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Learning and Technology Conference (L&T)

作者： Dahlia Omran Sahar Fawzi Ahmed Kandil Systems and Biomedical Engineering Faculty of Engineering Cairo University Giza Egypt Information Technology & Computer Science Nile University Giza Egypt

correct understanding of the Holy Quran is an essential duty for all Muslims. Tajweed rules guide the reciter to perform Holy Quran reading exactly as it was uttered by Prophet Muhammad peace be upon him. This work focused on the recognition of one Quranic recitation rule. Qalqalah rule is applied to five letters of the Arabic Alphabet (Baa/Daal/Jeem/Qaaf/Taa) having sukun vowelization. The proposed system used the Mel Frequency Cepstral Coefficients (MFCC) as the feature extraction technique, and the Convolutional Neural Networks (CNN) model was used for recognition. The available dataset consists of 3322 audio samples from different surahs of the Quran for four professional readers (Sheihk) AlHussary, AlMinshawy, Abdel Baset, and Ayman Swayed. The best results were gained using Ayman Swayed audio samples with a validation accuracy of 90.8%.

关键词： Training Neural networks Feature extraction Pattern recognition Convolutional neural networks Mel frequency cepstral coefficient

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Enhancing Schizophrenia Prediction Using Class Balancing and SHAP Explainability Techniques on EEG Data

Enhancing Schizophrenia Prediction Using Class Balancing and...

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Pattern Recognition systems (ICPRS), International Conference on

作者： Javiera T. Arias César A. Astudillo PhD in Engineering Systems University of Talca Curicó Chile Department of Computer Science Faculty of Engineering Universidad de Talca Curicó Chile

Machine learning (ML) makes predictions or supports decision making based on data, achieving high accuracy, saving time and resources, and even running real-time analysis. However, one drawback of these models is the lack of transparency in complex models, reducing confidence in sensitive fields such as health. This paper analyzes electroencephalogram (EEG) data to predict schizophrenia in patients. Three classifiers are compared, considering Support Vector Machines(SVM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Three metrics are used to measure the classification process, including accuracy (ACC), area under the curve (AUC) and F-l score (F1). XAI is incorporated into the pipeline to identify relevant features. XGBoost is the model with the best performance in predicting schizophrenia cases, reaching an ACC = 0.93, AUC = 0.93 and F1 score = 0.92, outperforming the SVM and AdaBoost algorithms. The SHAP explainability technique was applied on the XGBoost model, identifying the sex, IQ, delta T6, and delta Pz waves as the most relevant characteristics in the prediction processes. Based on the data analysis, we found that schizophrenia causes an alteration of the delta wave in an EEG, which is different to other mental illnesses. On the other hand, the study generates a specific impact by showing that XGBoost presents better results in 3 validation metrics (ACC, AUC, and F1 Score) compared to SVM and AdaBoost. It is shown that balancing classes is essential to obtain better ML predictions.

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Image Enhancement Using Adaptive Fractional Order Filter

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computer systems science & engineering 2023年第5期45卷 1409-1422页

作者： Ayesha Heena Nagashettappa Biradar Najmuddin M.Maroof Surbhi Bhatia Arwa Mashat Shakila Basheer Department of Electronics and Communication Engineering Bheemanna Khandre Institute of TechnologyBhalkiVisveswaraya Technological UniversityBelagaviIndia Department of Electronics and Communication Engineering Khaja Banda Nawaz College of EngineeringVisveswaraya Technological UniversityBelagaviIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-HofufAl-AhsaSaudi Arabia Faculty of Computing&Information Technology King Abdulaziz UniversityP.O.Box 344Rabigh21911Saudi Arabia Department of Information Systems College of Computer and Information Science Princess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia

Image enhancement is an important preprocessing task as the contrast is low in most of the medical images,Therefore,enhancement becomes the mandatory process before actual image processing should *** research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images,the proposed operators are based on Grunwald-Letnikov(G-L),Riemann-Liouville(R-L)and Caputo(Li&Xie),which are the definitions of fractional order *** this fractional-order,differentiation is well focused on the enhancement of echocardiographic *** provoked for developing a non-linear filter mask for image *** designed filter is simple and effective in terms of improving the contrast of the input low contrast images and preserving the textural features,particularly in smooth *** novelty of the proposed method involves a procedure of partitioning the image into homogenous regions,details,and ***,a fractional differential mask is appropriately chosen adaptively for enhancing the partitioned pixels present in the *** is also incorporated into the Hessian matrix with is a second-order derivative for every pixel and the parameters such as average gradient and entropy are used for qualitative *** wide range of existing state-of-the-art techniques such as fixed order fractional differential filter for enhancement,histogram equalization,integer-order differential methods have been *** proposed algorithm resulted in the enhancement of the input images with an increased value of average gradient as well as entropy in comparison to the previous *** values obtained are very close(almost equal to 99.9%)to the original values of the average gradient and entropy of the *** results of the simulation validate the effectiveness of the proposed algorithm.

关键词： Adaptive filter differential filter enhancement mask fractional differential mask fractional-order calculus hessian matrix

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Enabling Quadruple-D Compensation Coil Integration for Efficient Power Transfer to Receiver in Wireless Power Transfer systems for EV Charging

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Erel, Mehmet Zahid Imtiaz, Tabish Elsanabary, Ahmed Mubin, Marizan Soon, Tey Kok Mekhilef, Saad Ankara Yildirim Beyazit University Department of Energy Systems Engineering Ankara Turkey Universiti Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Port Said University Faculty of Engineering Department of Electrical Engineering Port Said Egypt Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Hawthorn Australia Presidency University Department of Electrical and Electronics Engineering Karnataka Bengaluru India

Wireless power transfer (WPT) presents a promising approach for charging electric vehicles (EVs), offering increased convenience, reliability, and safety for EV customers. Recently, the incorporation of multiple coil structures in wireless charging systems has addressed various operational challenges, enhanced power transfer, and provided a scalable solution for EVs. However, cross-coupling issues and space limitation problems arise when multiple coils are introduced to the WPT system. In this paper, a novel integrated compact coupler is proposed, including two naturally decoupled coils on the transmitter side, while both are coupled with the receiver coil to improve the power transfer capability of the system. ANSYS Maxwell is employed to simulate the proposed coupler and evaluate its performance under misalignment operations. The proposed wireless charging system inherently detects misalignment direction based on the variation of output voltage with respect to x-misalignment. As a result, the experimental prototype of the designed wireless WPT system presents a peak efficiency of 97.08% at 1.147-kW output power for an air gap of 120 mm under fully-aligned condition. Additionally, the designed compensation coil can transfer power of 243.76-W under fully-aligned condition and increases reliability in power transfer during misalignment operations. © 2013 IEEE.

关键词： Inductive power transmission

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Transmissive glucose concentration plasmonic Au sensor based on unclad optical fiber

Transmissive glucose concentration plasmonic Au sensor based...

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Bioengineering (ENBENG), Portuguese Meeting in

作者： Cristina Cunha Ana Sofia Assunção Catarina S. Monteiro Cátia Leitão João P. Mendes Susana Silva Orlando Frazão Susana Novais Department of Physics and Astronomy Faculty of Sciences of the University of Porto INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto Portugal Physics Department i3N University of Aveiro Aveiro portugal Faculty of Sciences of the University of Porto INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto Portugal CIQUP Chemistry Research Center Faculty of Sciences of the University of Porto INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto Portugal INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto Portugal

Using surface resonance (SPR) as a sensitivity enhancer, this work describes the development of a transmissive multimode optical fiber sensor with a gold (Au) thin film that measures glucose concentration. The fiber's cladding was initially removed, and an Au layer was then sputtered onto its surface to simultaneously excite SPR and reflect light, making the SPR sensor extremely sensitive to changes in the environment's refractive index. A range of glucose concentrations, from 0.0001 to 0.5000 g/ml, were tested on the sensor. A maximum sensitivity of 161.302 nm/(g/mL) was attained for the lowest glucose concentration, while the highest concentration yielded a sensitivity of 312.000 nm/(g/mL). The proposed sensor's compact size, high sensitivity, good stability and practicality make it a promising candidate for a range of applications, including detecting diabetes.

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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Communication, Control and Intelligent systems (CCIS)

作者： Prasanna V Suresh Kumar N Divyaprabha P R Sundaram Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Information Technology Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Ganesh College of Engineering Salem India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN–GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors’ information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python.

关键词： Analytical models Accuracy Data analysis Computational modeling Predictive models Feature extraction Data models Convolutional neural networks Cardiovascular diseases Biomedical imaging

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CSELM-QE: A Composite Semi-supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction

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Chinese Journal of Electronics 2020年第6期29卷 1016-1024页

作者： ZHAO Jianli WANG Wei SUN Qiuxia HUO Huan SUN Guoqiang GAO Xiang ZHU Chendi College of Computer Science and Engineering Shandong University of Science and Technology College of Mathematics and Systems Science Shandong University of Science and Technology Faculty of Engineering and Information Technology University of Technology Sydney

Wireless local area network(WLAN)fingerprint-based localization has become the most attractive and popular approach for indoor ***,the primary concern for its practical implementation is the laborious manual effort of calibrating sufficient location-labeled *** Semi-supervised extreme learning machine(SELM) performs well in reducing calibration *** SELM methods only use Received signal strength(RSS) information to construct the neighbor graph and ignores location information,which helps recognizing prior information for manifold *** propose Composite SELM(CSELM) method by using both RSS signals and location information to construct composite ***,the issue of unlabeled RSS data quality has not been *** propose a novel approach called Composite semisupervised extreme learning machine with unlabeled RSS Quality estimation(CSELM-QE) that takes into account the quality of unlabeled RSS data and combines the composite neighbor graph,which considers location information in the semi-supervised extreme learning *** results show that the CSELM-QE could construct a precise localization model,reduce the calibration effort for radio map construction and improve localization *** quality estimation method can be applied to other methods that need to retain high quality unlabeled Received signal strength data to improve model accuracy.

关键词： calibration graph theory indoor radio learning (artificial intelligence) RSSI wireless LAN signal strength data quality estimation method CSELM-QE Composite Semisupervised extreme learning machine unlabeled RSS Quality estimation radio map construction wireless local area network fingerprint-based localization indoor localization location-labeled fingerprints calibration effort traditional SELM methods received signal strength information location information Composite SELM method RSS signals composite graph unlabeled RSS data quality composite neighbor graph precise localization model localization accuracy Wireless local area network (WLAN) WiFi fingerprints Radio map construction Semisupervised extreme learning machine (SELM) Received signal strength (RSS) data quality estimation Location based services

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Bipartite Graph-Based Recommendation systems: Introducing the Enhanced Graph Convolutional Neural Network Method for Indonesian Tourism

Bipartite Graph-Based Recommendation Systems: Introducing th...

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Conference on New Media Studies (CoNMedia)

作者： Felix Indra Kurniadi Rudi Sutomo Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Faculty of Engineering & Informatics Universitas Multimedia Nusantara Banten Indonesia

Indonesia's tourism sector, a cornerstone of its economy, has seen significant growth with both domestic and international tourists, drawn by its diverse landscapes and cultural sites. In 2022, the country welcomed around 10 million international visitors, indicating the industry's potential. In this context, employing artificial intelligence for personalized travel recommendations is crucial. This study proposes an innovative approach, converting tourism data into a bipartite format focused on the user-place relationship. We introduce the Enhanced Graph Convolutional Neural Network (Enhanced GCN), tailored for the tourism sector's complexities. Compared to the traditional Graph Convolutional Network (GCN), the Enhanced GCN shows superior predictive accuracy, reducing the Mean Squared Error (MSE) to 2.38. This advance represents a significant step in providing more accurate, customized travel suggestions. However, the current MSE suggests room for further improvement. Future research will aim to refine the Enhanced GCN, enhancing its ability to meet and predict tourists' needs in Indonesia's dynamic tourism environment. Ultimately, the goal is to use artificial intelligence to transform how visitors experience Indonesia's cultural and natural offerings, thereby bolstering the tourism industry's growth..

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Exploring the IT’s Needs in Morocco Using Online Job Ads

Exploring the IT’s Needs in Morocco Using Online Job Ads

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International Symposium on Automatic Control and Emerging Technologies, ACET 2023

作者： Sellamy, Khadija El Farouki, Mohamed Sabri, Zineb Nouib, Hamza Qostal, Aniss Fakhri, Youssef Moumen, Aniss Laboratory of Research in Computer Science Faculty of Sciences University Ibn Tofail Kenitra Morocco Laboratory of Research Intelligent Processing and Security of Systems University Mohammed V Rabat Morocco Laboratory of Systems Engineering ENSA of Kenitra University Ibn Tofail Kenitra Morocco S.I.G.E.R Laboratory FST Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (纸本)9789819701254

Researchers have the opportunity to better understand the labor market and its needs as job offers/advertisements become more accessible. This study aims to better understand the expectations of employers in the IT sector by collecting and analyzing the data becoming job offers posted on job search websites in Morocco. In this work, we used multiple techniques of Text Mining and machine learning, for the scraping and the analysis of the offers. This sector is explored to draw descriptive and statistical conclusions related to the demand like Job titles, Required Skills, Years of Education, Experience, Contract type, Natural Languages, Telecommuting. And which are beneficial for several actors (job seekers, researchers, trainers, students, etc.) with the aim of better decision-making. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Commerce

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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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