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6th International Conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Verma, Divyanshu Vijayarajan, V. Rajkumar, S. Ramesh Pathy, M. Shool of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India School of Bio Sciences and Technology Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9783031840616

Hyperspectral imaging (HSI) emerges as a highly efficient means of information retention, particularly when leveraging the power of deep learning. The intricate task of non-destructively identifying rotten fruits is addressed through HSI, offering a non-invasive method for data collection that significantly streamlines the sorting process using advanced algorithms. Deep learning, employing neural network methodologies, facilitates the exploration of the intricate connection between spatial and spectral contexts within the data. The HSI system captures the wavelength information of refracted, reflected, and absorbed light across multiple bands. To address noise introduced by the machinery, a Standard Normal Variate (SNV) filter is applied, resulting in a refined dataset represented as a hyperspectral cube (3D). The workflow involves the utilization of an image pre-training convolutional neural network (CNN) on the collected dataset, subsequently fine-tuned to serve as a robust classifier. In the proposed model, a substantial majority of the dataset (70%) is dedicated to training the neural network, with the remaining portion (30%) reserved for the validation process. The findings demonstrate that this analysis approach, leveraging the CNN algorithm, yields remarkably accurate results (>90%) and proves to be highly effective in discerning fruit quality. The presented methodology showcases a promising synergy between hyperspectral imaging and deep learning, offering a reliable solution for non-destructive fruit quality assessment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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A Simple Algorithm to Secure Data Dissemination in Wireless Sensor Network 1

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Sonai, Veeramani Bharathi, Indira Department of Computer Science and Engineering School of Engineering Shiv Nadar University Chennai Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India

ISBN: (数字)9789819981298

ISBN: (纸本)9789819981281

Wireless sensor networks are heavily utilized in numerous applications in both industry and science. As the original data is transferred from the sensor node to the sink node, unauthorized individuals attempt to access it, breaching confidentiality. The first step in enhancing the security of data transmission in a wireless sensor network (WSN) is figuring out the best method for encrypting data as it moves from the sensors to the central node. In order to move encrypted data from different sensor nodes to sink nodes efficiently in wireless sensor networks, this paper introduces a simple data encryption technique. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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An Efficient Detection and Classification of Plant Diseases using Deep Learning Approach

An Efficient Detection and Classification of Plant Diseases ...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Jagadeesan, S. Deepakraj, E. Ramalingam, Venkadeshan Venkatachalam, Ilayaraja Vivekanandan, Manojkumar Manjula, R. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India University of Technology and Applied Sciences - Shinas Branch Information Technology Department Oman Vellore Institute of Technology School of Computer Science and Engineeing Vellore India School of Engineering and Applied Sciences Srm University-AP Department of Computer Science & Engineering Andhra Pradesh Amaravati India Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technology Kattankulathur India

ISBN: (纸本)9798350313413

Crop failure caused by disease-causing pests is an important problem. Farmers struggle with disease management and detection due to inadequate interventions. The goal is to create an automatic system to efficiently identify plant diseases from photos while reducing crop losses and increasing productivity. Machine learning algorithms offer a faster and cheaper alternative to visual inspection by experts. The main goal is to perform image analysis for early diagnosis and effective disease control. The use of CNN architecture for plant disease classification and detection offers a promising solution for plant health monitoring and risk mitigation. Given the threats to crop productivity and global food security, reliable methods for early detection and accurate classification are essential. CNNs enable efficient analysis of vast plant image databases, enabling accurate plant disease identification with speed and accuracy. The multi-layer CNN approach extracts feature and refines the representation, facilitating accurate prediction and accurate diagnosis. Transfer learning methods accelerate system development and allow adaptation to plant disease-specific databases. Combining computer vision algorithms with CNN architecture enables real-time monitoring, early disease detection and targeted intervention, reducing yield losses and improving crop management. This approach uses AI, image analysis, and plant pathology to solve the challenges of sustainable agriculture and plant diseases. System performance is measured by various performance metrics. © 2023 IEEE.

关键词： CNN-Based Approach Error Rate Plant disease Precision ROC

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Intelligent Analysis and Identification of Palm Gestures using ML Algorithms

Intelligent Analysis and Identification of Palm Gestures usi...

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2024 International Conference on computer, Electrical and Communication engineering, ICCECE 2024

作者： Roy, Debraj Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar De, Utpal Chandra Tripathy, Abinash School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications Kiit Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350386479

The area of human-computer interaction (HCI) has made significant strides in recent years, thanks in large part to the development of innovative technologies that aim to improve the way in which people and machines work together. One of these areas is the use of hand gestures to control gadgets and apps, which has the potential to create a form of interaction that is more intuitive and natural. The introduction of a palm gesture detection system that is both dependable and effective has the potential to completely alter the ways in which we interact with various electronic gadgets and software programmes. The objective is to develop a palm gesture recognition system that is capable of precise detection and classification of hand gestures in real time. The computer vision methods is applied to collect and interpret the hand movements. Following this, machine learning algorithms will be used to train the system on a huge dataset consisting of hand gestures. The system will be able to identify the hand gestures in real time and will categorise them according to predetermined categories. The experimental results are compared against other gesture recognition systems to study the accuracy of the system. © 2024 IEEE.

关键词： Support vector machines

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An Efficient Decision Support System for Stock Market Prediction using Technical Indicators as Features by Machine Learning Approaches 4

An Efficient Decision Support System for Stock Market Predic...

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4th International Conference for Emerging technology, INCET 2023

作者： Naga Pranava Shashank, V. Ramanan, Vimal S Altrin Lloyd Hudson, D. Sheik Abdullah, A. Vellore Institute of Technology School of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350335750

In this Research, we have built Machine Learning Models to predict the next day price for the Nifty Index, Reliance and TCS Stocks. We will use R Project Library, quantmod, TTR for data acquisition and technical analysis. We have used the R Project Machine Learning Library caret along with the daily data for the last five years (June 2017 - June 2022) for the NIFTY Index and the Reliance and TCS Stocks. Our data will consist of Open, High, Low, Last (Close) and Volume. Our objective is to predict the price change for the next day using 6 technical indicators as features which include RSI, Williams%R, etc. For building Machine Learning Models, we have used the R Library caret. The primary methods which are used are KNN, Classification Regression Trees, Naive Bayes, SVM with Radial Basis Function and DNN. For k-nearest neighbors, we are able to achieve an accuracy of 72.78%, for DNN 72.533%, for CART 72.36%, for NB 70.08%, and for SVM 72.69% for the prediction accuracy of the next day. © 2023 IEEE.

关键词： Data acquisition

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Jaya integrated binary whale optimized algorithm for preserving medical data

Jaya integrated binary whale optimized algorithm for preserv...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sudha, I. Ramesh, P.S. Jagadeesan, S. Murugan, Guru Vimal Kumar Gokulnath, C. Poongavanam, N. Department of Computer Science and Engineering SIMATS School of Engineering SIMATS Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India School of information Technology and Engineering Vellore Institute of Technology Tamilnadu Vellore India

ISBN: (纸本)9781032669663

Cloud Computing has the source of information to various sectors that the future generation duly depends on the resources of the cloud. Users can access a variety of network, storage, and platform features through the cloud. The users exploit the cloud’s resources in accordance with their needs, and the data is centralized for cloud storage. Hence, The main aim proposes a method called Jaya Binary Whale Optimization (JBWO), which combines the Binary Whale Optimization and Jaya algorithm (JBWOA) and modifies the Fully homomorphic encryption (FHE) Organism exhausting Advanced Encryption Standard (AES), for the cloud to start securely transmitting data.. By applying the proposed JBWO algorithm to generate the Data Protection coefficient (DP), the original data is retained. For the purpose of selecting the best resolution, privacy and utility parameters are used to calculate fitness. By combining the key vector and the Key Information Product (KIP) matrix with the EX-OR operation, the sanitized data are produced. By utilizing the key that the data owner provides, users can access the unique data. The strategy produced improved results in the analysis utilizing the Cleveland datasets and is capable of managing sensitive information with clearly specified privacy. The suggested privacy protection techniques effectively safeguard data while maintaining privacy, resulting in satisfied customers. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Cloud storage

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Advance Chess Engine: an use of ML Approach 3

Advance Chess Engine: an use of ML Approach

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Srivastava, Kushagra Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Mishra, Manoj Ranjan De, Utpal Chandra Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350381931

Several researchers have sought to create a software that can learn to play cognitive games with little or no prior understanding of the game's rules. A typical chess playing machine investigates all of the possible moves from a chessboard arrangement before deciding on the next best move. The Deep Blue chess engine's brute-force searching strategy has had a significant influence on artificial intelligence, although it has been discovered to be resource intensive. Using the evolutionary and adaptive computing strategy on learning from human grandmasters, this study proposes a relatively easy and fast approach to developing an intelligent chess engine that can aid and hint at probable moves inside the game. Chess-playing machines may now easily surpass human talent thanks to the introduction of Machine Learning, Deep Learning and Artificial Intelligence. The game of chess is examined through the prism of these algorithms in this study. The comparison of the model performance with the existing ones has been performed to show the efficacy of the model. © 2024 IEEE.

关键词： Engines

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Artist-Specific Analysis of Spotify using BI Tools 14

Artist-Specific Analysis of Spotify using BI Tools

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sharma, Varsha Atwal, Hetal Praveen Joe, I.R. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350335095

Spotify is one of the world's largest music audio streaming and media service provider with over 489 million monthly active users, including 205 million paying subscribers as of December 2022. The music from various artists around the globe can be found on Spotify where songs from these artists compete daily in order to top the charts. In this paper, we present an analysis of the music charts using many features that describe a song such as acousticness, danceability, etc., and present artist-specific suggestions that can help the artists know the genre of the songs that is most popular among the listeners and therefore, produce songs accordingly and hence help the artists gain popularity among the audience. © 2023 IEEE.

关键词： Audio acoustics

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A secured energy aware resource allocation and task scheduling based on improved cuckoo search algorithm and deep reinforcement learning for e-healthcare applications

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Measurement: Sensors 2024年 31卷

作者： Palani, S. Rameshbabu, K. Research Scholar School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Healthcare industries have begun using modern technologies and tools like cloud computing these days. When cloud computing and assisted technologies are used in the healthcare domain the storage cost improves along with the quality and time availability. The present work makes use of cryptography based architecture for improving the overall security aspects. However, the tasks of the user take a longer duration for the execution to complete when the needed resources are unavailable on the main server. ICSA (Improved Cuckoo Search Algorithm) with DRL (Deep Reinforcement Learning) to solve this issue. The main goal of this work is to increase overall security and reduce execution times of jobs. The objective also includes using underutilized resources. Scheduling jobs, Binary In-order Traversal Trees with weights are used. Subsequently, DRL algorithm has been used to decrease the space complexity by splitting the individual resources. There will be idle resources in a state space that are used in allocation of tasks. The scheduled tasks will then do a search on the resources based on the ICSA algorithm. The server will then distribute the resources to the action area after an optimal resource has been chosen and assigned to the job. DRL for resource allocation so that the inactive resource usage is minimized and the execution time is reduced. Finally, MTAC-IBEA (multi tenant authentication control with improved blowfish encryption algorithm) for decrypt health data. The outcomes of simulation demonstrate that the suggested method offers optimum work schedules, resource allocations, and security in e-healthcare systems. © 2023 The Authors

关键词： Reinforcement learning

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A Comprehensive Review on Early Diagnosis of Alzheimer's Disease Detection

A Comprehensive Review on Early Diagnosis of Alzheimer's Dis...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gayathri, G.S. Nachiyappan, S. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350305708

A progressive neurodegenerative Alzheimer's Disease (AD) shrinks (atrophy) the brain and kills brain cells. On a global scale, Finland and the United Kingdom have the highest prevalence of AD, with 54.7 and 42.7 cases per 100, 000 inhabitants respectively. In India, there are 14.60 AD cases for every 100, 000 people and it is ranked 137th in the world and 13th in India among the 50 causes of death. At the beginning of the disease, a person may forget things, or have trouble remembering recent conversations. As the disease progresses, a person may experience severe memory loss and difficulty performing everyday tasks. Consequently, the only way to predict AD at an early stage is to use treatments that prevent the disease from progressing. This paper provides an overview of the current approaches to AD diagnosis and detection, with a focus on the use of Biomarkers and discussed the advantages and disadvantages of Machine Learning (ML)/Deep Learning (DL) techniques in early detection of AD. It also reviews the collection of AD datasets and the pre-processing techniques used in the studies. Although DL methods gives the best outcome measures such as accuracy, precision, F-measures, recall and Area Under the Curve (AUC) still DL techniques have some limitations that can be overcome via Quantum Computing (QC). QC is currently emerging new technology based on quantum theory where qubits are used to store the data instead of bits. It accomplishes 1000 times faster and also solves the complex problem within a fraction of time than the classical computers. © 2023 IEEE.

关键词： Alzheimer's Disease Biomarkers Datasets Deep Learning Machine Learning Quantum Computing

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