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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Abdelsalam, Hany Souri, Alireza İnanç, Nihat Department of Software Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey

ISBN: (纸本)9798331539603

The global trend in renewable energy solutions has emphasized the urgent need for accurate forecasting of solar energy production. This study examines the potential of ensemble-based learning techniques in predicting solar energy production by leveraging historical data collected from 11 solar photovoltaic installations in Calgary, Canada, spanning from September 2015 to March 2023. The dataset was carefully preprocessed to handle missing values, duplicates, and to extract meaningful features that align with the nature of time series data. A range of ML models were implemented and assessed by several forecasting evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2). The experimental results demonstrate the superior performance of ensemble methods, particularly XGBoost and Light GBM, in solar energy forecasting. XGBoost achieved the best performance, with an MAE of 25.36, an MSE of 3599.66, and R2 of 0.957, outperforming models such as Random Forest and Neural Networks. The practical implications of these findings suggest that accurate solar energy forecasting can significantly enhance operational planning and optimize energy management in solar power plants, contributing to more reliable and efficient renewable energy systems. © 2024 IEEE.

关键词： ensemble methods machine learning renewable energy solar energy forecasting time series analysis XGBoost

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An Efficient Heterogeneous Ring Signcryption Scheme for Wireless Body Area Networks

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computer Systems Science & engineering 2023年第11期47卷 2061-2078页

作者： Qingqing Ning Chunhua Jin Zhiwei Chen Yongliang Xu Huaqi Lu Faculty of Computer&Software Engineering Huaiyin Institute of TechnologyHuai’an233003China

Wireless body area networks(WBANs)are an emerging technology for the real-time monitoring of physiological *** provide a mechanism for collecting,storing,and transmitting physiological data to healthcare ***,the open wireless channel and limited resources of sensors bring security *** ensure physiological data security,this paper provides an efficient Certificateless Public Key Infrastructure Heterogeneous Ring Signcryption(CP-HRSC)scheme,in which sensors are in a certificateless cryptosystem(CLC)environment,and the server is in a public key infrastructure(PKI)*** could solve the limitations of key escrow in identity-based cryptography(IBC)and certificate management for public keys in *** PKI is suited for the server because it is widely used on the ***,this paper designs a ring signcryption method that allows the controller to anonymously encrypt physiological data on behalf of a set of sensors,but the server does not exactly know who the sensor *** construction of this paper can achieve anonymity,confidentiality,authentication,non-repudiation,and integrity in a logically single *** the computational Diffie-Hellman(CDH)problem,the formal security proof is provided in the random oracle model(ROM).This paper demonstrates that this scheme has indistinguishability against adaptive chosen ciphertext attacks(IND-CCA2)and existential unforgeability against adaptive chosen message attacks(EUF-CMA).In terms of computational cost and energy usage,a comprehensive performance analysis demonstrates that the proposed scheme is the most *** to the three existing schemes,the computational cost of this paper’s scheme is reduced by about 49.5%,4.1%,and 8.4%,and the energy usage of our scheme is reduced by about 49.4%,3.7%,and 14.2%,respectively.

关键词： Wireless body area networks certificateless cryptosystem public key infrastructure security ring singncryption

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Quick Hidden Layer Size Tuning in ELM for Classification Problems

Mendel

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Mendel 2024年第1期30卷 1-14页

作者： Albtoush, Audi Fernandez-Delgado, Manuel Maarouf, Haitham Al Nawaiseh, Asmaa Jameel Faculty of Computer Science and Information Technology Jerash University Jordan Santiago De Compostela University Spain Software Engineering Department Mut’ah university Jordan

The extreme learning machine is a fast neural network with outstanding performance. However, the selection of an appropriate number of hidden nodes is time-consuming, because training must be run for several values, and this is undesirable for a real-time response. We propose to use moving average, exponential moving average, and divide-and-conquer strategies to reduce the number of training’s required to select this size. Compared with the original, constrained, mixed, sum, and random sum extreme learning machines, the proposed methods achieve a percentage of time reduction up to 98% with equal or better generalization ability. © 2024, Brno University of Technology. All rights reserved.

关键词： Knowledge acquisition

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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Fine-Tuning GPT-4o-Mini for Programming Questions Generation

Fine-Tuning GPT-4o-Mini for Programming Questions Generation

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2024 International Conference on computer and Applications, ICCA 2024

作者： Hesham, Alaa Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt

ISBN: (纸本)9798350367560

Creating programming questions that are both meaningful and educationally relevant is a critical task in computer science education. This paper introduces a fine-tuned GPT4o-mini model (C2Q). It is designed to generate meaningful questions by leveraging semantic feature extraction and well- crafted prompts. The approach addresses the limitations of traditional generative models, offering a deeper understanding of programming code and producing questions that are precise, diverse, and relevant to a given code snippets. The proposed framework incorporates essential code elements, such as control structures and method attributes, to generate questions that align with programming concepts. Evaluation metrics used were BLEU, ROUGE-1, and ROUGE-L to evaluate the model's performance. The findings reveal that the model achieves better structural coherence and conceptual relevance while focusing on contextual understanding over exact term matching. This work highlights the potential of the proposed approach to advance teaching and assessment methods in computer science. © 2024 IEEE.

关键词： Program processors

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Survey and Tutorial on Hybrid Human-Artificial Intelligence

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Tsinghua Science and Technology 2023年第3期28卷 486-499页

作者： Feifei Shi Fang Zhou Hong Liu Liming Chen Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Computer Science and Software Engineering East China Normal UniversityShanghai 200062China Faculty of Computing Ulster UniversityNewtownabbey BT370QBUK

The growing computing power,easy acquisition of large-scale data,and constantly improved algorithms have led to a new wave of artificial intelligence(AI)applications,which change the ways we live,manufacture,and do *** with this development,a rising concern is the relationship between AI and human intelligence,namely,whether AI systems may one day overtake,manipulate,or replace *** this paper,we introduce a novel concept named hybrid human-artificial intelligence(H-AI),which fuses human abilities and AI capabilities into a unified *** presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective *** scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward *** then examine the key underpinning techniques of H-AI,such as user profile modeling,cognitive computing,and human-in-the-loop machine ***,we discuss H-AI’s potential applications in the area of smart homes,intelligent medicine,smart transportation,and smart ***,we conduct a critical analysis of current challenges and open gaps in H-AI,upon which we elaborate on future research issues and directions.

关键词： hybrid human-artificial intelligence(H-AI) Internet of Things(IoT) artificial intelligence(AI)

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A Ternary-attention Capsule Feature Pyramid Network for Building Footprint Extraction from Remote Sensing Imagery

A Ternary-attention Capsule Feature Pyramid Network for Buil...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Zhang, Haiyan Li, Yazhou Wang, Meifeng Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

The up-to-date and accurate building footprint database plays a significant role in a large variety of applications. Recently, remote sensing images have provided an important data source for building footprint extraction tasks. However, due to topology variations, color diversities, and complicated rooftop and environmental scenarios, it is still a challenging task to realize fully automated and highly accurate extraction of building footprints from remote sensing images. In this paper, we propose a novel ternary-attention capsule feature pyramid network (TA-CapsFPN), which is formulated with a capsule feature pyramid network architecture and integrated with context-augmentation and feature attention modules, aiming at improving the building footprint extraction accuracy by combining the superior properties of capsule representations and the powerful capability of attention mechanisms. Quantitative evaluations and comparative analyses show that the TA-CapsFPN provides a promising and competitive performance in processing buildings of varying conditions. © 2023 IEEE.

关键词： Extraction

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Dysarthric Speech Recognition: A Comparative Study 12

Dysarthric Speech Recognition: A Comparative Study

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12th International Conference on Speech Technology and Human-computer Dialogue, SpeD 2023

作者： Shah, Dhvani Lal, Vanshika Zhong, Zihan Wang, Qianli Shahamiri, Seyed Reza The University of Auckland Faculty of Engineering Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350327977

Automatic Speech Recognition (ASR) systems are designed to convert spoken words into written text. These systems have the potential to greatly benefit individuals with speech impairments like dysarthria, improving their overall quality of life. However, there are significant challenges that researchers must address in order to develop an effective ASR system for atypical speech, specifically dysarthric speech. Among these challenges are the limited availability of dysarthric speech data and the need for an accurate interpretation model. In this study, we explored the development of dysarthric ASR systems, starting from traditional approaches that were specifically designed to recognize dysarthric speech. We then delve into deep-learning-based ASR systems and modern ASR engines that utilize transformers. Additionally, we highlighted the notable dysarthric speech collections and identified the key factors that impede the performance of dysarthric ASR systems. The findings indicate that although researchers have achieved gradual improvements in performance, further research is required to achieve practical performance levels. Likewise, it is important to establish guidelines and standards to accurately measure and benchmark the performance of dysarthric ASR systems in order to ensure their usefulness and effectiveness in real-world applications. © 2023 IEEE.

关键词： Speech recognition

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Transaction transmission model for blockchain channels based on non-cooperative games

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Science China(Information Sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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