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2nd Inter.a.ional Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Sampath Kini, K. Pushpakumar, r. Senthamil Selvan, r. Praveenkumar, G.D. Basi reddy, A. Nitte Deemed To be University Department of Computer Science and Engineering Karkala India Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Information Technology Avadi India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India Kongu Engineering College Department of Computer Technology Erode India School of Computing Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798350364699

Nowadays, the credit card industry is more concerned about credit card fraud than any other problem. The main idea is to examine optional techniques used for fraud detection and to identify the different types of credit card fraud. Numerous strategies exist for lowering the prevalence of credit card fraud, each tailored to the specific thr.a.s faced by banks and the credit card industry. Credit card fraud prevention is the primary goal of implementing these measures. Existing methods have several unresolved issues that cause legitimate credit card customers to be mistakenly labelled as fraudulent. The amount of fraud in the financial sphere may be inferred from this paper's emphasis on integrating the best classification algorithm out of four distinct methods. © 2024 IEEE.

关键词： Credit card fraud faced by bank Financial sphere mistakenly labelled Primary Goal

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Auto-Handover Gateway LoraWAN based on Autonomous Vehicles 10

Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles

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10th IEEE Infor.a.ion technology Inter.a.ional Seminar, ITIS 2024

作者： Pur.a.a, Iwan Pane, rahmadani Sooai, Adri Gabriel Edi, Fir.a. Chamid, Ahmad Abdul Adi, Puput Dani Prasetyo Universitas Labuhanbatu Faculty of Science and Technology Department of Information Technology Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Batam City Indonesia Muria Kudus University Department of Informatics Engineering Kudus Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This article presents a simulation study on the use of LoraWAN technology in autonomous vehicles. The research focuses on developing an Auto-Handover Gateway for.a.tonomous Vehicles, utilizing LoraWAN parameters such as bit rates, spr.a.ing factor (SF), packet loss, and received signal strength. These parameters are tested in simulations of autonomous vehicle movements, which include both regular.a.d random motion patterns. The aim is to assess the perfor.a.ce of LoraWAN in different scenarios and its impact on communication in autonomous vehicle systems. The simulation results show several key trends. As the vehicle moves further, the spr.a.ing factor (SF) incr.a.es, and bit rates tend to decr.a.e, indicating a drop in communication perfor.a.ce over longer distances. The research also identifies that the gateway with the closest proximity to the vehicle is selected, both in random and non-random movement scenarios. Additionally, packet loss incr.a.es as the distance between the vehicle and the gateway grows, highlighting the challenges of maintaining reliable communication over long distances. These findings provide insights into optimizing LoraWAN parameters for r.a.-time communication in autonomous vehicles, contributing to the development of intelligent vehicle networks. © 2024 IEEE.

关键词： Packet loss

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3DSC2-UNet: Advancing Br.a.t Cancer Segmentation Techniques Using the 3D Skipped Convolution Capsule-UNet

3DSC2-UNet: Advancing Breast Cancer Segmentation Techniques ...

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2024 Inter.a.ional Conference on Signal Processing, Computation, Electronics, Power.a.d Telecommunication, IConSCEPT 2024

作者： ramadass, Parthasarathy Sharmila, C. Mahendran, rakesh Kumar Hema, V. Kavitha, E. Sanmugasundaram, r. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science & Engineering Chennai India Dr. M.G.R. Educational and Research Institute Department of Information and Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Dr.M.G.R Educational and Research Institute Department of Physics Chennai India Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9798331540685

Br.a.t cancer (BC) persists as a substantial global health hurdle, demanding ongoing progress in the r.a.ms of detection, diagnosis, and therapeutic appr.a.hes. This thorough examination delves into cutting-edge technologies and methodologies that have sur.a.ed in the quest for enhanced results in BC management. Yet, investigations employing solitary imaging modalities have showcased restricted clinical effectiveness owing to their relatively diminished diagnostic precision and dependability when compared to multimodal systems. As a result, our.a.m is to cr.a.e a 3D Skipped Convolution Capsule UNet designed for the segmentation of br.a.t tumors, employing both mammogram and ultrasound imaging modalities. For the integration of these modalities, we utilize the Dynamic Fusion Net (DF-Net), incorporating two modules named the Molecular-Level Aligner Module and Biphasic Sequential Integration Module (BSInU). The process of segmentation takes place within our innovative framework, 3DSC2-UNet, encompassing four primary elements: (a) Graphical feature extraction, (b) Convolutional Capsule (ConvCaps) encoder module, (c) Synaptic skip connection, and (d) Convolutional decoder (Conv Decoder). The overall accuracy attained is 99.97%, accompanied by a Dice Similarity Coefficient (DSC) of 0.89 and an Average Sur.a.e Distance (ASD) of 2.5mm across the mammogram dataset. Moreover, the precision stands at 99.65%, accompanied by a Dice Similarity Coefficient (DSC) of 0.88 and an Average Sur.a.e Distance (ASD) of 2.4mm across the ultrasound dataset. The suggested 3DSC2-UNet, harnessing both mammogram and ultrasound, showcases exceptional perfor.a.ce in accuracy, ASD, and DSC metrics. This model holds the capability to diminish unwar.a.ted biopsy procedures, thereby advancing its prospects for clinical implementation. © 2024 IEEE.

关键词： Ultrasonic imaging

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Cloud based Weather Station using IoT technology to Track the Air Parameters 2

Cloud based Weather Station using IoT Technology to Track th...

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2nd Inter.a.ional Conference on Electronics and renewable Systems, ICEArS 2023

作者： Jayanthi, S. Srividhya, E. ramshanka, N. Srinivasan, T.r. Prabhakar, P.B. Edwin Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Jimma Institute of Technology Jimma University Faculty of Electrical and Computer Engineering Department of Computer Engineering Jimma Ethiopia New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350346640

The Internet of Things (IoT) technology is a viable alter.a.ive for monitoring meteorological data in a specific ar.a.and making the data accessible from anywhere in the world. This is based on the idea that IoT technology connects electronic sensors to the public internet. Data from the embedded system can be accessed via the internet from anywhere in the world. In some regions, it may be impossible to examine and monitor vital weather parameters utilizing wires and analog devices during some weather thr.a.s. This research proposes a comprehensive appr.a.h for monitoring the weather conditions in a particular region and making the infor.a.ion accessible in other parts of the globe using a system described in this study. The IoT is the underlying technology that enables this. It is a highly developed and effective method for connecting things to the internet and bringing the whole internet of things together in a network. This research work has developed an IoT-based forecast concerning sustainable development using an embedded system that can report weather status such as air pressure, heating rate, moisture, air quality, illuminance, and so on in r.a. time. The measurement data has been given as an input to Thingspeak's server for processing and assessment. © 2023 IEEE.

关键词： Internet of things

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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 Inter.a.ional 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 infor.a.ion 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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An Innovative Method to Detect Cyberbullying on Social Media 3

An Innovative Method to Detect Cyberbullying on Social Media

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3rd Inter.a.ional Conference on Electronics and renewable Systems, ICEArS 2025

作者： Pandiaraja, P. Muthumanickam, K. Geetha, M. Mahesh, P.C. Senthil Vedhashree, S.K. Selvarathi, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Kongunadu College of Engineering and Technology Department of Information Technology Tholurpatti Tamil Nadu Trichy621215 India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639113 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641021 India

ISBN: (纸本)9798331509675

Today social media has fundamentally altered global communication and infor.a.ion exchange. However, as these platforms have become more widely used, so has cyber-hatred which is a significant problem that has caught academics' attention. This article has been addressed by a variety of methods from deep learning and machine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using mathematical concepts. However, sentiment-oriented data offers an accurate picture of how the public r.a.s communications online, accurate classification necessitates a more "critical thinking"perspective. Two machine learning classifiers were employed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and multinomial naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm. © 2025 IEEE.

关键词： Adver.a.ial machine learning

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A KNN Algorithm Based Predictive Model for Heart Disease Progression

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recent Advances in computer Science and Communications 2023年第5期16卷 76-81页

作者： Jaffrin, Lijetha C. Srikanth, G. Umarani Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India

Background: Disease diagnosis is a useful phenomenon in healthcare. Machine learning classification methods would considerably improve the healthcare industry by providing a quick diagnosis of the disease. Thus, time could be saved for doctors. Nearly 17.9 million people expire due to heart disease every year. Objectives: World Health Or.a.ization (WHO) predicted that rate of death might incr.a.e by 24.5 million in 2030. Since heart illness was the major cause of death in comparison with other diseases today, it was the most challenging disease to diagnose. Methods: One of the r.a.ons for death due to heart disease was due to the fact that risks were not identified in the earlier stage. Earlier diagnosis of disease was very much impor.a.t. Machine Learning algorithms were used for predicting the prognosis of disease. results: Here K-NN algorithm was used to predict the presence of heart disease in an individual. Thus, patients were classified as either positive or negative for heart disease and this model enhanced medical care and reduced the cost. This gave us significant knowledge that helps us to predict the patients with heart disease. Conclusion: The Python sci-kit library was used to implement this in Anaconda Navigator's Spyder Integrated Development Environment. Experiments revealed that technique worked well and was more accurate than before. © 2023 Bentham Science Publishers.

关键词： Diseases

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Wireless Sensor Networks: revolutionizing Explosive Detection 5

Wireless Sensor Networks: Revolutionizing Explosive Detectio...

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5th Inter.a.ional Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sujith Kumar, P.S. Anand, C. Vasuki, N. ramesh, V. Santhoshkumar, S.P. Sree Buddha College of Engineering Department of Computer Science and Engineering Kerala Alappuzha India Nandha College of Technology Department of Information Technology Erode India Government College of Engineering Department of Computer Science and Engineering Erode India School of Computing Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India

ISBN: (纸本)9798331518097

One of the impor.a.t technologies for protecting civilian life from terrorist attacks is explosive detection techniques. The global incidence of terrorist activities has been considerably reduced by the development of explosive detection techniques, which has been made possible by ongoing technology improvements. Wireless sensor networks (WSNs) are one of these developments that has become an innovative tool for explosives detection. This work explores recent empirical research on the application of WSNs to explosives detection and includes statistical analysis of the investigations. Wireless sensor network with compatible specialized sensors is required for effective explosive detection. Three main wireless sensor system aspects that are relevant to explosives detection are covered in this research. It first looks at how scalable wireless sensors are in terms of explosives detection technologies. In the second section, it looks at the mobility and communication capabilities of WSNs and sensors in explosives detecting systems. Finally, it highlights potential hazards and weaknesses and addresses the crucial topic of data security inside wireless sensor networks used for explosive detection. The study also suggests incorporating drone assisted detection as a novel way to improve the coverage and effectiveness of explosives detection systems, adding to the continuous advancement of security technologies meant to shield civilian populations from terrorist thr.a.s. © 2024 IEEE.

关键词： Terrorism

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Exploring Network Dynamics: Community Detection and Influencer.a.alysis in Multidimensional Social Networks

Exploring Network Dynamics: Community Detection and Influenc...

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2024 IEEE Inter.a.ional Conference on Big Data, BigData 2024

作者： Kanavos, Andr.a. Vonitsanos, Gerasimos Karamitsos, Ioannis Al-Hussaeni, Khalil Ionian University Department of Informatics Corfu Greece University of Patras Computer Engineering and Informatics Department Patras Greece Rochester Institution of Technology Dubai United Arab Emirates Rochester Institute of Technology Computing Sciences Department Dubai United Arab Emirates

ISBN: (纸本)9798350362480

In the digital era, multidimensional social networks have become integral to daily communication, catering to diverse relational needs, from interpersonal to professional and commercial. This study utilizes two comprehensive datasets from Twitter to explore and visualize user interactions within these networks. Focusing on advanced community detection algorithms, we apply the Louvain and Label Propagation methods to delineate the structure of these communities and identify influential users effectively. Through systematic analysis, our research reveals significant insights into the dynamics of network clusters and the pivotal role of influencers. We demonstrate that community structures significantly influence in for.a.ion dissemination and user engagement, providing key data to optimize digital communication strategies in complex environments. The findings underscore the impor.a.ce of strategic influencer engagement and tailored community management in enhancing interaction within multidimensional social networks. Additionally, our results suggest that understanding the network's structural nuances can aid in developing targeted interventions that leverage influencer capabilities to maximize communication impact, illustrating potential applications across various sectors, including marketing, politics, and public health. © 2024 IEEE.

关键词： Tweets

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Comparative Analysis of Dynamic Data Placement Strategy in Distributed Cloud Environment 1st

Comparative Analysis of Dynamic Data Placement Strategy in D...

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1st Inter.a.ional Conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Shankar, B. Prabhu Singh, Uday Kumar Vijayalakshmi Viji, C. rajkumar, N. Stalin, M. Nachiappan, Balusamy Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bangalore India Prologis DenverCO80202 United States Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

In general, the dynamic data optimization technique is used to place the user’s data in a cloud system to reduce the latency, intercommunication traffic, data transfer cost, execution cost, load balancing, and bandwidth utilization. The user data is dispersed and stored in a distributed cloud environment, so it must ensure data availability and reliability in cloud data centers. These data placement techniques involve two steps: first user data identification and second identifying the node to place the data in a geographically distributed cloud. The primary goal of the data placement appr.a.h is to find the best or optimal node to store user data in terms of latency and storage costs. This comparative analysis provides various survey data placement algorithms and techniques used to analyze latency delay, execution time, access cost, load balance, server over, bandwidth, and server capacity with different parameters. This study also analyzes the merits and demerits of data placement strategies. This survey helps to concentrate on the dynamic data optimization technique in the Distributed or multiple cloud environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distributed cloud

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