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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Akshay, S. Yashwanth, M.A. Manohar, N. Jipeng, Tian Amudha, J. School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Karnataka Mysore India Computer Science College Zhongyuan University of Technology Department of Computer Science China School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350383652

Eye tracking is a method of measuring and analyzing the movements of the eyes as they move and focus on different objects in the visual field. This technique has been used in a wide range of fields, including psychology, neuroscience and human-computer interaction. Eye tracking provides valuable insights into how individuals process visual information, including attentional biases, cognitive processing, and decision-making. Traditionally, eye tracking has relied on licensed software and specialized hardware, making it costly and less accessible for some researchers and applications. However, with recent technological advancements and by leveraging the capabilities of webcams, researchers, and practitioners can now perform eye-tracking experiments without the need for expensive equipment. By utilizing webcams, researchers can conduct eye-tracking experiments in a cost-effective manner, making the method more accessible to a broader range of researchers and practitioners. © 2024 IEEE.

关键词： Human computer interaction

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Meta-learning with orthogonal softmax layer (MLOSL) for small sample hyperspectral image classification

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Multimedia Tools and Applications 2025年 1-30页

作者： Amoako, Prince Yaw Owusu Cao, Guo Shi, Hao Arthur, John Kingsley Agyenim, Yaw Oti Boateng School of Computer Science and Engineering Nanjing University of Science and Technology Xiaolingwei Nanjing City210094 China Department of Computer Science Valley View University Accra Oyibi Ghana ETSU Municipal Center East Tennessee State University Johnson CityTN United States

Recent research on Hyperspectral image classification (HSIC) has been much concerned with small sample size due to the challenges of obtaining large labeled data. Coupled with this is the challenge with optimization of the models’ structure and computational complexities. Meta-learning techniques have been proposed to improve classification accuracy on small training data samples. The existing meta-learning techniques still require several pre-collected labeled hyperspectral images (HSI) source datasets of large labeled samples for meta-training, which is undoubtedly a huge task and time-consuming. We propose a meta-learning with orthogonal softmax layer (MLOSL) for small sample HSIC. Orthogonality is introduced to address the structural and computational complexities of deep convolutional neural network (CNN) models for HSIC. An unsupervised meta-learning task construction extracts multiview spectral dimension features of different bands from the same sample, whereas the spatial dimension multiview features are extracted through data augmentation. The orthogonal softmax layer captures a wider variety of patterns in the data by promoting variation among the weight vectors, potentially improving generalization to unobserved samples and minimizing computational time. Label smoothing is added to the classification loss to lessen the impact of incorrectly classified samples and class imbalance. Experimental outcomes on four target datasets compared to existing state-of-the-art models indicate the competitiveness of the proposed model's small sample HSIC performance and its suitability in time-constraint applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Harmony in transcripts: a systematic literature review of transcriptome-wide association studies

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Journal of engineering and Applied science 2024年第1期71卷 167页

作者： Mashhour, Mahinaz A. Kandil, Ahmed Hisham AbdElwahed, Manal Mabrouk, Mai S. Biomedical Engineering Department Misr University for Science and Technology Giza Egypt Systems and Biomedical Engineering Department Cairo University Giza Egypt School of Information Technology and Computer Science Nile University Sheikh Zayed City Egypt

Transcriptome-wide association studies (TWAS) goal is to better understand the etiology of diseases and develop preventative and therapeutic approaches by examining the connections between genetic variants and phenotypes while overcoming the limitations of the genome-wide association study (GWAS). It is a valuable complement to GWAS, reducing the negative effects of multiple tests and enabling a more thorough investigation of gene expression patterns in various tissues. A systematic review is presented in this paper to identify articles that utilize TWAS to understand the genetic factors behind complex diseases. A detailed selection process was carried out using standard PRISMA criteria to select relevant articles for the review. Twenty-five articles passed the inclusion criteria and were selected for additional review. The studies cover a diverse range of disorders, including Tourette’s syndrome, Alzheimer’s disease, rheumatoid arthritis, and major depression. Leveraging gene expression data from different tissues and populations, these investigations successfully identified novel genes and pathways associated with the studied conditions. The collective findings highlight the transformative impact of integrative genomics in advancing our understanding of complex diseases, providing insights into potential therapeutic targets, and laying the foundation for precision medicine approaches. © The Author(s) 2024.

关键词： Genome

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Lexicon-based fine-tuning of multilingual language models for low-resource language sentiment analysis

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1116-1125页

作者： Vinura Dhananjaya Surangika Ranathunga Sanath Jayasena Department of Computer Science and Engineering University of MoratuwaMoratuwaSri Lanka School of Mathematical and Computational Sciences Massey UniversityPalmerston NorthNew Zealand

Pre-trained multilingual language models (PMLMs) such as mBERT and XLM-R have shown good cross-lingual transferability. However, they are not specifically trained to capture cross-lingual signals concerning sentiment words. This poses a disadvantage for low-resource languages (LRLs) that are under-represented in these models. To better fine-tune these models for sentiment classification in LRLs, a novel intermediate task fine-tuning (ITFT) technique based on a sentiment lexicon of a high-resource language (HRL) is introduced. The authors experiment with LRLs Sinhala, Tamil and Bengali for a 3-class sentiment classification task and show that this method outperforms vanilla fine-tuning of the PMLM. It also outperforms or is on-par with basic ITFT that relies on an HRL sentiment classification dataset.

关键词： deep learning natural languages natural language processing

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Automated AI-Powered Fruit Identification Using Convolutional Neural Network

Automated AI-Powered Fruit Identification Using Convolutiona...

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

作者： Sai Tejasree, K. Surendran, R. Rajakumar, B. Gomathi, R.M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India J.N.N Institute of Engineering Department of Artificial Intelligence and Data Science Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recognition. This method teaches the ML Model we clearly explained the Model using application's agent using PEAS and the application's task environment using the 6 dimensions by showing it tons of fruit images, so over time, it gets really good at spotting the differences and similarities between, say, a banana and a grape. We also used another technique called pattern recognition, which helps the computer pay attention to specific details like the fruit's color, shape, size and texture. Overcoming multiple obstacles in order to automatically identify the type of fruit from the picture. The variety of images is influencing the color, texture, and shape of many different types of fruits. When it came to fruit picture detection, Convolutional Neural Network (CNN) Algorithm outperformed standard support-vector-machine-based approaches using handcrafted features in terms of accuracy also, it is a lot faster to implement for new fruits. By integrating deep learning and pattern recognition techniques such as Convolutional Neural Network Algorithm we have got the accuracy of 84%, our system efficiently identified different fruit types from images, demonstrating the power and effectiveness of our methods. The goal of our project is to create a tool that can quickly and correctly identify many types of fruits in photos, which could be useful for things like sorting fruits in a grocery store or helping people learn about different fruits by using Convolutional Neural Network Algorithm. This is not just about teaching a computer to recognize fruits;it is about making technology that can understand and interact with the world in a way that is helpful to us. © 2025 IEEE.

关键词： Convolutional neural networks

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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Dynamic Multimedia Encryption Using a Parallel File System Based on Multi-Core Processors 4

Dynamic Multimedia Encryption Using a Parallel File System B...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Simi Margarat, G. Malathi, S. Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Grt Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruttani India Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India

ISBN: (纸本)9798350393354

A new parallel file system and multi-core processor-based dynamic multimedia encryption method is presented in this study. Multimedia encryption efficiency and security were the main goals, addressing massive data sets and high-speed processing. We created a dynamic encryption algorithm for multimedia content, supported by a strong parallel file system and tuned for multi-core processing. Multiple tests showed significant improvements in encryption and decryption times, retaining short durations for real-time applications. After encryption, the system maintained multimedia content quality and accuracy with data integrity rates nearing 100%. Balanced CPU utilization showed efficient resource use and processor load management. The system's excellent throughput efficiency showed its capacity to process enormous amounts of data. These results indicate that parallel file systems and multi-core CPUs may solve multimedia encryption problems. Adaptable and scalable, the system supports many multimedia formats and sizes. This paper proposes a multimedia encryption system that improves performance data security and integrity, paving the way for safe multimedia processing advances. © 2024 IEEE.

关键词： Cryptography

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Biomedical Data Management and Analytics in IOMT 8th

Biomedical Data Management and Analytics in IOMT

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8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Niha, K. Surendiran, B. Amutha, S. School of Computer Science and Engineering VIT Vellore Campus Tamil Nadu Vellore India Department of Computer Science and Engineering National Institute of Technology Puducherry India School of Computer Science and Engineering VIT Chennai Campus Chennai India

ISBN: (纸本)9789819732449

The IoMT is a device that works with different healthcare systems to help develop networking technologies. It reduces the unnecessary burden of visiting the hospital. Instead, it enables the transfer of medical related data with physicians through a secured network. Smart devices, such as wearable and medical monitors are used for healthcare use in the IoMT network. Telehealth and other services are adapted in homes and hospitals in other real-time locations. Approximately in the globe, 80% of healthcare have implemented IoT (Internet of Things) technologies and 20% are expected to do the same in the future. This paper focuses on four topics: (1) recent advances on Internet of medical things, (2) highlights the emerging data fusion process for data preparation, (3) discusses the life cycle of sensor devices and their protocols, and (4) processing data with different techniques and theories. In a nutshell, this paper gives a comprehensive overview of IoMT research advancement, application, data management, and analytics techniques along with the current research challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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EAMR-Net: A multiscale effective spatial and cross-channel attention network for retinal vessel segmentation

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Mathematical Biosciences and engineering 2024年第3期21卷 4742-4761页

作者： Prethija, G. Katiravan, Jeevaa School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Information Technology Velammal Engineering College Chennai600066 India

Delineation of retinal vessels in fundus images is essential for detecting a range of eye disorders. An automated technique for vessel segmentation can assist clinicians and enhance the efficiency of the diagnostic process. Traditional methods fail to extract multiscale information, discard unnecessary information, and delineate thin vessels. In this paper, a novel residual U-Net architecture that incorporates multi-scale feature learning and effective attention is proposed to delineate the retinal vessels precisely. Since drop block regularization performs better than drop out in preventing overfitting, drop block was used in this study. A multi-scale feature learning module was added instead of a skip connection to learn multi-scale features. A novel effective attention block was proposed and integrated with the decoder block to obtain precise spatial and channel information. Experimental findings indicated that the proposed model exhibited outstanding performance in retinal vessel delineation. The sensitivities achieved for DRIVE, STARE, and CHASE_DB datasets were 0.8293, 0.8151 and 0.8084, respectively. © 2024the Author(s).

关键词： Drops

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring 5

DL-Based Somnolence Detection for Improved Driver Safety and...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

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