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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Hoomod, Haider K. Al-Hamami, Mohammad Alaa Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Niaf, Jolan Rokan College of Education Mustansiriyah University Baghdad Iraq Applied Science University Manama Department of Management Information Systems Manam Bahrain Imam Al-Kadhim College Computer Techniques Engineering Department Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq Informatics Institute for Postgraduate Studies University of Information Technology and Communications Baghdad Iraq

ISBN: (纸本)9798350369106

With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined communication protocol. Other software programs can be linked to operate together using Message Queue Telemetry Transport Protocol (MQTT) or MQSeries components. Business integration software and middleware are common terms for this category of applications. With its suggested lightweight, easy-to-implement, and lowbandwidth system for billions of smart items, the MQTT protocol offers a wide range of potential applications, including home automation. In order to enhance the power savings in MQservice routing, we presented an intelligent assistant for optimizing routing model. For Internet of Things (IoT) purposes, it introduces an optimal routing model based on optimized hybrid machine learning-based anomaly detection (OHMLA). In order to extend the reach of a network, the offered approach allows Internet of Things devices to make efficient use of energy. To further enhance Intrusion Detection identification and get optimum network selection routes, the moth Salp Swarm technique (SSA) technique is used for the optimized hyperparameters. The model is capable of accurately detecting attacks that result in network congestion, causing delays in connection response and data transfer in IoT network. © 2024 IEEE.

关键词： Middleware

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Modeling Context and Content Features for Fake News Detection

SSRN

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SSRN 2023年

作者： Phan, Huyen Trang Nguyen, Ngoc Thanh Hwang, Dosam Seo, Yeong-Seok Department of Computer Engineering Yeungnam University Gyeongsan Korea Republic of Department of Applied Informatics Wroclaw University of Science and Technology Wroclaw Poland

With the emergence and rapid development of social networks, an increasing amount of news has been spreading. In addition to the benefits of factual information, there are always risks associated with the dissemination of fake news and preventing the spread of fake news has been a concern for researchers. Many methods have been proposed to detect fake news, but they do not fully extract important information related to news content and context, and rarely consider modeling the simultaneous exploitation of the news context and content in fake news detection. This study proposes a method to improve the performance of fake news detection by modeling features related to news context and content. First, we combine contextualized embeddings (e.g., BERT) and dependency-based embeddings (e.g., dependency-based GCN) to enhance the performance of the content representations of news and reviews posting them. Second, we combine all available review texts related to news belonging to the user. Third, we explore all the reviews that other users had posted about current news by clearly creating review representations posted by the same user about the same news. This leads the model to quickly memorize all reviews related to news from one user. Finally, we model the news content features and the modeled news context features to enhance the richness of the news feature representations. Experimental results on the PolitiFact and GossipCop datasets show improvement to the state-of-the-art method of more than three percentage points in the best case. © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Forecasting of Fires Hotspot in Tropical Region Using LSTM Algorithm Based on Satellite Data

Forecasting of Fires Hotspot in Tropical Region Using LSTM A...

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2022 IEEE Region 10 Symposium, TENSYMP 2022

作者： Kadir, Evizal Abdul Kung, Hsiang Tsung Rosa, Sri Listia Sabot, Andrew Othman, Mahmod Ting, Mark Universitas Islam Riau Department of Informatics Engineering Pekanbaru28284 Indonesia Harvard University Department of Computer Science CambridgeMA02134 United States Universiti Teknologi Petronas Department of Applied Mathematics Perak86400 Malaysia

ISBN: (数字)9781665466585

ISBN: (纸本)9781665466585

Raising of temperature due to global warming impacted to the number of fires hotspot globally, in tropical region some of the places a high risk such as wildfire. Fire in Indonesia is one of the big disasters because of forestry region and peat land that highly risk to wildfire especially in dry season. This research aims to do a forecasting number of fires hotspot in future time based on training data collected in previous years. Long Short-Term Memory (LSTM) algorithm applied in this forecasting, the advantages of LSTM model to forecast a timeseries data make the high prediction accuracy. Fires hotspot data was collected since year 2010 to 2021 with total number of datasets more than 700,000 hotspot and forecast for the future year 2022. Collected datasets analyzed use LSTM model divided into two classification which are training and test data with 70% and 30% respectively. Results shows a similar pattern of forecasting fires hotspot in 2022 compared into previous last 2 years which 2021 and 2020. Furthermore, to proof proposed algorithm is working fine then a forecast number of fire hotspot for year 2021 have been done which compared actual and forecasting data and percentage of error 4.56%. LSTM Algorithm is one of the models suitable to use in data forecasting in high volume time series data. © 2022 IEEE.

关键词： Fires

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Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

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computer Systems Science & engineering 2023年第5期45卷 1775-1787页

作者： Abdelhamied A.Ateya Amel Ali Alhussan Hanaa A.Abdallah Mona A.Al duailij Abdukodir Khakimov Ammar Muthanna Department of Electronics and Communications Engineering Zagazig UniversityZagazigSharqia44519Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Applied Probability and Informatics Peoples’Friendship University of Russia(RUDN University)Moscow117198Russia Center for Telecommunication Research School of Postgraduate Studies&ResearchSri Lanka Technological CampusPadukka10500Sri Lanka

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability,availability,and ultra-low *** requirements of such networks are the main challenges that can be handled using a range of recent technologies,including multi-access edge computing(MEC),artificial intelligence(AI),millimeterwave communications(mmWave),and software-defined *** aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service(QoS).This article considers developing a complex MEC structure for fifth and sixth-generation(5G/6G)cellular ***,we propose a seamless migration technique for complex edge computing *** developed migration scheme enables services to adapt to the required load on the radio *** proposed algorithm is analyzed for various use cases,and a test bench has been developed to emulate the operator’s *** obtained results are introduced and discussed.

关键词： 5G 6G mobile edge computing migration offloading quality of service

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Chaotic binary reptile search algorithm and its feature selection applications

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13931-13947页

作者： Abualigah, Laith Diabat, Ali Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Division of Engineering New York University Abu Dhabi Saadiyat Island Abu Dhabi129188 United Arab Emirates Department of Civil and Urban Engineering Tandon School of Engineering New York University BrooklynNY11201 United States Faculty of Computer Sciences and Informatics Amman Arab University Amman11953 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

Feature selection (FS) is known as the most challenging problem in the Machine Learning field. FS can be considered an optimization problem that requires an efficient method to prepare its optimal subset of relative features. This article introduces a new FS method-based wrapper scheme that mixes chaotic maps (CMs) and binary Reptile Search Algorithm (RSA) called CRSA, employed to address various FS problems. In this method, different chaotic maps are included with the main ideas of the RSA algorithm. The objective function is revealed to combine three objectives: maximizing the classification accuracy, the number of chosen features, and the complexity of produced wrapper models. To assess the achievement of the proposed methods, 20 UCI datasets are applied, and the results are compared with other well-known methods. The results showed the superiority of the introduced method in bettering other well-known techniques, particularly when applying binary RSA with Tent CM. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature Selection

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Estimation of Weibull Distribution Parameters by Analytical Methods for the Wind Speed of Jhimpir, Pakistan - A Comparative Assessment

Estimation of Weibull Distribution Parameters by Analytical ...

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2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2022

作者： Abbas, Ghulam Gu, Jason Asad, Muhammad Usman Balas, Valentina E. Farooq, Umar Khan, Irfan A. The University of Lahore Department of Electrical Engineering Lahore Pakistan Dalhousie University Department of Electrical and Computer Engineering Halifax Canada Aurel Vlacieu University of Arad Department of Automation and Applied Informatics Arad Romania Lab GalvestonTX77553-1675 United States

ISBN: (纸本)9781665492423

Assessing the potential of a wind farm requires looking into how the wind behaves throughout a certain time frame. One of the most popular ways to statistically model wind data is with the Weibull distribution. Estimating two parameters of the Weibull PDF is crucial for a better fit between the PDF and wind speed data. In this study, Weibull distribution parameters for 2019 wind speed data in the Jhimpir region of Pakistan are determined using four analytical techniques: the empirical method (EM), the maximum likelihood method (MLM), the method of moments (MoM), and the energy pattern factor (EPF) approach. Each technique is evaluated using several different metrics, including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), and the coefficient of correlation (R). Statistical analyses show that the shape (k) and scale (c) parameters of the Weibull distribution estimated by the EM, MLM, and MoM are quite close to one another compared to the ones obtained by EPF for the available data. The MATLAB environment-based numerical results expressed that the EPF method performed the best in terms of R and RMSE and worst in terms of MAE and MARE. © 2022 IEEE.

关键词： Weibull distribution

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Automated Fire Hydrant Monitoring and Maintenance using IoT Data Analytics and Naive Bayes Classifier

Automated Fire Hydrant Monitoring and Maintenance using IoT ...

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

作者： Sarasu, R. Nandagopal, V. Shibu, N.V. Lakshminarayanan, Suresh Raj, T Saju Rajmohan, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics Chennai India Mohan Babu University School of Engineering Department of Electrical and Electronics Engineering Tirupathi India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai India University of Technology and Applied Sciences Department of Engineering & Electronics South Al Batina Oman Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350375442

This paper introduces a new method for managing fire hydrants that uses a monitoring system based on the Internet of Things (IoT) and a Naive Bayes Classifier (NBC) for predictive maintenance. Water pressure, flow rate, and the physical state of fire hydrants are among the essential factors for the proposed system continually collecting data using an IoT sensor network. The system analyzes real-time data using NBC. These predictive capabilities reduce the potential of hydrant breakdowns during emergencies through preventive maintenance potential. It introduces a reliable IoT infrastructure for fire hydrant monitoring, improving the acquired data's granularity and timeliness. NBC provides a powerful instrument for early problem detection, significantly improving maintenance effectiveness. Since the technology is compatible with existing fire department procedures, it can easily integrate into current operational processes. Better public safety, more efficient use of resources, and more dependable fire hydrants are potential outcomes of responsive and proactive data-driven maintenance techniques. The findings improve overall fire safety and operational efficiency, with a 25% decrease in maintenance expenditures and a 40% increase in issue detection accuracy. © 2024 IEEE.

关键词： Predictive maintenance

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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Robotic Assisted Immersive Therapy System for Stroke Rehabilitation

Robotic Assisted Immersive Therapy System for Stroke Rehabil...

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E-Health and Bioengineering Conference (EHB)

作者： Robert Gabriel Lupu Adrian Burlacu Ovidiu Catarama Ionut Sopot Department of Computer Engineering Gheorghe Asachi Technical University of Iasi Iasi Romania Department of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iasi Iasi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

The number of stroke cases has increased dramatically in the last 30 years, and more and more people are left disabled because of stroke. It is vital to continue researching new ways to recover and improve their condition. Rehabilitation therapies are based on intensive and repeated training starting as soon as possible. However, the number of therapists is too few to treat the growing number of stroke survivors. Advanced technologies such as virtual reality (VR) and robot-assisted therapies have been introduced to improve stroke rehabilitation. VR-based rehabilitation immerses patients in simulated environments, providing visual feedback miming real-world activities. This approach motivates patients through interactive tasks while engaging their cognitive processes, which is crucial for neuroplasticity and motor recovery. Robot-assisted therapy complements VR by providing precise, repetitive movements and proprioceptive feedback. This paper presents a virtual reality- based and robotic-assisted upper limb neuromotor recovery system. The system combines VR immersion with robotic assistance, allowing patients to receive real-time guidance and support during exercise. The system adapts to the patient's condition, providing personalized visual and physical feedback to help relearn motor skills.

关键词： Training Somatosensory Visualization Robot kinematics Medical treatment Propioception Virtual reality Stroke (medical condition) Motors Real-time systems

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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