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Design and Implementation of a Mobile Application for Intelligent Attendance Tracking 1

Design and Implementation of a Mobile Application for Intell...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Nadhiya, S. Jain, Deepak Verma, Anupama Kumar, Saurabh Bhavadharani, S. Chandigarh University Dept. of Computer Science Engineering Chandigarh India Dhanalakshmi Srinivasan College Dept. of AI & Data Science Coimbatore India IIM Indore Dept. of Management Indore India Usha Martin University Dept. of Business Management & Comm Jharkhand India Mangalayatan University Dept. of Business Management & Commerce Aligarh India A.H. Edu. & Research Dept. of Commerce Meenakshi Chennai India

ISBN: (纸本)9798350356816

Taking attendance is an important task in schools, universities, and other institutions. However, traditional attendance taking methods such as paper-based attendance sheets or manual entry into an electronic system are time-consuming and prone to errors. In recent years, the emergence of facial recognition technology has opened up new possibilities for attendance management systems. This research paper discusses the development and implementation of a Smart Attendance System using Facial Recognition technology using OpenCV in educational institutions. The study explores the benefits, limitations, and ethical considerations associated with this technology and its implications for the future of attendance management systems. This model takes use of LBPH and gja, exfilaface recognition to increase accuracy over conventional models. After this, the connection of recognized faces is compared with the kdatabase containing student's faces. This model will be an effective method for controlling student attendance. © 2024 IEEE.

关键词： Students

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Modernized Application Development Using Optimized Low Code Platform 2

Modernized Application Development Using Optimized Low Code ...

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2nd Asian Conference on Innovation in Technology, ASIANCON 2022

作者： Phalake, Vaishali Joshi, Shashank Rade, Kuldip Phalke, Vijay College of Engineering Computer Engineering Dept. Pune India Institue of Management and Entrepreneurship Development Marketing Management Dept. Pune India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

Today, Low Code Development Platforms (LCDP) like Mendix, OutSystems, Zoho creators, Lansa, Quick base, and Salesforce works for modernize regular processes and quickly deliver new digitized applications. These platforms are capable to generate desktop apps, mobile apps, database apps and general-purpose apps. Basically, LCDPs has Integrated Development Environment (IDE) provides drag and drop, visual modelling, rapid application development and automatic code generation capabilities which makes them more popular. In proposed low code system, we have introduced optimization techniques for code to improve time and space complexity as well as to increase the sustainability of software. These tools often have What You See Is What You Get (WYSIWYG) editors and drag-and-drop interfaces to make development even easier. Once User Interface (UI) or visual model is prepared by using required tools/components;the respective source code will be automatically generated and optimized, then the application is ready for user to use. © 2022 IEEE.

关键词： Drops

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Dynamic Optimization of Provider-Based Scheduling for HPC Workloads 31

Dynamic Optimization of Provider-Based Scheduling for HPC Wo...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： Marino, Jacopo Risso, Fulvio Bighi, Mauro Politecnico di Torino Dept. of Control and Computer Engineering Torino Italy PUNCH Torino S.p.A Dept. of Information Technology Torino Italy

ISBN: (纸本)9798350301076

The vast array of cloud providers present in today's market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and cost structures. Consequently, selecting the optimal cloud provider and configuring the features of the chosen computing instance (e.g. virtual machines) proves to be a challenging task for users intending to execute HPC workloads. This paper introduces a novel component designed for effortless integration with existing HPC scheduling systems. This module's primary function is to facilitate the selection of the most appropriate cloud provider for each distinct job, thereby empowering dynamic and adaptive cost-minimization strategies. Through the application of data augmentation techniques and the employment of Continuous Machine Learning, the system is endowed with the capability to operate efficiently with cloud providers that have not been previously utilized. Furthermore, it is capable of tracking the evolution of jobs over time. Our results show that this component can achieve consistent economic savings, based on the quality of the data used in the training phase. © 2023 University of Split, FESB.

关键词： Machine learning

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Data Mining Techniques in Financial Fraud Detection 1

Data Mining Techniques in Financial Fraud Detection

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Rathore, Rachna Sharma, Yash Ambika, P. Upadhyay, Amit Kumar Mahajan, Samriti Kumar, Rupendra Jiwaji University Dept. of Mathematics Gwalior India CSE-APEX Chandigarh India Meenakshi A.H.E.R Dept. of Commerce Chennai India Mangalayatan University Dept. of Computer Engineering & Applications Aligarh India Lingayas Vidyapeeth University Dept. of Commerce & Management Faridabad India JS University Dept. Computer Science Shikohabad India

ISBN: (纸本)9798350356816

In today's world, financial fraud poses a real threat to global economies, necessitating robust detection systems. Utilizing data mining methods in fraud detection has emerged as a new approach, offering powerful tools, especially with large financial transaction datasets. This paper explores various Data Mining techniques applied in Financial Fraud Detection, including decision trees, neural networks, and clustering methods, highlighting their effectiveness in identifying fraud. As decision trees can easily classify & predict transactions based on this historical data, it helps to recognize fraudulent activities. Neural networks can improve the accuracy of predicting fraudulent transactions by detecting intricate patterns, they are capable & adaptable for learning even odd more. Data points that are close together with each other end up in the same cluster - revealing outliers such as fraud;which would appear to be a point very distant from its neighbours. The adoption of these methodologies within financial systems prevents fraud before a crime has been committed, allowing for the resources & time spent on manual investigation to be reallocated. In addition, the development of machine-learning solutions & further enhancements in artificial intelligence allows for specialized fraud prevention methods to evolve. This abstract emphasises the importance of data mining for financial security which also points at some on-going research in this area. © 2024 IEEE.

关键词： Support vector machines

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Continual Cell Instance Segmentation of Microscopy Images 48

Continual Cell Instance Segmentation of Microscopy Images

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chuang, Tzu-Ting Wei, Ting-Yun Hsieh, Yu-Hsing Chen, Chu-Song Yang, Huei-Fang National Sun Yat-sen University Dept. Information Management Taiwan National Taiwan University Dept. Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781728163277

A continual cell instance segmenter aims to continually learn to segment new objects while preserving the ability to localize and distinguish old objects without access to previous data. Besides catastrophic forgetting, background shift, where the background class could contain objects in the old and unseen future classes, could occur. In addition, as acquiring annotations is label-intensive, cell images can be partially labeled. In this paper, we present iMRCNN, which extends Mask R-CNN with knowledge distillation and pseudo labeling, to address these challenges. To preserve the learned skills, the current student distills knowledge from the former teacher at output and feature levels. Furthermore, we employ a pseudo labeling scheme, where the teacher is utilized to identify objects with no labels provided, to deal with background shift and partially labeled data. Experiments on two microscopy image sets demonstrate the effectiveness of iMRCNN over other alternatives in various incremental learning scenarios. © 2023 IEEE.

关键词： Distillation

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AI-enabled Clinical Decision Support System 6

AI-enabled Clinical Decision Support System

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6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Vasudeva Rao, Pv Ashwija Kanmani Kavana Tilak, Sk Kulal, Brinda Jyothika, Rs Sahyadri College of Engineering and Management Dept. of Information Science and Engineering Dept. of Computer Science and Engineering Mangalore India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

Each person's life is extremely important and vital for the country's development. Helping to limit the number of misdiagnoses can save many lives and strengthen families, as some families would lose their primary source of income as a result of misdiagnosis. Misdiagnosis is one of the significant errors in the medical field due to misjudgments by medical professionals eading to increased harm to patients. With 72 percent of errors occurring during the patient-practitioner encounter, it becomes increasingly important to reduce the error in real time. Lowering the mortality rate as a result of misdiagnosis would enhance and build the social well-being of the country. The area of general medicine is very vast and has more than 400 diseases and conditions under it. However, with differing and misleading symptoms for the diseases, it becomes rather confusing for a recent medical graduate to diagnose an individual within the limiting time frame for testing the patient. The developed solution is an artificial intelligence-based system that uses traditional machine learning algorithms and deep learning techniques to help new medical graduates and practitioners with limited experience reliably diagnose a patient's medical condition based on the patient's symptoms recorded during the clinical confrontation. The proposed solution is an AI-enabled Clinical Decision Support System in the form of a web application intended to assist medical graduates and healthcare professionals in accurately diagnosing a patient's health condition based on the symptoms observed during doctor-patient encounters. The developed system has achieved the highest accuracy by using the Ensemble technique which is a combination of Support Vector Classifier, Random Forest, and Naive Bayes technique for textual data analysis and ResNet architecture for analyzing image data. © 2022 IEEE.

关键词： Errors

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Risk Assessment and Safety-as-a-Service Provisioning in IoV Networks: A Roadmap 5

Risk Assessment and Safety-as-a-Service Provisioning in IoV ...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Pradhan, Patrali Chattopadhyay, Samiran Chowdhury, Subrata Rout, Suchismita Mishra, Manoj Ranjan Patra, Sudhansu Shekhar Haldia Institute of Technology Dept. of Computer Science and Engineering Haldia India Techno India University Kolkata India Sreenivasa Institute of Tech. and Management Studies Dept. of Computer Science and Engineering Andhra Pradesh Chittoor India Kiit Deemed to be University School of Computer Engineering Bhubaneswar India Kiit Deemed to be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350376685

This paper presents a framework for a Safety recommender system that offers Safety-as-a-Service (Safe-aaS) to on-road vehicles in IoT-based transportation systems. The proposed safety recommendation system integrates several phases, including preparing sensed data, self-supervised learning from the data, and predicting personalized safety plans. The customized safety plans, generated by analyzing on-road decision parameters such as dynamic road conditions, weather patterns, and driving assistance parameters, are suggested to registered users in real time through the Safe-aaS platform tailored to their specific *** a Safe-aaS enabled road transportation system, sensor nodes produce large volumes of data in a dynamic environment. Efficient analysis and storage of sensed data in edge devices poses a major challenge for service providers. Conversely, generating safety-related decisions with lower processing time ensures driver safety. The Safe-aaS-based recommender system provides safety-related decisions with low latency and processing delay. In the proposed recommendation system, a safety score is computed from the pre-processed sensed data across various parameters, followed by a user-specific similarity score and a corresponding risk score. We apply a fuzzy rule-based learning technique to predict optimized safety plans. To showcase the real-time scenario of our framework, we develop a user-friendly application interface at the primary stage. Extensive analysis shows that our system effectively detects on-road risks and adapts safety recommendations in dynamic weather and road conditions. © 2024 IEEE.

关键词： Sensor nodes

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Addressing Weight Competition in Long Short-Term Memory (LSTM) Networks for Improving Temperature Prediction 12

Addressing Weight Competition in Long Short-Term Memory (LST...

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12th International Conference on Awareness Science and Technology, iCAST 2023

作者： Lin, Kun-Wei Li, Yu-Fan Lin, Che-Yu Jiang, Zheng-Han Chen, Wei-Jian dept. Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan dept. Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9798350324693

This conference paper presents a study on the application of Long Short-Term Memory (LSTM) networks to predict temperature changes in the upcoming week. During our research, we encountered the issue of weight competition within the LSTM architecture, which can lead to suboptimal predictions. To overcome this challenge, three novel methods including Independent Time Series (ITS) Model, Arithmetic Segmented Time Series (ASTS) Model and Arithmetic Segmented Rolling Time Series (ASRTS) Model, respectively, were used to address weight competition and enhance the accuracy of temperature change forecasts. The effectiveness of these methods were evaluated through extensive experiments and compared against standard LSTM models. The results demonstrate the significance of mitigating weight competition to achieve more reliable and accurate temperature predictions. © 2023 IEEE.

关键词： Long short-term memory

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Modified Deep Neural Network Approach to Identify Heart Disease using IoMT: Artificial Neural Networks or Convolutional Neural Networks!

Modified Deep Neural Network Approach to Identify Heart Dise...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Nayak, Debasish Swapnesh Kumar Priyadarshini, Arpita Mahanta, Pabani Panigrahi, Soumyarashmi Meher, Sushanta Swain, Satyananda Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar752050 India Utkal University Department of Statistics Bhubaneswar India University Dept. of Computer Science & Engineering Bhubaneswar752050 India

ISBN: (纸本)9798350369175

Unconventional methods of predicting and treating heart disease (HD) are required because HD is still the top mortality on a global scale. This study delves into the potential of combining deep learning (DL) methods with IoMT devices to improve HD prediction and early diagnosis. Despite their usefulness, traditional risk assessment approaches can misrepresent the intricate relationship between cardiovascular risk factors. On the other hand, DL and ML algorithms can sift through mountains of data related to lifestyle, genetics, demographics, and clinical conditions to find complex relationships. To increase generalization performance across varied patient demographics and datasets, our study is focused on designing a novel DL architecture that is specialized for HD prediction. We optimize its design and hyperparameters. With feature engineering, when selecting features, we use linear discriminant analysis (LDA). In our work, two different DL techniques neural network and convolutional neural network (CNN) for HD identification are studied. We then evaluate the studied model's efficacy with measures including recall, accuracy, precision, F1-score, and Matthews Correlation Coefficient (MCC). By attaining 96% accuracy, 97% precision, 95% specificity, 97% F1-score, and 93% MCC on chosen features, the experimental findings show that a one-dimensional (1-D) CNN performs better than other models. Our suggested method, which combines DL and IoMT, offers a powerful answer to the problems of real-time health monitoring and HD risk assessment, which in turn improves early intervention and tailors treatment plans to each patient. The findings of this study highlight the promise of cutting-edge AI-driven approaches to revolutionizing cardiovascular healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

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Partial Discharge Classification with Transformer Neural Networks 14

Partial Discharge Classification with Transformer Neural Net...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Cheypoca, Thepjit Promphanich, Wiboon Thway, Aung Ye Hankae, Angelina Phimpissada Jeenmuang, Siwakorn Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Instrumentation and Control Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Computer Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Electrical Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Mechatronics Engineering Dept. Bangkok Thailand

ISBN: (纸本)9798350374605

This paper introduces an approach with the Transformer Neural Networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The phase value will be encoded into the charge array and Transformer Neural Network is constructed using Positional Embedding and Transformer Encoder Layer. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed © 2024 IEEE.

关键词： Electric corona

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