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

作者： Bhavana, Kapa Sumathi, R. Vanaja, Kandregula Manjula, S. Srinivasulu, T. Divya, Siripuri Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Department of Information Technology Andhra Pradesh Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331509675

Fruits are absolutely delicious for the most part, but more importantly, they are good for healthy life. Fruits are nature's candy and offer all sorts of health benefits besides the great taste. They provide necessary nutrients to the body so that it will not be susceptible to disease or other ailments. Fruits contain enough fiber to keep our digestive system in top health form. Fruits also helps to regulate the water level in body as well as your blood sugar levels, which means that will usually feel hungry less often if we consumes a lot of fruits. The easier it is for people to recognize that a fruit is ripe and ready for selling at the market allows the farmers, in some cases, to sell their crop early before it would normally spoil. If we look closely, we see that many times people don't buy something because it appears rotten or way past its peak ripeness, which demonstrates how products spoil right on time. This study proposed a user-convenient system for fruit ripeness detection using the convolutional neural network (CNN) based on RipeNet structure. © 2025 IEEE.

关键词： Fruits

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Adaptive design and implementation of automatic modulation recognition accelerator

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 779-795页

作者： Wang, Bin Wei, Xianglin Wang, Chao Li, Junnan Jiao, Xiang Fan, Jianhua Li, Peng School of Electronics and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China 63rd Research Institute National University of Defense Technology Nanjing210007 China

Automatic modulation recognition-oriented Deep Neural Networks (ADNNs) have achieved higher recognition accuracy than traditional methods with less labor overhead. However, their high computation complexity usually far exceeds the computation capacity of communication devices built on Field Programmable Gate Array (FPGA) platform. When solving the problem of insufficient resources, the complete operation of FPGA-based accelerator can be achieved by dividing the calculation into several parts and calculating them separately, but this will cause unaccepTable latency. In this backdrop, we develop a new ADNN model, named VT-CNN2+, to promote the recognition accuracy. Then, after stating the resources and latency problems for implementing VT-CNN2+ on the FPGA platform, an adaptive hardware accelerator is put forward. To implement the accelerator, Area folding is introduced to optimize resources consumption. Moreover, Literacy Optimization, Parallelism Optimization, Inter-layer Cascading, Temporary Cache and Data Loading Optimization are adopted to reduce latency. Afterwords, the two components in our accelerator are detailed, i.e., Once-designed module and Re-designed module. Finally, to evaluate the performance and adaptivity of our accelerator, a series of experiments are conducted on two different FPGA platforms, i.e., AX7350 and ZedBoard. Results show that our accelerator can successfully adapt to different FPGA platforms and it can remarkably reduce the processing latency. Moreover, our accelerator’s processing speed of 0.066249s per single data sample with much lower energy consumption is one order of magnitude faster than desktop-level Central Processing Units (CPUs), two orders of magnitude faster than embedded CPUs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Deep neural networks

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Self-supervised extracted contrast network for facial expression recognition

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Multimedia Tools and Applications 2025年第15期84卷 14977-14996页

作者： Yan, Lingyu Yang, Jinquan Xia, Jinyao Gao, Rong Zhang, Li Wan, Jun Tang, Yuanyan School of Computer Science Hubei University of Technology Wuhan430068 China Department of Computer Science Royal Holloway University of London SurreyTW20 0EX United Kingdom School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China Zhuhai UM Science and Technology Research Institute Zhuhai519085 China

Self-supervised Contrastive learning has recently demonstrated significant performance in Facial Expression Recognition (FER). However, existing methods fail to address inherent challenges such as similar and blurred facial expressions, and low-quality semantic pairs from image cropping. To tackle these challenges, this paper proposes a Lightweight Self-supervised Distilled Contrastive network for FER (LSDC-FER), which consists of a center suppressed cropping module and a multi-scale feature joint distillation module. The former improves the quality of face expression semantic pairs by face image localization and center suppressed cropping. The latter learns fine-grained features using knowledge distillation and explores multi-scale contextual information to improve feature learning. Experimental results show that LSDC-FER achieves 75.65% and 61.16% recognition accuracy on RAF-DB and FER-2013 public facial expression datasets, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Distillation

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Revealing Security Flaws in Cross-Modal Retrieval Models through Video Poisoning

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6184-6194页

作者： Jin, Ming Hu, Wenbo Hong, Richang Zhu, Lei Hefei University of Technology School of Computer and Information Hefei China Data Space Research Institute Hefei Comprehensive National Science Center China Tongji University School of Electronic and Information Engineering Shanghai China

Video-text cross-modal retrieval is widely studied to improve retrieval accuracy. However, the security of video-text cross-modal retrieval models receives little attention. If attackers exploit the security vulnerabilities in these models, it poses a significant threat to the retrieval models. Thus, identifying security flaws in video-text cross-modal retrieval models becomes the focus of our research. We are the first to design a video poisoning model to uncover security vulnerabilities in retrieval models. Existing poisoning models have certain limitations when it comes to exploiting vulnerabilities in retrieval models. These include failing to comprehensively embed malicious information into the original video and being unable to maintain visual consistency between the original and poisoned videos. These limitations can result in unsuccessful attacks on retrieval models and an inability to effectively identify security flaws within them. To address these shortcomings, we design an efficient poisoning model that embeds malicious information thoroughly into the original clean data to attack video-text cross-modal retrieval models. We are the first to use a poisoning model to attack retrieval models, thereby uncovering their security vulnerabilities. Second, we introduce a bi-level poisoning module to ensure that malicious information is thoroughly embedded into the original video, thereby enhancing the attack capability of the poisoning model. Finally, we design an adversarial module to improve visual consistency between the original and poisoned videos, thus enhancing the concealment of malicious information within the training data of retrieval models. Our poisoning model can identify security flaws in video-text cross-modal retrieval models, providing insights into improving the security of retrieval models. The effectiveness of our model is validated on the MSR-VTT, LSMDC, and MSVD datasets. © 1991-2012 IEEE.

关键词： Content based retrieval

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The Role of Ensemble Learning in Advancing Child Fetal Health: A Focus on Voting and Bagging Techniques 1

The Role of Ensemble Learning in Advancing Child Fetal Healt...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Swain, Satyaprakash Patra, Kumar Janardan Jayasingh, Suvendra Kumar Mohanty, Mihir Narayan Pattanayak, Binod Kumar Institute of Technical Education and Research Siksha 'O' Anusandhan Deemed to be University Department of Computer Science and Engineering Odisha Bhubaneswar India Odisha University of Technology and Research Department of Computer Science and Engineering Odisha Bhubaneswar India Institute of Management and Information Technology BPUT Department of Computer Science and Engineering Odisha Cuttack India Institute of Technical Education and Research Siksha 'O' Anusandhan Deemed to be University Department of Electronics and Communication Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350361780

The field of healthcare has witnessed significant advancements in leveraging technology for in-depth analysis of fetal health data, offering profound insights into child development. We meticulously gathered an extensive dataset comprising 1500 instances of child fetal health data sourced from New Born and Pediatric Health Centre, Cuttack, Odisha. We proposed a robust analytical pipeline for effectively classifying and predicting fetal health outcomes. The process initiated with meticulous data preprocessing and refinement, ensuring data integrity and quality. We use clustering techniques unveiled inherent patterns and groupings within the dataset, setting the stage for in-depth analysis and feature extraction. Further enriching the analysis, Principal Component Analysis (PCA) is deployed to streamline data dimensionality while retaining essential information. For prediction we use ensemble learning techniques. To predict the condition, we used voting ensemble method encompassing Decision Trees (DT), Support Vector Machines (SVM) and Naïve Bayes (NB), after predictive models are successfully constructed, showcasing notable accuracies of 92.28%, 83.89% and 82.22%. These results underscore the effectiveness of these models in predicting fetal health outcomes based on the comprehensive dataset. To further refine model performance, a meticulous GridSearchCV approach exhaustively explored optimal parameter values within predefined parameter grids, significantly enhancing predictive capabilities. Additionally, leveraging a bagging method, particularly Random Forest (RF), Logistic Regression (LR), and Gradient Boosting, further bolstered predictive accuracies. These bagging models demonstrated exceptional achieving accuracy of 94.02%, 93.22% and 92.28%. The systematic approach involving clustering, dimensionality reduction, ensemble learning, and parameter optimization resulted in highly accurate predictive models for fetal health assessment. Our research not only highligh

关键词： Decision trees

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Machine Learning and Vehicle Fault Diagnosis System with IoT Enabled Data

Machine Learning and Vehicle Fault Diagnosis System with IoT...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kannan, S. Rajes Partheeban, Pachaivannan Ramesh, Ramamoorthy Elamparithi, P. Navin Somasundaram, Krishnan Selvi, R. Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Department of Civil Engineering Chennai India National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli India Sri Muthukumaran Institute of Technology Department of Computer Scinece and Engineering Chennai India Dr.M.G.R. Educational and Research Institute Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348910

Vehicle systems are difficult for both Hardware implementation and Software implementation, so it is safeguarding is a difficult task. In vehicle trade, the maintenance process being utilized that effect in a fall of vehicle lifetime and also loss of capital. Predictive maintenance is needed to conquer these problems. The fault diagnosis system is proposed using PIC Microcontroller with efficient manner. The several parts in the vehicle such as vehicle engine, Gearbox, Power train unit, Airbag, Engine Control Units (ECUs), Chassis, ABS are establishing to apply the microcontroller. This article aims to clarify and review the ML frontiers involved in modern vehicle condition monitoring systems. The microcontroller operation helps to information about the individual health of the vehicle and gives/saves on the Internet of Things (IoT). Then, the web page receives the data and information will apply to the fault code diagnosis system. The fault diagnosis system helps to find the faults in the vehicle, such as the faults are precisely identified and establish the proper establishment of fault information in the fault diagnosis system. This work is combined effect of IoT data with machine learning to reduce the vehicle demages or fault condition monitoring. The performance analysis of the proposed system attains better performance than the other existing approaches. © 2023 IEEE.

关键词： Microcontrollers

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Efficient Smart Public Transit System Using RFID and IoT integration with MERN web applications

Efficient Smart Public Transit System Using RFID and IoT int...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Madasanal, Kartik Gudnavar, Anand Nagarmunoli, Sreedevi Madagouda, Basavaraj Naregal, Keerti Visvesaraya Technological University Jain College of Engineering and Research Department of Computer Science and Engineering Belagavi India Graphic Era University Department of Computer Science and Engineering Dehradun India S.G.Balekundri Institute of Technology Visvesaraya Technological University Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798331513894

This paper develops a Smart Public Transportation System using RFID technology, IoT integration, and a MERN-based web application for increasing the efficiency, accessibility, and user experience of urban transit systems. It addresses problems such as unreliable bus tracking, lack of support for multilingualism, and ineffective communication between operators and passengers. The key hardware components include RFID tags for bus identification, and IoT-enabled bus stations with RFID scanners, speakers, GSM modules for data transmission, and LCD displays. These ensure accurate real-time updates about the bus locations. On the software side, it is supported by a MERN stack, including MongoDB, ***, ***, and ***, providing a responsive web interface that's complemented by *** for dynamic route visualization and WebSockets for live updates. Multilingual support through i18next makes it inclusive for various user groups. The system is efficient in that it only updates when RFID scanners capture bus tags at certain checkpoints, thereby reducing server load much more than GPS-based systems. Additional features include route tracking, interactive maps, ticket pricing, and anonymous passenger chats. Crowd-sourced feedback mechanisms and an admin panel for bus management ensure the reliability and scalability of the system. This work shows the feasibility of cost-effective, scalable, and user-centric modernization of public transportation systems, paving the way for further enhancements such as integrated payment systems and AI-based route optimization. © 2025 IEEE.

关键词： Liquid crystal displays

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Closest Celestial Body Search Using KD Trees

Closest Celestial Body Search Using KD Trees

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Priya Ponnusamy, P. Shabariram, C.P. Umayal, V.R. Susmeta, A. PSG Institute of Technology and Applied Research Neelambur Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350348217

A Binary Search Tree is expanded into a KD Tree to handle the multi-dimensional key searches. A discriminator makes the KD tree different from the BST. This discriminator will take branching decisions at every level based on the key search. It can handle multi-dimensional coordinate object searching. K-d trees enable O(klogn) lookup times for the k nearest points to some point x. This is extremely useful, especially in cases where an O(n) lookup time is intractable already. In this work, a kd tree has been created based on how a celestial body is positioned in relation to the sun. It aims at finding the minimum in a particular dimension, searching for a celestial body and its nearest neighbor. © 2023 IEEE.

关键词： Nearest neighbor search

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Optimization-Inspired Graph Neural Network for Cellular Network Optimization

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6446-6459页

作者： He, Pengcheng Tang, Yijia Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China Tongji University School of Electronic and Information Engineering Shanghai China

The rapid development of wireless communications have driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model's generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model's applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. © 2002-2012 IEEE.

关键词： Graph neural networks

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