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Knowledge Augmented Significant Language Model-Based Chatbot for Explainable Diabetes Mellitus Prediction

Knowledge Augmented Significant Language Model-Based Chatbot...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Mazen Elfayoumi Mohamed AbouElazm Omar Mohamed Tamer Abuhmed Shaker El-Sappagh Faculty of Computer Science and Engineering Galala University Egypt Faculty of Computer Science Goldsmith UOL United Kingdom College of Computing and Informatics Sungkyunkwan University South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning models and in addition to providing precise, individualized, robust prediction of diabetes augmented by the percentage of its confidence, this chatbot provides detailed text-based explanations for its individual predictions and enhances user's understanding by retrieving and presenting similar cases using case-based reasoning techniques. Utilizing key health indicators such as hypertension status, heart disease presence, smoking history, BMI, HbA1c level, and blood glucose levels, the chatbot not only predicts the presence of diabetes but also educates users about the underlying reasons for each prediction. The system's explanation module promotes transparency and trust in the predictive process. The proposed architecture integrates the retrieval augmented generation (RAG) technique with prompt engineering across multiple LLMs, including Llama 3.1, GPT- 3.5, and Gemma2. This approach augments the chatbot's responses with contextually relevant data from similar past cases, thereby enhancing both relevance and accuracy. Furthermore, RAG minimizes LLM hallucinations while enhancing them with up-to-date, precise, personalized medical information. The objective of this chatbot is to offer healthcare professionals and patients a dependable, educational instrument for early detection of diabetes supported by transparent AI principles. This will have a positive effect on preventative healthcare measures.

关键词： Technological innovation Accuracy Large language models Computational modeling Retrieval augmented generation Medical services Predictive models Chatbots Diabetes Medical diagnostic imaging

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Enhance Low-Resolution Face Recognition Accuracy Using Median and Mean Filtering Techniques Based on Convolutional Neural Networks (CNN) 18

Enhance Low-Resolution Face Recognition Accuracy Using Media...

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18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Kurnia, Dian Ade Risnanto, Slmaet Sugiyatno Kusuma, Ajeng Nisa Naufal, Yogacitra Bin Mohd, Othman Sekolah Tinggi Manajemen Informatika Dan Komputer Ikmi Departemen of Computer Science Cirebon Indonesia Unversitas Sangga Buana Faculty of Engineering Departemen of Informatics Engineering Bandung Indonesia Universitas Bhayangkara Jakarta Raya Fakulti Informatics Jakarta Indonesia Unversitas Sangga Buana Bandung Indonesia Universiti Teknikal Malaysia Fakulti Teknologi Maklumat Dan Komunikasi Melaka Malaysia

ISBN: (纸本)9798331527686

The aim of this study is to evaluate the effectiveness of median and mean filtering techniques in enhancing the performance of deep learning-based face recognition systems on low-resolution images. The main challenge in face recognition lies in the low quality of images due to various factors such as lighting, distance, and camera resolution. Median filtering replaces each pixel with the median value of its neighbors, effectively removing salt-and-pepper noise, while mean filtering replaces each pixel with the average value of its neighbors, resulting in smoother images but potentially blurring important details. The results show that median filtering is more effective in preserving edge details and reducing speckle noise, whereas mean filtering excels at producing overall smoother images. The combination of these techniques is expected to enhance the accuracy of face recognition on low-resolution images, contributing to the development of more reliable and precise face recognition systems © 2024 IEEE.

关键词： Wiener filtering

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Leveraging Large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency

Leveraging Large Language Models for Smart Pharmacy Systems:...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abdelrahman Osheba Ahmed Abou-El-Ela Osama Adel Youssef Maaod Tamer Abuhmed Shaker El-Sappagh Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications.

关键词： Drugs Electric potential Hospitals Large language models Mortality Medical services Chatbots Real-time systems Safety Electronic medical records

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E-Commerce Supply Chains with Considerations of Cyber-Security 1

E-Commerce Supply Chains with Considerations of Cyber-Securi...

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1st International Conference on computer Science and Emerging Technologies, CSET 2023

作者： Zawaideh, Firas H. Abu-Ulbeh, Waheeb Majdalawi, Yousef I. Zakaria, Muhammad D. Jusoh, Julaily Aida Das, Sunanda Irbid National University Faculty of Science and Informaiton Technology Department of Cyber Security Irbid Jordan Al-Istiqlal University Faculty of Administrative Sciences and Informatics Cybersecurity Department Palestine Petra University Faculty of Management and Financial Sciences Business & E-commerce Department Amman Jordan Faculty of Informatics and Computing UniSZA Malaysia School of Computer Science and Engineering Jain University Bangalore India

ISBN: (纸本)9798350341737

As the global e-commerce landscape continues to expand rapidly, the efficiency and security of supply chains have become critical areas of concern. With the increasing reliance on digital platforms and the interconnected nature of supply chains, the vulnerability to cyber threats has escalated significantly. This research paper delves into the multifaceted domain of e-commerce supply chains with a primary focus on cyber-security considerations. The objective of this study is to assess the existing cyber-security measures and vulnerabilities within e-commerce supply chains while proposing effective strategies to mitigate potential risks. Through an extensive literature review, the paper highlights various cyber-security challenges faced by e-commerce enterprises, including data breaches, ransomware attacks, and supply chain disruptions caused by cyber-attacks on logistics partners. By analyzing real-world case studies and industry reports, the research provides valuable insights into the consequences of cyber-security lapses and their impact on supply chain resilience and consumer trust. The research also explores the technological advancements and best practices that can fortify e-commerce supply chains against cyber threats. It examines the role of encryption, multi-factor authentication, secure payment gateways, and cloud-based security solutions in safeguarding sensitive data and maintaining the integrity of supply chain operations. Additionally, the study investigates the importance of employee training and awareness programs to cultivate a cyber-security-oriented organizational culture. Furthermore, this paper considers the legal and regulatory aspects of cyber-security in the context of e-commerce supply chains. By examining international laws, industry standards, and compliance requirements, it highlights the responsibilities and obligations of e-commerce businesses to protect customer data and secure the flow of goods throughout the supply chain. The efficie

关键词： Cybersecurity

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Visualization of Syntax and Semantics for Simple Functional Language of Natural Numbers and Boolean Values 10

Visualization of Syntax and Semantics for Simple Functional ...

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10th IEEE Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022

作者： Perháč, Ján Ferencsik, Michal Zhukovskyy, Viktor Zhukovska, Nataliia Shatnyi, Serhii Technical University of Košice Faculty of Electrical Engineering and Informatics Department of Computers and Informatics Košice Slovakia National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

Building an abstract syntax tree is an integral part of compilers. We analyze a few of the popular techniques providing such analysis in the web environment. Based on the analysis of these solutions, we deal with the implementation of our parser solution, which also provides a graphical representation of abstract syntax trees as well as proof trees. This solution is then used in our application designed to interpret the expressions of the simple teaching functional language of natural numbers and boolean values. In addition, the application supports the evaluation (semantics) of expressions (step by step), and also their individual properties: the depth of the syntax tree, syntax tree nodes count, and the constants used in the expression. © 2022 IEEE.

关键词： Semantics

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Pre-trained Self-Attention Framework: An Efficient Mechanism for Source Separation 13th

Pre-trained Self-Attention Framework: An Efficient Mechanism...

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13th International Conference on Information Technology and its Applications, CITA 2024

作者： Minh Tan, Ha Fhadli, Muhammad Kim Quoc, Nguyen Vu, Duc-Quang Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh City Viet Nam Department of Informatics Faculty of Engineering Khairun University Ternate City Indonesia Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam

ISBN: (纸本)9783031741265

In this work, the pre-trained model of the self-attention framework is proposed for single-channel speech separation. Firstly, all layers in the pre-trained self-attention framework are frozen. The model is then retrained through three stages using the scheduling mechanism for learning rates and the layers of the framework are unlocked following the schedule. This way, the model is relearned, enhanced, and updated from previous knowledge. This is an effective way to improve the advanced model performance while significantly reducing the time and cost of a training model. This method is beneficial in applying existing models to perform a similar task or enhancing model performance. In this strategy, the pre-trained system outperforms the non-pre-trained system since the following phases of the model’s training repurpose characteristics extracted through the previously trained early phases. The suggested method is tested and assessed on a conventional dataset. The findings from experiments suggest that the methodology has higher performance than the baseline framework and outperforms current methods for the monaural speech separation task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： fine-tuning framework pre-trained model self-attention mechanism speech separation time masking framework Transformer

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High isolation and gain metamaterial based MIMO antenna for 5G millimiter wave applications

High isolation and gain metamaterial based MIMO antenna for ...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Elalaouy, Ouafae Aghoutane, Bilal El Ghzaoui, Mohammed Das, Sudipta Foshi, Jaouad El Faylali, Hanan University of Moulay Ismail Faculty of Sciences and Techniques Errachidia Errachidia Morocco Faculty of Sciences IbnTofail University Informatics Systems and Optimization Laboratory Department of Computer Science Kenitra Morocco Sidi Mohamed Ben Abdellah University Faculty of Sciences Dhar El Mahraz Fes Morocco Imps College of Engineering and Technology Department of Electronics and Communication Engineering W.B Malda India

ISBN: (纸本)9798350365306

In the contemporary era, harnessing millimeter-wave frequencies emerges as a compelling strategy to fulfill the swift data transfer requirements for 5G communications. Consequently, this study involves designing a metamaterial-based multiple-input multiple-output (MIMO) antenna with a focus on achieving high isolation and gain within the millimeter-wave spectrum. The suggested MIMO is printed on a Rogers RT5800 measuring 18 × 38 × 0.8 mm3. The single antenna element demonstrates a broad frequency coverage from 27.2 to 28.72GHz. To elevate the antenna's effectiveness, a two-port MIMO configuration is implemented by introducing a second antenna element parallel to benchmark antenna. However, the isolation between the two elements is deemed satisfactory. To address this, a 4 × 1 metamaterial unit cell is introduced, significantly enhancing isolation from 31 dB to 39 dB at 28 GHz. Furthermore, it improves the gain by 0.5 dBi at 28 GHz, elevating it from 8.5 to 9 dBi. The collective features of the suggested two ports MIMO antenna, encompassing its compactness and simplicity, position it as an excellent option for upcoming portable and shrink devices in Internet of Things uses. © 2024 IEEE.

关键词： Microwave antennas

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Co-creation through design: Exploring the role of prototyping in processes of co-creation in cross disciplinary collaborations 23

Co-creation through design: Exploring the role of prototypin...

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Proceedings of the 35th Australian computer-Human Interaction Conference

作者： Fahd Bin Malek Newaz Joakim Karlsen Department of Informatics University of Oslo Norway and Faculty of Computer Science Engineering and Economics Østfold University College Norway Faculty of Computer Science Engineering and Economics Østfold University College Norway

ISBN: (纸本)9798400717079

This article examines the role of toolkits and prototypes in processes of Co-creation in cross-disciplinary collaborations. This study focuses on a co-creation initiative involving student teachers, museum educators, teacher educators, and designers, aiming to strengthen school-museum collaborations in Norway. Based on observations, surveys, and interviews of a multi-event and multi-site project to foster co-creation among these stakeholders, we find that toolkits and prototypes as established resources in the field of co-design contributed positively to increased levels of co-creativity. Further, the findings show a large degree of variation in how the particular tools and techniques offered, were utilized by the stakeholders. We understand these variations to be welcome in line with the rationale signaled by co-creation. It underlines how supporting co-creation by design to a large degree is similar to meta-design, to design for further design in use by seeding and providing malleable and tailorable resources. Findings have implications for designers exploring how to adapt the tools and techniques of co-design in supporting cross-sectoral learning and innovation.

关键词： Co-creation Co-design Cross-disciplinary collaborations Cultural heritage Education Prototypes Research through design Toolkits

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Games for Scrum Team Collaboration in the Global Software Development Environment: A Literature Review 7

Games for Scrum Team Collaboration in the Global Software De...

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7th International Conference on informatics and Computing, ICIC 2022

作者： Hidayati, Anita Ismail, Iklima Ermis Yuly, Ade Rahma Edison, Henry Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia Blekinge Institute of Technology Department of Software Engineering Sweden

ISBN: (纸本)9798350345711

Software development companies commonly use Global Software Development (GSD) in their industry. A competent Scrum team supports the success of the GSD project. This research aims to identify the game components in the form of types, elements, and technologies that contribute to building experience for the Scrum team in a GSD environment. We used a Systematic Literature Review (SLR) with the Kitchenham and snowball method. From the 12 papers, we concluded that none of the games specifically focused on the competence of the Scrum team at GSD. The SLR results also identify ten game elements and technologies as a reference for game development to meet the challenges in GSD. The conclusion is that there are still great opportunities to develop a game that can improve the competence of the GSD Scrum team. Game development should refer to elements and identified technologies. © 2022 IEEE.

关键词： Software design

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Advancing Weed Detection in Agricultural Landscapes Using computer Vision 5

Advancing Weed Detection in Agricultural Landscapes Using Co...

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5th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2023

作者： Tan, Bei Ren Moung, Ervin Afnan Uda, Muhd. Nur Kin Teo, Kenneth Tze Angeline, Lorita School of Computer Science Taylor's University Subang Jaya Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

This paper explores the need for weed detection in agricultural landscapes, where weed infestations pose significant crop productivity challenges. To address this matter, computer vision techniques are utilised to create a weed detection algorithm that accurately detects weed patches inside agricultural field images. To ensure reliable model evaluation, the dataset is split into training and testing subsets before being fed into the YOLOv3 model. The YOLOv3 model performs well in terms of precision, with an average precision of 66.4% for crop regions and a notable precision of 92.5% for detecting weeds. These results highlight the potential of artificial intelligence-driven weed detection techniques to optimize crop management practices and foster sustainable agriculture in the modern era. This research advances precision agriculture by effectively addressing weed infestations, paving the way for more efficient and targeted weed control strategies, which ultimately lead to increased crop yield and improved agricultural sustainability. © 2023 IEEE.

关键词： computer vision

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