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A Globally Convergent Improved Hybrid Method of Quasi Newton and Conjugate Gradient Method 11

A Globally Convergent Improved Hybrid Method of Quasi Newton...

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11th International Conference on Electrical Engineering, computer Science and Informatics, EECSI 2024

作者： Hanan Wan Osman, Wan Farah Faculty of Computer Media and Technology Management Terengganu Malaysia

ISBN: (纸本)9798350355314

A new algorithm which combines the Quasi-Newton (QN) method and conjugate gradient (CG) method are presented in this paper. It is denoted as QN-WAM+ method and tested using twenty-six different standard test functions from small scale to large scale with four different initial points. All computations are conducted through Matlab software. It is then compared with the other hybrid methods based on number of iterations and CPU times and then analyzed using performance profile. From numerical results, it shows that the new algorithm with BFGS update formula as an approximation of Hessian matrix perform well and proved to be more efficient compared to other hybrid methods. Accordingly, the global convergent is theoretically prove and fulfil sufficient descent condition. © 2024 IEEE.

关键词： Conjugate gradient method

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Development of Interactive Websites to Promote Road Safety Awareness among Primary School Children - LINTAS 11

Development of Interactive Websites to Promote Road Safety A...

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11th International Conference on Electrical Engineering, computer Science and Informatics, EECSI 2024

作者： Aziz, Nur Sukinah Apandi, Ziti Fariha Mohd Sulaiman, Noor Suhana Rahman, Saadiah A. Zakaria, Nur Liyana Idayu Tun Mohd Hassan, Wan Nur Media and Technology Management University College TATI Faculty of Computer Terengganu Malaysia

ISBN: (纸本)9798350355314

Road safety education is essential for primary school children to reduce traffic-related injuries and fatalities. The Road Safety Department (JKJR) and the Malaysian Institute of Road Safety Research (MIROS) have developed a Road Safety Education (RSE) Module integrated into the Bahasa Melayu curriculum. This study focuses on designing and developing interactive websites that promote road safety awareness among primary school students. Through the use of interactive website, the study aims to create an engaging educational tool that captivates and informs young learners. Traditional methods, such as physical books, present challenges in maintaining students' interest. Moreover, there is a significant lack of interactive road safety education websites, particularly tailored to the needs of primary school students, like Pendidikan Keselamatan Jalan Raya (PKJR). This scarcity extends to the limited availability of game-based learning platforms for road safety education. The study addresses this gap by developing LINTAS, an initiative that offers a comprehensive set of educational tools, including flashcards and an interactive website, to engage children effectively. These resources aim to equip students with the knowledge to recognize road hazards, avoid accidents, and practice safe pedestrian behavior. By fostering an understanding of road safety through engaging digital tools, the project seeks to safeguard children's well-being during their daily commutes and everyday activities. Ultimately, the use of digital platforms in education encourages active learning, enhances academic performance, and instills a lasting awareness of road safety. © 2024 IEEE.

关键词： Students

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An Intrusion Detection System of Rough Set Theory Classification: Kyoto 2006+ Dataset 11

An Intrusion Detection System of Rough Set Theory Classifica...

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11th International Conference on Electrical Engineering, computer Science and Informatics, EECSI 2024

作者： Sulaiman, Noor Suhana Hashim, Nur Nadiah Mohd Yacob, Azliza Aziz, Nur Sukinah Nasir, Akhyari Mulyono, Sri Assegaf, Badieah Subroto, Imam Much Ibnu University College TATI Faculty Computer Media & Technology Management Terengganu Malaysia Universitas Islam Sultan Agung Informatics Department Semarang Indonesia

ISBN: (纸本)9798350355314

As telecommunication and information networks, which connect modern society through computers, smartphones, and other electronic devices, grow, so do security issues and cybercrime. The seriousness of cyberattacks in the context of network security has been highlighted by this rise in cybercrime. Simultaneously, machine learning has been thoroughly investigated for intrusion classification, with an emphasis on improving the accuracy of classifiers and the effectiveness of data mining models. This paper investigates the derivation of intrusion attack classification rules with Rough Set Theory (RST), a rule-based decision-making approach. To determine the efficacy of four different algorithms, including the Genetic Algorithm, experiments were carried out utilizing datasets that were made publicly available. When the Genetic Algorithm was used for rule construction in RST classification, the results were the best when compared to other rule generating techniques. Furthermore, this approach's capacity to precisely forecast all forms of attacks was shown when it was applied to a dataset of intrusion attacks that was made available to the public. Additionally, this methodology gives security professionals and developers useful information ahead of time, allowing them to take proactive measures when needed. © 2024 IEEE.

关键词： Network intrusion

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The Impact of Enhancing the k-Means Algorithm through Genetic Algorithm Optimization on High Dimensional Data Clustering Outcomes

The Impact of Enhancing the k-Means Algorithm through Geneti...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Symbiosis Institute of Business Management Nagpur India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the realm of unsupervised machine learning, the k-Means algorithm stands as a cornerstone for clustering high-dimensional data. However, its efficiency and accuracy can significantly dwindle as the dimensionality of the dataset increases. This paper introduces an innovative approach that integrates Genetic Algorithm (GA) optimization with the k-Means clustering process, aiming to enhance its performance on high-dimensional datasets. The proposed methodology leverages the evolutionary capabilities of Genetic Algorithms to optimize the initial centroid selection and clustering configuration of k-Means, thus addressing its sensitivity to initial conditions and potential to converge to local optima. We systematically detail the implementation of this hybrid algorithm, incorporating mathematical expressions to elucidate the optimization process. Through extensive experiments on synthetic and real-world datasets, we demonstrate the superior clustering outcomes of the enhanced k-Means algorithm in terms of accuracy, robustness, and computational efficiency. The results are presented via a series of graphs and tables, providing a comparative analysis against traditional clustering algorithms. Our findings indicate that the integration of GA into the k-Means algorithm significantly improves its performance on high-dimensional data, making it a powerful tool for data mining applications where conventional clustering methods fall short. This study not only proposes a novel clustering solution but also contributes to the ongoing discourse on optimizing machine learning algorithms for complex datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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AI-Driven Remote Parkinson's Diagnosis with BPNN Framework and Cloud-Based Data Security

AI-Driven Remote Parkinson's Diagnosis with BPNN Framework a...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India SRV Media Pvt. Ltd. India Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Symbiosis Institute of Business Management Nagpur India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the realm of medical diagnostics, the integration of Artificial Intelligence (AI) has shown promising advancements, especially in the diagnosis and management of chronic diseases such as Parkinson's Disease (PD). This paper presents a novel AI-Driven approach for Remote Parkinson's Diagnosis utilizing a Back Propagation Neural Network (BPNN) framework, coupled with a secure cloud-based data handling mechanism. The primary focus is to harness the computational prowess of BPNNs to analyze and interpret the subtle and complex patterns associated with Parkinson's symptoms, thus facilitating an accurate and early diagnosis. To address the critical concern of data privacy and security, which is paramount in the healthcare domain, we implement a robust cloud-based architecture ensuring the confidentiality, integrity, and availability of patient data. This architecture employs state-of-the-art encryption techniques alongside access control mechanisms to safeguard sensitive information against unauthorized access and potential cyber threats. The proposed system is evaluated through extensive experiments involving real-world datasets, demonstrating its effectiveness in diagnosing Parkinson's with high accuracy and efficiency. Furthermore, the implementation of this system on a cloud platform ensures scalability, making it accessible to a wider range of healthcare providers and patients. Through this study, we aim to contribute to the improvement of remote healthcare services for Parkinson's patients, offering a secure, accurate, and user-friendly diagnostic tool. © 2024 IEEE.

关键词： Data integrity

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Development of Fuzzy Based Models for Detection System with More Accuracy for the Implementation of Healthcare/Clinical System 4

Development of Fuzzy Based Models for Detection System with ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Meenakshi India Srv Media Pvt. Ltd India Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Pune India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

Many electronic infection detection systems often use binary classification techniques, which identify patient data as either pathology-indicating or normal in relation to different forms of infections. The Moni-ICU system, which is used in the critical care units of Vienna General Hospital, is noteworthy for introducing a novel third category called borderline. This categorization indicates patient data that partially satisfies the corresponding needs by closely aligning with virus-related clinical concepts or infections related to healthcare (HAI) surveillance categories. The probability of classifying patient data into normal, borderline, of pathological categories with respect to infection-related medical ideas and HAI definitions was calculated by integrating fuzzy sets of propositional fuzzy rules. This deviation from dichotomous approaches prevents the possible misunderstanding of borderline findings with normal ones, a major difficulty in binary classifications. Data collection included a thorough analysis of 5099 individuals treated over a two-year period, including adult patients across 10 ICUs at Vienna General Hospital. The Moni-ICU knowledge base, which includes fuzzy sets and rules for four top-level HAI definitions and 10 infection-related clinical concepts, made the categorization process easier. In order to verify the accuracy and dependability of the system, the assessment focused on how suitable borderline classifications were in relation to normal values for related HAI criteria. © 2024 IEEE.

关键词： Fuzzy rules

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Multi-Modal Facial Expression Recognition through a Hierarchical Cross-Attention Graph Convolutional Network

Multi-Modal Facial Expression Recognition through a Hierarch...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India SRV Media Pvt. Ltd. India Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the rapidly evolving field of computer vision and machine learning, recognizing human facial expressions with high accuracy remains a pivotal challenge. This paper introduces a novel Multi-Modal Facial Expression Recognition (MMFER) framework, leveraging a Hierarchical Cross-Attention Graph Convolutional Network (HCA-GCN) to enhance the accuracy and robustness of facial expression recognition. Our proposed HCA-GCN model systematically integrates multi-modal data sources, including visual imagery and depth information, to capture the complex and subtle nuances of human facial expressions. The hierarchical structure of our model allows for the efficient processing of multi-modal data through distinct pathways, ensuring that relevant features from each modality are effectively extracted and combined. The cross-attention mechanism within our framework meticulously aligns and emphasizes inter-modal features, fostering a synergistic integration that boosts the model's ability to discern intricate facial expressions. Through extensive experiments on benchmark datasets, our approach demonstrates significant improvements over existing methods, showcasing superior performance in recognizing a wide range of facial expressions under various conditions. The implementation details, including algorithms and mathematical expressions, are meticulously described to facilitate replication and further research. Results and discussions highlight the model's efficacy, underscored by comparative analysis and visualizations. Our findings indicate that the HCA-GCN model not only advances the state-of-the-art in facial expression recognition but also opens new avenues for research in multi-modal learning. © 2024 IEEE.

关键词： Spatio-temporal data

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Selection of Rendezvous Points Using Fuzzy Logic for Mobile Data Collection in Wireless Sensor Networks

Selection of Rendezvous Points Using Fuzzy Logic for Mobile ...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India Biib Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

Wireless Sensor Networks (WSNs) are a cornerstone of modern IoT applications, enabling data collection across various environments. The efficiency of these networks, however, is hampered by the limited energy resources of sensor nodes and the challenges inherent in mobile data collection. This paper proposes a novel approach to address these challenges through the strategic selection of rendezvous points (RPs) using fuzzy logic. Our method leverages the flexibility and decision-making process of fuzzy logic to evaluate and integrate multiple dynamic variables, including node energy levels, collector mobility patterns, and data priority, to dynamically determine optimal RPs. This approach not only facilitates efficient data transmission but also significantly conserves the energy of sensor nodes, thereby extending the network's lifespan. We present a comprehensive model incorporating a fuzzy inference system for RP selection, tailored to adapt to the changing conditions of WSNs. Through extensive simulations, our results demonstrate a marked improvement in network performance, showcasing reduced energy consumption and minimized data collection latency. The proposed fuzzy logic-based selection method offers a robust and adaptive solution to the challenges of mobile data collection in WSNs, promising enhanced efficiency and prolonged operational longevity of sensor networks. © 2024 IEEE.

关键词： Sensor nodes

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Enhancing Trust-Based Attacker Detection in 5G Social Networks Through Advanced Artificial Intelligence Control

Enhancing Trust-Based Attacker Detection in 5G Social Networ...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India Faculty of Management It & Business Analytics Pune India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the evolving landscape of 5G social networks, the surge in connectivity and data exchange has amplified the susceptibility to malicious attackers, undermining the trust and security integral to these networks. This paper introduces an innovative approach to bolster trust-based attacker detection mechanisms through the integration of advanced artificial intelligence (AI) control techniques. Leveraging the prowess of machine learning (ML) algorithms and AI-driven analytics, our proposed framework enhances the accuracy and efficiency of identifying potential threats within 5G social networks. By implementing a hybrid model that combines anomaly detection algorithms with trust scoring systems, we aim to establish a robust security posture that dynamically adapts to emerging threats. The core of our work lies in the development of a predictive model that utilizes deep learning techniques to analyze network behaviors and user interactions, facilitating the early detection of anomalous activities indicative of malicious intent. Through extensive simulation and real-world application, our results demonstrate a significant improvement in detection rates and a reduction in false positives, thereby fortifying the trust and reliability of 5 G social networks. This study not only contributes to the advancement of secure network architectures but also sets a new precedent for the application of AI in cybersecurity, offering a scalable and efficient solution to safeguard against sophisticated attackers. © 2024 IEEE.

关键词： 5G mobile communication systems

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Enhancing Quality of Service in Mobile Ad-Hoc Networks through Secure Routing with Multi-Constrained Network Feature Approximation

Enhancing Quality of Service in Mobile Ad-Hoc Networks throu...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

ISBN: (纸本)9798350359688

In the realm of Mobile Ad-Hoc Networks (MANETs), achieving optimal Quality of Service (QoS) amidst the challenges of security threats and dynamic network topology remains a paramount concern. This paper introduces a groundbreaking secure routing strategy empowered by a multi-constrained network feature approximation technique aimed at bolstering QoS in MANETs. The core innovation lies in the fusion of a multi-dimensional optimization framework with an advanced feature approximation algorithm, tailored to navigate the intricacies of MANETs' decentralized architecture and fluctuating conditions. By focusing on critical QoS parameters such as throughput, delay, packet delivery ratio, and jitter, alongside ensuring robust security measures, our proposed method significantly enhances the reliability and performance of data transmission within MANETs. A comprehensive simulation environment is utilized to evaluate the effectiveness of our approach, revealing substantial improvements in QoS metrics compared to conventional secure routing protocols. Through a meticulous examination of these results, the study underscores the proposed method's efficiency in managing the dual objectives of security and QoS optimization. This research not only marks a significant advancement in secure routing protocols for MANETs but also sets the groundwork for future explorations into integrating multi-constrained optimization techniques for network feature approximation in ad-hoc networks. © 2024 IEEE.

关键词： Constrained optimization

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