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Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

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GAN-CNN Ensemble: A Robust Deepfake Detection Model of Social Media Images Using Minimized Catastrophic Forgetting and Generative Replay Technique

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Procedia computer science 2024年 235卷 948-960页

作者： Preeti Sharma Manoj Kumar Hitesh Kumar Sharma Research Scholar School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun 248007 India Associate Professor Faculty of Engineering and Information Sciences University of Wollongong in Dubai UAE MEU Research Unit Faculty of Information Technology Middle East University Amman 11831 Jordan

Deep-fake photographs are difficult to discern from real ones, especially when utilized in social media platforms. Anyone can willfully create disinformation about public personalities, politicians, and celebrities using these deep fake photographs. So, it is an important need of society to work for an effective model for its detection. The models for deep fake detection commonly use CNN-based detectors. These detectors experience a drop in performance when used for transfer learning or continual learning techniques. A significant limitation in this process is CNN's catastrophe forgetting defect. For the solution of this problem, a Generative replay technique in the form of a GAN-CNN model is implemented that works to minimize this catastrophe forgetting issue that further helps for better detection. It involves generating and storing samples from previous tasks and then replaying them during the training of new tasks which makes CNN more robust to identify deep fakes. The GAN model used in this work is traditional DCGAN improved with necessary adjustments to achieve training stability. It is observed that the model attained a good accuracy of 98.67%(training),70.08% (testing) and minimum loss with a value of 0. 0337 for 100 epochs. Also, it acquired good precision values of 68% and 72%, Recall values are 74% and 66%, and F1 scores of 71% and 69% for classes 0 and 1 respectively. The model outcome is found stable and reliable in deep fake detection under dynamic training conditions. Optimum values of evaluation parameters ensure the model's capacity to learn new tasks preserving the existing task-learning knowledge.

关键词： Deep Learning CNN GAN Catastrophe forgetting CNN continual learning Lifelong learning GAN-CNN deep fake detector

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An integrated adaptive bilateral filter-based framework and attention residual U-net for detecting polycystic ovary syndrome

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Decision Analytics Journal 2024年 10卷

作者： Bedi, Pradeep Goyal, S.B. Rajawat, Anand Singh Kumar, Manoj Department of Computer Science & Engineering Galgotias University Greater Noida India Faculty of Information Technology City University Petaling Jaya Malaysia School of Computer Science & Engineering Sandip University Nashik India School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park United Arab Emirates MEU Research Unit Middle East University Amman 11831 Jordan

Introduction: PCOS is a global hormonal disorder in women, increasing the risk of certain cancers. Early diagnosis and treatment can manage it. While metabolic markers are typical for detection, MRI and ultrasound physiological features are gaining interest. Ultrasound is especially promising for early-stage detection. AI and ML are enhancing this detection process. Methodology: The methodology focuses on developing a hybrid model for detecting PCOS, an early indicator of gynecological cancer in women. This model incorporates adaptive bilateral filter-based image pre-processing and a novel attention residual u-net (AResUNet). The adaptive bilateral filtering allows for efficient noise reduction, while AResUNet's architecture ensures adaptability across both 2D and multi-modal images. Findings: The findings of the study demonstrate a significant improvement in PCOS detection through the proposed hybrid model. It has been observed that the AResUNet model performs exceptionally well on both 2D and multi-modal images. Conclusion: The 2% improvement over current models reflects the innovation and potential applicability of this approach, providing a promising direction for further research and clinical practice in managing PCOS and other related health concerns. © 2023 The Authors

关键词： Adaptive bilateral filter Attention residual UNet Gynecological disorder Machine learning Polycystic ovary syndrome Segmentation

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Public Infrastructure Monitoring in Developing Countries Through a Mobile Persuasive System

SSRN

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SSRN 2023年

作者： Obabueki, Omo-Okhirelen Ugah, Otozi John Ezeanya, Uchenna Christiana ICT Unit Lagos State University of Science and Technology Isolo Campus Lagos State Nigeria Department of Computer Science Ebonyi State University Ebonyi State Abakiliki Nigeria ICT Unit National Open University of Nigeria Enugu Study Centre Enugu State Enugu Nigeria

This research focuses on a mobile persuasive system for public assets monitoring in developing countries. Currently, there is a lack of maintenance of public infrastructures in developing countries like Nigeria due mostly to the non-establishment of an effective monitoring and reporting framework for the management and maintenance of public assets. This has led to a decay of public infrastructure procured with expensive and scarce fiscal resources across local, state, and federal governments. The mobile persuasive system for monitoring government infrastructure was designed using a five-step model for the design of persuasive technology that targets governments and their agencies with mobile crowdsourcing. It is made up of two subsystems, a mobile App for citizen reporting and a web App for administrative activities. This system collects image data on public assets such as roads, buildings, and solid waste evacuation status using smartphones. Descriptive information about the image is added and the image location is then mapped using geographical information system. The system will identify the WhatsApp contact of the government office responsible for the maintenance of the infrastructure. It will in turn send the images and locations to the identified office with a predefined persuasive message. It will also set reminder dates and resend information if there is no response. When the need arises, the system escalates and sends to higher authority until an appropriate response is obtained. The system also uses social categorization criteria for road maintenance prioritization and decision-making. It identifies social landmarks present in sent road complaint reports that target the government and its agencies. An object-oriented software development methodology was adopted. This system will help the government at all levels monitor and maintain their infrastructures timely. It would also ensure that citizens do not just make reports, but can monitor updates on their r

关键词： Crowdsourcing

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Three weak solutions for a (p, q)-Schrödinger-Kirchhoff type equation

arXiv

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arXiv 2024年

作者： Ahmed, Ahmed Ahmedatt, Taghi Aberqi, Ahmed University of Nouakchott Faculty of Science and Technology Mathematics and Computer Sciences Department Research Unit Geometry Algebra Analysis and Applications Nouakchott Mauritania National School of Applied Sciences Sidi Mohamed Ben Abdellah University Fez Morocco

In this manuscript, we investigate a (p, q)-Schrödinger-Kirchhoff equation involving a continuous positive potential that meets the del Pino-Felmer type conditions. Using Recceri’s classical variational approach...

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Industrial Digitalization Solutions for Precision Forestry Towards Forestry 4.0 1

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7th International Conference on Smart City Applications, SCA 2022

作者： Torres, Pedro M. B. Spencer, Geoffrey Neto, Luís Gonçalves, Gil Dionísio, Rogério Instituto Politécnico de Castelo Branco Castelo Branco6000-084 Portugal SYSTEC - Research Center for Systems and Technologies Porto4200-465 Portugal DiSAC - R &D Unit for Digital Services Applications and Content Castelo Branco6000-767 Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal

ISBN: (数字)9783031268526

ISBN: (纸本)9783031268519

This paper presents machine digitalization solutions with particular application in forest machines, such as harvesters and wood processing machines. In line with all the requirements of Industry 4.0, this type of machines also needs digitization to align with the concept defined as Forestry 4.0, where we think of a smarter forest in which all stakeholders, humans, forest producers, machines and factories communicate. For machine manufacturers is a step that must be taken to modernize machines, enabling remote access services for maintenance, productivity monitoring, and management of forest operations. It consists of developing cyber-physical systems around the machines with digital twins that allow the simulation and identification of faults that may occur. A solution is presented to enable CAN Bus communication between the controller, operator joysticks, and sensors/actuators, as well as a Digital Twin solution to emulate machine operations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Industry 4.0

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Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems (vol 14, 5434, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-33页

作者： Premkumar, Manoharan Sinha, Garima Ramasamy, Manjula Devi Sahu, Santhoshini Subramanyam, Chithirala Bala Sowmya, Ravichandran Abualigah, Laith Derebew, Bizuwork Department of Electrical & Electronics Engineering Dayananda Sagar College of Engineering Kumaraswamy Layout Bengaluru Karnataka 560078 India Department of Computer Science and Engineering Jain University Ramanagaram Bengaluru Karnataka India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science & Engineering GMR Institute of Technology Rajam Srikakulam Andhra Pradesh India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk 71491 Tabuk Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia 27500 Petaling Jaya Malaysia College of Engineering Yuan Ze University Taoyuan Taiwan Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Bushira Ethiopia Applied science research center Applied science private university Amman 11931 Jordan Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the gre

关键词： Computational intelligence Data mining Grey wolf optimizer K-means clustering Optimization algorithm

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Data preparation for artificial intelligence in medical imaging: Experiences from the ProCAncer-I initiative

Data preparation for artificial intelligence in medical imag...

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2023 IEEE EMBS Special Topic Conference on Data science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Kalokyri, Varvara Tachos, Nikolaos Sfakianakis, Stelios Nikiforaki, Katerina Karatzanis, Ioannis Kondylakis, Haridimos Mazzetti, Simone Regge, Daniele Papanikolaou, Nikolaos Marias, Konstantinos Fotiadis, Dimitrios Tsiknakis, Manolis FORTH-ICS Foundation of Research and Technology Hellas Institute of Computer Science Nik. Plastira 100 Crete Heraklion70013 Greece University Campus of Ioannina FORTH-BRI Ioannina45110 Greece University of Turin Department of Surgical Sciences Turin Italy Candiolo10060 Italy Champalimaud Foundation Champalimaud Research Computational Clinical Imaging Group Lisbon1400-038 Portugal FORTH-BRI Greece The Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department University Campus of Ioannina Ioannina45110 Greece

ISBN: (纸本)9798350383386

The ProCAncer-I project, an important initiative in the field of prostate cancer research, is exploiting the power of Artificial Intelligence (AI) to advance prostate cancer diagnosis, prognosis, and treatment. The success of AI in prostate cancer imaging within this context relies on meticulous data preparation, especially when dealing with clinical data that need to be integrated with imaging data. This paper explores the importance of data preparation to implement AI tools for prostate cancer imaging, and outlines the approach followed and challenges needed to be addressed in the context of ProCAncer-I, for ensuring data quality, privacy, and reliability. © 2023 IEEE.

关键词： Medical imaging

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A Bio-Medical Snake Optimizer System Driven by Logarithmic Surviving Global Search for Optimizing Feature Selection and its application for Disorder Recognition

arXiv

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arXiv 2024年

作者： Khurma, Ruba Abu Alhenawi, Esraa Braik, Malik Hashim, Fatma A. Chhabra, Amit Castillo, Pedro A. MEU Research Unit Faculty of Information Technology Middle East University Amman11831 Jordan Computer Science Department Al- Ahliyya Amman University Amman Jordan Software Engineering Department Al- Ahliyya Amman University Amman Jordan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan Faculty of Engineering Helwan University Egypt Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar143005 India Department of Computer Engineering Automatics and Robotics University of Granada Granada Spain

It is of paramount importance to enhance medical practices, given how important it is to protect human life. Medical therapy can be accelerated by automating patient prediction using machine learning techniques. To double the efficiency of classifiers, several preprocessing strategies must be adopted for their crucial duty in this field. Feature selection (FS) is one tool that has been used frequently to modify data and enhance classification outcomes by lowering the dimensionality of datasets. Excluded features are those that have a poor correlation coefficient with the label class, that is, they have no meaningful correlation with classification and do not indicate where the instance belongs. Along with the recurring features, which show a strong association with the remainder of the features. Contrarily, the model being produced during training is harmed, and the classifier is misled by their presence. This causes overfitting and increases algorithm complexity and processing time. The pattern is made clearer by FS, which also creates a broader classification model with a lower chance of overfitting in an acceptable amount of time and algorithmic complexity. To optimize the FS process, building wrappers must employ metaheuristic algorithms (MAs) as search algorithms. The best solution, which reflects the best subset of features within a particular medical dataset that aids in patient diagnosis, is sought in this study using the Snake Optimizer (SO). The swarm-based approaches that SO is founded on have left it with several general flaws, like local minimum trapping, early convergence, uneven exploration and exploitation, and early convergence. By employing the cosine function to calculate the separation between the present solution and the ideal solution, the logarithm operator was paired with SO to better the exploitation process and get over these restrictions. In order to get the best overall answer, this forces the solutions to spiral downward. Additionally, S

关键词： Evolutionary algorithms

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The Implementation of Artificial Intelligence Tools In Retail Sector Among Consumers - A New Evolution Traditional to Morden

The Implementation of Artificial Intelligence Tools In Retai...

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： Leena Jenefa Ashok Kumar S Bibiana Lim Chiu Yiong M. Jayakumar Lenin Lokesh B M. Sakthivel School of Management Hindustan Institute of Technology and Science Chennai India Learning and Teaching Unit Swinburne University of Technology Sarawak Campus Malaysia Department of Management Studies Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

AI technologies may serve numerous functions in entire retail sector in value chain, and employ the employees for the job done by using to implement AI operations in the value chain. Because of the increasing degree of competition in the retail industry, merchants must now provide higher-quality services and prioritize their clients more than ever before. Retailers have increasingly adopted AI-based solutions to study new potential in the retail industry. The focus of the research is to know the people mind set to use the digital sectors. Performance expectation refers to consumers' belief that technology would improve productivity and effectiveness in reaching goals. The Indian retail sector has emerged as one of the most rapidly increasing industries. The findings show the influence are favorably connected to performance expectation, but not correlated with anthropomorphism. Retailers are always seeking to unleash the industry's true potential by using cutting-edge technologies such as artificial intelligence-based data analytics, CRM systems.

关键词： Industries Productivity Performance evaluation Data analysis Customer satisfaction Object recognition Artificial intelligence Anthropomorphism Consumer electronics

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