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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Agrawal, Navin Kumar Kumar, Sandeep Bisht, Girish Singh Srivastav, Akanksha Bansla, Vandna Jain, Arpit Gla University Department Of Computer Engineering And Applications U.P. Mathura India Tula's Institute Department Of Computer Science And Engineering Uttarakhand Dehradun India Shivalik Engineering College Department Of Computer Science And Engineering Uttarakhand Dehradun India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering AP Vaddeswaram India

ISBN: (纸本)9798350366846

A mobile ad hoc network (MANET) uses mobile devices and wireless networking to stream video. Several methods have been developed to address the challenges associated with video streaming over MANETs. This paper designed a new protocol for Mobile Video of Demand (MVOD) system architecture over the MANET environment. The proposed MVOD Model is used to improve the QoS parameters of the video system in wireless ad-hoc networks. The MVOD protocol uses client caching algorithms and a staggered broadcasting method, and it compares its performance with that of an existing traditional protocol for three-tier architecture. When compared to QoS parameters such as service delay and bandwidth utilization, the proposed MVOD model performs better than the traditional approach when compared with various scenarios. Based on the importance and complexity of evaluating the techniques, we conclude that comparability problems exist in research. © 2024 IEEE.

关键词： Quality of service

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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Multibranch Co-training to Mine Venomous Feature Representation: A Solution to SnakeCLEF2024 25

Multibranch Co-training to Mine Venomous Feature Representat...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Wang, Peng Li, Yangyang Tan, Bao-Feng Zhou, Yi-Chao Li, Yong Wei, Xiu-Shen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

The SnakeCLEF2024 competition aims to develop an advanced algorithm capable of automatically identifying snake species from images. Accurate identification of snake species in snakebite cases can assist doctors in administering targeted antivenom, which is crucial for effective treatment. In this paper, we propose a multibranch co-training strategy based on Convolutional Neural Networks (CNNs) as the solution. During the training phase, our method consists of three branches which can be trained end-to-end. The first branch is used for the classification of all species and generates a gating coefficient. The second branch specifically focuses on venomous snakes, while the third branch concentrates on harmless species. The gating coefficient determines which of these branches will be utilized. During the inference phase, we only retain the first branch. Our solution significantly enhances the model's ability to distinguish between venomous and harmless snake species and achieve an accuracy of 69.83% and scored 83.57% on the track1 on the private leaderboard, which is the 1st place among all participants. The code is available at https://***/pengdadaaa/SnakeCLEF2024. © 2024 Copyright for this paper by its authors.

关键词： Convolutional neural networks

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An Efficient DDoS Attack Detection in SDN using Multi-Feature Selection and Ensemble Learning 4

An Efficient DDoS Attack Detection in SDN using Multi-Featur...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kachavimath, Amit V. Narayan, D.G. Department of Master of Computer Applications KLE Technological University Hubballi580031 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India

Software-defined networking (SDN) has significantly enhanced network agility by decoupling network control from hardware devices. This architectural shift exposes SDN infrastructures to increased risks from Distributed Denial of Service (DDoS) attacks, which can severely disrupt network services and render them unavailable to legitimate users. Despite the existence of various techniques for detecting specific types of DDoS attacks, there remains a need for more comprehensive work capable of accurately identifying multiple attack categories. We have addressed the gap by proposing a robust DDoS attack detection method in the SDN. We utilize advanced machine learning (ML) algorithms to analyze the InSDN dataset, employing advanced feature extraction techniques to improve detection accuracy and efficiency. Three feature extraction methods, Se-lectKBest, ANOVA F-value scores, and feature importance scores from RandomForest Classifier, were used to select the best ten features from 81. This feature selection, combined with ensemble learning, achieved an accuracy of 99.9%. The Receiver Operating Characteristic (ROC) curve, confusion matrix, and K-fold cross-validation were used to evaluate the performance of the proposed model. © 2025 The Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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Impact of nutrients in food quality and safety by machine learning classifier using internet of things

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International Journal of Information Technology (Singapore) 2024年第5期16卷 2803-2812页

作者： Balamurugan, S. Gurumoorthi, E. Devi, P.P. Maruthamuthu, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp Department of IT CMR College of Engineering and Technology Telangana Hyderabad India Department of Computer Applications Madanapalle Institute of Technology and Science AP Madanapalle India

The traditional Food Supply Chain (FSC) is challenged by a number of issues, including uncertainty, security, expense, complexity, and quality worries. A precise supply chain is required to address these problems. Managing the supply chain to distribute high-quality goods is a difficult task in the modern food sector. The efficient Tree Augmented Naive Bayes linked with FSC utilizing Internet of Things (IoT) is proposed in this paper to enable tracking, tracing, and managing the full food supply chain process, including supplier, exporter, and consumers. This paper's goals are to evaluate the food safety and create the best possible chronological data for the analysis of potential future assumptions. It also intends to connect the producer and the buyer by delivering high-quality food goods while using IoT technology to transport food from the producers to the customers. IoT-based food supply chain traceability is used to successfully transact data with ambiguous, unreliable, and inadequate data. In order to provide secure backdrop and food safety for FSC process utilizing IoT technology, the suggested effective Tree Augmented Naive Bayes algorithm would be able to overcome the challenges of conventional supply chain. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Bayes classifier algorithm Food safety and IoT FSC Traceability Tree augmented Naive Bayes

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A Review of Moving Object Detection Techniques for Night Time 5

A Review of Moving Object Detection Techniques for Night Tim...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Dua, Disha Bhat, Aruna Girdhar, Ashish Delhi Technological University Department Of Computer Science & Engineering Delhi India Kurukshetra University Department Of Computer Science & Applications Kurukshetra India

ISBN: (纸本)9798350330861

Moving Object Detection in low light conditions is a challenging task due to limited visibility and the presence of noise and objects in the captured images, low contrast, low brightness, low appearance information, and low Signal to Noise Ratio. The majority of night surveillance models suffer from lack of adaptability for different scenes and separation between detection and tracking. This review paper provides a comprehensive survey of recent advancements in Moving Object Detection Techniques for night-time situations. It presents an overview of various approaches proposed in the literature, including traditional and deep learning-based methods, and discusses their strengths and limitations. Moreover, the paper highlights evaluation metrics commonly used in the literature, the datasets they have been evaluated upon, along with a comparative analysis of the performance metrics & various limitations of different methods. Finally, the paper identifies the key research challenges and future directions for Moving Object Detection in Night Time viz. dim-lit environments, such as the need for more robust and efficient algorithms that can handle diverse environmental conditions and real-time applications. © 2023 IEEE.

关键词： Object detection

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Current Perspectives and Virtualisation solutions with SDN for IoT 2

Current Perspectives and Virtualisation solutions with SDN f...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Bhardwaj, Shanu Kumar, Shailender Girdhar, Ashish Department of Computer Science and Engineering Delhi Technological University Delhi India Department of Computer Science and Applications Kurukshetra University Kurukshetra India

ISBN: (纸本)9798350305418

During the past years, IoT has acquired a lot of consideration since it incorporates intelligent gadgets which empower many applications that work in our day-to-day existence. Due to this the rising number of clients and the interest in more different and specific applications have prompted a tremendous expansion in the network traffic. Managing different traffic requests from various applications is a difficult task for the current traditional networking architecture. Therefore, the paper provides a thorough analysis of the SDN and various other technologies-based network virtualization methods as well as current perspectives for the IoT. The representation provides the working of various up-going technologies such as Machine Learning, Edge Computing, and Virtualization with SDN to betray the performance of the SDN applications in today’s world. © 2023 IEEE.

关键词： edge computing internet of things machine learning network traffic software-defined networking

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Predictive Analysis of Telecom Customer Churn Using Machine Learning Techniques 1

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International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Baby Lavanya, K. Ismail Ansari, M.D. Gundu, Venkateswarlu Krishna Mohan, G. Mouleswararao, B. Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation AP Guntur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Guntur India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

Customer churn poses a significant challenge to the telecom industry as it directly impacts revenue and customer retention efforts. To tackle this concern, the present research suggests an all-encompassing machine learning oriented method for forecasting customer attrition within the telecommunications industry. The dataset is employed for training and testing the Machine Learning models, and their effectiveness is assessed through conventional metrics like accuracy, precision, recall, and the area under the curve of the receiver operating characteristic (AUC-ROC). Conducting an analysis of feature importance enables us to pinpoint the foremost factors that impact customer churn. The findings illustrate that the logistic regression model attains accuracy in its predictive capabilities for customer attrition. The approach offers a practical and accurate solution, aiding telecom companies in reducing churn, increasing customer loyalty, and ultimately improving business performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Telecommunication industry

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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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MACPGANA: design of a highly efficient multimodal agriculture commodity price prediction model via generative adversarial networks & autoencoders

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2355-2368页

作者： kambo, Rubi Pillai, Jyothi Soni, Sunita Urkude, Giridhar Department of Computer Science and Engineering Bhilai Institute Technology Chhattisgarh Durg India Department of Computer Science and Engineering Rungta College of Engineering and Technology Chhattisgarh Bhilai India Symbiosis Institute of Technology Nagpur Campus Symbiosis International(Deemed University) Pune India Department of Computer Applications Bhilai Institute of Technology Durg Chhattisgarh India

This paper presents MACPGANA, a novel multimodal agriculture commodity price prediction model leveraging Generative Adversarial Networks (GAN) and Autoencoders (AEs). The model integrates multimodal data to extract and represent features efficiently, employs GAN for feature selection, and uses predictive autoencoders to estimate inter-day price movements. A Q-Learning-based feedback loop ensures continuous optimization of prediction performance. MACPGANA achieves high accuracy, precision, and recall while maintaining low computational complexity, making it suitable for real-time and large-scale applications. The model’s effectiveness is demonstrated across diverse commodities, highlighting its potential for scalable and efficient agricultural price prediction. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Agriculture commodity price prediction Autoencoders (AE) Generative adversarial networks (GAN) Multimodal feature extraction Q-learning optimization Real-time price forecasting

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