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4th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2022

作者： Shehab, Mohammad Tarawneh, Omar Abusalem, Hani Shannag, Fatima Al-Omari, Walaa Amman Arab University Faculty of Computer Sciences and Informatics Amman Jordan University of South Carolina Aiken Department of Computer Science Engineering and Mathematics Aiken United States

ISBN: (纸本)9781665494113

Gradient-based optimizer (GBO) is one of the most promising metaheuristic algorithms, where it proved its efficiency in various fields. GBO combine two major search mechanisms population-based and gradient-based Newton. Thus, it has a strong ability in global search. However, it suffers from dealing with local search problems. In this paper, a new version introduces which integrates the feature of Simulating annealing method (SA) with the GBO (GBOSA) to enhance the local search technique. The proposed GBOSA has been compared with various popular algorithms and improved variants on a set of real-world engineering problems. The experiment results show that GBOSA outperformed the other algorithms in the literature. © 2022 IEEE.

关键词： Simulated annealing

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R-TAP: A Cloud-Based Multi-Region Road Traffic Accident Severity Prediction Model

R-TAP: A Cloud-Based Multi-Region Road Traffic Accident Seve...

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IEEE Industrial Electronics and Applications Conference (IEACon)

作者： Albe Bing Zhe Chai Bee Theng Lau Mark Kit Tsun Tee Chris McCarthy Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science & Software Engineering Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798350382976

ISBN: (纸本)9798350382983

The tremendous impact of road traffic accidents (RTAs) remains a global concern, especially in low- and middle-income countries (LMICs). While most LMICs have limited budget to afford advanced traffic monitoring systems, it is necessary to develop feasible solutions for enhancing road safety in these regions. Recent studies have extensively explored the applicability of machine learning (ML) models to predict RTA severity in different regions. However, there is limited exploration of training and evaluating the performance of ML model from a multi-region perspective. Further investigation is also necessary to formulate a cost-effective implementation strategy for LMICs to deploy ML models for real-time prediction. Hence, this paper contributes by proposing R-TAP, which deploys a TabNet-based model on the cloud for multi-region RTA severity predictions. A web application is also developed to receive inputs and visualise the prediction results. The TabNet model is trained on a multi-region dataset consisting of 42 features, produced by combining the individual RTA datasets collected from Australia, Brazil, Ethiopia, New Zealand, the UK, and the US. Evaluation with random forest and multinomial logistic regression showed that TabNet can achieve performance comparable to random forest. It also has the highest area under the receiver operating characteristics at 0.8857. R-TAP is also simulated using testing samples as real-time input, with an overall execution time of 4.104s per prediction. It is considered satisfactory and is expected to be useful for emergency responders to make informed decisions. This solution is expected to support LMICs in their roadmap towards enhanced road safety. Future research is essential to improve the predictive power and include datasets from more regions for a more inclusive RTA severity prediction.

关键词： Training Visualization Receivers Predictive models Road traffic Road safety Real-time systems Random forests Accidents Testing

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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Voltage Control of Distribution System with High Sharing of Photovoltaic Power Supply Using Grey Wolf Optimization Technique 23

Voltage Control of Distribution System with High Sharing of ...

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23rd International Middle East Power Systems Conference, MEPCON 2022

作者： Hussein, Mahmoud M. Amer, Mostafa Alkhalaf, Salem Senjyu, Tomonobu Hemeida, Ashraf Aswan University Faculty of Energy Engineering Department of Electrical Engineering Aswan81528 Egypt College of Science and Arts in Ar-Rass Department of Computer Qassim University Ar Rass52571 Saudi Arabia University of the Ryukyus Faculty of Engineering Dept. of Elect. and Electronics Engineering Nishihara903-0213 Japan

ISBN: (纸本)9781665463638

There are significant restrictions for potential control in the distribution systems that contain photovoltaic (PV) energy resources due to a variable nature of renewable sources and uncertainty of these sources. An optimized method for voltage control has been used in this paper assuming the control of both PV inverter reactive power and on load tap changer (OLTC) step control of distribution transformer. The method aims to mitigate the voltage raise (VR) and voltage drop (VD) and try to keep the voltage within the limits defined by utility companies. To make the control system achieve the desired goals of voltage control, grey wolf optimization technique (GWO) will be applied upon the objective function of the control system. The system under study is 33-IEEE bus system, with four different variable loads (commercials, industrial, residential and lighting) over a day. The time response shows that the implementation of both control technique of the PV inverter and OLTC is superior over implementation of PV-inverter alone. © 2022 IEEE.

关键词： Voltage control

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HADR: APPLYING DOMAIN RANDOMIZATION FOR GENERATING SYNTHETIC MULTIMODAL DATASET FOR HAND INSTANCE SEGMENTATION IN CLUTTERED INDUSTRIAL ENVIRONMENTS

arXiv

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arXiv 2023年

作者： Grushko, Stefan Vysocký, Aleš Chlebek, Jakub Prokop, Petr Department of Robotics Faculty of Mechanical Engineering VSB - Technical University of Ostrava Czech Republic Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB - Technical University of Ostrava Czech Republic

This study uses domain randomization to generate a synthetic RGB-D dataset for training multimodal instance segmentation models, aiming to achieve colour-agnostic hand localization in cluttered industrial environments. Domain randomization is a simple technique for addressing the "reality gap" by randomly rendering unrealistic features in a simulation scene to force the neural network to learn essential domain features. We provide a new synthetic dataset for various hand detection applications in industrial environments, as well as ready-to-use pretrained instance segmentation models. To achieve robust results in a complex unstructured environment, we use multimodal input that includes both colour and depth information, which we hypothesize helps to improve the accuracy of the model prediction. In order to test this assumption, we analyze the influence of each modality and their synergy. The evaluated models were trained solely on our synthetic dataset;yet we show that our approach enables the models to outperform corresponding models trained on existing state-of-the-art datasets in terms of Average Precision and Probability-based Detection Quality. © 2023, CC BY-SA.

关键词： Random processes

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P-ACOHONEYBEE: A Novel Load Balancer for Cloud Computing Using Mathematical Approach

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computers, Materials & Continua 2022年第10期73卷 1943-1959页

作者： Sunday Adeola Ajagbe Mayowa O.Oyediran Anand Nayyar Jinmisayo A.Awokola Jehad F.Al-Amri Computer Engineering Department Ladoke Akintola University of TechnologyLAUTECHOgbomosoNigeria Computer Science Department Ajayi Crowther UniversityOyoNigeria Graduate School Faculty of Information TechnologyDuy Tan UniversityDa NangVietnam Department of Computer Sciences Precious Cornerstone UniversityIbadanNigeria Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Cloud computing is a collection of disparate resources or services,a web of massive infrastructures,which is aimed at achieving maximum utilization with higher availability at a minimized *** of the most attractive applications for cloud computing is the concept of distributed information ***,privacy,energy saving,reliability and load balancing are the major challenges facing cloud computing and most information technology *** balancing is the process of redistributing workload among all nodes in a network;to improve resource utilization and job response time,while avoiding overloading some nodes when other nodes are underloaded or idle is a major ***,this research aims to design a novel load balancing systems in a cloud computing *** research is based on the modification of the existing approaches,namely;particle swarm optimization(PSO),honeybee,and ant colony optimization(ACO)with mathematical expression to form a novel approach called *** experiments were conducted on response time and *** results of the response time of honeybee,PSO,SASOS,round-robin,PSO-ACO,and P-ACOHONEYBEE are:2791,2780,2784,2767,2727,and 2599(ms)*** outcome of throughput of honeybee,PSO,SASOS,round-robin,PSO-ACO,and P-ACOHONEYBEE are:7451,7425,7398,7357,7387 and 7482(bps)*** is observed that P-ACOHONEYBEE approach produces the lowest response time,high throughput and overall improved performance for the 10 *** research has helped in managing the imbalance drawback by maximizing throughput,and reducing response time with scalability and reliability.

关键词： ACO cloud computing load balancing swarm intelligence PSO P-ACOHONEYBE honeybee swarm

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Training Artificial Neural Networks by Coordinate Search Algorithm

Training Artificial Neural Networks by Coordinate Search Alg...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Rokhsatyazdi, Ehsan Rahnamayan, Shahryar Miyandoab, Sevil Zanjani Bidgoli, Azam Asilian Tizhoosh, H.R. Lab Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada Rhazes Lab Mayo Clinic Department of Artificial Intelligence and Informatics RochesterMN United States

ISBN: (纸本)9781665430654

Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously;for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such

关键词： Artificial Neural Network (ANN) Coordinate Search Expensive Optimization Gradient-free Large-Scale Optimization Stochastic Gradient Descent (SGD)

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Machine Learning Approaches for Efficient Traffic Flow in Smart Cities 3

Machine Learning Approaches for Efficient Traffic Flow in Sm...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Kulkarni, Aniket Anitha, P. Valluri, John Yesudas Sunena Rose, M.V. Hemavathi, U. Hussein, Omar M. Software Qa Manager Drata Inc San DiegoCA United States Kalasalingam Academy of Research and Education Department of Computer Applications Tamil Nadu Srivilliputhur India Faculty of Management Studies Bengaluru India Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Avadi Chennai India Al-Farahidi University Medical Technical College Medical Instrumentation Engineering Baghdad Iraq

ISBN: (纸本)9798350354379

These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning (ML) techniques can handle large numbers of parameters in massive amounts of data and dynamic behaviour over time, they are replacing analytical and statistical methods in the solving of real-world problems. This research presents a convergence of machine-learning (ML) and deep-learning (DL) calculations for traffic stream prediction;this paves the way for flexible traffic signals, which can be implemented through the use of a calculation that adjusts the timing in accordance with the anticipated traffic flow or by controlling traffic signals to some degree. The proposed ML and DL models are constructed, validated, and tested using two publicly accessible datasets. The first depicts the total number of cars subjected to checks using various sensors at six junctions over the course of 56 days. In this research, ML and DL models are created using data from four of the six junctions. The excellent performance metrics obtained by all ML and DL computations suggest that their application to intelligent traffic light management is feasible. © 2024 IEEE.

关键词： Traffic congestion

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User Reviews Sentiments Integrated E-Commerce App Recommendation System: Comparative Analysis and Cold-Start Solution

User Reviews Sentiments Integrated E-Commerce App Recommenda...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Yesmin Akter Monika Chakraborty Mohammed Mahmudur Rahman Sanjida Yasmin Shanta Department of Computer Science & Engineering International Islamic University Chittagong Faculty of Science and Technology University Sains Islam Malaysia Malaysia

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Mobile applications come in a variety of genres, including gaming, entertainment, and e-commerce, and users frequently rely on ratings and reviews to determine the applications' dependability and quality. In order to match customer preferences, recommendation algorithms and sentiment analysis are essential. Personalized app suggestions improve discovery and satisfaction. Sentiment analysis, in particular, is essential for personalizing experiences since it evaluates the underlying sentiments of user comments and draws insights from user-generated information. Sentiment analysis and recommendation algorithms are combined in a novel way in app recommendation systems. Sentiment analysis is used in the study to classify user ratings of six e-commerce apps from the Google Play Store, ranging from “Very Negative” to “Very Positive.” In an effort to better customize the user experience, these feelings were analyzed using supervised machine learning techniques. The creation of a hybrid recommendation model, which addresses Cold-Start and New User issues, is the central focus of this research. It combines collaborative filtering (CF) and content-based filtering (CBF), and is further improved using a knowledge-based filtering approach. The goal of this strategy is to offer more precise and customized app suggestions. Metrics including RMSE, Precision, Recall, and F1-score were used to evaluate the models' performance, showing off their exceptional accuracy and predictive power in raising user engagement and satisfaction.

关键词： Sentiment analysis Technological innovation Filtering Reviews Collaborative filtering Knowledge based systems User experience Electronic commerce Fifth Industrial Revolution Recommender systems

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Equivariant Frames and the Impossibility of Continuous Canonicalization

arXiv

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arXiv 2024年

作者： Dym, Nadav Lawrence, Hannah Siegel, Jonathan W. Faculty of Mathematics Faculty of Computer Science Technion Israel Department of Electrical Engineering and Computer Science MIT MA United States Department of Mathematics Texas A&M University TX United States

Canonicalization provides an architecture-agnostic method for enforcing equivariance, with generalizations such as frame-averaging recently gaining prominence as a lightweight and flexible alternative to equivariant architectures. Recent works have found an empirical benefit to using probabilistic frames instead, which learn weighted distributions over group elements. In this work, we provide strong theoretical justification for this phenomenon: for commonly-used groups, there is no efficiently computable choice of frame that preserves continuity of the function being averaged. In other words, unweighted frame-averaging can turn a smooth, non-symmetric function into a discontinuous, symmetric function. To address this fundamental robustness problem, we formally define and construct weighted frames, which provably preserve continuity, and demonstrate their utility by constructing efficient and continuous weighted frames for the actions of SO(d), O(d), and Sn on point clouds. Copyright © 2024, The Authors. All rights reserved.

关键词： Probability distributions

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