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A hybrid deep learning framework for solar irradiation prediction based on regional satellite images and data

Neural Computing and Applications 2025年 1-37页

作者： Attya, Mohammed Abo-Seida, Osama Mohamed, Hatem Mohammed, Amgad Department of Information System Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt Department of Computer Science Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt

Accurate prediction of solar irradiation, particularly Diffuse Horizontal Irradiance (DHI), is essential for optimizing solar energy systems. This paper presents a hybrid framework that integrates satellite imagery and satellite-derived data to improve the precision of DHI forecasts. Two key datasets were employed: ground-based Solar Irradiation Measurement (SRM) and satellite-based Solar Irradiation (SSR). The proposed methodology utilizes machine learning models along two paths. The first path processes satellite imagery using advanced techniques, including pixel imputation with a modified Random Forest (RF) and Generative Adversarial Network (GAN), noisy region detection with Self-Organizing Maps (SOM), and noise removal through a Latent Diffusion Model (LDM). The second path handles tabular data through a diffusion probabilistic model designed for missing data imputation. These features are then merged and fed into a Long Short-Term Memory (LSTM) network, enhanced to capture seasonality and trends for accurate DHI predictions. The paper makes significant contributions by introducing a robust hybrid framework that leverages both satellite imagery and tabular data, incorporating novel preprocessing methods such as GAN-based pixel imputation and diffusion-based noise removal. The LSTM model is further adapted to handle seasonal and trend components, resulting in enhanced DHI forecasting accuracy. The proposed model demonstrates superior performance, achieving a Mean Squared Error (MSE) of 8.170 W/m2, Root Mean Squared Error (RMSE) of 1.749 W/m2, Mean Absolute Error (MAE) of 0.693 W/m2, and an R-squared value of 2.574, significantly outperforming conventional methods such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), and traditional LSTM models. Additionally, the model records a low Mean Absolute Percentage Error (MAPE) of 0.8183% and a computation time of 35.4 s, highlighting its efficiency. These results contribute to more accurate and reli

关键词： Landsat

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Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

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computers, Materials & Continua 2024年第6期79卷 4725-4740页

作者： Djeldjli Halima Benatiallah Djelloul Ghasri Mehdi Tanougast Camel Benatiallah Ali Benabdelkrim Bouchra Material Sciences Department Faculty of Material SciencesMathematics and Computer ScienceLaboratory of Sustainable Development and Computer Science(LSDCS)University of AdrarAdrar01000Algeria Department of Civil Engineering University of Sistan and BaluchestanZahedan98167-45845Iran Laboratory of Computer Production and Maintenance Engineering(LGIPM)University of LorraineChieulles57070France Laboratory of Energy Environment and Information System(LEEIS) University of AdrarAdrar01000Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first *** study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and *** proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN *** GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were *** model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

关键词： Solar energy systems genetic algorithm neural networks hybrid adaptive neuro fuzzy inference system solar radiation

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A Survey of information Security Roles and Trends in the Quranic Perspective 1

A Survey of Information Security Roles and Trends in the Qur...

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1st International Conference on Cyber Security and Computing, CyberComp 2024

作者： Yunus, Mohd Amin Mohd Rahman, Nurul Hidayah Ab Hamid, Isredza Rahmi A. Universiti Tun Hussein Onn Malaysia Applied Information System Faculty of Computer Science and Information Technology Parit Raja Malaysia Centre of Information Security Research Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia

ISBN: (纸本)9798350387728

In this study, we surveyed the existing information security literature related to the Quran to contribute to the knowledge gap(s) in seeking the role of information security in the Quran research area. Materials published in English were located between January 2019 and February 2024 by searching various academic databases, including Google Scholar, IEEEXplore, ACM Digital Library, Springer and scienceDirect. Thirty-two publications were collected and classified into ten domains of information security Common Body of Knowledge to identify the research trends. The results show that Domain 3 (Cryptography and Digital Watermarking) is the most studied area, followed by Domain 2 (Ethical and Legal Issues) with eight publications and Domain 5 (Access Control) with three publications. Other domains consist of only 1 or 2 publications each. Our observation also showed that three recurring themes of studies that are the implementation of information security techniques to preserve digital Quran authenticity, the association of Quran verses in supporting information security approaches;and the development of information security techniques from the Quran epistemology. Overall, exploring the research trends and interconnected areas could lead to advancements in information security practices and Quranic studies, fostering how traditional wisdom influences modern technologies. © 2024 IEEE.

关键词： authenticity ethical information security Quranic

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Enhancing Student's Performance Classification Using Ensemble Modeling

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第4期4卷 204-214页

作者： Nafea, Ahmed Adil Mishlish, Muthanna Shaban, Ali Muwafaq Shaban AL-Ani, Mohammed M. Ali Alheeti, Khattab M. Mohammed, Hussam J. Department of Artificial Intelligence College of Computer Science and IT University of Anbar Ramadi Iraq Department of Mathematics Faculty of Education for pure science University of Anbar Ramadi Anbar Iraq Department of Information System University of Anbar Ramadi Iraq Selangor Bangi Malaysia Department of Computer Science University of Anbar Ramadi Iraq

A precise prediction of student performance is an important aspect within educational institutions to improve results and provide personalized support of students. However, the predication accuracy of student performance considers an open issue within education field. Therefore, this paper proposes a developed approach to identify performance of students using a group modeling. This approach combines the strengths of multiple algorithms including random forest (RF), decision tree (DT), AdaBoosts, and support vector machine (SVM). Afterward, the last ensemble estimates as one of the bets logistic regression methods was utilized to create a robust and reliable predictive model because it considers The experiments were evaluated using the Open University Learning Analytics Dataset (OULAD) benchmark dataset. The OULAD dataset considers a comprehensive dataset containing various characteristics related to the student’s activities thereby five cases based on the utilized dataset were investigated. The experiment results showed that the proposed ensemble model presented its ability with accurate results to classify student performance by achieving 95% of accuracy rate. As a result, the proposed model exceeded the accuracy of individual basic models by using the strengths of various algorithms to improve the generalization by reducing the potential weaknesses of individual models. Consequently, the education institutes can easily identify students who may need additional support and interventions to improve their academic performance. © The Author(s), 2023.

关键词： Students

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An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

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computers, Materials & Continua 2022年第1期70卷 1721-1747页

作者： Sultan Noman Qasem Mohammed Al-Sarem Faisal Saeed Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Computer Science Department Faculty of Applied ScienceTaiz UniversityTaiz6803Yemen College of Computer Science and Engineering Taibah UniversityMedinaSaudi Arabia Information System Department Saba’a Region UniversityMareebYemen

Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health *** huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been *** COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social *** methods have been applied for detecting rumors and tracking their sources for COVID 19-related ***,very few studies have been conducted for this purpose for the Arabic language,which has unique ***,this paper proposes a comprehensive approach which includes two phases:detection and *** the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic Algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the *** experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.

关键词： Rumor detection rumor tracking similarity techniques COVID-19 social media analytics

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Preliminary Exploration on Node-To-Node Fault Tolerance Coordination in Distributed system

Preliminary Exploration on Node-To-Node Fault Tolerance Coor...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Saadoon, Muntadher Hamid, Siti Hafizah Ab. Sofian, Hazrina Altarturi, Hamza Nasuha, Nur Azizul, Zati Hakim Khalid, Haqi Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Selangor 43400 Malaysia

ISBN: (纸本)9798350302684

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism. © 2023 IEEE.

关键词： Fault tolerance

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Gamification using Octalysis Framework in Knowledge Management system for Vocational High Schools during the Covid-19 Pandemic 7

Gamification using Octalysis Framework in Knowledge Manageme...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Firdaus, Mgs. Afriyan Indah, Dwi Rosa Sazaki, Yoppy Ariefin, Eka Prasetyo Nuriza, Muhammad Fachri Rafly, Muhammad Universitas Sriwijaya Faculty of Computer Science Information System Department Palembang Indonesia Universitas Sriwijaya Data Structure and Accounting Information System Laboratory Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Informatics Management Department Palembang Indonesia

ISBN: (纸本)9798350345711

Knowledge management systems enable teachers and students to interact directly in the knowledge transfer process during the Covid-19 pandemic. In addition, applying the gamification concept in vocational disciplines could enhance the quality of teaching to increase motivation and student learning outcomes which tend to decline. Octalysis Framework, which supports the gamification concept, consists of eight cores that focus on creativity, self-expression, social dynamics, logic, thinking, and ownership depicted in an octagon making an application more fun and helpful to motivate users to enjoy and be involved in an activity. This condition is crucial and urgent in the Covid-19 pandemic that requires online and student-centered learning. This study discussed the application of Octalysis Framework-based Gamification in a computer Learning Knowledge Management system in Vocational High Schools during the Covid-19 Pandemic. The Gamification concept with the Octalysis Framework was implemented in the Knowledge Management system. Some game mechanics in a non-game context consisting of four phases, namely the Discovery, Onboarding, Scaffolding, and Endgame was applied to improve student performance and motivation in learning. Meanwhile, The Knowledge Management system software achieved an overall quality score of 5.14 out of 6 (85.6 percent of quality achieved) covering functionality, reliability, usability, efficiency, maintainability, and portability characteristics of the ISO 9126 quality indicator. © 2022 IEEE.

关键词： Students

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A Comparative Analysis on Three Duplication Elements in Copy-Move Forgery using PatchMatch-Based Detection Method 8

A Comparative Analysis on Three Duplication Elements in Copy...

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8th IEEE International Conference on Software Engineering and computer systems, ICSECS 2023

作者： Nor Azaimi, Nur Izzati Abd Warif, Nor Bakiah Ismail, Nor-Syahidatul N Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Centre for Information Security Research Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Department of Computer System and Networking Pekan Pahang Malaysia

ISBN: (纸本)9798350310931

Image forgery is the alteration of a digital image to hide some of the important and useful information. Copy-move forgery (CMF) is one of the most difficult to detect because the copied part of the image has the same characteristics as the original image. Most of the existing datasets only highlight additional attacks in the copied part. Since there are no categories of duplication elements in the datasets, this research analyzed three categories of duplication elements in CMF which are animals, food and non-living things using DEFACTO and CoMo3Dataset. The analysis is performed on PatchMatch-based detection method and the results show that the method able to maintain at least 83% for all duplication elements in both DEFACTO and CoMo3Dataset. Furthermore, the method is able to detect a minimum 92% score for the food category in both datasets. © 2023 IEEE.

关键词： Copy-Move forgery DEFACTO duplication regions PatchMatch

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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Alignment of Chan's Supply Value chain Waste Management Using Artificial Intelligence

Alignment of Chan's Supply Value chain Waste Management Usin...

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2023 International Seminar on Application for Technology of information and Communication, iSemantic 2023

作者： Winarno, Agus Nurhayati, Oky Dwi Gernowo, Rahmad Hasibuan, Zainal Arifin Diponegoro University Doctoral of Information System Departement Semarang Indonesia Universitas Dian Nuswantoro Information System Department Semarang Indonesia Universitas Diponegoro Information System School of Postgraduate Semarang Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350339215

Economy circulation can extend product cycle life, efficiency in using (extracting) natural resources, reduce waste generated from the production process and use recycled products as an increase in state revenue. The amount of waste does not show that the pattern of supply chain management in waste management is still not optimal, and the management of organic, inorganic waste (plastic, glass, wood, etc.) to hazardous and toxic waste (B3) which causes pollution, increases emissions impact on human health and the environment. Prevention to reduce excessive consumption patterns, waste management with the 3R pattern (reduce, reuse, and recycle), bio-physical-chemical processes for turning waste into new products, and waste incineration processes. Implementation of the Integrated Circular Economy system of waste in Indonesia with several stages of separating the waste into several waste groups, waste collection, waste transportation, waste treatment, and final waste treatment. Data collection in the form of data sets in the process of artificial intelligence or data mining for descriptive analysis, diagnostic analysis to find out the root of the problem and the background that occurs, predictive analysis to predict what will happen in the future, predictive analysis which is carried out at the latest and requires 4.0 technology supported by Artificial Intelligence (AI), Internet of Things (IoT) which is integrated with information systems in waste management to be able to improve people's welfare, reduce waste from upstream / households and provide government input to make policy technical, budgeting, operational efficiency, optimization of TPA and garbage transportation system. © 2023 IEEE.

关键词： Efficiency

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