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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rai, Preeti Nain, Vishal Dept. of Computer Science and Applications New Delhi India

ISBN: (纸本)9798331518592

The 2010 discovery of the Stuxnet malware signaled a sea change in the field of cyberwarfare, and the 21st century has seen an unparalleled spread of digital technology and their incorporation into vital infrastructure systems. Given this, Stuxnet is a sobering reminder of the weaknesses present in our globalized society. This paper aims to investigate the intricacies of the Stuxnet attack, including its origins, development, objectives, and significant implications for cybersecurity and international relations. By carefully analyzing this sophisticated cyberweapon, this study sheds light on how cyberthreats are evolving in the twenty-first century. © 2024 IEEE.

关键词： Critical infrastructures

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Feature Extraction Techniques and Their Role in Alzheimer's Disease Diagnosis: A Comprehensive Review 3

Feature Extraction Techniques and Their Role in Alzheimer's ...

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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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A Novel Music Recommendation System Using Filtering Techniques

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第4期48卷 595-610页

作者： Vashishtha, Srishti Varshney, Deepika Sarin, Eva Kaur, Simran Computer Science and Engineering Dept. Bharati Vidyapeeth’s College of Engineering GGSIPU New Delhi India Computer Science and Engineering Dept. Jaypee Institute of Information Technology Noida India

With the growth of the World Wide Web, a large amount of music data is available on the Internet. A large number of people listen to music online rather than downloading and listening offline. But only some sites provide personalized and accurately recommended songs while they listen to an auto-playing playlist. Hence the need for recommendation systems arises. Two approaches can be applied to the recommendation system: content based filtering and collaborative filtering. While in content-based filtering approach, analysis on the songs’ content which has been preferred by the user in history is done and the songs with relative similarity are recommended. While latter suggests songs that certain users of similar listening pattern have preferred. But collaborative filtering-based recommendation systems not only requires time to attain stability but also might recommend unsuitable music because it is not personalized to each user’s preference. Also, the latter requires the songs to be listened by a few users already to recommend it any other user. Hence for overall analysis, answers to few questions need to be implemented in recommendation systems: the very first one is how the properties should be analyzed, next one is how the analysis should be done and last is how the songs related to the user’s preference should be chosen. So, for suggesting the better system which solves these three questions as well as provides better and personalized recommendations, In this paper we build a collaborative filtering as well as content based filtering recommendation system, where various factors determine the essence of the songs, i.e. liveliness, keys used, loudness, pitch, valence, etc., are analyzed for comparison. From the experimental analysis in has been identified that content-based filtering technique performed best on KNN machine learning classifier with accuracy of 85%. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Collaborative filtering

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Moth flame optimization algorithm for efficient feature selection and hybrid classification technique for intrusion detection system

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Multimedia Tools and applications 2025年 1-21页

作者： Keerthanarani, U. Selvakumar, V. Singaravelan, S. Raja, S. Edwin Thenmozhi, K. Shunmugam, D. Arun Dept. of Computer Science and Engineering P.S.R Engineering College Sivakasi India Dept.Of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai Avadi India Dept. Of Computer Science and Engineering Coimbatore Institute of Engineering and Technology Coimbatore India Dept. Of Electronics and Communication Engineering P.S.R Engineering College Sivakasi India

The rapid expansion of the Internet of Things (IoT) brings numerous benefits but further presents fresh difficulties, especially in terms of security. The distributed and interconnected nature of IoT devices makes them susceptible to various security threats. An Intrusion Detection System (IDS) plays a crucial role in safeguarding IoT networks by monitoring, detecting, and responding to potential security incidents. The Internet of Things (IoT) connects billions of devices, sensors, and systems, transforming industries and everyday life. However, this extensive interconnectivity also introduces significant security risks, making Intrusion Detection Systems (IDS) a critical component for safeguarding IoT environments. Feature selection is a crucial aspect of building effective Intrusion Detection Systems (IDS). Selecting relevant features not only helps in achieving better classification accuracy but also contributes to reducing false alarm rates. In the proposed system a moth flame optimizer is utilized to select the efficient features from the dataset, overall accuracy of over 97%. Further it is classified employing Random Forest and J48 classifier which attains high classification accuracy and low false alarm rate. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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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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ROBOT PATH PLANNING WITH A HYBRID ARITHMETIC OPTIMIZATION ALGORITHM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第3期86卷 197-210页

作者： Song, Qiang Dept. of Computer Science and Software Engineering Zhaoqing University Zhaoqing China

To address the premature convergence and search stagnation of arithmetic optimization algorithm (AOA), the paper proposes a hybrid arithmetic optimization algorithm (HAOA) and applies it to the practical robot path planning (RPP) problem. In initialization phase, the chaotic mechanism is used to create high-quality individuals. In evolution phase, the stochastic disturbance method is applied to enhance the exchange of information between different individuals. The solution space information around the candidate solution was integrated by Levy flight to enhance gene diversity and enhance search performance. In order to better solver the RPP problem, the Spline interpolation method is adapted to the HAOA algorithm for path optimization, so as to smooth the path curve and ensure the planning accuracy. Finally, numeric experiments of benchmark functions and RPP instances verify HAOA’s excellent performance in accuracy and robustness. This research shows arithmetic optimization algorithm can quickly find the global optimal solution, and has fast convergence speed and accuracy. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Stochastic systems

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Time series forecasting using transformer neural network

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International Journal of computers and applications 2024年第10期46卷 880-888页

作者： Bhogade, Vaibhav Nithya, B. Dept. of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Time series forecasting is a vital component of data science, giving essential insights that help decision-makers to predict future trends across a number of sectors. This paper focuses on projecting complicated stock market price dynamics, weather data variations, and hourly traffic occupancy rates. The Informer-Based Time Series Forecasting (IBTSF) model, which employs a transformer-based architecture to capture long-term relationships and give probabilistic predictions, is *** model is assessed against Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model to benchmark its performance. Simulation findings demonstrate that the IBTSF model considerably outperforms other models across all datasets. For traffic data, the IBTSF model achieves a Mean Squared Error (MSE) of 10.1 and a Mean Absolute Scaled Error (MASE) of 1.69, compared to other model's larger mistakes. In stock data forecasting, the IBTSF model reports an MSE of 8.04 and a MASE of 1.53, again outperforming other models. Similarly, using meteorological data, the IBTSF model obtains an MSE of 86.12 and a MASE of 0.7, whereas other models fall short. These results underline the IBTSF model's resilience and accuracy in capturing subtle patterns and relationships within time series data, giving precise insights that benefit decision-making processes. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Prediction models

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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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A Novel Identity Authentication Mechanism forUnmanned Maritime Vessels Communication Based on MITRE ATT&CK Framework

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Journal of Marine science and Technology (Taiwan) 2024年第2期32卷 170-180页

作者： He, Jun-Xian Chang, Shih-Hao Dept. of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Dept. of Computer Science and Information Engineering National Taipei University of Technology Taipei City Taiwan

With the development of the smart shipping industry, unmanned vessel technology is rapidly evolving, accompanied by a demand for robust Internet of Things (IoT) communication security practices. Key communication technologies for the operation of unmanned vessels include external vessel communication. One crucial aspect is the verification of the identities of the parties involved in these communication systems, as this ensures secure and trusted interactions between unmanned vessels and port management authorities. Identity authentication plays a vital role in ensuring the secure communication of unmanned vessels. This paper aims to analyze potential risks related to identity authentication technology in unmanned vessel communication systems. To effectively address these risks, we propose leveraging digital certificates and blockchain technology to achieve device non-repudiation and substitution. Based on this technology, we organize and implement defense strategies using the MITRE ATT&CK framework. This framework provides a comprehensive and systematic approach to understanding potential threats and designing appropriate defense measures. By utilizing this framework, security teams can better comprehend vulnerabilities and formulate effective countermeasures. To validate the effectiveness of the proposed defense framework, we conducted simulation experiments. These experiments helped us evaluate the efficiency and reliability of the strategies in real-world scenarios. Through these experiments, we could ascertain whether the countermeasures effectively mitigated the identified risks and ensured the secure communication of unmanned vessels. © 2024 National Taiwan Ocean University

关键词： Risk assessment

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