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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Wijesinghe, W.A.K.D. Amarasinghe, A.G.L.D.P. Bandara, T.M.U.A. Ishara Gamage, Aruna Ganegoda, Devanshi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665408622

The tourism industry is one of the largest industries that contribute to the worldwide economy. Also, it is one of the best income sources in Sri Lanka. It makes a considerable amount of money directly and indirectly through contributing to the government budget, job market and foreign exchange earnings. Same as foreign visitors, a significant amount of domestic travelers contributes to the economy in Sri Lanka. Hence, the tourism industry is popular in Sri Lanka;this research aims to enhance travelers' travelling experience. When considering today's travel communities, there are a set of unanswered questions that travelers tend to find answers manually from forums and social media because of the lack of features in the current apps. This paper discusses the development of the Voyager - Smart travel guidance mobile application to fulfil the gap between current apps and user requirements. The mobile application mainly focuses on the following features;smart travel plan within the desired travel period, shortest and scenic path options, automatic review categorizing and rating system, place tagging and place feature extracting system, fuel data analyzer, and one-tap emergency guidance feature. Despite the previous mentioned features, the final mobile application includes a friends system between app users, group travel planning, and an in-app chat system. As a result of the research project, this user-friendly mobile application will provide a better user experience when travelling, and it will help to develop the tourism culture in Sri Lanka. © 2021 IEEE.

关键词： Sentiment analysis

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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DevFlair: A Framework to Automate the Pre-screening Process of software engineering Job Candidates 4

DevFlair: A Framework to Automate the Pre-screening Process ...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Jayasekara, R.T.R. Kariyawasam, K.G.S.S.K. Jayasinghe, S.L. Kudarachchi, K.A.N.D. Rajapaksha, Dilini Thelijjagoda, Samantha Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka School of Computing Technologies Royal Melbourne Institute of Technology Melbourne Australia SLIIT Business School Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The HR department of a technology company receives hundreds of job applications for each software engineering related vacancy. Evaluating a candidate by looking at the curriculum vitae may appear to be easy during the pre-screening process. However, an automated pre-screening process using Natural Language Processing and Machine Learning methodologies would help the recruiter to obtain a more accurate and deeper understanding of the candidate. In this paper we propose "DevFlair", a framework to automate pre-screening software engineering job candidates. DevFlair uses data from social media, GitHub, and open-ended questionnaires to predict the Big-Five personality traits, analyze technical skill expertise, and analyze the experience in using industry-related online platforms. After analysis, the candidates are ranked according to their personality and technical skill levels. We conduct the personality prediction experiments using a social media posts dataset annotated with gold-standard Big-Five personality labels. We train FastText classification models and compare their accuracy against other state of the art classification models. The comparisons conclude that the FastText classification models substantially outperform the state of the art classification models when predicting Openness, Conscientiousness, and Agreeableness personality traits. © 2022 IEEE.

关键词： Machine learning

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Application to Enhance the Programming Knowledge of Children

Application to Enhance the Programming Knowledge of Children

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Advancements in Computing (ICAC), International Conference on

作者： Anuga Dissanayake Vishwa Lakpriya Mahendran Sanjeevan Ahamed Fahmi Samanthi E.R. Siriwardana Mihiri Samaraweera Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This proposal outlines the development of a comprehensive educational platform aimed at bridging the information Technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical information Technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances information Technology education, equipping Sri Lankan students with essential programming skills to succeed globally.

关键词： Adaptation models Translation Machine learning algorithms Computational modeling Retrieval augmented generation Learning (artificial intelligence) Real-time systems Proposals information technology Programming profession

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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Guided Vision: A High Efficient and Low Latent Mobile App for Visually Impaired 3

Guided Vision: A High Efficient and Low Latent Mobile App fo...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Rizan, Tharik Siriwardena, Vishwa Raleen, Mohamed Perera, Lakshan Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

This paper presents a novel solution for visually impaired individuals. A mobile app is connected to an ESP32CAM and a remote server to help visually impaired individuals to navigate around their environment safely. A deep learning model is deployed in the mobile app to detect obstacles in real-Time without connecting to the internet. Other tasks such as reading texts, recognizing people, and describing objects are done in the remote server. We managed to connect the mobile app to the ESP32CAM and the remote server simultaneously. This was possible because the ESP32CAM is connected to the mobile app through Bluetooth. This gave the mobile the ability to connect to the remote server via the internet. To the best of our knowledge, no research has been done using Bluetooth to stream images to do object detection in a mobile app locally. Hence, our solution can detect obstacles locally and do other tasks mentioned previously in the remote server. This paper discusses how the ESP32CAM, obstacle detection module, face recognition module, text reading module, and object description module was implemented such that a low latent and highly efficient mobile app is created using minimal resources. © 2021 IEEE.

关键词： Face recognition

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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CTGGAN:A modified generative adversarial network for imbalanced CTG signal classification during labor

CTGGAN:A modified generative adversarial network for imbalan...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Mai, Peilin Feng, Junyuan Li, Luoyi Chen, Qinqun Liu, Guiqing Wei, Hang Guangzhou University of Chinese Medicine School of Medical Information Engineering Guangzhou China Shenzhen University College of Computer Science and Software Engineering Guangzhou China Guangzhou University of Chinese Medicine The First Affiliated Hospital Guangzhou China Guangzhou University of Chinese Medicine Intelligent Chinese Medicine Research Institute Guangzhou China

ISBN: (纸本)9798350386226

Cardiotocography (CTG) is a primary tool for real-time monitoring fetal heart rate (FHR) and uterine contraction (UC) signals during labor. Nowadays CTG signal classification models based on deep learning have facilitated end-to-end CTG interpretation through deep neural networks for automatic feature extraction. However, the extreme rarity of abnormal cases in clinical settings has posed great challenges for CTG deep learning. Meanwhile, most Generative Adversarial Network (GAN)-based studies focused on FHR signal generation and ignored the importance of UC signals during labor. Hence, we introduced CTGGAN, a modified GAN specifically designed to simulate both FHR and UC intrapartum signals simultaneously. Our approach incorporated Wasserstein Distance to prevent gradient vanishing during training, alongside a set of latent code vectors that enhance the generator controllability. We also integrated an auxiliary classifier to impose categorical constraints, thereby enriching the diversity of the generated samples. The experimental results demonstrated that CTGGAN effectively generated CTG signals that closely resembled real CTG signals. In addition, the incorporation of CTGGAN significantly improved performance, with a 19.18% increase in sensitivity over the traditional classification without GAN. Furthermore, the comparison with several state-of-the-art CTG classification models underscored the discriminative performance of our CTGGAN. In conclusion, this study effectively enhanced the detection of abnormal cases for intrapartum fetal monitoring. © 2024 IEEE.

关键词： Neonatal monitoring

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A Mutation-Guided Assessment of Acceleration Approaches for Continuous Integration: An Empirical Study of YourBase

A Mutation-Guided Assessment of Acceleration Approaches for ...

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International Mining software Repositories, MSR

作者： Zhili Zeng Tao Xiao Maxime Lamothe Hideaki Hata Shane McIntosh Software REBELs University of Waterloo Canada Division of Information Science Nara Institute of Science and Technology Japan Department of Computer Engineering and Software Engineering Polytechnique Montréal Canada Faculty of Engineering Shinshu University Japan

ISBN: (数字)9798400705878

ISBN: (纸本)9798350363982

Continuous Integration (CI) is a popular software development practice that quickly verifies updates to codebases. To cope with the ever-increasing demand for faster software releases, CI acceleration approaches have been proposed; however, adoption of CI acceleration is not without risks. For example, CI acceleration products may mislabel change sets (e.g., a build labeled as failing that passes in an unaccelerated setting or vice versa) or produce results that are inconsistent with an unaccelerated build (e.g., the underlying reasons for failure differ between (un)accelerated builds). These inconsistencies threaten the trustworthiness of CI acceleration *** this paper, we propose an approach inspired by mutation testing to systematically evaluate the trustworthiness of CI acceleration. We apply our approach to YourBase, a program analysis-based CI acceleration product, and uncover issues that hinder its trustworthiness. First, we study how often the same build in accelerated and unaccelerated CI settings produce different mutation testing outcomes. We call mutants with different outcomes in the two settings "gap mutants". Next, we study the code locations where gap mutants appear. Finally, we inspect gap mutants to understand why acceleration causes them to survive. Our analysis of ten open-source projects uncovers 2,237 gap mutants. We find that: (1) the gap mutants account for 0.11%–23.50% of the studied mutants; (2) 88.95% of gap mutants can be mapped to specific source code functions and classes using the dependency representation of the studied CI acceleration product; and (3) 69% of gap mutants survive CI acceleration due to deterministic reasons that can be classified into six fault patterns. Our results show that even deterministic CI acceleration solutions suffer from trustworthiness limitations, and highlight the ways in which trustworthiness could be pragmatically improved.

关键词： Accelerometers Codes Source coding Life estimation software Complexity theory Reliability

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AI Assisted Diagnosis of Dengue Amid Arboviral Diseases, Severity Prediction, and Post Effects

AI Assisted Diagnosis of Dengue Amid Arboviral Diseases, Sev...

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International Conference on information Technology Research (ICITR)

作者： Nivasheni B Fernando V.G.S.O. Srihari M Sarah M.I.L. Vishan Jayasinghearachchi Samadhi Chathuranga Rathnayake Dept. of Computer Science Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

This paper aims to examine the applicability of machine learning models for aiding in the diagnosis and management of arboviral illnesses, especially Dengue. The study focuses on three key objectives: Diagnosis of Dengue, prediction of prognosis of severe disease, and the identification of the post dengue effects. Multiple strategies were used to address these challenges. They are feature engineering based multiclass classification of arboviral diseases, enhancement of standard conventional machine learning algorithms for binary classification of severe Dengue and finally MLSOL enhancing ensemble model for drawing future effects of Dengue infections. The comparison of traditional models included both Random Forest, XGBoost, and also ensemble method while applying the feature selection methods of SFA and RFE. This study presents the comparison of traditional individual models and optimal ensemble method for the multiclass classification in arboviral diseases. In the case of binomial classification, the best accuracy level was 0.93 achieved by the Random Forest with RFE feature selection compared to Logistic Regression as well as the Gradient Boosting. Furthermore, the study proposed an MLSOL augmented ensemble approach to handle label imbalance problem in the dataset which in turn substantially enhanced the prediction accuracy. This approach decreased Hamming Loss to 0. 12 and increased the F-measure to 0.82, this has the capability of handling the imbalanced dataset and improving the predictive accuracy and performance. This machine learning framework offers great potential for clinical use in such aspects as early intervention and in effects following Dengue fever. It is meant to help healthcare practitioners and policy makers on how to better prevent and combat Dengue in Sri Lanka.

关键词： Accuracy Machine learning algorithms Medical services Feature extraction Ensemble learning Object recognition Prognostics and health management Viruses (medical) Random forests Standards

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