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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Choudha, Shaurya Singh, Aniruddh Kumar, Aman Gouthaman, P. School of Computing College of Engineering and Technology Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur603203 India

ISBN: (纸本)9798331518097

Weather forecasting is an important function in a variety of activities such as agriculture, environment monitoring, and disaster management among others. Today with the development of technology, vast amounts of meteorological data are able to be collected and analyzed with the intention of improving prediction accuracy. The purpose of this work is to analyze the contribution of Big Data frameworks such as, Hadoop, MapReduce and Apache Spark in effective weather prediction processes. In a distributed system, historical weather data, satellite images and sensor readings are kept and manipulated through the HDFS storage. For effective data manipulation, MapReduce and Spark are used along with Hive and Pig for searching large datasets. Predictive analytics are performed using machine learning algorithms included in the MLlib package of Spark. These results are presented using the Tableau application, which shows the speed and accuracy of this Big Data approach in comparison to classical ones. The proposed work ends with a look into the prospects of Big Data in the field of weather forecasting and relevant problems in this area. © 2024 IEEE.

关键词： MapReduce

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Identifying illicit transactions in Bitcoin Tumbler services using Supervised Machine Learning Algorithms 12

Identifying illicit transactions in Bitcoin Tumbler services...

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12th IEEE International Conference on Advanced computing, ICoAC 2023

作者： Ezhilmathi, S. Selvakumara Samy, S. Srm Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350318210

Digital currencies have emerged as a new platform for money laundering as the first blockchain platform to support smart contracts, and the number of transaction records for Bitcoin has expanded substantially in recent years. Due to privacy services such as tumblers, which conceal the identities of payers and payees, pseudonymous digital currencies are frequently referred to as anonymous address and untraceable. A large proportion of accounts participating in Bitcoin transactions are engaging in illegal activity. According to the digital ledger, a transaction was transferred from one of many potential payers to one of many possible payees. As a consequence, it is vital to have a dependable system for classifying accounts and keeping track of the transactions linked with each account. We utilized machine learning techniques to apply multi-class classification methods to a Bitcoin transaction record obtained from *** in order to improve the model's accuracy. Experiment results show that ML algorithms like logistic regression, random forests, and multilayer perceptron's with the macro-average F1 score for bitcoin tumblers outperform state-of-the-art approaches for identifying unknown digital currency accounts, with the RF model outperforming LR and MLP by 97% accuracy. © 2023 IEEE.

关键词： Blockchain

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A new strategy for fabrication of unique heterostructured titanium laminates and visually tracking their synchronous evolution of strain partitions versus microstructure

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Journal of Materials science & Technology 2022年第12期107卷 70-81页

作者： Hao Ding Xiping Cui Zhiqi Wang Tao Zhao Yuchen Wang Yuanyuan Zhang Hongtao Chen Lujun Huang Lin Geng Junfeng Chen School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China Center for Analysis Measurement and ComputingHarbin Institute of TechnologyHarbin 150001China School of Materials Science and Engineering Harbin University of Science and TechnologyHarbin 150080China School of Materials Science and Engineering Fuzhou UniversityFuzhou 350116China

Heterostructured(HS)material with extraordinary mechanical properties has been regarded as one of the most promising structural ***,we reported a new strategy for preparing heterostructured pure titanium laminates that possess a good combination of strength and ductility by combining gradient structure(GS)and heterogeneous lamella structure(HLS).The deformation characteristic versus microstructure evolution of GS/HLS titanium laminates,namely the strain partitions between different-sized grains(480–25μm)was visualized using a scanning electron microscope(SEM)equipped with electron backscattered diffraction(EBSD)mode combined with the digital image correlation(SEM-DIC)with an ultrahigh spatial resolution for the first *** a result,the hetero-deformation of unique GS/HLS structure by the characteristic of strain partitions could be accurately *** the hetero-deformation could result in the hetero-deformation induced(HDI)stress strengthening and HDI hardening,which were regarded as the key reason that the resulting GS/HLS Ti laminates showed a superior combination of strength and *** could promote a more in-depth understanding of the strengtheningtoughening mechanism of heterostructured material.

关键词： Gradient structure Heterogeneous lamella structure Digital image correlation Strain partition

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Singe Image Dehazing using Deep Neural Networks 2

Singe Image Dehazing using Deep Neural Networks

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2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Doshi, Nisarg Bhavsar, Sagar Rajeswari, D. Srinivasan, R. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350315905

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear *** first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r.t SSIM values. © 2023 IEEE.

关键词： Image fusion

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The Near Optimal Identification of Food Ingredients for Enhanced Dining Experience

The Near Optimal Identification of Food Ingredients for Enha...

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2024 IEEE International Conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Naveen, Shivam Goyal, Yashvardhan Kowsigan, M. Ramamoorthy, S. Madhavan, P. SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies College of Engineering and Technology Tamilnadu Chengalpattu603203 India

ISBN: (纸本)9798331522988

The importance of restaurants, canteens, and various food service outlets is increasingly recognized in economies at multiple levels. This paper introduces an innovative automated menu ordering system that enhances the customer experience during the ordering process. Currently, many establishments rely on manual methods for operations such as menu ordering and payments. This paper highlights a transition to a novel solution utilizing button-based menu displays, allowing customers to place orders seamlessly and intuitively. The system's uniqueness lies in elevating the dining experience for patrons while providing a vital tool for restaurant owners to improve service efficiency and customer satisfaction. By streamlining the ordering process, the system reduces wait times and human error, creating a more engaging environment. This automated approach marks a transformative step in restaurant operations, establishing a new standard for customer engagement in the food service industry. © 2024 IEEE.

关键词： Food ingredients

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Vehicle-Assisted Service Caching for Task Offloading in Vehicular Edge computing

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IEEE Transactions on Mobile computing 2025年第7期24卷 6688-6700页

作者： Jiang, Hongbo Cai, Jianghao Xiao, Zhu Yang, Kehua Chen, Hongyang Liu, Jiangchuan Hunan University College of Computer Science and Electronic Engineering Hunan Changsha China Hunan University Shenzhen Research Institute Shenzhen518055 China Zhejiang Laboratory Research Center for Graph Computing Hangzhou311100 China Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada

The development of artificial intelligence (AI) enables vehicular edge computing (VEC) servers to be able to provide more intelligent services. However, the limited storage resources of VEC servers constrain the deployment of intelligent service contents, which greatly restricts the intelligence level of the VEC network. To resolve this problem, we first design a novel vehicle-assisted VEC network architecture and further propose VaCo, a Vehicle-assisted Collaborative caching system. VaCo allows VEC servers to download the cached service content from any vehicle in the VEC network to support task offloading. VaCo mainly considers the real-time scheduling problem of vehicle storage resources under the dynamic VEC network and the benefit problem caused by invoking vehicle resources under the highly dynamic load environment. VaCo models the vehicle storage resources as an independent resource pool and deploys a cross-VEC server content retrieval mechanism to achieve unified and efficient management of the storage resources of the vehicle cluster and the VEC server cluster. Then, we propose a multi-swarm collaborative optimization scheme to jointly optimize the service failure rate and cost, and further propose a Pareto-based optimization scheme to ensuring that VaCo can correctly evaluate the benefits of invoking vehicle resources in a dynamic VEC network. Finally, we implement VaCo and conduct extensive evaluations on real-world dataset. The experimental results on the real trajectory dataset show that VaCo can effectively utilize vehicle resources and ensure the benefits of both vehicles and VEC servers simultaneously. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Precision Forecasting of Stock Prices: Leveraging XGBoost and Technical Indicator for Advanced Predictive Modeling 1

Precision Forecasting of Stock Prices: Leveraging XGBoost an...

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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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An Innovative URL-Based System Approach with ML Based Prevention 5

An Innovative URL-Based System Approach with ML Based Preven...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Nongmeikapam Thoiba Rani, Jyoti Sharma, Prince Mishra, Akriti Yadav, Ankit Raj, Shivani Chandigarh University Department of Computer Science and Engineering Punjab India University Institute of Computing Chandigarh University Punjab India

ISBN: (纸本)9798350383522

This paper delves into the pervasive threat of phishing in the e-commerce sector, where adversaries impersonate financial institutions to perpetrate identity theft. The modus operandi involves sending deceptive emails urging users to click on fraudulent links, leading them to fake websites mimicking authentic ones. Focused on URL-based detection, the study employs sophisticated algorithms and machine learning techniques, utilizing a dataset for training and testing to identify phishing URLs. The paper outlines various types of phishing attacks, including deceptive, spell, clone, whale, link manipulation, and voice phishing. Prevention strategies, such as spam avoidance, secure communication, and user education, are discussed. The methodology involves frontend and backend development using Python, JavaScript, HTML, CSS, PHP, and MySQL, incorporating machine learning for analysis. The website provides a user-friendly interface with a reporting option, contributing to a robust defense against phishing threats. Results indicate the effectiveness of URL-based detection, emphasizing the importance of staying updated on evolving phishing trends and employing a multifaceted approach for enhanced organizational cybersecurity. © 2024 IEEE.

关键词： data security phishing phishing detection URL

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Efficient Intelligent Based Compliance, Detection, Tracking, and Proximity Model for Traffic System

Efficient Intelligent Based Compliance, Detection, Tracking,...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Revadala, Abhinay Reddy, Sri Ram Charan Ram Kumar, J. Kumar, Manoj School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India University of Wollongong Faculty of Engineering and Information Sciences Dubai United Arab Emirates

ISBN: (纸本)9798350375237

The Intelligent Traffic Management System (ITMS) is a cutting-edge solution designed to address multiple aspects of traffic management and safety. This advanced system incorporates various technologies to tackle issues such as helmet compliance, traffic signal violations, number plate identification, cell phone usage tracking, and proximity-based police station alerts. By leveraging the power of artificial intelligence and machine learning algorithms, the ITMS can accurately detect and enforce helmet compliance among motorcyclists, ultimately reducing the risk of head injuries in road accidents. Furthermore, the system utilizes advanced video analytics to detect violations of traffic signals, ensuring that motorists adhere to traffic rules and regulations. Additionally, the ITMS employs automatic number plate recognition (ANPR) technology to quickly and efficiently identify vehicles with illegal or expired license plates, enabling law enforcement agencies to take appropriate action. Moreover, the system includes a cell phone usage tracking feature which uses computer vision techniques to identify drivers who are using their phones while driving, contributing to a decrease in distracted driving incidents. Lastly, by introducing proximity-based police station alerts, the ITMS enables prompt response from law enforcement agencies by automatically notifying the nearest police station in the event of a traffic violation or accident. In summary, the Intelligent Traffic Management System provides a comprehensive and holistic approach to traffic management and safety through its features of helmet compliance, traffic signal violation detection, number plate identification, cell phone usage tracking, and proximity-based police station alerts. © 2024 IEEE.

关键词： Traffic signals

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Deep Learning Empowered Natural Language Understanding Voicebot's for farmers: Kissan Samvaad Initiative 8

Deep Learning Empowered Natural Language Understanding Voice...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Jayalakshmi, V. Deepa, R. Ganesan, R. Sriram, N. Surendran, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Computer Science and Engineering chennai India Kg College of Arts and Science Department of Computer Applications Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The 'Kissan Samvaad' work focuses on implementing a closed-domain Chatbot using the RASA Open Source Framework to address the lack of timely access to expert agricultural advice for farmers. Despite the rapid growth of Deep Learning and NLU based Chatbots in various fields, farmers often rely on peer knowledge to solve agricultural problems. Kissan Samvaad aims to bridge this gap by allowing farmers to interact with the Chatbot to specify farmers need. By identifying intents and entities in user inputs, the Chatbot retrieves relevant information from its database to provide remedies and solutions using BERT. Initial testing of Kissan Samvaad has shown promising results, indicating its potential to serve as a valuable resource for farmers. © 2024 IEEE.

关键词： agriculture chatbot kissan samvaad natural language understanding (nlu) RASA RNN

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