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Advances in Electrical and Communication Technologies (ICAECOT), International Conference on

作者： Rania Ibtissam Ben Melouka Hadda Ouguissi Nail Alaoui Messouda Benmelouka Sofian Kirech Arwa Mashat Dept. of Electronics Laboratoire d’Electronique de Puissance et Commande Industrielle (LEPCI) University of Ferhat AbbasSétif -1- Sétif Algeria Faculty of Technology Materials Energetic Systems Renewable Energies and Energy Management Laboratory (LMSEERGE) Ammar Thelidji University of Laghouat Laghouat Algeria Laboratoire de Recherche Modélisation Simulation et Optimisation des Systémes Complexes Réels University of Djelfa Djelfa Algeria Telecommunications and Smart Systems Laboratory University Ziane Achour of Djelfa Djelfa Algeria Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University P. O. Box 344 Rabigh Saudi Arabia

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

This research investigates the use of metamaterial integration and the miniaturisation process as a novel approach to antenna design. To enhance the frequency utilisation and minimise the antenna size, we suggest a novel method. The interaction between the two separate ring resonators relies on this resonant component using this manner. The characteristics examined in this experiment include the resonance frequency, the absence of response, the wavelength, the quantity of radiation, and the antenna’s gain and power output. CST is responsible for conducting all simulation effects.

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Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms

arXiv

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arXiv 2023年

作者： Saifullah, Shoffan Drezewski, Rafal Yudhana, Anton Pranolo, Andri Kaswijanti, Wilis Suryotomo, Andiko Putro Putra, Seno Aji Khaliduzzaman, Alin Prabuwono, Anton Satria Japkowicz, Nathalie Institute of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Center of Knowledge Engineering and Data Science Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang65145 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia College of Computer and Information Hohai University Nanjing211100 China Department of Informatics Faculty of Industrial Technology Universitas Ahmad Dahlan Yogyakarta55166 Indonesia Faculty of Agricultural Engineering and Technology Sylhet Agricultural University Sylhet3100 Bangladesh Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science American University WashingtonDC20016 United States

This study explored the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection for precise poultry hatchery practices. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset (200 single egg images) using augmented images (rotation, flip, scale, translation, and reflection). The training results demonstrated that all models achieved high accuracy, indicating their ability to learn and classify chicken eggs’ fertility. However, variations in accuracy and performance were observed when these models were evaluated on the testing datasets. The InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. It demonstrated excellent performance in all parameters of the evaluation metrics for both training and testing datasets. When evaluated on the testing datasets, it achieved an accuracy of 0.98, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.96 for identifying non-fertile eggs. The higher performance is attributed to its unique architecture, efficiently capturing features at different scales, which leads to improved accuracy and robustness. Further optimization and fine-tuning of the models might be necessary to address the limitations in accurately detecting fertile and non-fertile eggs using other models. This study highlighted the potential of CNN-transfer learning for nondestructive fertility detection and emphasized the need for further research to enhance the models’ capabilities and to ensure accurate *** Codes CS-Class: 68T07, 68T45, 68U10, ICT-Class: 94A08 © 2023, CC BY-SA.

关键词： Image classification

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Module Placement Scheme Using MPC4.5 with Markov Chain Process for Mobile Fog computing Environment

Module Placement Scheme Using MPC4.5 with Markov Chain Proce...

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International Conference on computing, Communication, and Intelligent Systems (ICCCIS)

作者： Rasmita Patro Sudhansu Shekhar Patra Lalbihari Barik Ajaya Dev Prusty Rabindra K. Barik School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Computer Applications KIIT Deemed to be University Bhubaneswar India Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Kingdom of Saudi Arabia

ISBN: (数字)9781728185293

ISBN: (纸本)9781728185309

Mobile Fog computing (MFC) is an elongation to cloud computing in which the processing is performed near the IoT devices. It reduces latency and energy consumption in the system and the idle choice for healthcare applications, smart cities, etc. In MFC, the mobile devices (MDs) select the best fog device (FD) and offload FDs' task. The submitted applications to the system are divided into modules, and the best FD has been selected for the module using the Module Placement C4.5 (MPC45) algorithm. If the MD's power consumption is more than that the power consumption of Wi-Fi, then only offloading has been performed. There are many factors, such as authenticity, confidentiality, availability, capacity, integrity, cost, and speed are used as the decision parameters for selecting the best FD. In the present research paper, a novel module placement scheme has proposed for optimizing the MPC45 with the Markov chain. The proposed approach is compared with the state of art algorithms in terms of power consumption, performance, and response time, which shows that the proposed method of task offloading is better compared to the other existing ones.

关键词： Power demand Smart cities Markov processes Time factors Task analysis Wireless fidelity Edge computing

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Usability Assessment on Symmetric Cryptography Learning Module

Usability Assessment on Symmetric Cryptography Learning Modu...

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International Conference on Computer & information Science (ICCIS)

作者： Irfan Syamsuddin Arif Bramantoro Politeknik Negeri Ujung Pandang Makassar Indonesia Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Saudi Arabia

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154688

Teaching information security requires lecturer to introduce several tools to encourage students performing hands on practices in better way in order to enhance their understanding. In this study, a novel learning module as a tool for student to perform symmetric cryptography exercises is introduced. It is called NgeXTEA, a novel learning module to describe how a simple yet powerful symmetric cryptography called Extended Tiny Encryption Algorithm (XTEA) works. The main objective of the study is to conduct assessment from usability point of view on NgeXTEA learning module using System Usability Scale (SUS) method. Finally, SUS based assessment indicated that the learning module is considered useful to enhance students' understanding and skills on the given topic.

关键词： Usability Encryption Tools Education Computer security information security Graphical user interfaces

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IoT-Based Wearable Devices for Patients Suffering from Alzheimer Disease

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Contrast Media and Molecular Imaging 2022年第0期2022卷 3224939页

作者： Salehi, Waleed Gupta, Gaurav Bhatia, Surbhi Koundal, Deepika Mashat, Arwa Belay, Assaye Yogananda School of Artificial Intelligence Computer and Data Sciences Shoolini University HP Solan 173229 India Department of Information Systems College of Computer Science and Information Technology King Faisal University Al Hasa Saudi Arabia Department of Systemics School of Computer Science University of Petroleum and Energy Studies Uttrakhand Dehradun India Faculty of Computing and Information Technology King Abdulaziz University Rabigh Saudi Arabia Department of Statistics Mizan-Tepi University Ethiopia

The disorder of Alzheimer's (AD) is defined as a gradual deterioration of cognitive functions, such as the failure of spatial cognition and short-term memory. Besides difficulties in memory, a person with this disease encounters visual processing difficulties and even awareness and identifying of their beloved ones. Nowadays, recent technologies made this possible to connect everything that exists around us on Earth through the Internet, this is what the Internet of Things (IoT) made possible which can capture and save a massive amount of data that are considered very important and useful information which then can be valuable in training of the various state-of-the-art machine and deep learning algorithms. Assistive mobile health applications and IoT-based wearable devices are helping and supporting the ongoing health screening of a patient with AD. In the early stages of AD, the wearable devices and IoT approach aim to keep AD patients mentally active in all of life's daily activities, independent from their caregivers or any family member of the patient. These technological solutions have great potential in improving the quality of life of an AD patient as this helps to reduce pressure on healthcare and to minimize the operational cost. The purpose of this study is to explore the State-of-the-Art wearable technologies for people with AD. Significance, challenges, and limitations that arise and what will be the future of these technological solutions and their acceptance. Therefore, this study also provides the challenges and gaps in the current literature review and future directions for other researchers working in the area of developing wearable devices. © 2022 Waleed Salehi et al.

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Recognizing User Activity Using a Smartphone's Accelerometer and Deep Neural Network Classifier

Recognizing User Activity Using a Smartphone's Accelerometer...

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International Conference on computing, Engineering, and Design (ICCED)

作者： Syech Pranata Teddy Mantoro Media Anugerah Ayu Anton Satria Prabuwon Deshinta Arrova Dewi Sampoerna University Jakarta Indonesia Faculty of Computing and Information Technology King Abdulaziz University Rabigh Saudi Arabia Faculty of Information Technology INTI International University Nilai Negeri Sembilan Malaysia

Along with today's fast-growing technology, machines/devices especially mobile devices have been developed using many sensors to simplify the user's activities. One of the most known and frequently used sensors is called accelerometer, daily used as a step counter, image stabilization, and user interfaces control. However, activity recognition is considered a difficult task due to the reality that each activity has its unique features and there is no clear analytical way to analyze sensor data into specific forms of action in general. This study examines the potential and exciting ability of the accelerometer to recognize user activity by making simple prototype to support the implementation of this user activity recognition. After data acquisition, deep learning classifier will be used to differentiate activities. This research will show the efficiency and utilization of using accelerometer combined with deep learning in recognizing user activity, which can be associated with many applications for advance study such as falling detection, abnormality detection, and prediction of human behavior.

关键词： Accelerometers Deep learning Image sensors Neural networks Prototypes Activity recognition User interfaces

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Estimation of Missing Values Using Hybrid Fuzzy Clustering Mean and Majority Vote for Microarray Data 16

Estimation of Missing Values Using Hybrid Fuzzy Clustering M...

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16th International Learning and technology Conference, L and T 2019

作者： Kumaran, Shamini Raja Othman, Mohd Shahizan Yusuf, Lizawati Mi Yunianta, Arda School of Computing Faculty Engineering Universiti Teknologi Malaysia Skudai Johor81310 Malaysia Faculty of Computing and Information Technology Rabigh King Abdulaziz University Jeddah Makkah Saudi Arabia

Rapid development in microarray experiments leads toward generating a large amount of genes expressions. Handling such a massive amount of gene expressions is a challenge using traditional methods especially multiple missing expression values exist. This article proposed a novel hybrid fuzzy clustering mean and majority vote to yield better performance. The proposed method will be evaluated with other three imputation methods and trained with four publicly available microarray datasets. The experimental results show that the proposed method has promising results that able to replace the missing data as well as achieves high accuracy rates and low root mean square error values. © 2019 The Authors. Published by Elsevier B.V.

关键词： Gene expression

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Melayu Grid: Sharing Language Service to Bridge Cultural Difference

Melayu Grid: Sharing Language Service to Bridge Cultural Dif...

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International Conference on Computer & information Science (ICCIS)

作者： Arif Bramantoro Irfan Syamsuddin Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah Saudi Arabia Politeknik Negeri Ujung Pandang Makassar Indonesia

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154688

Today grid computing powered by web service technology has been a research interest for decades. Several grids of language services are available in Internet as part of daily activity. Melayu Grid is proposed to share Indonesian, Malay and regional language resources. To ease the sharing, each language is located as a distinctive concept in language service ontology and eventually measured its semantic closeness to other languages. By utilizing semantic closeness, it is expected that the endangered language resource that has less amount of its speaker can take advantage by replacing it with other language resource that has better resources and speakers. This project implements service grid architecture as a culture grid for Indonesia and Malaysia to balance cultural differences and conflicts in these two neighboring countries. Multicriteria optimization is utilized to adapt the multi culture feature of several language services. It substitutes several regional languages services that are coming from the same language families or having similar linguistic vocabularies, syntax, morphology and semantics.

关键词： Ontologies Semantics Cultural differences Web services Engines Global communication Biomedical imaging

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An Adaptive Normalized Google Distance Similarity Measure for Extractive Text Summarization

An Adaptive Normalized Google Distance Similarity Measure fo...

引用

International Conference on Computer & information Science (ICCIS)

作者： Albaraa Abuobieda Ahmed Hamza Osman Faculty of Computer Studies International University of Africa Khartoum Sudan Faculty of Computing and Information Technology King Abdulaziz University Rabigh Kingdom of Saudi Arabia

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154688

No doubt, for each clustering algorithm running improper similarity calculation, that can lead to reduce the clustering accuracy. Hence, several applications that employ such algorithm are affected negatively and generate improper results. In a previous work, we found that employing the Normalized Google Distance (NGD) similarity measure to cluster document's sentences for text summarization problem is unreasonable; since NGD was basically designed to work with large databases. On the other hand, a term-weighting approach is used widely to define document's contents. In this paper, a term-weighting approach is integrated with the NGD similarity measure to adopt the latter from being able to work in small database (single document). Differential Evolution (DE) algorithm is used to train and test the proposed method. The DUC2002 dataset is preprocessed and used as a test bed. The results showed that our proposed method could outperform the previous work in terms of F-score evaluation measure as well as outperformed the standard baseline methods Microsoft Word and Copernic Summarizer.

关键词： Internet Mathematical model Databases Weight measurement Clustering algorithms Frequency measurement Benchmark testing

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Reinforcement learning for Pedestrian evacuation Simulation and Optimization during Pandemic and Panic situation

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Journal of Physics: Conference Series 2021年第1期1817卷

作者： Sharaf J. Malebary Ahmad Hoirul Basori Entisar Soliman alkayal Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Rabigh Makkah 21911 Saudi Arabia

The pandemic of Covid-19 has drawn significant attention of the people around the world. The current situation has revealed that this virus infected more than 50 million people globally. On the other hand, the study on crowd simulation can demonstrate the behaviour of massive people that gather in the same location. The importance of this study can lead to safe evacuation in case an outbreak happens. This paper proposed a solution of innovative crowd simulation supported by reinforcement learning and pandemic factors. The study has successfully demonstrated the entering and leaving the various exit and entrance. The experiment is performed with a different mode such as two elevators for exit and entry, staircase and six lanes of gates. As a result of the experimental study, it reveals around 87% of agent behaviour has similarity compared to a real-life simulation performed by previous research. It means the simulation is reflecting the real-life human behaviour when the evacuation process has occurred. The future study can extend to a contagion model of crowd behaviour where an agent can influence each other in a particular situation.

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