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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rai, Preeti Ambooj Kukkar, Sania Dept. of Computer Science and Applications New Delhi India

ISBN: (纸本)9798331518592

Feature extraction is essential for breaking down complicated datasets into more digestible and useful formats, particularly in medical imaging. Accurate and timely diagnosis is critical in the context of Alzheimer's disease (AD), and MRI scan feature extraction algorithms have demonstrated significant potential. This study looks at a number of feature extraction methods, their applications across several fields, and their specific use in Alzheimer's disease diagnosis. By examining MRI-based research, we highlight important advancements and contributions in the field, emphasizing the techniques' potential to improve diagnostic accuracy. Despite these developments, there are still things that can be done to optimize feature extraction processes, such as reducing computational costs, enhancing model generalization, and refining feature selection for improved clinical outcomes. The paper continues by discussing the challenges of applying these methods in real-world clinical settings, including differences in patient demographics and imaging procedures. In conclusion, advanced feature extraction methods-particularly those that enable early detection, lower computing requirements, and provide more precise interpretations of MRI data-that employ deep learning and machine learning algorithms-have the potential to revolutionize Alzheimer's disease detection. The paper also makes recommendations for future research directions to get beyond current limitations and maximize the usage of feature extraction techniques for Alzheimer's disease diagnosis. © 2024 IEEE.

关键词： Neurodegenerative diseases

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MOPO-HBT: A movie poster dataset for title extraction and recognition

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Multimedia Tools and applications 2024年第18期83卷 54545-54568页

作者： Ghosh, Mridul Roy, Sayan Saha Banik, Bivan Mukherjee, Himadri Obaidullah, Sk Md Roy, Kaushik Dept. of Computer Science Shyampur Siddheswari Mahavidyalaya Howrah India Dept. of Computer Science & Engineering Aliah University Kolkata India Dept. of Electronics & Electrical Communication Engineering IIT Kharagpur Kharagpur India TISA Lab Dept. of Computer Science West Bengal State University Barasat India

Real-world images often encompass embedded texts that adhere to disparate disciplines like business, education, and amusement, to name a few. Such images are graphically rich in terms of font attributes, color distribution, foreground-background similarity, and component organization. This aggravates the difficulty of recognizing texts from these images. Such characteristics are very prominent in the case of movie posters. One of the first pieces of information on movie posters is the title. Automatic recognition of movie titles from images can aid in efficient indexing as well as information conveyance. However, it is accompanied by other texts like the names of actors, producers, taglines, dates, etc. Though the organization of components is somewhat similar across different film industries like Tollywood (West Bengal), Bollywood (Mumbai), and Hollywood (Los Angeles), the graffiti patterns differ in multifarious instances. To address the problem of movie title understanding, we propose a dataset named MOvie POsters-Hollywood Bollywood Tollywood (MOPO-HBT) that encompasses movie posters from the aforementioned industries. The entire dataset is publicly available (http://***/11564) for research purposes. The baseline results of title identification and recognition were obtained with a CNN-based (Convolutional Neural Network) approach, wherein the titles were extracted using the M-EAST (Modified-Efficient and Accurate Scene Text) detector model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Character recognition

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AI in CS Education: Opportunities, Challenges, and Pitfalls to Avoid

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ACM Inroads 2024年第3期15卷 52-57页

作者： Vahid, Frank Dept. of Computer Science and Engineering University of California Riverside United States

Csinstructors realize education may be impacted by generative AI (artificial intelligence). This article describes (1) opportunities, like 24/7 help or auto-grading, (2) challenges, like increased cheating or student isolation, and (3) pitfalls to avoid, like inequitable access or false cheating accusations. The descriptions come from 15 CS educators and practitioners, from diverse institutions, who met at a 2023 NSF workshop on AI in CS education. The article aims to assist the CS community to take advantage of generative AI to improve courses, embark on new research, treat students equitably, and ultimately improve CS education for instructors and students. © 2024 Association for Computing Machinery. All rights reserved.

关键词： Students

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HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization of Biomedical Research Articles using Seq2Seq Transformers 23

HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization...

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23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Malik, Hemang Pradeep, Gaurav Seth, Pratinav Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Data Science & Computer Applications India Dept. of Computer Science & Engineering India

ISBN: (纸本)9798891761308

Lay summarization aims to generate summaries of technical articles for non-experts, enabling easy comprehension for a general audience. The technical language used in research often hinders effective communication of scientific knowledge, making it difficult for non-experts to understand. Automatic lay summarization can enhance access to scientific literature, promoting interdisciplinary knowledge sharing and public understanding. This has become especially important for biomedical articles, given the current global need for clear medical information. Large Language Models (LLMs) (Yao et al., 2024), with their remarkable language understanding capabilities, are ideal for abstractive summarization, helping to make complex information accessible to the public. This paper details our submissions to the BioLaySumm 2024 Shared Task: Lay Summarization of Biomedical Research Articles (Goldsack et al., 2024). We fine-tune and evaluate sequence-to-sequence models like T5 across various training dataset settings and optimization methods such as LoRA (Hu et al., 2021b) for lay summarization. Our submission achieved the 53rd position overall.. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Flood Detection from Satellite Images Using Self-Attention Empowered EfficientNetV2 Mechanism 7

Flood Detection from Satellite Images Using Self-Attention E...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Selva Jeba, G. Chitra, P. Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India Thiagarajar College of Engineering Dept. of Computer Applications Madurai India

ISBN: (纸本)9798350304268

Floods occur when water overflows onto normally dry land and are a destructive natural disaster. In recent times, deep learning models have demonstrated their remarkable capabilities in identifying objects and classifying images. As a result, the utilization of deep learning in remote sensing, especially for the crucial task of flood detection, has become an increasingly prominent and promising area of research and application. This study proposes a lightweight yet powerful feature extraction framework by leveraging a self-attention(SA) based EfficientNetV2. The EfficientNetV2 architecture serves as the backbone of the model and has exhibited effectiveness in image classification tasks. By incorporating self-attention mechanisms, the model can capture spatial relationships within remote sensing images, enhancing its accuracy and efficiency in flood detection. The model was trained and assessed using the SEN12-FLOOD dataset, and the outcomes demonstrate that the suggested framework exhibits enhanced flood detection performance, achieving an accuracy of 95.9%. © 2023 IEEE.

关键词： attention mechanism deep learning EfficientNetV2 flood detection remote sensing

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A Few-Shot Learning for HAR Using 3D Siamese Network 15

A Few-Shot Learning for HAR Using 3D Siamese Network

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

作者： Guanghui, Hua Kumar, G. Hemantha Aradhya, V.N. Manjunath Punithavalli University of Mysore Dept. of studies in Computer Science Mysuru India Jss Science and Technology University Dept. of Computer Applications Mysuru India Bharathiar University Department of Computer Applications Coimbatore India

ISBN: (纸本)9798350370249

In artificial intelligence, deep learning techniques were applied in a wide field due to their ability to automatically learn complex patterns from raw data, such as image classification, object detection, image generation, and video analysis in computer vision. These applications are just the tip of a substantial iceberg. With the development of deep techniques to achieve state-of-art accuracy, learn precisely relevant features from raw data, and improve the robustness of models to make them versatile solutions. The traditional deep learning techniques require vast computational resources, including high-performance computer GPUs or TPUs, and can be expensive in terms of hardware. In Human Activity Recognition Systems (HARs). The video needs more computational resources than image processing. In this paper, we proposed a few-shot learning approach based on a 3D Siamese Network with dept. video sequences to address the action video similarity or dissimilar problem. Convolutional Neural Networks (CNNs) are well-suited for images and spatial data processing. The 3DCNN is an extension of CNNs to incorporate an additional temporal dimension to capture the temporal pattern. From the 3D Siamese network, we extract more effective features with spatial-temporal action information from raw data. We employed the 3DCNNs Siamese Network (3DCNNs+SN) with shared weight to recognize or classify a new class of data without new sample data only based on learning training existing sample constructed model. In addition, we divided the benchmark dataset MSR Action 3D dataset into sub-dataset1 (SD-1), sub-dataset2 (SD-2) and sub-dataset3(SD-3) to train and validate our proposed method. For dissimilar sub-dataset 2 we got a 98.1% accuracy, 33% on high-similar sub-dataset 3 and 63.5% on merged sub-dataset SD-4 from sub-dataset2 (SD-2) and sub-dataset3(SD-3). © 2024 IEEE.

关键词： Spatio-temporal data

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A Machine Learning Model for Predicting Internet Addiction in Teenage and Young Students 3

A Machine Learning Model for Predicting Internet Addiction i...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Johar, Geetika Patel, Ravindra University Institute of Technology Rgpv Dept. of Computer Science & Engineering Bhopal India University Institute of Technology Rgpv Dept. of Computer Applications Bhopal India

ISBN: (纸本)9798350384598

Internet addiction is becoming one of the critical issues among teenagers and young university students. This habit not only negatively impacts the student's learning performance, but also affects the student's physical and mental health. This paper is focusing on designing a Machine Learning (ML) model for accurately identifying internet addiction. The model incorporates data preprocessing and feature selection techniques for reducing the computational complexity. Next, five different ML algorithms have been implemented and compared to identify appropriate ML algorithms. The identified ML model has been trained using the behavioral features of the students for predicting internet addiction. The experiments have been carried out and the performance of the models has been measured. The results demonstrate that a random forest classifier provides 83% accurate detection for university students and 96% accurate results are provided by an SVM-based classifier for teenager internet addiction detection. Therefore, by using the entire observation of experiments it is found that the proposed model can successfully identify internet addiction in students by analyzing the student's behavioral features. © 2024 IEEE.

关键词： Contrastive Learning

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Blockchain Technology: Reshaping Telemedicine and Telehealth Services for Emergency Management

Blockchain Technology: Reshaping Telemedicine and Telehealth...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Neelam Mittal, Varsha Rawat, Vandana Kumar, Priyansh Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Uttrakhand Dehradun India Graphic Era Deemed to Be University Dept. of Computer Applications Uttrakhand Dehradun India

ISBN: (纸本)9798331508685

Both consumers and healthcare providers have benefited greatly from the quick development of software-based technology. The dominance of blockchains is impending as the medical care sector moves quickly towards digitization and customization. The development of blockchain provided answers to several problems that had long plagued the healthcare sector. However, not only in healthcare due to its capabilities, blockchain technology has found great success in a variety of industrial fields. The important factors propelling the growth of the healthcare blockchain market are the rise in data breaches, privacy concerns, fake pharmaceuticals, and provided answers to numerous problems that had long plagued the healthcare sector. Although telemedicine had been developing for a while, the covid-19 epidemic gave it the necessary kick. In order to facilitate social isolation, the pandemic made it necessary to expand virtual healthcare and distant therapy. Over the past year, the telemedicine market has experienced rapid expansion. In the current study, the adaptation issues and prospective prospects for blockchain technology in the telehealth and telemedicine sectors are *** on a systematic review, we have determined the existing state of the use of blockchain technology in a number of healthcare-related areas, the ideal state that would be achieved, the research gap regarding the usage of blockchain technology in a number of healthcare-related application areas, as well as a number of potential research agendas for future studies. © 2025 IEEE.

关键词： Telemedicine

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A MMAMF Mitigation Strategy for Overcoming Cyber Vulnerabilties in a Cyber-Physical System 3

A MMAMF Mitigation Strategy for Overcoming Cyber Vulnerabilt...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ahuja, Sidak Rana, Lakshya Wason, Ritika Arora, Parul Hoda, M.N. Arora, Devansh Dept. of Computer Science and Applications Delhi India Sprinklr Gurugram India

ISBN: (纸本)9798331518592

The rise in the frequency and complexity of cyber-attacks against all enterprises alike, has fueled the research on mitigation techniques for cyber-physical vulnerabilities. The 2014 Sony Pictures cyber-attack was a significant event that was unique in its nature, orchestration as well as effects. This manuscript provides a detailed assessment of the cyber physical vulnerabilities exposed by the attackers while assessing the implications for future mitigation strategies. The manuscript also lays an effective mitigation framework to avoid such attacks in the future on any significant, cyberphysical systems. © 2024 IEEE.

关键词： Cyber attacks

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J-NECORA: A Framework for Optimal Resource Allocation in Cloud-Edge-Things Continuum for Industrial applications With Mobile Nodes

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IEEE Internet of Things Journal 2025年第11期12卷 16525-16542页

作者： Pettorali, Marco Righetti, Francesca Vallati, Carlo Das, Sajal K. Anastasi, Giuseppe Univ. of Pisa Dept. of Information Engineering Italy Missouri Univ. of Science and Technology Dept. of Computer Science RollaMO United States

In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of MNs. If the optimal allocation does not exist, J-NECORA provides a best-effort allocation that minimizes the delay. The proposed framework is highly modular and flexible, and can be easily customized to different scenarios. We validated its effectiveness through simulation experiments, focusing on a specific use case. For the considered scenarios, the performance predicted by our framework matches quite closely the simulation results. © 2014 IEEE.

关键词： Cloud platforms

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