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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Srinidhi, Vaishnav Victor, Akila School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350305258

Deep learning has become increasingly important in the diagnosis of Alzheimer's disease due to its ability to analyze vast amounts of medical data with exceptional accuracy. The goal of this study is to conduct research on computer aided diagnosis to improve the accuracy and efficiency of diagnosing Alzheimer's disease, and to make the diagnosis process more accessible to patients in remote or underserved areas. By training deep neural networks on large datasets, we can identify subtle patterns in patient data that may indicate the presence of Alzheimer's disease. Furthermore, deep learning can help personalize treatment plans and monitor disease progression over time, leading to better patient outcomes. In this study, we shall introduce a novel computer Aided Diagnosis (CAD) algorithm using transfer learning. Keeping security in mind, the models have been trained in a federated environment to ensure that satisfactory results are obtained while maintaining differential privacy. © 2023 IEEE.

关键词： Alzheimer's Disease computer Vision Federated Algorithm Transfer Learning

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DIEONet:Domain-Invariant information Extraction and Optimization Network for Visual Place Recognition

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computers, Materials & Continua 2025年第3期82卷 5019-5033页

作者： Shaoqi Hou Zebang Qin Chenyu Wu Guangqiang Yin Xinzhong Wang Zhiguo Wang School of Computer Science and Technology Xinjiang UniversityUrumqi830046China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Institute of Public Security Kash Institute of Electronics and Information IndustryKashi844000China

Visual Place Recognition(VPR)technology aims to use visual information to judge the location of agents,which plays an irreplaceable role in tasks such as loop closure detection and *** is well known that previous VPR algorithms emphasize the extraction and integration of general image features,while ignoring the mining of salient features that play a key role in the discrimination of VPR *** this end,this paper proposes a Domain-invariant information Extraction and Optimization Network(DIEONet)for *** core of the algorithm is a newly designed Domain-invariant information Mining Module(DIMM)and a Multi-sample Joint Triplet Loss(MJT Loss).Specifically,DIMM incorporates the interdependence between different spatial regions of the feature map in the cascaded convolutional unit group,which enhances the model’s attention to the domain-invariant static object *** Loss introduces the“joint processing of multiple samples”mechanism into the original triplet loss,and adds a new distance constraint term for“positive and negative”samples,so that the model can avoid falling into local optimum during *** demonstrate the effectiveness of our algorithm by conducting extensive experiments on several authoritative *** particular,the proposed method achieves the best performance on the TokyoTM dataset with a Recall@1 metric of 92.89%.

关键词： Visual place recognition domain-invariant information mining module multi-sample joint triplet loss

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An AgroBot: Natural Language Processing Based Chatbot for Farmers 4

An AgroBot: Natural Language Processing Based Chatbot for Fa...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Marla, Anushka Paul, Rejath Saha, Arupam Kumar Basha, Niha Kamal Balasundaram, Anandhakrishnan School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350300888

In an industry that is enduring a rapid transformation, rowers are gaining access to new technologies that help them improve agricultural yields and resource management. TensorFlow-built chatbots can provide instantaneous assistance to producers. TensorFlow is a machine learning (ML) framework for data automation, model monitoring, and model retraining. It was used to analyse more than 80 million call records from the government portal Kisan Call Canter (KCC) using multiple data preprocessing techniques in conjunction with TensorFlow. Training enabled the chatbot to distinguish between agricultural questions, crop management, soil health, and pest control intentions and entities. This chatbot intends to implement a voice recognition module that will enable users to submit speech-based queries rather than text-based ones. In the future, the chatbot can be trained to comprehend images with high precision and be able to identify crops, provide recommendations on how to increase crop productivity, and promote the need for sustainable farming. The automaton has been evaluated using metrics for precision and error reduction. It's conceivable that TensorFlow-trained chatbots could revolutionize agriculture by providing producers with real-time data and advice on how to increase their yields and revenue. © 2023 IEEE.

关键词： Crops

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Incorporation of Two-Fold Trust in Group Recommender System to Handle Popularity Bias

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SN computer science 2024年第2期5卷 259页

作者： Richa Bedi, Punam Gupta, Shivani Kumar, Ranjeet Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India Department of Computer Science University of Delhi Delhi New Delhi India School of Computer Science Engineering and Technology Vellore Institute of Technology Tamilnadu Chennai India School of Electronics Engineering Vellore Institute of Technology Tamilnadu Chennai India

In recent years, there has been a rise in work which focuses on various issues of recommender systems other than accuracy. Popularity bias is one factor that causes the list of recommendations to deviate from the user’s expectation and causes biased list to generate. This paper proposed an approach that handles the popularity bias in trust aware group recommender system using trust propagation. The proposed work considers two factors, novelty and serendipity, to evaluate the presence of bias. By incorporating trust, along with novelty, diversity, and unexpectedness, the diverse list of recommendation that maintains accuracy should be generated. Trust propagation combines trustworthy user’s opinion indirectly involves enhancing the accuracy. The trust propagation and randomness in trust propagation ensures the trade-off between the accuracy and bias of the recommendations. A prototype is developed using the tourism dataset and compared using traditional approach of recommendation using various accuracy measures and coverage. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Diversity Novelty Popularity bias Recommender system Relevancy

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An Automated Recommendation Model for E-Commerce Web Application Using Machine Learning 2

An Automated Recommendation Model for E-Commerce Web Applica...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Sheik Abdullah, A. Suseendhar, B. John, Sharon Jefrin, Tanisha School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798350366570

The rapid evolution of the Internet and technological advancements have revolutionized the retail landscape, giving rise to e-commerce as a dominant force in the industry. In response to the increasing demand for online shopping, businesses are compelled to develop innovative, user-friendly web applications to cater to evolving consumer needs. This abstract introduces an innovative e-commerce web application designed to transform the online shopping experience. Leveraging cutting-edge technologies, intuitive user interfaces, and personalized recommendations, our web application aims to enhance convenience, engagement, and customer satisfaction. Through a comparative analysis, we demonstrate that our solution surpasses existing methods. Our e-commerce web application boasts intuitive search and filtering capabilities, empowering users to easily navigate through a vast product catalogue. Moreover, sophisticated recommendation algorithms, driven by machine learning techniques, provide personalized product suggestions based on users' browsing and purchasing history, significantly enhancing the overall shopping experience. The web application ensures seamless performance, even during peak traffic periods and also offers integrated secure payment gateways for customers confidence and peace of mind while completing transactions. By combining advanced technology with user-centric design, this e-commerce web application sets a new standard for online shopping, promising increased convenience, engagement and satisfaction for users. © 2024 IEEE.

关键词： Marketplaces

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Data Secure Storage Mechanism for IIoT Based on Blockchain

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computers, Materials & Continua 2024年第3期78卷 4029-4048页

作者： Jin Wang Guoshu Huang R.Simon Sherratt Ding Huang Jia Ni School of Computer Science and Mathematics Fujian University of TechnologyFuzhou350118China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Nanjing University of Information Science and TechnologyNanjing210044China

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of *** technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the *** the traditional blockchain,data is stored in a Merkle *** data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based ***,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of *** solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC ***,this paper uses PVC instead of the Merkle tree to store big data generated by *** can improve the efficiency of traditional VC in the process of commitment and ***,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of *** mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

关键词： Blockchain IIoT data storage cryptographic commitment

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Music Genre Classification Using Federated Learning

Music Genre Classification Using Federated Learning

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World Conference on information systems for Business Management, ISBM 2022

作者： Gupta, Lakshya Meedinti, Gowri Namratha Popat, Anannya Perumal, Boominathan School of Computer Science and Engineering Vellore Institute Of Technology Vellore India

ISBN: (纸本)9789811974465

Federated learning (FL) is a decentralized privacy-preserving machine learning technique that allows models to be trained using input from multiple clients without requiring each client to send all of their data to a central server. In audio, FL and other privacy-preserving approaches have received comparatively little attention. A federated approach is implemented to preserve the copyright claims in the music industry and for music corporations to ensure discretion while using their sensitive data for training purposes in large-scale collaborative machine learning projects. We use audio from the GTZAN dataset to study the use of FL for the music genre classification task in this paper using convolutional neural networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Execution Time Analysis of Multithreading and Multiprocessing on Seam Carving Algorithm

Execution Time Analysis of Multithreading and Multiprocessin...

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2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Joshi, Aditya Deepak Bhattacharyay, Rajarshi Luhadia, Garvit Vijayarajan, V. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350333817

The objective of this research is to demonstrate the performance difference exhibited by the seam carving algorithm when executed sequentially on a conventional CPU as opposed to when it is run parallelly. Multithreading and multiprocessing were two of the parallelization methods used in this project. These execution times were recorded, and an execution time analysis was performed. In the end, the researchers compared the two to determine which parallelization technique performed better. The researchers believe that the applications of this algorithm when parallelized can be significantly expanded. This expansion could potentially allow it to be used in applications such as thumbnail generation, responsive web development, and a complete contentaware alternative to image resizing (skewing and stretching). © 2023 IEEE.

关键词： Image processing

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Hyperparameter Optimization of Machine Learning Models for Monitoring Menstrual Mental Health 1

Hyperparameter Optimization of Machine Learning Models for M...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Chawla, Tisha Mittal, Shubh Srinivasan, Kathiravan Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350335774

Monitoring and tracking mood disorders associated with Menstruation are becoming crucial to avoid impairment in the mental health of a woman, prevent disruption in family life and relationships, and allow women to maintain a proper balance in their work life. Every sentiment linked during the menstruation cycle can be identified for improving fecundability and health management. This study aims to analyze the feelings of women during Premenstrual dysphoric disorder (PMDD) and the actual menstrual cycle using text-mining methods. We used sentimental analysis in a Reddit dataset to identify positive and negative emotions in women. In the proposed framework, public sentiments were first processed and data was balanced using Synthetic Minority Over-sampling Technique (SMOTE) followed by classification using different machine learning frameworks like Support Vector Machine (SVM), Decision Tree Classifier, Logistic Regression, Random Forest, AdaBoost Classifier and Boosting Classifier. The findings of this study exhibit that women evoke both positive and negative sentiments. However, compared to other feelings the negative emotion is at its inception. The result shows that Random Forest with tuned hyperparameters gives us the best accuracy of 94% when compared to other baseline and ensemble machine learning models with different feature extraction techniques. © 2023 IEEE.

关键词： Support vector machines

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A Prognostic Framework for Post-Operative Patient Survival Prediction in IoMT 16

A Prognostic Framework for Post-Operative Patient Survival P...

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16th International Conference on COMmunication systems and NETworkS, COMSNETS 2024

作者： Mittal, Shubh Rahman, Saifur Pal, Shantanu Karmakar, Chandan School of Computer Science Vellore Institute of Technology India School of Information Technology Deakin University Melbourne Australia

ISBN: (纸本)9798350383119

The study presents an Internet of Medical Things (IoMT) framework designed to predict patient survival outcomes through the evaluation of a post-thoracic surgery scenario. We employ a multi-layered IoMT framework that integrates various sensors and medical devices for real-time data collection, efficient data transmission, and data analysis. Utilizing a set of eight traditional and ensemble machine learning classifiers, along with neural networks optimized using grid search, we establish a baseline performance for the framework's capability in predicting post-surgical survival rates. However, as individual machine learning classifiers exhibit suboptimal performance across the performance metrics used, we combine the individual strengths of these classifiers to construct a stacking approach. The stacked classifier which incorporates a multi-layer perceptron as the final estimator achieved significant results, including a high accuracy of 0.90, precision of 0.87, and recall of 0.93. These metrics not only indicate a high post-operative survival detection rate but also demonstrate a balance of low bias and high variance performance, ensuring that the model is both accurate and reliable in varying IoMT scenarios. © 2024 IEEE.

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