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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

The investigation aims to evaluate the performance of machine learning techniques, particularly the XGBoost regression method, for stock price prediction with the help of technical indicators. The research targets the adjusted closing price to reduce the autocorrelation problem in time series. The Five Days and Ten Days Exponential Moving Averages (EMA-5 and EMA-10) are involved in refining feature selection. The performance of the model was evaluated using different error metrics, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results indicate that the XGBoost model, combined with EMA-5 and EMA-10, achieves high predictive performance, obtaining an r2 value of 0.98. It implies that incorporating short-term EMAs into advanced machine learning techniques drastically improves stock price forecasting. © 2025 IEEE.

关键词： Time series

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Novel Lightweight Encryption Scheme Based on Transformation Matrix and ChaCha20 Algorithm for Secure Snap Transmission

Novel Lightweight Encryption Scheme Based on Transformation ...

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2024 IEEE International Conference on Intelligent Systems and advanced Applications, ICISAA 2024

作者： Sellam, V. Kumar, M. Ranjith Sudeesh Kumar, V. Mahendira Kumar, S. Tharun Kaarthik, G.K. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engg. Chennai India

ISBN: (纸本)9798331539948

A revolution for ultra-security in image transmission, due to the innovation and upgrade of cryptographic algorithms occurred during recent years. Proposed image encryption algorithm with ChaCha20 Our matrix-based shuffling not only improves image security but also adds to the complexity of data stealth. The scheme is made up of four main operations: Rearranging pixels in the image with a transform matrix, encryption using ChaCha20, decryption to get back the original image and finally descrambling it by applying inverse transformation changes. In this way, integrity is maintained and image privacy becomes secure. The provided transformation matrix makes scrambling with reversible operations and speed possible against all of the known standard attacks to which ChaCha20 encryption is subjected. The suggested method, led to a Peak Signal Noise Ratio of 361.20 and zero Mean Squared Error indicating a top-quality reconstructed image concerning the original image. The study is raising the security alerts provided by ChaCha20, a relatively new but efficient and high-fidelity encryption solution that makes it an attractive option in today's increasingly digitalized environment where data security has become everyone's top priority. © 2024 IEEE.

关键词： Signal to noise ratio

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Fake Social Media Profile Identification and Report Using Machine Learning

Fake Social Media Profile Identification and Report Using Ma...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Subramaniam, Suthashini Sangodiah, Anbuselvan Iftikhar, Aqsa Kiran, Ajmeera Kalsalingam Academy of Research and Education Department of Computer Science and Engineering School of Computing TamilNadu Srivilliputtur India Kampar Malaysia School of Computing Faculty of Computing and Engineering Quest International University Perak Ipoh30250 Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

In the digital age, Online Social Networking platforms are integral to daily life, but they also attract malicious bots that mimic human behaviour and pose cybersecurity threats. This literature review focuses on recent Machine Learning-based techniques for detecting and classifying bots on major social media platforms. It covers supervised, semi-supervised, and unsupervised methods, dataset details, and feature categories. While the technology is advancing, an ongoing challenge lies in the creation of ever- evolving bots to evade detection. The study offers insights into the evolving landscape of cybersecurity and the need for continual research in this field, but potential drawbacks may include data privacy concerns, The pervasive influence of the internet and social media as platforms for real-time information dissemination and their potential to shape perceptions and lives. It highlights the concern that unverified and malicious content can significantly impact users' perceptions. The paper offers a taxonomy for classifying malicious information at various stages and presents technologies to combat the issue from content generation to containment. The results illustrate the efficacy of machine learning in improving the dependability and safety of social media platforms, offering a scalable approach to address the increasing problem of online deception. © 2024 IEEE.

关键词： Bot (Internet)

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Enhanced Real-Time Object Detection on Customized Dataset with YOLOv9 Architecture

Enhanced Real-Time Object Detection on Customized Dataset wi...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Adithiyan, P.V. Kumar, M Ranjith Rahul, K. Nikhil, V. Annamalai, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engg. Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350379525

YOLO has developed into a primary real-time object identification platform for applications such as video surveillance systems, autonomous vehicles, and robots. This research proposes an improved real-time object recognition approach based on the YOLOv9 architecture, trained on a football-specific custom dataset. The 255 annotated images in the dataset are separated into training, validation, and test sets with an 80-15-5 split ratio;there are 204 images in the training set, 38 in the validation set, and 13 in the test set. Our gelan-c model achieves remarkable detection performance with 467 layers, 25,414,044 parameters, and 102.5 GFLOPs. Upon validation, Gelan-c shows an aggregate mAP50 of 75.9%, with mAPs ranging from 32.2% (ball) to 93.7% (goalkeeper) depending on the specified class. The impressive aspect of the Gelan-C is its 31.6 ms picture processing rate, which assures real-time performance. The methodology covers environmental setup, preparation of data, YOLOv9 architecture introduction, and experimental evaluation to demonstrate the model's football-related recognition capabilities. The suggested approach has potential applications in sports event monitoring and evaluation due to its reliability and efficacy in detecting football players, referees, and the ball. © 2024 IEEE.

关键词： Image annotation

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Application of Machine Learning and Deep Learning Algorithms in Predicting Virtual Network Functions for Network Function Virtualization 14

Application of Machine Learning and Deep Learning Algorithms...

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

作者： Spandana, Chereddy Srisurya, Ippatapu Venkata Priyadharshini, A.R. Krithika, S. Sridevi, S. Prasanna Kumar, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Computer Science and Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

Network functions virtualization (NFV) is a methodology that replaces hardware middleboxes with more flexible software alternatives called virtualized network functions (VNFs). These VNFs need to be created and their resource allocation needs to be changed as per traffic demands to dynamically adapt to changes. However, determining the appropriate number of resources to be allotted for each VNF instance is challenging, as fixed resource consumption is often assumed in existing optimization methods. This can lead to either resource wastage or poor service quality. A novel machine learning algorithm trained on actual VNF data, which includes performance measures and resource needs is proposed to address this issue. These trained models can accurately predict the number of resources needed for each VNF to handle a specific traffic load. These machine learning models are incorporated into an algorithm for simultaneous VNF placement and scaling, and their effect on the final VNF placements is evaluated. © 2023 IEEE.

关键词： Network function virtualization

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Semantic Analysis Using Deep Learning for Predicting Stock Trends 2

Semantic Analysis Using Deep Learning for Predicting Stock T...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Galphade, Manisha Nikam, V.B. Yedurkar, Dhanalekshmi Singh, Prabhishek Stephan, Thompson Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India Department of Computer Engineering & IT Veermata Jijabai Technological Institute Mumbai India Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Unveiling Security Vulnerabilities in NFTs: A Comprehensive Risk Assessment 4

Unveiling Security Vulnerabilities in NFTs: A Comprehensive ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Verma, Ravi Chandrawanshi, Kamlesh Soni, Gaurav Jain, Garima Nigam, Shekhar Jain, Neelesh Vit Bhopal University School of Computing Science and Engineering Bhopal India Samrat Ashok Technological Institute Department of Computer Science Vidisha India Sam Global University School of Computer Science and Engineering Bhopal India Prestiage Institute of Science and Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331518981

The biggest invention of Non-Fungible Tokens System (NFTs), that defines its role digitally using the self-generated concept of proof of ownership, which comes as a result of continues progress and advancement in Blockchain technology This study aims to provide a comprehensive understanding of NFTs, their foundational technology, and the platforms enabling their trade. It highlights the key challenges of implementing NFT technology, focusing on issues related to ownership, governance, security, privacy, and environmental concerns. The research explores the inherent risks in the evolving NFT ecosystem, offering insights into the nature and scale of these threats. Additionally, it proposes a set of actionable solutions for participants and stakeholders to mitigate these risks. By categorizing core hazards within a simplified taxonomy, this paper identifies key vulnerabilities and outlines strategies for addressing them to enhance the safety and sustainability of the NFT space. © 2024 IEEE.

关键词： Differential privacy

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ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED CRIMINAL DETECTION USING A DEEP LEARNING APPROACH

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED...

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

作者： Jagadeesan, S. Sujigarasharma, K. Manjula, R. Vetriselvi, T. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305708

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases. © 2023 IEEE.

关键词： CNN Criminal detection Face detection Machine learning

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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AquaVision Dehaze and Enhancement: A Robust Algorithm for Improving Underwater Image Quality in Aquatic Environments 2

AquaVision Dehaze and Enhancement: A Robust Algorithm for Im...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Vijayalakshmi, M. Sasithradevi, A. Spoorthi, J.S. Prakash, P. School of Electronics Engineering Vellore Institute of Technology Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Electronics Engineering Madras Institute of Technology Chennai India

ISBN: (数字)9781837242535

ISBN: (纸本)9781837242672

Underwater imaging is frequently challenged by light scattering, leading to haze, color distortion, and visibility loss. To address these issues, we introduce the AquaVision Dehaze and Enhancement algorithm to improve underwater image quality. Extensive testing on a dataset from an aquaculture environment demonstrates that the algorithm significantly enhances image clarity. Quantitative evaluations reveal high performance across various image quality metrics, with scores of UICM (19.6463), UISM (18.1490), UIConM (26.7463), and UIQM (13.3312). Furthermore, the algorithm delivers competitive results in no-reference image quality metrics, including NIQE (3.6780), Entropy (7.5942), PIQE (52.1444), and BRISQUE (35.9847). These results highlight the algorithm's effectiveness in addressing the challenges of underwater imaging and illustrate the effectiveness through an ablation study. The AquaVision Dehaze and Enhancement algorithm offers a robust solution for improving the quality making it a valuable tool for advancing research in aquatic environments. © The Institution of engineering & technology 2024.

关键词： Aquaculture

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