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arXiv 2024年

作者： Yuan, Shuai Qin, Hanlin Yan, Xiang Akhtar, Naveed Mian, Ajmal The School of Optoelectronic Engineering Xidian University Xi’an710071 China The School of Computing and Information Systems Faculty of Engineering and IT The University of Melbourne ParkvilleVIC3052 Australia The Department of Computer Science and Software Engineering The University of Western Australia Perth6009 Australia

This is the pre-acceptance version, to read the final version please go to IEEE TRANSACTION ON GEOSCIENCE AND REMOTE SENSING on IEEE Xplore. Infrared small target detection (IRSTD) has recently benefitted greatly from U-shaped neural models. However, largely overlooking effective global information modeling, existing techniques struggle when the target has high similarities with the background. We present a Spatial-channel Cross Transformer Network (SCTransNet) that leverages spatial-channel cross transformer blocks (SCTBs) on top of long-range skip connections to address the aforementioned challenge. In the proposed SCTBs, the outputs of all encoders are interacted with cross transformer to generate mixed features, which are redistributed to all decoders to effectively reinforce semantic differences between the target and clutter at full levels. Specifically, SCTB contains the following two key elements: (a) spatial-embedded single-head channel-cross attention (SSCA) for exchanging local spatial features and full-level global channel information to eliminate ambiguity among the encoders and facilitate high-level semantic associations of the images, and (b) a complementary feed-forward network (CFN) for enhancing the feature discriminability via a multi-scale strategy and cross-spatial-channel information interaction to promote beneficial information transfer. Our SCTransNet effectively encodes the semantic differences between targets and backgrounds to boost its internal representation for detecting small infrared targets accurately. Extensive experiments on three public datasets, NUDT-SIRST, NUAA-SIRST, and IRSTD-1K, demonstrate that the proposed SCTransNet outperforms existing IRSTD methods. Our code will be made public at https://***/xdFai/SCTransNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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Code Smell Detection Using Whale Optimization Algorithm

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computers, Materials & Continua 2021年第8期68卷 1919-1935页

作者： Moatasem M.Draz Marwa S.Farhan Sarah N.Abdulkader M.G.Gafar Department of Software Engineering Faculty of Computers and InformationKafrelsheikh UniversityKafr ElsheikhEgypt Faculty of Informatics and Computer Science British University in EgyptCairoEgypt Department of Information Systems Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Faculty of Computer Studies Arab Open UniversityCairoEgypt Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam bin Abdulaziz UniversityKharjSaudi Arabia Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr ElsheikhEgypt

software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their *** smells arise as one of the obstacles in the software *** are characteristics of software source code that indicate a deeper problem in *** smells appear not only in the design but also in software *** smells introduce bugs,affect software maintainability,and lead to higher maintenance *** code smells can be formulated as an optimization problem of finding the best detection *** researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be *** research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their *** research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection *** this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass *** proposed framework adopts if-then detection rules during the software development life *** rules identify the types for both medium and large *** are conducted on five open-source software projects to discover nine smell types that mostly appear in *** proposed detection framework has an average of 94.24%precision and 93.4%*** accurate values are better than other search-based algorithms of the same *** proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and ***,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

关键词： software engineering intelligence search-based software engineering code smell detection software metrics whale optimization algorithm fisher criterion

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Development of a Model for Discriminating Electroencephalograms of Patients with Mild Cognitive Impairment and Healthy Individuals Using a Support Vector Machine 13

Development of a Model for Discriminating Electroencephalogr...

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Tanabe, Akifumi Nakatani, Kyosuke Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Kamae-Shigenobu, Kazue Shinosaki, Kazuhiro Graduate School of Information Science University of Hyogo Kobe Japan School of Life Science and Technology Tokyo Institute of Technology Yokohama Japan Aino University Department of Medical Engineering Ibaraki Japan Institute for Datability Science Osaka University Suita Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Aomori University Faculty of Software and Information Technology Aomori Japan Osaka University Cyber Media Center Suita Japan Asakayama General Hospital Sakai Japan

ISBN: (纸本)9798350373332

We performed machine learning using spectrum analysis and power spectra of electroencephalograms (EEGs) of patients with mild cognitive impairment (MCI). Spectrum analysis was performed on the EEGs of MCI patients and healthy individuals, and the power spectrum values of the two groups were calculated and compared. Based on the power spectrum values of the theta and alpha bands, models for discriminating MCI patients were created using a support vector machine (SVM), and their accuracies were compared. In the spectrum analysis, the theta band showed significantly higher power spectrum values in the MCI group. In the comparison of the accuracies of SVM models, the model using the theta band showed higher values in parameters, such as accuracy rate. These results suggested that the power spectrum values of the theta band are useful for discriminating MCI patients. © 2024 IEEE.

关键词： Electroencephalography

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Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

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computer systems Science & engineering 2023年第10期47卷 375-391页

作者： Sultan Alahmari Saud Yonbawi Suneetha Racharla ELaxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa King Abdulaziz City for Science and Technology P.O Box 6086Riyadh 11442Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of AIML Aditya Engineering CollegeSurempallemAndhra PradeshIndia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed *** spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine *** accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural *** is significant for the prediction and growth monitoring of crop *** the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature *** article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm *** proposed HMAODL-CTC model mainly intends to categorize different types of crops on *** accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image *** addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature *** hyperparameter tuning of the dilated CNN model,the HMAO algorithm is ***,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC *** comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.

关键词： Crop type classification hyperspectral images agricultural monitoring deep learning metaheuristics

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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Examining the Use of Non-fungible Tokens (NFTs) as a Trading Mechanism for the Metaverse 1

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29th European systems, software and Services Process Improvement, EuroSPI 2022

作者： Yilmaz, Murat Hacaloğlu, Tuna Clarke, Paul Department of Computer Engineering Engineering Faculty Gazi University Ankara Turkey Metaverse Research and Development Laboratory Gazi University Ankara Turkey Department of Information Systems Engineering Atilim University Ankara Turkey School of Computing Dublin City University Dublin Ireland Lero the Science Foundation Ireland Research Centre for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

The notion of a metaverse seems hard to define but encourages the impression that it can be considered as a new virtual metaphysical landscape that somehow goes beyond our geographical locations and understanding (i.e., independent of time and space). Based on virtual reality, augmented reality, and blockchain, it is envisioned as an independent but extended world that is planned to be a digital virtuality entrenched not only in our old habits such as gaming and entertainment but also in virtual asset trade. In particular, trading is a pillar of the virtual economy, and auction houses will be crucial for Metaverse trading. This exploratory study examines the possibility of using an auction environment to improve the trading capabilities in a virtual universe. We investigate the cases of creating a virtual auction house with the potential of social trading of virtual assets with crypto coins and bartering. To this end, we built a virtual auction house and tested it initially using a set of scenarios. Our preliminary findings suggest that creating a virtual trading environment would be beneficial as an environment for buying and selling virtual assets and exploring their consequences. © 2022, Springer Nature Switzerland AG.

关键词： Augmented reality

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Smart Farmer Education System for Disease Identification, Severity Assessment and Infestation Management (Brinjal) 17

Smart Farmer Education System for Disease Identification, Se...

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17th IEEE International Conference on Industrial and Information systems, ICIIS 2023

作者： Bandara, Keshara Vidhanaarachchi, Samitha Nanayakkara, A.A.E. Kulasinghe, Kavindu Rajapakshe, Sanka Wickramasinghe, Asela Yapa, Kanishka Sri Lanka Institute of Information Technology Department of Information Technology New Kandy Road Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka University of Ruhuna Faculty of Agriculture Department of Soil Science Mapalana Kamburupitiya Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350323634

The agriculture industry is one of the most significant sources of foreign exchange and employment in the Sri Lankan market. Therefore, small crops play a crucial role in ensuring the food security of the population as they are integral components of Sri Lankan cuisine. However, industry experts have identified inefficient disease and pest management as a hindrance to production. This issue requires immediate attention and proper action, which can be effectively addressed through the utilization of ICT technologies. Consequently, this research paper proposes a smart system that aims to assist farmers and agriculture professionals by providing them with farmer education, disease and infestation management tools, as well as progression level calculations. The implementation of this system aims to uplift the agriculture industry in Sri Lanka. The developed 'AI-based mobile application system for Brinjal Diseases,' which is based on analyzing the eating patterns of leafhopper damage, presents an innovative approach to identify the most significant threats to Brinjal, specifically focusing on leafhopper damage. This proposed system effectively informs various stakeholders, including the Area Agricultural Research Officer, Seed Certification and Plant Protection Center (SCPPC), Agricultural Service Center, Office of the Register of Pesticides (ROP), Plant Protection Service (PPS), as well as farmers, regarding the dispersion and prevention of brinjal diseases and pest infections. © 2023 IEEE.

关键词： Crowdsourcing

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Process Discovery Automation: Benefits and Limitations

Process Discovery Automation: Benefits and Limitations

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Intelligent Methods, systems, and Applications (IMSA)

作者： Reem Kadry Montasser Iman M. A. Helal Software Engineering Department Faculty of Computer Science October University for Modern Sciences and Arts Giza Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt

Process discovery algorithms incorporating domain knowledge can have varying levels of user involvement. It ranges from fully automated algorithms to interactive approaches where the user makes critical decisions about the process model. Designing domain knowledge using process discovery techniques faces various challenges. These challenges could cause some issues with existing approaches. Acquiring domain knowledge with domain experts, integrating domain knowledge with process data, scalability to handle large complex data sets, and ensuring data quality are examples of these challenges. In this survey, we assess recent work with varying levels of automation in process discovery to enhance the analysis and understanding of business processes within an organization. Current work can be classified into two categories: fully automated or semi-automated process discovery. We conclude that semi-automated process discovery gives a better opportunity for involving users. Also, the use of deep learning algorithms in automation gives better performance than machine learning algorithms.

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities

The Reliability of Information Sources: Student Perceptions ...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Anita Mihalovicsne Kollar Jozsef Katona Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the university of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources; social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape.

关键词： Economics Power engineering Accuracy Social networking (online) engineering profession Education Reliability engineering Information filters Information age Reliability

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