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

作者： Valarmathi, B. Gupta, N. Srinivasa Chellatamilan, T. Santhi, K. Reddy, R. Hemadri Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789811996375

Mobility is the biggest issue for blind individuals. In order to move around, they must rely on others. Therefore, we are creating a navigational aid for blind people, to help the blind people in the world from various difficulties they face in day-to-day life. The World Health Organization (WHO) reported that a survey was conducted in 2014 and that there are an estimated 285 million visually impaired persons worldwide, of which 37 million are blind, with over 15 million of them being Indian. The majority of blind persons depend on other people for their activities since INDIA accounts for 21% of the global population of blind people (Khanam et al. in Int J Adv Res Sci Eng 7:195–199, 2018). Smart shoes play an important role. Obstacle detection is performed using ultrasonic sensor which is also done by many people in the previous projects or researches. The new concept that is included in this paper is that it can also detect any flame and potholes around the person using flame sensor and ultrasonic sensor, respectively. And we use shock sensor to detect any shock that occurs. If the smart assistive device or smart shoes senses an object, a pothole, a fire (from about three feet away), or a shock in front of the user, a buzzer will sound. These sensors are integrated using Arduino Uno. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Obstacle detectors

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An efficient frame preemption algorithm for time-sensitive networks using enhanced graph convolutional network with particle swarm optimization

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Measurement: Sensors 2024年 31卷

作者： Kannamma, R. Umadevi, K.S. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Numerous time-sensitive applications are being made possible by the Internet of Things (IoT) phenomenon, which calls for real-time transmission to provide communication services. Applications that depend on low latency have been made possible because of the 5G Ultra-Reliable and Low-Latency Communication (URLLC) scenario. Widely regarded as a possible paradigm for achieving the deterministic transmission guarantees for 5G, Time-Sensitive Networking (TSN) has gained much attention. TSN, on the other hand, is a hybrid traffic system that combines best-effort and time-sensitive traffic, necessitating efficient routing and frame preemption to provide a predictable and limited latency. On the other hand, time-sensitive and non-time-sensitive traffic optimization significantly broaden the issue area and increase the difficulty of establishing a solution. This research presents Enhanced Graph Convolutional Network-based Deep Reinforcement Learning (EGCN-based DRL) solutions for the joint optimization problem in real-world communication contexts. The EGCN is included in deep reinforcement learning to obtain the network's spatial dependency and enhance the generalization performance of the suggested technique (DRL). Specifically, the EGCN approximates the graph convolution kernel using the first-order Chebyshev polynomial, which reduces the complexity of the algorithm and improves the feasibility of the task. Particle Swarm Optimization (PSO) is also employed to hasten the convergence of the model training process. The objective of TSN is to provide deterministic and time-critical communication capabilities over Ethernet networks. TSN aims to enable reliable, low-latency, and synchronized communication between devices, ensuring that time-sensitive data and control messages are delivered with high precision and determinism. The suggested EGCN-based DRL algorithm surpasses cutting-edge techniques in terms of average end-to-end latency, and it also converges quickly, according

关键词： Reinforcement learning

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Traffic Crash Severity: Predicting Property Damage and Injury Collision Through Machine Learning Models 7th

Traffic Crash Severity: Predicting Property Damage and Injur...

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7th International Conference of Transportation Research Group of India, CTRG 2023

作者： Agrawal, Toshini Mohan, Ranju Department of Computer Science and Engineering Vellore Institute of Technology Madhya Pradesh Bhopal466114 India Department of Civil and Infrastructure Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India

ISBN: (纸本)9789819799428

The types of collisions during road crashes and the factors contributing to them are many. Chances of collisions can be reduced by predicting the probability of types of collisions at various locations and providing drivers with real-time details of the risk of collision while they travel. This paper aims to predict the severity of collisions at intersections, specifically two types of collisions—‘Property damage’ and ‘Injury Collision’, through machine learning models. The paper uses a dataset from the Kaggle website which the police department of Seattle regularly updates. It consists of values of different variables such as road condition, weather condition, junction type, and the attention span of the driver, hitting a parked car, light condition, and speeding. Four different conventional machine learning algorithms, namely, Random Forest, K-Nearest Neighbour, Decision tree, and Linear Regression are applied for collision prediction. The Random Forest model is observed to have the highest accuracy (0.7506) followed by the K-Nearest Neighbours algorithm (0.7442), Decision Tree (0.7385) and Linear regression (0.6999). The most featured attributes were weather conditions, road conditions and temperature and predicted more property damage than injury collision. An ensemble of the above models is also tried for crash prediction and observed as with the highest accuracy (0.7519). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Random forests

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Database Concentration Method for Efficient Image Retrieval Using Clustering and Image Tag Comparison 3rd

Database Concentration Method for Efficient Image Retrieval...

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3rd International Conference on Soft Computing and its engineering Applications, icSoftComp 2021

作者： Shimgekar, Soorya Ram Pathi, Preetham Reddy Vijayarajan, V. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9783031057663

As the world moves towards a digital civilization, we generate more and more data along the way. With this increase in the number of images on the internet and with social media companies, faster and scale-able image retrieval algorithms are needed for efficiency. In this paper, we propose a tag based image retrieval algorithm which also uses clustering for better efficiency in large data sets. Although there are multiple existing algorithms that use tags most of them use tag completion but not new tag generation. So, they cannot be used for completely unlabelled data. The proposed algorithm makes use of IBM’s(International Business Machines) VisualRecognitionV3 to generate new tags for completely unlabelled data. We make use of K-Means algorithm to firstly cluster the images into different clusters based on features extracted using ImageNet weights of ResNet50 architecture. We then use IBM’s VisualRecognitionV3 to label the folders based on its contents. Similar steps are also carried out with query image and using word-vectorization and cosine similarity, top-n most relevant folders are found out. Then relevant images from these best folders are saved as output based on similarity of image features, found using ResNet-50 model on ImageNet Weights. © 2022, Springer Nature Switzerland AG.

关键词： Business machines

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Cyclone Chronicles: A Comprehensive Survey of Advancements in Cyclone Prediction Techniques 2

Cyclone Chronicles: A Comprehensive Survey of Advancements i...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Raj, Abhineet Tiwari, Tanmay Madurwar, Ayush Sandosh, S. Integrated Mtech in CSE specialization In Business Analytics Vellore Institute of Technology Chennai India School Of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350340235

Tropical cyclones represent formidable natural phenomena, demanding constant progress in prediction methodologies. The survey paper amalgamates diverse insights from recent research, offering an extensive overview of the state of tropical cyclone prediction. This study analyzes the stat-of-the-art approaches, such as Support Vector Machine and polynomial regression models to achieve pre cise cyclone tracking. Additionally, this study examines the critical role of satellite imagery in automation, shedding light on the automated techniques employed. Furthermore, this study explores the emerging applications of Generative Adversarial Networks (GANs) for learning physical characteristics within cyclone data distribution, enhancing predictive capabilities. It also addresses challenges like rapid intensification, presenting prototype visualization tools and bias correction techniques as innovative solutions. The survey underscores the significance of machine learning models and ensemble forecasting in elevating the precision of cyclone predictions. © 2023 IEEE.

关键词： Bias correction Convolutional Neural Networks Deep learning Ensemble forecasting Generative Adversarial Networks Machine learning Satellite imagery Tropical cyclone prediction Weather forecasting

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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A Proficient Approach in Crop Recommendation System using Gradient Boosting Machine Technique 3

A Proficient Approach in Crop Recommendation System using Gr...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kumar, Kandukuri Joseph Reddy, N Praneeth Kumar Vasu, Andhavarapu Sandosh, S. Business Analytics Vellore Institute of Technology Integrated Mtech in CSE Specialization Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343632

The Crop Recommendation System (CRS), a powerful data-driven tool, has revolutionized farming practices by delivering precise crop recommendations. Decision Trees and Logistic Regression are employed as initial models to assess crop choices and further enhance accuracy with Gradient Boosting Machine (GBM), which consistently outperforms other methods. CRS meticulously processes essential factors, including Nitrogen (N), Phosphorus (P), Potassium (K), Temperature, Humidity, Rainfall, and pH levels. These inputs lead to highly personalized recommendations, tailored to specific farming contexts. CRS significantly boosts agricultural productivity, providing farmers with insights for optimal crop selection, increased yields, and efficient resource allocation. It promotes sustainability by encouraging environmentally responsible farming practices. In modern agriculture, CRS emerges as a pivotal asset, contributing to productivity and global food security. Its ability to guide farmers toward sustainable practices positions it as a driving force for positive change in the agricultural sector, offering a path toward a more sustainable and food-secure future. © 2023 IEEE.

关键词： Agricultural Decision Support System Crop Modeling Data-driven Agriculture Machine Learning Algorithms Precision Farming Remote Sensing Technologies

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Investigation of Aadhaar Card Enrolment in Government Schools Using R 1

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7th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2023

作者： Ahsan, Sameer Sakthivel, V. Subbulakshmi, T. Krishnamurthy, Vallidevi School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

ISBN: (数字)9789819937585

ISBN: (纸本)9789819937578

Aadhaar card is the only universal identity accepted across the country. Having Aadhaar card is important as it provides you with unique identity. Aadhaar card data is managed by the unique identification authority of India (UIDAI). We are going to do analysis on Aadhaar card enrolment whether the student is enrolled in school is enrolled with the Aadhaar card or not. If the student is not enrolled with the Aadhaar card he/she may not get the benefit of the government scheme. There are many governments related scheme which are provided to the citizens those who have Aadhaar card like direct transfer benefit etc. It will also help in uniquely identifying the student in case of similarity or any other issue. Aadhaar card analysis can also be used to manage the strength of the students in school and provide better education to all. We have done the data analysis of two district schools to check what is status of the Aadhaar card enrolment and see what is the improvement rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Students

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Prediction of Covid-19 Recovery Chances Using Supervised Machine Learning Classification 5th

Prediction of Covid-19 Recovery Chances Using Supervised Mac...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Dhanalakshmi, R. Nivashini, A. Vijayaraghavan, N. Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering MNM Jain Engineering College Chennai India Department of Mathematics KCG College of Technology Chennai India

ISBN: (纸本)9789819767311

Predictive analytics if combined with machine learning approaches have the potential to play a significant role in forecasting the spread of respiratory infections. Machine learning approaches aid in the mining of data to better assess and forecast the presence or absence of covid-19 infection. In this paper, XGBoost Ensemble Classification using a fine-tuned approach is being used. Various machine learning algorithms are being analyzed and XGBoost Ensemble Classification algorithm is used to forecast and predict the covid-19 infected patient recovery chances that is the possible outcome of a covid-19 infected patient along with the prediction probability of recovery. The suggested categorization model is applied to a variety of situations. When compared to other ensemble models like random forest, hybrid ensemble and other individual classifiers such as decision tree, logistic regression, K-nearest neighbor, support vector machine and Naive Bayes, the XGBoost Ensemble Classification model gave the highest accuracy. Predicting the correct class is important. Thus, the proposed system discusses the possibility of automated prediction for covid-19 infected patient recovery chances along with the probability of recovery and the effectiveness of such predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Enhancement in Quality of Routing Service Using Metaheuristic PSO Algorithm in VANET Networks

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Soft Computing 2023年第5期27卷 2739-2750页

作者： Javadpour, Amir Rezaei, Samira Sangaiah, Arun Kumar Slowik, Adam Mahmoodi Khaniabadi, Shadi School of Computer Science and Technology Guangzhou University Guangzhou510006 China Bernoulli Institute for Mathematics and Computer Science University of Groningen Groningen Netherlands School of Computing Science and Engineering VIT University Vellore India Department of Industrial Engineering and Management National Yunlin University of Science and Technology Douliu Taiwan Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland Department of Computer Science and Engineering USM Engineering Campus Pulau Pinang George Town14300 Malaysia

Vehicular ad hoc Networks (VANETs) can be designed in a way to organize road protection with no specific need for any fixed infrastructure. Accordingly, the movement of all vehicles can be planned according to perceived information, and Quality of Services Routing (QoSR) algorithms can be pressured according to its available options, paths, and links, and according to the criteria and reliability of the QoSR. Ensure that QoSR is aware of the environment of the network of vehicles, including location of vehicles, direction, and speed. This study is to reduce the effects of unpredictable problems on the best pathway to replace the broken path/link. A QoSR with Particle Swarm Optimization (QoSR-PSO) is used in this article for improving QoSs in vehicular ad hoc networks. By modeling the behavior of a group of particles, particle swarm optimization algorithms find the best possible solution to the problem. In order to perform simulation experiments, NS2 simulator and VanetMobisim have been used. The comparison results with benchmark studies show the improvement in packet delivery rate (PDR), delay, Packet Drop, and overload. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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