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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Kumar, M. Ranjith Priyanga, S. Anusha, Janani Srinivasan Chatiyode, Veda Santiago, Jerome Chaudhary, Dharminder School of Computer Science and Engineering Vellore Institute of Technology Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engg Chennai India

ISBN: (纸本)9798350373110

The telecommunications industry has a significant challenge in customer churn, especially for companies aiming for sustainable growth in a competitive market. To overcome this issue, we suggested a method utilizing Long Short-Term Memory (LSTM) networks, a deep learning strategy that has revolutionized predictive modeling. We used 4250 customer profiles, each containing 19 features including consumption data and plan options, to train and test the LSTM model. Data preprocessing included encoding categorical variables, normalizing numerical features, and eliminating irrelevant variables to maintain data integrity and authenticity. The LSTM model was fine-tuned to recognize significant patterns rather than simply memorizing data. We also incorporated an additional layer to assess diverse information types, which improved the model's ability to process sequential data effectively. The proposed model achieved a prediction accuracy of 95.3% in identifying potential customer churn, demonstrating its effectiveness in predicting when customers might leave their telecommunications provider. These results demonstrate how LSTM networks can reduce customer attrition in the telecom industry, making them a valuable tool for companies aiming to retain customers and maintain growth in a competitive market. The study's findings pave the way for innovative applications of deep learning techniques in customer retention strategies across diverse industries. This breakthrough has the potential to transform business approaches to customer relationship management and loyalty programs. © 2024 IEEE.

关键词： Commerce

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TizenRT OS Based Data Compression Algorithms for IoT Devices 4

TizenRT OS Based Data Compression Algorithms for IoT Devices

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chamak, Tanmoy Roy Kaushik, Sachi Singhal, Shiven Dadheech, Aditya Anitha, Prof. A. Jenicka, Prof. S. Thapa, Vidisha School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Samsung Research Institute Karnataka Bangalore India

ISBN: (纸本)9798350306118

The paper investigates the integration and adaptation of the LZ4 compression algorithm within the context of TizenRT, in addition to existing algorithms Miniz and LZMA. TizenRT, a real-Time operating system tailored for Internet of Things (IoT) devices, presents unique challenges in balancing compression efficiency and resource constraints. The study evaluates the performance of LZ4 alongside Miniz and LZMA in terms of compression ratios, processing overhead, and memory utilization. Experimental results demonstrate the feasibility of LZ4's adoption in TizenRT, highlighting its potential to enhance data compression and decompression operations while adhering to the stringent requirements of IoT devices. The findings contribute to the optimization of data management strategies within the TizenRT ecosystem. © 2023 IEEE.

关键词： Internet of things

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Smart Monitoring and Irrigation Regulation via IoT and Cloud

Smart Monitoring and Irrigation Regulation via IoT and Cloud

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Konatham Sumalatha Kumar Harshdeep Department of Database Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore Computer Science and Engineering Vellore Institute of Technology Vellore

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The increasing demand for agricultural produce and the strain on global water resources highlight the need for innovative solutions to improve water efficiency in farming. This work introduces an IoT-powered Smart Irrigation System designed to optimize water usage, ensuring sustainable crop cultivation. Traditional irrigation methods often lead to inefficiencies, wasting water and increasing costs, as they fail to consider varying soil moisture and environmental conditions across fields. This can result in suboptimal crop growth and resource depletion. To overcome these challenges, the proposed system integrates IoT technology with smart irrigation practices. Sensors deployed in the field collect real-time data on soil moisture, temperature, humidity, and light intensity, which a central controller then processes. Using cloud-based analytics, the controller adjusts irrigation schedules and water distribution to match current conditions precisely. The system's primary goal is to create an eco-friendly, cost-effective solution that reduces water waste, lowers operational costs, and minimizes environmental impact. Scalable and adaptable to various crops and environments, this smart irrigation system promises to improve crop yields, promote sustainable agriculture, and address water scarcity challenges globally.

关键词： Temperature sensors Irrigation Cloud computing Data analysis Costs Soil moisture Water resources Monitoring Wireless fidelity Underwater vehicles

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Neural Network Aided Kalman Filter to Maximize Accuracy

Neural Network Aided Kalman Filter to Maximize Accuracy

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2022 International Conference for Advancement in technology, ICONAT 2022

作者： Agarwal, Varun Niraj Kanshi, Avaneesh Melarkode, Navneet Vinod Krishna, Hemanth Hota, Malaya Kumar School of Computer Science Engineering Vellore Institute of Technology Vellore India School of Electronics and Engineering Vellore Institute of Technology Department of Communication Engineering Vellore India

ISBN: (纸本)9781665425773

With the growing need for data, and ever-growing demand for prediction and error-correction, Kalman Filters are undoubtedly at the forefronts of real-time estimation. While these filters are designed to achieve convergence shortly after getting exposed to the data, the filters might not be able to maximize all the data it can extract from the system. In order to extract most of the information that is otherwise unusable by the Kalman Factor, an initial assumption of a pre-trained Machine Learning model that correlates a feedable parameter with the unusable data is made. The feedable parameter is then given to the Kalman Filter along with the other standard parameters which boosts the accuracy by adding another dimension to the filter. © 2022 IEEE.

关键词： Kalman filters

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An with effective ensemble classifier for network intrusion detection system

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Peer-to-Peer Networking and Applications 2025年第2期18卷

作者： Nandhini, U. Santhosh Kumar, S.V.N. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

The increasing frequency of network attacks poses a significant challenge in maintaining network security. In response to security vulnerabilities, the employment of Intrusion Detection Systems (IDS) has become crucial. An IDS is a software application, which monitors network traffic to monitor and identify various malicious actions in the network. Network Intrusion Detection Systems (NIDS) specifically examine network traffic to identify and monitor the various malicious activities identified by the network nodes. However, many existing IDS solutions encounter difficulties in achieving optimal feature selection, aiming for high classification accuracy while minimizing false alarm rates. In the proposed system, the Linear Discriminant Analysis (LDA) technique is employed to reduce the dataset dimensionality. Further, an Enhanced Whale Optimization Algorithm (EWOA) is utilized for efficient feature selection for global optimization tasks. To perform classification, an ensemble classifier is proposed which employs Random Forest (RF) and XGBoost. The main objective of the proposed approach is to improve classification accuracy while minimizing the false alarm rate by combining the benefits of RF and XGBoost. The proposed work is implemented by using the python jupyter framework. The experimental results show that the proposed system has an overall classification accuracy of 96.08%, a false alarm rate of 0.01%, and an overall detection rate of 94.03%, by using the NSL-KDD dataset when it is compared with their counterparts. © The Author(s).

关键词： Application programs

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Analyzing the Trade-Off Between Complexity Measures, Ambiguity in Insertion System and Its Application in Natural Languages 11th

Analyzing the Trade-Off Between Complexity Measures, Ambigu...

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Mahendran, Anand Kannan, Kumar Hamada, Mohammed Laboratory of Theoretical Computer Science Higher School of Economics Moscow Russia School of Computer Science and Engineering Vellore Institute of Technology Vellore India Software Engineering Lab The University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9789811988240

Insertion is one of the basic operations in DNA computing. Based on this basic operation, an evolutionary computation model, the insertion system, was defined. For the evolutionary computation model defined above, varying levels of ambiguity and basic descriptional complexity measures have been defined. In this paper, we define twelve new (descriptional) complexity measures based on the integral parts of the derivation, such as axioms, strings to be inserted, and contexts used in the insertion rules. Later, we analyze the trade-off among the complexity measures and the existing ambiguity levels. Finally, we examine the application of the analyzed trade-off in natural languages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Economic and social effects

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Dynamic Convolutional Attention for Classification of Diabetic Retinopathy 7

Dynamic Convolutional Attention for Classification of Diabet...

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7th IEEE Conference on Information and Communication technology, CICT 2023

作者： Bharathraj, S. Sudharson, S. Annamalai, R. Senthil Prakash, P.N. Amrita School of Computing Chennai India Vellore Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350305173

Deep learning algorithms in medical image processing have showed considerable promise, notably in the identification of DR. However, due to the disease's complexity and variability, precisely identifying DR remains difficult. A recently developed deep learning approach known as DCA has shown promising outcomes in a range of image classification applications, including medical imaging. The DCA model excels at recognising essential parts and patterns by using attention mechanism to focus on significant areas and exclude irrelevant ones. Our findings add to the body of knowledge about deep learning approaches in medical picture processing. We discovered that combining DCA approaches with ResNet-50 and ensembling it with other state of art models improved DR classification accuracy considerably. The ensemble model, with weights assigned to each architecture, outperformed the standalone ResNet-50-DCA model's accuracy of 73.59%, precision of 75.52%, and recall of 74.37%. The ensemble technique maximises performance while minimising weaknesses by harnessing the strengths of diverse models. © 2023 IEEE.

关键词： Convolutional neural networks

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A Deep Learning Based Social Distance Analyzer with Person Detection and Tracking Using Region Based Convolutional Neural Networks for Novel Coronavirus

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Journal of Mobile Multimedia 2022年第3期18卷 541-560页

作者： Prabakaran, N. Sree Sai Kumar, Suvarna Kranthi Kiran, P. Supriya, Pabbidi School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Tamil Nadu Vellore 632014 India

With its staggering spread, the continuous novel coronavirus Covid flare-up has caused a worldwide disaster. Populace weakness develops because of an absence of productive helpful prescriptions and a shortage of antibodies against the infection. Since there are no antibodies accessible as of now, social detachment is viewed as a sufficient insurance (standard) against the transmission of the illness. With the ascent in cases, the public authority has ordered a base actual division of 2 meters in all open spaces as a security measure. Utilizing PC vision on video reconnaissance, we made an AI device to forestall the spread of the (novel coronavirus). A social separating analyzer AI apparatus that utilizes video observing from CCTV cameras and robots to control social removing convention. The made AI device was introduced in broad daylight spaces and guaranteed the distance between gatherings of individuals. If the hole was excessively close, the red line showed up, demonstrating a higher danger of being influenced, trailed by the green and yellow light, showing a protected line, and the other, demonstrating a generally safe of being influenced. © 2022 River Publishers.

关键词： CCTV cameras Convolutional neural network (CNN) Drones Novel coronavirus Region-based convolutional neural network (RCNN) YOLO V3

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An Intelligent RL-Based Scheduler to Control Flooding in a Renewable Energy Powered Automatic Water Dam Control System

An Intelligent RL-Based Scheduler to Control Flooding in a R...

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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Ray, Prishita Geraldine Bessie Amali, D. College of Computing and Information Science Cornell University IthacaNY United States School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350325539

The use of non-renewable energy to operate large-scale systems is proving detrimental to the environment through pollution as well as leading to its depletion. As an alternative, the focus of industries, both private and public, is shifting towards the profitable use of renewable energy sources where it is available in abundance for such purposes. Water dams have been built above rivers to tap this natural resource to supply water to the nearby population. However, in the rainy season, excessive flooding wreaks havoc in the surrounding areas, leading to crop failures, damage to households and dam infrastructure. To counter this problem, this paper proposes an intelligent scheduler for an efficient renewable-energy powered automatic water dam control system which is modelled as a non-convex optimization problem. The scheduler uses the Soft Actor Critic with Emphasized Recent Experience and Prioritized Experience Replay Scheduler (SEPS), as the automatic Reinforcement Learning (RL) based scheduling algorithm to manage flooding in the control system through irrigation, hydroelectricity generation and reservoir storage such that it can sufficiently satisfy the needs of domestic households while also ensuring its efficient working. Simulated sensor readings record the current state of the environment and the proposed scheduler can make decisions based on these environmental variables with less human oversight and great accuracy. Experimental results clearly indicate that the SEPS scheduler outperforms its RL counterpart Proximal Policy Optimization Scheduler (PPOS) and the Natural Evolution Strategies Scheduler (NESS) in terms of average rewards obtained for managing floods efficiently and using renewable energy to run the control system. © 2023 IEEE.

关键词： Reservoirs (water)

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Eye Gaze Detection Based on Computational Visual Perception and Facial Landmarks

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computers, Materials & Continua 2021年第8期68卷 2545-2561页

作者： Debajit Datta Pramod Kumar Maurya Kathiravan Srinivasan Chuan-Yu Chang Rishav Agarwal Ishita Tuteja V.Bhavyashri Vedula School of Computer Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India School of Information Technology and Engineering Vellore Institute of Technology(VIT)Vellore632014India Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan

The pandemic situation in 2020 brought about a‘digitized new normal’and created various issues within the current education *** of the issues is the monitoring of students during online examination situations.A system to determine the student’s eye gazes during an examination can help to eradicate *** this work,we track the users’eye gazes by incorporating twelve facial landmarks around both eyes in conjunction with computer vision and the HAAR *** aim to implement eye gaze detection by considering facial landmarks with two different Convolutional Neural Network(CNN)models,namely the AlexNet model and the VGG16 *** proposed system outperforms the traditional eye gaze detection system which only uses computer vision and the HAAR classifier in several evaluation metric *** proposed system is accurate without the need for complex ***,it can be implemented in educational institutes for the fair conduct of examinations,as well as in other instances where eye gaze detection is required.

关键词： computer vision convolutional neural network data integrity digital examination eye gaze detection extraction information entropy

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