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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Lu, Xiqin Funabiki, Nobuo Ueda, Kiyoshi Kao, Wen-Chung Okayama University Department of Information and Communication Systems Okayama Japan Nihon University Department of Computer Science Koriyama Japan National Taiwan Normal University Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

To assist self-study of C programming, we have proposed three types of introductory exercise problems: grammar-concept understanding problem (GUP), value trace problem (VTP) and phase fill-in-blank problem (PFP). GUP and VTP aim at code reading study whereas PFP aims at code writing study. Students can solve them using the browser-based answer interface that has the automatic marking function. In this paper, we present their application results to first-year undergraduate students taking a C programming course in Nihon University, Japan. The same instances were assigned twice this time, where they were assigned once in the previous year. The results show that the correct answer rate was improved for any type as the course elapsed, where that for PFP was no sufficient. Thus, the effectiveness of the proposal is confirmed for basic C programming study. © 2023 IEEE.

关键词： Students

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Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals

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computer systems science & Engineering 2023年第2期44卷 1517-1529页

作者： Ashit Kumar Dutta Yasser Albagory Manal Al Faraj Yasir A.M.Eltahir Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Respiratory Care College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical ***,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classiﬁcation Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG *** major intention of the OSAE-SSCEEG technique is toﬁnd the sleep stage disorders using the EEG biomedical *** OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization ***,the classiﬁcation of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)***,the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm(COA)and it leads to boosted classiﬁcation efﬁ*** order to ensure the enhanced performance of the OSAE-SSCEEG technique,a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG tech-nique over the recent methods.

关键词： Biomedical signals EEG sleep stage classiﬁcation machine learning autoencoder softmax parameter tuning

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Towards Blockchain-based Decentralized Employee Transfer System 6

Towards Blockchain-based Decentralized Employee Transfer Sys...

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6th International Conference on Blockchain Computing and Applications, BCCA 2024

作者： Zaib, Alam Mustafa, Ehzaz Khan, Ahsan Khattak, Hasan Ali Ibrahim, Isa Ali Namoun, Abdallah Shuja, Junaid Ahmad, Raja Wasim Department of Computer Science Comsats University Islamabad Abbottabad Campus Pakistan Faculty of Computing & Information Technology Sohar University Sohar Oman School of Information and Communication Technology Federal University of Technology Imo Owerri1526 Nigeria AI Centre Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Department of Computer Science Southeast Missouri State University Cape GirardeauMO United States Department of Computer & Information Sciences Ajman University Ajman United Arab Emirates

ISBN: (纸本)9798350351538

Employees are the main driving force in organizations that undergo recruitment, promotion, and transfer. There are many employee management systems, but most of them are centralized. Centralization leads to problems such as lack of transparency, auditability issues, vulnerability, and a single point of failure. Blockchain technology addresses these concerns. Despite some blockchain-based HRM and employee management systems, none of the existing blockchain-based systems have addressed the problems of employee transfers. We propose a secure and transparent blockchain-based employee transfer (E-transfer) system to address this. The proposed system uses smart contracts for transfer management, recording outcomes on an unchangeable ledger. We conducted tests on Hyperledger Fabric for our proposed system. Our study aims to introduce an automated, auditable, and efficient transfer process. The findings of the study validate the effectiveness of the blockchain-based E-transfer system. © 2024 IEEE.

关键词： Smart contract

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Changing Lineup Classifier Ensemble for Drifting Imbalanced Data Streams

Changing Lineup Classifier Ensemble for Drifting Imbalanced ...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Weronika Wegier Maciej Maczyński Michał Woźniak Department of Systems and Computer Networks Wrocław University of Science and Technology Wrocław Poland

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Currently, most data requires stream processing. This kind of processing causes new challenges in classifier learning method, such as data difficulties for example data imbalance since we cannot observe the entire set of data simultaneously or concept drift, which is the possibility of changes in the probabilistic characteristics of the processed data. These challenges were addressed by proposing a new learning algorithm that maintains consistency with the current data distribution by training a classifier ensemble with the changing lineup and applying dedicated mechanisms to account for the imbalance of the incoming data stream. The proposed IMB-WAE (Imbalance Weighted Aging Ensemble) method has been evaluated on the basis of exhaustive computer experiments conducted on a large number of real and synthetic data streams assessing the impact of its parameters on classification quality and confirming its quality compared to the state-of-the-art algorithm.

关键词： Training Learning systems Concept drift Noise Probabilistic logic Data models Classification algorithms Ensemble learning Data mining Synthetic data

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Non-stationary Imbalanced Data Stream Classification with Weighted One-Class Ensemble

Non-stationary Imbalanced Data Stream Classification with We...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Jakub Klikowski Michał Woźniak Department of Systems and Computer Networks Wrocław University of Science and Technology Wrocław Poland

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

The presented study focuses on imbalanced data stream classification that requires fast processing of incoming data without memorizing it. An additional difficulty is the imbalanced data distribution, i.e., significant disparities among objects from different classes, which can change over time. We have proposed a novel classifier for a non-stationary and imbalanced data stream to solve the task mentioned above One-Class Support Vector Machine Weighted Ensemble (OCWE). OCWE, on the one hand, uses a pool of binary classifiers to deal with the disparity between classes. On the other hand, the ability of the classifier ensemble to follow the changes in data distributions is provided by updating its lineup based on currently incoming training examples. The performance of the proposed method has been evaluated through extensive experimental studies on synthetic and real data streams. The results prove that OCWE can outperform state-of-the-art classifiers for the considered task.

关键词： Hands Training Support vector machines Conferences Data mining

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： Data integrity

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Enhancement of IoT based Flood Detection and Prevention using Arduino UNO with WiFi Module 9

Enhancement of IoT based Flood Detection and Prevention usin...

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9th International Conference on Advanced Computing and communication systems, ICACCS 2023

作者： Charaan, R. M. Dilip Shobana, J. Krishnamoorthy, P. Princy, B. Anni Abinaya, R.J. Murugesan, K. Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India SRM Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu India Karpagam Academy of Higher Education Department of Electronics and Communication Engineering Tamil Nadu India Loyola Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798350397376

The overflow of water from a lake or river usually causes flooding. Sometimes, a dam breach might result in the unexpected release of vast quantities of water. Some of the water seeps into the ground, flooding the region. In a station, rivers are involving the riverbanks. Along with a lack of goods and houses, businesses, and offices, street infrastructure floods contain bacteria, sewage from waste sites, and chemical spills, which later cause a number of diseases. The rate of change in river stage in real time, which can assist signal the gravity and immediacy of this hazard, is a crucial piece of information for flood predictions. Understanding the type of storm that produced the moisture, including its duration, strength, and actual extent, is important for determining the possible severity of the flood. In this system, an Arduino Uno is connected to four separate sensors: a humidity sensor, a flow sensor, a float sensor, and an ultrasonic sensor. The float sensor detects when the water is full. With the aid of IOT, these sensor combinations are utilised to predict floods, alert the appropriate authorities, and sound an immediate alarm in adjacent communities to rapidly relay information about potential floods. These sensors provide data via the IOT's WiFi module. When flooding conditions are detected, the system warns the nearby villages and places and estimates how long it would take for help to reach at a particular location. The technology also determines when it might be considered a flood and gives them a window of time to leave in case it does. © 2023 IEEE.

关键词： Floods

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⁠Marine Ecosystem Monitoring Based on Remote Sensing Using Underwater Image Analysis for Biodiversity Conservation Model

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Remote Sensing in Earth systems sciences 2024年第4期7卷 309-318页

作者： Rao, Chandana Narasimha Rao, A. Venkateswara Shanmugasundar, G. Hamid, Junainah Abd Haldorai, Anandakumar Naidu, G. Rama Sapthami, I. Department of Chemistry Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Andhra Pradesh Guntur 522302 India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai 600044 India Management and Science University Selangor Shah Alam Malaysia Center for Future Networks and Digital Twin Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore 641202 India Department of Electronics & amp Communication Engineering Aditya University Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The growing focus on underwater observation and use of marine resources in recent years has led to an active research area in underwater image processing and analysis. Because of its importance in marine engineering as well as aquatic robotics, underwater picture enhancement serves as a preprocessing step to increase performance of high-level vision tasks like underwater object detection as well as recognition. While a number of studies demonstrate that underwater image enhancement methods (synthetic underwater environment image) increase the detectors’ F-measure, no research has examined the connection between these two jobs. This research proposed novel technique in remote sensing–based underwater image analysis in ecosystem monitoring in biodiversity conservation model using machine learning techniques. Here, the underwater-based remote sensing image based on marine ecosystem has been collected and analysed for noise removal with normalisation. The processed image feature extracted and classified using Gaussian spatial gradient neural network (GSGNN) and multilayer pyramid Bayes network (MPBN). The output images show coral surface texture. The experimental analysis has been carried out for various underwater dataset in terms of kappa coefficient, classification accuracy, random precision, F-measure, and normalised square error. Proposed technique kappa coefficient is 94%, classification accuracy is 98%, random precision is 95%, F-measure is 93%, and Normalised square error is 63%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Biodiversity conservation model Ecosystem monitoring Machine learning Remote sensing Underwater image analysis

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UAV -Based Object Tracking Using Siamese networks and Kalman Filtering

UAV -Based Object Tracking Using Siamese Networks and Kalman...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramanan, S.V. Rajasekar, M. Shanmugapriya, G. Sujitha, R. Akshya, J. Choudhry, Mani Deepak P Pg Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Vsb college of Engineering Department of Computer Science and Engineering Technical Campus Coimbatore India Dr.N G.P Institute of Technology Department of Computer Science and Business Systems Coimbatore India Christ The King Engineering College Department of Information Technology Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

The ability to match features and keep track of objects in changing dynamic environments is still an important problem, particularly due to varying noise levels, diverse datasets, and high dimensional feature spaces. Euclidean Distance and Cosine Similarity based matching are existing methods that commonly do not generalize well, particularly in the case of cross domain scenarios, where noise and motion dynamics are powerful factors to limit the accuracy of tracking. However, Deep Metric Learning approaches can extract robust features at the cost of computational efficiency and are unsuitable for real-time applications. This research proposes a Siamese Network framework to solve these problems by using robust feature matching and multi-object tracking. Based on that, the proposed framework uses a dual-branch network architecture in which feature embeddings extracted from paired inputs are mapped through the same shared latent space. Furthermore, the adaptive learning rate leads to efficient convergence during the training. Experimental results show that the Siamese Network not only achieves better accuracy than the existing methods and the more such as Deep Metric Learning but also performs significantly more efficient computation. With an accuracy of 90.2 % at a precision of 88.5%, the framework's inference time is much faster than that of Deep Metric Learning at 2.0ms. Furthermore, the proposed method performs well on the generalization over datasets with a 15% improvement of cross-domain matching accuracy on Euclidean Distance. By striking this balance between accuracy, efficiency and adaptability, it shows its suitability for dynamic and noisy environments for real-time tracking. The proposed method fills existing gaps and achieves a new state of the art for feature matching and tracking performance. © 2025 IEEE.

关键词： Object tracking

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BER Improvement of Cooperative Spectrum Sharing of NOMA in 5G Network 3

BER Improvement of Cooperative Spectrum Sharing of NOMA in 5...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Hassan, Mohamed Saeed, Rashid A. Mokhtar, Rania A. Hamid, Khalid Ali, Elmustafa Sayed Odeh, Nidhal Singh, Manwinder Khalifa, Othman O. Islam, Shayla Lovely Professional University Department of Wireless Communication Punjab Phagwara144001 India Taif University Department of Computer Engineering College of Computers and Information Technology Taif21944 Saudi Arabia University of Science & Technology Department of Communication Systems Engineering Omdurman Sudan Department of Electronics Engineering Khartoum Sudan University of Wollongong School of Electrical Computer and Telecommunications Engineering Australia International Islamic University Malaysia Department of Electrical and Computer Engineering Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Institute of Computer Science and Digital Innovation Ucsi University Malaysia Department of Computer Science Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350319897

Non-orthogonal multiple access is one of the best methods for addressing the needs of 5G wireless services (NOMA). The proliferation of mobile devices in recent years has increased the importance of cooperative spectrum sharing and utilization in wireless communication due to increases in the bit error rate (BER) brought on by collisions and interference. In this study, MIMO and Massive MIMO (M-MIMO) in the downlink (DL) NOMA power domain (PD) in conjunction with the Cooperative Cognitive Radio Network are proposed as two new ways for improving and evaluating BER in the 5G network (CCRN). Customers of NOMA compete for available channels on the CCRN in this first strategy. The second approach creates a dedicated channel for NOMA users. Three scenarios with varying distances, power location coefficients, and transmission power are used to evaluate the proposed methods in the MATLAB software program. It is assumed that four users will share a 90 MHz BW using QPSK modulation in all three scenarios. When evaluating the performance of the suggested system under the presumption of frequency-selective Rayleigh fading, concurrent interference cancellation (SIC) and unstable channel conditions are also considered. © 2023 IEEE.

关键词： 5G mobile communication systems

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