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5th International Conference on applied Technologies, ICAT 2023

作者： Armijos, Carlos Cambizaca, Juan Abril-Ulloa, Victoria Espinoza-Mejía, Mauricio Faculty of Engineering University of Cuenca Cuenca Ecuador Technologies Applied to Health Research Group Faculty of Medical Sciences University of Cuenca Cuenca Ecuador Computer Science Department University of Cuenca Cuenca Ecuador

ISBN: (纸本)9783031589553

This paper presents the development of a conversational agent embedded in a mobile application aimed at improving the safety of medication use in patients with diabetes, a highly prevalent chronic disease. One of the main challenges in the management of diabetes is the prevention of drug interactions and the understanding of how food can affect treatment. The conversational agent uses a database of drug, food, and pharmacological interaction information to provide both preventive and informative recommendations. In the first scenario, it alerts users to possible interactions between the medications they are planning to take. In the second scenario, it provides information on contraindications, suggests food alternatives, and points out possible drug-food and drug-drug interactions. The design of the conversational agent allows it to understand and answer questions in natural language, making it easier for users to intuitively access relevant information. The usability of the tool was evaluated, resulting in an overall positive perception by users. In addition, the system identifies drug-drug and drug-food interactions and provides useful recommendations as indicated by experts. Although areas for improvement were identified and specific adaptations are needed to fully meet the needs of users, the implementation of the agent through a mobile application has the potential to prevent dangerous interactions for patients with diabetes and significantly improve their quality of life. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Drug interactions

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KTI-RNN:Recognition of Heart Failure from Clinical Notes

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Tsinghua science and Technology 2023年第1期28卷 117-130页

作者： Dengao Li Huiting Ma Wenjing Li Baofeng Zhao Jumin Zhao Yi Liu Jian Fu College of Data Science Taiyuan University of TechnologyJinzhong 030600China Department of Statistics and Applied Probability University of CaliforniaSanta BarbaraCA 93106USA College of Mining Engineering Taiyuan University of TechnologyTaiyuan 030024China College of Information and Computer Taiyuan University of TechnologyJinzhong 030600China

Although deep learning methods have recently attracted considerable attention in the medical field,analyzing large-scale electronic health record data is still a difficult *** particular,the accurate recognition of heart failure is a key technology for doctors to make reasonable treatment *** study uses data from the Medical Information Mart for Intensive Care *** with structured data,unstructured data contain abundant patient ***,this type of data has unsatisfactory characteristics,e.g.,many colloquial vocabularies and sparse *** solve these problems,we propose the KTI-RNN model for unstructured data *** proposed model overcomes sparse content and obtains good classification *** term frequency-inverse word frequency(TF-IWF)model is used to extract the keyword *** latent dirichlet allocation(LDA)model is adopted to extract the topic word *** models enable the expansion of the medical record text ***,we embed the global attention mechanism and gating mechanism between the bidirectional recurrent neural network(BiRNN)model and the output *** call it gated-attention-BiRNN(GA-BiRNN)and use it to identify heart failure from extensive medical *** show that the F 1 score of the proposed KTI-RNN model is 85.57%,and the accuracy rate of the proposed KTI-RNN model is 85.59%.

关键词： heart failure diagnosis text classification deep learning

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A Pruned Multi-Scale Skip Connected and Asymmetric-Convolution-Based U-Net Model for Land Cover Classification

A Pruned Multi-Scale Skip Connected and Asymmetric-Convoluti...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Almusawi, Muntather Parameshachari, B.D. Sunil, G. Reddy Muvva, Vijaya Bhaskar Siri, Dharmapuri The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Sr University Department of Computer Science & Artificial Intelligence Warangal India University of Technology and Applied Sciences -Shinas College of Computing and Information Sciences Department of Information Technology Shinas Oman Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350375442

Recently, land cover classification became an evolutionary study in Remote Sensing (RS) satellite interpretations. While extracting features from satellite images, spatial information causes the overfitting issue. In addition, computational complexity is occurring in land cover segmentation due to the usage of complex architectures. Thus, in this research, Multi-scale Skip connected and Asymmetric-Convolution-based U-Net (MACUNet) with pruning method is introduced to reduce the computational complexity. To reduce the overfitting, Batch Normalization (BN) layer is employed, simultaneously 3D Convolutional Neural Network (3D CNN) for feature extraction. Initially, the input is taken as satellite images from the GID dataset, where spatial and temporal features are extracted using 3D CNN model with BN layer. After that, these features are given as input to the MACUNet method to improve segmentation performance. Then, classification is done with class_num value utilization based on identified pixel region maps and classified into six classes such as build up, forest, water, farmland, meadows and others. From the results, the proposed pruned-MACUNet model produces better results than existing GeohashNet in terms of Mean Intersection over Union (MIoU) as 80.45% and f1-score as 90.23% respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Preventing Diabetes: Substituting Processed Foods and Nutritional Chatbot Assistance 1

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5th International Conference on applied Technologies, ICAT 2023

作者： Solano, Pablo Herrera, Víctor Abril-Ulloa, Victoria Espinoza-Mejía, Mauricio Faculty of Engineering University of Cuenca Cuenca Ecuador Technologies Applied to Health Research Group Faculty of Medical Sciences University of Cuenca Cuenca Ecuador Computer Science Department University of Cuenca Cuenca Ecuador

ISBN: (数字)9783031589539

ISBN: (纸本)9783031589522

Type 2 Diabetes Mellitus (T2DM) is one of the biggest threats to Ecuador’s health. The intake of processed foods has been linked to a higher risk of T2DM. This paper proposes FoodSub, a mobile application to recommend substitutes for processed foods using the NOVA Classification. Nutrient-based food clustering is used to identify substitute pairs between processed and unprocessed foods. The recommendations are supported and personalized using a knowledge graph that contains foods, dietary guidelines, and user information. In addition, a chatbot is implemented to answer simple questions about foods. This chatbot is developed using a Large Language Model (LLM) to query the knowledge graph. The mobile application and the chatbot are evaluated in terms of usability;both perform well, but there is room for improvement. Additionally, the recommendations’ performance is evaluated through expert verification. The recommendations perform well when issues like food transformation processes, flavor, context, or meal time are not relevant. Future work will consider the enhancement of the chatbot and the improvement of substitute recommendations for the relevant cases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Nutrition

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A Framework for Blockchain Based E-Voting System for Iraq

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第10期16卷 210-222页

作者： Wahab, Yousif Mohammed Ghazi, Alaan Al-Dawoodi, Aras Alisawi, Muthana Abdullah, Sirwan Saber Hammood, Layth Nawaf, Asmaa Yaseen Computer Science College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Gazi University Ankara Turkey Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Faculty of Applied and Human Sciences University Malaysia Perlis Perlis Malaysia Information Technology and Communication Department Rafidain Bank Iraq Altinbas University Istanbul Turkey Computer Science College of Computer Science University of Anbar Kirkuk Iraq

Election is a basic democratic tool that offers an official mechanism for the people to express their opinions in order to form a government by democratic means. Electronic voting has evolved into the most significant application of e-governance and e-democracy. Few countries have recently taken the opportunity to test and use electronic voting systems. Also, many countries expect that internet voting will become a reality during the next decade. In the conventional voting system, the electors authenticate themselves by displaying credentials;this move is open to the public and is validated by poll workers. During this authentication, they are manually checked before being eligible to vote. The standard system of voting is more costly and needs more human capital. Because of these factors, the whole world is heading toward the trend of e-voting. Electronic voting devices are supposed to be the answer to the shortages. A secure e-voting system must meet many criteria, including uniqueness, performance, fairness, stability, safety, authentication, and anonymity. Block-chain is a modern security solution, which is capable of securing e-voting system. Blockchain is distributed ledger. Each block contains only one transaction. Block chain provides security using cryptography and hashing. Blockchain provides a perfect infrastructure to provide secure e-voting ***. Blockchain along with smart contracts need hour to improvise an e-voting system. This paper presents a secure blockchain based framework for an e-electronic voting in Iraq. © 2022

关键词： Smart contract

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UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detection 14

UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detec...

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14th Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis, WASSA 2024

作者： Šmíd, Jakub Přibáň, Pavel Král, Pavel Department of Computer Science and Engineering University of West Bohemia Faculty of Applied Sciences Czech Republic NTIS – New Technologies for the Information Society Univerzitní 2732/8 Pilsen301 00 Czech Republic

ISBN: (纸本)9798891761568

This paper presents our system built for the WASSA-2024 Cross-lingual Emotion Detection Shared Task. The task consists of two subtasks: first, to assess an emotion label from six possible classes for a given tweet in one of five languages, and second, to predict words triggering the detected emotions in binary and numerical formats. Our proposed approach revolves around fine-tuning quantized large language models, specifically Orca 2, with low-rank adapters (LoRA) and multilingual Transformer-based models, such as XLM-R and mT5. We enhance performance through machine translation for both subtasks and trigger word switching for the second subtask. The system achieves excellent performance, ranking 1st in numerical trigger words detection, 3rd in binary trigger words detection, and 7th in emotion detection. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Intelligent Tracing and Process Improvement of Pathology Workflows using Character Recognition 3

Intelligent Tracing and Process Improvement of Pathology Wor...

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3rd International Health Data Workshop, HEDA 2023

作者： Hatlem, Markus Rabbi, Fazle Stünkel, Patrick Leh, Friedemann Faculty of Social Sciences University of Bergen Norway Department of Pathology Haukeland University Hospital Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway

A pathology laboratory processes various types of tissue and cell specimens and plays a vital role in the diagnostic process. However, pathology departments are currently facing a significant challenge due to the steady increase in incoming specimens. Increasing the workforce to match the influx is generally not feasible, so Information and Communication Technology (ICT) is seen as a potential solution. One area where ICT can be applied is in process monitoring and tracing. The increase in incoming specimens has caused queues within the laboratory, resulting in more time spent locating and retrieving individual specimens. Existing methods of tracing specimens, such as barcodes or alphabetic sorting, also require manual labor, adding further overhead. In this paper, we propose a lightweight application of optical character recognition (OCR) for specimen tracing, as part of a larger research project to optimize pathology processes at a large regional hospital in Bergen. We present a specific solution that integrates into a general process monitoring environment, and we compare different implementation techniques, particularly edge detection and neural networks. Our preliminary results indicate that this implementation can achieve an accuracy of up to 93.41%, increase sorting speed up to 54% and save up to 35% of time spent in manual sorting activities. We conclude our findings with a general discussion and outlook onto other areas where this solution could theoretically be applied. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Process control

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Enhancing IoT-Edge Computation with Data Forwarding based Decentralized Deep Neural Networks 7

Enhancing IoT-Edge Computation with Data Forwarding based De...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Prasad, B.M.G. Jopate, Rachappa Savita, Pankaj Basi Reddy, A. Shankar, B. Prabu Arunkumar, M.S. Department of Cse Andhra Pradesh Kurnool518452 India University of Technology And Applied Sciences - Al Mussanah Department of Information Technology Al Muladha Muscat Oman Sagar Institute of Science & Technology Bhopal Organization-Sagar Institute of Science & Technology Department of Cse Madhya Pradesh Bhopal India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Alliance University Alliance College of Engineering And Design Department of Computer Science And Engineering Karnataka Banglore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Computer Science And Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371406

The incorporation of edge computing has become essential in the big data and Internet of Things age to tackle issues related to latency, real-time processing, and data scale. This paper delves into the imperative need for edge computing within the context of big data environments. Distributed deep learning methodologies are explored, encompassing a diverse array of deep learning models. Particularly, a novel paradigm named Data Forwarding based Decentralized Deep Neural Network (DF-DDNN) is introduced to achieve low latency IoT-Edge computation. In order to reduce latency in Io T ecosystems, the DF-DDNN model makes use of edge devices' processing capabilities. By strategically distributing data processing and computation at the edge, this model seeks to overcome inherent limitations of conventional Io T architectures, with a keen focus on network, processing, and overall latencies. The research provides a thorough performance evaluation that compares the DF-DDNN model's performance versus conventional IoT models in a number of areas. The outcomes demonstrate how much better the suggested DF-DDNN model is than traditional IoT systems. Notably, improvements are evident in terms of reduced network latency, accelerated processing speeds, and overall latency mitigation. This research underscores the critical importance of embracing edge computing in the context of big data and deep learning applications. The DF-DDNN model emerges as a promising avenue for advancing IoT-Edge computation, effectively addressing latency concerns and augmenting the performance of IoT-enabled systems. © 2023 IEEE.

关键词： Edge computing

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A Systematic Literature Review of Blockchain Technology

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第10期16卷 97-108页

作者： Ghazi, Alaan Alisawi, Muthana Wahab, Yousif Mohammed Al-Dawoodi, Aras Abdullah, Sirwan Saber Hammood, Layth Nawaf, Asmaa Yaseen Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Faculty of Applied and Human Sciences University Malaysia Perlis Perlis Malaysia Computer Science College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Gazi University Ankara Turkey Information Technology and Communication Department Rafidain Bank Iraq Altinbas University Istanbul Turkey Computer Science College of Computer Science University of Anbar Kirkuk Iraq

The Blockchain technology moves data nodes and data streams from one informatics center to another based on the importance of Bitcoins, resulting in a dedicated, public, and secure network for interpretation and development of the ensuing information chain reaction. Blockchains are frequently formed by connecting interrelated patterns of information in order to conduct a secure transaction. Blockchain has the characteristics of decentralization, transparency, immutability, autonomy, open-source, anonymity, and consensus. Block chains are being utilized in a variety of real-world applications, including business and economics, financial technology, and digital currencies. This article does a comprehensive review of the literature on blockchain technology. While searching for the keyword "blockchain" as a subject, we identified a total of 1882 entries in these databases. After removing the less common record kinds, the papers were reduced to 1246 articles, which were then used for further investigation. © 2022

关键词： Smart contract

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Transmission Line Fault Detection and Classification Using Feature Extraction Based Self-Attention Convolutional Neural Network with Time Series Image 3

Transmission Line Fault Detection and Classification Using F...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Almusawi, Muntather Babu, D. Raja Mazumder, Debarshi Bala Dhandayuthapani, V. Saranya, N. Naga College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India College Of Computing And Information Sciences University Of Technology And Applied Sciences Department Of It Shinas campus Oman Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

The transmission lines continuously experience the number of shunt faults and its effective in practical system increases the instability, load damages and line restoration cost. This work implements an advanced self-attention convolutional neural network (SAT-CNN) method for fault detection and classification (FDC) of high voltage transmission lines experiencing a constant number of shunt faults. Faults cause load damage in real-time applications, increase instability, and increase the cost of line restoration. Therefore, a precise model is needs to identify and categorize the flaws to quickly restore he problematic phases. In this research, implemented SAT-CNN feature extraction model with imaging-based on time series that can accurately detect and classify faults. By employing a number of input signals, including, current, voltage, and combined current and voltage signals, at different sampling frequencies, the efficacy of implemented SAT-CNN model is evaluated. Implemented SAT-CNN method obtains high performance when compared to existing methods including weight-sharing network (WSCN), Truncated singular value decomposition and Human urbanization algorithm based Recurrent Perceptron Neural Network (TSVD-HUARPNN), and Graph convolutional neural network (GCN), and result achieved 99.90% accuracy value. © 2024 IEEE.

关键词： Convolutional neural networks

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