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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Winata, Andreas Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

In hybrid cloud-edge systems, data processing is distributed between cloud servers and edge servers, this makes data serialization critical for efficient data transfer between servers. This study evaluates the efficiency of multi-language data serialization of Human Resources Information System (HRIS) in a hybrid cloud-edge environment. Because lack of existing studies that specifically evaluate multi-language data serialization for hybrid cloud-edge HRIS, this study conducted a Systematic Literature Review (SLR) to get the list of current state of the art of data serialization methods and then used this result to evaluate the methods. By focusing on HRIS employee data from the employee management module, this study measures both data serialization and deserialization using performance metrics of time, data size, and CPU usage. The results indicate that no methods excel in all metrics, but FlatBuffers, Protocol Buffer, and Apache Avro can provide balanced effectivity across the metrics. The study gives valuable insight for selecting data serialization methods, architecture, and systems requirements in hybrid cloud-edge HRIS to ensure the performance efficiency of the system. © 2024 The Authors.

关键词： Cloud platforms

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Exploring the Role of Generative AI in Constructing Knowledge Graphs for Drug Indications with Medical Context 15

Exploring the Role of Generative AI in Constructing Knowledg...

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15th International Conference on Semantic Web Applications and Tools for Health Care and Life sciences, SWAT4HCLS 2024

作者： Alharbi, Reham Ahmed, Umair Dobriy, Daniil Lajewska, Weronika Menotti, Laura Saeedizade, Mohammad Javad Dumontier, Michel Department of Computer Science University of Liverpool Liverpool United Kingdom School of Science and Technology University of Camerino Camerino Italy Institute for Data Process and Knowledge Management Vienna University of Economics and Business Vienna Austria Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway Department of Information Engineering University of Padua Padova Italy Department of Computer Science Linköping University Linköping Sweden Institute of Data Science Maastricht University Maastricht Netherlands

The medical context for a drug indication provides crucial information on how the drug can be used in practice. However, the extraction of medical context from drug indications remains poorly explored, as most research concentrates on the recognition of medications and associated diseases. Indeed, most databases cataloging drug indications do not contain their medical context in a machine-readable format. This paper proposes the use of a large language model for constructing DIAMOND-KG, a knowledge graph of drug indications and their medical context. The study 1) examines the change in accuracy and precision in providing additional instruction to the language model, 2) estimates the prevalence of medical context in drug indications, and 3) assesses the quality of DIAMOND-KG against NeuroDKG, a small manually curated knowledge graph. The results reveal that more elaborated prompts improve the quality of extraction of medical context;71% of indications had at least one medical context;63.52% of extracted medical contexts correspond to those identified in NeuroDKG. This paper demonstrates the utility of using large language models for specialized knowledge extraction, with a particular focus on extracting drug indications and their medical context. We provide DIAMOND-KG as a FAIR RDF graph supported with an ontology. Openly accessible, DIAMOND-KG may be useful for downstream tasks such as semantic query answering, recommendation engines, and drug repositioning research. © 2024 Copyright for this paper by its authors.

关键词： Knowledge graph

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Detection of Employee Fatigue Based on Image Processing Using Deep Learning Model 4

Detection of Employee Fatigue Based on Image Processing Usin...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Ethan, Michael Kusuma, I. Gede Putra Bina Nusantara University Computer Science Department Binus Graduate Program Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

Fatigue in workplace is a common thing shared by all employees. Continuous exposure of fatigue could lead to negative productivity for companies. Current research on fatigue detection mostly focused to detect fatigues from a single 2D image data, meanwhile research on fatigue detection using each frames from video data is needed for better detection for fatigue process. The main advantage of using each timesteps of frames from video data which makes the model is able to predict long term dependency of fatigue person. To solve this problem, a deep learning model is proposed that could detect employee fatigue based on image processing using video data. Three models are used to train data using Convolutional Neural Network (CNN), which are Time Distributed CNN LSTM, 3D CNN, and 3D CNN LSTM. Out of those three models, the best model to detect fatigue person from video data is Time Distributed CNN Model with F-1 Score value of 0.77 and Accuracy Score of 0.76 for testing data. The model that gives best inference time is also Time Distributed CNN Model with the average value of 382 miliseconds to detect fatigues from 58 testing data with each of the data has duration of 3 seconds and 12 frames. © 2024 IEEE.

关键词： Video analysis

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Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

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International Journal of Crowd science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： data quality

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Quantum-enhanced Vehicle Detection and Speed Monitoring using OpenCV

Quantum-enhanced Vehicle Detection and Speed Monitoring usin...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Simonthomas, S. Naresh, B. Rohith, K. Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Chennai India Aarupadai Veedu Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331541170

Quantum based vehicle detection is the innovative integration of Quantum Machine Learning (QML) techniques with classical computer vision methods to enhance vehicle detection and speed tracking systems using OpenCV. Traditional approaches in vehicle detection and speed tracking are often hindered by computational inefficiencies and accuracy limitations in complex traffic scenarios. Our proposed hybrid quantum-classical framework aims to overcome these complications by exploiting the computational advantages of quantum computing. In this study, we employ Quantum Principal Component Analysis (QPCA) for feature extraction and data dimensionality reduction, optimizing the input data for improved processing efficiency. The core detection mechanism integrates Quantum Neural Networks (QNNs) with classical Convolutional Neural Networks (CNNs), enhancing the model's accuracy in identifying vehicles within video frames. For speed tracking, we apply quantum optimization techniques to refine the tracking of vehicle positions across consecutive frames, ensuring precise speed calculation. Using OpenCV for real-time video processing and Qiskit for quantum computations, the findings from the experiments indicate significant improvements in both detection a precision, accuracy and computational efficiency. The hybrid quantum-classical model showcases the potential of quantum computing in advancing computer vision applications, particularly in intelligent transportation systems. This research highlights the transformative impact of QML techniques on practical applications and paves the way for further advancements in quantum-enhanced vehicle detection and speed tracking. © 2024 IEEE.

关键词： Convolutional neural networks

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A Mathematical and Dynamical Model for Analyzing H-Shaped PM Vernier Motor for Electric Motorcycle Mid-Drive Applications

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IEEE Transactions on Energy Conversion 2025年第1期40卷 529-543页

作者： Ghods, Mehrage Faiz, Jawad Bazrafshan, Mohammad Amin Gorginpour, Hamed Toulabi, Mohammad Sedigh University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran Persian Gulf University Department of Intelligent Systems Engineering and Data Science Bushehr*** Iran Oakland University Department of Electrical and Computer Engineering RochesterMI48309 United States

The permanent magnet (PM) Vernier machines enhance torque density and decrease cogging torque compared to conventional permanent magnet synchronous motor. This paper presents a novel fractional-slot H-shaped PM Vernier machines, which incorporates unique design for the PM housing and rotor core construction. These designs are specifically optimized for the small-scale electric vehicles application. The choice of the PM arrangement is based on the combination of V-shaped and spoke-array PM topologies. The aim is to achieve a higher torque density than V-shaped PM machines, while reducing cogging torque compared to spoke-array PM machines. By using this methodology, leakage fluxes in the terminal region of the PMs reduce, resulting in an augmentation of both flux-linkage and power factor. A prototype 680 W motor was fabricated with 12 slots and 8 poles to verify the accuracy and effectiveness of the proposed model. Mathematical model and dynamical analysis are proposed for the analytical prediction of machine performance. Due to the unique shape feature of the machine, the conformal mapping approach is applied to design the rotor core. To validate the model, the predicted performance of the motor was compared with those obtained by finite element analysis and experiments. © 1986-2012 IEEE.

关键词： Permanent magnets

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Cross-Layer Optimization Strategies for Energy-Efficient and Reliable Machine-Type Communication in LPWANs 2

Cross-Layer Optimization Strategies for Energy-Efficient and...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Amanullah, M. Manoranjini, J. Sathiya, V. Helen Prabbu Sankar, P. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering Department of Artificial Intelligence and Data Science Chennai India Panimalar Engineering College Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331534950

In the massive Machine-Type Communication (mMTC), the exponential growth of Internet of Things (IoT) devices over Low Power Wide Area Networks (LPWANs) presents substantial issues regarding energy efficiency and stability. The existing frameworks frequently fails in the the intricate relationships among the MAC-layer and physical parameters. To enhanced cross-layer evaluation methodology that integrates adaptive data rate (ADR) optimization, real-time energy consumption monitoring, and dynamic downlink message handling to address the problems. To maintain stability and performance with the network situations, the framework makes use of a Control-Lyapunov function. To enable effective resource allocation uses the Successive Convex Approximation that improve the effects of various transmission systems under quasi-static and dynamic channel conditions. To verify the cross-layer interactions and it impacts the energy efficiency, the proposed method begins with the data collection and it moves to payload size and packet length, are regularly changed to examines the affect throughput and energy consumption. The findings show the high-density node situations, energy consumption that reduced while retaining reliable communication. Furthermore, the results show the interdependencies among network parameters, highlighting the necessity of an integrated strategy. This framework helps to ensure the mMTC scenarios that optimized by improving the long-term viability and dependability of IoT networks. © 2024 IEEE.

关键词： Resource allocation

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Hierarchical Encoder-Decoder with Addressable Memory Network for Diagnosis Prediction 28th

Hierarchical Encoder-Decoder with Addressable Memory Networ...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Mingxia Xiong, Yun Zhang, Yao Yu, Philip S. Zhu, Yangyong Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Department of Computer Science University of Illinois Chicago Chicago United States

ISBN: (纸本)9783031306778

Deep learning methods have demonstrated success in diagnosis prediction on Electronic Health Records (EHRs). Early attempts utilize sequential models to encode patient historical records, but they lack the ability to identify critical diseases for patient health conditions. Besides, some works focus on the hierarchical structure of EHR data during learning patient representations, but neglect it in diagnosis prediction process, which leads to insufficient utilization of hierarchical information. To tackle these challenges, we propose a new Hierarchical Encoder-Decoder with Addressable Memory Network for Diagnosis Prediction named HAMNet. Specifically, we employ a hierarchical encoder-decoder framework to utilize the hierarchical structure of historical visits both during representation learning and diagnosis prediction. Furthermore, we propose the addressable memory network to distinguish core diseases towards patient health status at next visit through the well-designed addressing mechanism. It employs feed-forward layers to build the memory network, which can automatically update the memory module through gradient backpropagation without explicit read/write operations. Finally, we evaluate the performance of HAMNet on a large real-world EHR dataset MIMIC-III, and it achieves the state-of-the-art performance compared with baselines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Watch Product Categorization through Conv2D Sequential Covolutional Neural Network with Truncated Normal Kernel Initializer 2

Watch Product Categorization through Conv2D Sequential Covol...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Shyamala Devi, M. Harivishnu, K.C. Jamal Mohammed, R. Jagan, S. Gurucharan, G. Mohamed Arshad, M. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350371314

For the sake of customer satisfaction and high profitable revenue, the products are designed in the Ecommerce application along with the product category. Based on the product category, prices may differ in a broad range. So, the application must be able to categorize the products before releasing the price voucher to the customers. The absence of effective product categorization, made it challenging to provide deep learning algorithm that results in the usage of modern tools towards product classification. This paper proposes Truncated Normal Kernel Initializer Conv2D (TNKIC) Sequential model for categorizing the watch product. The suggested TNKIC model includes the Watche Image dataset from KAGGLE which consists of 2000 pictures divided into 10 groups. To classify watch goods, pictures are preprocessed and fitted using the TNKIC model. Three convolutional layers, a max pooling layer, and three dense layers that are weight initialized using a Truncated Normal Kernel initializer are preprocessed on the watch pictures in order to classify the watch products. The watch images are splitted into training and testing images. The TNKIC model is examined to determine how well the suggested model works in the watch product categorization than the current conV2D models after being trained with watch images. When compared to current conV2D models, implementation observations using Python on a Gtx Geforce Tesla V100 Nvidia GPU workstation utilizing 64 and 250 training intervals show that the suggested TNKIC model classifies the watch product category with the accuracy of 97.88%. © 2024 IEEE.

关键词： Deep learning

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Early Diagnosis of Schizophrenia in Patients Using Deep Learning Techniques 10

Early Diagnosis of Schizophrenia in Patients Using Deep Lear...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Priyanka, G. Nivaashini, M. Pavithra, S. Srimaha, T.S. Kanishgaa, P.R. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

While Brain, emotion, and behaviours abnormalities are hallmarks of schizophrenia, a complex and long-lasting mental illness. Its symptoms are divided into three categories: positive, negative, and cognitive. Positive symptoms include delusions, disordered thinking, abnormal gait, and hallucinations. Diminished ability to express emotions, alogia, anhedonia, avolition, and social disengagement are detrimental symptoms. Deficiencies in working memory, problems paying attention, and trouble making decisions are examples of cognitive symptoms. Environmental variables, genetic predisposition, and neurochemical imbalances interact intricately in an etiology of schizophrenia. Typically, antipsychotic drugs, psychotherapy, and social support are used in conjunction for treatment. The management of symptoms and enhancement of the general health of persons diagnosed with schizophrenia need early intervention and customized treatment strategies. The CNN, BiLSTM, GRU, and Transformer deep learning models are implemented in this study, and their respective performances are assessed. The transformer and GRU models outperform the other models when hyperparameter adjustment is carried out to increase the models' efficacy. © 2024 IEEE.

关键词： Diseases

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