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18th IEEE International Conference on Advanced Networks and Telecommunications systems, ANTS 2024

作者： Kotha, Aditya Venkanna, U. International Institute of Information Technology Department of Computer Science and Engineering Naya Raipur India National Institute of Technology Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350391725

Autonomous vehicles (AVs) increasingly rely on vehicle-to-everything (V2X) networks for communication. However, due to the devices' heterogeneity, they are more susceptible to attacks like distributed denial of service (DDoS), resulting in packet loss and transmission delays. Existing solutions to these issues often create bottlenecks and lack post-attack recovery mechanisms. To solve these problems, this paper proposes RAPID-AV, an ML-based approach that uses programmable P4 switches for real-time detection and mitigation of DDoS attacks in AV networks. The proposed system uses a threefold strategy: collect traffic statistics from AV, use P4-based programmable packet processing for anomaly detection, and use majority voting to mitigate attacks. In our experiments, we found that the decision tree algorithm achieves an accuracy of 94.33% in distinguishing traffic from DDoS traffic. In addition, RAPID-AV demonstrated a nearly 10% reduction in CPU load during attacks and maintained consistent communication between AV and RSU. This approach can help improve AV network security while addressing the limitations of existing solutions. © 2024 IEEE.

关键词： Network security

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Assessment Software Reliability in Component-Based Software systems: A Comprehensive Review 6

Assessment Software Reliability in Component-Based Software ...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Tagen, Ibrahim Ayman Wassif, Khaled T. Sayed, Sabah Moawad, Ramadan Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt Department of Computer Science Faculty of Computers and Information Technology Future University in Egypt New Cairo Egypt

ISBN: (纸本)9798350373875

The reliability of Component-Based Software systems (CBSS) is a critical factor in the success and longevity of software applications across various domains. This comprehensive review synthesizes methodologies and findings from thirteen key studies, offering a panoramic view of the landscape of CBSS reliability assessment. We explore a spectrum of approaches, from traditional statistical models to advanced computational intelligence techniques, evaluating their effectiveness, challenges, and contexts of applicability. Our review reveals a trend towards the integration of machine learning and fuzzy logic in reliability estimation, reflecting the complexity and dynamic nature of CBSS. Moreover, we identify a significant emphasis on predictive models that cater to early detection of reliability issues, underscoring the proactive aspect of modern software reliability engineering. The findings suggest a need for more adaptable and context-sensitive methods that can accommodate the evolving nature of software components and their interactions. This review not only charts the current state of CBSS reliability assessment but also highlights future directions for research, including the integration of real-time data and the development of more robust models for uncertainty management. Our study serves as a crucial resource for researchers and practitioners seeking to enhance the reliability of CBSS, ultimately contributing to the development of more dependable software systems. © 2024 IEEE.

关键词： Machine learning

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PSO-CHS Routing Protocol for Energy Consumption Enhancement in Wireless Sensor Networks 4

PSO-CHS Routing Protocol for Energy Consumption Enhancement ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Fakhri, Israa Sabri Marhoon, Haydar Abdulameer Hussein, Mohsin Hasan College of Computer Science and Information Technology University of Kerbala Department of Computer Science Karbala Iraq Scientific Research Center Al-Ayen University Department of Computer Science Information and Communication Technology Research Group Thi-Qar Iraq

ISBN: (纸本)9798350352931

A region's Wireless sensor networks consist of sensors distributed over a region that collect and send environmental data to a central station. It measures humidity and temperature. The formation is based on many key ideas, including the routing system that optimizes data flow to the base station. Routing introduces limits and obstacles to better results. One challenge is choosing the cluster head, who aggregates data from nearby sensors and sends it to the base station. Many methods are used to choose the cluster head. Instead of GSTEB, DCBRP, and ETSRP, which are laborious and time-consuming, modern cluster head identification methods use artificial intelligence. This study selects the cluster head quickly and accurately using PSO-CHS (particle swarm optimization-Cluster Head Selection Routing Protocol). It improves execution time by 84.1%, delay matrix by 44.3%, and energy matrix by 80.4% over previous protocols. PSO-CHS design promotes energy efficiency, network durability, and packet loss reduction. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A Performance Analysis of Boosting Algorithms for the Identification of Card Fraud 21

A Performance Analysis of Boosting Algorithms for the Identi...

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21st IEEE International Conference on computer Applications, ICCA 2024

作者： Han, Sai Saw Wai, Khaing Khaing Faculty of Information Science Myeik Myanmar University of Computer Studies Yangon Department of Information Technology Support and Maintenance Yangon Myanmar

ISBN: (纸本)9789998601673

A cashless society is becoming more and more of a reality because of the quick development of technology. This has made it easier for fraudsters to commit their crimes, to a certain extent. To solve this issue, fraud classification systems are used to identify fraudulent activity after it has already occurred. In this experiment, the performance of classification models for this fraud detection analysis was built and compared using five deep boosting algorithms: Adaboost, CatBoost, Gradient Boosting, XGBoost, and LightGBM (LGBM). On the IEEE-CIS Fraud Detection Dataset, which Vesta Corporation provided, this study was carried out. The models were then evaluated using various performance metrics, including the Accuracy measure, F1 Score, and Area Under the Receiver Operating Characteristic Curve (AUC). The research goal is to identify the best boosting algorithm that exhibits high classification performance and high AUC and accuracy scores. An analysis of the boosting ensemble algorithms was conducted in order to accomplish this goal. For the experiment, the dataset was preprocessed using feature selection and scaling methods. The metric comparison showed that LGBM is ranked second and XGBoost has the best classification. Since lowering the false negative rate (FNR) is the main objective of a fraud detection system, XGBoost obtains the highest fraud detection score. Overall, XGBoost achieves the highest predictive accuracy when compared to the other methods. © 2024 IEEE.

关键词： Crime

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A Novel information Model for Software Interface Reliability in the Software Development Life Cycle 15

A Novel Information Model for Software Interface Reliability...

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15th International Conference on Emerging Ubiquitous systems and Pervasive Networks / 14th International Conference on Current and Future Trends of information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Alnahdi, Noha Alnanih, Reem Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

The reliability of software interfaces has become increasingly recognized as a crucial factor impacting the quality, manageability, and efficiency of various software systems. Traditional approaches to software reliability assessment often overlook interface-level reliability, focusing primarily on system-level metrics. However, the growing emphasis on user-centered design has amplified the demand for reliable and user-friendly software interfaces. This paper introduces a novel information model for evaluating the reliability of software interface design, which is crucial for enhancing assessment approaches in software engineering. The proposed model integrates usability testing and reliability attributes early in the software development life cycle (SDLC), particularly in the interface design phase, to enhance the system's overall performance and user experience. The paper describes the current state of models and methods used to evaluate software reliability at the interface design level, outlining the problems that the proposed new model aims to solve. It presents the theoretical basis for the model and provides information on its integration into the SDLC. Analytical examples are used to corroborate the proposed model, demonstrating its usefulness in the early project stages and its ability to improve software interface reliability and practicality. The paper also discusses challenges in adoption and suggests areas for future theoretical and empirical research. By developing a more comprehensive perspective on software quality, this work paves the way for practical, reliable, and user-friendly interfaces across various software domains. © 2024 The Authors.

关键词： Data reliability

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Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

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computer systems science & Engineering 2023年第11期47卷 1571-1591页

作者： Ghoroub Talal Bostaji Emad Sami Jaha Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia

Human recognition technology based on biometrics has become a fundamental requirement in all aspects of life due to increased concerns about security and privacy ***,biometric systems have emerged as a technology with the capability to identify or authenticate individuals based on their physiological and behavioral *** different viable biometric modalities,the human ear structure can offer unique and valuable discriminative characteristics for human recognition *** recent years,most existing traditional ear recognition systems have been designed based on computer vision models and have achieved successful ***,such traditional models can be sensitive to several unconstrained environmental *** such,some traits may be difficult to extract automatically but can still be semantically perceived as soft *** research proposes a new group of semantic features to be used as soft ear biometrics,mainly inspired by conventional descriptive traits used naturally by humans when identifying or describing each ***,the research study is focused on the fusion of the soft ear biometric traits with traditional(hard)ear biometric features to investigate their validity and efficacy in augmenting human identification *** proposed framework has two subsystems:first,a computer vision-based subsystem,extracting traditional(hard)ear biometric traits using principal component analysis(PCA)and local binary patterns(LBP),and second,a crowdsourcing-based subsystem,deriving semantic(soft)ear biometric *** feature-level fusion experiments were conducted using the AMI database to evaluate the proposed algorithm’s *** obtained results for both identification and verification showed that the proposed soft ear biometric information significantly improved the recognition performance of traditional ear biometrics,reaching up to 12%for LBP and 5%for PCA descriptors;when fusing all three capa

关键词： Ear biometrics soft biometrics human ear recognition semantic features feature-level fusion computer vision machine learning

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Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

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Neural Computing and Applications 2024年第34期36卷 21671-21702页

作者： Amer, Dina A. Soliman, Sarah A. Hassan, Asmaa F. Zamel, Amr A. Computer Science Department Higher Technological Institute Tenth of Ramadan44228 Egypt Management Information Systems Department Faculty of Management Economics and Business Technology Egyptian Russian University Badr11829 Egypt Computer and Systems Engineering Department Faculty of Engineering Zagazig University Zagazig44519 Egypt

Wireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing meth

关键词： Particle swarm optimization (PSO)

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An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation

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computer systems science & Engineering 2023年第1期44卷 281-295页

作者： R.Prabhu S.Rajesh Department of Computer Science and Engineering AAA College of Engineering and TechnologySivakasiTamilnaduIndia Department of Information Technology Mepco Schlenk Engineering College SivakasiTamilnadu India

Cloud computing distributes task-parallel among the various *** with self-service supported and on-demand service have rapid *** these applications,cloud computing allocates the resources dynami-cally via the internet according to user *** resource allocation is vital for fulfilling user *** contrast,improper resource allocations result to load imbalance,which leads to severe service *** cloud resources implement internet-connected devices using the protocols for storing,communi-cating,and *** extensive needs and lack of optimal resource allo-cating scheme make cloud computing more *** paper proposes an NMDS(Network Manager based Dynamic Scheduling)for achieving a prominent resource allocation scheme for the *** proposed system mainly focuses on dimensionality problems,where the conventional methods fail to address *** proposed system introduced three–threshold mode of task based on its size STT,MTT,LTT(small,medium,large task thresholding).Along with it,task mer-ging enables minimum energy consumption and response *** proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme(EDS)and Decentralized Virtual Machine Migration(DVM).With a Network Manager-based Dynamic Scheduling,the proposed model achieves excellence in resource allocation compared to the other existing *** obtained results shows the proposed system effectively allocate the resources and achieves about 94%of energy efficient than the other *** evaluation metrics taken for comparison are energy consumption,mean response time,percentage of resource utilization,and migration.

关键词： Cloud computing resource allocation load balance dynamic scheduling dimensionality reduction

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Dynamic Insights: Well-being Activity Infused Fine-Tuning of Large Language Models

Dynamic Insights: Well-being Activity Infused Fine-Tuning of...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Mohammed, Hamza Haruna Kiss, Gabriel Serrano, J. Artur Lindseth, Frank Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Department of Computer Science Trondheim Norway Norwegian University of Science and Technology Faculty of Medicine and Health Sciences Department of Neuromedicine and Movement Science Trondheim Norway

ISBN: (纸本)9798350362480

The fast development of Large Language Models (LLMs) has made transformative applications in several fields attainable or possible. However, language models must often be more effective in specialized areas, especially health and prevention. This paper presents a novel method for fine-tuning LLMs using activity-related data to optimize them for the applicability of such models in health and wellbeing applications. We thus empirically evaluate this approach on the Cardiac Exercise Research corpus for fine-tuning the LLMs. The fine-tuning utilizes Quantized Low-Rank Adaptation (QLoRA) to ensure the models' size remains small while maintaining high performance and accuracy to keep the semantic understanding and relevance with health-related queries. Our results in answering domain-related prompts showed an improved user satisfaction and sentiment scores, providing strong confidence in the method's effectiveness. This study highlights the potential of domain-specific LLMs in advancing personalized healthcare. It instills a sense of optimism about the future of healthcare and the seamless integration of AI within health prevention and well-being domains. © 2024 IEEE.

关键词： Semantics

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Cyclic Transfer Learning for Mandarin-English Code-Switching Speech Recognition

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IEEE Signal Processing Letters 2023年 30卷 1387-1391页

作者： Nga, Cao Hong Vu, Duc-Quang Luong, Huong Hoang Huang, Chien-Lin Wang, Jia-Ching National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan Thai Nguyen University of Education Department of Computer Science and Information System Thai Nguyen7905218 Viet Nam Fpt University Department of Information Technology Can Tho900000 Viet Nam National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Transfer learning is a common method to improve the performance of the model on a target task via pre-training the model on pretext tasks. Different from the methods using monolingual corpora for pre-training, in this study, we propose a Cyclic Transfer Learning method (CTL) that utilizes both code-switching (CS) and monolingual speech resources as the pretext tasks. Moreover, the model in our approach is always alternately learned among these tasks. This helps our model can improve its performance via maintaining CS features during transferring knowledge. The experiment results on the standard SEAME Mandarin-English CS corpus have shown that our proposed CTL approach achieves the best performance with Mixed Error Rate (MER) of 16.3% on testman, 24.1% on testsge. In comparison to the baseline model that was pre-trained with monolingual data, our CTL method achieves 11.4% and 8.7% relative MER reduction on the testman and testsge sets, respectively. Besides, the CTL approach also outperforms compared to other state-of-the-art methods. © 1994-2012 IEEE.

关键词： Speech recognition

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