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International Conference on Computing, engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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

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

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Creative Communication and Innovative technology (ICCIT), IEEE

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

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

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.

关键词： Deep learning Training Correlation Accuracy System performance Predictive models Data models Planning Stakeholders Long short term memory

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Health professional students' preparedness for E-Health

Libyan International Medical University Journal

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Libyan International Medical University Journal 2022年第2期04卷 74-81页

作者： Adel A. Al-Tawaty Ehab Omar Elfallah Department of Pediatrics Faculty of Medicine Libyan International Medical University Benghazi Libya Department of Software Engineering Faculty of Information Technology Libyan International Medical University Benghazi Libya

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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Dogodo: IoT Based Mobile Application to Provide Essential Health Services to Dogs 3

Dogodo: IoT Based Mobile Application to Provide Essential He...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Thilakarathne, L.V.I.S. Salay, M.S. Wijethilaka, M. G. R Chethana Fernando, T.S. Sriyaratna, Disni Rupasinghe, Shalini Sri Lanka Institute of Information Technology Faculty of Computing Department of Infotmation Tchnology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Voice of dogs can be heard by people who listen to them. The more you listen, the more you learn about the dogs. This study proposes a platform to identify and observe dogs' behavior and their activities by using the newest technologies. The proposed system will mainly cover the relevant areas that are supposed to be covered to full fill the pet owners' expectations by providing necessary services such as internal health, voice recognition, and emotion translations and external issues such as skin diseases, breeding patterns, and breeding outcomes. Our priority is to emphasize necessary services in the mobile application and provide fluid services with fewer interruptions. Primarily research will offer an IoT device and a mobile application that covers the dog's internal health and external health consecutively. The study revealed current developments and the challenges and possibilities related to the system. © 2021 IEEE.

关键词： Speech recognition

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A Method of Deep Reinforcement Learning for Simulation of Autonomous Vehicle Control 16

A Method of Deep Reinforcement Learning for Simulation of Au...

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16th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2021

作者： Huynh, Anh T. Nguyen, Ba-Tung Nguyen, Hoai-Thu Vu, Sang Nguyen, Hien D. Faculty of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam Faculty of Information Systems University of Information Technology Ho Chi Minh City Viet Nam Faculty of Computer Science University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789897585081

Nowadays autonomous driving is expected to revolutionize the transportation sector. Carmakers, researchers, and administrators have been working on this field for years and significant progress has been made. However, the doubts and challenges to overcome are still huge, regarding not only complex technologies but also human awareness, culture, current traffic infrastructure. In terms of technical perspective, the accurate detection of obstacles, avoiding adjacent obstacles, and automatic navigation through the environment are some of the difficult problems. In this paper, an approach for solving those problems is proposed by using of Policy Gradient to control a simulated car via reinforcement learning. The proposed method is worked effectively to train an agent to control the simulated car in Unity ML-agents Highway, which is a simulating environment. This environment is chosen from some criteria of an environment simulating autonomous vehicle. The testing of the proposed method got positive results. Beside the average speed was well, the agent successfully learned the turning operation, progressively gaining the ability to navigate larger sections of the simulated raceway without crashing. Copyright © 2021 by SCITEPRESS - Science and technology Publications, Lda. All rights reserved

关键词： Reinforcement learning

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Two-Layer Smart System to Predict HIV Risk

Two-Layer Smart System to Predict HIV Risk

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International Conference on Sensing technology, ICST

作者： Trinh Duc Minh Nguyen Viet Tien Le Mai Hoang Long Nguyen Phuc Hau Nguyen Thi Phuoc van Nguyen Hanoi - Amsterdam High school Hanoi Vietnam Faculty of Electronic Engineering Hanoi University of Industry HAUI Hanoi Vietnam PT Automotive - AIS FPT Software Ho Chi Minh Vietnam Faculty of Information Technology Electric Power University Hanoi Vietnam Department of Information Technology Thanh Do University Hanoi Vietnam

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

In this work, a two-layer privacy protection system was proposed for HIV risk prediction. The system involves various clinic centres, each using its data to build a model to predict HIV risk. The models from each clinic are gathered at a central server/cloud to create a global model. To protect the individual clinic models, they are encrypted before being sent to the server. At the server, the encrypted models are aggregated to create the global model without decryption. The encrypted global model is then distributed to each clinic for use. This method ensures that patient information from each clinic is protected by two layers: modelling and encryption. We used data from Vietnam 2005 to validate the proposed framework. The performance of the proposed method is superior the local model in terms of accuracy and AUC (Area Under The Curve). The enhancement is larger than 10% in comparison with recent works.

关键词： Privacy Federated learning Predictive models Smart systems Data models Sensors Electronic healthcare Servers Protection Homomorphic encryption

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Exploring Multi-Perspective Document Structures for News Discourse Profiling

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2349-2359页

作者： Hang Yang Dianbo Sui Yubo Chen Kang Liu Jun Zhao School of Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China School of Computer Science and Software Engineering Harbin Institute of Technology Weihai China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Learning the discourse structures of news articles is vital for obtaining valuable structural information around main events, which has been shown useful for many natural language processing applications. This paper proposes the extraction of graphs explicitly representing the discourse structure, coreference structure, and semantic similarities within a document. Concept embeddings are then updated using a weighted combination of feature embeddings, modeled after capsule networks, and resulting in improved context-dependent representation of the content of each news article. Experimental tests on the NewsDiscourse dataset demonstrate the effectiveness of our proposed method.

关键词： Speech processing Routing Computational modeling Weapons Encoding Transformers Representation learning Natural language processing Context modeling Artificial intelligence

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Towards Tolerating Soft Errors for Embedded Systems

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SN Computer Science 2021年第2期2卷 1-10页

作者： Sadi, Muhammad Sheikh Ahmed, Waseem Jürjens, Jan Khulna University of Engineering & Technology Khulna Bangladesh Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia IST/ISST University of Koblenz-Landau and Fraunhofer Institute for Software and Systems Engineering Koblenz/Dortmund Germany

Exponential growth in the number of transistors for each chip along with increasing clock frequencies and operational voltages and decreasing load capacitance are aggravating the possibility of occurrence of soft errors in embedded systems. Transistors on current chips have components separated by only a few hundred atoms;hence, a small voltage glitch can alter the state of the transistor, thus causing soft errors in the systems. The impact will be a matter of great concern when the line widths will shrink further. These complicated linkages among the components in chips directly affect the reliability of embedded systems and cause them to be sensitive to soft errors. The common approach to address such errors is focused on post-design phases that are complex and costly to implement. However, reliability, which is a vital non-functional attribute of a system, should be validated at the design phase, particularly for critical systems. This paper proposes an efficient approach to measure and minimize the potential threats of soft errors for embedded systems in the early design phase of system-level design. The methodology is validated against a system model that must have high reliability. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Criticality analysis Embedded systems Heuristic metrics Reliability risks Soft errors

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Revisiting Knowledge Transfer for Success Enterprise System Project

Revisiting Knowledge Transfer for Success Enterprise System ...

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2021 International Conference on information technology, ICIT 2021

作者： Hussien, Jamal Abdullateef, Mansoor Kahtan, Hasan Sulaiman, Riza Bangi Malaysia Abdulgabber Abdulhak Consultant Researcher Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665428705

This paper re-examines the relationship between the Knowledge Transfer (KT) and the project management process groups. Knowledge Transfer as one of the knowledge management life-cycle is supposed to have a remarkable impact on the enterprise system success. However, project failure is one of the main problems in enterprise systems management and lack of proper documentation on the results of previous projects and knowledge transfer are the main reasons. In this paper, we have found that enterprise system project divided into three stages, with (6) process groups in the Pre-Implementation stage, (6) process groups during-Implementation stage, and (3) process groups in the post-Implementation, however, there are eleven (11) roles who are evolving to anticipate the fifth results (Project Proposal, Evaluation Ratings, Project Charter, Project Plan, Project Documents), to increase the understanding of knowledge in each project stages to improve the enterprise system project success. © 2021 IEEE.

关键词： Project management

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