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12th International Conference on computer Communication and informatics, ICCCI 2022

作者： Rooban, S. Manimegalai, R. Eshwar, Idupulapati Venkata Sai Vasista, Ranga P.L.N. Gupta, Puvvada Venkata Madhava Electronics and Communication Engineering Koneru LakshmaiahEducation Foundation Andhra Pradesh Guntur522502 India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering TN Coimbatore District 641062 India

ISBN: (纸本)9781665480352

This paper presents the precautionary robot which can warn and also help the people from affecting with deadly corona virus. We know that the Novel Corona virus had shown a great impact on the world population, many of the people are dying due to this deadly virus because of not following the safety measures and not taking any precautions to overcome the ***-care workersare playing a major role in this pandemic by helping the affected patients to recover from the virus infections. Not only health-care workers but also robots are playing a major part in this pandemic. Robots are being used in laboratories and hospitals for various purposes like disinfection, spraying, manufacturing of medicines/vaccines, surveillance and many more applications. Robots are also equally contributed as healthcare workers around the world to adapt the present needs in order to combat with COVID-19. Mainly in this pandemic robot act as a tool, used in applications which are focused primarily in the area of health and services. The proposed work is to design and implement a robot for the covid applications. This robot is proposed by analyzing the present day's scenario on this pandemic. The proposed robot is user friendly and works particularly for 4 functions like mask detection on people, sanitizer spraying/disinfecting, temperature detection, and finally moving from one place to another place in the hospital or wherever it is required. © 2022 IEEE.

关键词： Robots

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Urban Pluvial Flood Prediction Using Machine Learning Models

Urban Pluvial Flood Prediction Using Machine Learning Models

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Innovations in computer Science & Software engineering (ICONICS), International Conference on

作者： Waseemullah Tahir Hassan Sajid Majeed Muhammad Qasim Memon Department of Computer Science & Information Technology NED University of Engineering and Technology Karachi Pakistan Department of Remote Sensing & Geo-Informatics Institute of Space Technology Karachi Pakistan Department of Information and Computing University of Sufism and Modern Sciences Bhitshah Pakistan

ISBN: (数字)9798331504342

ISBN: (纸本)9798331504359

Urban pluvial flooding causes threats to human lives, economy, infrastructure, and ecosystems around the world, particularly in developing countries. Pakistan is among those developing countries that have become vulnerable to urban flooding due to global warming, climate change risks, rapid urbanization, and the occurrence of extreme and unusual rainfall events in the monsoon season. To effectively mitigate the adverse effects of pluvial flooding, there is a pressing need for an advanced and accurate predictive model that is capable of predicting urban pluvial flooding. The aim of this research is to utilize the potential of machine learning (ML) techniques and algorithms to accurately predict the occurrence of urban pluvial flooding, for this study we have worked on the monsoon rainfall data of multiple cities of Sindh province in Pakistan. We made an attempt to use multiple ML models especially logistic regression (LR), random forest (RF), k nearest neighbor (KNN), and support vector machine (SVM) to propose the best and accurate predictive model. The developed models were evaluated and assessed using various performance evaluation metrics which includes accuracy, recall, precision and F1 score. The best among proposed models was created using LR which achieved an accuracy of 97%, while the accuracy of other models which were created using SVM, KNN and RF were 96%, 93%, and 92% respectively.

关键词： Support vector machines Radio frequency Accuracy Rain Machine learning algorithms Computational modeling Biological system modeling Predictive models Nearest neighbor methods Floods

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Introducing QuantConn: Overcoming Challenging Diffusion Acquisitions with Harmonization 15th

Introducing QuantConn: Overcoming Challenging Diffusion Acqu...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Newlin, Nancy Schilling, Kurt Koudoro, Serge Chandio, Bramsh Qamar Kanakaraj, Praitayini Moyer, Daniel Kelly, Claire E. Genc, Sila Yang, Joseph Yuan-Mou Wu, Ye Adluru, Nagesh Nath, Vishwesh Pathak, Sudhir Schneider, Walter Gade, Anurag Consagra, William Rathi, Yogesh Hendriks, Tom Vilanova, Anna Chamberland, Maxime Pieciak, Tomasz Ciupek, Dominika Vega, Antonio Tristán Aja-Fernández, Santiago Malawski, Maciej Ouedraogo, Gani Machnio, Julia Thompson, Paul M. Jahanshad, Neda Garyfallidis, Eleftherios Landman, Bennett Department of Computer Science Vanderbilt University NashvilleTN United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville United States Indiana University Bloomington BloomingtonIN United States Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC Los AngelesCA United States Developmental Imaging Murdoch Children’ Research Institute Melbourne Australia Murdoch Children’s Research Institute Melbourne Australia Turner Institute for Brain and Mental Health School of Psychological Sciences Monash University Melbourne Australia Department of Neurosurgery Royal Children’s Hospital Melbourne Australia Neuroscience Research Murdoch Children’s Research Institute Melbourne Australia Department of Paediatrics University of Melbourne Melbourne Australia School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Waisman Center Department of Radiology University of Wisconsin Madison United States NVIDIA Santa Clara United States Learning Research and Development Center University of Pittsburgh Pittsburgh United States Brigham and Women’s Hospital Boston United States Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computer Science and Mathmatics Eindhoven University of Technology Eindhoven Netherlands LPI ETSI Telecomunicación Universidad de Valladolid Castilla y León Spain Sano Centre for Computational Medicine Kraków30-054 Poland Aix-Marseille Université Marseille France Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States

ISBN: (纸本)9783031869198

White matter alterations are increasingly implicated in neurological diseases and their progression. Diffusion-weighted magnetic resonance imaging (DW-MRI) has been included in many international-scale studies to identify alterations in white matter microstructure and connectivity. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variations in acquisition protocols, sites, and scanners. Specifically, there is a need to harmonize the preprocessing of DW-MRI datasets to ensure that compatible and reproducible quantitative metrics are derived from each site, including (1) bundle-wise microstructure measures, (2) features of white matter fiber bundles, and (3) connectomics measures. In the MICCAI CDMRI 2023 QuantConn challenge, participants are provided raw data from the same individuals taken with two different acquisition protocols on a single 4 tesla scanner in the same scanning session and asked to preprocess the data in order to minimize acquisition differences while retaining biological variation. Here, we outline the testing framework, provide baseline pre-harmonized results, and discuss the learning implications of this challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photointerpretation

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Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework 3

Enhanced Student Stress Prediction in Higher Education Using...

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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 Amudha, K. 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 Mullana Ambala India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance. © 2025 IEEE.

关键词： Students

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Applications of Blockchain technology in Modern Education System – Systematic Review

Applications of Blockchain Technology in Modern Education Sy...

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International Conference on computer Communication and informatics (ICCCI)

作者： P. Chinnasamy D. Roja Ramani Ramesh Kumar Ayyasamy B. Jency A Jebamani S. Dhanasekaran V. Praveena Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Information Technology Kalasalingam Academy of Research and Education Deemed to be University Srivilliputtur TamilNadu India Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore India

The development of new technologies and also the provision of great connections are becoming increasingly important in all aspects of our everyday situations. This advanced technology also brings a variety of flaws, most importantly security-related problems. Distributed ledger technology, amongst the most prominent technologies in the previous few years, is gaining traction due to its ability to just provide privacy in a variety of domains, including logistics management, transportation, as well as other industries. The educational industry should also make use of the important features blockchain technology offers. This technology is now being considered by schools and universities, particularly universities, as an approach to enhance the teaching-learning process and encourage the participation of all stakeholders including such undergraduates, professors, and even family members. E-transcripts, digitized certificates and credentials, data storage, plus access control will all benefit from it. This detailed research analysis discusses the adaptability of blockchain technology in the education sector.

关键词： Industries Privacy Systematics Transportation Memory Blockchains Stakeholders

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Exploring the RFID mutual authentication domain

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International Journal of computers and Applications 2021年第2期43卷 127-141页

作者： Mohsin, Syed Muhammad Khan, Imran Ali Abrar Akber, Syed Muhammad Shamshirband, Shahaboddin Chronopoulos, Anthony T. Department of Computer Science Virtual University of Pakistan Lahore Pakistan Department of Computer Science COMSATS Institute of Information Technology Islamabad Pakistan Wuhan China Department for Management of Science and Technology Development Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science University of Texas at San Antonio San AntonioTX United States Department of Computer Engineering & Informatics University of Patras Rio Greece

Mutual Authentication is the initial and the most significant line of defense in any open and trusted network system. Weaknesses in mutual authentication protocol may lead to eavesdropping, unauthorized access to the tag or reader data, Denial of Service attack, counterfeiting attack, de-synchronization attack, location tracking, cloning attack, timing attack, man-in-the-middle attack, spoofing attack, and many more. As a result of continuous focus on research of mutual authentication protocols for Radio Frequency Identification (RFID) systems, numerous solutions to address the security issues of RFID system have been proposed. A Comprehensive survey of existing state-of-the-art RFID mutual authentication protocols is presented in this paper. The ultimate objective of this survey paper is to categorize the existing RFID mutual authentication protocols on the basis of their working principle along with their strengths and weaknesses. This categorization of RFID mutual authentication protocols will be beneficial for new researchers to have a quick overview and understanding of the domain and will serve as an input to devise more effective and efficient algorithms for RFID mutual authentication. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Surveys

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AI-Driven Cybersecurity and Functional Framework for Remote Laboratories: A Software engineering Perspective

AI-Driven Cybersecurity and Functional Framework for Remote ...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nesreen Otoum Enrique Arias Jesús Jover Alfaro Department of Software Engineering University of Petra Amman Jordan Department of Computer Systems University of Castilla-La Mancha Albacete Spain Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Remote laboratories have reshaped the way engineering courses provide hands-on experimentation by enabling students to control physical lab equipment via online interfaces. nevertheless, their ease, However, these platforms face challenges such as security vulnerabilities, performance instability, and limited user support. This paper introduces a layered framework that integrates artificial intelligence (AI) into a secure, modular design. Using conventional software engineering techniques, our approach improves cybersecurity through intelligent threat detection, automated resource balancing, and context-aware user interaction. We provide a comprehensive explanation of the framework's architecture, which methodically addresses critical requirements. This includes a proactive cybersecurity apparatus that adapts to emergent threats, as well as real-time device control and data acquisition. Ultimately, this work aims to assist educators and institutions build or modernize remote lab environments that offer robust protection and seamless learning outcomes, even under rapidly shifting demands.

关键词： Remote laboratories computer architecture Threat assessment Real-time systems Resource management Reliability computer security Protection Standards Software engineering

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High-Gain Wideband CP S-shaped Slot Antenna with Metasurface Reflector for UHF-RFID Readers

High-Gain Wideband CP S-shaped Slot Antenna with Metasurface...

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2022 International Electrical engineering Congress, iEECON 2022

作者： Supreeyatitikul, Nathapat Boonpoonga, Akkarat Phongcharoenpanich, Chuwong King Mongkut's Institute of Technology Ladkrabang School of Engineering Bangkok10520 Thailand Faculty of Engineering King Mongkut's University of Technology North Bangkok Research Center of Innovation Digital and Electromagnetic Technology Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9781665402064

This research proposes a high-gain wideband circularly polarized (CP) S-shaped slot antenna with metasurface (MTS) reflector for UHF-RFID handheld readers. The proposed antenna consisted of the S-shaped slot antenna with coplanar waveguide feed on the upper substrate and 4 × 4 periodically-arranged plus-sign-shaped slot MTS unit cells on the lower substrate functioning as a reflector. The MTS reflector was utilized to realize the high-gain of the CP S-shaped slot antenna. To achieve wide impedance bandwidth (IBW) and axial ratio bandwidth (ARBW), the evolution of the antenna was developed in four stages. The simulated IBW and ARBW of the Antenna IV (proposed antenna) at 0.9 GHz (center frequency) were 37.7% (0.66-1 GHz) and 27.7% (0.72-0.97 GHz), respectively. In addition, the maximum gain was 7.56 dBic at 0.78 GHz with left-hand circular polarization (LHCP) radiation characteristics. The dimension of the proposed antenna was 0.35?0 × 0.35?0 × 0.195?0. © 2022 IEEE.

关键词： Coplanar waveguides

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Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Chou, Ka Seng Wong, Kei Long Aguiari, Davide Tse, Rita Tang, Su-Kit Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Department of Computer Science and Engineering Alma Mater Studiorum University of Bologna Bologna Italy Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates UCLA Samueli Computer Science University of California Los Angeles Los Angeles United States

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

Monitoring electric vehicles’ battery situation and indicating the state of health is still challenging. Temperature is one of the critical factors determining battery degradation over time. We have collected more than 2.3 million discharging samples via a custom Internet of Thing device for more than one year to build a machine-learning model that can forecast the battery pack’s average temperature in real-world driving. Our best Bi-LSTM model achieved the mean absolute error of 2.92 ∘ C on test data and 1.7 ∘ C on cross-validation for prediction of 10 min on the battery pack’s temperature. © 2023, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Lithium-ion batteries

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Optimal Sizing of Security Constrained Unit Commitment Problem Integrated with Renewable Energy Sources and PEVs 15

Optimal Sizing of Security Constrained Unit Commitment Probl...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Dhawale, Pravin G. Kumar, Vikram Bath, S.K. Verma, Chaman Simona Raboaca, Maria Filote, Constantin Kumar, Deepak School of Electrical and Electronics Engineering Lovely Professional University Phagwara India University of Calgary Calgary Canada Maharaja Ranjit Singh Punjab Technical University Department of Electrical Engineering Bhatinda India Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania Stefan Cel Mare University of Suceava Universitatii Street Faculty of Electrical Engineering and Computer Science Suceava720229 Romania Apex Institute of Technology Chandigarh Univerity Punjab Mohali India

ISBN: (纸本)9798350321388

Optimal sizing of SCUCP with solar energy and PEVs is crucial for electric power system operation and planning. This paper presents a Chaotic Arithmetic Optimization Algorithm to minimize cost while optimizing the sizing of solar energy and PEV systems. The proposed framework integrates probabilistic forecasting models for solar power generation and realistic EV charging profiles based on travel patterns and infrastructure characteristics. It aims to find optimal sizes for Solar power plants and EV charging infrastructure, considering system demand, cost, emissions, and reliability. The Chaotic Arithmetic Optimization Algorithm efficiently solves the optimization problem by accounting for the complexities of SCUCP. The research evaluates the proposed approach using benchmark test systems of varying dimensions and power systems. The results enable informed decision-making by identifying optimal capacities for solar power plants and charging infrastructure, leading to improved system reliability, reduced emissions, and minimized costs. © 2023 IEEE.

关键词： Solar energy

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