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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Mazhar, Tehseen Iqbal, Muhammad Iqbal, Muhammad Ghadi, Yazeed Yasin Rehman, Ateeq Ur Abbasi, Waseem Department of Computer Science Virtual University of Pakistan Lahore54000 Pakistan Institute of Computing and Information Technology Gomal University Dera Ismail Khan29220 Pakistan Department of Computer Science and Software Engineering Al Ain Universit Abu Dhabi15322 United Arab Emirates Department of Electrical Engineering Government College University Lahore54000 Pakistan Department of Computer Science and IT Superior University Sargodha Pakistan

ISBN: (纸本)9783031776168

IoT-based applications make innovative use of millions of sensors. Research has risen in the medical and technological fields in the previous ten years. The Internet of Things impacts the development of medical research. Improved medical care is possible because the Internet of Things can link sensors, medical equipment, and remote healthcare professionals. Patient treatment has been enhanced in terms of effectiveness, cost, accessibility, and safety. Due to COVID-19, receiving medical treatment in the convenience of one’s home is more important than ever. To enhance healthcare, internet of things devices is utilized to monitor patients. His article examines how the Internet of Things has improved, enabling technology, healthcare services, and healthcare-related challenges. IoT system concerns are also taken into consideration. This effort aims to help future researchers understand the topic better by giving a thorough overview of all of HIoT’s many uses. The applications, technology, and challenges of the healthcare industry are all examined in this article. As a result, the IoT has the potential to improve healthcare’s effectiveness and ability to treat patients. The idea put out in this article is that the Internet of Things may be made widely used, lucrative, and easily available everywhere. The integration provides a faster calculation. The last section includes a number of practical recommendations and suggestions for implementing the IoT healthcare system during COVID-19 and future severe pandemics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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Multi-Objective Evolving Long-Short Term Memory Networks with Attention for Network Intrusion Detection

SSRN

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SSRN 2022年

作者： Wei, Wenhong Chen, Yi Lin, Qiuzhen Ji, Junkai Wong, Ka-Chun Li, Jianqiang School of Computer Science and Technology Dongguan University of Technology Dongguan China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong

Cyber security has received more and more attention, as people use more Internet applications in their life and are concerned about the security of their personal data. Intrusion Detection System (IDS) is an important security tool that can detect abnormal Internet network activities. Deep Learning (DL) techniques have been widely applied in the field of IDS and achieved promising performance in terms of detection accuracy. However, the design of DL architectures usually requires professional knowledge and dramatically impacts the performance of the DL model. Moreover, due to the existence of a small ratio of abnormal traffic in a large number of network traffic, this serious imbalance data problem leads to poor performance of the DL model in detecting minority attack classes. To alleviate these problems, this paper proposes a hybrid evolutionary DL model (called EvoBMF) to detect intrusion behaviors in the network, which uses bidirectional Long-short Term Memory (BiLSTM) for preliminary feature extraction, Multi-Head Attention (MHA) for further capturing features and global information of the network traffic, and full-connected layer module to do final classification. To deal with the problem of parameter tuning manually in the DL model when tackling different tasks, the EvoBMF model is first encoded as the chromosome of Multi-objective Evolutionary Algorithm (MOEA) and then its two conflicting objectives (the complexity and classification ability of the model) will be optimized. Later, a state-of-the-art MOEA (MOEA/D-DRA) is modified to optimize the above two objectives, which can obtain the optimal architecture for EvoBMF. The proposed EvoBMF model can be easily deployed in Cloud computing scenarios to resist network intrusion. Moreover, in order to alleviate the severe imbalance in routine network traffic, the synthetic minority over-sampling technique is introduced to generate representative samples of minority classes in order to improve the overall performanc

关键词： Brain

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Mutation Testing Techniques for Android Applications: A Comparative Study

Mutation Testing Techniques for Android Applications: A Comp...

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Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Ibrahim Anka Salihu Asma'u Usman Ndukwe-Oke Eke Rosziati Ibrahim Muhammad Bello Kusharki Department Software Engineering Nile University of Nigeria Abuja Nigeria Department Computer Science Abdu-Gusau Polytehcnic Talata Mafara Nigeria Department of Computer Science Nile Univeristy of Nigeria Abuja Nigeria Department Software Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Department of Information and Communication Technology National Defence College Abuja Nigeria

ISBN: (数字)9798350358834

ISBN: (纸本)9798350358841

Mobile applications are becoming increasingly used to achieve various computing needs. Hence, it is essential to guarantee quality of the applications. software testing has been the main activity for ensuring the quality of software, however, it is a critical and costly activity. Furthermore, code coverage is commonly used as metric for verifying the effectiveness of testing technique. However, numerous researchers and experts claimed that considering code coverage only to ascertain the testing quality is not enough to ensure quality of apps. In view of this, mutation testing has been introduced to complement the procedure by assuring that apps behave as expected and are released free of faults. Several techniques/tools were proposed based on different syntax and mutation operators. This study presents a comparative study of six (6) mutation techniques/tools for Android applications to highlight their strengths and weakness which will give an insight to researchers on the directions for further research.

关键词： software testing Codes Costs Syntactics software Mobile applications Testing

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A High-Gain DC-DC Boost Converter for Solar Microgrid Systems Using a Switching Inductor and a Capacitor

A High-Gain DC-DC Boost Converter for Solar Microgrid System...

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： S. Prasanth S. Priyadarsini R. Lakshmi G. Sneha S. S. Ajitha R. Saravanakumar Konnaiah Gari Narendra Department of Computer Science and Engineering Unnamalai Institute of Technology Kovilpatti Tamil Nadu India Department of Computer Science and Engineering PSR Engineering College Sivakasi Tamil Nadu India Department of Elecrical and Electroncis Engineering Siddharth Institute of Engineering and Technology Puttur India Department of Electronics and Communication Engineering St. Joseph's Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India Department of Electonics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

This paper outlines the design and implementation of a solar microgrid-specific high-gain DC-DC booster converter that makes use of a variable inductor and capacitors. To improve the suitability of photovoltaic (PV) panels for incorporation into microgrids, the proposed converter efficiently increases their low-voltage outputs. Conventional boost converters can't offer that kind of voltage increase because they lose too much performance and put too much strain on their internal components. The converter increases the voltage gain dramatically while maintaining a high degree of efficiency thanks to a switching inductor-capacitor network. The main breakthrough is an inventive multi-stage energy transfer and storage device that cleverly employs a switching inductor and capacitor. This configuration increases the output voltage while decreasing the voltage and current ripples, which in turn reduces the amount of electromagnetic interference (EMI) and increases the life of the components. Thorough assessments of the converter's working via theoretical modeling and simulation studies confirm its performance across several load and input situations common to solar microgrids. The prototype's testing findings show a significant voltage increase and a peak efficiency of over 95% when compared to traditional boost converters. Moreover, the converter's reliable performance over a wide range of voltage sources and workload scenarios proves its suitability for practical solar applications. As a result, the proposed high-gain boost converter will greatly improve the efficiency and reliability of solar microgrids, leading to a rise in the use of renewable energy sources in DGS.

关键词： Photovoltaic systems Renewable energy sources Capacitors Electromagnetic interference Voltage Switches Microgrids DC-DC power converters Inductors Switching circuits

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Cybersecurity in Piezoelectric Biosensor for Sensing Application

Cybersecurity in Piezoelectric Biosensor for Sensing Applica...

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Innovations in Intelligent Systems and Applications Conference

作者： Ahmet Faruk Görmüş Ieva Markūnienė Kenan Şenturk Serkan Gönen Mehmet Ali Banşkan Gökçe Karacayilmaz Ercan Nurcan Yılmaz Technology Transfer Office Istanbul Gelisim University Istanbul Turkiye Mechanical Engineering Kaunas University of Technology Kaunas Lithuania Mechatronic Engineering Istanbul Gelisim University Istanbul Turkiye Software Engineering Cyber Security Research and Application Center Istanbul Gelisim University Istanbul Turkiye Computer Engineering Cyber Security Research and Application Center Istanbul Gelisim University Istanbul Turkiye Computer Engineeringr Istanbul Gelisim University Istanbul Turkiye Electrical - Electronics Engineering Gazi University Ankara Turkiye

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

Biosensors play a crucial role in various healthcare applications such as continuous health monitoring, targeted drug delivery, and nanosurgery. Enhancing the performance of biosensors involves integrating them with higher bandwidth external networks like 5G, necessitating a seamless connection between the biological environment and technological infrastructure to convert biochemical signals into electromagnetic signals. While sensor technologies provide real-time monitoring of vital parameters like pulse, blood pressure, and body temperature, their reliance on wireless communication networks poses security risks. This study investigates the security vulnerabilities in wireless communication infrastructure within the healthcare sector. Using an infrastructure model of an instant patient pulse monitoring system, the study identifies open ports via port scanning and conducts attacks, including Distributed Denial of Service (DDoS) and Man-in-the-Middle (MITM) attacks, resulting in data interception and potential exposure of patient information. The analysis of these attacks leads to proposed solutions aimed at addressing system vulnerabilities and fortifying the wireless communication infrastructure's security. Additionally, a model utilizing artificial intelligence is developed to detect and prevent future attacks, contributing significantly to enhancing overall system security.

关键词： Wireless communication Temperature sensors Temperature measurement Biological system modeling Medical services Security Biosensors computer crime Biomedical monitoring Monitoring

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EATS: Efficient Adaptive Test Case Selection for Deep Neural Networks

EATS: Efficient Adaptive Test Case Selection for Deep Neural...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Huanze Meng Zhiyi Zhang Yuchen Ding Shuxian Chen Yongming Yao Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China School of Mathematical Science Yangzhou University Yangzhou China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China Army Engineering of PLA Nanjing China

ISBN: (数字)9798350365634

ISBN: (纸本)9798350365641

As deep neural network (DNN) has made significant advancements across various fields, systematically testing DNN has become increasingly crucial. To uncover potential faults within DNN, a vast number of test cases and their correct labels are required, but the process of labeling is time-consuming and labor-intensive. To alleviate the burden on developers, test case selection techniques for DNN models have been proposed, assisting in the selection of test cases from large datasets that are more likely to reveal model faults. In this study, we introduce an efficient adaptive test case selection method based on the principle of uniform distribution of test cases, named EATS. In addition, we propose a test case optimization method and an image similarity calculation method. The optimization method can save time during the test case selection process, while the image similarity calculation method computes the degree of difference between images based on model uncertainty. EATS leverages the model’s uncertainty to achieve a more uniform distribution of selected test cases, aiming to select test cases that can induce a diversity of model fault predictions, thereby optimizing model performance. We conducted comparative experiments of EATS and other test case selection strategies on four common datasets and their corresponding DNN models. The experimental results show that EATS outperforms other methods in terms of the uniformity of test case distribution, diversity of errors discovered, and model optimization. It also demonstrates excellent time efficiency.

关键词： Adaptation models Uncertainty Costs Computational modeling Optimization methods Artificial neural networks Predictive models

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Nuclei Instance Segmentation in Colon Histology Images with YOLOv7 2nd

Nuclei Instance Segmentation in Colon Histology Images with...

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2nd International Conference on Advanced engineering, technology and Applications, ICAETA 2023

作者： Yıldız, Serdar Memiş, Abbas Varlı, Songül Department of Computer Engineering Faculty of Electrical and Electronics Engineering Yıldız Technical University İstanbul Turkey Department of Software Engineering Faculty of Engineering and Natural Sciences İstanbul Health and Technology University İstanbul Turkey Health Institutes of Türkiye İstanbul Turkey BİLGEM TÜBİTAK Kocaeli Turkey

ISBN: (纸本)9783031509193

In histology image analysis, instance-based nuclei segmentation is one of the challenging tasks within the segmentation-guided studies since it is quite troublesome to detect each distinct nuclei instance of each nuclei type in images in contrast to the semantic segmentation in which all the image pixels of a nuclei type are labelled with the same mask ID although the segmented region may comprise of multiple instances. In this paper, an instance-based medical image segmentation task is addressed, and in this context, instances of multiple types of nuclei in colon histology images are aimed to be delineated distinctly. For the instance-based segmentation of the nuclei in colon histology images, the YOLOv7 algorithm and its built-in instance segmentation module are utilized. In the experimental studies performed on Colon Nuclei Identification and Counting (CoNIC) Challenge 2022 colon histology image dataset by using a 5-fold cross-validation performance evaluation strategy, nuclei instances belonging to 6 classes as the neutrophil, epithelial, lymphocyte, plasma, eosinophil and connective were segmented. To calculate the overall system accuracy, the quantification metrics of mean average precision (mAP) and mean panoptic quality (mPQ) were measured. In performance evaluations, quite promising accuracy values were obtained. The mAP values of 0.2885 and 0.2903, and mPQ values of 0.1659 and 0.1704 were observed by using the YOLOv7 algorithm. To the best of our knowledge, this is the first nuclei instance segmentation study with YOLOv7. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Colon histology images CoNIC 2022 dataset Nuclei instance segmentation Nuclei segmentation YOLOv7

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Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement

Machine Learning in Higher Education using RF and SVM for Pe...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati Taruna Anand D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Ambala India Department of Humanities and Social Sciences Graphic Era (Deemed to be) University Dehradun India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions.

关键词： Support vector machines Radio frequency Training Accuracy Education Symbols Feature extraction Tokenization Integrated circuit modeling Principal component analysis

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Reliability of a Reconfigurable Network with Segment Switching

Reliability of a Reconfigurable Network with Segment Switchi...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： Le Anh Tu V. A. Bogatyrev E. A. Abramova Faculty of Software Engineering and Computer Technology ITMO University Saint Peterburg Russia Department Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation Saint Petersburg Russia

The article proposes models of the structural reliability of a multipath routing network with the possibility of its reconfiguration when switching path segments. The models are focused on optimizing the placement of segment switching nodes, aimed at achieving maximum network performance with the option of connecting request sources to the available communication paths with servers. The study considers the options for connecting each source of requests with one of the paths or with all paths for connecting to servers. The paper shows the existence of an optimal placement of path switching nodes, in which the maximum reliability of the system is achieved.

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When Metaverses Meet Vehicle Road Cooperation: Multi-Agent DRL-Based Stackelberg Game for Vehicular Twins Migration

arXiv

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arXiv 2023年

作者： Kang, Jiawen Zhang, Junhong Yang, Helin Ye, Dongdong Hossain, M. Shamim The Guangdong University of Technology China The School of Informatics Xiamen University China The Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Vehicular Metaverses represent emerging paradigms arising from the convergence of vehicle road cooperation, Metaverse, and augmented intelligence of things. Users engaging with Vehicular Metaverses (VMUs) gain entry by consistently updating their Vehicular Twins (VTs), which are deployed on RoadSide Units (RSUs) in proximity. The constrained RSU coverage and the consistently moving vehicles necessitate the continuous migration of VTs between RSUs through vehicle road cooperation, ensuring uninterrupted immersion services for VMUs. Nevertheless, the VT migration process faces challenges in obtaining adequate bandwidth resources from RSUs for timely migration, posing a resource trading problem among RSUs. In this paper, we tackle this challenge by formulating a game-theoretic incentive mechanism with multi-leader multi-follower, incorporating insights from social-awareness and queueing theory to optimize VT migration. To validate the existence and uniqueness of the Stackelberg Equilibrium, we apply the backward induction method. Theoretical solutions for this equilibrium are then obtained through the Alternating Direction Method of Multipliers (ADMM) algorithm. Moreover, owing to incomplete information caused by the requirements for privacy protection, we proposed a multi-agent deep reinforcement learning algorithm named MALPPO. MALPPO facilitates learning the Stackelberg Equilibrium without requiring private information from others, relying solely on past experiences. Comprehensive experimental results demonstrate that our MALPPO-based incentive mechanism outperforms baseline approaches significantly, showcasing rapid convergence and achieving the highest reward. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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