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3rd International Conference on Advanced Research in computing, ICARC 2023

作者： Sumanathilaka, T.G.D.K. Weerasinghe, Ruvan Priyadarshana, Y.H.P.P. Informatics Institute of Technology School of Computing Colombo006 Sri Lanka University of Colombo School of Computing Colombo007 Sri Lanka

ISBN: (纸本)9798350347371

With the social and technological revolution, the usage of social media platforms and instant message services strengthens native language compatibility in the digital arena. The Sinhala and the Romanized Sinhala became the prominent typing languages among the general Sri Lankan community. Informal short-hand-based typing and short net acronyms were used for easier Sinhala typing. But Typing Romanized Sinhala using ad-hoc transliterations and getting the expected output in native Sinhala is less accurate and time-consuming. Therefore, this study aims to introduce a novel reverse transliterator which can back transliterate and suggest Romanized Sinhala to Sinhala words. The Transliterator has been modelled using the Statistical approach with Trigram and Rule-based model for back transliteration purposes and Knowledge-based with a Trie data structure for suggesting purposes. The proposed solution is capable of transliterating both formal and informal shorthand Romanized Sinhala. This hybrid model used in the study is capable of efficient transliteration with the word level accuracy of 0.84. This proposed model can be used in digital platforms to enhance the usability of native Sinhala communication in a much more efficient way. © 2023 IEEE.

关键词： Knowledge based systems

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Web-Based Mental Health Predicting System Using K-Nearest Neighbors and XGBoost Algorithms 8th

Web-Based Mental Health Predicting System Using K-Nearest Ne...

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8th International Visual informatics Conference, IVIC 2023

作者： Zulkefli, Nurul Farhanaa Diah, Norizan Mat Ismail, Azlan Hanum, Haslizatul Fairuz Mohamed Ibrahim, Zaidah Arif, Yunifa Miftachul School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia

ISBN: (纸本)9789819973385

Problems with mental health are common presently and have been a worry for a long time. Mental health problems, like anxiety, depression, and panic attacks, can be caused by numerous things. Therefore, recognising the start of mental disease is becoming increasingly crucial to maintaining a good life balance. This study uses machine learning to identify any possible mental health disorders in an individual to attain this goal. The investigation employed supervised machine learning to predict mental health status, namely K-Nearest Neighbors (KNN) and XGBoost, with performance evaluation criteria including accuracy, precision, recall, and F1 score. When these two algorithms were compared, it was discovered that XGBoost produced a more effective prediction model, which was then employed to develop a web-based mental health prediction system. The web-based method creates a questionnaire for mental health issues. Based on the user’s responses to the questions, the system will predict his or her mental health status as normal, depression, anxiety, stress, loneliness, or regularity. Every component of the system, including buttons and forms, has been successfully tested using functionality tests. Moreover, the system’s advantages, weaknesses, and future study directions are identified. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nearest neighbor search

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A classifier design for micro bubble generators based on deep learning technique

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Materials Today: Proceedings 2023年 80卷 2684-2696页

作者： Qaddoori, Ayman S. Saud, Jamila H. Hamad, F.A. Informatics Institute for Postgraduate Studies Iraqi Commission for Computer and Informatics Baghdad Iraq College of Science Mustansiriyah University Baghdad Iraq School of computing Engineering and Digital Technology Teesside University Middlesbrough United Kingdom

This paper aims to study the sizes of bubbles generated by the micro-bubble generator device (MBGs) in the water because of its impact on the dissolved oxygen percentage in the water and we find this in aquaculture where oxygen is important for marine life and in many applications. Where we have designed a classifier for micro-bubbles of different sizes and the effectiveness of the device has been proven in generating bubbles whose size ranges from 20 to 50 and calculating their numbers in the image by converting the image from (RGB) to (HSV), it has proven its effectiveness in maintaining lighting and neglecting color information Which is not important in identifying bubbles in the image, as well as increasing the processing speed, and then finding edges in the image, using canny edge detection, which has proven its great effectiveness in finding weak and strong edges. Where we then used the (Circular Hough Transform) algorithm to identify the tiny bubbles in the image, calculate their average diameter and numbers in the image, and find the overlapping bubbles between them by using (Two Thresholds) one for the edge and the other for the center of the bubble and then making (Segmentation) for these bubbles and inserting them on (Gaussian Normal Distribution) Do not neglect the dark bubbles, because in most cases they are unreal bubbles and may be distortions in the image or reflections in the lighting. And then make (zero paddings) for the extracted images to make them equal in size and avoid the distortion that will happen in the image if another method is used to make all bubbles of equal dimensions to prepare them for classification. And then we calculated the (mean square error) of the resulting bubble images to avoid repeating the same bubble in (datasets). The efficiency of the classifier was calculated by using deep learning technology. Where we built a (Convolution Neural Network) consisting of (15) layers and then we trained these layers on the images resul

关键词： Feature extraction

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An enhanced LSTM method for financial market forecasting 2

An enhanced LSTM method for financial market forecasting

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2nd International Conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Sun, Yu Mutalib, Sofianita Guangdong University of Science and Technology Guangdong Dongguan523083 China School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam40450 Malaysia

ISBN: (纸本)9781510689053

To effectively enhance the prediction accuracy of financial time series and improve the interpretability of the structural parameters of the prediction model, this paper proposes a long short-term memory (LSTM) stock price prediction model optimized in two stages based on Bayesian and Particle Swarm Optimization (BPSO-LSTM). This model represents an innovative advancement of the traditional LSTM framework, specifically designed to address complex nonlinear problems characterized by long-term dependencies, particularly in stock indices. Initially, the Bayesian algorithm is employed to optimize the core parameters of the LSTM model, establishing a foundation for subsequent Particle Swarm Optimization. Following this, an adaptive particle swarm algorithm is utilized to further refine the core parameters obtained through Bayesian Optimization, ensuring a high degree of compatibility between the characteristics of stock data and the network topology, thereby significantly improving the prediction accuracy of stock prices. Results from the prediction of the S&P 500 and Hang Seng Index indicate that the two-stage BPSO-LSTM model not only enhances prediction accuracy but also demonstrates strong universal applicability. © 2025 SPIE.

关键词： Particle swarm optimization (PSO)

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Game-Based Mobile Application for Tarannum Learning 8th

Game-Based Mobile Application for Tarannum Learning

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8th International Visual informatics Conference, IVIC 2023

作者： Nadziman, Muhammad Irfan Mohd Hanum, Haslizatul Fairuz Mohamed Adnan, Nur Aina Khadijah Diah, Norizan Mat Bakar, Zainab Abu School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia School of Science and Technology Asia eUniversity Selangor Subang Jaya Malaysia

ISBN: (纸本)9789819973385

The melodious recitation of Quran verses, known as Tarannum, impacts the reciters’ and audiences’ understanding and perception. Several experts on Tarannum melodies often train their apprentices in continuous lessons covering techniques on voice activation and correct recitations. The availability of an interactive application can help promote constant training and effectively complement the lessons. An interactive approach to Tarannum lessons is not widely available and is often developed for recognising the Arabic alphabet rather than practising melodies and recitations. A mobile application is one of the most widely implemented learning and training applications, as it offers interactive feedback to motivate users to continue the lessons repetitively. The lack of mobile applications to promote Tarannum learning reduces the interest in becoming competent reciters. This is because traditional melody training requires one-on-one instruction from an instructor to perfect the melody. The lack of an interactive Tarannum application affects Muslims who cannot practice the Tarannum skills and foundation through conventional learning sessions. Therefore, this project proposes a mobile learning application for Tarannum. The application can assist Muslims in learning Tarannum by improving their melodic recitation techniques and, simultaneously, can give the user an enjoyable experience by proposing a game-based learning approach. This was done in response to the need for an application to assist Muslims in learning Tarannum and evaluating their Tarannum techniques. For the extended project, the application can be enhanced by implementing an algorithm for Tarannum melody detection that will be more helpful for people who want to learn Tarannum. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mobile computing

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Edge Load Balancer for OTT Platforms

Edge Load Balancer for OTT Platforms

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2023 International Conference on Green Energy, computing and Intelligent technology, GEn-CITy 2023

作者： Herbert Raj, P. Ravi Kumar, P. Computer Science University of Southampton Malaysia School of Computing and Informatics University Technology Brunei Brunei

ISBN: (纸本)9781839539596

In this modern world scope of an edge network attained pinnacle due to proliferation of mobile and portable devices. The aim of the edge network is to develop a high-speed data communication network that connects entire world. Immense quantity of data is produced due to lot of IIoT (Industrial Internet of Things) devices, IoT(Internet of Things) devices, Sensors, Mobile phones and portable wireless gadgets. This immense quantity of data must be processed and sent back to the customers without any delay in the network. Edge datacentres are responsible for this information processing. Unfortunately, datacentres are incapable of processing this huge volume of data due to its limited resource availability. In order to alleviate this issue, Edge computing was introduced and edge architecture could back huge data volume because edge can handle heterogeneous networks. This paper proposes Spring Cloud Load Balancer to handle these issues in Edge. This helps in handling user request and gives swift reply to the customers so that customers can enjoy fast service without any latency and flawless communication. © The Institution of Engineering & technology 2023.

关键词： Internet of things

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Review of AI Technologies for Enhancing the Lives of Visually Impaired Individuals: Applications, Outcomes, and Future Directions

Review of AI Technologies for Enhancing the Lives of Visuall...

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2024 International Conference on Information and Communication technology for Development for Africa, ICT4DA 2024

作者： Deriba, Fitsum Gizachew Wubineh, Betelhem Zewdu Teka, Mengistu Zenebe University of Eastern Finland School of Computing Yliopistokatu 2 JoensuuFI-80100 Finland Wroclaw University of Science and Technology Faculty of Information and Communication Technology Wroclaw Poland Wachemo University School of Computing and Informatics Hosaena Ethiopia

ISBN: (纸本)9798331527792

Imagine an environment where individuals with disabilities enjoy a significantly improved quality of life, not only because of the support they receive but also due to advancements in artificial intelligence (AI). While existing studies focus on exploring specific aspects of AI technologies for visually impaired (VI) individuals, there is limited research exploring the outcomes of designed and implemented AI technologies on these individuals. This study examines the current state of AI technologies in supporting VI individuals. We systematically reviewed 263 articles published between 2008 and 2023, of which 33 met the inclusion criteria for our study. This rigorous selection process ensured that only the relevant studies conducted with AI technology for VI were included in our analysis. The results identified several applications developed for various purposes, predominantly 45% focused on AI technology for independent navigation, followed by technology that addresses daily activities issues (21%). Our findings indicate that limited AI technologies have been developed for educational purposes and use in finance and e-commerce. The outcomes of using AI technology demonstrate improvements in navigation and mobility, as well as enhanced independence and quality of life. While our study identifies gaps in existing AI technology for VI, it also contributes to various stakeholders, including developers, users, and researchers, by providing a comprehensive understanding of the current state of AI technology for individuals with VI. © 2024 IEEE.

关键词： Assistive technology

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity 5

A Review on Leveraging Language Agnostic Automated Text Data...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Wijesundera, Sarith Peiris, Suresh Michael University of Westminster Department of Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages. © 2025 IEEE.

关键词： Transfer learning

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Joint Active and Passive Beamforming Design in Intelligent Reflecting Surface(IRS)-Assisted Covert Communications:A Multi-Agent DRL Approach

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China Communications 2024年第9期21卷 11-26页

作者： Gao Ang Ren Xiaoyu Deng Bin Sun Xinshun Zhang Jiankang School of Electronic Information Northwestern Polytechnical UniversityXi’an 710072China Key Laboratory of Near Ground Detection and Perception Technology Wuxi 214035China Department of Computing and Informatics Bournemouth UniversityUK

Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals ***,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication *** the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert *** this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG)approach for the joint active and passive beamforming(Joint A&P BF)optimization without the channel estimation,where the base station(BS)and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE)*** to the‘centralized training and distributed execution’feature of MADDPG,each agent can execute the active or passive beamforming independently based on its partial observation without referring to *** results demonstrate that the proposed deep reinforcement learning(DRL)approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.

关键词： covert communications deep reinforcement learning intelligent reflecting surface

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A-MASA: Arabic Multi-Domain Aspect-Based Sentiment Analysis Datasets 6

A-MASA: Arabic Multi-Domain Aspect-Based Sentiment Analysis ...

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6th International Conference on AI in Computational Linguistics, ACLing 2024

作者： Eid, Yomna Zayed, Hala Medhat, Walaa School of Information Technology and Computer Science Nile University Giza Egypt Centre for Informatics Science Nile University Giza Egypt School of Computing and Information Sciences Egypt University of Informatics Cairo Egypt

The rapid growth of Natural Language Processing (NLP) applications has gained interest in various languages, including Arabic. One critical NLP task is Aspect-Based Sentiment Analysis (ABSA). ABSA involves identifying the sentiment expressed toward specific aspects or attributes of entities mentioned in a piece of text, rather than determining the overall sentiment. Despite the increased use of Semitic languages like Arabic in NLP over the past decade, there remains a lack of resources and datasets in Arabic, particularly in dialectical Arabic. Additionally, existing ABSA datasets are often limited to a single domain, which does not support all types of ABSA, such as multi-domain ABSA, nor do they allow for testing a model's generalization capabilities. This study addresses these limitations by constructing multi-domain ABSA datasets in Arabic for the tasks of aspect extraction and aspect polarity detection. The datasets include both real and synthetic data, covering dialectical and modern standard Arabic, and comprising 6,500 records across five domains. These datasets can be utilized in single-domain, cross-domain, and multi-domain experiments for ABSA. Furthermore, we evaluated the constructed datasets using various transformer-based models and assessed the impact of generating synthetic data on the diversity and balance of the data, as well as on model performance. The results reported in this research demonstrate that the data is valid for use in ABSA tasks. Moreover, the models used for evaluation achieved competitive or superior results compared to existing models on various datasets. Additionally, the annotation guidelines we created are presented as recommended practices applicable to different tasks. All datasets used in this study are publicly available for research purposes. © 2024 Elsevier B.V.. All rights reserved.

关键词： Data assimilation

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