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5th International Conference on Advancements in Computing, ICAC 2023

作者： Mudunkotuwa, Kavindi Senadeera, Chamath Gunawardana, Beuni Pathiraja, Onara Wijendra, Dinuka Fernando, Harinda Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems and Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study presents a comprehensive assessment of water quality in Sri Lankan reservoirs using available data sources. The study focuses on the identification of microorganisms through image analysis, watercolor analysis in satellite-based images detection of water quality, and the inference of water quality index using relevant physical parameters. By analyzing images, the study quantifies microorganisms, providing insights into microbial diversity and potential risks. Image processing techniques enable the identification of contaminants based on watercolor analysis. Utilizing satellite data, it examines variations in water quality parameters, providing a comprehensive assessment of overall water quality. The derived water quality index, using relevant physical parameters, quantitatively assesses and compares water quality across different reservoirs. The findings contribute to understanding water quality in Sri Lankan reservoirs, supporting informed decision-making and sustainable water resource management. © 2023 IEEE.

关键词： Water quality

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Toward Drought Modeling in South Asia: Machine Learning Approaches, Challenges, and Opportunities

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IEEE Access 2025年 13卷 87654-87671页

作者： Mana Saleh Al Reshan Muhammad Owais Raza Naeem Ahmed Mahoto Adel Rajab Asadullah Shaikh Mohamed A. Elmagzoub Khairan D. Rajab Department of Information Systems College of Computer Science and Information Systems Najran University Najran Saudi Arabia Emerging Technologies Research Laboratory (ETRL) College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Computer Engineering Istanbul Sabahattin Zaim University İstanbul Türkiye Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Network and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools, which offer accurate and reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction, Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1) The selection of the region for this study, for this study South Asia(SA) is selected as region of interest (ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, and advances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This review encapsulates the indicator and metrics (Complex Machine learning and deep learning models) for drought modeling which are most relevant to the SA region. This study sum up as most common challenges in drought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack of explainable and interpretable models, and unavailability of data for model uncertainty analysis. This study proposes that these problems can be solved with modern machine learning techniques such as explainable machine learning and federa

关键词： Droughts Machine learning Asia Deep learning Indexes Water resources Remote sensing Predictive models computer science Climate change Environmental monitoring Economics

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Time Series Facebook Prophet Model and Python for COVID-19 Outbreak Prediction

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computers, Materials & Continua 2021年第6期67卷 3781-3793页

作者： Mashael Khayyat Kaouther Laabidi Nada Almalki Maysoon Al-zahrani Department of Information Systems and Technology Collage of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia Department of Computer and Network Engineering Collage of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia and University of TunisElmanarTunisia

COVID-19 comes from a large family of viruses identied in 1965;to date,seven groups have been recorded which have been found to affect *** the healthcare industry,there is much evidence that Al or machine learning algorithms can provide effective models that solve problems in order to predict conrmed cases,recovered cases,and *** researchers and scientists in the eld of machine learning are also involved in solving this dilemma,seeking to understand the patterns and characteristics of virus attacks,so scientists may make the right decisions and take specic ***,many models have been considered to predict the Coronavirus outbreak,such as the retro prediction model,pandemic Kaplan’s model,and the neural forecasting *** research has used the time series-dependent face book prophet model for COVID-19 prediction in India’s various ***,we proposed a prediction and analysis model to predict COVID-19 in Saudi *** time series dependent face book prophet model is used to t the data and provide future *** study aimed to determine the pandemic prediction of COVID-19 in Saudi Arabia,using the Time Series Analysis to observe and predict the coronavirus pandemic’s spread daily or *** found that the proposed model has a low ability to forecast the recovered cases of the COVID-19 *** contrast,the proposed model of death cases has a high ability to forecast the COVID-19 ***,obtaining more data could empower the model for further validation.

关键词： COVID-19 time series analysis prediction face book prophet model python

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Validating Perception of Hyperspectral Textures in Virtual Reality systems

Validating Perception of Hyperspectral Textures in Virtual R...

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24th Eurographics Conference on Visualization, EuroVis 2022

作者： Díaz-Barrancas, F. Cwierz, H. Gil-Rodríguez, R. Pardo, P.J. Department of Psychology Justus-Liebig Universität Germany Department of Computer and Network Systems Engineering University of Extremadura Spain

ISBN: (纸本)9783038681854

Virtual reality (VR) environments are increasingly offering higher quality content. They use different computing techniques to improve the final user experience. In this work, we create different light sources and introduce hyperspectral textures for the object reflectance to boost the VR environment's quality. In addition, we perform a quantitative study to demonstrate that hyperspectral textures improve the final quality of the content in virtual reality systems. © 2022 The Author(s) Eurographics Proceedings © 2022 The Eurographics Association.

关键词： Textures

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Hospital Scheduling systems Current Challenges and Emerging Solutions

Hospital Scheduling Systems Current Challenges and Emerging ...

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2024 Global Digital Health Knowledge Exchange and Empowerment Conference, gDigiHealth.KEE 2024

作者： Musa, Nura Shifa Rafique, Saida Hafsa Mathew, Elezabeth College of Engineering Al Ain University Al Ain United Arab Emirates United Arab Emirates University Department of Information Systems and Security Al Ain United Arab Emirates United Arab Emirates University Department of Computer and Network Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798331531331

Hospital scheduling systems are integral components of healthcare management, addressing the complex challenge of optimizing appointment schedules. In the realm of healthcare research, the Appointment Scheduling Problem (ASP) has garnered significant attention due to its multifaceted nature. This problem is particularly pronounced in the outpatient setting, where effective scheduling systems can significantly impact patient satisfaction and overall health care efficiency. Recent comprehensive reviews highlighted the complexity of the Appointment Scheduling Problem within healthcare services, emphasizing the need for innovative solutions and proposing new scheduling models. This paper reviews literature on the topic of hospital scheduling systems, then looks into the current challenges prevalent in it and the emerging solutions that are discussed in literature. The review offers opportunities for innovation and improvements in scheduling procedures while highlighting difficulties with allocation of resources, appointment scheduling, and technology integration. Using advanced technologies into healthcare such as blockchain and AI-driven algorithms, are said to be integral to move in the direction of care that is patient-centric and efficient operations. The study highlights limitations in hospital scheduling systems, including scalability and generalizability, operational uncertainties, need for real-world implementation, and lack of consideration for emergency cases. To improve patient outcomes and the efficiency of healthcare scheduling, ongoing research and development, stakeholder involvement, user-centred design, and longitudinal research are essential. In summary, hospital scheduling systems represent a dynamic area of research, addressing the intricate interplay between patient satisfaction, resource optimization, and technological advancements. © 2024 IEEE.

关键词： User centered design

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Descriptive Exam Evaluation through Semantic Mining and Analysis

Descriptive Exam Evaluation through Semantic Mining and Anal...

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2023 Human-Centered Cognitive systems, HCCS 2023

作者： Akhtar, Raybal Safyan, Muhammad Iqbal, Muddesar Sarwar, Sohail University of Management and Technology Department of Informatics and Systems Lahore Pakistan Government College University Lahore Department of Computer Science Lahore Pakistan Prince Sultan University Department of Communication & Network Engineering Riyadh Saudi Arabia Bahria University Islamabad Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350359183

Online examination evaluation methods are used as an alternative to the traditional examination's way of evaluation methods. They offer a number of advantages while addressing issues associated with outdated conventional approaches. Online evaluation methods are mainly used for objective and short answers based exams but may not suitable for assessing the depth knowledge in candidates. So, candidate's descriptive examinations are conducted to assess their depth of knowledge. Descriptive evaluation methods have been put into practice for quite some time but they greatly rely on keyword-matching techniques between the questions and answers. Due to this limitation, the semantic meanings of these answers are ignored. In current research, the deficiencies in prevalent systems have been addressed by calculating the semantic and syntactic similarity between answers. Moreover, the repetitive words and similarity between counteractive answers has also been considered. We have used SQuAD1 dataset in current research. Our model has achieved a value of 0.88 in terms of accuracy for while computing similarity among listed items. © 2023 IEEE.

关键词： Semantics

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On a novel tracking differentiator design based on iterative learning in a moving window

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Control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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Aspect-Based Sentiment Analysis for Polarity Estimation of Customer Reviews on Twitter

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computers, Materials & Continua 2021年第5期67卷 2203-2225页

作者： Ameen Banjar Zohair Ahmed Ali Daud Rabeeh Ayaz Abbasi Hussain Dawood Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddah21589Saudi Arabia Air University Islamabad44000Pakistan Department of Computer Science Islamabad44000Pakistan Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21589 Saudi Arabia

Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some ***,sentiment analysis is currently a hot topic of *** particular,aspect-based sentiment analysis concerns the exploration of emotions,opinions and facts that are expressed by people,usually in the form of *** is crucial to consider polarity calculations and not simply categorize reviews as positive,negative,or ***,the available lexicon-based method accuracy is affected by limited *** of the available polarity estimation techniques are too general and may not reect the aspect/topic in question if reviews contain a wide range of information about different *** paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis(ABSA-PER).ABSA-PER has three major phases:data preprocessing,aspect co-occurrence calculation(CAC)and polarity estimation.A multi-domain sentiment dataset,Twitter dataset,and trust pilot forum dataset(developed by us by dened judgement rules)are used to verify *** outcomes show that ABSA-PER achieves better accuracy,i.e.,85.7%accuracy for aspect extraction and 86.5%accuracy in terms of polarity estimation,than that of the baseline methods.

关键词： Natural language processing sentiment analysis aspect co-occurrence calculation sentiment polarity customer reviews twitte

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ImConv_RNN: Improved Convolutional Recurrent Model Based Channel Estimation for 6G networks 4

ImConv_RNN: Improved Convolutional Recurrent Model Based Cha...

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

作者： Kulkarni, Raghavendra Suresh Kumar Kondeti, Venkata Satya Pillai, Binu Sudhakaran Surendran, R. T-Mobile Usa Inc Principal Engineer Department of Network Technology BellevueWA United States Senior Systems Engineer Department of Professional Services Axyom. Core North AndoverMA United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

This research introduces a novel deep learning for channel estimation for the 6G networks. The channel estimation for the 6G networks is employed using the proposed Improved Convolutional Recurrent (ImConv-RNN) model. In this, the loss function optimization is employed using the proposed ChSO algorithm. The chaotic mapping based on Chebyshev is included in the skill optimization assist the algorithm to enhance the convergence rate. The proposed ImConv-RNN model is evaluated based on RMSE, R2, MAE and MAPE acquired the values of 0.0136, 0.9584, 0.0114, and 5.8969 respectively. © 2025 IEEE.

关键词： 6G networks channel estimation chebyshev mapping recurrent model skill optimization algorithm spatio-temporal feature extraction

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Multimedia Fingerprinting Codes Resistant to Linear Attacks and Adversarial Noise

Multimedia Fingerprinting Codes Resistant to Linear Attacks ...

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2023 International Symposium on networks, computers and Communications, ISNCC 2023

作者： Fernandez, Marcel Kabatiansky, Grigory Kamel, Ibrahim Miao, Ying Rabie, Tamer Farouk The Technical University of Catalonia Department of Network Engineering Spain Center for Next Generation Wireless and IoT Skolkovo Institute of Science and Technology Russia University of Sharjah Electrical and Computer Engineering United Arab Emirates Institute of Engineering Information and Systems University of Tsukuba Japan

ISBN: (纸本)9798350335590

It has recently been shown that there are no multimedia fingerprinting codes that can find all malicious users when they use arbitrary linear attacks plus adversarial noise. It is shown that such codes exist if the complete recovery property is limited to the IPP property, i.e., the property to find at least one malicious user. Moreover, we extend this property to a property that allows us to detect all users whose contribution to the forgery is large enough. Efficient decoding (tracing traitors) algorithms are developed for these codes. © 2023 IEEE.

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