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A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data

computers, Materials & Continua 2022年第1期70卷 73-89页

作者： Harshita Patel Dharmendra Singh Rajput Ovidiu Petru Stan Liviu Cristian Miclea School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India Technical University of Cluj Napoca Faculty of Automation and Computer ScienceCluj Napoca400114Romania

Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other *** Imbalanced distribution of data is a natural occurrence in real world datasets,so needed to be dealt with carefully to get important *** case of imbalance in data sets,traditional classifiers have to sacrifice their performances,therefore lead to *** paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this *** have adapted the‘existing algorithm modification solution’to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing *** K nearest neighbor is a non-parametric classification method that is mostly used in machine learning *** classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced *** proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional *** show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.

关键词： Machine learning fuzzy classification nearest neighbor adaptive approach optimal weights

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Revolutionizing Heart Disease Prediction with Quantum-Enhanced Support Vector Machines 2

Revolutionizing Heart Disease Prediction with Quantum-Enhanc...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Jessika, Sharon S. Mantri, Harsh Chaudhry, Angad Bhathena, Jehan Sharukh Alam, Syed Rayan Sai, Gautham S.A. Natarajan, B. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India School of CSE VIT Vellore Campus Tamilnadu Vellore India School of CSE VIT Chennai Campus Tamilnadu Chennai India

ISBN: (纸本)9798331504106

Heart diseases are encountered as a leading root for higher death cases around the globe. However, the death rate can be reduced by taking proper treatment procedures and medical inspections. The early identification of heart diseases poses huge challenges to the physicians due to the complexity of the root causes. The emergence of intelligent decision-making support systems aids in predicting the diseases at the earliest. The recent innovations handle a huge amount of earlier data and produce accurate results as expected. The introduction of quantum computing-based machine learning approaches produces outstanding results in prediction and classification. The proposed research work aims to improve the heart disease prediction rate by incorporating Quantum Support Vector Machine Algorithms (QSVM). The outcomes of this research work shows the improved results in terms of prediction accuracy. Further, the additional evaluation metrics state the significance of the proposed work. ©2024 IEEE.

关键词： Support vector machines

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A Smart Assistive Device for the Blind People 7th

A Smart Assistive Device for the Blind People

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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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Design and Implementation of a Decentralized Voting System Using Volta Blockchain 5

Design and Implementation of a Decentralized Voting System U...

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5th IEEE International Women in technology Conference, WINTECHCON 2024

作者： Arthi, M. Suganthi Evangeline, C. Bharadwaj, Rohit Avrit, M. Jaswant, Gogineni Siranjeevi, G.P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India

ISBN: (纸本)9798350374070

The drive for secure, open, and easily accessible voting systems has spurred the advance of decentralized blockchain-powered voting systems. This paper outlines the design and implementation of a decentralized voting system that utilizes the Hardhat Ether for testing and deployment, the Volta blockchain, Solidity smart contracts, and MetaMask for user interaction. Our approach ensures the confidentiality and integrity of votes by harnessing the irreversible and transparent properties of blockchain technology, enabling participation from any location with internet connectivity. Users can safely cast their ballots straight from their web browsers thanks to the inclusion of MetaMask, improving accessibility and usability. Furthermore, the application of Hardhat Ether simplifies the development process, enabling reliable deployment and testing protocols. This paper discusses the architecture, functionality, and security considerations of our decentralized voting system, highlighting its potential to revolutionize democratic processes worldwide. © 2024 IEEE.

关键词： Voting machines

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Homomorphic Encryption and Prim's Process for Multi-Keyword Exploration in Encrypted Cloud Data for Trades 1

Homomorphic Encryption and Prim's Process for Multi-Keyword ...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Prakash, A. Vignesh, C. Chandru Suresh, M. Pavan, Jampanisriram Ahamad, S.K. Shamshad Rama, M. QIS College of Engineering and Technology AP Ongole India School of Computer Science and Engineering Vellore Institute of Technology Department of Internet of Things Tamilnadu Vellore India

ISBN: (纸本)9798350345957

Due to its low administration costs and convenient accessibility, cloud storage is quickly becoming the norm in today's online infrastructure. By letting cloud users save their massive data in the cloud, the cloud storage system offers crucial advantages. Improving cloud storage's performance at data retrieval requires addressing concerns about data privacy and security. This study presents a new algorithm for bolstering security, called Modified Ring-based Fully Homomorphic Encryption and Prim's (MRFHE-P). The ranking function determines how the index is built for keywords and files. Prim's algorithm generates the dynamic tree based on the ordered keywords. The document is encrypted using a variant of the RSA technique called the Modified Ring-Based Fully Homomorphic Encryption Algorithm (MRFHEP). The encrypted files are indexed in a Two-way Hash Table so that users can quickly get the necessary data. Afterwards, the cloud user uses a shared symmetric key and private key to decrypt the encrypted data. The experimental results show that the suggested technique significantly reduces the time needed to encrypt and decrypt data compared to the current standard. The proposed technique also speeds up indexing and searches for a variety of documents. There was also a decrease in the amount of time spent searching and indexing, and an increase in the efficiency with which keywords could be extracted. © 2023 IEEE.

关键词： Cryptography

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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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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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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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