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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Anand, Taruna Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Ambala India University Department of Humanities and Social Sciences Dehradun India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions. © 2025 IEEE.

关键词： Teaching

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Improved Healthcare System with Quantum Computing

Improved Healthcare System with Quantum Computing

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Chandrasekaran, Sriramakrishnan Agrawal, Rashmi Dutt, Vishal Vyas, Narayan Solution Architect Tcs India Manav Rachna International Institute of Research and Studies India Aryabhatta College Department of Computer Science Ajmer India Mds University Department of Computer Science Ajmer India

ISBN: (纸本)9798350322309

Health care is playing a major role in designing any kind of prediction model and there is a large amount of data related to different diseases is available over the globe. A major part of machine learning lies in the identification of the data related to different diseases and make them helpfulfor people. The major implementation is in the identification ofthe safest medical data systems if the new challenging task where we can implement quantum computing as the main lead to process the medical data and making the new data systems which are most secured to manage the medical data which is useful to identify the fastest method to form a drug and also to manage in the identification of the safe drug mechanism for thedisease which is not even in identification. Quantum computingis a challenging area that is like an extension to the current scenario of medical imaging. Improved healthcare system using machine learning and quantum computing gives a new age to medical sciences to identify the approximate solutions to the different medical diseases. This article works on the implementation of such medical advancements to a specificdisorder based on human genetics and DNA sequencing. The Quantum computing approach succeeded in recognizing the approximate medical sequences for a particular disease. © 2023 IEEE.

关键词： Diagnosis

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Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture

Optimizing Crop Traits with ANN and Genetic Innovations for ...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Mahendran, Radha Danda, Ramanakar Reddy Anjum, Asma Kamde, Rashmi Abhay Firake, Bhagyashree Shailesh Wasnik, Jayshree Suhas Vels Institute of Science Technology and Advanced Studies Department of Bioinformatics Chennai India Architect Telangana Hyderabad India Hkbk College of Engineering Visvesvaraya Technological University Department of Computer Science and Engineering Bangalore India K J Somaiya Polytechnic Department of Electrical Engineering Maharashtra Mumbai India

ISBN: (纸本)9798331521394

Sustainable agriculture requires new strategies to tackle difficulties such as rising food demand, environmental pressures, and resource constraints. This study introduces a sophisticated approach that combines artificial neural networks (ANNs) with genetic advancements to enhance crop characteristics. The system attains enhanced accuracy (98.1%), F1 score (0.97), and reduced RMSE (0.012) by utilising high-dimensional genetic, phenotypic, and environmental data, surpassing baseline models. The technique integrates ANN predictions with gene-editing frameworks, guaranteeing accurate interventions and effective trait optimisation. The results demonstrate considerable improvements in forecast accuracy and resource efficiency compared to current techniques, creating a scalable, data-driven framework to promote sustainable agriculture practices worldwide. © 2025 IEEE.

关键词： Precision agriculture

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Analysis of Various COVID-19 Prediction Techniques

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IEIE Transactions on Smart Processing and Computing 2021年第4期10卷 323-329页

作者： Arora, Rakesh Kumar Gupta, Manoj Kumar Bhati, Bhoopesh Singh Department of Computer Science and Engineering Krishna Engineering College UP India Department of Computer Science and Engineering Trinity Institute of Innovations in Professional Studies UP India Department of Computer Science and Engineering Ambedkar Institute of Advanced Communication Technologies and Research Delhi India

There is no precise treatment available for COVID-19, but treatment of a number of signs/symptoms is possible. This treatment is provided in accordance with the clinical position of the patient. Furthermore, supplementary care is proven as efficient for infected people. COVID-19 prediction techniques follow various steps, which include dataset collection, pre-processing, feature extraction, and classification. Numerous COVID-19 prediction techniques have been proposed in the literature. In this paper, various techniques are systematically reviewed on the basis outcomes, advantages, and disadvantages. Copyrights © 2021 The institute of Electronics and Information Engineers

关键词： COVID-19

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Blockchain Ethereum-Based Decentralized Secure Ride-Sharing Framework for Carpooling System

Blockchain Ethereum-Based Decentralized Secure Ride-Sharing ...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： J. Wessly R. Durga Department of Computer science Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Carpooling involves sharing a car for a journey, typically with two or more individuals. Car-sharing systems play a crucial role in addressing urban challenges by providing shared vehicles and decreasing the dependence on private cars. Car-sharing systems present security concerns due to transmitting sensitive information through a shared centralized network, including identity, location, and access codes. It is crucial to develop secure authentication methods to prevent unauthorized access and use of this information for illegitimate purposes. To address the problem, this research presents a Blockchain Ethereum-based Decentralized Secure Ride-sharing Framework (BEDSRF) for carpooling systems. The study introduces a Role-based Access Control (RBAC) method to determine user roles for accessing their information. Then, based on these roles, the data is encrypted using the Elliptic Curve Cryptography (ECC) method. The BEDSRF approach verifies each node and transmits the information within the decentralized network. Additionally, the Key Policy Authentication Scheme (KPAS) is utilized to authenticate the user's key and enable role-based decryption for the end user. Furthermore, experiments were carried out to confirm the efficiency and resilience of the proposed approach. The findings indicate that the proposed method outperforms existing carpooling schemes in terms of both security and authentication performance.

关键词： Privacy Shared transport Authentication Elliptic curve cryptography Ubiquitous computing Blockchains Security Automobiles Reliability Resilience

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Preprocessing and feature extraction of MRI Liver Tumour images using A novel multi-class identification (NMCI) framework

Preprocessing and feature extraction of MRI Liver Tumour ima...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： A. Bhagya S. Perumal Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

One of the leading and most serious causes of death worldwide is liver tumours. Currently, one of the most well-known computer Vision disciplines (CV) is Medical Imaging (MI), which aids radiologists and physicians in the early detection and diagnosis of liver tumours. In order to diagnose patients, radiologists and doctors read hundreds of images using manual or semi-automated technologies, such as Magnetic resonance imaging (MRI). Consequently, a fully automated approach is required to use MRI scans, the most common and extensively used imaging modality, for early tumour detection and diagnosis. Implementing machine learning (ML) techniques in finding the tumour affected parts are the main focus of the proposed work. The multiclass liver tumour classification procedure followed in the tumour identification process follows, Random Forest (RF), Random Tree (RT), Logistic Model Tree (LMT) and J48 with multiple automated Region of Interest (ROI). Hemangioma, cyst, hepatocellular carcinoma, and metastasis are the four basic tumour classes, which are to be classified before severity increases. Transformation procedures followed in image processing are conversion of collected MRI images into grayscale, then boosting the contrast with histogram equations. The Gabor filter was used to minimise noise, and an image sharpening technique was used to enhance image quality. Furthermore, employing texture, binary, histogram, and rotational, scalability, and translational (RST) algorithms, 55 characteristics of various pixel dimensions were obtained for each ROI. Out of these 55 characteristics, 20 optimised features were chosen for classification using the correlated feature selection approach. RF and RT outperformed J48 and LMT, with respective accuracy of 97.48% and 97.08%, according to the collected data. The new framework that has been suggested will aid doctors and radiologists in the diagnosis of liver tumours.

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An Improved Deep Forest Classification and Intelligent Segmentation for skin Cancer Prediction Technique Inspired by Whale Optimization

An Improved Deep Forest Classification and Intelligent Segme...

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Advances in Modern Age Technologies for Health and Engineering science (AMATHE), International Conference on

作者： Siva Prasad Reddy K.V S. Meera Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Fast malignant melanoma tumor identification is vital for successful therapy. Melanoma has now been universally acknowledged to be the most lethal form of malignancy over all else since, even if not caught and addressed promptly, it possesses a substantially higher propensity to migrate towards remaining regions of physique. Definitive presentation of various disorders relies largely on quasi therapeutic machine vision / procuring of disorders scanned pics. These methods offer a tool for autonomous image enhancement that allows for a quick and precise appraisal of the abscess. Deficiency of vast samples constitutes one of primary hurdles for designing a trustworthy autonomous classification scheme. This research suggests integrating Deep—learning & Image—processing to develop an algorithm for artificially recognizing melanoma. This survey's phase involves amassing a repository of dermoscopy pics, pre—Processing, separation utilizing hybridized Fuzzy-C-Means, and weighted enhancement employing the whale-optimization-algo. Following the subdivision of the dermoscopic imagery, the chapped skin cellular membranes traits are captured utilizing extraction of features. For the layering of the retrieved attributes, Long -shortterm -memory {LsTm}, bidirectional LsTm && Deep-Forest predictors are deployed. After using freely searchable sample group, the reported classifier reliability was Ninety Seven Percent && the segmentation effectiveness reached ninety three percent.

关键词： Hair Training Object segmentation Melanoma Feature extraction Solids Skin

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Intelligent network intrusion detection system using optimized deep CNN-LSTM with UNSW-NB15

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International Journal of Information Technology (Singapore) 2025年 1-17页

作者： Thaljaoui, Adel Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Preparatory Institute for Engineering Studies of Gafsa Street Al Khayzourane-Zarroug Gafsa 2112 Tunisia

In the era of interconnected technologies, the ongoing threat of intrusion attacks poses significant risks, exposing individuals and organizations to potentially severe repercussions. Network Intrusion Detection System (IDS) has a crucial role in securing these networks. In this paper, we propose an hybrid model combining the convolution neural network (CNN) and the long short-term memory (LSTM) with hyper-parameters optimization using Bayesian optimization method for attacks detection in IoT. We train and test our hybrid model using the benchmark dataset UNSW-NB15 for evaluating its effectiveness in intrusion detection. The performance of our model is assessed using various metrics, including accuracy, precision, recall, and F1-score. In addition, we conduct a comparative study to highlight the effectiveness of our proposed model, which achieved remarkable accuracy on the testing set. This evaluation underscores the model's potential for attacks detection. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Convolutional neural network (CNN) Deep learning Internet of Things (IoT) Intrusion detection system (IDS) Long short-term memory (LSTM) Optimization

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Improved Gaussian Grasshopper Optimization Algorithm (IGGOA) and Deep Belief Network (DBN) For Fetal Health Classification

Improved Gaussian Grasshopper Optimization Algorithm (IGGOA)...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： B. Jansi V. Sumalatha Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The effective method for continuously monitoring the fetus’s health is called Cardiotocography (CTG). For the past 60 years, CTG has been used in clinical settings, allowing medical professionals to identify early indicators of fetal deterioration. Prior research has solely focused on the classification accuracy of CTG datasets that include the entire feature set, ignoring the classifier computing time. Improved Gaussian Grasshopper Optimization Algorithm (IGGOA) and Deep Belief Network (DBN) are introduced to categorize CTG data. Several factors contribute to the introduction of IGGOA to feature selection: 1) Due to its fragile tail, Gaussian Mutation (GM) is introduced to generate a new offspring closer to the original feature. 2) The development of Levy Flight (LF) has created a more straightforward way to solve the local optimum because grasshoppers have more vital attraction forces;3) OBL has been used to update the present population, and GOA capability is used to determine the FHR classification. In the DBN classifier, CTG samples are categorized as Normal (N), Suspect (S), and Pathological (P). DBN has been analyzed as a Restricted Boltzmann Machine (RBM). Every sub-network hidden layer in the DBN classifier acts as the layer visible to the layer after it. The Softmax function has been used to determine the hidden layer results. The accuracy is about $97.3659 \%$, which is better than other models.

关键词： Accuracy Fetal heart rate Computational modeling Sociology Tail Feature extraction Probability distribution

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A Mango Disease Prediction for Smart Agiculture using Machine Learning Algorithms

A Mango Disease Prediction for Smart Agiculture using Machin...

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

作者： Lavanya K A. Packialatha Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The country's economy relies heavily on agriculture, and the health of the crops is essential to its success. Crop diseases that go undetected can cost the agriculture industry. Thus early detection and identification are crucial. It is possible to avoid crop diseases from destroying the harvest if they are accurately diagnosed and detected. Because healthy and diseased plants appear identical in their early stages, farmers cannot distinguish between the two by watching the crop leaf. India exports vast mangoes, making it an economically and environmentally significant fruit. About 1500 mango species are grown in India, with over 1000 commercial types. There are a lot of diseases that harm mangoes, affecting their look, taste, and economy. The "Prediction of Disease in Mango Fruit Crops" A complex warning system that uses machine learning and IoT. One of the main objectives is to develop a system that can forecast disease outbreaks on mango fruit harvests using historical weather information and crop yield Mango trees in India are plagued by a fungus called Anthracnose, which is the most frequent disease of its kind. Anthracnose, a highly contagious fungus, requires a quick and accurate method of diagnosis. As a result, an in-depth examination of the plants is essential before initiating any control measures. This study examines machine learning(ML) and deep learning (DL) strategies for detecting and classifying mango plant diseases. The performance of ML and DL-based classification models for mango crops and their datasets and feature extraction approachesare examined in this work. Finally, a variety of issues involved in plant disease identification are explored.

关键词： Support vector machines Deep learning Plant diseases Accuracy Costs Biological system modeling Crops Data models Agriculture Internet of Things

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