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13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022

作者： Zhang, Jie Hong, Jie Huang, Dafei Li, Meng Yan, Shiyu Gong, Helin School of Computing University of South China Hunan Hengyang China Engineering and Technology Research Center of Software Evaluation and Testing for Intellectual Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

ISBN: (数字)9781665486903

ISBN: (纸本)9781665486903

Metamorphic testing can effectively alleviate the test oracle problem. However, the program under test needs to be executed one more time;it is particularly significant to select the metamorphic relation with the highly fault-detecting ability and improve the test cost-effectiveness. Due to the limitations of existing evaluation methods in engineering applications, a validity evaluation method for metamorphic relation is presented based on single factor and multi-factor mutation scores. It can guide their priority ranking and selection. Experiments show that this method can significantly reduce test costs. © 2022 IEEE.

关键词： Cost effectiveness

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Machine Learning based Voltage Regulation Technique using Smart Rotating Magnetic Inverter

Machine Learning based Voltage Regulation Technique using Sm...

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2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022

作者： Hussain, Muhammad Majid Siddique, Muhammad Javed, Waqas Razaq, Abdul Akhter, Muhammad Naveed Malik, Farhan Hameed University of South Wales Department of Electrical and Electronic Engineering Cardiff United Kingdom NFC Institute of Engineering and Technology Department of Electrical Engineering Multan Pakistan Glasgow Caledonian University School of Computing Engineering and Built Enviornment Glasgow United Kingdom Abertay University School of Design and Informatics Dundee United Kingdom Rachna Colllege of Engineering and Technology Department of Electrical Engineering Gujranwala Pakistan Abu Dhabi Polytechnic Department of Electromechnical Engineering Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

In this paper, a control methodology for power distribution line voltage regulations is presented. The salient features of this rotating magnetic inverter (RMI) include novel architecture with reduced electronic components and simple circuitry rather than conventional inverters, making it very economical and easy to implement. A new adaptive control based on a fuzzy logic approach is discussed in this paper, enabling the inverter to work more efficiently for dynamic voltage regulation. This proposed control method is best to mitigate the effect of voltage sags and swells in power distribution lines. On dynamic load variations, the system becomes unstable, and output contains voltage harmonics. Moreover, the RMI is a novel contribution from the authors as it has a unique structure for rotation of magnetic fields. The claim about the performance of the proposed RMI is tested and verified through MATLAB simulation. © 2022 IEEE.

关键词： Dynamic loads

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Diabetes Prediction using Enhanced and Optimized DRL-CNN Approaches

Diabetes Prediction using Enhanced and Optimized DRL-CNN App...

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Learning and Technology Conference (L&T)

作者： Usha Nandhini A K. Dharmarajan Bhisham Sharma Subrata Chowdhury Imed Ben Dhaou School of Computing VISTAS Chennai India Department of Information Technology VISTAS Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies Chittoor Andra Pradesh India Department of Computer Science Hekma School of Engineering Computing and Design Dar Al-Hekma University Jeddah Saudi Arabia

This study develops an advanced automated prediction system using Machine Learning (ML) techniques to identify diabetes early. The research employs the WBSMOTE method for data preprocessing, addresses class imbalances with the WB-SMOTE approach, and introduces two novel classification methods, DRL-CNN and DRL-OCNN. Reliable datasets from sources like The Data World repository, PIMA Indian Diabetes (PIDD) dataset, and Real Time (RT) Diabetes Dataset are used, with an emphasis on data consistency and feature relevance. If we examine these datasets, it contains general attributes and no internal specific attributes related to diabetes is used. So, prediction results will be what type of diabetes we won’t get any specific characteristics and risk factors. Evaluation is based on Accuracy, Precision, and Recall criteria. The DRL-OCNN model, integrating SGD optimization and Delta Rule Learning, outperforms other ML models with a accuracy of 96%, precision of 0.93 and recall of 0.91, demonstrating its potential for accurate early diabetes identification and classification.

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A Novel Method to Predict Chronic Kidney Disease using Optimized Deep Learning Algorithm

A Novel Method to Predict Chronic Kidney Disease using Optim...

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Learning and Technology Conference (L&T)

作者： I. Preethi K. Dharmarajan Bhisham Sharma Subrata Chowdhury Imed Ben Dhaou School of Computing Sciences VISTAS Chennai India Department of Information Technology VISTAS Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies Chittoor Andhra Pradesh India Department of Computer Science Hekma School of Engineering Computing Design Dar Al-Hekma University Jeddah Saudi Arabia

Chronic Kidney Disease (CKD), a significant global health issue and has a major impact on a vast number of people. In the early stages, it is impossible to identify the disease’s symptoms. Very few people are aware of this illness and are able to foresee the symptoms earlier. The disorder is considered as potential risk factor. As a result, a potent deep learning framework is used in this paper to reduce the risk of acquiring these diseases as a way to prevent them. The machine learning algorithms used in early disease prediction, has been found to be computationally expensive, frequently overfit, and underperform in terms of accuracy since they must examine the large amount of clinical data until the model converges. Therefore, in this paper three novel work has been proposed an efficient novel hybrid feature selection strategy RFITLO algorithm is used to find the optimal features that gives the major contribution is classifying the CKD disease. Then two proposed classification algorithms namely Enhanced Multi-Layer Perceptron (HW-MLP) and Optimized Multi-Layer Perceptron (PKD-OMLP) are used in prediction model to capture the complex patterns and optimize the learning algorithm to predict the CKD at prior stage from the data gathered in Kaggle, Real Time and UCI Machine Learning Repository Dataset. In order to measure the classifications of disease, performance measures including accuracy, precision and recall are analyzed. The experimental findings show that the PKD-OMLP strategy produces better results than the proposed HW-MLP and other conventional approaches like Support Vector Machine (SVM), Linear Regression (LR) and Multi-Layer Perceptron (MLP). Among the preceding four models PKD-OMLP renders the best outcome as per its performance level producing a high accuracy of 94.89% on testing Real Time (RT) CKD dataset comparatively with other datasets such as Kaggle and UCI repository. Therefore, these proposed algorithms can support clinicians to enable secured an

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INNSE: Invertible neural network-based DNA image storage with self-correction encoding

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Computational and Structural Biotechnology Journal 2025年 27卷 2492-2502页

作者： Yanfen Zheng Xiaokang Zhang Lijun Sun Ben Cao Bin Wang Qiang Zhang Pan Zheng School of Computer Science and Technology Dalian University of Technology Dalian Liaoning 116024 China The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian Liaoning 116622 China Department of Accounting and Information Systems University of Canterbury Upper Riccarton Christchurch 8140 New Zealand

DNA storage has become an alternative to silicon-based storage of media. However, when used to store multimodal data such as images, it fails to take full advantage of the characteristics of high correlation and variability present in such data, and currently remains inferior in terms of encoding density and time complexity. It also has the shortcoming of weak performance in terms of error correction methods to address insertion and deletion errors caused by error crosstalk. Therefore, we proposed an invertible neural network-based DNA image storage self-correction encoding method (INNSE) that uses the invertibility of network to perform up-down sampling processing on image and video data, thereby greatly reducing the number of DNA sequences required for data storage. A codebook generator was then designed to encode low-dimensional vectors into DNA sequences that meet various constraints. Finally, a self-correction method was constructed to achieve DNA sequence error correction at the base level without adding any redundant information. Our experimental results showed that, compared to the Yin-Yang algorithm, the encoding density of INNSE was increased by 3×, encoding time complexity was reduced by 95.26%. Under a simulated error rate of 2%, PSNR and SSIM were raised by 80.88% and 63.82%, respectively, proving that INNSE has significant advantages in encoding performance and error correction ability.

关键词： Codebook generator DNA storage Invertible neural network Self-correction

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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Smart Captures- An Advanced Attendance System using Facial Recognition

Smart Captures- An Advanced Attendance System using Facial R...

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Cognitive computing and engineering Education (ICCCEE), International Conference on

作者： Reena Gunjan Anannya Mital Muskan Jain Satyajit Pangaonkar Virendra Shete Prakash Rokade Department of Computer Science Engineering MIT School of Computing MIT Art Design and Technology University Pune India School of Computer Science and Engineering Avantika University Ujjain Madhya Pradesh India Department of Electronics and Communication Engineering MIT School of Engineering and Science MIT Art Design and Technology University Pune India Cloud and Infrastructure Services Infosys Ltd Pune Maharashtra India

Facial Recognition is one of the most productive and important techniques in image processing. It uses Machine Learning and Artificial Intelligence techniques to identify, collect, store, and evaluate face characteristics so that they can be matched to photos of people in a database. Nowadays, many kinds of research are focusing on human detection mainly in the context of marking students’ attendance. Marking attendance is a procedure of recognizing students’ faces and marking their attendance for that class. The need for this application was generated because after the pandemic use of touch biometric systems was suspended in many places and the manual process of taking attendance and maintaining paper records was a very tedious job and this could be manipulated very easily by anyone. Some attendance systems with face recognition already got developed but they were very basic and only had the feature of just recognizing the face once and marking the attendance of that student. So, by bringing in some unique features, here, developed ‘Smart Captures’, an advanced attendance system for universities, schools, and education-based places. Smart Captures was entitled to capturing the students’ movement from the classroom (in & out) and marking their attendance according to the set constraints using IoT, thereafter, making it visible to the faculties on their mobile application. This provided the data analytics for regular attendance on the dashboard to the admin with the administrative power using the application.

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Investigation of the mesmeric effect of substituents in DSSC based on thiophene and oxadiazole. Structural and optoelectronic study using the DFT approach

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Interactions 2025年第1期246卷 1-15页

作者： Agda, Fatima Nebbach, Diae Louazri, Latifa Ninis, Ouafae Merimi, Imane Ajana, Mohammed Aziz Bouachrine, Mohammed Laboratory Molecular and Computational Chemistry: Design Synthesis and Reactivity. Department of Chemistry Faculty of Science Moulay Ismail University Meknes Morocco Regional Center for Education and Training Professions Meknes Morocco Energy Materials and Computing Physics Research Group ENS Abdelmalek Essaadi University Tetouan Morocco Advanced Materials and Process Engineering Laboratory Faculty of Science Ibn Tofail University Kenitra Morocco National School of Architecture National Road N2 Compus de Savoir Technolopole Oujda Oujda60000 Morocco Faculty of Sciences Mohammed First University Oujda Morocco

In this work, we have developed a range of organic Donor- π -Acceptor;we are changing the π-spacers by adding novel substitutions (F, CH3, and CN) to the base molecule D-TTOxTT-A for dye-sensitized solar cells (DSSCs). Our aim is to improve the photovoltaic performance of these engineered organic dyes. We use the B3LYP (d,p) and TD/CAM-B3LYP (d,p) to study the impact of different types of the introduction of the substitution in the spacer, on the sensitizer characteristics, with the aim of highlighting the links between structure and property. Based on these results, it is possible to use these dyes as promising sensitizers for DSSCs. In addition, M4 could be an ideal compound for high throughput DSSC due to its planar geometry, low energy gap (1.73eV), higher absorption wavelength (449.3nm), longer lifetime (2.366 ns), lower ΔGreg (-0.64 eV), large hardness (-0.869 eV) and reasonable energy reorganization (0.488eV). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Thiophene

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Cryptographic Protocols for Securing Internet of Things (IoT) 15

Cryptographic Protocols for Securing Internet of Things (IoT...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Manimekala, B. Aravind, T. Sounderrajan, T. Rajakumareswaran, V. Kondreddi, Krishnaveni Mohanambal, K. Christ University Department of Computer Science Yeshwanthpur Bengaluru India Vel TecRangarajanDr.Sagunthala R&d Institute of Science and Technology Department of Cse School of Computing India Erode Sengunthar Engineering College Department of Ai and Data Science Tamil Nadu Thuduppathi India Erode Sengunthar Engineering College Department of Computer Science and Design Tamil Nadu Thuduppathi India Chaitanya Bharathi Institute of Technology Electrical and Electronics Engineering Ranga Reddy Telangana Gandipet500075 India Bannariamman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350370249

Cryptographic protocols are used to relax the ever-developing quantity of linked gadgets that make up the net of things (IoT). Those cryptographic protocols have been designed to make certain that IoT tool traffic stays cozy and personal, even while nevertheless allowing tool-to-device and cloud-to-tool communications. Examples of these protocols consist of TLS/SSL, PGP/GPG, IPsec, SSL VPN, and AES encryption. Every one of these protocols enables authentication, message integrity, and confidentiality via encryption and key trade. Moreover, a lot of these protocols are carried out in the form of diverse hardware and software answers, such as smart playing cards and gateways, to make certain that IoT traffic is secured. With the appropriate implementation of those cryptographic protocols, establishments can ensure that their IoT facts are blanketed and securely transmitted. © 2024 IEEE.

关键词： Authentication Communications Cryptographic EverDeveloping Gadgets Protocols

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhu, Yujiao Chen, Mingzhe Wang, Sihua Liu, Yuchen Li, Gaolei Yin, Changchuan Quek, Tony Q.S. Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Network Beijing100876 China Singapore University of Technology and Design 487372 Singapore University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China

ISBN: (纸本)9798350351255

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method. © 2024 IEEE.

关键词： Gaussian distribution

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