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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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Bangla Social Media Cyberbullying Detection Using Deep Learning 1st

Bangla Social Media Cyberbullying Detection Using Deep Learn...

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1st International Conference on Intelligent Systems and Data science, ISDS 2023

作者： Rodela, Anika Tasnim Nguyen, Huu-Hoa Farid, Dewan Md. Huda, Mohammad Nurul Department of Information and Communication Technology Bangladesh University of Professionals Mirpur Cantonment Dhaka Bangladesh College of Information and Communication Technology Can Tho University 3/2 Street Ninh Kieu District Can Tho City Viet Nam Department of Computer Science and Engineering United International University United City Madani Avenue Badda Dhaka1212 Bangladesh

ISBN: (纸本)9789819976485

The growth of social media over the past decade has been nothing short of phenomenal. An exponential increase has been seen on platforms like Facebook, Twitter, Instagram, LinkedIn, and YouTube, in their user base, accumulating billions of active users worldwide. Technology advancements, the ubiquitous use of smartphones, and the innate human desire for connection don’t always contribute constructively;they might additionally end up in spreading violence in the form of bullying of others which is known as cyberbullying. As a result of cyberbullying, the victims will frequently experience anxiety, depression, and other mental diseases which can even result in suicide. Therefore the extensive need of detecting and controlling cyberbullying motivated us to automate this process. In this paper, we have introduced an approach to detect cyberbullying from social media data by using deep learning models. We have used Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network in the proposed hybrid approach along with word embedding technique called fastText. We achieved an accuracy of 91.63% with the proposed model by using a publicly available dataset containing 16,073 samples which outperformed all the state of the art models. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects

arXiv

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arXiv 2024年

作者： Fu, Da Rong, Mingfei Kim, Eun-Hu Huang, Hao Pedrycz, Witold Shandong University Department of Computer Science and Technology Shandong Weihai264200 China Linyi University Department of Big Data Shandong Linyi276000 China Suwon University Department of Computer Science Suwon Gyeonggi-do Hwaseong-si18323 Korea Republic of Alberta University Department of Electrical & Computer Engineering EdmontonT6G 2H5 Canada

Accurate classification of fine-grained images remains a challenge in backbones based on convolutional operations or self-attention mechanisms. This study proposes novel dual-current neural networks (DCNN), which combine the advantages of convolutional operations and self-attention mechanisms to improve the accuracy of fine-grained image classification. The main novel design features for constructing a weakly supervised learning backbone model DCNN include (a) extracting heterogeneous data, (b) keeping the feature map resolution unchanged, (c) expanding the receptive field, and (d) fusing global representations and local features. Experimental results demonstrated that using DCNN as the backbone network for classifying certain fine-grained benchmark datasets achieved performance advantage improvements of 13.5-19.5% and 2.2-12.9%, respectively, compared to other advanced convolution or attention-based fine-grained backbones. © 2024, CC BY-NC-ND.

关键词： Image enhancement

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Dynamic Human Digital Twin Deployment at the Edge for Task Execution: A Two-Timescale Accuracy-Aware Online Optimization

arXiv

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arXiv 2024年

作者： Yang, Yuye Shi, You Yi, Changyan Cai, Jun Kang, Jiawen Niyato, Dusit Shen, Xuemin The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Department of Electrical and Computer Engineering Concordia University MontréalQCH3G 1M8 Canada The School of Automation Guangdong University of Technology Guangzhou510062 China The School of Computer Science and Engineering Nanyang Technological University Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Human digital twin (HDT) is an emerging paradigm that bridges physical twins (PTs) with powerful virtual twins (VTs) for assisting complex task executions in human-centric services. In this paper, we study a two-timescale online optimization for building HDT under an end-edge-cloud collaborative framework. As a unique feature of HDT, we consider that PTs’ corresponding VTs are deployed on edge servers, consisting of not only generic models placed by downloading experiential knowledge from the cloud but also customized models updated by collecting personalized data from end devices. To maximize task execution accuracy with stringent energy and delay constraints, and by taking into account HDT’s inherent mobility and status variation uncertainties, we jointly and dynamically optimize VTs’ construction and PTs’ task offloading, along with communication and computation resource allocations. Observing that decision variables are asynchronous with different triggers, we propose a novel two-timescale accuracy-aware online optimization approach (TACO). Specifically, TACO utilizes an improved Lyapunov method to decompose the problem into multiple instant ones, and then leverages piecewise McCormick envelopes and block coordinate descent based algorithms, addressing two timescales alternately. Theoretical analyses and simulations show that the proposed approach can reach asymptotic optimum within a polynomial-time complexity, and demonstrate its superiority over counterparts. Copyright © 2024, The Authors. All rights reserved.

关键词： Computation offloading

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Large AI Model for Delay-Doppler Domain Channel Prediction in 6G OTFS-Based Vehicular Networks

arXiv

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arXiv 2025年

作者： Xue, Jianzhe Yuan, Dongcheng Ma, Zhanxi Jiang, Tiankai Sun, Yu Zhou, Haibo Shen, Xuemin School of Electronic Science and Engineering Nanjing University Nanjing210023 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Channel prediction is crucial for high-mobility vehicular networks, as it enables the anticipation of future channel conditions and the proactive adjustment of communication strategies. However, achieving accurate vehicular channel prediction is challenging due to significant Doppler effects and rapid channel variations resulting from high-speed vehicle movement and complex propagation environments. In this paper, we propose a novel delay-Doppler (DD) domain channel prediction framework tailored for high-mobility vehicular networks. By transforming the channel representation into the DD domain, we obtain an intuitive, sparse, and stable depiction that closely aligns with the underlying physical propagation processes, effectively reducing the complex vehicular channel to a set of time-series parameters with enhanced predictability. Furthermore, we leverage the large artificial intelligence (AI) model to predict these DD-domain time-series parameters, capitalizing on their advanced ability to model temporal correlations. The zero-shot capability of the pre-trained large AI model facilitates accurate channel predictions without requiring task-specific training, while subsequent fine-tuning on specific vehicular channel data further improves prediction accuracy. Extensive simulation results demonstrate the effectiveness of our DD-domain channel prediction framework and the superior accuracy of the large AI model in predicting time-series channel parameters, thereby highlighting the potential of our approach for robust vehicular communication systems. © 2025, CC BY-NC-ND.

关键词： Prediction models

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Deep learning based text detection using resnet for feature extraction

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Multimedia Tools and Applications 2023年第30期82卷 46871-46903页

作者： Huang, Li-Kun Tseng, Hsiao-Ting Hsieh, Chen-Chiung Yang, Chih-Sin Institute of Bioinformatics and Structural Biology College of Life Science National Tsing Hua University No. 101 Sec. 2 Kuangfu Rd. East Dist. Hsinchu City300 Taiwan Department of Information Management National Central University No. 300 Zhongda Rd Zhongli District Taoyuan City320 Taiwan Department of Computer Science and Engineering Tatung University No. 40 Sec. 3 Jhongshan N. Rd Jhongshan District Taipei City104 Taiwan

Popular deep learning models for text segmentation include CTPN, EAST, and PixelLink. However, they are not very well capable of dealing with the images containing densely distributed characters, and those characters may be connected. For these problems, the ResNet with excellent sensitivity for feature extraction is used to replace those embedded convolution neural networks in the main structures of CTPN and EAST. The experimental results showed that a better feature extraction network could significantly improve the precision of text localization. Noteworthy, the results indicate that the accuracy of modified EAST with ResNet101 would be the highest with a deeper depth and larger width of ResNet. The accuracy of text segmentation on ICDAR 2015 is 83.4% which is 7% higher than the original PVANET-EAST. The text detection accuracy is 83.9% on the untrained scanned document. Also, it achieved an accuracy of 86.3% when applied to self-collected Chinese calligraphy. Those results demonstrated that text detection using ResNet is a better improvement for OCR applications. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Feature extraction

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Number plate recognition system for vehicles using machine learning approach 3rd

Number plate recognition system for vehicles using machine l...

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3rd International Conference on Innovative Computing and Communication, ICICC 2020

作者： Amzad Hossain, Md. Suvo, Istiaque Ahmed Ray, Amitabh Ariful Islam Malik, Md. Mridha, M.F. Department of Computer Science and Engineering Bangladesh University of Business and Technology Rupnagar R/A Mirpur-2 Dhaka1216 Bangladesh

ISBN: (纸本)9789811551475

In recent times, the deep learning techniques in particular Convolutional Neural Networks (CNNs) are extensively used in computer vision and machine learning field. Machine learning technique provides high accuracy in different classification tasks like as MNIST, CIFAR-100, ImageNet, and CIFAR-10. However, there are lots of research being conducted for Bangla number plate recognition in the last decade. None of them are used to deploy a physical system for the Bangla Number plate recognition system because of their poor recognition accuracy. In this research work, we proposed a new algorithm for vehicle number plate recognition based on Connected Component Analysis (CCA) and Convolutional Neural Networks (CNNs). We have implemented the CCA technique for number plate detection and character segmentation. Which produced 92.78% accuracy for number plate detection and 97.94% accuracy for character segmentation. Along with that, we have also implemented a CNN model for character recognition and used a dataset "PlateNumbers" for training this model. The dataset consists of 408 (120 × 110) character images in 17 classes. It’s a standard and very first dataset. So finally, we have produced 96.91% accuracy in the character recognition stage by implementing our CNN model. The results of our research work indicate that the performance of the system is noticeable. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Character recognition

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Advanced Persistent Threat Identification with Boosting and Explainable AI

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SN computer science 2023年第3期4卷 271页

作者： Hasan, Md. Mahadi Islam, Muhammad Usama Uddin, Jasim Department of Computer Science and Engineering Asian University of Bangladesh Ashulia Dhaka 1341 Bangladesh School of Computing and Informatics University of Louisiana at Lafayette 104 E University Ave Lafayette 70503 LA United States Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Western Avenue Wales Cardiff CF5 2YB United Kingdom

Advanced persistent threat (APT) is a serious concern in cyber-security that has matured and grown over the years with the advent of technology. The main aim of this study is to establish an effective identification model for APT attacks to prevent and reduce their influence. Machine learning has the potential as well as substantial background to detect and predict cyber-security threats including APT. This study utilized several boosting-based machine learning methods to predict various types of APTs that are consistent in cyber-security domain. Furthermore, Explainable Artificial Intelligence (XAI) was coupled with the predictions to provide actionable insights to the domain stakeholders as well as practitioners in this domain. The results, particularly XGBoost with weighted F1 score of 0.97 and SHapley Additive exPlanations (SHAP)-based explanation, prove that boosting methods as well as machine learning models paired with XAI are indeed promising in handling cyber-security-related dataset problems which can be extrapolated towards new avenues of challenging research by effectively deploying boosting-based XAI models. © 2023, The Author(s).

关键词： APT Boosting Cyber-security Explainability Machine learning XAI

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Enhancement of Information Carrying and Decoding for Visual Cryptography with Error Correction

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ACM Transactions on Multimedia Computing, Communications and Applications 2023年第1期20卷 23-23页

作者： Yang, Ching-Nung Wu, Xiaotian Chung, Min-Jung Department of Computer Science and Information Engineering National Dong Hwa University No. 1 Sec. 2 Da Hsueh Road Shou-Feng Hualien97401 Taiwan Department of Computer Science Jinan University No. 601 Huangpu West Avenue Guangzhou510632 China

Recently, three visual cryptography schemes with t-error-correcting capability (VCSs-tEC) were introduced for preventing the shadows carrying additional information from being corrupted by noise interference. However, the concerns on VCS-tEC, such as the average amount of carrying information, decoding of information from shadows, and the demonstration way of a secret, should be considered and improved. In this article, two schemes, namely the (k, n) probabilistic VCS-tEC (PVCS-tEC) and the (2, n) deterministic VCS-tEC (DVCS-tEC), are proposed. The concept of probabilistic VCS is combined with the Bose-Chaudhuri-Hocquenghem code (BCH code) for designing the (k, n) PVCS-tEC. Furthermore, some constant-weight BCH codewords are adopted to build the (2, n)-DVCS-tEC. Comprehensive results and experiments are demonstrated to clarify the enhancement of information-carrying and decoding by the two proposed methods. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Error correction

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Multi-band Wireless Communication Networks: Fundamentals, Challenges, and Resource Allocation

TechRxiv

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TechRxiv 2023年

作者： Aboagye, Sylvester Saeidi, Mohammad Amin Tabassum, Hina Tayyar, Yamin Hossain, Ekram Yang, Hong-Chuan Alouini, Mohamed-Slim The Department of Electrical Engineering and Computer Science York University TorontoONM3J 1P3 Canada The Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T 5V6 Canada The Department of Electrical and Computer Engineering University of Victoria VictoriaBCV8W 2Y2 Canada The Computer Electrical and Mathematical Science Engineering Division King Abdullah University of Science and Technoloyg Thuwal Saudi Arabia

The sub-6 GHz spectrum and the millimeter wave frequency band, with its huge spectrum, have been exploited in the past years to meet the traffic demands of wireless communication networks. However, the limited and licensed spectrum of these bands cannot support the massive connectivity requirements, the exponential growth of data traffic, and the strict quality-of-service requirements for 6G and beyond wireless systems. Extremely high frequencies, such as optical and terahertz, which offer much wider transmission bandwidths with extreme data rate capabilities, are expected to play key roles in the 6G and beyond era. As a result, future-generation wireless networks will transition from single-band and heterogeneous networks to multi-band networks (MBNs), where various frequency bands coexist. Despite the great potential of MBNs, they face novel challenges from channel modeling, transceiver and antenna design, programmable simulation platforms, standardization activities, and resource allocation. This paper provides a tutorial overview from the communication design perspective of the various frequency bands, elaborating on the above issues. Then, we introduce and examine typical MBN architectures for future networks and provide a detailed overview of state-of-the-art resource allocation problems for existing MBNs that typically operate on two frequency bands. The considered resource allocation optimization problems and solution techniques are discussed comprehensively. We then identify key performance metrics and constraint sets that should be considered for resource allocation optimization in future MBNs and provide numerical results to depict how various system parameters and user behaviors can influence their performance. Finally, we present several potential research issues as future work for the design and performance optimization of MBNs. © 2023, CC BY.

关键词： Resource allocation

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