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6th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Do, Tri Nhut Tran, Quang Vinh Nguyen, Hoai Nhan Nguyen, Minh Son University of Information Technology Vietnam National University Faculty of Computer Engineering Ho Chi Minh City71308 Viet Nam

ISBN: (纸本)9798350327410

In today's era of booming artificial intelligent (AI) applications, smart camera products are widely used in many different practical application environments such as observation, monitoring, recognition and identification. Due to the working characteristics, the smart camera is always used continuously for a long time, increasing the energy consumption. Therefore, smart cameras need a solution to optimize power consumption to ensure system performance while employing artificial intelligence (AI) algorithms in image processing. Designing a smart camera system requires careful attention to this aspect. In this paper, a 2-frequency conversion mode method is proposed to improve the energy efficiency of smart camera systems using AI accelerators. Two test scenarios were conducted covering a series of test cases to demonstrate that the energy consumption of the system is significantly improved compared to previous methods. © 2023 IEEE.

关键词： Electric power utilization

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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A New Co-Simulation Approach of Active Power Curtailment for Voltage Optimization in PV-Rich Distribution Networks 6

A New Co-Simulation Approach of Active Power Curtailment for...

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6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

作者： Dubravac, Marina Znidarec, Matej Fekete, Kresimir Topic, Danijel Josip Juraj Strossmayer University of Osijek Osijek Croatia Computer Science and Information Technology Faculty of Electrical Engineering Osijek Croatia

ISBN: (纸本)9798350328752

In recent years, distribution networks have faced technical issues caused by the increasing integration of pho-tovoltaics. Voltage level violations are addressed as the main technical issue for distribution system operators. However, photo-voltaics are inverter-based sources and can participate in voltage control. The photovoltaic inverter has several modes of operation, but due to the high R/X ratio of the distribution network, the volt-watt control mode represents the most attractive for researchers. This paper presents an optimal power flow-based model for voltage optimization using active power curtailment from photovoltaics as a control variable. The optimization problem minimizes the voltage deviation. The optimal power flow-based active power curtailment model is realized using a new co-simulation approach that interconnects the computational intelligence optimization method and power system simulation software. The proposed model is applied to an unbalanced IEEE European low voltage distribution feeder test system. The optimization model has been evaluated over three case studies which differ according to the level of photovoltaic integration. Results show that the proposed optimal power flow-based active power curtailment approach improves voltage profiles in distribution feeders and mitigates voltage issues caused by photovoltaics. However, due to unbalanced power flow and the assumption of equal active power curtailment per each phase of photovoltaic, voltages are reduced even in phases where the allowable limit is not exceeded. Secondly, this objective function formulation has a shortcoming since it tries to reduce differences of voltages below the reference value and observes this as a deviation, thus increasing its value. Finally, with that approach losses in active distribution feeder are indirectly affected and reduced. © 2023 IEEE.

关键词： Electric power distribution

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P-ACOHONEYBEE: A Novel Load Balancer for Cloud Computing Using Mathematical Approach

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computers, Materials & Continua 2022年第10期73卷 1943-1959页

作者： Sunday Adeola Ajagbe Mayowa O.Oyediran Anand Nayyar Jinmisayo A.Awokola Jehad F.Al-Amri Computer Engineering Department Ladoke Akintola University of TechnologyLAUTECHOgbomosoNigeria Computer Science Department Ajayi Crowther UniversityOyoNigeria Graduate School Faculty of Information TechnologyDuy Tan UniversityDa NangVietnam Department of Computer Sciences Precious Cornerstone UniversityIbadanNigeria Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Cloud computing is a collection of disparate resources or services,a web of massive infrastructures,which is aimed at achieving maximum utilization with higher availability at a minimized *** of the most attractive applications for cloud computing is the concept of distributed information ***,privacy,energy saving,reliability and load balancing are the major challenges facing cloud computing and most information technology *** balancing is the process of redistributing workload among all nodes in a network;to improve resource utilization and job response time,while avoiding overloading some nodes when other nodes are underloaded or idle is a major ***,this research aims to design a novel load balancing systems in a cloud computing *** research is based on the modification of the existing approaches,namely;particle swarm optimization(PSO),honeybee,and ant colony optimization(ACO)with mathematical expression to form a novel approach called *** experiments were conducted on response time and *** results of the response time of honeybee,PSO,SASOS,round-robin,PSO-ACO,and P-ACOHONEYBEE are:2791,2780,2784,2767,2727,and 2599(ms)*** outcome of throughput of honeybee,PSO,SASOS,round-robin,PSO-ACO,and P-ACOHONEYBEE are:7451,7425,7398,7357,7387 and 7482(bps)*** is observed that P-ACOHONEYBEE approach produces the lowest response time,high throughput and overall improved performance for the 10 *** research has helped in managing the imbalance drawback by maximizing throughput,and reducing response time with scalability and reliability.

关键词： ACO cloud computing load balancing swarm intelligence PSO P-ACOHONEYBE honeybee swarm

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A Classification Technique for Online Shoppers' Purchasing Intention 1

A Classification Technique for Online Shoppers' Purchasing I...

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1st International Conference on Logistics, ICL 2024

作者： Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Fadel, Ali Hussein Abbas, Ali Hashim Jaber, Mustafa Musa Ali, Rabei Raad Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor86400 Malaysia School of Information Technologies and Engineering ADA University BakuAZ1008 Azerbaijan University of Diyala Department of Computer Science Diyala32001 Iraq Imam Ja'afar Al-Sadiq University Department of Computer Engineering and Information Technology AI-Muthanna Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10021 Iraq Northern Technical University Department of Computer Engineering Technology Mosul41000 Iraq

ISBN: (纸本)9798331518844

Online shopping has become a recent outburst using electronic devices that allow consumers to shop online, as there are factors that influence shopping intention. The factors include consumers' attitudes, subject norms, behavior control, perception of usefulness, considered risks, and trust. This study aims to classify the intent of online consumers' shopping. Subsequently, based on these measures, this classification can provide fundamental and prioritized suggestions for performance improvements in online shopping. The classification is performed by using four data mining algorithms, which are random forest (RF), decision tree (DT), support vector machines (SVMs), and k-nearest neighbors (k-NN). Classification of online shoppers has been implemented based on the online shopping behavior dataset and critical performance metrics, including accuracy, precision, recall, and F1-measure. Essential factors have been considered in the online shopping behavior dataset that was used. The dataset has been preprocessed to check missing values and change the data to an appropriate numerical type. Then, the classification algorithms are applied to extract the required results. The results of detecting consumer shopping behavior showed that RF has outperformed other algorithms and achieved an average accuracy of 90.24%. The average classification accuracies of the DT, SVM, and k-NN are 89.59%, 89.38%, and 87.06%, respectively. The result of this work is hoped to assist in identifying online shoppers' behavior categories in practical aspects. © 2024 IEEE.

关键词： Support vector machines

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Knowledge Management Trends for Measuring Academic Performance: A New Conceptual Framework 4

Knowledge Management Trends for Measuring Academic Performan...

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4th International Conference on Electrical engineering and Informatics, ICon EEI 2024

作者： Yanti, Novi Defit, Sarjon Okfalisa Universitas Islam Negeri Sultan Syarif Kasim Riau Faculty of Science and Technology Department of Informatics Engineering Pekanbaru Indonesia Universitas Putra Indonesia Yptk Padang Faculty of Computer Science Department of Information Technology Padang Indonesia

ISBN: (纸本)9798331540791

Research in the area of knowledge management for improving academic performance has been on the rise in recent years. The effectiveness of knowledge management in improving the quality of decision-making in higher education is a new challenge in the drive for university performance improvement. Therefore, this paper tries to propose one conceptual framework used to measure academic performance in universities. By applying knowledge management process integrated with Business Intelligence System with data-driven analysis concept. This can provide new insights into the theme of academic knowledge management processes for future research opportunities. The model developed is based on comprehensive research developments on research trends and mapping of knowledge management articles and academic performance as well as a systematic literature review. This literature review examines the current state, clusters, and main topics of research related to knowledge management based on authors, sources, cited articles, countries of article authors, and journals that are productive in producing articles. Herein, 429 articles are utilized as metadata taken from the Scopus database published in 2018-2024. Hence, the data analysis of scientific articles is then reviewed using a bibliometric approach and article mapping with using tool VOSviewers to get a network visualization image. The findings of bibliometric analysis point to a rise in publications and a strong level of interest among academics in the field of knowledge management for performance evaluation in higher education institutions. © 2024 IEEE.

关键词： Research and development management

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A Quantum Q-Learning Model for the Capacitated Vehicle Routing Problem 17

A Quantum Q-Learning Model for the Capacitated Vehicle Routi...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Xu, Jiahua Xie, Ningyi Lee, Xinwei Saito, Yoshiyuki Asai, Nobuyoshi Cai, Dongsheng University of Tsukuba Graduate School of Science and Technology Japan Singapore Management University School of Computing and Information Systems Singapore University of Aizu Graduate School of Computer Science and Engineering Japan University of Aizu School of Computer Science and Engineering Japan University of Tsukuba Faculty of Engineering Information and Systems Japan

ISBN: (纸本)9798331530471

The research on Variational Quantum Algorithms (VQAs) has gained significant momentum because of their promising practicality in the noisy intermediate-scale quantum (NISQ) era. Recent studies highlight the potential of VQAs in reinforcement learning (RL) by replacing classical components with parameterized quantum circuits (PQCs). Building on this, we propose a quantum Q-learning model for the Capacitated Vehicle Routing Problem (CVRP), combining a PQC with an RL training process. We present a PQC for embedding both static and dynamic states in RL environments. The proposed PQC approximates the Q-value function by using the expectation values of different observables. Experimental results demonstrate that the proposed PQC outperforms the existing PQC for the CVRP with fewer quantum resources. Furthermore, our method shows competitive performance compared to the classical counterpart. © 2024 IEEE.

关键词： Vehicle routing

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Dual-Band Reflectarray Antennas Using Integrated Resonant and Non-Resonant Natures of Metallic Waveguide Elements at Millimeter Wave Frequencies

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Radio Science 2023年第1期58卷 1-13页

作者： Chou, H.-T. Wang, N.-N. Fang, M. Wang, L.-Q. Akkaraekthalin, P. Torrungrueng, D. Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Research Center of Innovation Digital and Electromagnetic Technology Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut's University of Technology North Bangkok Bangkok Thailand

Pure metallic reflectarray antennas have good power efficiency and low fabrication cost, limited to single-band operation in the past. A dual-band reflectarray antenna is developed for two directional beams by two feeds. The reflecting elements are metal waveguides and have novel properties of both resonant and non-resonant modes at the two frequency bands. The low and high-frequency bands can be easily separated by the cutoff frequency of waveguide modes. The phase-changing mechanisms via ray optics and fundamental waveguide modes, respectively, provide simple formulations for the elemental structure design of directional beams. Radiation characteristics of the linear and circular polarizations are cross-examined with a good performance by full-wave simulations using HFSS, FEKO, and CST at 28 and 60 GHz bands for 5G front-haul network applications. In particular, we have used HFSS to simulate the reflecting elements' basic characteristics of phase variation, FEKO to simulate the linear polarization antenna case, and CST to simulate the circular polarization antenna case, respectively. © 2023. American Geophysical Union. All Rights Reserved.

关键词： Cutoff frequency

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Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakarta Passenger Prediction 3

Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakar...

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

作者： Siswanto, Joko Hendry Rahardja, Untung Sembiring, Irwan Lisangan, Erick Alfons Hakim, Muhammad Iman Nur Wibowo, Feri Polytechnic of Road Transportation Safety Road Transportation System Engineering Tegal Indonesia Satya Wacana Christian University Faculty of Information Technology Indonesia University of Raharja Faculty of Science and Technology Tangerang Indonesia Satya Wacana Christian University Faculty of Computer Science Indonesia The University of Western Australia Computer Science and Software Engineering Perth Australia Polytechnic of Road Transportation Safety Automotive Engineering Technology Tegal Indonesia Muhammadiyah University Purwokerto Faculty of Engineering and Science Banyumas Indonesia

ISBN: (纸本)9798350367492

A crucial role in the BRT transportation system's planning, development, and operation is the prediction of passenger numbers. Using time-series data, it is necessary to develop careful prediction models, appropriate techniques, and an indepth understanding of the number of BRT Transjakarta passengers. A prediction model is sought based on comparing combined LSTM and BiLSTM experiments using three evaluation matrices and time. Historical data used from daily passenger data for 13 BRT Transjakarta corridors (1/01/2021-31/12/2023). The best prediction model was obtained from the BiLSTM-CNN combination with the lowest MSLE (0.0425), MAPE (0.1598), and SMAPE (15.9387) evaluation matrix values. However, it required a longer time (00:02:14). Predictions of passenger numbers on the 13 Transjakarta BRT corridors for the next 12 months can be made simultaneously by predicting fluctuations occurring simultaneously. The strongest positive correlation is in corridor 9-6, while the strongest negative correlation is in corridor 12-5. The prediction results must be understood by stakeholders, managers, and technopreneurs to develop and support appropriate strategies to increase the number of BRT passengers. © 2024 IEEE.

关键词： Prediction models

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