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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Al-Khowarizmi Ramlan, Fitria Wulandari Efendi, Syahril Lubis, Arif Ridho Riza, Ferdy Amrullah Sary, Yoshida Universitas Muhammadiyah Sumatera Utara Department of Information Technology Medan Indonesia National University Ireland College of Engineering and Informatics Galway Ireland Universitas Sumatera Utara Department of Computer Science Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering & Informatics Medan Indonesia Universitas Muhammadiyah Sumatera Utara Department of Information System Medan Indonesia

ISBN: (纸本)9798350305968

The crude palm oil (CPO) industry is highly competitive due to fluctuating and unpredictable prices. To accurately predict future CPO prices, a forecasting technique is necessary. This can be achieved through various regression techniques and data mining approaches. This paper proposes a Multivariate Adaptive Regression Splines (MARS) model, which utilizes the ECoS machine learning algorithm to obtain optimal forecasting values. This research mainly focuses on forecasting CPO prices using the ECoS-MARS approach. The functional basis (BF) validation has yielded an accuracy of 0.0532%. Furthermore, validation has been conducted to achieve accurate forecasting values, resulting in an accuracy of 0.11714%. The best parameter combination for this forecasting model consists of a learning rate of 0.6 for both learning rates 1 and 2, a sensitivity threshold of 0.3, and an error threshold of 0.05. © 2023 IEEE.

关键词： Forecasting

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Ubi-Care: An Elderly Life Support Healthcare Framework Based on Ubiquitous Personal Online Data Stores

Ubi-Care: An Elderly Life Support Healthcare Framework Based...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Chen, Hong Zhou, Tao Wu, Bo Graduate School of Human Sciences Waseda University Saitama359-1192 Japan Daiichi Institute of Technology Department of Information and Electronics System Engineering Taito Tokyo110-0005 Japan School of Computer Science Tokyo University of Technology Hachioji Tokyo192-0982 Japan

ISBN: (纸本)9781665410205

In recent years, the global aging population has intensified, leading to a sharp increase in social security benefits and caregiving costs. The elderly face greater health risks, and their behaviors often indicate signs of crises and illnesses. The rapid development of IoT technology offers new solutions for detecting abnormal signs, thereby promoting healthy aging, independent living, and social participation for the elderly. However, the diversity of IoT devices has led to the phenomenon of personal data silos. When the elderly leave their usual IoT environment, the issue of continuity in healthcare services becomes more pronounced. To address this issue, this study proposes a decentralized Ubi-Care framework. The framework aims to achieve complete separation of data and applications. It is based on the ActivityPub protocol and integrates various data from wearable devices, smart home sensors, and social networks, storing this data in a ubiquitous personal online data store (UPOD) and assigning different roles based on data type. UPOD applications support bidirectional following, allowing users to access data associated with relevant roles, addressing issues related to data categorization, sharing, privacy, and security. Additionally, this study proposes a method for utilizing complete UPOD data to perform anomaly detection based on the hidden Markov model (HMM). By effectively integrating IoT data into UPOD and enhancing data interoperability. The data-sharing model proposed in this study also facilitates elderly individuals and their family members in sharing relevant data as needed, while ensuring privacy protection. The methods proposed in this study will help prevent accidental injuries and enable early diagnosis of diseases, providing strong technical support for elderly healthcare. © 2024 IEEE.

关键词： Electronic health record

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Analysis of EMI Mitigation Techniques in High Speed Printed Circuit Board 2

Analysis of EMI Mitigation Techniques in High Speed Printed ...

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2nd International Conference for Innovation in technology, INOCON 2023

作者： Anandan, M. Sajitha, L.P. Geerthik, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of ECE Chennai India Agni College of Technology Department of Information Technology Chennai India R.M.K Engineering College Department of Computer Science and Business System Chennai India

ISBN: (纸本)9798350320923

To serve the numerous services, today's wireless communication devices require microwave network systems with multiband and multifunctional capabilities. A growing number of microwave circuits and parts that can handle several frequency bands and larger bandwidths are needed to support the unprecedented systems. The issues in EMI/EMC have grown as a result of research and advances in contemporary wireless network communication equipment. Generally speaking, there are several ways to reduce different EMI issues. Therefore, the necessary tunable and downsized components are required to make these systems smaller, lighter, and less expensive. Most studies on emission measurements have been done in the time domain recently and one of the widely used techniques is the creation of EMI band pass filters. This article presents band pass filters with HIDDS (Hybrid Inter Digital Defected Ground Structure). Additionally, a number of uses for the hybrid S-parameters are provided by fusing Agilent ADS circuit tool and full-wave EM solvers. To provide a complete EM response of the filter construction, 3D EM simulations are performed. The analysis of the simulated findings with the results of previous experiments has revealed that HIDDS (Hybrid Inter Digital Defected ground Structure) provides minimal insertion loss and a larger range of stop band frequencies. Therefore, wireless systems might benefit from the band pass filters with HIDDS that have been proposed. © 2023 IEEE.

关键词： Defected ground structures

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ML-Driven HR system: Candidate Assessment for Enhanced Recruitment Outcomes

ML-Driven HR System: Candidate Assessment for Enhanced Recru...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： G C Dixon S Srithanujan H D T N Ranaweera R.M.P M Ariyarathne K L Weerasinghe N Gamage Department of Information Technology SriLanka Institure of Information Technology Sri Lanka Department of Computer System Engineering SriLanka Institure of Information Technology Sri Lanka Department of Computer Science and Software Engineering SriLanka Institure of Information Technology Sri Lanka

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This study developed and implemented a machine learning-driven HR system designed to enhance recruitment outcomes, particularly in online interviews. Challenges such as candidate impersonation, interview question memorization, and deepfake manipulations were addressed. The system incorporated several critical processes utilizing advanced algorithms: pre-interview shortlisting employed Support Vector Machines (SVM) achieving 92% accuracy; cognitive skills assessment utilized adaptive testing methods powered by fine-tuned Large Language Models (LLMs) with 88% accuracy; emotional intelligence evaluation was conducted using deep learning-based emotion detection algorithms attaining 85% accuracy; face verification leveraged Convolutional Neural Networks (CNNs) for identity and deepfake detection with 90% accuracy; online video interviews were analyzed through integrated audio-visual processing techniques; language proficiency testing employed Natural Language Processing (NLP) models reaching 87% accuracy; analysis of thinking patterns was performed using sentiment analysis algorithms with 83% accuracy; and candidate grading was facilitated by ensemble learning methods ensuring consistent and reliable evaluations. The results demonstrated the system’s effectiveness in verifying candidate authenticity and assessing competence, providing a robust solution for modern recruitment challenges.

关键词： Support vector machines Deepfakes Analytical models Sentiment analysis Renewable energy sources Accuracy Reliability Interviews Recruitment Testing

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Enhanced Condition Monitoring of Low Voltage Motors in Harsh Environments with Ladybug Beetle Optimization and Context-Aware Graph Convolutional Networks 4

Enhanced Condition Monitoring of Low Voltage Motors in Harsh...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Rajasekar, P. Tiwari, Praveen Prakash Sharmila, L. Garg, Akriti Srinivas, Rayudu Tallapragada, V. V. Satyanarayana Srm Institute of Science and Technology Department of Data Science and Business System Tamil Nadu Kattankulathur603203 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Mahatma Jyotiba Phule Rohilkhand University Department of Electrical Engineering Uttar Pradesh Bareilly243006 India Aditya College of Engineering & Technology Department of Electrical and Electronics Engineering Andhra Pradesh Surampalem533437 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798331540364

Low voltage motors are essential in many applications in contemporary industrial settings, as they power machines and guarantee efficient operations. However, these motors often operate in harsh environments where they are exposed to factors such as extreme temperatures, moisture, dust, and vibrations, which can lead to premature wear and failure. This paper proposes a hybrid method for low voltage motor condition monitoring in challenging industrial environments. The proposed method is the combined execution of Ladybug Beetle Optimization algorithm (LBOA) and Heterogeneous Context-Aware Graph Convolutional Network (HCAGCN). Hence it is named as LBOA-HCAGCN technique. Initially, the input data is collected from Triboelectric vibration sensors. Then, the collected data is fed into preprocessing utilizing Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMKMCKF). This filter normalizes and cleans the data by effectively removing noise. Recursive Hilbert Transform (RHT) is used to extract time domain features such as mean, Root Mean Square (RMS), Peak-to-Peak Value, Skewness, and Kurtosis. Then the extracted features are given to HCAGCN for classify the motor health conditions such as normal, broken-bar, bowed-bar, bowed-rotor, faulty bearing, and voltage imbalance. In general, HCAGCN does not express adapting optimization strategies to determine optimal parameters to classify motor conditions. Hence, the LBOA is used to optimize the weight parameter of the HCAGCN which accurately classify motor conditions. The proposed LBOA-HCAGCN is implemented in MATLAB. Performance indicators such as Mean Absolute Error (MAE), Precision, and Accuracy were used to analyze the effectiveness of the proposed approach. Comparing the proposed LBOA-HCAGCN methodology to other existing techniques like Deep Residual Neural Network (DRNN), Convolutional Neural Network (CNN), and Recursive Neural Network (RNN), it achieves 25.8%, 26.4%, and 24.7% greater accuracy, 26.7%, 29.4%, and

关键词： Mean square error

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Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

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computer systems Science & engineering 2022年第12期43卷 1219-1230页

作者： Yanman Li Jun Zhang Yang Hu Yingnan Zhao Yi Cao NARI Nanjing Control System Ltd. Nanjing210032China School of Computer Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and computer Engineering University of WindsorWindsorN9B 3P4Canada

Safety helmet-wearing detection is an essential part of the intelligentmonitoring system. To improve the speed and accuracy of detection, especiallysmall targets and occluded objects, it presents a novel and efficient detectormodel. The underlying core algorithm of this model adopts the YOLOv5 (YouOnly Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (CompleteIntersection Over Union) Loss function, and the Mish activation function. First,it applies the attention mechanism in the feature extraction. The network can learnthe weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accuratebounding box regression. Third, it utilizes Mish activation function to improvedetection accuracy and generalization ability. It builds a safety helmet-wearingdetection data set containing more than 10,000 images collected from the Internetfor preprocessing. On the self-made helmet wearing test data set, the averageaccuracy of the helmet detection of the proposed algorithm is 96.7%, which is1.9% higher than that of the YOLOv5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.

关键词： Safety helmet wearing detection object detection deep learning YOLOv5 Attention Mechanism

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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait Ahmed Younes Shdefat AymanNabil Asif Nawaz Muhammad Rizwan Rashid Rana Zohair Ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

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computer systems Science & engineering 2023年第5期45卷 1759-1773页

作者： Manar Ahmed Hamza Haya Mesfer Alshahrani Sami Dhahbi Mohamed K Nour Mesfer Al Duhayyim ElSayed M.Tag El Din Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt

Wireless Sensor Networks(WSN)has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of *** spite of this,it is challenging to design energy-efficient *** routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of *** order to solve the restricted energy problem,it is essential to reduce the energy utilization of data,transmitted from the routing protocol and improve network *** this background,the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol(DEAOA-MHRP)for *** aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in *** accomplish this,DEAOA-MHRP model initially integrates the concepts of Different Evolution(DE)and Arithmetic Optimization Algorithms(AOA)to improve convergence rate and solution ***,the inclusion of DE in traditional AOA helps in overcoming local optima *** addition,the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and *** order to ensure the energy efficient performance of DEAOA-MHRP model,a detailed comparative study was conducted and the results established its superior performance over recent approaches.

关键词： Wireless sensor network routing multihop communication arithmetic optimization algorithm fitness function

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technology Integration in Cultural Heritage Preservation Enhancing Community Engagement and Effectiveness 3

Technology Integration in Cultural Heritage Preservation Enh...

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3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Lukita, Chandra Lutfiani, Ninda Salam, Rahmat Pangilinan, Greian April Setiani Rafika, Ageng Ahsanitaqwim, Ridhuan University of Catur Insan Cendekia Master of Management Cirebon Indonesia University of Raharja Master of Information System Tangerang Indonesia University of Muhamadiyah Jakarta Master of Administrative Sciences Jakarta Indonesia Centro Escolar University Medical Technology Manila Philippines University of Raharja Department of Computer System Tangerang Indonesia Kreatif Desain Department of Engineering Tangerang Indonesia

ISBN: (纸本)9798350367492

In the digital era, the preservation of cultural heritage faces unique challenges and opportunities. This research explores the integration of technology with traditional cultural practices, aiming to ensure sustainable preservation and promotion of cultural heritage. A key component of the study is the application of SmartPLS, a structural equation modeling tool, to analyze relationships between variables, including Technological Adoption (TA), Cultural Engagement (CE), Preservation Effectiveness (PE), Digital Documentation (DD), and Community Involvement (CI). Preliminary data indicate that Technological Adoption accounts for approximately 45.5% of the variance in Cultural Engagement, while Digital Documentation contributes to 38% of the variance in Preservation Effectiveness. Community Involvement influences Preservation Effectiveness by 30%. The research employs qualitative methods, including interviews and case studies, to evaluate various digital preservation initiatives. The study aims to provide a framework for integrating technology into cultural preservation, guidelines for effective documentation, and policy recommendations for sustainable cultural tourism. It holds significant implications for cultural organizations, communities, and policymakers, offering a pathway to ensure that cultural traditions are not only remembered but also experienced and appreciated by future generations in a rapidly evolving digital landscape. © 2024 IEEE.

关键词： Digital Documentation Engagement Heritage Preservation SmartPLS technology Integration

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Cloud-Based IoT Solutions for Smart Grids: Advancing Smart Technologies in Energy Management 3

Cloud-Based IoT Solutions for Smart Grids: Advancing Smart T...

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3rd IEEE International Conference on Electrical Power and Energy systems, ICEPES 2024

作者： Antonius Alijoyo, Franciskus Vidhate, Deepak A. Kaur, Chamandeep Nivedan, V. Rani, V. Kanpur Balakumar, A. School of Business and Information Technology STMIK LIKMI Bandung Indonesia Dr. Vithalrao Vikhe Patil College of Engineering Department of Information Technology Maharashtra Ahmednagar India Jazan University Department of Computer Science Jazan Saudi Arabia Sri Ramakrishna College of Arts & Science Department of Electronics and Communication System Coimbatore India Hindustan Institute of Technology and Science Chennai India K.Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798350390728

The incorporation of Internet of Things (IoT) cloud computing solutions with smart grids signifies a significant advancement in energy management. Traditional methods, though beneficial, often lack scalability and real-time capabilities, limiting their effectiveness in managing the complexity of modern energy systems. In this paper, a novel approach cloud-based IoT technologies to address these shortcomings and enhance smart grid functionalities. Existing energy management methods heavily rely on localized monitoring and control systems, constraining scalability and real-time responsiveness. Additionally, these methods struggle to handle the immense data generated by IoT devices dispersed throughout the grid. The proposed approach tackles these challenges by utilizing cloud computing to store, process, and analyze IoT sensor data in real-time. The novelty of this method lies in seamlessly integrating cloud-based platforms with IoT devices across the smart grid. Through utilising cloud resources, the limitations of traditional approaches are overcome, enabling scalable, real-time oversight and management of grid assets. The methodology involves deploying IoT sensors across the grid to compile information about energy usage, generation, and distribution, which is then transmitted to cloud servers for analysis and decision-making. Through simulations and real-world experiments, the effectiveness of this approach in optimizing grid performance, improving reliability, and facilitating demand-response programs is demonstrated. The results exhibit significant enhancements in grid efficiency and resilience, showcasing the potential of cloud-based IoT solutions to transform energy management in smart grids. Overall, this proposed method presents a promising pathway towards establishing more efficient, sustainable, and resilient energy infrastructure for the future. © 2024 IEEE.

关键词： Cloud platforms

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