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Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

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computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

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The Impact of Collusive Interest Flooding Attack on NDN Network Performance 10

The Impact of Collusive Interest Flooding Attack on NDN Netw...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Jupriyadi Negara, Ridha Muldina Mulyana, Eueung Syambas, Nana Rachmana Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Telkom University School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350377057

Collusive Interest Flooding Attack (CIFA) is a denial of service (DoS) attack in Named Data Networking (NDN) networks. Using a fake server, this attack develops the Interest Flooding Attack (IFA) attack. The attacker sends a large number of requests to the fake server. Sending requests to fake servers is intended to avoid existing flood attack detection systems. In this paper, an analysis of the impact of CIFA attacks is carried out by carrying out attacks and varying network topology configurations. This is done to get a more comprehensive idea of the possible impact of the attack. The performance metric used is the interest satisfaction ratio (ISR) value, which describes the ratio of the number of data packets consumers receive to the total interest packet requests sent. The results of the simulations show that CIFA attacks can reduce the performance of the NDN network. This can be seen based on the ISR value, which decreases when a CIFA attack occurs. By increasing the size of the content store (CS) on the router, you can increase the ISR value so that the impact of attacks can be minimized. © 2024 IEEE.

关键词： Denial-of-service attack

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Developing Energy Autonomous and Cable-less Multi-gateway LoRa Networks

Developing Energy Autonomous and Cable-less Multi-gateway Lo...

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2023 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2023

作者： Valkanis, Anastasios Beletsioti, Georgia A. Kantelis, Konstantinos Nicopolitidis, Petros Papadimitriou, Georgios Louta, Malamati Aristotle University of Thessaloniki Department of Informatics Thessaloniki Greece University of Western Macedonia Faculty of Engineering Department of Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350302561

Long Range (LoRa) networks are a reliable and efficient solution to serve Internet of Things (IoT) applications in a long range rural and forest areas. The capability of the gateways, both for wireless connection with the backbone network and be powered by renewable energy resources, makes possible the development of energy autonomous and without the need of cabling LoRa networks. This perspective greatly facilitates the network installation in hard-to-reach areas, as well as reducing their operating costs. An important obstacle, for the operation of energy autonomous LoRa networks, is the need for continuous operation of the gateways defined by the LoRaWAN protocol, leading to increased power consumption. For this reason, it is necessary the creation of a protocol that will reduce the energy consumption of the gateways, while maintaining the quality of the services provided in the IoT applications. In this paper we present a new hybrid protocol for energy autonomous and cable-less multi-gateway LoRa networks. The simulation results showed that the proposed protocol ensures a more than 50% reduced energy consumption for the gateways, as well as increased efficiency and reliability compared to the existing protocol, at the cost of slightly increased delay in data transmission. © 2023 IEEE.

关键词： Energy efficiency

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Decision Making in Precision Agriculture - The Case of VEL OS Intelligent Decision Support System 14

Decision Making in Precision Agriculture - The Case of VEL O...

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Theodorou, Dimitrios Mantas, Nikolaos Karampelia, Ioanna Dimokas, Nikos Kyriakidis, Thomas Louta, Malamati University of Western Macedonia Faculty of Engineering Department of Electrical and Computer Engineering Kozani Greece University of Western Macedonia Department of Informatics Kastoria Greece

ISBN: (纸本)9798350318067

Precision agriculture includes Integrated Pest Management (IPM), which focuses on preventing and controlling pests and diseases through a combination of biological, traditional, and chemical practices. An Intelligent Decision Support System (VELOS) was developed for plant protection in bean cultivations in the Prespa region of Greece. VELOS utilizes remote sensing, GPS mapping, and sensor technologies to assist in collecting and generating massive amounts of high-quality data. This information is then used to monitor crop development, detect pests and diseases, and assess the efficacy of pest management strategies. The collected information is exploited by the Decision Support Systems (DSS) at the core of IPM. The DSS assists in saving resources and minimizing the overall cost while improving overall pest management efficiency. It also provides an intelligent decision support mechanism for pest management methods, such as which pesticides to apply and when to use them. A DSS can also help pest control practitioners monitor the effectiveness of pest management measures and modify them appropriately. This feedback loop can provide effective disease management, avoiding the danger of serious crop losses or property damage. The proposed Intelligent Decision Support System (VELOS) is responsible for generating suitable plant protection recommendations as well as scheduling their implementation. © 2023 IEEE.

关键词： Precision agriculture

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Artificial Intelligence-Based System 'SiMoniK' for MSMEs 9

Artificial Intelligence-Based System 'SiMoniK' for MSMEs

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Lestari, Sri Yulmaini Aswin Sulyono Nul Fikri, Ruki Rizal Informatics Engineering Study Program Faculty of Computer Science Bandar Lampung Indonesia Management Study Program Faculty of Economics Business Bandar Lampung Indonesia

ISBN: (纸本)9798350306835

Artificial intelligence was recently implemented in various fields. One of these applied artificial intelligence systems was the Performance Monitoring System "SiMoniK"for MSMEs. This system was designed through the C4.5 algorithm and the Porat-Rank method. The C4.5 algorithm was applied to classify the MSMEs into three classifications, e.g. the micro enterprise classification, the small enterprise classification, and the medium enterprise classification. Moreover, the Porat-Rank method was also applied to distribute the solving-problem recommendations and to improve the independence of MSMEs. The result of this study was that the C4.5 algorithm could classify the MSME data correctly in the form of an accuracy value of 99.2%. Besides, the Porat-Rank method could also provide recommendations in the form of outstanding quality with an NDCG mean value of 0.87. © 2023 IEEE.

关键词： Artificial intelligence

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Lip-reading: Phoneme-to-text Machine Translation using Large Language Models

Lip-reading: Phoneme-to-text Machine Translation using Large...

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： El-Ghomari, Dounia El-Bialy, Randa Chen, Daqing Polytech Nantes School of Engineering Nantes France British University in Egypt Faculty of Informatics and Computer Science Egypt London South Bank University School of Engineering London United Kingdom

ISBN: (纸本)9798350376739

An effective conversion of recognised phonemes to words has a significant impact on the overall performance of an automatic lip-reading system. In this paper, transformer-based models have been examined for phoneme-to-text translation. The benchmark BBC Lip-reading Sentence 2 (LRS2) data is used in this study, and the T5 model (Text-To-Text Transfer Transformer) and the GPT-2 model are considered the conversion models. A detailed discussion is provided on the analysis procedure, data pre-processing, model implementation, and the metrics employed for model evaluation. The experimental results have demonstrated that the T5 model can achieve a substantial improvement over the GPT-2 model with a superior accuracy and fluency in the phoneme-to-text translation, offering a potentially promising solution for enhancing the precision of automatic translation and speech processing systems. This work contributes to the development of more robust applications in the field of speech recognition and communication technologies. © 2024 IEEE.

关键词： Natural language processing systems

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Challenges on Cloud Computing Migration Strategy for Music Industry: A Systematic Literature Review

Challenges on Cloud Computing Migration Strategy for Music I...

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2023 International Conference on informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Wirasti, Hijir Della Seta, Henki Witarsyah, Deden Prabu, Hamonagan K Azzahro, Ayu Hananto, Bayu Telkom University Faculty of Engineering Industry Bandung Indonesia Universitas Pembangunan Nasional Veteran Informatics Study Program Computer Science Faculty Jakarta Indonesia

ISBN: (纸本)9798350307306

Cloud computing has emerged as a transformative technology that offers numerous benefits to various industries, including the music industry. Cloud computing has revolutionized the way businesses operate and has had a significant impact on various industries, including the music industry Cloud computing, with its scalability, flexibility, and cost-effectiveness, presents an opportunity for the music industry to leverage technology to enhance its operations and meet the evolving demands of the digital era. This paper presents a systematic literature review on cloud computing migration strategies specifically tailored for the music industry. The review aims to identify existing research, frameworks, and best practices related to cloud migration in the music industry, as well as highlight the challenges and opportunities associated with such migrations. Through an analysis of relevant literature, this study provides valuable insights to assist music industry stakeholders in developing effective cloud migration strategies. © 2023 IEEE.

关键词： Cloud computing architecture

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IOT Based Smart Parking System Using Ensemble Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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New Skills and Knowledge for Digital Entrepreneurs in the age of Artificial Intelligence 19

New Skills and Knowledge for Digital Entrepreneurs in the ag...

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19th European Conference on Innovation and Entrepreneurship, ECIE 2024

作者： Orozova, Daniela Angelova, Nadezhda Zlatev, Zlatin Department of Informatics and Mathematics Faculty of Economics Trakia University Stara Zagora Bulgaria Department of Electrical Engineering Automation Computer Systems and Communications Faculty of Technics and Technologies Trakia University Yambol Bulgaria

ISBN: (纸本)9781917204149

Digital technologies are becoming increasingly complex and integrated, leading to significant transformations in society and the economy. The article aims to explore and summarize the new opportunities and potential risks of the widespread use of artificial intelligence (AI) in all aspects of life, to define the new skills and necessary knowledge of digital entrepreneurs and to highlight the need for transformation in modern education. Recognizing that the relationship between technology and business is two-way and becoming stronger, revealing that well-prepared employees are a guarantee of success and prosperity of companies in various fields, we try to focus on the main groups of qualities, skills and basic knowledge of students in the age of artificial intelligence. The development of the Internet, expansion of connectivity through social networks, the advent of AI, 3D printing, and immersive technologies like Augmented Reality, and Virtual Reality, require new knowledge and skills, leading to new challenges in education. Qualified personnel in this modern world must have solid professional training and systemic thinking (knowledge, skills, accumulated information), developed cognitive abilities, and personal skills based on collecting and analyzing large amounts of diverse information from heterogeneous sources. Questions arise: how can multiple information sources be combined effectively, and how can the fusion of multiple sources provide additional information to support decision-making processes? Combining information obtained from the real world makes the results heterogeneous and more informative. It follows the need to develop machine learning methods to extract relevant information from increasingly complex data sets. The goal is to improve the accuracy of the applied classification algorithms by combining predictions from multiple models, as well as obtaining a more stable final classification evaluation, effective handling of noisy data, adaptation to c

关键词： Adversarial machine learning

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A computer Vision System to Classify the Quality of Central Sulawesi Cocoa Beans Using Convolutional Neural Networks (CNN) 3

A Computer Vision System to Classify the Quality of Central ...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Angreni, Dwi Shinta Anshori, Yusuf Wirdayanti Arifin, Suci Ramadhani Humaira, Fitrah Maharani Universitas Tadulako Faculty of Engineering Departement of Information Technology Palu Indonesia Universitas Dipa Makassar Departement of Informatics Engineering Makassar Indonesia Politeknik Elektronika Negeri Surabaya Departemen of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350394559

The cocoa industry serves as a vital economic pillar, holding a pivotal role in the daily lives of farmers in Central Sulawesi. However, a prevailing challenge for cocoa cultivators in this region resides in the inherently subjective nature of cocoa bean quality assessment, often necessitating the involvement of a substantial human workforce. Addressing this challenge requires the implementation of computer vision technology capable of autonomously grading cocoa bean quality. Such a system not only expedites the evaluation process but also enhances the precision and consistency in determining the quality of the beans. The study employed Convolutional Neural Networks (CNN) methodology to analyze images of cocoa beans sourced from Central Sulawesi farmers. The research demonstrated a remarkable accuracy of 94.66%, accompanied by a minimal loss of 0.1195. The model demonstrates strong performance on both training and validation sets, indicating effective learning of relevant patterns. Consistent accuracy and loss trends across epochs signify stable training without notable overfitting or underfitting. The minimal gap between training and validation metrics reflects the model's effective generalization to unseen data. This technological integration promises to revolutionize cacao quality assessment, offering a sophisticated and efficient solution for the benefit of Central Sulawesi's cacao farming community. © 2023 IEEE.

关键词： computer vision

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