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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Milutinovic, Nemanja Cabarkapa, Slobodan Zivkovic, Miodrag Antonijevic, Milos Mladenovic, Djordje Bacanin, Nebojsa Singidunum University Technical Faculty Belgrade Serbia Academy of Technical and Art Applied Studies Belgrade Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia College of Academic Studies Dositej Belgrade Serbia

ISBN: (纸本)9781665474511

From 2015 to 2022, healthcare 4.0 has made revolutionary impacts on health services. It includes machine learning (ML), internet of things (IoT), fog computing and cloud computing. The utilization of machine learning approaches supplied by IoT advances employing fog and cloud computing principles improves the performance and accuracy of healthcare models. These concepts bounded together are distinguished in their application with the researchers as they dominate alongside the best results. Inspirited by the mathematical traits of sine and cosine functions, the sine cosine algorithm (SCA) generates numerous initial random candidate solutions with the goal of fluctuation outwards or towards the ideal answer. The metaheuristic algorithm can be applied for optimization of an artificial neural network (ANN) on which the Healthcare 4.0 relies. The solution has been tested on four diverse datasets in this field as well as the results of those tests have been compared to those of other hybrid solutions with the use of same datasets as the suggested solution. The results are in the favor of the novel method, as it obtains general advantage over all tests. © 2023 IEEE.

关键词： Multilayer neural networks

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Electrodermography and Heart Rate Sensing for Multiclass Emotion Prediction in Virtual Reality: A Preliminary Investigation

Electrodermography and Heart Rate Sensing for Multiclass Emo...

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2021 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2021

作者： Bulagang, Aaron Frederick Mountstephens, James Teo, Jason Faculty of Computing and Informatics Universiti Malaysia Sabah Malaysia

ISBN: (纸本)9781665415910

This paper demonstrates a method for classifying multi-model emotions using a combination of Heart Rate (HR) and Electrodermography (EDG) signals with SVM (Support Vector Machine) as the classifier in Virtual Reality (VR). A wearable was used during the experiment to acquire the subject's HR and EDG signals simultaneously while watching 360O videos in VR. The acquired signals are then classified with SVM in a multi-class model for valence and arousal. The experiment conducted is for 10 intra-subject classifications, in which two subjects achieved the best accuracy of 99.5%, while for inter-subject classification of 10 subjects achieved 66.0%, This paper demonstrates that combined signals of HR and EDG can provide high accuracy for multi-class emotion classification in VR. ©2021 IEEE

关键词： Support vector machines

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Evaluation of Smart Community Engagement in Riyadh, Saudi Arabia 8th

Evaluation of Smart Community Engagement in Riyadh, Saudi Ar...

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8th International Visual informatics Conference, IVIC 2023

作者： Zamin, Norshuhani Mary, Mervin Esckalin Muzaffar, Abdul Wahab Ku-Mahamud, Ku Ruhana Residi, Mohd Azhar Ibrahim College of Computer Studies De La Salle University Manila Philippines College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia School of Computing Universiti Utara Malaysia Kedah Malaysia Faculty of Leadership and Management Universiti Sains Islam Malaysia Negeri Sembilan Nilai Malaysia

ISBN: (纸本)9789819973385

COVID19 has created a global effect on economies, people and governments around the world. This includes the Kingdom of Saudi Arabia (KSA). Although the KSA’s peak of reported COVID cases was from March to July 2020 the pattern shows significant reduction from August 2020 onwards. Nonetheless, many people are still losing their jobs and businesses are shutting down. Due to the prolonged lockdown period announced by the KSA government from 9 March 2020, there has been an increased, 70%, demand for digital services during the lockdown, as compared to the previous year (according to the government figures). This is due to the high restriction of movements for everyone. People started to use online services to purchase items and food;meanwhile, lessons in schools and universities remained online to the date this proposal was written. Many people are also working from home. Online systems such as Tabaud, Mawid, Tawakkalna and Tetamman Many apps were developed for use during the pandemic. For example, the Tabaud, Mawid, Tawakkalna and Tetamman apps for smartphones are among the latest by KSA government efforts to combat and contain the virus. The KSA introduced Vision 2030 to empower their people to diversify the economy of the country. In Vision 2030, the KSA has prioritized the rapid growth of ICT as a building foundation of digital development. This study focuses on evaluating the smart community engagement in Riyadh focusing on the level of acceptance and execution from the perspective of the public and relevant agencies. This research seeks to understand the level of community engagement in Riyadh towards the use of ICT in improving quality of life. This could help move forward the development of the smart community concept involving the engagement of relevant authorities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart city

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Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network

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Computers, Materials & Continua 2023年第11期77卷 2157-2178页

作者： Gul Nawaz Muhammad Junaid Adnan Akhunzada Abdullah Gani Shamyla Nawazish Asim Yaqub Adeel Ahmed Huma Ajab Department of Information Technology The University of HaripurHaripur22060Pakistan Faculty of Computing and Informatics University Malaysia SabahSabah88400Malaysia Department of Environmental Sciences COMSATS University Abbottabad CampusAbbottabad22010Pakistan Department of Chemistry COMSATS University Abbottabad CampusAbbottabad22010Pakistan

Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate *** proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)*** centralizes the control plane and separates it from the data *** simplifies a network and eliminates vendor specification of a *** of this open nature and centralized control,SDN can easily become a victim of DDoS *** proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate *** Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)*** proposed DNN model scans the data to find the correlated features and delivers high-quality *** model enhances the security of SDN and has better accuracy as compared to previously proposed *** choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing *** model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss *** accuracy increases to 99.80%as we increase the number of epochs to 100 *** proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed *** can handle a huge amount of structured and unstructured data and can easily solve complex problems.

关键词： Distributed denial of service(DDoS)attacks software-defined networking(SDN) classification deep neural network(DNN)

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Classification Based on Texture Analysis for Synthetic Hazy Image from Mount Kelud Haze Image Density 8

Classification Based on Texture Analysis for Synthetic Hazy ...

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8th International Conference on Electrical, Electronics and Information Engineering, ICEEIE 2023

作者： Nugroho, Fresy Basid, Puspa Miladin Safitri A. Arif, Yunifa Miftachul Diah, Norizan Mat Bahtiar, Firma Sahrul Nurhayati, Hani Kurniawan, Fachrul Priandani, Nurizal Dwi Dermawan, Dodik Arwin Universitas Islam Negeri Maulana Malik Ibrahim Informatics Engineering Faculty of Science and Technology Malang Indonesia School of Computing Sciences College of Computing Informatics and Media Universiti Teknologi Mara Shah Alam Malaysia Library and Information Science Universitas Islam Negeri Maulana Malik Ibrahim Faculty of Science and Technology Malang Indonesia Manajemen Informatika Program Vokasi Universitas Negeri Surabaya Surabaya Indonesia

ISBN: (纸本)9798350340501

This paper classifies the simulations of homogeneous synthetic images, heterogeneous synthetic hazy images, and original hazy images taken from CCTV (Close Circuit Television) of Mt. Kelud crater using the GLCM (Gray Level Co-Occurrence Matrix) method. The average feature values obtained using the GLCM (Gray Level Co-Occurrence Matrix) method are used to compare the similarity of gray feature values of the three and then classify thin, medium, and thick images. The results for classifying thin haze, medium haze, and thick haze on the homogeneous synthetic hazy image test data obtained an accuracy value of 50%, a precision value of 46%, and a sensitivity value of 65%. As for the classification of thin, medium, and thick fog on heterogeneous synthetic hazy images, test data obtained an accuracy value of 42%, a precision value of 32%, and a sensitivity value of 48%. © 2023 IEEE.

关键词： Textures

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A Model for Forex Market Price Prediction using Deep Learning

A Model for Forex Market Price Prediction using Deep Learnin...

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2022 International Conference on Innovation and Intelligence for informatics, computing, and Technologies, 3ICT 2022

作者： Makiya, David Shibwabo, Bernard ULisboa Faculty of Science Department of Informatics Lisbon Portugal Strathmore University School of Computing and Engineering Sciences Nairobi Kenya

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

The forex markets are full of uncertainties that are largely influenced by the forces of demand and supply. The rates usually adjust depending on the prevailing status of the economy, politics and the monetary policies. The forex market consists of multiple dealers and online forex trading platforms. Predicting forex market prices is a complicated process and subjective in nature for forex dealers, economists and business people. The potential to make losses due to poor speculative guesses is quite high for multinational corporations located in more than one economy. The aim of this study is to develop a model for forex market price prediction. We develop the model using the Data-Driven modelling technique. We source the Central Bank of Kenya's (CBK) historical dataset to achieve this. The dataset is divided into training and testing data by a splitting of 80-20. We construct the model by combining time-series regression with resilient backpropagation neural networks in the select currencies. The unique behavior of each of the currencies required a separate implementation of the model output. This results in increased accuracy and lower error levels, hence efficiency and optimality. Successful predictions are conducted up-to eight months forward. We realize accuracy levels ranging from 88-98% and SSE of 0.496-2.667. The wider the historical data range, the higher the accuracy and consequently, the longer the prediction horizon. © 2022 IEEE.

关键词： Neural networks

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Improving stability of detected topics in learning analytics 45

Improving stability of detected topics in learning analytics

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45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022

作者： Rako, S. Simic, D. Slibar, B. Gusic-Mundar, J. University of Zagreb University Computing Center Zagreb Croatia University of Zagreb Faculty of Organization and Informatics Varaždin Croatia

ISBN: (纸本)9789532331035

Due to exponential increase in the number of published research papers, it is becoming more and more difficult to review current research in any discipline. It is especially true for disciplines undergoing fast development like learning analytics and educational data mining. Natural language processing offers opportunity to facilitate extraction of information/knowledge from a large volume of documents. In an attempt to organize papers on application of learning analytics and educational data mining for supporting self-regulated learning, we have used Latent Dirichlet Allocation to identify topics in a set of 107 research papers. Results of Latent Dirichlet Allocation depend on the initial choice of a random number generator seed, and replicated runs do not result in the same topics. Stability of allocation of documents to topics was analyzed using Jaccard similarity coefficient. The proposed method enables identifying stabile clusters of research papers sharing the same topics. © 2022 Croatian Society MIPRO.

关键词： Data mining

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Legal Maxims Artificial Intelligence Application for Sustainable Architecture And Interior Design to Achieve the Maqasid of Preserving the Life and Money

Legal Maxims Artificial Intelligence Application for Sustain...

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2022 Engineering and Technology for Sustainable Architectural and Interior Design Environments, ETSAIDE 2022

作者： Tarshany, Yasser Mohamed Abdelrahman Al Moaiad, Yazeed El-Ebiary, Yousef A. Baker Mediu Faculty of Islamic Sciences Kuala Lumpur Malaysia Mediu Faculty of Computer and Information Technology Kuala Lumpur Malaysia UniSA Faculty of Informatics and Computing Terengganu Malaysia

ISBN: (数字)9781665442855

ISBN: (纸本)9781665442855

Nowadays, buildings are changing rapidly to achieve Sustainable Architecture and Interior Design. Therefore, it needs many Legal Maxims to assist them. Without the proper rules, numerous problems will emerge in the present and the future. Legal Maxims are crucial in helping to achieve the Maqasid of preserving life and money. These Legal Maxims are acts judged by the intentions behind them-Acts are judged by the intention behind them, hardship begets ease, certainty is not overruled by doubt, there should be no harm and no reciprocating harm, and custom is the basis of judgment. Hence this paper will discuss these Legal Maxims and demonstrate how they will achieve the Maqasid of preserving life and money for sustainable Architecture and Interior Design. © 2022 IEEE.

关键词： Architectural design

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Framework for Analyzing Intruder Behavior of IoT Cyber Attacks Based on Network Forensics by Deploying Honeypot Technology 5

Framework for Analyzing Intruder Behavior of IoT Cyber Attac...

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5th International Conference on Information and Communications Technology, ICOIACT 2022

作者： Felix, Michael Safitri, Cutifa Mandala, Rila President University Faculty of Computing Cikarang Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

This paper proposes an improved framework or methodology for analyzing the activities of network intruders and malwares in the area of cybercrime by utilizing honeypot technology and packet analyzer based on network forensics fundamentals to finally identify those intruders based on their intrusion scenarios. Learning on how new intruder and malware attacks aids organizations to be more aware and give the advantage to prepare their cyber-security systems against any attacks and solve real-world problems. The main advantage of this proposed method is resulted in the form of framework that can be served as a guideline to analyze attackers and overcome the discovered vulnerabilities along with strengthening the existing cyber-security systems for preventing future attacks. The proposed hypothesis is proven through a real experiment on a virtual machine using a virtual server. The literature reviews about the topic described are raised to support and validate our findings, a model is used to simulate the experiment processes done by the authors, therefore, it is found that the proposed framework and scenario in this study is possible to be achieved. © 2022 IEEE.

关键词： Cybersecurity

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Intelligent PSO approach for QoS-Aware Web Service Selection 3

Intelligent PSO approach for QoS-Aware Web Service Selection

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3rd International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2022

作者： Abdel-Salam, Mahmoud El-Hasnony, Ibrahim M. Elhoseny, Mohamed Tarek, Zahraa Faculty of Computers and Information science Mansoura University Egypt College of Computing and Informatics University of Sharjah United Arab Emirates

ISBN: (纸本)9781839538186

Various providers can supply the various services, in a service-oriented setting, each with its own unique set of non-functional Quality of Service (QoS) qualities. Web service selection efficiency will always decay as the number of offered services grows because many already-existing services share identical QoS characteristics. There must be a method for choosing which individual services will go into other related fields so that it may fulfill its consumers' expectations for quality of service. Many services exist with the same QoS and provide the same functionality therefore choosing the best services from many candidates is a research issue. When selecting services of good quality and with less computation time, it will affect the efficiency of the next operations such as service composition and service recommendation. In this paper, a new improved Particle Swarm Optimization (PSO) algorithm is proposed to solve the mentioned issue of service selection. To find the global optimal solution for selecting dynamic web services while maintaining the quality of service, this study introduces a new single parameter and evolution-based enhancement system to improve PSO-based web service selection (IPSO-SS). The IPSO tries to avoid the local optimum to find the global solution for the problem of service composition. IPSO-SS is practical and effective in the service selection process, as evidenced by theoretical experimental and analysis findings. The experiments conducted on QWS datasets showed that the proposed approach IPSO-SS outperformed other approaches in terms of computation time and convergence rate. © IET Conference Proceedings 2022.

关键词： Quality of service

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