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检索条件"机构=Faculty of Computing and Information Technology"

共 7420 条记录，以下是61-70 订阅

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Design and Implementation of a Secure Documentation System for Robust Document Transfer and Access Control 6

Design and Implementation of a Secure Documentation System f...

引用

6th International Conference on Advancements in computing, ICAC 2024

作者： Jayathilaka, Mishara Weerasinghe, Hiranya Gunawardana, Kithmi Samarasinghe, Pinidu Fernando, Harinda Siriwardene, Deemantha Faculty of Computing Sri Lanka Institute of Information and Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

Traditional documentation systems often lack the robust security measures required to handle sensitive data, leaving them vulnerable to unauthorized access, data breaches, and insider threats. Addressing these vulnerabilities, our system introduces a custom-built, secure document editor along with a unique file format compatible solely with this platform. This specialized format limits unauthorized access by preventing compatibility with other software, ensuring document security. To further safeguard data, each document is fortified with a digital signature, generated through biometric and cryptographic information, guaranteeing the authenticity and integrity of the content. In addition, forensic data is actively employed for anomaly detection, allowing the system to identify potential insider threats by analyzing unusual access patterns or document interactions. Real-time alerts for breach events enhance responsiveness, enabling swift action against potential security incidents. Tailored for high-security environments, this solution effectively addresses critical documentation concerns, prioritizing data confidentiality and integrity. © 2024 IEEE.

关键词： Data integrity

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Deep Learning Framework for Exploring Customized CNN with Customer Demographics and Salon Visuals 6

Deep Learning Framework for Exploring Customized CNN with Cu...

引用

6th International Conference on Advancements in computing, ICAC 2024

作者： Hewage, Randika Rathnayake, Samadhi Thilakarathna, Thusithanjana Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

This research explores the integration of visual aesthetics and demographic data to enhance customer satisfaction in the beauty salon industry through a custom Convolutions Neural Network (CNN) model. Traditional salon experiences rely on visual elements such as interior design, ambiance, and branding, which shape customer perceptions. However, the influence of demographic factors such as age, gender, and income on these visual cues has been under explored. To address this gap, a CNN model was developed to combine visual and demographic inputs, enabling the prediction of customer preferences and segmentation. By analyzing images of salon environments and integrating customer data, the model offers actionable insights for salon owners to optimize design and services. The findings show that the custom CNN, enhanced with self-attention mechanisms, achieved high accuracy in predicting customer preferences, with a training accuracy of 98.1 % and a validation accuracy of 98.7%. This approach has practical applications in improving salon strategies and customer satisfaction. © 2024 IEEE.

关键词： Sales

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Decoding Diabetics: Diabetic Retinopathy Detection using Machine Learning 6

Decoding Diabetics: Diabetic Retinopathy Detection using Mac...

引用

6th International Conference on Advancements in computing, ICAC 2024

作者： Arunthavarajah, Venuganth Tissera, Wishalya Rajendran, Karthiga Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

Diabetic Retinopathy, a serious complication linked with diabetes, involves the progressive damage of retinal blood vessels and can lead to severe vision loss or blindness if not detected and treated promptly. This research paper explores the application of Machine Learning (ML) techniques for diagnosing and detecting Diabetic Retinopathy. The study examines essential components including data gathering, preprocessing, model training, and evaluation, highlighting the critical role of early detection and timely treatment in preventing severe visual impairments. Unlike traditional screening methods that depend on manual evaluation by ophthalmologists, which can be labor-intensive and limited in remote or underserved regions, ML algorithms offer a scalable and efficient solution for Diabetic Retinopathy screening through retinal image analysis. This research demonstrates the effectiveness of ML models in automating the detection process. By employing advanced preprocessing techniques like data augmentation and using sophisticated Convolutional Neural Network (CNN) architectures, the proposed method achieves high accuracy and reliability in identifying various stages of Diabetic Retinopathy. The integration of these technologies showcases the potential of ML to revolutionize Diabetic Retinopathy screening practices, enhancing accessibility and improving patient care. The findings of this study highlight the capacity of ML to significantly advance Diabetic Retinopathy detection and management, offering a more efficient and automated approach that could greatly benefit individuals at risk of this condition. © 2024 IEEE.

关键词： Convolutional neural networks

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Adaptive Learning Tool to Enhance Educational Outcomes for Students with Inattentive Attention Deficit Hyperactivity Disorder (ADHD) 6

Adaptive Learning Tool to Enhance Educational Outcomes for S...

引用

6th International Conference on Advancements in computing, ICAC 2024

作者： Thawalampola, Oshada Jayasuriya, Danuja Kariyawasam, Supun Makawita, Madushi Wijendra, Dinuka Joseph, Jenny Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

This paper presents an adaptive learning tool to enhance educational outcomes for students with inattentive ADHD. The platform comprises four key components: content chunking and simplification with interactive content, an adaptive quizzing system, a personalized ADHD identifier and break recommendation system, and a tool to maximize learning potential. The content chunking component transforms traditional educational materials into ADHD-friendly formats using Natural Language Processing (NLP) technologies, making them more accessible and engaging. The adaptive quizzing system employs a modified spaced repetition algorithm to optimize memory retention and engagement through personalized quizzes. The personalized ADHD identifier uses a hybrid Convolutional Neural Network (CNN) model to analyze webcam data, tracking head movements and facial emotions to provide real-time break recommendations, enhancing focus and cognitive performance. The final component integrates machine learning and data mining to adaptively assign tasks and provide personalized feedback, fostering a supportive learning environment. Preliminary evaluations indicate significant improvements in academic performance and engagement among ADHD-diagnosed students. This research underscores the potential of adaptive learning technologies in creating inclusive educational environments addressing the unique challenges faced by students with ADHD. © 2024 IEEE.

关键词： Adversarial machine learning

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How Much You Can Get at the Cost of Your Privacy

How Much You Can Get at the Cost of Your Privacy

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Albalawi, Umar University of Tabuk Faculty of Computing and Information Technology Tabuk47512 Saudi Arabia

ISBN: (纸本)9798350362480

It feels as though nearly every traditional household appliance now has an internet-of-things counterpart. These IoT devices help us accomplish menial tasks and provide convenience in almost every part of our lives, but is there anything put at risk by using these devices? Do consumers need to be more aware of how these services function? The goal of this research is to raise awareness on the privacy concerns associated with using IoT based surveillance systems, remote patient monitoring systems, and smart home/hub devices. The experimental result shows the challenge of anomaly detection and intrusion classification is increased when we trying to identify the specific malicious such as: Spyware, Malware, Trojan Horse, and Distributed Denial of Service Attack DDoS. Therefore, in the IoT ear, relying on just anomaly countermeasure is not enough. This paper concludes with actionable recommendation for developers and users to enhance IoT privacy. © 2024 IEEE.

关键词： Differential privacy

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Deltrux: Insider Threat Detection of End-User Behavior Analysis Using Machine Learning 3

Deltrux: Insider Threat Detection of End-User Behavior Analy...

引用

3rd International Conference for Advancement in technology, ICONAT 2024

作者： Wishvaranga, K.L.S. Gunawardana, M.P.V.A. Perera, M.B.C. Deshan, M.A.P. Abeywardena, Kavinga Yapa Pandithage, Dinithi Sri Lanka Institute of Information Technology Malabe Faculty of Computing Sri Lanka

ISBN: (纸本)9798350354171

In the evolving threat landscape insider threats present a significant challenge within the enterprise environments. Since traditional signature-based analytics approaches focus only on the previously known signature types, it is unable to detect various threats depending on the dynamic activities performed by users. Using a behavioral analysis approach can detect threats by analyzing the dynamic behaviors of end users, and this could also detect zero-day threats. This study introduces a sophisticated, machine learning-based insider threat detection system specifically designed for end-user devices. The system leverages user behavior analytics to identify deviations from normal user activities, focusing on the components such as mouse movements, keystroke dynamics, communication patterns, operating system (OS) calls, and malicious data encryption attempts. The system can dynamically analyze user behaviors to identify insider threats in real-time. Successful detection of Insider threats on each endpoint devices, will pass an alert to a centralized dashboard called 'Deltrux'. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancing Marine Communication and Sustainability: A LoRa Based Ad Hoc Network System for the Fisheries Industry 6

Advancing Marine Communication and Sustainability: A LoRa Ba...

引用

6th International Conference on Advancements in computing, ICAC 2024

作者： Kumari, Chamodya Bandara, Pinsara Weerasinghe, Lahiru Madhuthisara, Rithma Tissera, Wishalya Vidhanaarachchi, Samitha Peruma Godapitiya, Virajini Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

The Sri Lankan fisheries industry faces significant challenges, including communication gaps and safety risks. This research introduces a novel solution by developing a LoRa-based ad hoc network integrated with blockchain technology to address these issues. The proposed system enables real-time data communication between fishing boats and land stations, offering features such as emergency alerts, and barrier avoidance. Blockchain integration provides secure communication and verification of registered vessels. The primary goals of this research are to enhance communication efficiency, and improve safety through early warning systems. By implementing this system, we aim to promote the sustainability and economic growth of Sri Lanka's fisheries industry. © 2024 IEEE.

关键词： Fisheries

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Enhancing Diabetic Retinopathy Management Through Personalized Recommendations with Reinforcement Learning 13

Enhancing Diabetic Retinopathy Management Through Personaliz...

引用

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Herath, H.M.A.I.B. Sandakalum, H.G.D. Samoon, T.F. Thushan, P.V. Kasthurirathna, Dharshana Krishara, Jenny Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350380583

Diabetic retinopathy (DR) is one of the main causes of vision impairment among diabetes patients, which leads to the requirement of effective management strategies to stop its progression. In traditional management methods, this process is often less personalized, which results in poor dietary and exercise practices among patients. This paper presents a mobile application, 'Retina-Care,' created to give out personalized dietary and exercise recommendations using Q-Iearning - a method of reinforcement learning. This proposed system dynamically learns and adapts to individual user behaviors, which leads to users engaging with the system more and adhering to the recommendations given. The paper addresses the cold start problem which can be seen in reinforcement learning systems, through a combination of epsilon-greedy methods, initialized Q-tables, and integration of an expert system. The results received through simulations and user testing indicate that the proposed strategy was successful in giving recommendations that were suitable to the unique medical conditions and preferences of the diabetic retinopathy patients. The said findings show that reinforcement learning can be used to enhance the effectiveness of diabetic retinopathy management systems, offering a promising solution for broader health care applications. © 2024 IEEE.

关键词： Reinforcement learning

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Todly: Multimodal Approach for Toddler Safety and Well-Being Using Advanced Machine Learning 6

Todly: Multimodal Approach for Toddler Safety and Well-Being...

引用

6th International Conference on Advancements in computing, ICAC 2024

作者： Dewmini, Ashani Shashindi, Maleesha Uththara, Kaveen Sathushka, Gehan Manathunga, Kalpani Karunathilaka, Supipi Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

This study presents 'Todly,' an intelligent toddler monitoring system designed as a mobile application to ensure the safety and well-being of toddlers through cutting-edge technology. 'Todly' integrates advanced machine learning algorithms and neural networks to enhance child safety and provide caregiver support. This approach features face emotion and gesture detection, danger object detection, crying pattern analysis, and a chatbot assistant for new mothers. These components work together seamlessly to offer caregivers real-time insights into a child's emotional state, identify potential hazards, understand the reasons behind a toddler's crying, and provide timely support and guidance. The architecture is designed to be scalable and adaptable, making it suitable for various environments, such as homes, daycare centers, and pediatric healthcare facilities. Its user-friendly interface ensures accessibility for users with different levels of technical expertise, while the robust support infrastructure facilitates smooth operation and maintenance. The integrated solution demonstrated strong performance across its various components, achieving an accuracy of 89.9% in object detection, 85% in emotion detection, and 90.8% in cry analysis, encompassing all cry types. The chatbot's mT5 model also achieved BLEU scores of 38.7 for Sinhala-to-English translation and 31.2 for English-to-Sinhala translation. © 2024 IEEE.

关键词： Gesture recognition

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Success Factors of Requirement Engineering that Influence Software Development Efficiency 6

Success Factors of Requirement Engineering that Influence So...

引用

6th International Conference on Advancements in computing, ICAC 2024

作者： Yapa, Thilini Nawinna, Dasuni Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

Due to the rapid advancement of technology, the software industry has become one of the major industries all over the world. Many organizations invest large amounts of money to develop software products. Requirement engineering is one of the major phases in the software development process. It is a challenging and time-consuming task which should carefully be done because requirement engineering plays a vital role in shaping a project's success and efficiency. Software development teams face significant challenges in making functional software products that meet client's requirements due to lack of clear requirements. This study explores the critical success factors of requirement engineering that influence software development efficiency. A study was conducted to gather data from business analysts within a leading telecommunication organization and its subsidiaries across three countries. The data was thoroughly analyzed through a structured analysis using Smarts PLS. The success factors considered in this study are business analyst's skills, quality of the requirement, customer involvement and team capability. The results highlighted that software development efficiency is strongly influenced by requirements engineering factors, including the skills of the Business Analyst, customer involvement, and team capability, each with a significance value of less than 0.05. By addressing requirement engineering challenges and implementing standardized procedures derived fromcritical success factors, organizations can streamline their requirement engineering processes, and this study provides valuable insights to enhance requirement engineering best practices fostering software development efficiency. © 2024 IEEE.

关键词： Enterprise software

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