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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

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7th international conference on Contemporary computing and informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 ieee.

关键词： Electronic health record

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Towards Generating Contracts for Scientific Data Analysis Workflows

Towards Generating Contracts for Scientific Data Analysis Wo...

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Vu, Anh Duc Kehrer, Timo University of Bern Switzerland German Informatics Society Germany

ISBN: (纸本)9798350355543

To increase the dependability and portability of scientific data analysis workflows (DAWs), recent work has proposed contract-driven design of DAWs, providing verifiable expectations and obligations to ensure that tasks run in a proper environment and produce correct results. However, the specification of suitable contracts is still left to the discretion of DAW developers, imposing labor-intensive manual work which likely hampers the widespread adoption of contracts in scientific practice. We report about work-in-progress of developing a pipeline empowered by Large Language Models for automatically generating code contracts from logical workflow descriptions. We instantiate this pipeline within the workflow system Nextflow, and evaluate its contract generation capabilities in an experiment using real-world Nextflow modules. Our findings indicate that we generate a substantial amount of contracts serving as starting point for DAW developers. Our approach demonstrates potential in assisting domain scientists with contract-driven design of DAWs, laying the groundwork for its future adoption. © 2024 ieee.

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Modified Metaheuristic Optimization Algorithm for Hyperparameter Tuning in Mental Health Applications 6

Modified Metaheuristic Optimization Algorithm for Hyperparam...

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6th international conference on Mobile computing and Sustainable informatics, ICMCSI 2025

作者： Kozakijevic, Stanislava Jovanovic, Luka Tedic, Smiljana Jankovic, Ninoslava Zivkovic, Miodrag Bacanin, Nebojsa Faculty of Media and Communication Singidunum University Belgrade Serbia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (纸本)9798331522667

The World health organization considers a mentally healthy person to be able to manage life’s stresses, work productively, and contribute to their communities. However, mental disorders, affecting 970 million people in 2019, often reduce quality of life, necessitating accurate diagnosis and therapy. Affective disorders, encompassing depressive and manic episodes, vary in severity and pattern, with conditions like bipolar disorder and recurrent mood disorders identified through ICD-10 classifications. Diagnosis, typically made by expert teams, remains challenging due to the complex and variable nature of mental disorders, leading to frequent misdiagnoses that hinder treatment progress and erode patient trust. Artificial intelligence (AI) holds promise in supporting mental health diagnosis by identifying subtle patterns in symptoms and validating expert conclusions. While AI’s specialized nature poses limitations, integrating it into clinical practice can improve diagnostic accuracy and reduce errors. High quality models can assist doctors by ruling out less likely conditions and confirming diagnoses. Optimizing AI models, however, requires effective hyperparameter selection, a complex task classified as NP-hard. Metaheuristic algorithms offer a solution, enabling efficient parameter tuning and improving AI model performance. Given the critical implications of accurate mental health diagnoses, leveraging AI and optimization techniques is essential to enhance outcomes and improve lives. This work proposed a modified metahetusirc aimed at optimizing classification accuracy. When evaluated on a genuine dataset the best performing models attain an accuracy of 0.972222 suggesting feasibility in real world situations. © 2025 ieee.

关键词： Patient treatment

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Design Parental Involvement Monitoring Students' Academic Performance on University Mobile Apps 8

Design Parental Involvement Monitoring Students' Academic Pe...

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8th international conference on informatics and computing, ICIC 2023

作者： Yusuf, Nabila Adelia Tolle, Herman Aknuranda, Ismiarta Brawijaya University Department of Informatics Engineering Malang Indonesia Brawijaya University Department of Information System Malang Indonesia

ISBN: (纸本)9798350342604

The role of parents in monitoring student academic activities is important. Many students in university fail because of a lack of monitoring and supervision from parents and the university. Most academic information is not channeled efficiently to the parents due to various factors, lack of awareness, the busyness of parents, and limited access to academic information. Therefore, a platform is needed for parents to monitor all Student's academic activities easily and efficiently through University App. This study uses the Design Thinking method with a special function on Notification and context awareness features to increase the User's attention on monitoring students' progress. The usability evaluation was then conducted to evaluate the solution design's effectiveness, efficiency, and satisfaction. The effectiveness rate is 'Success' on all tasks, while the efficiency result is 0.161 goals/second. The satisfaction rate using the System Usability Scale (SUS) has a score of 86 with the level of 'Very Good,' and the User Experience Questionnaire (UEQ) produces an 'Excellent' benchmark in 6 aspects. The solution design produces an ideal model for parents to monitor Student academic progress using mobile applications. © 2023 ieee.

关键词： Students

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Synergy of Value Engineering and Green Energy Systems in Innovating Building Construction 10

Synergy of Value Engineering and Green Energy Systems in Inn...

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10th international conference on Smart computing and Communication, ICSCC 2024

作者： Kumara, I Nyoman Indra Ariana, I Komang Agus Tapa, I Gede Fery Surya Cipta Indrashwara, Decky Putu Merta Karunia Putri, Luh Putra, I Ketut Anzas Dwi Anggara Universitas Pendidikan Nasional Faculty of Engineering and Informatics Denpasar Indonesia

ISBN: (纸本)9798350363104

This research explores the synergistic integration of value engineering methods and green energy systems in Denpasar Health Polytechnic building construction innovations. The application of value engineering methods using Dell'Isola Theory in material substitution methods functions as a structured framework to streamline project value by balancing quality, performance and cost, while green energy systems emphasize development sustainability and energy efficiency. Through careful analysis and strategic application, this research explains how the combination of these methodologies improves cost efficiency, sustainability, and performance in building construction projects. These findings underscore the potential for innovation and progress in creating built environments that are environmentally conscious and economically viable. In addition, the integration of green energy systems not only increases energy efficiency but also contributes to reducing environmental impacts and supports sustainable development goals. © 2024 ieee.

关键词： Green buildings

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Exploring the Frontier of Technological progress in Plant Biotechnology 6

Exploring the Frontier of Technological Progress in Plant Bi...

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6th international conference on Contemporary computing and informatics, IC3I 2023

作者： Aggarwal, Aarohi Mehta, Ankit Saraswat, Manish Shekhar Independent Researcher Haryana Panchkula India Lloyd Institute of Engineering & Technology Greater Department of Computer Science Uttar Pradesh Noida India

ISBN: (纸本)9798350304480

Innovative solutions to major issues in agriculture and sustainability are now available because of the fusion of bio-inspired computing and plant biotechnology. This study examines how genetic algorithms and neural networks, two bio-inspired technologies, can be used to improve crop qualities, forecast plant responses, and manage crops more effectively. We demonstrate their potential influence on developing plant biotechnology by discussing their benefits and applications to conventional approaches. We showcase effective implementations, ranging from better planting schedules to creating disease-resistant crops. Its capabilities are further enhanced by combining omics and phenotyping technologies with bio-inspired computing. We discuss issues including data integration difficulties and moral concerns with regard to genetically modified creatures. Computer scientists and biotechnologists will be able to work together thanks to this multidisciplinary approach. In summary, we can conclude that combining bio-inspired computing with plant biotechnology has the potential to advance sustainable agriculture. This combination offers a crucial step toward resilient food systems and environmental stewardship as we move toward precision farming. © 2023 ieee.

关键词： Bio-inspired computing Biofortification Bioinformatics and Analytics CRISPR-Cas Genome Editing Nanotechnology Plant-Machine Interfaces Synthetic Biology Techniques

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Multimodal Contrastive Learning for Protein-Protein Interaction Inhibitor Prediction

Multimodal Contrastive Learning for Protein-Protein Interact...

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2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Zitong Wang, Zhixian Zhao, Lingling Wang, Junjie Wang, Chunyu Harbin Institute of Technology Faculty of Computing Harbin China Nanjing Medical University School of Biomedical Engineering and Informatics Department of Medical Informatics Nanjing China

ISBN: (纸本)9798350386226

Protein-protein interactions (PPIs) are crucial for various cellular activities and disease development, and modulating PPIs using small molecule inhibitors (PPIIs) has gradually become a promising therapeutic strategy. Recently, researchers have proposed several machine learning methods to screen PPIIs, but most of the works focused on unimodal representations of molecules or combining multimodal features in a naive splicing manner. Meanwhile, current research progress is being slowed by the lack of large-scale PPII datasets. To address these issues, we propose MCLPPII, a unified multimodal contrastive learning framework for PPII prediction. MCLPPII extracts comprehensive molecular information from four modalities and effectively combines them through an adaptive feature fusion method. Furthermore, we propose a three-stage training strategy to enhance the PPII prediction capability of MCLPPII by leveraging self-supervised pre-training on a large unlabeled dataset. We evaluate MCLPPII on nine PPI targets and two downstream tasks, including the PPI inhibitor identification task and potency prediction task. Experimental results show that MCLPPII achieves competitive performance. The source code and datasets are freely available at https://***/1zzt/MCLPPII. © 2024 ieee.

关键词： Contrastive Learning

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Enhancing Quantum Search: The Potential of Partitioned Grover's Algorithm 1

Enhancing Quantum Search: The Potential of Partitioned Grove...

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1st international conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Bounceur, Ahcene Alhashmi, Saadat M. Nour, Mohamed Abdalla Seker, Huseyin University of Sharjah College of Computing and Informatics Sharjah United Arab Emirates

ISBN: (纸本)9798331541064

Grover's Algorithm is a prominent quantum search algorithm in the rapidly advancing quantum computing study. By exploiting superposition and quantum parallelism, it has the potential to outperform classical brute-force approaches. Nevertheless, the current practical application of quantum technology in decrypting intricate passwords is restricted by its early stage, namely the restricted qubit quantities and short coherence durations. This work presents an innovative distributed quantum computing paradigm that addresses these difficulties by partitioning the password search space among several quantum processors. Each processor executes Grover's Algorithm on a subset of the search space, minimizing the computational complexity and quantum resource requirements for each individual processor. The partial findings are subsequently transmitted to a central classical computer, which amalgamates them to obtain the ultimate password. This distributed methodology not only allows for practicality with current quantum systems but also provides a scalable paradigm that can adapt to the progress of quantum computer technology. By decomposing the quantum brute-force search problem into smaller, more controllable elements, this approach can significantly decrease the time complexity of password retrieval, which has significant consequences for cryptography and cybersecurity. © 2024 ieee.

关键词： Quantum computers

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An Enhanced Method with Part of Speech Tagging and Named Entity Recognition Techniques Towards Advanced Persistent Threat in Cyber Threat Intelligence: Work in progress 11

An Enhanced Method with Part of Speech Tagging and Named Ent...

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11th international conference on Electrical Engineering, Computer Science and informatics, EECSI 2024

作者： Permana, Dendi Renaldo Stiawan, Deris Rini, Dian Palupi Afifah, Nurul Ningrum, Septiani Kusuma Budiarto, Rahmat Universitas Sriwijaya Indonesia Universitas Sriwijaya Computer Engineering Dept Indonesia Universitas Sriwijaya Informatic Engineering Dept Indonesia Universitas Sriwijaya COMNETS Research Group Indonesia College of Computing and Information Albaha University Saudi Arabia

ISBN: (纸本)9798350355314

This research enhances the development of natural language processing (NLP) by integrating part of speech (POS) tagging and named entity recognition (NER) techniques to annotate unstructured data from advanced persistent threat (APT) news. This paper uses combination of these two techniques with deep learning methods, specifically bidirectional long short-term memory (BiLSTM) and conditional random field (CRF) to automate data annotation. The objective is to automatically convert the unstructured APT data into structured threat information expression (STIX) format. The proposed approach can be further developed into cyber threat intelligence (CTI) system for information exchange and early warning against cybercrime. This research is currently in the preliminary stage with progress including data collection and preprocessing, and BiLSTM model building. The expected outcome is a model that capable of APT data labeling to enhance CTI systems. © 2024 ieee.

关键词： Cyber attacks

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Simplified Artificial Neural Network Configuration in R Programming for Predictive Modelling 8

Simplified Artificial Neural Network Configuration in R Prog...

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8th ieee international conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Razak, Tajul Rosli Jarimi, Hasila Ahmad, Emy Zairah Universiti Teknologi MARA School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia University Kebangsaan Malaysia Solar Energy Research Institute Selangor Bangi Malaysia Universiti Teknikal Malaysia Melaka Fakulti Teknologi Dan Kejuruteraan Elektrik Durian Tunggal Malaysia

ISBN: (纸本)9798350315516

The configuration of Artificial Neural Networks (ANNs) in the context of predictive modelling can provide considerable difficulty owing to the complex nature of their arrangements and the need for meticulous hyperparameter adjustment the present study addresses the issue by proposing a more straightforward methodology for configuring Artificial Neural Networks (ANNs) through the R programming language the methodology presented in this study offers a systematic and comprehensive framework, ensuring accessibility and simplicity of implementation. This approach aims to enhance the usability of Artificial Neural Networks (ANNs) for practitioners who need advanced machine learning knowledge. In order to demonstrate the applicability of the proposed methodology, a series of experiments were conducted on a case study in sustainable energy research. This study makes a valuable contribution to academic discipline by establishing a connection between artificial neural network (ANN) theory and its practical application. This study aims to enhance the accessibility of artificial neural network (ANN) setup and provide significant insights to further progress predictive modelling, specifically focusing on sustainable energy research. © 2023 ieee.

关键词： Neural networks

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