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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Swatisipra Das Mohammad Sahil Naveen Kumar Pandit Rojalina Priyadarshini Sarada Prasad Gochhayat Postgraduate Dept. of Computer Science Fakir Mohan University Odisha India Dept. of Computer Science and Engineering C.V. Raman Global University Odisha India Dept. of Computer Science and Engineering Indian Institute of Technology Jammu J&K India

Cloud computing enables on-demand computation on remote servers and computers. Thanks to the adaptability and scalability of the infrastructure for data storage, processing, and management. To solve the security problems arising in cloud identity management techniques such as, dependency on a third-party token provider, leakage of user’s details, single point of failure, etc., decentralized cloud identity management systems came into the picture. Blockchain is a decentralized database providing immutability and transparency to recorded transaction data. The current token-based decentralized cloud identity management systems have limitations, including the absence of data access management procedures and a lack of security features. This paper suggests a Blockchain based Secure Cloud Identity and Access Management (BSCIAM) model for secure identity and access management, utilizing encryption and key sharing techniques. The proposed model has been implemented using Ethereum smart contracts for token-based identity management.

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An Analytical Approach of Higher Order Electric Vehicle System Reduction Using Truncation Based Approximation

An Analytical Approach of Higher Order Electric Vehicle Syst...

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International Conference on Power Electronic Drives

作者： Sameer Singh V. P. Singh U. K. Yadav Akhilesh Mathur Suryansh Kumar Pandey Shailendra Kumar Krishna Murari Electrical Engineering M. National Institute of Technology Jaipur RJ India Electrical Engineering Indian Institute of Technology Bhilai Chhattisgarh India Dept. of Electrical Engineering and Computer Science The University of Toledo Ohio USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

In this research, reduction of fifth-order electric vehicle (EV) transfer function (TF) is accomplished by exploiting direct truncation method. The higher order TF (HOTF) of order five is approximated to obtain the reduced order transfer function (ROTF) of comparatively lower order. The truncation of numerator and denominator coefficients is done to determine the desired EV ROTF. The graphical representation of step, error, Nichols and Bode responses are presented in the support of EV ROTFs obtained for EV HOTF. The error indices data and time domain specifications data are also provided in tabulated form to prove the efficacy of proposed ROTFs.

关键词： Uncertainty Transfer functions Electric vehicles Power electronics Data models Time-domain analysis

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IoT-Based Diagnosis and Recommendation System for Chronic Diseases Using Patient Health Records 3

IoT-Based Diagnosis and Recommendation System for Chronic Di...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Umamaheswaran, S.K. Gangodkar, Durgaprasad Samatha, V. Chakrapani, I.S. Dharamvir Buddhi, Dharam Sri Sai Ram Engineering College Department of Mathematics Tamil Nadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India AP Nellore524003 India PRRandVS Govt College Dept.of Zoology AP India The Oxford College of Engineering Dept. of MCA Bangalore560068 India Uttaranchal University Uttaranchal Institute of Technology India

ISBN: (纸本)9798350399264

The proliferation of IoT applications, notably in the sphere of health care, has led to discussion of patient health records utilising data gathered from IoT-connected devices. Biological data from patients' medical records is mined for health analysis and diagnosis. Certain types of illness, known as chronic illnesses, are completely quiet yet devastating if left untreated. Recent years have seen an uptick in interest from academics in the utilization of patient health information data for the pre-emptive identification of chronic diseases. On the other side, healthcare and medical assistance have benefited enormously from the implementation of recommender systems that use machine learning techniques. Using an Internet of Things device, this research implemented a medical recommendation system to aid in the early diagnosis and management of chronic conditions. The current technique made use of the dataset of digitised patient health records that is housed in the Physio Net data repository. The current dataset contains patient health records that have been documented in accordance with the diseases that have been identified and the doctor's diagnosis. The recommended technique uses K-nearest neighbour classification to identify the kind of ailment before using collaborative filtering to select the optimal course of treatment for patients. The outcomes of using the suggested methodology show that this technique, which is based on using patient symptom similarity, produces better results than previous methods and has a high precision in diagnosing and forecasting chronic illnesses. After determining the kind of disease using the closest neighbour classification method, the collaborative filtering strategy is utilised to select the optimal course of therapy for patients. The outcomes of using the suggested methodology show that this technique, which is based on using patient symptom similarity, produces better results than previous methods and achieves better accuracy

关键词： Recommender systems

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Personalized Federated Learning for Smart Healthcare Using SDN-Driven Edge Computing

Personalized Federated Learning for Smart Healthcare Using S...

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IEEE Southeastcon

作者： Deborsi Basu Ritu Chaudhary Uttam Ghosh Raja Datta G. S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Dept. of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India Dept. of Computer Science and Data Science School of Applied Computational Sciences MMC TN USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The advancement in distributed Federated Learning (FL) techniques is transforming next-generation edge computing, especially in e-Helathcare systems where data integrity and security are critical concerns. Collecting, processing and analyzing such substantial medical data is highly challenging due to their non-identical and versatile nature. Conventional FL approaches often show unsatisfactory and inaccurate results while operating on those data. Traditional FL methods also lack optimal dynamic management mechanisms for edge devices in ehealthcare, which is crucial to ensure that only authorized devices participate in handling any system failures and new registrations. To tackle such important aspects, in this work, we propose a personalized FL technique to handle abrupt label shifts among clients' data. The proposed approach further targets to achieve personalized models for local edge clients. A statistical batch normalization is proposed on customized edge servers, keeping the data specificity intact. To make the proposed system more secure and robust, a Software Defined Network-based (SDN-based) centralized server is used that allows only authorized and efficient edge clients to participate. The proposed comprehensive experiments on two healthcare datasets demonstrate better accuracy than traditional FL methods with enhanced security and privacy.

关键词： Data privacy Federated learning Data integrity Medical services Servers Security Software defined networking Next generation networking Batch normalization Edge computing

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Fine-Tuning Prophet model for application in Agro-Industry to forecast Pesticide consumption

Fine-Tuning Prophet model for application in Agro-Industry t...

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Green Industrial Electronics and Sustainable Technologies (GIEST), IEEE International Conference on

作者： Snehasis Dey Utpal Chandra De Bibhuti Bhusan Dash Sudhansu Shekhar Patra Tapaswini Samant Shobhan Banerjee Electronics and Telecommunication Engineering College of Engineering Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Dept. of Computer Science & Engr. Indian Institute of Information Technology Ranchi India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

Forecasting plays a key role in several industries to predict the probable requirement of a certain product or service over a period of time. This may or may not be dependent on certain external factors depending upon which, a suitable method needs to be chosen to perform the forecasting task. For a given data distribution, the best-fit model needs to be figured out after fine-tuning its hyperparameters to acquire the most optimal forecasts. In this paper, we have used the Prophet model of Facebook, an additive regression model to forecast the requirement of pesticides to be used for agricultural needs, and presented an overview of how fine-tuning the parameters leads to optimal curve fits, hence resulting in better-quality forecasts. We have also studied the impact of the size of the train data on the overall quality of predictions. Hence after combining both the data size and parameter values, the curve fits and predictions have been visually illustrated.

关键词： Industrial electronics Additives Social networking (online) Pesticides Predictive models Data models Agriculture Forecasting Tuning

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A Comprehensive Review of Leap Motion Controller-Based Hand Gesture Datasets

A Comprehensive Review of Leap Motion Controller-Based Hand ...

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Next Generation Electronics (NEleX), International Conference on

作者： Bharatesh Chakravarthi Prabhu Prasad B M Raju Imandi Pavan Kumar B N School of Computing and Augmented Intelligence Arizona State University Tempe Arizona USA Dept. of Computer Science and Engineering Indian Institute of Information Technology Dharwad India Dept. of Computer Science and Engineering Indian Institute of Information Technology Sri City Chittoor India

This paper comprehensively reviews hand gesture datasets based on Ultraleap's leap motion controller, a popular device for capturing and tracking hand gestures in real-time. The aim is to offer researchers and practitioners a valuable resource for developing and evaluating gesture recognition algorithms. The review compares various datasets found in the literature, considering factors such as target domain, dataset size, gesture diversity, subject numbers, and data modality. The strengths and limitations of each dataset are discussed, along with the applications and research areas in which they have been utilized. An experimental evaluation of the leap motion controller 2 device is conducted to assess its capabilities in generating gesture data for various applications, specifically focusing on touchless interactive systems and virtual reality. This review serves as a roadmap for researchers, aiding them in selecting appropriate datasets for their specific gesture recognition tasks and advancing the field of hand gesture recognition using leap motion controller technology.

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Fine-Tuning Prophet model for application in Agro-Industry to forecast Pesticide consumption 1

Fine-Tuning Prophet model for application in Agro-Industry t...

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1st IEEE International Conference on Green Industrial Electronics and Sustainable Technologies, GIEST 2024

作者： Dey, Snehasis De, Utpal Chandra Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Samant, Tapaswini Banerjee, Shobhan College of Engineering Electronics and Telecommunication Engineering Bhubaneswar India Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Indian Institute of Information Technology Dept. of Computer Science & Engr. Ranchi India

ISBN: (纸本)9798331540142

关键词： Pesticides

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An Integrated Approach to Enhance Legal Information Retrieval of Sri Lankan Supreme Court Verdicts

An Integrated Approach to Enhance Legal Information Retrieva...

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Advancements in Computing (ICAC), International Conference on

作者： Asel Jayasooriya Akeel Ahamed Yomal Bandara Chamod Gavindya Dharshana Kasthurirathna Lakmini Abeywardhana Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept.of Computer Science & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Legal professionals and law students often grapple with the arduous task of locating prior Supreme Court rulings for argument preparation and academic study, leading to a laborious and resource-intensive process. This study introduces an innovative approach to streamline the retrieval of legal information from Sri Lankan Supreme Court verdicts. The method centers on developing a Custom Named Entity Recognition (NER) model, boasting a remarkable accuracy exceeding 90%. This model efficiently extracts crucial legal entities from Supreme Court rulings, readily available on the Ministry of Justice's Supreme Court website. The high accuracy ensures precise entity extraction, followed by systematic organization within a dedicated database. Subsequently, a knowledge graph is formed by linking recorded legal entities, reducing information retrieval time to a mere 179 milliseconds, significantly outperforming existing methods. Moreover, a BART summarization model is crafted to generate concise, accurate, and insightful summaries of Supreme Court decisions, boasting an impressive ROGUE1 score of 85%. This approach revolutionizes legal information retrieval, delivering a user-friendly platform that enhances the identification of cases and fosters a deeper understanding, ultimately elevating the quality of legal research and practice.

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On Brooks' Theorem

arXiv

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arXiv 2022年

作者： Gopalan, Sajith Saxena, Sanjeev Dept. of Computer Science and Engineering Indian Institute of Technology Guwahati781039 India Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur208 016 India

In this note we give two proofs of Brooks’ Theorem. The first is obtained by modifying an earlier proof and the second by combining two earlier proofs. We believe these proofs are easier to teach in computer science ...

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Analysis of Defect Associated with Powder Bed Fusion with Deep Learning and Explainable AI 15

Analysis of Defect Associated with Powder Bed Fusion with De...

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15th International Conference on Knowledge and Smart technology, KST 2023

作者： Pratap, Ayush Sardana, Neha Utomo, Sapdo John, A. Karthikeyan, P. Hsiung, Pao-Ann Indian Institute of Technology Dept. of Metallurgical and Materials Engineering Ropar Rupnagar India National Chung Cheng University Grad. Ins. Ambient Intelligence and Smart Systems Chiayi County Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9781665477123

Research into the detection, classification, and prediction of internal defects using surface morphology data of parts created via powder bed fusion-type additive manufacturing has become a hot topic in the previous decade thanks to the development of deep learning. However, there is no other evidence to evaluate the model other than accuracy and metrics. In this paper, a novel data set is compiled from various literature and other sources to evaluate the black box model using explainable artificial intelligence (XAI). The data set contains three major powder bed fusion defects: gas porosity, lack of fusion, and balling. The anomaly was initially found using convolutional neural networks (CNN) and transfer learning. Based on test data, a model comparison was performed to determine the best accuracy and an F1 score. VGG16 has outperformed all other models in terms of accuracy, with an F1 score of 98.6 percent. Further, the model has been compared with the existing state-of-the-art model for classification in the domain of powder bed fusion defects. Finally, VGG16 was employed to interpret and explain the test data set. The LIME explanations revealed that the feature predicted by the model was present in conjunction with the fault. As a result, we are confident that the proposed model with XAI would considerably improve the fairness and trustworthiness of the output result in the powder bed fusion field. This can also aid in the automation of additive manufacturing in the realm of Industry 4.0. © 2023 IEEE.

关键词： computer vision

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