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The Role of Blockchain Integration in Edge Computing for 6G IoT: Challenges and Applications 7

The Role of Blockchain Integration in Edge Computing for 6G ...

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7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Alawadhi, Abdulwadood Almogahed, Abdullah Shtayt, Bushra Abdullah Barraood, Samera Obaid Adawy, Mohammad Ibrahim Al-Jamili, Omar InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Kedah Sintok Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Parit Raja Malaysia Information Technologies Management Management Technical College of Basra Southern Technical University Basra Iraq Department of Computer Science College of Computers and Information Technology Hadhramout University Hadhramout Yemen Faculty of Information Technology The World Islamic Sciences and Education University Amman Jordan School of Computing Universiti Utara Malaysia Kedah Sintok Malaysia

ISBN: (纸本)9798331519513

Blockchain is the technology on which cryptocurrencies operate within the 6G Internet of Things. Due to its flexibility, it has received significant adoption in numerous applications as a possible distributed data management solution. Nonetheless, a significant obstacle that impedes blockchain's potential is its constrained scalability, which limits its capacity to facilitate applications requiring frequent transactions. Conversely, edge computing, an innovative technology, addresses the limitations of conventional cloud-based systems by decentralizing cloud functionalities and alleviating problems such as elevated latency and insufficient security. Nonetheless, edge computing presently encounters challenges related to decentralized governance and security. As a result, the integration of blockchain and edge computing within a cohesive framework offers substantial benefits, such as dependable network access and governance, decentralized storage, and computing at the peripheries. This integration effectively mitigates many security problems and guarantees data integrity in edge computing contexts. This article offers a comprehensive analysis of blockchain technology, its attributes, and its relevance in the context of 6G. Additionally, it investigates the amalgamation of blockchain and edge computing, analyzes diverse application cases, and addresses the related issues. Finally, the report delineates contemporary research trajectories and draws conclusions based on the findings. © 2024 IEEE.

关键词： Mobile edge computing

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HeadweAR: Development of Augmented Reality Based on Hat Tester to Support Product Testing 5

HeadweAR: Development of Augmented Reality Based on Hat Test...

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5th International Conference on Advancement in Data science, E-Learning and information Systems, ICADEIS 2023

作者： Juma'At, Saiful Anwar Fudzee, Mohd Farhan Md Nugraha, Ryan Adhitya Syamsuar, Dedy Kontagora, Ibrahim Umar Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and IT Parit Raja86400 Malaysia Telkom University Information System Department Bandung Indonesia Bina Nusantara University School of Information Systems Information Systems Department Indonesia Niger State Polytechnic Department of Computer Science Niger State Zungeru Nigeria

ISBN: (纸本)9798350303414

Augmented reality technology application in variety of sectors is becoming more common in line with progress of technological advances towards the digital future. However, the experience of online shopping that lacks in virtual dressing room deter consumers to make the purchase decisions. Existing application for virtual dressing room is yet to include headwear products in software applications. Therefore, this project is proposed to develop an augmented reality mobile application that helps user to visualize virtual hat onto user's head. The targeted users for this project are adults aged 20 to 30 years old who are the customers of The Cap City. Multimedia Mobile Content Development (MMCD) model is implemented to develop the headweAR application. Based on user testing results, more than 80% of the respondents left positive feedback on the proposed application. Development with additional 3D models and multiple face tracking are some of the future works of this markerless AR-based application. © 2023 IEEE.

关键词： Augmented reality

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Investigation on the Functional Requirements for Talent Management System – A Case of Performing Arts Freelancers in Malaysia 11th

Investigation on the Functional Requirements for Talent Mana...

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11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Bahar, Nurhidayah Subaramaniam, Kasthuri Masoud, Adnan Shibghatullah, Abdul Samad Wahab, Mohd Helmy Abd Center for Software Technology and Management Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia Parit Raja Malaysia

ISBN: (纸本)9789811681288

This study examines job seeking and hiring process between freelancers and agency or event organizer in performing arts industry in Malaysia. The main aim of this study is to develop a set of system functional requirements for Talent Management System – a job portal that can be used to automate and computerized job seeking and hiring process. To achieve the aim, the study employed a qualitative research approach and followed an object-oriented analysis and design with the Unified Process methodology. A total of 10 interviews were conducted with freelancers, agencies, and organizers to gain an in-depth understanding of the process, constraints, and concerns about the current practice. The finding from this study highlighted the important system’s functions and provides guidelines on the system design and implementation. Hence, it offers some insights to the system developers who will be working on a similar system. Additionally, this study adds some knowledge to the literature related to talent management system and performing arts. Future studies may want to further design the data access layer, system interfaces, controls, and security. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： User interfaces

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Effects of the Number of Network’s Order Used in a Higher Order Neural Network on Time Series Prediction 11th

Effects of the Number of Network’s Order Used in a Higher O...

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11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Husaini, Noor Aida Ghazali, Rozaida Arbaiy, Nureize Lasisi, Ayodele Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Batu Pahat Parit Raja86400 Malaysia Department of Mathematical Sciences Faculty of Science Augustine University Ilara-Epe Lagos Nigeria

ISBN: (纸本)9789811681288

Higher Order Neural Networks (HONN) with backpropagation algorithm is conducted for predicting the time series event, which is a challenging problem under investigation. Many factors can affect the results of time series prediction. One of the most important factors is the architecture of a HONN which consists of layer, network’s order and output layer. In this study, we are focusing on the numbers of network’s order (or hidden nodes for ordinary Neural Network). Other factors are kept unchanged. The network is also tested with few other metaheuristic learning algorithms and compared to Multilayer Perceptron. Experimental results demonstrate that the effects of the numbers of network’s order on time series prediction are significant. Together with proper network’s order setup, practical analysis of results shows that 5-5-1 for PSNN-MCMC and 6-4-1 for FLNN-MCMC are the optimal network combination of input-hidden-output. The accuracy rate for both network models is around 0.037% to 0.498%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time series

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Developing Novel T-Swish Activation Function in Deep Learning

Developing Novel T-Swish Activation Function in Deep Learnin...

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2022 International Conference on IT and Industrial Technologies, ICIT 2022

作者： Javid, Irfan Ghazali, Rozaida Syed, Irteza Husaini, Noor Aida Zulqarnain, Muhammad University of Poonch Rawalakot Department of Computer Science and Information Technology AJK Pakistan Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Parit Raja Malaysia Tunku Abdul Rahman University College Faculty of Computing and Information Technology Jalan Genting Kelang Kuala Lampur Malaysia The Islamia University of Bahwalpur Faculty of Computing Punjab Pakistan

ISBN: (数字)9781665489454

ISBN: (纸本)9781665489454

Activation functions are an essential component of neural network construction. It is used in the hidden layer to achieve the regulation that lies in the learning capability of the model from the training dataset. Activation functions employed in the output layer play a significant part to determine the type of predictions that the model can produce. It is a significant area that has to be studied further to increase the efficacy of a deep neural network. As a result, our research in this area has grown. Several activation functions were established throughout time to remedy the inadequacies of the earlier functions. Over 40 activation functions, some of which are well-known, have been postulated, such as the Tanh, Mish, sigmoid, swish, and Rectified Linear Unit, however not entirely. Using datasets such as CIFAR-100, CIFAR-10, MNIST, and Fashion-MNIST, we developed a new activation function Tunable Swish (T-Swish) that increases enactment on object classification tasks. Then, to validate its applicability, we compared it to well-known activation functions like ReLU, sigmoid, Tanh, and Swish using Network In-Network, ResNet-56 version 1, LeNet-5, and U-Net architecture. The T-Swish function, in particular, enables quick network learning, which is excellent in the Transfer Learning technique. © 2022 IEEE.

关键词： Deep neural networks

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Optimization Techniques in Reinforcement Learning for Healthcare: A Review

Optimization Techniques in Reinforcement Learning for Health...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Dahiru Adamu Aliyu Emelia Akashah Patah Akhir Nurul Aida Osman Jabir Abubakar Salisu Yahaya Saidu Jameel Shehu Yalli Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Malaysia Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

One paradigm for machine learning that is transforming is reinforcement learning, or RL, promising significant healthcare improvements through personalized treatment recommendations, optimized resource allocation, and enhanced predictive modeling. Despite these successes, challenges such as model interpretability and real-world integration persist. This paper reviews various optimization techniques employed in RL, focusing on Proximal Policy Optimization (PPO) due to its stability, efficiency, and applicability in healthcare. Applications of PPO include adaptive treatment strategies, personalized medication management, and optimized clinical workflows. Additionally, the paper discusses case studies demonstrating RL’s potential to reduce medication errors, enhance treatment protocols, predict adverse events, and personalize radiation therapy. A comprehensive database search initially retrieved 708 articles; after removing duplicates and screening based on titles and abstracts, 23 articles were considered, ultimately narrowing down to 15 articles following the application of exclusion criteria. The findings demonstrate RL’s significant impact on healthcare, enhancing treatment effectiveness, optimizing service selection, and improving patient care through advanced techniques like Deep RL and multi-agent systems. Future research should focus on developing sophisticated adaptive treatment models and validating RL applications through clinical trials, underscoring RL’s potential to revolutionize healthcare by making it more effective, efficient, and patient centric.

关键词： Adaptation models Reviews Computational modeling Stability criteria Medical services Reinforcement learning Data models Radiation therapy Complexity theory Optimization

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Centralized RSU Deployment Strategy for Effective Communication in Multi-hop Vehicular Adhoc Networks (VANETs) 6th

Centralized RSU Deployment Strategy for Effective Communicat...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Rashid, Sami Abduljabbar Audah, Lukman Hamdi, Mustafa Maad Abdulsattar, Nejood Faisal Mutar, Mohammed Hasan Alkhafaji, Mohamed Ayad Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Parit Raja86400 Malaysia Department of Computer Engineering Technology Al-Maarif University College Al-Anbar Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Baghdad66002 Iraq National University of Science and Technology DhiQar Iraq

ISBN: (纸本)9789819940707

Due to the advent of smart cities, vehicular technologies are high focus to perform intelligent transmission. Road traffic control and accident control becomes a primary tasks in Vehicular Adhoc Networks (VANETs). In VANETs communication is ineffective without presence of Road Side Units (RSUs). In some places due to lack of RSUs data transmission is improve as well as end to end delay and routing overhead is increased during the time of communication so effective RSU deployment is very essential to provide effective communication in multi-hop VANETs. In this paper, Centralized RSU Deployment Strategy is proposed to control and monitor the traffic density of the vehicle. The parameters which are considered for the process of centralized RSU deployment strategy are centralization scale, past transmission rate and location identification. To analysis the performance of the network the simulation is carried out in NS2.35 and SUMO. The parameters which are taken for the performance analysis are packet delivery ratio, end to end delay, routing overhead and packet loss. In order to execute comparative analysis the recent research works are considered which are AVRCV and RDSTDV. From the outcome it is understood that the proposed CRDSMV approach achieves 70–200 ms lower end to end delay, 7–14% improved packet delivery ratio, and 50–300 packets lower routing overhead and 50–240 Kbps improved throughputs when compared with the earlier methods AVRCV and RDSTDV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Roads and streets

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Deep Learning Approach for Football Match Classification of English Premier League (EPL) Based on Full-Time Results 7th

Deep Learning Approach for Football Match Classification of ...

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7th International Conference on the Applications of science and Mathematics, SCIEMATHIC 2021

作者： Muszaidi, Muhaimin Mustapha, Aida Binti Ismail, Shuhaida Razali, Nazim Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Panchor Johor84600 Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johor84600 Malaysia

ISBN: (纸本)9789811689024

The trend of modeling the football match classification has become increasingly popular in the last few years, thus many classification models have been proposed with the point of evaluating the attributes that lead a football team to win, draw or lose a certain match. There are two types of approaches has been considered for classification of football matches results, which include the statistical approaches and deep learning approaches. This paper proposes a Multilayer Perceptron (MLP) and Dense Neural Network (DNN) to evaluate the performance of the classification football matches results in the terms of home win (H), away win (A), and draw (D) for games under the English Premier League (EPL). The experiment revealed that MLP produced better classification accuracy rate of 78.42% as compared to 67.63% by the standard DNN. Nonetheless, the performance of DNN can be further improved by means of hyperparameter tuning, whereby DNN achieved the highest accuracy of 70.53% when modeled in a three-dense layers with the size of 16 nodes and trained with 200 epochs. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Comparative Analysis of Statistical and Machine Learning Methods for Classification of Match Outcomes in Association Football 7th

Comparative Analysis of Statistical and Machine Learning Me...

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7th International Conference on the Applications of science and Mathematics, SCIEMATHIC 2021

作者： Zulkifli, Syazira Mustapha, Aida Binti Ismail, Shuhaida Razali, Nazim Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Panchor Johor84600 Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johor86400 Malaysia

ISBN: (纸本)9789811689024

Association football is one of the most popular sports in the world, having a large fan base that draws media and entertainment platforms to it. The three major difficulties to developing highly accurate match result prediction models, especially in prediction match outcomes, are data availability and quality [1], model assumptions [2], and testing various models and parameters [3]. The primary goal of the study is to identify the best model in predicting football match outcomes. The football dataset was obtained from the top 5 leagues in Euro. Exploratory data analysis had been conducted to better understand the dataset. Models used in predicting the football match outcomes include Logistic Regression, Artificial Neural Networks, and XGBoost. The predictive performance of three classification models was compared in terms of accuracy, precision and recall. The results showed that the Artificial Neural Networks achieved highest accuracy of 0.6788, followed by Logistic Regression (0.668) and XGBoost (0.654). These results are hoped to be used as benchmark results for future experiments in the area of football match classification. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Deploying Fuzzy Rough Set and Artificial Immune System Algorithms for Air Quality Prediction 11th

Deploying Fuzzy Rough Set and Artificial Immune System Algor...

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11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Lasisi, Ayodele Ghazali, Rozaida Ismail, Lokman Hakim Husaini, Noor Aida Department of Mathematical Sciences Faculty of Science Augustine University Ilara-Epe Lagos106101 Nigeria Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Parit Raja86400 Malaysia Department of Design Engineering and Architecture Faculty of Civil and Environmental Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Parit Raja86400 Malaysia

ISBN: (纸本)9789811681288

The widespread of air pollution due to emissions of highly detrimental concentrates on human life needs paramount attention. Devising air prediction strategy that accurately analyze air quality level through gathering of useful information allow for relevant organizations to disseminate promptly control measures. This paper proposes the use of artificial immune system (AIS) algorithms consisting of Immunos algorithms (Immunos-1, Immunos-2, Immunos-99), CLONal selection ALGorithm (CLONALG), clonal selection classification algorithm (CSCA), artificial immune recognition system algorithms (AIRS1, AIRS2, Parallel AIRS2) for air quality prediction. The fuzzy rough set selects pertinent data features summarizing interpretations of the data. Comparative simulations reveal that the Parallel AIRS2 produced superlative results to other algorithms with detection rate of 96.40%. Effective prediction performance can be generated with AIS algorithms having highest detection rates and lowest false alarm rates. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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