OkIP Books

Towards Machine Learning Maintenance in Airport Infrastructure

Tarik Lahna — Sat, 07 Sep 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Chapter Abstract:
The total operating costs of airport infrastructures include substantial upkeep expenses. Maintenance management methods are becoming increasingly important because any damage can lead to significant consequences at the airport. Many classic maintenance methods, like "run to failure" or preventive methods, are lacking in different fields because they're expensive and show a non-maintenance management approach. Many methods related to BIM will detect all possible failures and resolve them before they occur, a type of management known as "predictive maintenance." Predictive maintenance enables you to maximize the availability of engineering systems, prevent downtime, reduce downtime, and increase safety.

Cite this paper as:
Lahna T. (2024). Towards Machine Learning Maintenance in Airport Infrastructure. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIS24#21. https://doi.org/10.55432/978-1-6692-0007-9_17

Presented at:
The 2024 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online on October 2, 2024

Contact:
Tarik Lahna
lahnatk@ucla.edu

Prospect for Blockchain Applications & Perspectives: a Vision from French Occitanie

Emma Junior Cheukap Djampou, Bernard Kamsu-Foguem — Tue, 03 Sep 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:
A blockchain is a technology for storing and transmitting information without a control body. This document presents the point of view of digital players in Occitanie, a French region bordering Spain, on blockchain development projects in different sectors of production of goods and services, including energy and health care. The prospects and challenges of blockchain applications are also mentioned.

Cite this paper as:
Cheukap Djampou E. J., Kamsu-Foguem B. (2024). Prospect for Blockchain Applications & Perspectives: a Vision from French Occitanie In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIS24#19. https://doi.org/10.55432/978-1-6692-0007-9_16

Presented at:
The 2024 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online on October 2, 2024

Contact:
Bernard Kamsu-Foguem
bkamsufo@yahoo.fr

Towards Performance in Aeronautics: The Role of Artificial Intelligence

Amina Zaoui, Bernard Kamsu-Foguem, Samuel Fosso-Wamba, Dieudonne Tchuente — Tue, 03 Sep 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

AI integration is growing interest in the aeronautics sector. Common AI technologies include autonomous intelligent systems, predictive analytics and machine learning. The benefits of AI integration include cost reduction, improved customer satisfaction, time savings, enhanced safety and security, better decision-making, problem-solving, and operational efficiency. By implementing new AI technologies aeronautics companies enhance their performance.

Cite this paper as:
Zaoui A., Kamsu-Foguem B., Fossa-Wamba S., Tchuente D. (2024). Towards Performance in Aeronautics: the Role of Artificial Intelligence. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIS24#17. https://doi.org/10.55432/978-1-6692-0007-9_15

Presented at:
The 2024 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online on October 2, 2024

Contact:
Amina Zaoui
amina.zaoui17@gmail.com

Split-Federated Reinforcement Learning for IoMT Data and Task Management in Edge-Fog-Cloud Infrastructure

Sonia Ikken, Doha Bitaqa — Tue, 03 Sep 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

Workload management in edge-fog-cloud infrastructure for the Internet of Medical Things (IoMT) involves significant costs and difficulties, primarily due to the challenge of managing tasks and the unpredictability of data generated by IoMT devices while processing real-time patient health states. Deep Reinforcement Learning (DRL) shows promise in efficiently addressing dynamic data placement and task offloading. However, DRL suffers from poor scalability in distributed multi-node IoT environments. To address these challenges, Federated Learning (FL) and DRL are combined to provide potential solutions, but they face coordination complexities, privacy concerns, high computational and communication costs, and scalability issues. In this paper, a new solution called Split Federated Hybrid Decisionbased DRL (SF-HDRL) is introduced to tackle these challenges.

Our algorithm combines continuous and discrete actions within a splitting actor-critic architecture to achieve joint optimized data placement, as well as node selection for task execution, while minimizing IoMT operational costs. Simulations indicate that the proposed SF-HDRL algorithm is superior in scalability and workload management costs compared to existing optimal nonfederated and federated approaches.

Cite this paper as:
Ikken S., Bitaqa D. (2024). Split-Federated Reinforcement Learning for IoMT Data and Task Management in Edge-Fog-Cloud Infrastructure. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIS24#15. https://doi.org/10.55432/978-1-6692-0007-9_14

Presented at:
The 2024 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online on October 2, 2024

Contact:
Sonia Ikken
sikken@cesi.fr

Sustainability: The Main Pillar of Industry 5.0

Hassane Zaoui, Samia Zaoui, Bernard Kamsu-Foguem, Dieudonne Tchuente — Fri, 30 Aug 2024 00:00:00 +0000

The implementation of sustainability principles into Industry 5.0 is crucial in the face of climate change and rising global environmental issues. This study aims to analyze the interaction between Industry 5.0 and sustainability. The review of 464 papers extracted from the Scopus database demonstrates the increasing annual scientific production and illustrates the growing interest in the topic by researchers and scholars. The analysis of clusters provides scope on the dimensions of Industry 5.0 and sustainability with the domination of the environmental aspects like climate change, energy efficiency, environmental impact and sustainable development. The importance of the digital technology is also revealed.

Machine Learning-Based SOC Prediction for Lithium-ion Batteries in Electric Vehicles

Mon, 26 Aug 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

Developing an effective and accurate battery management system that can predict the state of charge of electric vehicles is essential to enhance the safety and efficiency of electric vehicles. The assessment of the state of charge of the battery is important not only for determining the amount of energy that is available from the battery but also for determining how long the battery will last. This paper provides a brief understanding of how the state of charge estimation was predicted before and after the era of Machine Learning (ML). In addition, it proposes an accurate and fast state of charge estimation for lithium-ion batteries in electric vehicle applications using machine learning. The proposed model is designed to be generalizable across various inputs, applicable to both new and old batteries, and robust under different charging and discharging scenarios. Model performance and accuracy were evaluated using predefined metrics such as Root Mean Square Error and Mean Absolute Error. Among the four machine learning algorithms that achieved an approximate error of 0.75, while Multiple Linear Regression (MLR) model was chosen for its lightness and speed in training and testing. The findings of this research contribute to the advancement of Battery Management System (BMS) design and implementation for enhancing the efficiency and safety of Lithium-Ion Batteries (LIBs) in real-world driving scenarios. In the future, further research might be conducted to study the implementation of a variety of deep learning algorithms, as well as the estimation of battery health and the remaining useful life.

Cite this paper as:

Eid A. M., Nassif A. B., Ghenai C., Youssef H. Y., Alkhaja L. A., Almazrouei H. H. (2024). Machine Learning-Based SOC Prediction for Lithium-ion Batteries in Electric Vehicles. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAPE24#11. https://doi.org/10.55432/978-1-6692-0007-9_12

Presented at:
The 2024 OkIP International Conference on Advances in Power and Energy (CAPE) in Oklahoma City, Oklahoma, USA, and Online, on October 3, 2024

Contact:
Ali Bou Nassif
anassif@sharjah.ac.ae

Experimental Study of Parallel-Connected DC-DC Buck-Boost Converters FPGA Chaos Controlled

Ammar Natsheh, hanh Hai Nguyen, Preetha Sreekumar — Sat, 24 Aug 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

Chaos control is used to design a controller that is able to eliminate the chaotic behavior of nonlinear dynamic systems that experience such phenomena. This paper discuss the use of the FPGA as a controller of a parallel-connected DC-DC buck-boost converter, the goal of this paper is to build a controller that is capable of controlling the output current of a photovoltaic cells and minimize the effect of the module buck-boost converter chaotic behavior on the output voltage. To achieve this goal this paper presents two different methods, FPGA control the duty cycle and the frequency of the output controlling signal, this technique is done through software (FPGA code), and a delayed feedback control scheme in a module converter in the continuous-current conduction mode (CCM) using MATLAB/SIMULINK simulation. Thus, this paper shows the FPGA capabilities in the power industry and it’s specifies a guideline to overcome some of the obstacles when dealing with an FPGA as a buck-boost converter controller, and MATLAB/SIMULINK simulation results show the effectiveness and robustness of the scheme.

Cite this paper as:

Natsheh A., Nguyen T. H., Sreekumar P. (2024). Experimental Study of Parallel-Connected DC-DC Buck-Boost Converters FPGA Chaos Controlled. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CEST24#21. https://doi.org/10.55432/978-1-6692-0007-9_11

Accepted for:
The 2024 OkIP International Conference on Energy and Sustainable Technologies (CEST) in Oklahoma City, Oklahoma, USA, and Online, on April 3, 2024

Contact:
Ammar Natsheh
ammar_natsheh@yahoo.com

Power Quality Improvement of a Standalone Power Supply System Powered by PV, Wind, and Batteries via LSTM-based ANN Controllers

Sat, 24 Aug 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

Standalone electric power supply systems that integrate wind and photovoltaic (PV) technologies are widely employed in various applications. These systems require a battery storage system to ensure a continuous power supply, regardless of changes in loads, wind speed, and solar irradiance. To ensure a reliable and uniform power supply, several modifications need to be implemented in these hybrid systems. Power quality is an essential parameter in autonomous power systems, especially those that depend on hybrid energy sources. The battery should undergo the process of charging when there is an excess of energy and discharging when there is a demand from the loads. To achieve this, a bidirectional power flow circuit is utilized to establish a connection between the battery and the network, incorporating an appropriate controlling mechanism. Even in scenarios where there is only partial shading, a Modified Invasive Weed Optimization technique is employed to achieve the most efficient power operation of the photovoltaic (PV) system. Additionally, a Sliding Mode Control strategy is utilized for the wind turbine. This research investigates the application of multiple photovoltaic (PV) systems and wind turbines, each configured with different arrangements of parallel and series combinations of PV panels. The goal is to determine the appropriate rating for the power supply system. The control units in this hybrid standalone power system utilize artificial neural network (ANN) controllers that are based on long short-term memory (LSTM). The aforementioned controllers, when combined with the proposed control methodologies, improve power quality in various scenarios. The results of this investigation are presented using Hardware-in-the-Loop (HIL) on the OPAL-RT platform to evaluate the efficacy of the proposed methodology.

Cite this paper as:

Molu R., Mbasso W.F., Naoussi S., Wira P., Ambe H., Saatong K. T. (2024) Power Quality Improvement of a Standalone Power Supply System Powered by PV, Wind, and Batteries via LSTM-based ANN Controllers. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CEST24#20. https://doi.org/10.55432/978-1-6692-0007-9_10

Presented at:
The 2024 OkIP International Conference on Energy and Sustainable Technologies (CEST) in Oklahoma City, Oklahoma, USA, and Online, on April 3, 2024

Contact:
Reagan J. J. Molu
molureagan@yahoo.fr

Strategic Energy Management System for Improving the Power Quality of PV-Wind-Battery-based Standalone Microgrids

Sat, 24 Aug 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

The global community is currently engaged in the exploration of various techniques to utilize renewable energy sources with the objective of mitigating the effects of global warming and decreasing dependence on fossil fuels. The deployment of solar and wind energy has experienced substantial adoption in various geographical areas worldwide. However, the predictability and controllability of solar irradiance and wind speed are constrained. Therefore, it is crucial to incorporate an energy storage system to optimize the utilization of these energy sources by converting them into electrical energy. Within the domain of medium power applications, batteries are recognized as a prominent selection. However, batteries require significant maintenance and are susceptible to self-discharge, resulting in a gradual decrease in storage capacity over time. Hydrogen storage is a feasible alternative for high-power applications, offering a cost-efficient solution in comparison to batteries. The stored hydrogen can be utilized for various applications, including transportation and electricity generation. An electrolyzer is an electrochemical device that employs electrical energy to efficiently perform the process of water electrolysis, which involves the decomposition of water into its elemental components, namely oxygen and hydrogen. Nevertheless, the slow heat transfer dynamics hinder the rapid generation of hydrogen, necessitating the implementation of a novel control technique to improve production efficiency in response to changes in solar irradiance and wind speed. The deployment of boost, buck, and DC to DC bidirectional converters is utilized to maintain a consistent voltage at the DC-link across different operational scenarios. A novel control algorithm has been developed to ensure power quality at the 3-phase AC load bus and efficiently manage energy in the hybrid standalone system. The outcomes of this inquiry, conducted utilizing MATLAB/Simulink, are exhibited to evaluate the efficiency in various scenarios.

Cite this paper as:

Mbasso W.F., Molu R. J., Naoussi S.R.D., Ambe H., Donfack E. F., Saatong K. T. (2024) Strategic Energy Management System for Improving the Power Quality of PV-Wind-Battery-based Standalone Microgrids In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CEST24#17. https://doi.org/10.55432/978-1-6692-0007-9_9

Presented at:
The 2024 OkIP International Conference on Energy and Sustainable Technologies (CEST) in Oklahoma City, Oklahoma, USA, and Online, on April 3, 2024

Contact:
Wulfran Fendzi Mbasso
fendzi.wulfran@yahoo.fr

Hybrid Parallelization for Accelerating Visibility-Graph Construction and Community Detection on Temporal Data

Xun Jia, Minzhang Zheng, Liwei Wang, Shuangshuang Jin — Fri, 23 Aug 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

In the present era, vast amounts of time series data, particularly in biology, require efficient analysis due to high-dimensional datasets from advanced technologies. Transforming time series into networks and applying community detection methods can uncover dynamic patterns as temporal network communities. However, the large size of these datasets often extends analysis time. High Performance Computing (HPC) addresses this by accelerating traditional applications. This article presents an HPC-optimized version of the visibility-graph-based temporal community detection method. Enhancing the original algorithm with parallel processing, including shared memory and message passing models, improves adaptability. Experiments using an artificial sine series and two real-world datasets—biological time series and power consumption patterns—on Clemson's Palmetto Cluster demonstrate significant performance improvements and scalability over the original approach.

Cite this paper as:
Jia X., Zheng M., Wang L., Jin S. (2024) Hybrid Parallelization for Accelerating Visibility-Graph Construction and Community Detection on Temporal Data. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. AHPC24#06. https://doi.org/10.55432/978-1-6692-0007-9_8

Presentated at:
The 2024 OkIP International Conference on Advances in High-Performance Computing (AHPC) in Oklahoma City, Oklahoma, USA, and Online on October 3, 2024.

Contact:
Xun Jia
xunj@g.clemson.edu

Enriching Release Notes with Reddit Feedback: Understanding How User Feedback Can Augment Maintaining Software Updates

Fri, 23 Aug 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

The past decade has shown that the number of software components has increased, as well as their inter-connectivity and update cycles. Maintaining daily software updates is a time-consuming, arduous, and risky task. One common question is which update to review and perform next. Our previous work leveraging software release notes showed limitations in classifying and prioritizing breaking and high-impact updates. In this research, we enrich release notes with Reddit-based user feedback to understand how it potentially improves maintaining software updates. Towards the objective, we review five research questions useful to a clinical ecosystem use case scenario, involving nine software components, each enriched with at least 100 Reddit posts. The results show that enriched Reddit user feedback complements release notes and improves the classification of breaking and high-impact updates. For example, identifying an average of 5% critical breaking updates posted by Reddit users improves the early evaluation of potentially dysfunctional software components. Additionally, linking an update to an average of 10 other component helps in identifying potential dependencies among related components. Given the results, our prototyping efforts illustrate how, for example, a clinical ecosystem can accelerate maintaining software updates by proposing next update recommendations. By integrating user feedback from Reddit, we aim to better understood where user feedback is useful in maintaining software updates.

Cite this paper as:

Singh B., Daftary S. M., Rudra M., Veed S. P., Berhe S., Maynard M., Khomh F. (2024) Enriching Release Notes with Reddit Feedback: Understanding How User Feedback Can Augment Maintaining Software Updates. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. SERD24#11. https://doi.org/10.55432/978-1-6692-0007-9_7

Presentated at:
The 2024 OkIP International Conference on Software Engineering Research & Development (SERD) in Oklahoma City, Oklahoma, USA, and Online on October 3, 2024.

Contact:
Bhavya Singh
b_singh13@u.pacific.edu

Reducing Errors in Electronic Medical Records

Lynda Sellami, Khaled Sellami, Pierre Tiako — Thu, 22 Aug 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

As electronic medical records (EMR) are used in e-health by healthcare professionals and patients, there is a need to facilitate the efficiency and usability of these systems, reducing adverse patient outcomes due to EMR-related user errors. To pinpoint and mitigate errors within the electronic health record, we formulated a methodology designed to uncover entry errors within the electronic health record. This approach involves meticulously examining the electronic health record during data input and cross-referencing it with the patient's medical history to uncover potential entry discrepancies.

Cite this paper as:
Sellami L., Sellami K., Tiako P.F. (2024) Reducing Errors in Electronic Medical Records. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIF24#13. https://doi.org/10.55432/978-1-6692-0007-9_2

Presented at:
The 2024 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online on April 3, 2024

Contact:
Lynda Sellami
slynda1@yahoo.fr

Exploring Vulnerabilities: An In-Depth Analysis of Security Weaknesses Leading to Dark Web Exposure and Improved Security Defenses Using Artificial Intelligence (AI) & Machine Learning (ML)

Joseph Squillace, Justice Cappella, Andrew Sepp — Wed, 21 Aug 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

In modern society, demand and value of stolen personally identifiable information (PII) has risen dramatically. A significant portion of this information can be found within the Dark Web. Growing exposure and exchange of PII is largely a resultant of inadequate organizational security measures. This investigation uses Artificial Intelligence (AI) and Machine Learning (ML) to explore the underlying causes of data breach events to examine how adaptation of more robust security strategies can mitigate PII exposure.

Cite this paper as:

Squillace J., Cappella J., Sepp A. (2024) Exploring Vulnerabilities: An In-Depth Analysis of Security Weaknesses Leading to Dark Web Exposure and Improved Security Defenses Using Artificial Intelligence (AI) & Machine Learning (ML).
In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIF24#15. https://doi.org/10.55432/978-1-6692-0007-9_5

Presented at:
The 2024 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 3, 2024

Contact:

Joseph Squillace
jms10943@psu.edu

Text Toxicity Level Detection using Deep Contextualized Embedding Models

Omar Elgendy, Ali Bou Nassif, Bassel Soudan — Wed, 21 Aug 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

Toxic text is a critical aspect of social media, particularly in today's digital landscape. With the spread of online communication, it has become increasingly easy for individuals to spread harmful or offensive content. Toxic texts include the spread of misinformation, the promotion of hate speech, bullying, and the erosion of trust in online communities. Text toxicity detection algorithms can help to identify and mitigate these negative effects by automatically flagging potentially harmful content. This allows social media platforms to intervene and take appropriate action, such as removing the content or warning the user. Usually, social media platforms offer a reporting strategy which acts after a human decision is made. However, social media now requires an automated system to do this task. In this work, we proposed a Deep learning Regression model to predict the toxicity level in text. Additionally, we fine-tuned multiple Bert models for this task. Our work was evaluated using Mean Square Error, Root Mean Square Error and Mean Absolute Error compared to the testing set of the data and we got for the base model MSE of 0.562, RMSE of 0.750 and MAE of 0.364 but for BERT we got MSE 0.403, RMSE 0.635 and MAE 0.232.

Cite this paper as:
Elgendya O., Nassifa A. B., Soudana B., (2024) Text Toxicity Level Detection using Deep Contextualized Embedding models. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIF24#9. https://doi.org/10.55432/978-1-6692-0007-9_3

Presented at:
The 2024 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 3, 2024.

Contact:
Omar Elgendya
oelgendy@sharjah.ac.ae

Heart Failure Prediction using Machine learning with Metaheuristic feature selection techniques

Omar Elgendy, Ali Bou Nassif, Bassel Soudan — Wed, 21 Aug 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

Heart failure is a major complication of cardiovascular diseases (CVDs), which are the leading cause of mortality globally. However, early treatment and detection of heart failure may increase the chance of survival. Using current clinical data, machine learning (ML) algorithms present an effective approach for predicting heart failure. Utilizing ML algorithms and feature selection using metaheuristic methods, we present a novel framework in this research for the prediction of heart failure. We employ several ML algorithms and perform feature selection using four meta-heuristic techniques: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO). The performance of each combination is evaluated and compared based on the F-score metric, which we aim to maximize. (PSO) has achieved the highest when choosing the relevant features which increased the overall accuracy from 0.83 to 0.90. The results indicate that our proposed framework can effectively identify relevant features and improve the predictive performance of ML algorithms for heart failure. Furthermore, we provide a comprehensive comparison of the meta-heuristic techniques, highlighting their advantages and limitations for feature selection in heart failure prediction.

Cite this paper as:
Elgendya O., Nassifa A. B., Soudana B., (2024) Heart Failure Prediction using Machine learning with Metaheuristic feature selection techniques. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. AHIT24#15. https://doi.org/10.55432/978-1-6692-0007-9_4

Presented at:
The 2024 OkIP International Conference on Advances in Health Information Technology (AHIT) in Oklahoma City, Oklahoma, USA, and Online, on October 3, 2024.

Contact:
Omar Elgendya
oelgendy@sharjah.ac.ae

Using Artificial Intelligence (AI) and Machine Learning (ML) to Disrupt the Negative Impact of Disinformation on Digital Sovereignty and Social Stability Through Cognitive Security in Elections

Joseph Squillace, Justice Cappella — Wed, 21 Aug 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

Artificial Intelligence (AI) and Machine Learning (ML) stand at the forefront of election security. However, the rapid ascension of AI and ML as future tenets of modern election integrity have required these resources be protected to safeguard democracy from disinformation, misinformation, and media manipulation; securing tomorrow by protecting technology today.

Cite this paper as:

Squillace J., Cappella J. (2024) Using Artificial Intelligence (AI) and Machine Learning (ML) to Disrupt the Negative Impact of Disinformation on Digital Sovereignty and Social Stability Through Cognitive Security in Elections .
In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIF24#17. https://doi.org/10.55432/978-1-6692-0007-9_6

Presented at:
The 2024 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 3, 2024

Contact:
Joseph Squillace
jms10943@psu.edu

Scheduling for high performance computing with reinforcement learning

Sat, 17 Aug 2024 00:00:00 +0000

This is a Chapter in:

Book:
Competitive Tools, Techniques, and Methods

Print ISBN 978-1-6692-0008-6
Online ISBN 978-1-6692-0007-9

Series:
Chronicle of Computing

Chapter Abstract:

Job scheduling for high performance computing systems involves building a policy to optimize for a particular metric, such as minimizing job wait time or maximizing system utilization. Different administrators may value one metric over another, and the desired policy may change over time. Tuning a scheduling application to optimize for a particular metric is challenging, time consuming, and error prone. However, reinforcement learning can quickly learn different scheduling policies dynamically from log data and effectively apply those policies to other workloads. This research demonstrates that a reinforcement learning agent trained using the proximal policy optimization algorithm performs 18.44% better than algorithmic scheduling baselines for one metric and has comparable performance for another. Reinforcement learning can learn scheduling policies which optimize for multiple different metrics and can select not only which job in the queue to schedule next, but also the machine on which to run it. The agent considers jobs with three resource constraints (CPU, GPU, and memory) while respecting individual machine resource constraints.

Cite this paper as:
Hutchison S., Andresen D., Hsu W., Parsons B., Neilsen M. (2024) Scheduling for high performance computing with reinforcement learning. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. APDC24#6. https://doi.org/10.55432/978-1-6692-0007-9_1

Presented at:
The 2024 OkIP International Conference on Advances in Parallel and Distributed Computing (APDC) in Oklahoma City, Oklahoma, USA, and Online, on April 3, 2024

Contact:
Scott Hutchison
scotthutch@ksu.edu

Competitive Tools, Techniques, and Methods

Pierre Tiako — Fri, 16 Aug 2024 00:00:00 +0000

This book introduces a collection of selected papers from the 2024 OkIP Academia & Industry Convention conferences held Online and in Oklahoma City, Oklahoma, USA, on April 1-4 and October 1-3, 2024. The document includes articles addressing various challenges and perspectives on intelligent, sustainable, and business applications. Several papers present recent progress and contributions to sustainability and energy, while others deal with advances in machine learning and agent-based systems.

New Book Chapter Publication Model
OkIP is pleased to introduce its first Continuous Chapter Publishing (CCP) model. CCP means that each paper, after peer review, acceptance, and revision, will be published as a chapter of the book. The internal pagination of each chapter begins on page 1 rather than a continuous pagination of chapters throughout the book. In addition, each chapter has a unique Chapter Citation ID number, which replaces the page numbers in the citation. This change allows each Chapter to be published online immediately in its final form and the whole publication time becomes shorter.

ABOUT THIS BOOK

Print: ISBN 978-1-6692-0008-6
Online: ISBN 978-1-6692-0007-9

Preface

Table of Contents (coming soon)

Detailed Table of Contents (coming soon)

IN-PROGRESS BOOK TABLE OF CONTENTS

Part I: Competitive Tools, Techniques, and Applications in Healthcare and Society

>> Heart Failure Prediction using Machine learning with Metaheuristic feature selection techniques.
Omar Elgendy, Ali Bou Nassif, Bassel Soudan

>> Simulating Disease Transmission Models Via Agent Interaction Using Large Language Models
Elie Alhajjar and James Chen

>> Predicting Severity and Guiding Treatment: Precision Lung Adenocarcinoma Care with Regression Model Artificial Intelligence
Abhishek Shukla, Chandra Sindhu

>> Reducing Errors in Electronic Medical Records
Lynda Sellami, Khaled Sellami, Pierre F. Tiako

>> ...

Part II: Competitive Tools, Techniques, and Applications in Energy and Computing

>> Strategic Energy Management System for Improving the Power Quality of PV-Wind-Battery-based Standalone Microgrids
Wulfran F. Mbasso, Reagan J. J. Molu, Serge R. D. Naoussi, Harrison Ambe, Emmanuel F. Donfack, Kenfack T. Saatong

>> Scheduling for high performance computing with reinforcement learning
Scott Hutchison, Daniel Andresen, William Hsu, Benjamin Parsons, Mitchell Neilsen

>> Machine Learning-Based SOC Prediction for Lithium-ion Batteries in Electric Vehicles
Abdulrahman M. Eid, Ali B. Nassif, Chaouki Ghenai, Heba Y. Youssef, Latifa A. Alkhaja, Hajar H. Almazrouei

>> Power Quality Improvement of a Standalone Power Supply System Powered by PV, Wind, and Batteries via LSTM-based ANN Controllers
Reagan J. J. Molu, Wulfran F. Mbasso, Serge R. D. Naoussi, Patrice Wira, Harrison Ambe, Kenfack T. Saatong

>> Experimental Study of Parallel-Connected DC-DC Buck-Boost Converters FPGA Chaos Controlled
Ammar Natsheh, Thanh H. Nguyen, Preetha Sreekumar

>> Hybrid Parallelization for Accelerating Visibility-Graph Construction and Community Detection on Temporal Data
Xun Jia, Minzhang Zheng, Liwei Wang, Shuangshuang Jin

>> Sustainability: The Main Pillar of Industry 5.0
Hassane Zaoui, Samia Zaoui, Bernard Kamsu-Foguem, Dieudonne Tchuente

>> ...

Part III: Competitive Tools, Techniques, and Applications in Artificial Intelligence

>> Recent Surveys on Optimal Transport for Machine Learning
Bernard Kamsu Foguem, Pierre F. Tiako, S. L. Msouobu Gueuwou, Cheick A. K. A. Kounta

>> Using Artificial Intelligence (AI) and Machine Learning (ML) to Disrupt the Negative Impact of Disinformation on Digital Sovereignty and Social Stability Through Cognitive Security in Elections
Joseph Squillace, Justice Cappella

>> Enriching Release Notes with Reddit Feedback: Understanding How User Feedback Can Augment Maintaining Software Updates
Bhavya Singh, Suhasi M. Daftary, Mythry Rudra, Simhika P. Veed, Solomon Berhe, Marc Maynard, Foutse Khomh

>> Text Toxicity Level Detection using Deep Contextualized Embedding models
Omar Elgendy, Ali B. Nassif, Bassel Soudan

>> Exploring Vulnerabilities: An In-Depth Analysis of Security Weaknesses Leading to Dark Web Exposure and Improved Security Defenses Using Artificial Intelligence (AI) & Machine Learning (ML)
Joseph Squillace, Justice Cappella, Andrew Sepp, Joan Young

>> ...

Author Index (In Progress)

Gender Bender Bot - The Effect of (Not) Following Gender Stereotypes in Conversational Agent Design

Fabian Hildebrandt — Sat, 27 Jan 2024 00:00:00 +0000

This is a Chapter in:

Book:
Smart and Sustainable Applications

Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Series:
Chronicle of Computing

Chapter Abstract:
Conversational agents (CAs), are increasingly becoming a common presence in our daily lives (e.g., Alexa or ChatGPT). Research has shown that designing CAs humanlike (e.g., through social cues such as a human name or avatar) results in a higher perception of humanness, which increases and service satisfaction by the user. In this context, CAs are exclusively designed to portray stereotypical genders (e.g., combining a female name and avatar). To challenge this quasi-standard, a 2x2 experiment (male/ female avatar x male/ female name) with 262 participants was conducted to investigate the effect of gender-mixed CAs. Our results indicate that users of CAs with a stereotypical gender report higher service satisfaction and a partially higher perception of social presence for male CAs.
However, the results do not reveal any differences in perceived empathy and competence. Thus, it appears that users prefer stereotypically CAs, which is in sync with current practice.

Keywords:
Conversational Agents, Anthropomorphism, Avatar, Name, Gender, Stereotypes

Cite this paper as:
Hildebrandt F. (2024) Gender Bender Bot - The Effect of (Not) Following Gender Stereotypes in Conversational Agent Design. In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_12

Presented at:
The 2023 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023

Contact:
Fabian Hildebrandt
fabian.hildebrandt@tu-dresden.de

Improving Human-Agent Team Performance through Audio-Visualized Communication

Habib Laraba, Prosper Julius Mavunga, Jonathan Baradza — Sat, 27 Jan 2024 00:00:00 +0000

This is a Chapter in:

Book:
Smart and Sustainable Applications

Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Series:
Chronicle of Computing

Chapter Abstract:

In this paper, we deal with human-agent teams involving software agents and human agents collaborating to resolve a common problem. We propose experimenting the impact of integrating audio-visual interactive skills on the team performance: vocal communication act is integrated in the autonomous agent so that it can explain its behavior to human using voice as a communication channel; visual communication act is modeled to allow humans to track software agents’ movement in the environment. We carry out a series of tests using the testbed BW4T (Block World for Teams) to highlight the evolution of agents’ communication with new interactive capabilities partly adapted from DEFACTO(Demonstrating Effective Flexible Agent Coordination of Teams through Omnipresence) experiment . We propose implementing that experiment in Jason platform well adapted to develop such very complex systems where BDI (Believe Desire Intention) agents need to interact in complex ways with humans and/or with each other. The result is a significant improvement in team performance assessed through the completion of the joint task due to a better understanding of the overall situation of the task workflow.

Keywords:
Human-agent teams, team performance, audio-visual interactive skills, BDI agents, Jason.

Cite this paper as:
Laraba H., Mavunga, P.J., and Baradza J. (2024) Improving Human-Agent Team Performance through Audio-Visualized Communication. In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_11

Accepted for:
The 2023 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023

Contact:
Habib Laraba
habib.laraba@univ-constantine2.dz

Optimal Transport-based Loss Functions for Machine Learning

Bernard Kamsu Foguem, Pierre Tiako, Cheick Abdoul Kadir A. Kounta — Sat, 27 Jan 2024 00:00:00 +0000

This is a Chapter in:

Book:
Smart and Sustainable Applications

Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Series:
Chronicle of Computing

Chapter Abstract:

This short paper briefly reports the essential facets of the article (Kamsu-Foguem & al., 2022) presented and discussed as a Journal First paper. The article overviews generative neural networks whose loss functions are based on optimal transport with the Wasserstein distance. This tool of mathematical origin allows interesting automatic learning to be obtained in a reasoning time under Lipschitz constraints. As the proposed studies are based on Wasserstein Generative Adversarial Networks (WGAN), we conclude this report with a short discussion on how WGAN currently supports critical, intelligent applications in our society and nearly all industry sectors.

Keywords:
Geometry, Neural Network, Deep Learning, WGAN, Intelligent Applications

Cite this paper as:
Kamsu-Foguem B., Tiako P.F. & Kounta C.A.K.A. (2024) Optimal Transport-based Loss Functions for Machine Learning. In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_10

Presented at:
The 2023 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023

Contact:
Bernard K. Foguem
Bernard.Kamsu-Foguem@enit.fr

Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments

Nisal Hemadasa, Marcus Venzke, Volker Turau, Yanqiu Huang — Sat, 27 Jan 2024 00:00:00 +0000

This is a Chapter in:

Book:
Smart and Sustainable Applications

Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Series:
Chronicle of Computing

Chapter Abstract:

Indoor Positioning Systems have gained significance in numerous industrial applications. While state-of-the-art solutions are accurate, their reliance on external infrastructures can lead to considerable costs, deployment complexities, and privacy concerns, making them suboptimal for specific contexts. Recent advancements in machine learning have surfaced as a potential solution, leveraging data solely from onboard IoT sensors. Nonetheless, the optimal machine learning models for IoT's resource constraints remain uncertain. This research introduces an indoor positioning system using motion and ambient sensors tailored for factories and similar settings with predetermined paths. The problem is framed as multivariate time series classification, comparing various ML models. A novel dataset simulating factory assembly lines is utilized for evaluation. Results demonstrate models achieving over 80% accuracy, with 1 Dimensional-Convolutional Neural Networks showing the most balanced performance followed by Multilayer Perceptrons, considering accuracy, memory footprint and latency. Decision Trees exhibit the lowest memory footprint and latency, rendering its potential for practical implementation.

Keywords:
Indoor Positioning, Machine Learning, Sensor Fusion, Multivariate Time Series Classification

Cite this paper as:

Hemadasa N., Venzke M., Turau V., Huang Y. (2024) Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments. In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_9

Presented at:
The 2023 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023

Contact:
Nisal Hemadasa
nisal.hemadasa@tuhh.de

Geothermal Potential Assessment of the Bare-Bakem Region (Cameroon Volcanic Line): Contributions From Geophysical and Geothermometric Studies

Ange Merveille Meguedong Guedia, Dieudonne Bisso, Lionel Mbida Yem — Sat, 27 Jan 2024 00:00:00 +0000

This is a Chapter in:

Book:
Smart and Sustainable Applications

Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Series:
Chronicle of Computing

Chapter Abstract:

In order to determine the geothermal potential of the municipality of Baré-Bakem, located in both the sedimentary basin of Moungo and the volcanic region of Nkongsamba (Cameroon Volcanic Line), geophysical measurements using electrical resistivity tomography were conducted along a 1km profile between two opposing thermal sources spaced 20 meters apart. The profile was acquired in the SW-NE direction using a 945m-long array with 64 electrodes spaced at 15 meters intervals. The ZZRes2Dinv44 software was employed to generate investigative images. The obtained results along the profile reveal the presence of two geothermal zones of interest trapped within permeable sedimentary formations at depths ranging from approximately 20 to 110 meters and close to the surface. From a geothermometric perspective, chemical geothermometers such as silica and Na-K-Ca were calculated by collecting water samples from these thermal sources and analyzing the physicochemical parameters at the Laboratory of Geochemical Analysis of Waters (LAGE/IRGM) in Nkolbisson. The calculations indicate that the Baré-Bakem locality exhibits temperatures ranging from 51 to 90°C for near-surface anomalies and temperatures ranging from 283 to 300°C for deeper anomalies. These geothermal resources possess high energy potential suitable for electricity production.

Keywords:
Geothermal, Tomography, Chemical Geothermometers, Geothermal Energy.

Cite this paper as:
Meguedong A.M.G., BISSO D. & MBIDA L. (2024) Geothermal Potential Assessment of the Bare-Bakem Region (Cameroon Volcanic Line): Contributions From Geophysical and Geothermometric Studies. In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_8

Presented at:
The 2023 OkIP International Conference on Green Research & Technology (CGRT) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023

Contact:
Ange Merveille Meguedong Guedia
guediaangemerveille@gmail.com

The Effectiveness of Carbon Taxes - An Analysis Across Multiple Countries

Robert Marshall — Sat, 27 Jan 2024 00:00:00 +0000

This is a Chapter in:

Book:
Smart and Sustainable Applications
Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Series:
Chronicle of Computing

Chapter Abstract:

With worldwide emissions of carbon dioxide contributing to global warming, countries are looking for ways to lower local emissions. One strategy for reducing carbon emissions is through national carbon taxes. These taxes vary both in terms of amount of the tax and industries included in the tax. With the first carbon taxes imposed thirty years ago, it is possible to assess effectiveness and challenges associated with carbon taxes.

Keywords:
Carbon Tax, Emissions, Carbon Dioxide, Effectiveness.

Cite this paper as:

Marshall R.(2024) The Effectiveness of Carbon Taxes - An Analysis Across Multiple Countries. In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_7

Accepted for:
The 2023 OkIP International Conference on Green Research & Technology (CGRT) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023

Contact:
Robert Marshall
robbym1302@gmail.com

Digital Economy and Artificial Intelligence in Subsahara Industrialization

Irene Tiako, Bernard Nguekeng, Pierre Tiako — Fri, 26 Jan 2024 00:00:00 +0000

This is a Chapter in:

Book:
Smart and Sustainable Applications
Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Series:
Chronicle of Computing

Chapter Abstract:
This article examines the effects of the digital economy and artificial intelligence on the industrialization of sub-Saharan African economies. Thus, from a sample of 47 countries observed over the period 2003 – 2021, we estimate a model in panel data by the GMM system method. Overall, our results show that the digital economy and artificial intelligence contribute significantly to improving industrialization. In addition, they keep their robustness against the use of competing estimators. We suggest more investment in technological infrastructure, better human capital formation for a better contribution of the digital economy and artificial intelligence to industrial development.

Keywords:
Sub-Saharan Africa, GMM in System, Digital Economy, Artificial Intelligence, Industrialization

Cite this paper as:
Tiako I, Nguekeng B., Tiako P.F (2024) Digital Economy and Artificial Intelligence in Subsahara Industrialization.
In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_4

Presented at:
The 2023 OkIP International Conference on Electronic Commerce and Applications (CECA) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023

Contact:
Irene Tiako
tiakonga1@gmail.com

Review of a Time-variant bandpass filter for Fast Analog FEE

Hertz Pancha, Evariste Wembe Tafo, Bernard Essimbi Zobo — Fri, 26 Jan 2024 00:00:00 +0000

This is a Chapter in:

Book:
Smart and Sustainable Applications

Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Series:
Chronicle of Computing

Chapter Abstract:

A time-variant circuit is examined in the context of readout electronics for silicon sensors. It consists of a time-variant differentiation and a two-stage integrator. To lower mean power consumption, the circuit can alternate between the on and off states. This will improve noise performance and restore output undershoot and offset.

Keywords:
Integration, Time variant circuit, undershoot, Offset.

Cite this paper as:
Pancha H., Wembe Tafo E., Essimbi Zobo B. (2024) Review of a Time-variant bandpass filter for Fast Analog FEE. In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_6

Presented at:
The 2023 OkIP International Conference on Advances in Power and Energy (CAPE) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023

Contact:
Evariste Wembe Tafo
twembee@yahoo.fr

A Real-Time Energy Management of a PV/Battery/Grid-Connected System Under Uncertainties

Fri, 26 Jan 2024 00:00:00 +0000

This is a Chapter in:

Book:
Smart and Sustainable Applications

Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Series:
Chronicle of Computing

Chapter Abstract:

Building up grid-connected Hybrid Renewable Energy Systems (HRES) is one of the major challenges of developing countries. This relies on the feasibility study and moreover on the optimization of the system built. This paper aims to provide a good framework for Energy Management Systems (EMS) strategies. It consists of PV/Battery/loads connected to the power grid. Hence, the Hybrid Micro Grid System (HMGS) obtained is subject to meteorological uncertainties due to shadowing of the solar panels. Two approaches have been applied in this work: heuristic method using State Machine Logic (State Flow Method) and Linear Programming. These methods have been implemented using MATLAB R2018a. A thorough discussion describes the results of the simulations using both methods for two meteorological conditions: clear and cloudy periods. Four scenarios are presented in accordance to the specifications above-mentioned. A real-time analysis is performed for a specific case study on a daily basis.

Keywords:
Microgrid, Energy Storage System, Solar Array, Linear Programming, Energy Management System.

Cite this paper as:
Mbasso Fendzi W., Naoussi Dzonde S.R., Wira P., Molu R.J.J., Saatong K.T. (2024) A Real-Time Energy Management of a PV/Battery/Grid-Connected System Under Uncertainties. In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_5

Presented at:
The 2023 OkIP International Conference on Advances in Power and Energy (CAPE) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023

Contact:
Fendzi Mbasso Wulfran
fendzi.wulfran@yahoo.fr

Perspectives of Using Load Balancing for Supporting Devices in Reconfigurable Ubiquitous Environments

Lynda Sellami, Khaled Sellami, Pierre Tiako — Fri, 26 Jan 2024 00:00:00 +0000

This is a Chapter in:

Book:
Smart and Sustainable Applications

Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Series:
Chronicle of Computing

Chapter Abstract:

The user can access data and services anytime and from any location, thanks to ubiquitous computing. As a result, device collaboration is essential. The devices and networks become overloaded as a result. As a result, the network becomes unstable, and network activity is suspended. By addressing these load-balancing issues with the new necessity of ubiquitous environments, these systems may be able to move toward greater user/device collaboration and mobility in the future. For various types of devices, this position paper shows how reconfiguration is one of the critical solutions for reducing energy usage in ubiquitous surroundings. We suggest a new way to adjust the distribution of device components as soon as high energy consumption is detected to lower and/or balance the energy consumption.

Keywords:
Ubiquitous Computing, Load Balancing, Device Energy Consumption

Cite this paper as:
Sellami L, Sellami K., Tiako P.F,(2024) Perspectives of Using Load Balancing for Supporting Devices in Reconfigurable Ubiquitous Environments. In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_3

Presented at:
The 2023 OkIP International Conference on Advances in High-Performance Computing (AHPC) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023

Contact:
Lynda Sellami
lynda.sellami@univ-bejaia.dz

Optimal Energy Management Strategy for Microgrids in Developing Countries: A Focus on Battery Energy Storage System

Fri, 26 Jan 2024 00:00:00 +0000

This is a Chapter in:

Book:
Smart and Sustainable Applications

Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Series:
Chronicle of Computing

Chapter Abstract:

Microgrid failure is a significant concern in developing countries and rural areas, necessitating effective energy management strategies to enhance post-failure reliability. The battery energy storage system plays a crucial role in microgrids by managing imbalances, mitigating voltages, and improving system reliability. This paper presents a method for optimizing the energy storage system in a photovoltaic-connected microgrid. The method controls battery discharge and charge operations based on load requirements, considering fluctuations in demand over a day. Simulations using MATLAB R2022a employed state flow analysis and linear programming to minimize variable electricity prices before and after islanding events. The results demonstrate significant improvements in energy management by reducing total variable electricity costs. The proposed method enhances the performance and resilience of microgrid systems, addressing challenges associated with intermittent renewable energy and potential system failures. Effective energy storage management enables risk mitigation, improved reliability, and enhanced utilization of renewable resources, ultimately contributing to sustainable and resilient energy systems.

Keywords:
Island, Microgrid, Energy System Storage, Photovoltaic System, Energy Management Strategy.

Cite this paper as:

Molu R.J.J., Naoussi Dzonde S.R., Wira P., Mbasso Fendzi W., Saatong K. T. (2024) Optimal Energy Management Strategy for Microgrids in Developing Countries: A Focus on Battery Energy Storage System. In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_2

Presented at:
The 2023 OkIP International Conference on Advances in Power and Energy (CAPE) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023

Contact:
Reagan Jean Jacques Molu
molureagan@yahoo.fr

Predictive analytics-based evaluation of performance of public bus transportation San Antonio, Texas as a case study

Izzat Alsmadi, Mohammad Al-Ramahi — Fri, 26 Jan 2024 00:00:00 +0000

This is a Chapter in:

Book:
Smart and Sustainable Applications

Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Series:
Chronicle of Computing

Chapter Abstract:

Citizens in large cities utilize public transportation as an alternative to self-driving for several reasons, such as avoiding traffic congestion and parking costs and utilizing their time for other things (e.g. reading a book or responding to emails). While large cities provide public transportation as a service to their citizens, they need to consider optimizing their budget and ensuring that public transportation is available and reliable. Using our case study, the public bus transit system in the city of San Antonio, Texas, in this paper, we used predictive analytics models to evaluate the performance of public bus transportation. We used time point stops as the target variable in order to evaluate their impact on the overall performance of the system. We also evaluated methods for the detection of potential bus-time savings and reported several examples of possible savings.

Keywords:
Predictive Analytics, GTFS, Transportation Intelligence

Cite this paper as:
Izzat Alsmadi I., Al-Ramahi M. (2024) Predictive analytics-based evaluation of performance of public bus transportation San Antonio, Texas as a case study. In: Tiako P.F. (ed) Smart and Sustainable Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0005-5_1

Presented at:
The 2023 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 2-5, 2023.

Contact:
Izzat Alsmadi
izzat.alsmadi@tamusa.edu

Smart and Sustainable Applications

Pierre Tiako — Thu, 25 Jan 2024 00:00:00 +0000

This book introduces a collection of selected papers presented at the 2023 OkIP Academia & Industry Convention conferences held Online and in Oklahoma City, Oklahoma, USA, on October 2-5, 2023.
The document is composed of three Parts. Part I includes articles addressing various challenges and perspectives on Intelligent and Sustainable Applications. Recent progress and contributions to Sustainability and Energy appear in Part II. Lastly, Part III presents advances in Machine Learning and Agent-Based Systems.

ABOUT THIS BOOK

Print ISBN 978-1-6692-0006-2
Online ISBN 978-1-6692-0005-5

Detailed Preface

Table of Contents

Detailed Table of Contents

Part I Intelligent and Sustainable Applications

>> Predictive analytics-based evaluation of performance of public bus transportation San Antonio, Texas as a case study
Izzat Alsmadi, Mohammad Al-Ramahi

>> Optimal Energy Management Strategy for Microgrids in Developing Countries: A Focus on Battery Energy Storage System
Reagan Jean Jacques Molu, Serge Raoul Naoussi Dzonde, Patrice Wira, Wulfran Mbasso Fendzi, Kenfack Tsobze Saatong

>> Perspectives of Using Load Balancing for Supporting Devices in Reconfigurable Ubiquitous Environments
Lynda Sellami, Khaled Sellami, Piere F. Tiako

>> Digital Economy and Artificial Intelligence in Subsahara Industrialization
Irene Tiako, Bernard Nguekeng, Piere F. Tiako

Part II Sustainability and Energy

>> A Real-Time Energy Management of a PV/Battery/Grid-Connected System Under Uncertainties
Fendzi Mbasso Wulfran, Dzonde Naoussi Serge Raoul, Molu Reagan Jean Jacques, Kenfack Tsobze Saatong

>> Review of a Time-variant bandpass filter for Fast Analog FEE
Hertz Pancha, Evariste Wembe Tafo, Essimbi Zobo Bernard

>> The Effectiveness of Carbon Taxes - An Analysis Across Multiple Countries
Robert Marshall

>> Geothermal Potential Assessment of the Bare-Bakem Region (Cameroon Volcanic Line): Contributions From Geophysical and Geothermometric Studies
Ange Merveille Meguedong Guedia, Dieudonne Bisso, Lionel Mbida Yem

Part III Machine Learning and Agent-Based Systems

>> Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments
Nisal Hemadasa, Marcus Venzke, Volker Turau, Yanqiu Huang

>> Optimal Transport-based Loss Functions for Machine Learning
Bernard K. Foguem, Pierre F. Tiako, Cheick Abdoul Kadir A. Kounta

>> Improving Human-Agent Team Performance through Audio-Visualized Communication
Habib Laraba, Prosper Julius Mavunga, Jonathan Baradza

>> Gender Bender Bot - The Effect of (Not) Following Gender Stereotypes in Conversational Agent Design
Fabian Hildebrandt

Author Index

A Novel Agent-Based Intrusion Detection System for Wireless Body Area Network

Lynda Sellami, Khaled Sellami, Pierre Tiako — Fri, 22 Sep 2023 00:00:00 +0000

This is a Chapter in:

Book:

Intelligent Computing and Consumer Support Applications

Series:
Chronicle of Computing

Chapter Abstract:

The objective of e-health is to assist patients in improving health care through integrating a wireless body network, communication infrastructure, and hospital network. The patient monitoring system assists patients in better understanding their health daily. The mobility and dynamism offered by e-health services expose the health system to the risk of attacks and intrusions. However, securing patient information and confidentiality is essential to ensure quality care. Current research on security in e-health focuses on implementing authentication, encryption, and trust-based solutions for implanted and wearable medical devices. These solutions are often computationally expensive and challenging to implement on medical devices with limited resources. This paper proposes a novel intrusion detection system based on agent technology to protect patients' medical data. The proposed method detects network-level intrusions as well as anomalies in sensor data. Our model was experimented with by simulating a hospital network topology. Our simulation results demonstrate that we can achieve high detection accuracy.

Keywords:
eHealthcare, Wireless Body Area Network, Security, Intrusion Detection System, Agent Technology

Cite this paper as:

Sellami L., Sellami K., Tiako P. F. (2023) A Novel Agent-Based Intrusion Detection System for Wireless Body Area Network. In: Tiako P.F. (ed) Intelligent Computing and Consumer Support Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0003-1_8

Presented at:
The 2022 OkIP International Conference on Advances in Health Information Technology (AHIT) in Oklahoma City, Oklahoma, USA, and Online, on October 3-6, 2022

Contacts:
Lynda Sellami
slynda1@yahoo.fr

Advances and Intelligent Prospects in Veterinary Sterilization Equipment

Tom Jobe, Kedar Pai, Chris Bratcher — Fri, 22 Sep 2023 00:00:00 +0000

This is a Chapter in:

Book:

Intelligent Computing and Consumer Support Applications

Series:
Chronicle of Computing

Chapter Abstract:

This paper describes the recent advances in the development of veterinary sterilization equipment. An analysis of autoclaves, dry-heat, chemical, and plasma sterilizers utilized in veterinary applications is performed to illustrate the state of available commercial instruments. The technical and commercial benefits of each technology are examined in the comparison, including the environmental impact. Prospects of sterilization using Artificial Intelligence tools are briefly discussed.

Keywords:
Environmental Impact, Sterilization, Veterinary, Gas Plasma, Air Plasma, Intelligent Sterilization Perspectives

Cite this paper as:
Jobe T., Pai K., Timmons C., Bratcher C. (2023) Advances in Veterinary Sterilization Equipment. In: Tiako P.F. (ed) Intelligent Computing and Consumer Support Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0003-1_7

Presented at:
The 2022 OkIP International Conference on Green Research & Technology (CGRT) in Oklahoma City, Oklahoma, USA, and Online, on October 3-6, 2022

Contacts:
Tom Jobe
tom.jobe@bioinspirellc.com

Safety of The Cameroonian Consumer in eCommerce

Irene Tiako, Pierre Tiako — Thu, 21 Sep 2023 00:00:00 +0000

This is a Chapter in:

Book:
Intelligent Computing and Consumer Support Applications

Series:
Chronicle of Computing

Chapter Abstract:

The exponential development of Information and Communication Technologies (ICT) has considerably impacted e-technologies, particularly e-commerce, e-banking, e-payment, etc. This rapid evolution is materialized in Cameroon by the national high-bandwidth fiber optic backbone of more than 100,000 km serving all departments. International connectivity is diversified, and an ACE (Africa Coast to Europe) submarine cable is under construction with a landing point in Kribi. An optical fiber connects Cameroon to Brazil. One of the players in e-commerce is the consumer who purchases using this technology through which enormous financial resources are transited. These financial means, which are in digital form, attract malicious people who would like to exploit the flaws of e-commerce to take advantage of them. The latter will carry out multiple attacks to weaken the system.
In Cameroon, do regulatory and technological measures sufficiently protect the consumer when shopping online? First, we will present the e-commercial environment with all the links composing the potential attacks. In the second step, we will present the measures to protect the Cameroonian consumer when shopping. This protection is achieved both by regulations and by the technological means implemented by Cameroon.

Keywords:
Information and Communication Technologies, eCommerce, eBanking, ePayment, Regulation, Consumer Protection.

Cite this paper as:
Tiako I., Tiako P.F. (2023) Safety of Cameroonian Consumer in eCommerce. In: Tiako P.F. (ed) Intelligent Computing and Consumer Support Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0003-1_6

Presented at:
The 2022 OkIP International Conference on Electronic Commerce and Applications (CECA) in Oklahoma City, Oklahoma, USA, and Online, on October 3-6, 2022

Contacts:
Irene Tiako
tiakonga1@gmail.com

Automatic Test Generation for Microservices Based on Consumer-Driven Contracts

Shaheen Zahedi, Samad Paydar — Thu, 21 Sep 2023 00:00:00 +0000

This is a Chapter in:

Book:
Intelligent Computing and Consumer Support Applications

Series:
Chronicle of Computing

Chapter Abstract:

Microservices offer a shorter time-to-market by improving productivity with maximizing the automation of the software development lifecycle. However, when dealing with such systems, there are important design principles like their solidness and test coverage, which should be considered to be able to co-operate well in a distributed environment especially from the architecture level. However, for the provider of a service, it might be challenging to maintain the obligations needed by the consumer. Therefore, in this approach a form of agreement is written by the consumer of the service, which is called contract. In this paper, a language agnostic, no-code approach is proposed for conducting the consumer-driven contract concept, through automatically converting the contracts into a set of solid test suites, that runs on each deployment. Consequently, if the service provider alters the returned data in a way that breaks the expectations of the consumer, this breaking change will be detected early by running the auto-generated test suites. The proposed approach also provides an insight for the provider, on how their service is being used and how changes can affect the consumers. Then it gets evaluated with a dataset of 56 open-source projects available on Github which use consumer-driven contract testing. The results demonstrate that for 56 real source projects, the proposed approach has been able to generate 450 tests from the contract files in the sources and 90% of the generated test suites passed.

Keywords:
Software Engineering, Microservices, Testing, Consumer Driven Contract Testing, Test Generation, Mutation Testing, Software Development, Distributed Environments

Cite this paper as:
Zahedi S., Paydar S. (2023) Automatic Test Generation for Microservices Based on Consumer-Driven Contracts. In: Tiako P.F. (ed) Intelligent Computing and Consumer Support Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0003-1_5

Presented at:
The 2022 OkIP International Conference on Software Engineering Research & Development (SERD) in Oklahoma City, Oklahoma, USA, and Online, on October 3-6, 2022

Contacts:
Shaheen Zahedi
sh.zahedi@mail.um.ac.ir
Samad Paydar
s.paydar@um.ac.ir

LitP: Language-integrated Tensor Parallelism

Philipp Kramer — Thu, 21 Sep 2023 00:00:00 +0000

This is a Chapter in:

Book:
Intelligent Computing and Consumer Support Applications

Series:
Chronicle of Computing

Chapter Abstract:

LitP, is a new data-parallel programming model for .NET respectively C#. It has been designed specifically to run data-parallel calculations in a managed runtime system. It can be used to solve a wide range of problems. Programs are described in vectorized form, but abstract from low-level architectural details of the target hardware. This allows for a good compromise between simplicity and performance. The presented library currently targets GPUs and CPUs, but is in principle well suited to any kind of general purpose, parallel processing unit.

Keywords:
Programming Model, DSL, SIMD, Data-Parallel, Data-Flow, Reactive System, GPU, Compilation

Cite this paper as:
Kramer P. (2023) LitP: Language-integrated Tensor Parallelism. In: Tiako P.F. (ed) Intelligent Computing and Consumer Support Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0003-1_4

Presented at:
The 2022 OkIP International Conference on Advances in High-Performance Computing (AHPC) in Oklahoma City, Oklahoma, USA, and Online, on October 3-6, 2022

Contact:
Philipp Kramer
philipp.kramer@ost.ch

Assessing the Impact of Interactivity in the Cognitive Domain: A Noise Ratings Case Study

Flaithri Neff, Patrick Duffy, Malachy Ronan — Thu, 21 Sep 2023 00:00:00 +0000

This is a Chapter in:

Book:
Intelligent Computing and Consumer Support Applications

Series:
Chronicle of Computing

Chapter Abstract:

The widescale implementation of Emergency Remote Teaching during the pandemic has normalized the use of blended and online learning-tools in third-level education. However, many of these digital tools are not designed to achieve specific learning objectives, leading to mixed results reported by instructors and students. Although such tools aim to make the educational experience an active one for students, there is limited empirical data on their effectiveness on knowledge-acquisition and learning. These issues are more pronounced in some disciplines compared to others, and in this paper, we present an assessment of a newly-developed interactive application relevant to music-technology - a discipline that is traditionally reliant on campus-based, in-studio experiences. In a user-study comparing a traditional approach and the use of this interactive application to present the topic ‘Noise Ratings’ to undergraduates, results show significant improvements on questions in the “Understand” and “Application” taxonomy levels between pre- and post-time intervals.

Keywords:
Remote Teaching, Interactive Learning Application, Online Learning, Blended Learning, Noise Ratings, Bloom’s Taxonomy, Cognitive Domain.

Cite this paper as:
Neff F., Duffy P., Ronan M. (2023) Assessing the Impact of Interactivity in the Cognitive Domain: A Noise Ratings Case Study. In: Tiako P.F. (ed) Intelligent Computing and Consumer Support Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0003-1_3

Presented at:
The 2022 OkIP International Conference on Learning and Technology in Education (CLTE) in Oklahoma City, Oklahoma, USA, and Online, on October 3-6, 2022

Contact:
Flaithri Neff
Flaithri.Neff@tus.ie

Robustness Analysis of Deep Reinforcement Learning for Online Portfolio Selection

Marc Velay, Bich-Lien Doan, Arpad Rimmel, Fabrice Popineau, Fabrice Daniel — Thu, 21 Sep 2023 00:00:00 +0000

This is a Chapter in:

Book:
Intelligent Computing and Consumer Support Applications

Series:
Chronicle of Computing

Chapter Abstract:

Online Portfolio Selection (OLPS) requires a careful mix of assets to minimize risk and maximize rewards over a trading episode. The stochastic, non-stationary aspect of the market makes decision-making very complex. Heuristic methods relying on historical returns were traditionally used to select assets that found a balance of risk and reward. However, improvements in modeling time series from Neural Networks led to new solutions. Deep Reinforcement Learning (DRL) has become a popular approach to solve this problem, but its methods rarely reach a consensus among publications. In other fields, solutions using non-Markovian state representations are frequent. Crafting rewards to improve agent learning is common but has effects on the resulting behaviors. The resulting processes are rarely compared to other recent State-of-the-Art solutions but to heuristic algorithms. The proliferation of approaches motivated us to benchmark them using traditional financial metrics, and evaluate their robustness over time and across market conditions. We aim to evaluate the contributions to measured performance from each method in market representation, policy learning and value estimation.

Keywords:
Online Portfolio Selection, Deep Reinforcement Learning, Resource Allocation, Robustness Analysis

Cite this paper as:
Velay M., Doan B.-L., Rimmel A., Popineau F., Daniel F. (2023) Robustness Analysis of Deep Reinforcement Learning for Online Portfolio Selection. In: Tiako P.F. (ed) Intelligent Computing and Consumer Support Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0003-1_2

Presented at:
The 2022 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 3-6, 2022

Contact:
Marc Velay
marc.velay@centralesupelec.fr

Autonomous Systems Functional Safety Overview with Multimodality and Explainability Perspectives

Pierre Tiako, Bernard Kamsu-Foguem — Thu, 21 Sep 2023 00:00:00 +0000

This is a Chapter in:

Book:

Intelligent Computing and Consumer Support Applications

Series:
Chronicle of Computing

Chapter Abstract:

Functional safety is crucial in automation systems, particularly for autonomous vehicles. It is important because it protects humans, systems or vehicles, and operating environments from harm. Automated systems can expose operators to severe safety risks. Functional safety aims to minimize safety risks associated with autonomous systems to protect operators, the environment with nearby infrastructure and people, and the systems themselves. This paper overviews multiple facets of Artificial Intelligence (AI) techniques that reduce functional safety risks. As AI and Machine Learning (ML) progress theoretically and in their application, we face new technical challenges in dealing with Multimodality and Explainability. We will discuss these concepts before briefly providing their perspectives on minimizing safety risks in autonomous systems.

Keywords:
Functional Safety, Machine Learning, Autonomous Vehicle, Soft Error, Explainability, Multimodality, Deep Learning, Neural Network

Cite this paper as:

Tiako P.F., Kamsu-Foguem B. (2023) Autonomous Systems Functional Safety Overview with Multimodality and Explainability Perspectives. In: Tiako P.F. (ed) Intelligent Computing and Consumer Support Applications. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0003-1_1

Presented at:
The 2022 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 3-6, 2022

Contact:
Pierre Tiako
tiako@ieee.org

Intelligent Computing and Consumer Support Applications

Pierre Tiako — Wed, 20 Sep 2023 00:00:00 +0000

This book introduces a collection of selected papers presented online at the 2022 OkIP Academia & Industry Convention conferences in Oklahoma City, Oklahoma, USA, on October 3-6, 2022.
The document is composed of three Parts. Part I (three chapters) includes articles addressing various challenges and perspectives in Deep Learning and the Cognitive Domain. Recent progress appears in Part II (two chapters). Lastly, Part III (three chapters) presents advances in Sustainability, health IT, and the Digital Economy, emphasizing Consumer Assistance and Support.

THIS BOOK TABLE OF CONTENTS

Preface

Part I Machine Learning and Cognitive Applications

>> Autonomous Systems Functional Safety Overview with Multimodality and Explainability Perspectives
Pierre F. Tiako, Bernard Kamsu-Foguem

>> Robustness Analysis of Deep Reinforcement Learning for Online Portfolio Selection
Marc Velay, Bich-Lien Doan, Arpad Rimmel, Fabrice Popineau, Fabrice Daniel

>> Assessing the Impact of Interactivity in the Cognitive Domain: A Noise Ratings Case Study
Flaithri Neff, Patrick Duffy, Malachy Ronan

Part II Programming, Software, and Data Engineering

>> LitP: Language-integrated Tensor Parallelism
Philipp Kramer

>> Automatic Test Generation for Microservices Based on Consumer-Driven Contracts (CDC)
Shaheen Zahedi, Samad Paydar

Part III Safety and Consumer Assistance Applications and Supports

>> Safety of the Cameroonian Consumer in E-Commerce
Irene Tiako, Pierre F. Tiako

>> Advances and Intelligent Prospects in Veterinary Sterilization Equipment
Tom Jobe, Kedar Pai, Chris Timmons, Chris Bratcher

>> A Novel Agent-Based Intrusion Detection System for Wireless Body Area Network
Lynda Sellami, Khaled Sellami, Pierre F. Tiako

Author Index

Biosensors for supporting the Green Economy

Tom Jobe, Eric Reese — Tue, 08 Mar 2022 00:00:00 +0000

This is a Chapter in:

Book:
Automated Systems, Data, and Sustainable Computing

Series:
Chronicle of Computing

Chapter Abstract:

This paper describes how the numerous forms of biosensors perform necessary functions in research, medicine, and industrial uses. It describes recent developments that allow biosensors to be used to reduce resources in power, materials and costs while improving our quality of life.

Cite this paper as:
Jobe T., Reese E. (2022) Biosensors for supporting the Green Economy. In: Tiako P.F. (ed) Automated Systems,
Data and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_12

Contact:
Tom Jobe
tom.jobe@bioinspirellc.com

Stand-alone Electric Vehicle Charging Station Using FPGA

Tue, 08 Mar 2022 00:00:00 +0000

This is a Chapter in:

Book:
Automated Systems, Data, and Sustainable Computing

Series:
Chronicle of Computing

Chapter Abstract:

This paper presents a practical work, which builds a stand-alone electric vehicles (EVs) charging station utilizing solar panels to produce the DC power. This form of power is later converted to AC power that will be used to charge the EVs. The operation of the stand-alone charging station is run in such way that it is supplied by photovoltaic (PV) power produced by the solar panels installed. The DC power obtained by the PV cells is then taken to a DC/DC converter for regulation and boosting purposes, this high voltage will then be taken to a controlled DC/DC bidirectional converter to step up the voltage to a level suitable for charging the electric vehicle. The stand-alone EV charging station is firstly modeled and built through a hardware-in-the-loop with Compact RIO and LabView. Then, the experimental prototype is implemented to verify the operation of this charging station.

Cite this paper as:
Natsheh A., AlShammar E., Alkhaja M., AlBlooshi N., Hai N., Almheiri S., AlAsaad S., Ismail S. (2022) Stand-alone Electric Vehicle Charging Station Using FPGA. In: Tiako P.F. (ed) Automated Systems, Data, and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_11

Contact:
Ammar Natsheh
ammar_natsheh@yahoo.com

Complementarity and Variability of Wind and Solar Energy in Complex Terrain

Radian Belu, Darko Koracin — Tue, 08 Mar 2022 00:00:00 +0000

This is a Chapter in:

Book:
Automated Systems, Data, and Sustainable Computing

Series:
Chronicle of Computing

Chapter Abstract:

Wind and solar energy are expected to play a major role, in the near future electricity generation mix. However, wind and solar energy-based generation are intermittent and non-dispatchable, not being suitable to supply base-load electric power. Their greater penetration and grid integration are critical issues due to their inherent intermittency and variability. Moreover, there are strong evidence that wind and solar energy are showing complementary over appropriate time and space scales. This work investigates such spatiotemporal complementarity and variability as a means by which electricity planners, developers, and grid operators might advance uses and grid integration of wind energy. Over 14 years of synchronous wind velocity and solar radiation measurements at several sites, located in complex terrain of Nevada are used in this study. To do so we used auto-correlations and cross-correlations in wind speed and solar radiation time series, by applying detrended fluctuation analysis and detrended cross-correlation analysis.

Cite this paper as:
Belu R., Karocin D. (2022) Complementarity and Variability of Wind and Solar Energy in Complex Terrain. In: Tiako P.F. (ed) Automated Systems, Data, and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_10

Author Contact:
Radian Belu
radian_belu@subr.edu

Blockchain Interoperability Method for the Cross-organizational Transactions: A Meta-Meta-Model

Ebtehal Nassar, Iman Helal, Sherif Mazen — Tue, 08 Mar 2022 00:00:00 +0000

This is a Chapter in:

Book:
Automated Systems, Data, and Sustainable Computing

Series:
Chronicle of Computing

Chapter Abstract:

Nowadays, blockchain systems are attracting attention in both academic and industry fields. Their main advantages are to their provided capabilities in security, and immutability. However, these systems are still immature growth, as there is a lack of templates for blockchain development. Interoperability can be considered one of the challenges while developing new blockchain systems, due to the heterogeneity of the blockchain systems infrastructure, consensus protocol, privacy level, etc. Although many organizations aim to utilize blockchain systems, they suffer from the limitations of running cross-organizational transactions over different blockchain systems. The current cross-organizational solutions depend on how to make a connection between the different organizations’ applications to reach choreography. In this paper, we will address the current interoperability challenges in blockchain systems and their impact on the running of cross-organizational transactions. Moreover, we will illustrate the usage of Model-Driven Engineering (MDE) to model the different aspects of blockchain systems. Finally, we will present an MDE-based solution to reach organizational interoperability as a basis for modeling hybrid applications that can process cross-organizational transactions.

Cite this paper as:
Nassar E., Helal I., Mazen S. (2022) Blockchain Interoperability Method for the Cross-organizational Transactions: A Meta-Meta-Model. In: Tiako P.F. (ed) Automated Systems, Data, and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_9

Contact:
Ebtehal Nassar
ebtehal.yahia@fci-cu.edu.eg

Practical Understanding and Taxation of the Digital Economy in Cameroon

Irene Tiako, Dawandala, Pierre Tiako — Tue, 08 Mar 2022 00:00:00 +0000

This is a Chapter in:

Book:
Automated Systems, Data, and Sustainable Computing

Series:
Chronicle of Computing

Chapter Abstract:

In a context marked by the crash of oil prices and increased spending on security challenges, the search for innovative sources for broadening tax revenues in Cameroon is crucial. One of the ways to reach this goal is for the government to better tax the Digital Economy (DE), which is undergoing a significant evolution in all sectors of activity, hence the interest that guided the choice of this topic. The goal is to ensure that the current tax system is sufficiently strengthened to meet new digital-based business models better. The tax avoidance strategies practiced by digital multinationals, in general, have led to the question of how to impose taxes on them appropriately. Two practical cases are dealt with Western Union and Canal + to answer this question. In addition to reviewing tax documentation, a technical questionnaire was submitted to a sample of forty-two employees of the General Directorate of Taxation (GDT) employees. We have used descriptive statistics to analyze the data collected. The results confirmed the study's assumptions and indicate that the current tax system is not sufficiently equipped to tax companies in the digital economy. So, we have proposed some new national measures to improve the current fiscal policies and strengthen human and technical capacity.

Cite this paper as:
Tiako I., Dawandala, Tiako P.F. (2022) Practical Understanding and Taxation of the Digital Economy in Cameroon. In: Tiako P.F. (ed) Automated Systems, Data, and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_8

Author Contact:
Irene Tiako
tiakonga1@gmail.com

The Role of Deuterium-Depleted-Water as a Health Supplement

Eric Reese, Tom Jobe — Tue, 08 Mar 2022 00:00:00 +0000

This is a Chapter in:

Book:
Automated Systems, Data, and Sustainable Computing

Series:
Chronicle of Computing

Chapter Abstract:

This paper is a review of the published literature concerning the potential health benefits of Deuterium-Depleted-Water (DDW), focusing specifically on the potential efficacy of DDW in the role of cancer prevention and treatment. A review of studies using DDW to treat numerous cancer types has been completed and is summarized. Articles and publications that describe the potential benefits of the use of DDW as a general health and dietary supplement are also summarized.

Cite this paper as:
Reese E., Jobe T. (2022) The Role of Deuterium-Depleted-Water as a Health Supplement. In: Tiako P.F. (ed) Automated Systems, Data, and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_7

Author Contact:
Eric Reese
eric.reese@bioinspirellc.com

Detecting and Preventing Approach for Electronic Medical Records in E-Health

Lynda Sellami, Khaled Sellami, Pierre Tiako, Djilali Idoughi — Tue, 08 Mar 2022 00:00:00 +0000

This is a Chapter in:

Book:
Automated Systems, Data, and Sustainable Computing

Series:
Chronicle of Computing

Chapter Abstract:

E-Health is designed to support the activities of patients, including the transmission of their medical history between healthcare professionals, the renewal or adaptation of prescriptions remotely, and the transmission of their test results online. Ensuring the security and confidentiality of patient information is a crucial point, hence the need to protect patient medical data and ensure their well-being at the same time. In this article, we propose to develop an IDS for the protection of patient medical data. Our work aims to overcome intrusion problems by developing an intrusion detection system based on legitimate healthcare professional authentication, signals and prevents intruders from breaking in. The proposed model protects patients and ensures the security of medical resources.

Cite this paper as:
Sellami L., Sellami K., Tiako P., Idoughi D. (2022) Detecting and Preventing Approach for Electronic Medical Records in E-Health. In: Tiako P.F. (ed) Automated Systems, Data, and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_6

Author Contact:
Lynda Sellami
slynda1@yahoo.fr

Initial GPU Optimization of Template Modeling Score

Hannah Johnson, Armon White, Yogesh Kale, Elijah MacCarthy — Tue, 08 Mar 2022 00:00:00 +0000

This is a Chapter in:

Book:
Automated Systems, Data, and Sustainable Computing

Series:
Chronicle of Computing

Chapter Abstract:

Accuracy in the prediction of protein structures is key in understanding the biological functions of different proteins. Numerous measures of similarity tools for protein structures have been developed over the years, and these include Root Mean Square Deviation (RMSD), as well as Template Modeling Score (TM-score). While RMSD is influenced by the length of the protein and therefore the similarity between superimposed models can be affected by divergent loops in the models, TM-score is rather a robust and a more accurate method. TM-score, however, is much slower than RMSD in terms of calculations for the optimal superimposed model. Here, we present initial optimization work on GPU-TM-score, a GPU accelerated Template Modeling Score for fast and accurate measuring of similarity between protein structures. Our optimization is based on OpenACC parallelization and performance analysis of bottleneck regions and the KABSCH algorithm for the calculation of optimal superimposition within parallel architectures. Our initial results indicate an average 3.14× speedup compared to original TM-score on a benchmark set of 20 protein structures. This speedup is recorded on an Nvidia Volta V100 GPU compared to an AMD EPYC 7742 64-core processor.

Cite this paper as:
Johnson H., White A., Kale Y., MacCarthy E. (2022) Initial GPU Optimization of Template Modeling Score. In: Tiako P.F. (ed) Automated Systems, Data, and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_5

Author Contact:
Elijah MacCarthy
emaccarthy@lanecollege.edu

Adaptive Development of Parallel Power System Dynamic Simulation Application in Python

Cong Wang, Liwei Wang, Shuangshuang Jin — Tue, 08 Mar 2022 00:00:00 +0000

This is a Chapter in:

Book:
Automated Systems, Data, and Sustainable Computing

Series:
Chronicle of Computing

Chapter Abstract:

Due to its intensive computational demands for real-time operation and diagnosis, large-scale power system dynamic simulation requires high-performance computing technologies to accelerate its computation on advanced computing platforms. In this paper, leveraging high-level Python and its parallel scientific computing libraries, three parallel power system dynamic simulation applications are adaptively developed using native MPI for Python on CPU, PETSc for Python on CPU, and CuPy on GPU with dedicated data manipulation strategies and implementations, respectively. Their computational performance is compared using different sizes of testing systems and indicates that: 1) MPI and PETSc can make a decent performance for small and moderate-size systems on limited CPU resources, and 2) GPU has better potential in speeding up dynamic simulation for larger and more complex systems. The results demonstrate Python's suitability in parallelizing power system modeling and simulation with fast computational performance and easy development.

Cite this paper as:
Wang C., Wang L., Jin S. (2022) Adaptive Development of Parallel Power System Dynamic Simulation Application in Python. In: Tiako P.F. (ed) Automated Systems, Data, and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_4

Author Contact:
Cong Wang
cong2@g.clemson.edu

Using microgrids to improve the grid resilience in the aftermath of extreme events

Radian Belu — Tue, 08 Mar 2022 00:00:00 +0000

This is a Chapter in:

Book:
Automated Systems, Data, and Sustainable Computing

Series:
Chronicle of Computing

Chapter Abstract:

The adverse effects of extreme events on the grid resulted in growing demands to address grid resilience or provide emergency services. A microgrid, covering small geographical area is less vulnerable to such events. Through its features, such as local generation, optimal load and power scheduling or islanding operation can significantly increase system reliability and resilience. Optimized generation scheduling and energy management models are implemented, by using simplified microgrid component, composite load models, and load prioritization.

Cite this paper as:
Belu R. (2022) Using microgrids to improve the grid resilience in the aftermath of extreme events. In: Tiako P.F. (ed) Automated Systems, Data, and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_3

Author Contact:
Radian Belu
radian_belu@subr.edu

Detecting Unsatisfiable Conjunctive Property Path Queries under Shape Expression Schema

Yuuki Maedaa, Nobutaka Suzuki — Mon, 07 Mar 2022 00:00:00 +0000

This is a Chapter in:

Book:

Automated Systems, Data, and Sustainable Computing

Series:

Chronicle of Computing

Chapter Abstract:

Shape Expression (ShEx) is a novel schema language proposed for RDF/graph data. For a ShEx schema S and a query q, q is said to be unsatisfiable if for any valid data D under S q reports an empty answer over D. In general, the size of RDF/graph data is very large, and thus it is inefficient to perform unsatisfiable queries on such data. Therefore, it is desirable that we can detect unsatisfiable queries efficiently before executing them. In this paper, we consider Conjunctive Property Path, a generalization of Property Path defined in SPARQL 1.1, as the query language. First, we propose an algorithm for determining satisfiability of Conjunctive Property Path queries under ShEx schema. Then we conducted a preliminary experiment, which results suggest that the proposed algorithm determines if a given Conjunctive Property Path query is satisfiable efficiently.

Cite this paper as:
Maedaa Y., Suzuki N. (2022) Detecting Unsatisfiable Conjunctive Property Path Queries under Shape Expression Schema. In: Tiako P.F. (ed) Automated Systems, Data and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_2

Contact:
Nobutaka Suzuki
nsuzuki@slis.tsukuba.ac.jp

Getting Together for Computing Cross-fertilization and Broader Participation

Pierre Tiako, Nancy Mead, Manos Tentzeris, Akhil Kumar, Tom Jobe, Pierre Bourque — Mon, 28 Feb 2022 00:00:00 +0000

This is a Chapter in:

Book:
Automated Systems, Data, and Sustainable Computing

Series:
Chronicle of Computing

Chapter Abstract:

This paper briefly presents the diversity and quality of the work presented by distinguished speakers, from academia and industry, at the 2021 Inaugural OkIP Academia & Industry Convention. Speakers’ short biography is also presented. For maximum cross-fertilization and broader participation, they talked in various areas of computing, including (1) threat modeling and machine learning, (2) sustainable energy harvesting and wireless power transfer systems, (3) blockchain technology and its implications in business applications and healthcare IT, (4) biosensors in a green economy, and (5) software engineering body of knowledge guide.

Cite this paper as:
Tiako P.F., Mead N.R., Tentzeris M.M., Kumar A., Jobe T., Bourque P. (2022) Getting Together for Quality Cross-fertilization and Broader Participation. In: Tiako P.F. (ed) Automated Systems, Data, and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_1

Contact:
Pierre Tiako
tiako@ieee.org

Automated Systems, Data, and Sustainable Computing

Fri, 25 Feb 2022 00:00:00 +0000

This book introduces a collection of papers presented at the 2021 Inaugural OkIP Academia & Industry Convention. This document is very concise due to the short period to organize it (July-October), the current COVID situation, and the “inaugural” nature of the event. We are consistent with “Big things have small beginnings” from the story of T.E. Lawrence. The document is composed of twelve small conferences listed in the front matter of this document. The conferences were held simultaneously (exact location and dates) online and at the Moore-Norman Tech Campus of Oklahoma City, Oklahoma, USA, on November 15-18, 2021. Unfortunately, the event had to be conducted online due to technical issues with the Internet at that location. This book is composed of an introductory chapter and three Parts. Part I (four chapters) includes articles that address various challenges in Automated Systems, Software & Data Engineering, and Security. Recent progress in Heath IT, Business & Digital Economy, Data, and Process appears in Parts II (four chapters). Lastly, Part III (three chapters) presents advances in Sustainability, Energy, and Power.

THIS BOOK TABLE OF CONTENTS

Preface

Getting Together for Computing Cross-fertilization and Broader Participation
Pierre Tiako, Nancy Mead, Manos Tentzeris, Akhil Kumar, Tom Jobe, Pierre Bourque

Part I Automated Systems, Software & Data Engineering, and Security
Detecting Unsatisfiable Conjunctive Property Path Queries under Shape Expression Schema
Yuuki Maedaa, Nobutaka Suzuki

Using microgrids to improve the grid resilience in the aftermath of extreme events
Radian Belu

Adaptive Development of Parallel Power System Dynamic Simulation Application in Python
Wang Cong, Liwei Wang, Shuangshuang Jin

Initial GPU Optimization of Template Modeling Score
Hannah Johnson, Armon White, Yogesh Kale, Elijah MacCarthy

Part II Heath IT, Business & Digital Economy, Data, and Process
Detecting and Preventing Approach for Electronic Medical Records in E-Health
Lynda Sellami, Djilali Idoughi, Khaled Sellami, Pierre Tiako

The Role of Deuterium-Depleted-Water as a Health Supplement
Eric Reese, Tom Jobe

Practical Understanding and Taxation of the Digital Economy in Cameroon
Irene Tiako, Pierre Tiako, Dawandala

Blockchain Interoperability Method for the Cross-organizational Transactions: A Meta-Meta-Model
Ebtehal Nassar, Iman Helal, Sherif Mazen

Part III Sustainability, Energy, and Power
Complementarity and Variability of Wind and Solar Energy in Complex Terrain
Radian Belu, Darko Koracin

Stand-alone Electric Vehicle Charging Station Using FPGA
Ammar Natsheh, Eman AlShammar, Maryam Alkhaja, Noora AlBlooshi, Nguyen Hai, Salama Almheiri, Salma AlAsaad, Shamma Ismail

The Role of Biosensors in a Green Economy
Tom Jobe, Eric Reese

Author Index