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Deep Neural Network Empowered Movie Recommender System Using Hesitant Fuzzy Bi Objective Clustering

Journal of The Institution of Engineers (India): Series B 2024年 1-11页

作者： Shrivastava, Vineet Kumar, Suresh Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Haryana Faridabad India

The movie recommender system is a highly influential and practical tool that assists individuals in efficiently choosing films to watch. Although recommender systems have been extensively used in academic research for various objectives, such as suggesting movies and recommending books, there has been a lack of focus on providing personalized movie recommendations for individual users. This research presents a new method for recommending movies that combines the Hesitant Fuzzy Clustering technique with a Convolutional Spiking Neural Network Movie Recommender System. The first phase entails obtaining input data from benchmark datasets such as MovieLens 100 K and MovieLens 1 M. These datasets are analysed using Ternary Pattern and Discrete Wavelet Transforms along with Hesitant Fuzzy Bi-objective Clustering technique to choose clusters depending on the retrieved attributes. After that, recommendation of movies uses a Deep Convolutional Spiking Neural Network to forecast user preferences. The efficiency of the proposed model is specifically compared to recent existing methods, like the Multi-Model Trust Based Movie Recommender Scheme (MT-ML-MRS) and the Graph-Dependent Hybrid Movie Recommendation Scheme (GHRS-MRS), particularly for movie recommendations. The results demonstrate a substantial enhancement, as the suggested model achieves 2.30% and 4.71% greater accuracy for MovieLens 100 k and MovieLens 1 M datasets respectively. The proposed system shows significant improvement over some traditional recommendation models indicating avenues for future research in scalable and intricate systems. © The Institution of Engineers (India) 2024.

关键词： Convolutional neural networks

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Secure Block Chain-Based Healthcare Sensitive Data Prediction Using Pragmatic Quasi-Identifiers in a Decentralized Cloud Environment

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SN computer science 2024年第1期5卷 41页

作者： Punithavathi, S. Jeyalaksshmi, S. Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India Hindustan College of Arts and Science Chennai India Department of Information Technology School of Computing Science Vels Institute of Science Technology and Advanced Studies Chennai India

Security is essential for all facts, information sharing around the internet and maintaining personalized information. In recent days, the healthcare industry needs privacy-preserving to keep personalized data from others containing sensitive information. Due to increasing security breaches, fundamental sharing problems with personalized details shared on the internet lead to security problems. To resolve this problem, we propose a Secure Blockchain-based Healthcare-sensitive data prediction using a Pragmatic quasi identifier in a decentralized cloud environment. Pragmatic quasi-sensitivity Identification (PQSI) based Privacy-preserving for securing personalized records Using Hyper Recurrent feature classification for improved cloud security. Initially, the sensitivity fitness value of the feature is estimated through Intensive feature success rate (IFSR) and clustered by marginal subset features. By marginalizing the sensitivity threshold frequency rate, the components are extracted by pragmatic quasi-sensitive identifier and classified using Hyper recurrent Neural classification (HRNC) to find the sensitive and non-sensitive records based on frequency fitness weight to split and store perception as individually in the private cloud. Further, to improve the security level Elliptic Curve Cryptography (ECC) based master node authentication technique is used in the blockchain concept. Peer end Block chain principle is applied to secure the sensitive data. To ensure the sensitive records are sanitized into data blocks to assure in a blockchain environment. This proposed system produces higher prediction accuracy than other methods and achieves higher sensitivity and specificity rate performance. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing ECC Hyper recurrent feature classification Privacy preservation Security Sensitive data analysis

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Artificial Intelligence based Food Nutrients Recommendation System using Enhanced Customer Behavior Learning Approaches 8

Artificial Intelligence based Food Nutrients Recommendation ...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Jeyanthi, P. Durga, R. VEL'S Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Digital marketing has made significant steps in leveraging advanced technologies such as machine learning to provide personalized recommendations to food and nutritional product companies. Artificial Intelligence (AI), a branch of computer science aimed at imitating thought processes, learning abilities and knowledge management, is increasingly used in experimental nutrients recommendation. The food and nutrition product industry, using data science techniques to recommend products based on specific needs and preferences. Previous method analysis the kids' food product analysis failed in feature analysis based on product buying consumer behavior in digital marketing. To overcome further problems, a new method called Spectral Spider Optimization with Fuzzy Neural Network (SSO-FNN) with Support Vectored Neural Networks (SVN2) to advertise the best food items using maximum threshold values for selecting the best features is proposed. The SVN2 is prediction result based on recommendation food, accuracy, precision, recall, f1-score, and error rate and time complexity. These metrics uses confusion matrix evaluation and its outperforms the previous approaches. © 2024 IEEE.

关键词： Fuzzy neural networks

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Enhancing Car Pooling Systems Transactions using Consortium Blockchain-based Randomized Integrity Distributed Exchange Approach 8

Enhancing Car Pooling Systems Transactions using Consortium ...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Wessly, J. Durga, R. VEL'S Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Ridesharing is a process of optimizing fuel usage by enabling multiple individuals to share a vehicle while traveling along the same route. It allows frequent travelers to share their trips with others, resulting in reduced travel expenses and decreased traffic congestion. Many current vehicle possibilities depend on centralized authority privileges to perform, making systems susceptible to precise points of failure and privacy concerns from internal and external attackers. The centralized model of ride-sharing zones has raised significant concerns about data privacy, security, and transparency. In addition, traditional Car Pooling Systems (CPS) may lead to car owners misusing customer data. In order to tackle this issue, the new approach in the novel introduces the Consortium Blockchain-based Randomized Integrity Distributed Exchange (CB-RIDE) approach for secure CPS information sharing in the network. It starts with the utilization of a Solidarity Route Matching Scheme (SRMS) to identify the same route for CPS. Then, it encrypts the cashless transaction and trip information using the Secure Hash Algorithm 256 (SHA-256) method. Additionally, the key verifies using the Proof of Authentication (PoA) approach to decrypt the CPS information through the peer end. The proposed approach utilizes the decentralized nature of blockchain to protect users' trip and payment information and ensure the immutability of their transactions ledger. The experimental result demonstrates the proposed efficiency in terms of security and authentication performance compared to other methods. © 2024 IEEE.

关键词： Traffic congestion

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Harmonizing Healthcare Perspectives and In-Depth Strategy for Unifying Diverse Medical Big Data Sources 10

Harmonizing Healthcare Perspectives and In-Depth Strategy fo...

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10th International Conference on advanced Computing and Communication Systems, ICACCS 2024

作者： Manikandan, A. Thirumal, S. Kumar, N. Vels Institute of Science Technology &advanced Studies Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The healthcare sector has generated vast amounts of data, posing a significant challenge in terms of integration. Recently, deep learning frameworks have gained extensive utilization for forecasting relationships within healthcare systems. Specifically, the success of the Heterogeneous Multi-Layer Deep Learning Network (HMLDN) in seamlessly amalgamating diverse medical data for enhanced diagnostics and storage is explored in this paper. The initial discussion delves into capsule optimized networks and attention maps, laying the groundwork for their application in the formation of HMLDN to achieve efficient data integration. © 2024 IEEE.

关键词： Electronic health record

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A Novel Approach to Predict Cardiovascular Disease with Extra Trees Classifiers and Particle Swarm Optimization 8

A Novel Approach to Predict Cardiovascular Disease with Extr...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Ezhilvani, G. Thailambal, G. VEL'S Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Research indicates a progressive rise in mortality rates associated with Cardio-vascular disease. The primary causes of heart diseases and reduced blood flow through vessels stem from stress and unhealthy lifestyles. The World Health Organization suggests that current mortality rates are elevated due to widespread gadget addiction and persistent stress in daily life. Machine learning-based prediction systems have been proposed for cardiovascular diseases, leveraging their ability to uncover hidden features and complex relationships. However, these systems often lack precision in identifying the exact cause of the observed outcomes. Specifically, the study aims to enhance the predictive accuracy and robustness of the Extra Trees Classifier by employing novel data preprocessing techniques. Preprocessing helps in removing irrelevant and noisy data, thereby improving the signal-to-noise ratio and enhancing the classifier's performance. By preprocessing the data, it becomes feasible to extract and select relevant features, facilitating better discrimination between classes and boosting the classifier's predictive power. To improve features clarity the Novel Extra Trees Classifier (NETC) techniques perform advanced feature selection and data transformation approaches exploring the invisibility, aiming to further improve its predictive accuracy of 9 2. 7 % and generalization capabilities. When combined with other techniques, evaluate PSO optimization's effectiveness in enhancing cardiovascular disease prediction models, including accuracy and ROC curve analysis. © 2024 IEEE.

关键词： Feature Selection

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A Mango Disease Prediction for Smart Agiculture using Machine Learning Algorithms 8

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

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

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

ISBN: (纸本)9798350376425

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. © 2024 IEEE.

关键词： Smart agriculture

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Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

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Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte Kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology School of Computer Science and Engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Dopamine MoS_2 Biosensor Multiwalled carbon nanotubes Machine learning Complex mixture

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Chronic Kidney Disease (CKD) Prediction Using Stochastic Deep Radial Basis for Feature Extractiona Residual Neural Network

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SN computer science 2024年第7期5卷 962页

作者： Jayaprabha, M.S. Vishwa Priya, V. Vels Institute of Science Technology & amp Advanced Studies (VISTAS) Tamil Nadu Chennai India Department of Computer Science Vels Institute of Science Technology & amp Advanced Studies (VISTAS) Tamil Nadu Chennai India

Chronic Kidney Disease (CKD) is an increasingly common health issue, affecting a significant portion of the global population. Accurate prediction of CKD progression is crucial for early intervention and personalized healthcare. However, existing models often fail to adequately capture the temporal dependencies in disease progression, resulting in suboptimal performance. Current predictive models for CKD are limited by their inability to manage categorical variables effectively and address overfitting while capturing the necessary temporal subtleties. This study aims to address these limitations by developing a novel approach that enhances the accuracy and reliability of CKD progression predictions. The proposed method integrates a unique Automated Stochastic Deep Radial Basis (SDRB) for feature extraction, a Residual Neural Network (ResNet) for classification, and a Bidirectional Long Short-Term Memory (biLSTM) network for temporal prediction. This combination is designed to handle the complexities of CKD data, manage categorical variables, and mitigate overfitting while capturing temporal *** model significantly outperforms current state-of-the-art models, achieving 92% accuracy, 91% recall, and 93% precision. The SDRB component improves feature extraction, ResNet enhances classification, and biLSTM effectively captures temporal dependencies, resulting in a robust and reliable CKD progression prediction system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bidirectional LSTM Chronic Kidney Disease Feature extraction Residual neural network Temporal prediction

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Cloud Resource Allocation using Deep Learning Techniques-A Study 4

Cloud Resource Allocation using Deep Learning Techniques-A S...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Mahalakshmi, V. Poornima, V. Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331515720

The current era of information processing, communication, and technological advances provides resources to clients on demand. The client and service provider determine how much need there is for the resources. The fitness function and resource management strategy determine the best way to allocate cloud resources. Cloud Computing (CC) is a popular technology these days with the use of centralised computing facilities. Clients can utilize these resources to finish calculations, and the CC center will provide the user with the program's processed results. CC is useful for both consumer and business users. Users can save computing expenditures by avoiding the need to buy numerous PCs by investing in a cloud server. In huge-scale distributed computing, allocation of resources is essential as networks of machines face challenging optimization issues. This study is to analyse and evaluate the cloud resource optimization techniques such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), (Long Short term memory (LSTM) and Reinforcement learning (RL) for the allocation of resources in cloud. © 2024 IEEE.

关键词： Cloud platforms

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