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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Li, Yuan Lu, Xiaofen Yao, Xin Southern University of Science and Technology The Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

ISBN: (纸本)9798350314588

Evolutionary algorithms (EAs) combined with constraint handling techniques (CHTs) are very effective in solving constrained optimization problems (COPs). Recently, negatively correlated search (NCS) has been shown to be powerful for real-world multimodal optimization problems (MMOP). However, there has been few works on utilizing NCS to solve COPs. In this paper, we present a novel constrained optimization evolutionary algorithm (COEA) named NCS-E which combined NCS with a new CHT ensemble method to deal with COPs. It integrates three complementary CHTs as voters, and individuals are first voted by each voter, then the individual with more weighted votes is considered better. In addition, the negative correlation information of the voted individuals is utilized to adaptively adjust the voting weights. To demonstrate the performance of NCS-E, NCS-E is compared with four NCS-based COEAs and two state-of-the-art methods, MAgES and MAgES-VMCH, on 57 real-world COPs. The statistical results show that NCS-E exhibits the best performance among the compared methods. Moreover, NCS-E was applied to solve the Deep Neural Network pruning problem and compared with the state-of-the-art pruning method OLMP on three reference models. Empirical results show that the proposed method improves the performance of OLMP on all three DNN pruning tasks. © 2023 IEEE.

关键词： Constrained optimization

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Application of a High Power Fabry-Perot QW Laser Diode on Multi-Channel Coherent Optical System

Application of a High Power Fabry-Perot QW Laser Diode on Mu...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Ghosh, Shalmoli Raisa, Sharmila O'Sullivan, Maurice Laperle, Charles Hui, Rongqing Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS66045 United States Ciena Corporation NepeanONK2K 0L1 Canada

We investigate the property of a Fabry-Perot quantum-well laser diode which emits multiple spectral lines with a total power exceeding 120mW. We show its application as an optical source in a multi-channel coherent op... 详细信息

ISBN: (纸本)9781957171395

关键词： Fabry-Perot interferometers

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Modeling source code in bimodal for program comprehension

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Neural Computing and Applications 2024年第22期36卷 13815-13832页

作者： Wen, Dongzhen Zhang, Xiaokun Diao, Yufeng Zhao, Ziyun Jiang, He Lin, Hongfei School of Computer Science and Technology Dalian University of Technology 2rd Ling Gong Road Liaoning Dalian116024 China Department of Software Engineering School of Software Technology Dalian University of Technology No. 321 Tuqiang Street Dalian Economic and Technological Development Zone Liaoning Dalian116024 China School of Computer Science and Technology Inner Mongolia University for Nationalities 536 West Huolinhe Street Inner Mongolia Tongliao028000 China

Source code is an intermediary through which humans communicate with computer systems. It contains a large amount of domain knowledge which can be learned by statistical models. Furthermore, this knowledge can be used to build software engineering tools. We find that the functionality of the source code depends on the programming language-specific token which build the base structure, while identifiers provide natural language information. On this basis, we found that the knowledge in the source code can be sufficiently learned more when modeling the source code in bimodal. This paper presents the bimodal composition language model (BCLM) for source code modeling and representation. We analyze the effectiveness of bimodal modeling, and the results show that the bimodal approach has great potential for source code modeling and program comprehension. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Domain Knowledge

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Optimizing Resource Utilization Using Vector Databases in Green Internet of Things

Optimizing Resource Utilization Using Vector Databases in Gr...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Kumari, Rani Sah, Dinesh Kumar Cengiz, Korhan Nauman, Ali Ivković, Nikola Mihaljević, Ivan Birla Institutue of Technology Department of Computer Science and Engineering Ranchi India Siksha 'o' Anusandhan Department of Computer Science and Engineering Bhubaneswar India Istinye University Department of Computer Engineering Istanbul Turkey University of Hradec Kralove Faculty of Informatics and Management Department of Information Technologies Kralove50003 Czech Republic Yeungnam University Department of Information and Communication Engineering 280 Daehak-Ro Gyeongsan Gyeongbuk38541 Korea Republic of University of Zagreb Faculty of Organization and Informatics Pavlinska 2 Varaždin42000 Croatia

ISBN: (纸本)9798350370218

With the rapid proliferation of Internet of Things (IoT) devices and the ever-increasing volume of sensor data, optimizing resource utilization has become crucial for building sustainable and efficient IoT systems. In this study, we propose a novel approach for optimizing resource utilization in Green IoT through efficient storage and retrieval in vector databases. Our approach leverages specialized data structures, including k-d trees and ball trees, to achieve improved storage efficiency and accelerated retrieval performance for high-dimensional sensor data. We conducted extensive experiments to evaluate the effectiveness of our proposal, comparing it with traditional database approaches. The results demonstrate significant improvements in storage efficiency, with vector databases requiring considerably less storage space compared to traditional databases. Moreover, our approach enables fast and accurate retrieval of high-dimensional sensor data, reducing query times and enhancing real-time data analysis and decision-making capabilities. The technical achievements of our proposal offer promising prospects for the development of sustainable and efficient IoT systems in various domains, such as environmental monitoring, healthcare, and smart cities. Our work contributes to advancing the field of Green IoT by addressing the challenges of resource utilization and query performance through efficient storage and retrieval in vector databases. © 2023 IEEE.

关键词： Green IoT Indexing Techniques Resource Utilization Specialized Data Structures Vector Databases

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Neural Network-Based Obstacle and Pothole Avoiding Robot 4th

Neural Network-Based Obstacle and Pothole Avoiding Robot

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4th International Conference on Trends in Cognitive Computation engineering, TCCE 2022

作者： Al Arafat, Md. Mahedi Hossain, Mohammad Shahadat Hossain, Delowar Andersson, Karl Department of Computer Science and Engineering University of Chittagong Chittagong4331 Bangladesh Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Department of Computer Science Electrical and Space Engineering Lulea University of Technology SkellefteåSE-931 87 Sweden

ISBN: (纸本)9789811994821

The main challenge of any mobile robot is to detect and avoid obstacles and potholes. This paper presents the development and implementation of a novel mobile robot. An Arduino Uno is used as the processing unit of the robot. A Sharp distance measurement sensor and Ultrasonic sensors are used for taking inputs from the environment. The robot trains a neural network based on a feedforward backpropagation algorithm to detect and avoid obstacles and potholes. For that purpose, we have used a truth table. Our experimental results show that our developed system can ideally detect and avoid obstacles and potholes and navigate environments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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Machine Learning-Based Intrusion Detection Systems for the Internet of Drones: A Systematic Literature Review

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IEEE Access 2025年 13卷 96681-96714页

作者： Mostafa Ogab Sofiane Zaidi Abdelhabib Bourouis Carlos T. Calafate Department of Mathematics and Computer Science Research Laboratory on Computer Science’s Complex Systems (ReLa(CS)2) University of Oum El Bouaghi Oum El Bouaghi Algeria Department of Computer Engineering (DISCA) Universitat Politècnica de València Valencia Spain

The Internet of Drones (IoD) is a dynamic network architecture in which multiple drones, equipped with communication, sensing, and computation capabilities, are interconnected through Internet of Things (IoT) technologies to perform coordinated tasks autonomously. This infrastructure enables seamless real-time data exchange and collaborative operations across diverse applications, ranging from surveillance to delivery services, while ensuring adaptability, scalability, and security in dynamic aerial environments. However, the IoD introduces new security challenges, as drones are highly vulnerable to various cyberthreats and cyberattacks. Existing Intrusion Detection Systems (IDS) for IoD face several limitations, including high false positive rates, resource constraints of drones, limited adaptability to evolving attack patterns, and a lack of standardized datasets for benchmarking, despite ongoing research efforts. Moreover, there is a lack of a comprehensive study that systematically consolidates existing research. In this paper, we present a systematic literature review to examine the current research area of intrusion detection systems for IoD, focusing on the effectiveness of implemented machine learning models, employed datasets, existing challenges and limitations, as well as emerging trends and future research directions. This review follows PRISMA guidelines, with peer-reviewed journal articles and conference papers selected as the inclusion criteria. Publications relevant to the topic are sourced from a range of databases, including Scopus, IEEE Xplore, scienceDirect, SpringerLink, ACM Digital Library, and MDPI, covering a 10-year period from 2014 to 2024. From an initial pool of 1,909 records, 62 relevant reports are selected to address the identified research questions. The selected studies are categorized according to publication year, venue, journal, drone domain, IDS type, utilized algorithms, datasets, attack classifications, and software environment

关键词： Drones Security Intrusion detection Systematic literature review Autonomous aerial vehicles Machine learning Real-time systems Taxonomy Systematics Surveys

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A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems

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ACM Transactions on Evolutionary Learning and Optimization 2023年第4期3卷 1-25页

作者： Yazdani, Delaram Yazdani, Danial Yazdani, Donya Omidvar, Mohammad Nabi Gandomi, Amir H. Yao, Xin Department of Computer Engineering Mashhad Branch Azad University Mashhad*** Iran Faculty of Engineering & Information Technology University of Technology Sydney Ultimo2007 Australia AI Lab British Antarctic Survey CambridgeCB3 0ET United Kingdom School of Computing Leeds University Business School University of Leeds LeedsLS2 9JT United Kingdom Obuda University Budapest1034 Hungary Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method. © 2023 Copyright held by the owner/author(s).

关键词： Resource allocation

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Ensemble Multifeatured Deep Learning Models and Applications: A Survey

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IEEE Access 2023年 11卷 107194-107217页

作者： Abimannan, Satheesh El-Alfy, El-Sayed M. Chang, Yue-Shan Hussain, Shahid Shukla, Saurabh Satheesh, Dhivyadharsini Mumbai410206 India King Fahd University of Petroleum and Minerals SDAIA-KFUPM Joint Research Center for Artificial Intelligence Information and Computer Science Department Dhahran34464 Saudi Arabia King Fahd University of Petroleum and Minerals Interdisciplinary Research Center of Intelligent Secure Systems Information and Computer Science Department Dhahran34464 Saudi Arabia National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan School of Business MaynoothW23 F2H6 Ireland Lucknow226002 India Vellore632014 India

Ensemble multifeatured deep learning methodology has emerged as a powerful approach to overcome the limitations of single deep learning models in terms of generalization, robustness, and performance. This survey provides an extended review of ensemble multifeatured deep learning models, and their applications, challenges, and future directions. We explore potential applications of these models across various domains, including computer vision, medical imaging, natural language processing, and speech recognition. By combining the strengths of multiple models and features, ensemble multifeatured deep learning models have demonstrated improved performance and adaptability in diverse problem settings. We also discuss the challenges associated with these models, such as model interpretability, computational complexity, ensemble model selection, adversarial robustness, and personalized and federated learning. This survey highlights recent advancements in addressing these challenges and emphasizes the importance of continued research in tackling these issues to enable widespread adoption of ensemble multifeatured deep learning models. It provides an outlook on future research directions, focusing on the development of new algorithms, frameworks, and hardware architectures that can efficiently handle the large-scale computations required by these models. Moreover, it underlines the need for a better understanding of the trade-offs between model complexity, accuracy, and computational resources to optimize the design and deployment of ensemble multifeatured deep learning models. © 2013 IEEE.

关键词： computer vision

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VigilantAI: Real-time detection of anomalous activity from a video stream using deep learning

VigilantAI: Real-time detection of anomalous activity from a...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Javed, Danish Arshad, Usama Peerzada, Shuhrabeel Saud, Muhammad Ramiz Ali, Nisar Ali, Raja Hashim Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

In an era where artificial intelligence (AI) solutions are increasingly integrated into various sectors, we have utilized Artificial Intelligence for enhancing public safety through realtime detection of illegal activities such as robberies and threats at gunpoint using CCTV footage. With the proliferation of deep learning in object detection, the study focuses on deploying the YoloV5 model, trained on a custom dataset compiled from diverse CCTV sources and movies, to identify specific criminal actions. One of the major problems faced in this field is the availability of a large robust labeled dataset on which a deep learning model can be trained. For this purpose, we have created our own dataset by converting various CCTV footage and movies into images, and then labeling them with the correct class. In addition, we also augmented data by using various data augmentation techniques for the chosen images. This dataset, enriched through augmentation techniques and annotated with bounding boxes, allows for the precise detection of threats, achieving an accuracy rate of 85%. Our system stands out by not only spotting these activities but also by instantly alerting security personnel, facilitating a rapid response to potentially dangerous situations. This capability is important for law enforcement agencies worldwide, offering them an advanced tool to act swiftly and prevent crimes, thereby enhancing public security. The essence of our work demonstrates the practical application and significant impact of AI in bolstering security measures, providing a solid foundation for future enhancements in the field. Through this initiative, we aim to foster a safer environment in public spaces, reducing crime rates and increasing the general public’s sense of safety. © 2024 IEEE.

关键词： Crime

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Enhancing Fake News Detection Using BERT: A Comparative Analysis of Logistic Regression, RFC, LSTM and BERT

Enhancing Fake News Detection Using BERT: A Comparative Anal...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ramzan, Alishba Ali, Raja Hashim Ali, Nisar Khan, Ayesha Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

The research analyzed how to identify fake news effectively, understanding the developing capabilities to differentiate between misinformation and the correct nature of news in today’s complex media environment. Numerous approaches were tested: mostly the ways of determining if a certain message is valid or false. With application of Logistic Regression, Random Forest and lastly LSTM models, the research has been able to achieve 98.7%, 98.8%, and 95% accuracies respectively, showing that these traditional and advanced techniques are very effective. However, it became apparent that these models performed poorly as soon as tested on a new dataset and the discrepancy was not being able to understand context while being trained. Understanding the context is the key in news identification, so the research turned to BERT, the Transformer model pre-trained with huge contextual data to perform this task. Drawing on its deep and diverse knowledge base, BERT has shown a remarkable aptitude for sorting news articles into fake/real categories based on their context. Logistic Regression, Random Forest, and LSTM models demonstrated that although they were able to build models that were highly accurate up to 99% on familiar data, their accuracy dropped disproportionately as soon as new data was given. BERT, despite having a lower overall accuracy of 84% demonstrated a better sensitivity to contextual nuances in the news data. Here, the importance of contextualized conception in the sphere of fake news detection should be emphasized as a way to take advantage of BERT’s complicated comprehension which is a promising alternative of more accurate and effective identification as the media landscapes of the modern times may be complex and diverse. Despite the fact that the classical models surpass in planned environments, BERT’s capability of contextualization made it irreplaceable for evidence as the news sources in uncommon circumstances can digress from base. © 2024 IEEE.

关键词： Logistic regression

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