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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： latha, J. KarthickMurugan, S. logalakshmi, A. Department of Computer Science and Engineering R.M.K. Engineering College India Department of Computer Science and Engineering R.M.D. Engineering College India Department of Computer Science and Engineering Thiruthangal Nadar College India

ISBN: (纸本)9798350305708

Effectively identifying computer network attacks is still a challenge. This is due to recent attempts by cybercriminals to spoof intrusion detection systems (IdS) by altering packet contents. Additionally, the computer networks constantly add a large number of additional devices. Additionally posing a security risk to computer networks are these novel gadgets. To regulate computer network flows and offer security beforehand, you must do a thorough examination of the elements of your network, including the IdSs, the methodologies and devices used, particular kinds of attacks, and the tools that are used. This essay discusses intrusion detection methods, methodology, and tactics in addition to exploring innovative attack types, defenses, and current scientific study in the subject. © 2023 IEEE.

关键词： IdS methodologies IdS technologies IdS tools Intrusion detection system

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An Evolutionary Approach for Multiple Flying Base Stations deployment in NOMA-Based Networks

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9814-9827页

作者： Jabbari, Mohammad Reza Ni, Jianbing lu, Ning Yam, Scott S.-H. Chan, Francois Queen's University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada Royal Military College of Canada Department of Electrical and Computer Engineering KingstonONK7K 7B4 Canada

This paper addresses the problem of deploying a Non-Terrestrial Network (NTN) using multiple Flying Base Stations (FBSs) to serve multiple Stationary Ground Users (SGUs). Our objectives are to minimize the number of active FBSs, reduce overlapping coverage areas, and increase data rates for SGUs. We formulate this as a multi-objective, non-convex Non-linear Mixed Integer Programming (NlMIP) problem, incorporating both deployment and resource allocation practical constraints. To manage the problem's complexity, we decompose it into two sub-problems: FBS deployment and resource allocation optimization. To optimize the 2d deployment of FBSs in horizontal space, we propose a general, low-complexity heuristic method based on a statistical variant of the Self-Organizing Map (SOM) algorithm, named Evolutionary Bayesian SOM (EBSOM). Then, utilizing a Non-Orthogonal Multiple Access (NOMA) communication scheme between FBSs and SGUs, we maximize the downlink sum rate while adhering to constraints on FBS positions, transmission power, and bandwidth availability. In this stage, the deployment of FBSs is further refined to enhance resource allocation by dividing the problem into two non-convex sub-problems, which are solved iteratively using Surrogate Optimization (SO). Numerical simulations demonstrate the effectiveness of our strategy, particularly in reducing the number of active FBSs, minimizing overlapping areas, and improving network sum rates. © 1967-2012 IEEE.

关键词： Heuristic methods

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Integrated normal discriminant analysis in mapreduce for diabetic chronic disease prediction using bivariant deep neural networks

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International Journal of Information Technology (Singapore) 2024年第8期16卷 4915-4929页

作者： Ramani, R. dhinakaran, d. Edwin Raja, S. Thiyagarajan, M. Selvaraj, d. Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

This study presents the Normal discriminant Feature Selection based Regressive deep Neural MapReduce (NdFS-RdNMR) framework designed for efficient prediction of diabetic chronic diseases using input datasets. The primary aim of NdFS-RdNMR is to enhance accuracy and recall in handling large datasets for chronic disease prediction. The framework integrates the Normal discriminative Preprocessing Model (NdPM) and bivariant regressive deep artificial neural network with MapReduce (BRdANNMR) classifier. Utilizing the Pima Indian diabetic dataset as input, NdFS-RdNMR conducts feature preprocessing through NdPM to extract relevant features for disease prediction. Non-traditional datasets are transformed into traditional formats via parameter rescaling to fit within predefined value ranges. Min–max normalization is applied to improve system accuracy while preserving data relationships. The BRdANNMR classifier utilizes bivariant regression analysis in the mapping phase to generate intermediary outcomes, which are then classified using a bipolar activation function in the reducer process. The framework achieves high accuracy and recall in early diabetes disease prediction, offering valuable insights for medical practitioners and researchers. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Artificial neural network Classifier Feature selection MapReduce Normalization Regression analysis

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Implementing a Robust Method for detecting Human Actions in Health Monitoring by Employing Sensors on Mobile Internet of Things devices and Utilizing a One-dimensional Convolutional Neural Network

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Journal of Technology in Behavioral Science 2025年第1期10卷 79-91页

作者： Vignesh, T. Thyagharajan, K.K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Electronics and Communication Engineering R.M.D Engineering College Chennai India

This research is focused on developing a precise human action recognition system by utilizing accelerometer and gyroscope sensor data. The aim is to analyze various human actions like jogging, sitting, standing, walking, going upstairs, going downstairs, and falling, using multiple sensors found in modern smartphones. Previous studies have explored human action recognition using different sensors, cameras, and machine learning and deep learning techniques. However, some existing approaches, particularly those based on channel state information (CSI), have shown less satisfactory outcomes. In contrast, this paper employs deep learning techniques, specifically one-dimensional convolutional neural networks (1d-CNN), for more accurate human action analysis. One significant advantage of the proposed system is its potential as a wearable device. The paper will introduce a one-dimensional convolutional neural network tailored for human action recognition and an optimized approach for determining the magnitude of accelerometer's X, Y, and Z coordinates, as well as gyroscope sensor values. By utilizing these coordinates, the One-dimensional Convolutional Neural Networks can effectively classify various human actions like jogging, sitting, standing, walking, going upstairs, going downstairs, and falling. To evaluate the performance of the 1d-CNN approach, a comparison is made with other methods, including SOM, SVM, and FCM. The results demonstrate that the proposed method (1d-CNN) outperforms other classification techniques with an impressive accuracy of 92.42 per cent. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Acceleration Gyroscope Human action recognition One-dimensional convolutional neural network Sensors

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Video Transcript Extraction and Summarization Using Transfer learning 7th

Video Transcript Extraction and Summarization Using Transfer...

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7th International Conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022

作者： Mehta, Varun deshpande, Tushar Pandey, Ravikumar Kandoi, Tanishq Nair, Sindhu Department of Computer Engineering D.J. Sanghvi College of Engineering Mumbai India

ISBN: (纸本)9789811996375

The number of videos accessible on web platforms is continuously growing. Global access to the content is provided, especially in videos or audio. Unlike images, where data can be gathered from a single frame, videos require a viewer to watch the entire thing to fully understand the context, and this presents a significant challenge to information extraction. Previously, seq2seq models were used to predict the summaries of paragraphs or transcripts. But recent advancements in transfer learning bypass the accuracy obtained by the standard NlP algorithms such as text rank and seq2seq models. Also, the summarization type focused on by most of the standard models is extractive summarization. Therefore, Hugging Face Transformers are used in this study to summarize the generated transcripts as they help us to perform abstractive summarization and also give greater accuracy than previous standard NlP algorithms and *** system proposed in this paper converts audio chunks from input videos into transcripts. The summarization model, based on natural language processing and transfer learning, is then given these transcripts. This paper presents a comparative analysis for some transformer models and selects the one which performs better than other models and incorporates it in the proposed system. The developed model accepts user-supplied video links as input and outputs a summary like description of video. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： Natural language processing systems

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Proactive cyber defense through a comprehensive forensic layer for cybercrime attribution

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3555-3572页

作者： Patil, Rachana Y. Patil, Yogesh H. despande, Himani Bannore, Aparna Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune 411044 India Department of Instrumentation Engineering D. Y. Patil College of Engineering Akurdi Pune 411044 India Thadomal Shahani Engineering College Mumbai India SIES Graduate School of Technology Mumbai India

The extension of digital competences in the ever-changing world of technology has transported global organizations new opportunities like never before. But this expansion has also flickered a dramatic surge in cybercrime, which carriages a huge problem that needs inventive replies. The current methods of cybercrime investigation are inadequate, predominantly when it comes to finding the source of attacks. While SSl safeguards secure data transmission, it does not offer forensic support, and the TCP/IP protocol suite does not form any protocols that are intended to be operative in cybercrime investigations. To rectify these difficulties, this paper proposes adding a forensic layer below the SSl layer. This novel feature consists of three protocols that reinforce user authentication and verification: IVAP, which is intended to brace the authentication and identification;Pro_dEC, which confirms that digital evidence is composed in a forensically comprehensive manner;and Cybercrime Attribution, which supports to attribute cybercrimes by tracking the source and path of data packets. The steadiness of court procedures amended by these protocols, which bid a specific toolbox for precise threat tracing and acknowledgement. Enhanced fact-finding capabilities and national safety are the main outcomes of this preventative approach to shore up cybersecurity and make systems tougher in the face of ever-changing cyber threats. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： AVISPA BAN logic Cyber security digital forensics Forensic layer

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Kathak dance Movement Recognition and Correction Using Google's Mediapipe 14

Kathak Dance Movement Recognition and Correction Using Googl...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Solkar, Ahmed dhingra, Glen Chavan, Khushi Patil, Nilesh D.J. Sanghvi College of Engineering Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350335095

Indian Classical dance (ICd) is a continuing tradition of India. This legacy has traditionally been guarded by gurus (teachers). They practice and teach the tradition to their Shishyas, who are frequently undocumented. Consequently, historical preservation is limited in time and scope. The advent of digital multimedia technology has enabled the preservation of history by preserving its accessibility over time. However, more efforts must be made to implement effective technology in dance pedagogy or maintain ICd history. This document describes a tutoring system for the ICd dialect Kathak. Using Media Pipe Holistic, Machine learning Models, and Fast dynamic Time Warping to detect postures and motions, we develop a system that can assist a trainee dancer in identifying deviations in her dancing postures and movements relative to previously recorded performances. © 2023 IEEE.

关键词： Motion estimation

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A Multimodal Approach to Multispeaker Summarization and Mind Mapping for Audio data 3

A Multimodal Approach to Multispeaker Summarization and Mind...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Modi, Hridayraj Patel, Aunali Joshi, Isha Kanani, Pratik D.J. Sanghvi College of Engineering Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

With recent global events resulting in a shift in the work setting and way of life worldwide, most educational institutes and corporate organizations have been forced to conduct their day-to-day operations online. lectures and meetings that are conducted online can be downloaded and users can store these recorded meetings. Owing to the latest technological advancements, storage capacities are no longer a hindrance when storing and using this data. However, storing these recorded meetings will only be helpful when users can access and browse them quickly. This paper proposes implementing a multi-speaker speech summarization model to benefit from the content captured in meeting recordings. Browsing through audio-visual data for information can be a time-consuming task for humans since manual summary generation would require a person to sit through the whole meeting. This can be overcome by combining Natural language Processing techniques with text summarization to generate the transcript of a meeting. By performing speaker diarization, the various speakers in a multi-speaker meeting are identified, and accordingly, labels are assigned to every participant in the transcript. The model then performs summarization for individual speakers as well as the whole meeting and creates a mind map to represent the meeting minutes visually. Thus, this paper provides an efficient manner of reviewing meetings and labelling them for future reference. © 2023 IEEE.

关键词： Mapping

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Neurodegenerative disease detection using deep Convolutional GANs and CNN 8

Neurodegenerative Disease Detection using Deep Convolutional...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： deshpande, Tushar Chavan, Khushi Gandhi, Priya Mangrulkar, Ramchandra D.J. Sanghvi College of Engineering Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350334012

Over the past ten years, advances in deep machine learning techniques, high-speed computing infrastructure development, and an improved understanding of deep learning algorithms have created new opportunities for advanced analysis of neuroimaging data. Neuroscientists can now use the data from neuroimaging to diagnose neurodegenerative diseases. Yet, due to the similarities in disease characteristics, it is challenging to identify such disorders from neuroimaging data accurately. The reason for such results is the current or inevitable limited availability of neuroimaging data. Thus, this paper suggests an unsupervised generative modeling technique using deep Convolutional Adversarial Networks to produce synthetic images (dCGANs). This method uses the AdNI dataset, which contains data for four neurodegenerative diseases, namely: Alzheimer's disease(Ad), Mild Cognitive Impairment(MCI), Early Mild Cognitive Impairment(EMCI), late Mild Cognitive Impairment(lMCI), and subsequently uses dCGAN on the small quantity of data, so increasing the dataset's size and variety by utilizing GAN. To outperform the conventional deep learning techniques, the artificial images, and the original dataset images are combined and trained into a convolutional neural network (CNN). © 2023 IEEE.

关键词： Neuroimaging

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YOlO-Based Video Processing for CCTV Surveillance 5

YOLO-Based Video Processing for CCTV Surveillance

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Terdal, Sujata Sayyada, F. Fatima, Ameena Reddy, Amulya Koppal, Manasi P.D.A. College of Engineering Department of Computer Science and Engineering Kalaburagi India

ISBN: (纸本)9798350321425

A visual surveillance system is primarily employed for the analysis and interpretation of object behaviours within a given video scene. Surveillance system is important because it acts as a deterrent to crime, helping prevent theft, vandalism, assault and enhances public safety by monitoring public spaces and allowing for a timely response to emergencies. The performance of a surveillance system using traditional cameras is vastly superior during daytime as compared to night time, this is primarily due to the availability of natural light during the day whereas at night in the absence of ambient light it's difficult for detecting objects. The developed system that detects anomalous activity during the day as well as night and alert the authorized user. The proposed system captures the video using CCTV camera and converts the video into frames, these frames undergo pre-processing to detect objects using YOlO algorithm. In the event of any anomalies or suspicious activities detected within the surveillance system, an alert message is promptly sent to the concerned user or designated authority. The results of the system demonstrate that the accuracy of the proposed methods reaches 98.3%. © 2023 IEEE.

关键词： Object detection

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