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2022 International Conference on Digital Transformation and Intelligence, ICDI 2022

作者： Ja'ffar, Halimatul Sa'adiah Labadin, Jane King, Tiong Wei Lian, Cynthia Kon Mui Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology Samarahan Malaysia Swinburne University of Technology Faculty of Engineering Computing and Science Kuching Malaysia

ISBN: (纸本)9798350397000

Public health controls the re-emergence of infectious diseases by achieving herd immunity establishment in the population. This paper aims to consolidate two factors affecting herd immunity namely individuals' trust in public health and vaccines as well as wane of immunity in individuals into one epidemiological model in maintaining herd immunity. The model formulation adopts Individual-Based Model (IBM) approach to address the heterogeneity effects of individuals' attributes. The individuals' vaccination decision is modelled with imitation dynamics based on game theory. The model simulations use the real parameters obtained for Pertussis in Malaysia. By using different probability of individuals acquire trust, the simulation results are validated with the real Pertussis prevalence where the Root-Mean-Square-Percentage-Error (RMSPE) is used as the accuracy measurement metric. Around 70 percent to 80 percent of individuals must acquire trust towards public health and vaccines so that the simulation results become more fitted to the real Pertussis prevalence. In addition, the modified herd immunity threshold formula is derived from the model and is analyzed through parameter sensitivity analysis. Comparing to the general formula of herd immunity threshold, the modified herd immunity threshold formula provides a higher threshold for the establishment of herd immunity due to additional parameters included in the formula. The disruption of herd immunity because of the wane of immunity in individual would be countered by the vaccination of individuals who trust in public health and vaccines, hence establishing the herd immunity in the population. (Abstract) © 2022 IEEE.

关键词： Vaccines

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Solar Photovoltaic Panels Detection Using Machine Learning Algorithms 2

Solar Photovoltaic Panels Detection Using Machine Learning A...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Salameh, Dania Dannoun, Riham Zawahra, Isra Sara, Mutaz Rasmi Abu Alkhateeb, Jawad H Taqatqa, Sameh Kanan, Mohammad Palestine Ahliya University Faculty of Engineering and Information Technology Bethlehem Palestine Prince Mohammad Bin Fahd University College of Computer Engineering and Science Khobar Saudi Arabia University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798350373363

Solar Photovoltaic Panels Detection Using Machine Learning Algorithms. This research paper deals with designing and developing algorithms capable of automatically recognizing small-sized solar photovoltaic arrays in light of high-resolution atmospheric data. Achieving this automatic recognition is crucial to provide a faster and lower-cost alternative for obtaining vital in-formation about small solar, such as geographic location, capacity, and energy production efficiency. This paper exploits machine learning algorithms to achieve the most of aerial data. This research aims to provide an effective solution that improves efficiency and accuracy in identifying and inspecting small solar photovoltaic arrays. By using a dataset of measurements of aerial images, this research presents a variety of machine-learning algorithms that aim to verify whether the measurements of an object captured in the image are identical to a polygon measurement. The research begins by presenting the linear regression algorithm, the random forest algorithm, the decision tree algorithm, and the KNN algorithm. The results indicate that the linear regression algorithm significantly outperforms other algorithms, highlighting its promising potential in identifying solar cell arrays. © 2023 IEEE.

关键词： Solar energy

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Metaverse with Mobile Twin Edge Computing: Empowering Optimal Efficiency and Accuracy

Metaverse with Mobile Twin Edge Computing: Empowering Optima...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Bibi, Asma Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Software Engineering Islamabad Pakistan Kohsar University Department of Computer Science Murree Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9798350349719

The concept of Digital Twin has been widely used by researchers to represent physical entities in computer-generated reality in the metaverse. In this research, a novel concept of 'Mobile Twin' is coined. Mobile Twin serves as a replica of a mobile device in the virtual environment enabling communication. The purpose of introducing Mobile Twin in the metaverse is to bring mobility and benefits similar to tangible mobile devices while eliminating the need for individual energy sources, storage limitations, resource usage, and efficiency. The true benefits of a mobile twin can be achieved with an architecture where computing nodes are decentralized, data processing is near the network's edge with faster response times and reduced latency. For this purpose, the mobile twin-based applications of metaverse are built over edge computing-based network architecture. This idea introduced the aspect of 'Revolutionary Ultra-low latency (RULL)' in the metaverse. © 2024 IEEE.

关键词： Electric power utilization

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Investigating the Use of Data Mining for Knowledge Discovery 3

Investigating the Use of Data Mining for Knowledge Discovery

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sunil, M.P. Pandey, Megha Garima Janakiraman, V. Maral, Vikas B. Das, Sidhant Department of Electronics and Communication Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

Fact mining has emerged as one of the most promising information discovery strategies in tremendous and complex databases. The tourism quarter advantages highly from records mining, as it can provide specific insights into how customers view and transact with the industry. In recent years, fact mining has grown exponentially within the tourism region, making it vital for researchers to apprehend how it can be implemented to discover meaningful information and inform strategic choice-making. This paper examines the potential of fact mining for know-how discovery in tourism, along with supervised and unsupervised learning techniques, artificial intelligence, predictive modeling, and information visualization. The authors talk about the results of technological improvements in information mining and the ability of tourism organizations to benefit from its use. They then speak of realistic examples of statistics mining programs inside the tourism area, from tracking client reviews to forecasting vacationer trends. An assessment of the prevailing literature is presented to assess knowledge discovery strategies inside the field of tourism. Ultimately, the authors recommend avenues for destiny studies in this location. © 2023 IEEE.

关键词： conclusion forecasting knowledge presented technology

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Advancing Stroke Prediction: Explainable Machine Learning Models and Data Augmentation Strategies 25

Advancing Stroke Prediction: Explainable Machine Learning Mo...

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25th International Arab Conference on information technology, ACIT 2024

作者： Salma, Rahma Abu Al-Qerem, Ahmad Iessa, Abedalhakeam Rajab, Mohammed Alauthman, Mohammad Jeadi, Mohammad Elrashidi, Ali Zarqa University Faculty of Information Technology Computer Science Department Zarqa13110 Jordan University Headquarter University of Anbar Ramadi Iraq University of Petra Department of Information Security Amman11196 Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Stroke, a leading global disability cause, affects 12 million people annually, with 6.5 million dying. Preventing at least half of strokes requires public policy action and increased awareness. Early diagnosis is crucial and enhances patients' opportunities to survive a stroke. This research looks into how well machine learning (ML) models can predict when strokes will happen and how data augmentation affects those predictions.. Five ML models (Logistic Regression, Decision Tree, K-Nearest Neighbors, XGBoost, and Random Forest) are assessed and their performance is compared using an imbalanced and augmented dataset. Data augmentation techniques, including Random Oversampling, SMOTE, and Cluster Centroids, are employed to address class imbalance. The results demonstrate that some models achieve relatively high accuracy without data augmentation (XGBoost: 0.95). However, data augmentation techniques further improve performance across all models. Random Oversampling leads to the highest Accuracy for Random Forest (0.99), while SMOTE achieves the best Precision for Logistic Regression (1.00). The results found that age, occupation type, BMI, blood glucose level, and history of cardiovascular disease are risk factors for stroke. This study highlights the effectiveness of ML models, particularly when combined with data augmentation, for stroke prediction and emphasizes the importance of specific features in identifying stroke risk. © 2024 IEEE.

关键词： Logistic regression

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Optimizing Large-Scale Data Processing with Artificial Intelligence 3

Optimizing Large-Scale Data Processing with Artificial Intel...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Khurana, Saniya Kumar, Ritesh Beemkumar, N. Shukla, Anupam Nirmala, D. Meshram, Vidula V. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

massive-scale statistics processing presents precise demanding situations in extracting insights, as the sheer amount of records can be overwhelming for classic analytics systems. Artificial Intelligence (AI) can offer a powerful answer for coping with large-scale records units, because it leverages system mastering algorithms to construct sophisticated fashions and automate complex evaluation. AI algorithms are also adaptive and incredibly green, which reduces the time it takes to mine valuable insights from big information units. This paper will study the diverse approaches that AI can enhance large-scale information processing and explore the numerous advantages of deploying AI in statistics processing workflows. The dialogue will include the advantages of leveraging AI for both supervised and unsupervised getting to know tasks in addition to the technical concerns around deciding on the proper AI technology for information processing jobs. Subsequently, the paper will present actual- world applications of AI in huge-scale records processing and evaluate the ability for groups to gain an aggressive aspect by deploying AI of their information pipelines. © 2023 IEEE.

关键词： Aggressive Applications information Numerous Present

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AI-Based Algorithm for Zero-Day Attack Detection Using Reinforcement Learning

AI-Based Algorithm for Zero-Day Attack Detection Using Reinf...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Ola Nasir Waleed Amer Ala Hamarsheh Ali Mahmoud Ali Amani Abu Zaid Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Secyrity Faculty of Information Technology Zarqa University Zarqa Jordan Department of computer science Faculty of Information Technology Arab American University Department of Computer Network Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Zero-Day attacks represent one of the most challenging threats in modern cybersecurity, as they exploit unknown vulnerabilities and bypass traditional detection systems. In this paper, we propose a hybrid threat detection system that integrates three key components: a Random Forest-based supervised classification model for detecting known attack patterns, an autoencoder-based anomaly detection module to uncover novel threats, and a Reinforcement Learning (RL) agent to dynamically optimize response strategies in real-time. The proposed system achieves a detection Accuracy of 97.8% for known attacks. The autoencoder, trained exclusively on normal traffic, employs a dynamically determined threshold (set at 0.8409) to flag anomalous behavior indicative of Zero-Day attacks. Furthermore, the RL agent adapts quickly to evolving threats, effectively filtering out 53.2% of benign traffic while blocking 39.7% of attack traffic and quarantining 7.1% of ambiguous cases for further inspection. Experimental results demonstrate that this integrated approach not only enhances top-level threat prevention but also significantly reduces false alerts.

关键词： Heuristic algorithms Prevention and mitigation Autoencoders Reinforcement learning Threat assessment Real-time systems computer security Anomaly detection Random forests Testing

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An Application of Multilayer Perceptron for the Prediction of River Water Quality 6th

An Application of Multilayer Perceptron for the Prediction o...

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

作者： Ghazali, Rozaida Fuzi, Norfar Ain Mohd Mostafa, Salama A. Khattak, Umar Farooq Ali, Rabei Raad Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Batu Pahat Parit Raja86400 Malaysia School of Information Technology UNITAR International University Petaling Jaya47301 Malaysia Department of Computer Engineering Technology Northern Technical University Mosul41000 Iraq

ISBN: (纸本)9789819930098

This study examines the use of an intelligent system to predict the quality of water flowing through the Johore Rivers. A neural network model of multilayer perceptron has been developed to predict the quality of water in three rivers, namely the Bekok River, Sayong River, and Johor River. The model has been trained and tested with the values of the pH parameters as input variables. The neural network model’s performance has been assessed using three performance metrics: mean squared error, CPU time, and prediction accuracy. By analyzing the relationship between the residuals and the model values, it will be possible to assess the model’s suitability. Based on the results from the implementation of the multilayer perceptron model, a method is developed for forecasting and monitoring the parameters associated with water quality obtaining an average accuracy of prediction score of 99.93%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water quality

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Experimental evaluation of MU-MIMO in TDD environment for 5G NR using exploiting channel reciprocity

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International Journal of information technology (Singapore) 2024年第7期16卷 4651-4666页

作者： Kumar, Rajesh Sinwar, Deepak Singh, Vijander Department of Computer and Communication Engineering Manipal University Jaipur Rajasthan Jaipur 303007 India Department of IoT and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur 303007 India Department of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering NTNU - Norwegian University of Science and Technology Ålesund Norway

5G New Radio (NR) operates in two frequency ranges viz. Frequency Range 1 (sub-6 GHz band) and Frequency Range 2 (millimeter wave communication). It utilizes Time Division Duplex (TDD) communication, allowing the same frequency band for uplink and downlink transmission using sounding reference signals. These signals help estimate channel conditions for scheduling downlink traffic. Multi-User Multiple Input Multiple Output (MU-MIMO) technology enables simultaneous data transmission to multiple users using multiple antennas. This study examines MU-MIMO performance in TDD by analyzing variables like radio conditions, modulation schemes, code rates, precoding algorithms, and sub-carrier spacing. Simulation results show that Zero Forcing (ZF) precoding outperforms Block Diagonalization (BD) and Regularized Zero Forcing (RZF) in good radio conditions. However, in bad radio conditions, RZF performs better than ZF. Choosing higher code rates in 16 and 64-QAM and lower code rates in 256-QAM from the MCS table improves throughput. This indicates that MCS and user selection are crucial for higher throughput in TDD-based MU-MIMO for 5G NR. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： 5G new radio Beamforming MU-MIMO Precoding Sounding reference signal Time division duplex

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