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Optical Technology and Measurement for Industrial Applications Conference 2023

作者： Hii, K.U. Su, H.T. Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Kuching93350 Malaysia

ISBN: (纸本)9781510663411

An octuple-pass configuration for reflectance confocal system is demonstrated. By utilizing the lens in quadrantal division as well as retro-reflection approach, the same propagating beam is configured to pass the lens eight times and directed to hit the same scanning point on the test surface as much as four times. With the increased number of passes, it is shown that the axial resolution of octuple-pass system is four times better than that of the conventional double-pass system. The effects of the beam size and its incident orientation on the lens are also investigated. © 2023 SPIE · 0277-786X ·

关键词： Reflection

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Enhanced Windows Fuzzy Firewall for DOS Attack Prevention 1

Enhanced Windows Fuzzy Firewall for DOS Attack Prevention

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Himanshu Sharma, Manubhav Sujatha, G. Srm Institute of Science and Technology Faculty of Engineering Department of Networking and Communications School of Computing Chennai India

ISBN: (纸本)9798350359756

Winguardian, an enhanced version of FR-Winfirewall, enhances the security characteristics of the Windows Firewall. The native firewall is necessary, although it is not very efficient against DoS attacks. The improved version of Winguardian addresses this weakness by incorporating cutting-edge security features to counter a variety of DoS attacks, including those based on TCP, UDP, and ICMPv4 and ICMPv6 protocols. It protects Windows systems better than a lot of other firewalls. In addition to handling IPv6 traffic, the Winguardian also monitors HTTP and FTP packets to further improve its security features. This makes it an all-inclusive security tool for Windows users, ensuring that different kinds of network traffic are analysed and shielded from potential *** put it briefly, the Winguardian strengthens Windows-based systems' defences against online threats by providing a strong and intelligent security solution. © 2023 IEEE.

关键词： Denial-of-service attack

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Integration of Voice and Handwritten Tests Analysis for Enhanced Detection of Parkinson's Disease using Machine Learning 8

Integration of Voice and Handwritten Tests Analysis for Enha...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Mohamed Ziyadh, M. Avinash, Bavisetti Siva Sreekumar, K. Amaran, Sibi Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Kattankulathur SRM Nagar Tamilnadu Chennai603203 India Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology Kattankulathur SRM Nagar Tamilnadu Chennai603203 India

ISBN: (纸本)9798350376425

This research introduces a novel approach to improving Parkinson's disease (PD) detection by combining voice and handwriting analysis. By leveraging machine learning (ML) and deep learning (DL) techniques, this hybrid model aims to enhance diagnostic accuracy. Traditional approaches have focused on either voice or handwriting analysis independently. However, this study demonstrates the potential of integrating both modalities to achieve superior results. Preliminary findings suggest that the combined analysis of voice and handwriting data using ML and DL techniques yields higher accuracy compared to single-modality approaches. This study outlines the methodology, challenges, and potential implications of this hybrid approach for early PD diagnosis. By combining voice and handwriting analysis, this research offers a promising avenue for improving the detection and management of Parkinson's disease. © 2024 IEEE.

关键词： Contrastive Learning

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Deep Learning-Based Approach for Arabic Visual Speech Recognition

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Computers, Materials & Continua 2022年第4期71卷 85-108页

作者： Nadia H.Alsulami Amani T.Jamal Lamiaa A.Elrefaei Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt

Lip-reading technologies are rapidly progressing following the breakthrough of deep *** plays a vital role in its many applications,such as:human-machine communication practices or security *** this paper,we propose to develop an effective lip-reading recognition model for Arabic visual speech recognition by implementing deep learning *** Arabic visual datasets that have been collected contains 2400 records of Arabic digits and 960 records of Arabic phrases from 24 native *** primary purpose is to provide a high-performance model in terms of enhancing the preprocessing ***,we extract keyframes from our ***,we produce a Concatenated Frame Images(CFIs)that represent the utterance sequence in one single ***,the VGG-19 is employed for visual features extraction in our proposed *** have examined different keyframes:10,15,and 20 for comparing two types of approaches in the proposed model:(1)the VGG-19 base model and(2)VGG-19 base model with batch *** results show that the second approach achieves greater accuracy:94%for digit recognition,97%for phrase recognition,and 93%for digits and phrases recognition in the test ***,our proposed model is superior to models based on CFIs input.

关键词： Convolutional neural network deep learning lip reading transfer learning visual speech recognition

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An Artificial Intelligence Curriculum for Secondary School Students in Malaysia: Study Protocol 13

An Artificial Intelligence Curriculum for Secondary School S...

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13th IEEE International Conference on engineering Education, ICEED 2024

作者： Yin Sim, Serena Shing Lau, Bee Theng Amirul Islam, Fakir M Ming Than, Joel Chia Swinburne University of Technology Faculty of Engineering Computing and Science Kuching Malaysia School of Health Sciences Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9798350367416

The rapid evolution of artificial intelligence (AI) in the Fourth Industrial Revolution (IR 4.0) has increased the demand for AI talents globally, yet there remains a shortage, including in Malaysia. It has brought attention to the need to foster AI literacy among the younger generation through education, an area where research studies could be improved in developing countries, especially Malaysia. The current research aims to design and develop an intervention, an AI curriculum tailored for secondary school students in Malaysia, especially in Sarawak. The paper begins with a literature review, highlighting the current state of AI education in secondary schools globally and identifying the need for AI education. The methodology employs a design-based research (DBR) approach, allowing for iterative refinement of the intervention. A combination of quantitative and qualitative methods will evaluate the effectiveness of the intervention. The study targets lower secondary school students from the urban divisions in Sarawak, selecting 383 students for the baseline recruitment and 180 students for the intervention through a multistage cluster random sampling method. The study expects to improve the students' perceived competence, attitude, and motivation towards AI through the intervention. © 2024 IEEE.

关键词： Students

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Trading Automation Using PineScript

Trading Automation Using PineScript

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Rathore, Soumya Prakash, Harshit Balachander, T. School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350375237

This research project presents a comprehensive initiative that combines the development of an efficient website scraper with an algorithmic trading system. The website scraper, designed to extract diverse data elements such as text, images, links, and product information, aims to empower students and new learners in the field with the ability to access, analyze, and comprehend web data for applications like market research and competitive analysis. The trading system utilizes a indicator based on Relative Strength Index (RSI) and Bollinger Bands for real-time Buy and Short signals in index trading. Parameters and real-time display enhance adaptability and analysis. The initiative includes the integration of a simulator into a financial platform, offering users a realistic experience to understand investment strategies, mitigate risks, and optimize financial performance. The simulator, the real stock market using historical and real-time data, provides a risk-free environment for users to practice investment plans. The approach aims to make internet investing more accessible and informed, bridging the gap between data extraction, analysis, and algorithmic trading for a comprehensive understanding of investment opportunities in a dynamic market environment. © 2024 IEEE.

关键词： Websites

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Electric Power Scam Prediction Using Machine Learning Techniques 8th

Electric Power Scam Prediction Using Machine Learning Techni...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Suresh, A. Vishnoi, Mahendra Mishra, Aditya Kumar Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TamilNadu Chengalpattu603203 India

ISBN: (纸本)9789819765805

Electricity fraud is causing huge losses in both the public and private sectors. The proposition of this paper uses a temporarily controlled learning program to include potential theft data from non-specific data on the existence of multiple sample theft. There are two basic applications, one anticipating the use of power and the other to create a broken application and analysis space. Both are rushing for power protection in creating configuration, redesign, and functionality. Because of the significant costs of energy acquisition, as a restricted measure of energy assets, the efficient and effective use of energy assets is an important part of social and financial development in any country. This work introduces two accounting frameworks for the detection of theft of high- and low-power consumers who rely on machine learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Impact of Data Poisoning Attack on the Performance of Machine Learning Models

Impact of Data Poisoning Attack on the Performance of Machin...

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International Conference on Data science and Network engineering, ICDSNE 2023

作者： Das, Dipan Roy, Sharmistha Sahoo, Bibhudatta Department of Computer Science & Engineering NIT Rourkela Rourkela India Faculty of Computing and Information Technology Usha Martin University Ranchi India

ISBN: (纸本)9789819967544

The twenty-first century has witnessed widespread adoption of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). These techniques have provided reliable solutions in various areas, including statistics, information theory, and mathematics. Given the prevalence of ML techniques, there exist various adversaries which question the robustness of ML models. Adversaries aim to manipulate models to their advantage, reducing their performance and accuracy. Data poisoning attack is one such adversary in which the attacker manipulates models by introducing specially crafted poisoned data into the training dataset. This paper presents the performance analysis of different machine learning models with and without the influence of data poisoning attack to predict the probability of diabetes and its effect on accuracy and precision. It has been observed that the SVM (RBF) classifier performs best on clean data, while the KNN classifier is highly affected by data poisoning, with a lesser impact on the SVM (Linear kernel) classifier. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reliability analysis

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Cervical Cancer Risk Prediction using SMOTE and Explainable AI

Cervical Cancer Risk Prediction using SMOTE and Explainable ...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Agarwal, Priyansh Chaudhary, Annanya Sarvanan, T.R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence India Srm Institute of Science and Technology Faculty of Engineering & Tech. Department of Computational Intelligence India

ISBN: (纸本)9798331508685

Cervical cancer, accounting for 7.9% of cancers in women globally, is a critical health challenge, particularly due to its asymptomatic nature in early stages. This study proposes a machine learning-driven framework to predict cervical cancer risk using clinical data, addressing class imbalance through SMOTE and optimizing model performance via GridSearchCV. By evaluating five classifiers - K-Nearest Neighbors, Random Forest, Support Vector Classifier, Logistic Regression, and Decision Trees - this research identifies the Decision Tree model as the most robust, achieving superior precision, recall, and accuracy metrics. Additionally, Explainable AI techniques such as SHAP and LIME were employed to enhance interpretability, offering clinicians actionable insights into key risk factors driving predictions. The results demonstrate the effectiveness of machine learning in facilitating early detection and personalized treatment strategies for cervical cancer, thereby advancing its integration into clinical applications. © 2025 IEEE.

关键词： Lung cancer

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AI-Progressions in Automated Facial Paralysis Evaluation: A Comprehensive Survey of Traditional to Deep Learning Approaches 4

AI-Progressions in Automated Facial Paralysis Evaluation: A ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kiruthiga, S. Anbuananth, C. Dhilipkumar, V. Annamalai University Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331523923

Deep learning mechanisms allow computers to solve complex real-time problems with complex neural networks, it employs a vital role in health sector, for assisting the medical practitioners with quick and accurate decision making. Various studies on this research area proves that deep learning approaches are more effective in detecting facial paralysis, a common visible symptom of acute stroke. The sealing sign of stroke is facial weakness which destroys the newline muscle movements in the face on one or both sides of the nerves in the brain stream. Accurate and timely detection is most important and need of the golden hour, to patients effectively. Human assessment and traditional diagnostic methods have demonstrated poor inter rated reliability, demanding improved diagnostic tools. By analysing facial asymmetry and other relevant features like Face feature analysis, extracting characteristics such as eye shape, movement, and wrinkles from static images to detect facial weakness is done using various deep learning techniques. This work encompass the real-time challenges such as curating datasets, ethical concerns and data privacy of patients. The objective of this paper is to review the potentiality of various traditional to deep learning algorithms in automising the facial paralysis detection for accurate stroke diagnosis in patients. AI models can provide objective assessments that enhance the reliability of stroke detection. © 2025 IEEE.

关键词： Convolutional neural networks

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