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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Thakur, Gaurav Agarwal, Arun Kumar Singh, Ajay Yamsani, Nagendar Uttaranchal University Department of Civil Engineering Uttaranchal Institute of Technology Dehradun India School of Applied & Life Sciences Uttaranchal University Dehradun India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350381689

This research focuses on accurately predicting the compressive strength of Fiber Reinforced Geopolymer Concrete (FRGC) using machine learning techniques, considering the material's complexity and diverse factors influencing its strength. A machine learning model was developed to forecast the compressive strength of FRGC, leveraging data analysis and comparison of regression techniques. Linear Regression, Lasso Regression, and Ridge Regression were assessed for their predictive accuracy. Feature selection methods were employed to identify influential factors impacting compressive strength. The Random Forest Regressor emerged as the most effective model, exhibiting the lowest Root Mean Square Error (RMSE). The findings offer a valuable tool for engineers to optimize FRGC properties, enhancing its strength and sustainability for various applications. This study underscores the potential of machine learning in streamlining material design processes in civil engineering. © 2024 IEEE.

关键词： Geopolymer concrete

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Human Activity Recognition for People with Knee Abnormality Using Surface Electromyography and Knee Angle Sensors 8

Human Activity Recognition for People with Knee Abnormality ...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350396546

With the onset of technological advancements, biosensors are being effectively used in a variety of contexts, including the diagnosis of diseases, the promotion of their prevention and rehabilitation, the monitoring of patient health, and human activity recognition (HAR). Recently, HAR research results have been obtained and applied in many commercial applications such as fall detection using smart watch sensors, activity classification using smart home sensors. However, previous HAR models utilizing wearable sensors primarily utilized data from healthy individuals, and such models are frequently inaccurate when applied to individuals with medical mobility impairments. In this work, surface electromyography and goniometer sensors were used to categorize various types of rehabilitation activities based on HAR models developed for individuals with knee abnormalities. To achieve the goal of our study, a deep residual network called ResNeXt was proposed to detect human activities with high performance. To investigate the classification performance of deep learning models, a 5-fold cross-validation technique was applied for both training and testing. Based on the results of our study, the combination of features extracted from the goniometer and electromyograph signals resulted in the highest F1-score (93.31%) and the best accuracy (93.52%). © 2023 IEEE.

关键词： Deep learning

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IoT-Enabled Smart Manhole Management System for Real-time Status, Water Level, and Gas Detection

IoT-Enabled Smart Manhole Management System for Real-time St...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Imran, Md Asif Masfequier Rahman Swapno, S.M. Chhabra, Gunjan Kaushik, Keshav Anzum Mahi, Md. Arif Islam, Md Babul Haque, Rezaul Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Graphic Era Hill University Department of Cse Uttarakhand Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Modeling Electronic and System Engineering Unical Dept. of Computer Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Modeling Electronic and System Engineering Unical Department of Computer Rende Italy

ISBN: (纸本)9798350375237

Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water level, and gases. We tested the status, gas, and water levels of our revolutionary, innovative manhole in several places. Our system's Blink app position tracking of our smart manhole is critical. This capability improves monitoring efficiency and speeds up issue resolution. Our work revolutionizes municipal utility maintenance and safety by optimizing manhole performance. Integrating a Node MCU, GPS module, gas sensor, ultrasonic sensor, and Android Blynk software creates a complete real-time monitoring and data-collecting system. Combining these technologies, our Manhole Monitoring System improves safety and streamlines maintenance, making municipal infrastructure more robust and efficient. This paper highlights the promising features of IoT and shows how it may be used to improve the smart manhole. In the future, this creative method may result in a more durable and better intelligent city. © 2024 IEEE.

关键词： Maintenance

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A Study of Different Machine Learning Approaches for Relation Discovery Task

A Study of Different Machine Learning Approaches for Relatio...

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2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： AL-Aswadi, Fatima N. Za’al Alma’aitah, Wafa Chan, Huah Yong Gan, Keng Hoon Iqbal, Zahid Al-Dhamari, Ahlam Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Faculty of Computer Science and Engineering Hodeidah University P.O. Box 3114 Hodeidah Yemen Department of Intelligence Systems Faculty of Artificial Intelligence Al-Balqa Applied University Salt19117 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor11800 Malaysia Department of Computer Science Air University Kharian Pakistan Department of Computer Science University of Gujrat Gujrat Pakistan Department of Electrical and Computer Engineering Curtin University Malaysia Sarawak Miri98009 Malaysia

ISBN: (纸本)9781837243235

Relation extraction and classification, collectively referred to as relation discovery, are fundamental tasks in the ontology construction process. In recent years, numerous studies have dedicated their efforts to devising hybrid approaches, supporting inclusive innovation in computational linguistics and ontology engineering. These approaches merge traditional methods with Machine Learning (ML) or Deep Learning (DL) techniques to tackle relation discovery tasks. This research examines the applicability and efficacy of four distinct models— Support Vector Machine (SVM), Naive Bayes (NB), k-Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—in relation extraction and classification. Two different datasets, ACL RD-TEC 2.0 and ACLRelAcS, are utilized, alongside two distinct text representation methodologies, BOW and TF-IDF-BOW. An in-depth analysis of the results, including the advantages and limitations of each model, is presented in the results and discussion sections. © The Institution of engineering & Technology 2025.

关键词： Deep learning

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Utilizing Artificial Intelligence for Plant Phenotyping in Soilless Farming: An Innovative Deep Learning Approach on a Unique Dataset 1

Utilizing Artificial Intelligence for Plant Phenotyping in S...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Reddy, D. Jaya Narayana Krishna Priya, R. Kumar, J. Arun Prasad, P. Dharani Nawaz, Mustafa Venu, Nookala G Pullaiah College of Engineering and Technology India University of Technology and Applied Sciences Research and Consultancy Department Governorate of Musandam Oman Vel Tech Rangarajan Sagunthala R&d Institute of Science and Technology Department of Ece India Dept of Pharmacology Tirupati India Sri Sairam Institute of Technology Department of Computer Science and Engineering India Centre for IoT Madhya Pradesh Gwalior474005 India

ISBN: (纸本)9798350377002

In the dynamic landscape of agriculture, the convergence of technological advancements and sustainable practices has become imperative. The emergence of soilless farming, driven by hydroponics and aeroponics, presents a promising solution to address food security challenges while mitigating environmental concerns. Central to the success of soilless farming is efficient plant phenotyping, which traditionally relies on labour-intensive methodologies. However, this paradigm is shifting with the integration of Artificial Intelligence (AI) and Deep Learning techniques. This research endeavours to pioneer an innovative approach to plant phenotyping in soilless farming by harnessing the power of AI. Leveraging a unique dataset meticulously curated from controlled hydroponic and aeroponic environments, our study aims to redefine the boundaries of agricultural research and practice. By employing state-of-the-art Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), we dissect complex phenotypic traits encompassing leaf morphology, biomass accumulation, and physiological responses. Through iterative model refinement and validation, we strive to develop a robust framework capable of real-time phenotypic assessment across diverse plant species and growth stages. By synthesizing diverse environmental conditions and perturbations, we augment the original dataset, enhancing model generalization and adaptability. Moreover, through transfer learning techniques, Furthermore, the scalability and accessibility of AI technologies pave the way for democratizing agricultural innovation, fostering inclusive growth and resilience in the face of global challenges. © 2024 IEEE.

关键词： Hydroponics

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A Novel Approach Using OCR Technology to Combat Deceptive Click Attacks 3rd

A Novel Approach Using OCR Technology to Combat Deceptive Cl...

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3rd International Conference on Innovations in Computing Research, ICR 2024

作者： Lian, Yu-Sheng Hsu, Fu-Hau Hwang, Yan-Ling Chen, Jian-Xin Huang, Jian-Hong Wang, Hao-Jyun Wu, Min-Hao Department of Computer Science and Information Engineering National Central University Taoyuan32001 Taiwan School of Applied Foreign Languages Chung Shan Medical University Taichung40201 Taiwan Department of Artificial Intelligence College Xiamen City University Fujian Xiamen361000 China

ISBN: (纸本)9783031655210

On May 25, 2018, the European Union implemented the General Data Protection Regulation (GDPR), which requires websites related to Europe to inform users and obtain their consent to use their data, including cookies, on web pages. While this regulation may seem reasonable, the method of obtaining consent has resulted in users having to click on the "Accept" buttons on every new website to achieve the best website experience. However, inadvertently developing a habit of automatically agreeing without fully understanding the implications may lead to unknowingly consenting to malicious activities perpetrated by individuals with deceptive intentions on the internet. To counter this, we propose developing a browser extension utilizing OCR technology to provide the Chrome browser with visual perception capabilities. If successfully implemented, this extension will revolutionize how we interact with websites, ensuring that our actions align with our intentions and protect us from potential harm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Websites

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Problems in Video Conferencing-Mediated Teaching: Experiences of Three User Roles of a University in Bangladesh

Problems in Video Conferencing-Mediated Teaching: Experience...

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Advances in Computing,Communication, Electrical, and Smart Systems (iCACCESS), International Conference on

作者： Nafees Mansoor Anatte Rozario Muhammad Golam Kibria Md Saifuddin Khalid Department of Computer Science and Engineering University of Liberal Arts Bangladesh Department of Applied Mathematics and Computer Science Technical University of Denmark (DTU) Kongens Lyngby Denmark

Video-conferencing systems (VCS) became a defacto tool for online and hybrid teaching during COVID-19 and the gradual return to physical classrooms. As part of the EdViCon Erasmus+ project, towards defining the scenarios for improving teaching with additional portable tools by university teachers, this study reports users' problems in the context of the University of Liberal Arts Bangladesh (ULAB). Classroom observations, problem-tree analysis, interviews, and focus-group discussions with academics, e-learning and IT consultants, and students provide different perspectives on the central problems, underlying causes, and the effects of VCS use during live teaching sessions. While teachers emphasized the lack of resources and technical barriers, students' problem statement focused on the reduced quality of interactions due to the differences between VCS-mediated and physical classrooms, and the IT & e-learning professionals emphasized technological issues (audio, tone, lack of facial expression etc.), lack of collaboration, and differences in expectations among participants. The central contribution of this paper is three diagrams showing problem tree analysis from the perspectives of the three roles. Each of the causes, effects, and problem statements will be considered as point-of-view statements for generating ideas for supporting hardware and software tools and training on technology and pedagogy,

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Robust Detection of Tables in Documents Using Scores from Table Cell Cores

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SN computer science 2022年第2期3卷 1-19页

作者： Ajij, Md. Pratihar, Sanjoy Roy, Diptendu Sinha Hanne, Thomas Department of Computer Science and Engineering National Institute of Technology Meghalaya Shillong India Department of Computer Science and Engineering Indian Institute of Information Technology Kalyani India FHNW University of Applied Sciences and Arts Northwestern Switzerland School of Business Riggenbachstrasse 16 Olten 4600 Switzerland

Table detection is an essential step in many document analysis systems. Tabular data are a pivotal form of information representation that can organize data in a conventional structure for comfortable and quick information retrieval and comparison. Detection of table structures in PDF files or images is a challenging task because of the variability of table layouts, and sometimes the tabular structures’ similarities with non-tabular elements like charts, plots, etc. In this work, we have presented a table detection method using a geometric analysis of the table cell cores that represents the table cell texts. The proposed method works by analyzing the text gap information, and hence it can detect the table cell cores, irrespective of the presence of the table boundary lines and cell-separating rule-lines. Experimentations have been done on various document images of complex structures from well-known datasets. The detection accuracies obtained by us corroborate the usefulness of the proposed method. © The Author(s) 2022.

关键词： Document analysis Information retrieval Table recognition

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Utilizing Blockchain Technology for Financial Services in Parallel, Distributed, and Grid Computing Frameworks

Utilizing Blockchain Technology for Financial Services in Pa...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Chokkamreddy, Prakash Palagati, Anusha Rao, Akurathi Lakshmi Pathi Maheswari, P. Mahadevan, Vanitha Balan, Santhosh Kumar School of Management Studies Guru Nanak Institutions Technical Campus Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications KSR College of Arts and Science for Women Tiruchengode India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Oman

ISBN: (纸本)9798331510299

Application of blockchain in financial services has opened new ways of efficiency in transaction processing, assets management and security. The application of parallel, distributed, and grid computing with blockchain in improving the functionality of financial services like real time financial transactions, high frequency trading, portfolio and risk analysis is examined in this paper. It has been ascertained that namely parallel and distributed computing together with blockchain are the most suitable for facing increasing needs in data in the sphere of financial services. It also investigates the use of grid computing frameworks and their effects on the correspondingly balanced flows of medium amongst multiple systems. Various issues such as security, privacy violations, and adherence to the law are also reviewed, and existing prospects for developing through smart contracts and consensus mechanisms using blockchain. Exploring how some of the most successful pilot use-cases of blockchain are applicable for future growth in the financial services industry offers insights into how blockchain will soon enter mainstream as a disruptive technology in the broader global financial sector. © 2024 IEEE.

关键词： Smart contract

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Enhancing Healthcare Recommendations for Special-Need Patients Using Deep Learning and Blockchain Security 15th

Enhancing Healthcare Recommendations for Special-Need Patien...

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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Mudhafar, Mustafa Raparthi, Mohan Thota, Vijayakumar Sivakumar, B.N. Degadwala, Sheshang Department of Medical Physics College of Applied Medical Sciences University of Kerbala Karbala56001 Iraq Alphabet Life Science DallasTX75063 United States NSB Business School Bangalore University Bangalore India Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9789819786305

Special needs patients deserve increasingly specialised and secure healthcare supervision as the healthcare environment evolves. Our ground-breaking approach combines deep learning and blockchain security to enhance healthcare recommendations for people with special needs. This novel approach might assist with the difficulties of providing tailored healthcare advice. To get the best results, we mix two efficient technologies. Deep learning allows you to detect specific patterns in large patient datasets. Modern neural networks and algorithms are what power it. In the recommended approach, deep learning insights resulted in 92% accuracy, 89% precision, 94% recall, and a 91% F1 score. Deep learning allows for extremely accurate and customised healthcare suggestions. Special needs individuals need customised treatment regimens due to their specific medical challenges. Blockchain security is critical for ensuring patient data validity and confidentiality. To secure patient data, blockchain technology generates a distributed, immutable ledger and an open, auditable database. Encryption protects data's privacy, prohibiting unauthorised access to patient data. These developments in healthcare advice may increase treatment quality and patient trust in the system. Our analysis showed positive and game-changing findings, with the suggested technique outperforming traditional methods. These results illustrate the system's effectiveness and potential to revolutionise special-needs healthcare. This study reveals that the proposed approach is durable and has the potential to significantly alter healthcare recommendations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Deep learning Healthcare Integration Patients Recommendations Security Special needs Technology Treatment

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