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28th International Conference on Knowledge based and Intelligent information and Engineering systems, KEs 2024

作者： Hasnine, Mohammad Nehal Nguyen, Ho Tan Ueda, Hiroshi Research Centerfor Computing andMultimedia Studies Hosei University Koganei Tokyo184-8584 Japan

seamless learning refers to a learning model in which students can accomplish their learning tasks with the help of technology whenever they are curious in different real-life learning contexts. In the era of artificial intelligence, seamless learning technologies are expected to collect big data from mobile and computer devices, enable analytics to have an in-depth understanding of learning behaviors and patterns, and be flexible in employing suitable educational models to facilitate learning and teaching. This paper aims to build a seamless learning platform for language learners and language instructors by coining the extension of seamless learning based on the theories of mobile learning, ubiquitous learning, and task-based language learning. In order to facilitate formal and informal learning for foreign language learners across individual and social learning spaces, we present Taskcave- a seamless learning platform. Taskcave assists language instructors in assigning vocabulary learning tasks to the students and monitoring their learning activities using an analytic dashboard. Taskcave allows the students to log their learning activities digitally, interact with their classmates, engage in collaborative learning, and receive feedbacks from the teacher. © 2024 The Authors.

关键词： Adversarial machine learning

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On-device diagnostic recommendation with heterogeneous federated BlockNets

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science China(Information sciences) 2025年第4期68卷 33-49页

作者： Minh Hieu NGUYEN Thanh Trung HUYNH Thanh Toan NGUYEN Phi Le NGUYEN Hien Thu PHAM Jun JO Thanh Tam NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Information Technology HUTECH University School of Information and Communication Technology Hanoi University of Science and Technology Commonwealth Scientific and Industrial Research Organization (CSIRO)

The evolution of edge computing has advanced the accessibility of E-health recommendation services, encompassing areas such as medical consultations, prescription guidance, and diagnostic assessments. Traditional methodologies predominantly utilize centralized recommendations, relying on servers to store client data and dispatch advice to ***, these conventional approaches raise significant concerns regarding data privacy and often result in computational inefficiencies. E-health recommendation services, distinct from other recommendation domains, demand not only precise and swift analyses but also a stringent adherence to privacy safeguards, given the users' reluctance to disclose their identities or health information. In response to these challenges, we explore a new paradigm called on-device recommendation tailored to E-health diagnostics, where diagnostic support(such as biomedical image diagnostics), is computed at the client *** leverage the advances of federated learning to deploy deep learning models capable of delivering expert-level diagnostic suggestions on clients. However, existing federated learning frameworks often deploy a singular model across all edge devices, overlooking their heterogeneous computational capabilities. In this work, we propose an adaptive federated learning framework utilizing BlockNets, a modular design rooted in the layers of deep neural networks, for diagnostic recommendation across heterogeneous devices. Our framework offers the flexibility for users to adjust local model configurations according to their device's computational power. To further handle the capacity skewness of edge devices, we develop a data-free knowledge distillation mechanism to ensure synchronized parameters of local models with the global model, enhancing the overall accuracy. Through comprehensive experiments across five real-world datasets, against six baseline models, within six experimental setups, and various data distribution scenario

关键词： intelligent recommendation federated learning heterogeneous devices E-health diagnostics

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Industrial Control Anomaly Detection based on Distributed Linear Deep learning

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computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： shijie Tang Yong Ding Huiyong Wang School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace Technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical systems(CPs)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the secure Water Treatment(sWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPs deep learning MLP(multi-layer perceptron)

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Bidirectional LsTM-based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： sheik Mohideen shah Meganathan selvamani Mahesh Thyluru Ramakrishna surbhi Bhatia Khan shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long short-Term Memory(LsTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LsTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LsTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMsE and an 18.8%improvement in MAE over conventional unidirectional LsTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including sVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMsE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LsTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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Marine Predators Algorithm with Deep learning-based Leukemia Cancer Classification on Medical Images

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computer Modeling in Engineering & sciences 2024年第10期141卷 893-916页

作者： sonali Das saroja Kumar Rout sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz s.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster squeezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Engineering Educators' Adoption and implementation of project-based learning: Experiences from a south Indian University 13

Engineering Educators' Adoption and implementation of Projec...

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13th IEEE International Conference on Teaching, Assessment and learning for Engineering, TALE 2024

作者： Dijo, Anjal P Anantha, Krishnan K. P. subheesh, N.P. John, Aby Anzar, s.M. sachidananthan, sajin Federal Institute of Science and Technology Department of Computer Science and Engineering India College of Engineering Department of Computer Science and Engineering Kerala India Srm University - Ap Teaching Learning Centre Andhra Pradesh 522502 India Rudn University Faculty of Philology Moscow Russia Tkm College of Engineering Department of Electronics and Communication Engineering Kollam India Curriculum Research and Development U-DNA Educational Foundation Andhra Pradesh Anantapur India

ISBN: (纸本)9798350376234

This research investigates the attitudes and perceptions of engineering educators in Kerala, India, towards the implementation of project-based learning (PjBL) within the framework of APJ Abdul Kalam Technological University (APJAKTU), Kerala. Despite the growing emphasis on experiential learning, only limited research studies focus on understanding the attitudes and perceptions of engineering faculty members towards PjBL. Against this backdrop, the present study aims to critically examine the readiness of engineering educators in adopting and identifying PjBL strategies for its effective integration into engineering curricula. The paper explores various dimensions of engineering educators' engagement with PjBL, including faculty interest levels, satisfaction of implementation, perceived effectiveness, and career implications. The data were collected from engineering faculty members by distributing a comprehensive questionnaire titled project-based learning in Engineering Education in Kerala (PLEEK), uniquely designed by the researchers. The research findings reveal that over 90% of the surveyed faculty members have shown keen interest in implementing PjBL, while the remaining respondents exhibited a neutral stance. This underscores a noteworthy receptivity to PjBL among engineering educators in Kerala. suggestions from participants indicate that, if implemented correctly, PjBL would be highly effective, although time constraints pose a significant challenge. The results obtained are pivotal as they provide an explicit idea of the current status of the engineering education settings and the discrepancies between traditional theoretical instruction and project-based learning. © 2024 IEEE.

关键词： Engineering education

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Regression based prediction of higher heating value for refuse-derived fuel using convolutional neural networks predicted elemental data and spectrographic measurements

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Waste Disposal and sustainable Energy 2024年第3期6卷 429-437页

作者： Baki Osman Bekgoz Zerrin Günkaya Kemal Ozkan Metin Ozkan Aysun Ozkan Müfide Banar Department of Computer Engineering Meselik CampusEskisehir Osmangazi University26480EskisehirTürkiye Department of Environmental Engineering Iki Eylul CampusEskisehir Technical University26555EskisehirTürkiye Center of Intelligent Systems Applications Research Meselik CampusEskisehir Osmangazi University26480EskisehirTürkiye

Higher heating value(HHV)is the key parameter for replacing Refuse-Derived Fuel(RDF)with fossil fuels in the cement *** can be measured with a bomb calorimeter or predicted from direct elemental data by using regression *** methods require the continuous use of special laboratory equipment and are time *** overcome these limitations,this study aims to predict the HHV value of RDF from predicted elemental data by using regression ***,once the predicted elemental data are generated,there will be no need to have continuous elemental data to predict *** elemental data were generated from direct elemental data and Near Infrared(NIR)camera-based spectrometric data by using a deep learning model.A convolutional neural networks(CNN)model was used for deep learning and was trained with 10,500 NIR image samples,each of which was 28×28×*** regression models(Linear,Tree,support-Vector Machine,Ensemble and Gaussian process)were applied for HHV *** to these results,higher R2 values(>0.85)were obtained with Gaussian process models(except for the Rational Quadratic model)for the predicted elemental *** the Gaussian models,the highest R2(0.95)but the lowest Root Mean square Error(RMsE)(0.0563),Mean squared Error(MsE)(0.0317)and Mean Absolute Error(MAE)(0.0431)were obtained with the Mattern 5/2 *** results of predictions from predicted elemental data were compared to predictions from direct elemental *** results show that the regression from predicted elemental data has an adequate prediction(R2=0.95)compared to the prediction from the direct elemental data(R^(2)=0.99).

关键词： Deep learning Higher heating value Refuse-derived fuel Regression spectrographic measurement

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An Improved Binary Quantum-based Avian Navigation Optimizer Algorithm to select Effective Feature subset from Medical Data:A COVID-19 Case study

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Journal of Bionic Engineering 2024年第1期21卷 426-446页

作者： Ali Fatahi Mohammad H.Nadimi-shahraki Hoda Zamani Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad ***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad ***Iran

Feature subset selection(Fss)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic ***,existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method,resulting in suboptimal solutions that hinder diagnosis and prediction *** paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm(IBQANA)for Fss in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic *** proposed IBQANA’s contributions include the Hybrid Binary Operator(HBO)and the Distance-based Binary search strategy(DBss).HBO is designed to convert continuous values into binary solutions,even for values outside the[0,1]range,ensuring accurate binary *** the other hand,DBss is a two-phase search strategy that enhances the performance of inferior search agents and accelerates *** combining exploration and exploitation phases based on an adaptive probability function,DBss effectively avoids local *** effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets,with feature numbers ranging from 8 to 10,***'s effectiveness is evaluated regarding the accuracy,fitness,and selected features and compared with seven binary metaheuristic ***,IBQANA is utilized to detect *** results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical *** proposed method presents a promising solution to the Fss problem in medical data preprocessing.

关键词： Feature subset selection Optimization Binary metaheuristic algorithms Bioinspired Machine learning Medical datasets

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Intelligent Guitar Chord Recognition Using spectrogram-based Feature Extraction and AlexNet Architecture for Categorization

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International Journal of Advanced computer science and Applications 2025年第4期16卷 758-767页

作者： Korade, Nilesh B. salunke, Mahendra B. Bhosle, Amol A. sangve, sunil M. Joshi, Dhanashri M. Asalkar, Gayatri G. Kadu, sujata R. sarwade, Jayesh M. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India Department of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune412101 India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Bibwewadi Pune411037 India Department of Computer Engineering Terna Engineering College Maharashtra Mumbai India Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Deep learning

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A Rapid Classification of Cross-Contaminations in Aviation Oil Using Impedance-Driven supervised Machine learning

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IEEE sENsORs JOURNAL 2024年第22期24卷 38209-38221页

作者： De Pascali, Chiara Bellisario, Daniele signore, Maria Assunta sciurti, Elisa Radogna, Antonio Vincenzo Francioso, Luca Nunzio Natl Res Council CNR Inst Microelect & Microsyst IMM I-00185 Rome Italy Univ Salento Dept Expt Med Lecce I-73100 Italy

Real-time monitoring of lube oil plays a crucial role in ensuring optimal machinery performance, preventing failures, and facilitating timely maintenance strategies. The approach proposed in this work, based on impedance spectroscopy and supervised machine learning (ML), addresses this need by a novel solution to a multiclass classification problem of cross-contaminations in aviation lubricant. Impedance measurements were performed at room temperature by immersing a microfabricated sensor in 16 aged oil samples containing increasing concentrations of water and aviation fuel. Two datasets were constructed: the first based on impedance components spectra and the second based on dissipation factor spectra. A data pre-processing and augmentation method was proposed for generating synthetic examples from the measured data. Both datasets were independently used to train three supervised classifiers, whose performance was evaluated based on three different approaches of dataset split ratio and k-fold cross-validation. The 1-nearest neighbors (NN) classifier proved to be the most effective in reducing false positives (FPs), false negatives (FNs), and computational running time. The best results were obtained by employing a split ratio of 60:40 and threefold cross-validation scheme on the impedance components-based dataset, yielding an accuracy of 99.8%.

关键词： Oils Impedance sensors Lubricants Monitoring Pollution measurement Impedance measurement Contamination Electrodes Fuels data augmentation impedance spectroscopy lubricant machine learning monitoring

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