Physical Sciences and Mathematics | Open Access Articles

Offline Handwritten Chinese Character Using Convolutional Neural Network: State-Of-The-Art Methods, Yingna Zhong, Kauthar Mohd Daud, Ain Najiha Binti Mohamad Nor, Richard Adeyemi Ikuesan, Kohbalan Moorthy

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Given the presence of handwritten documents in human transactions, including email sorting, bank checks, and automating procedures, handwritten characters recognition (HCR) of documents has been invaluable to society. Handwritten Chinese characters (HCC) can be divided into offline and online categories. Online HCC recognition (HCCR) involves the trajectory movement of the pen tip for expressing linguistic content. In contrast, offline HCCR involves analyzing and categorizing the sample binary or grayscale images of characters. As recognition technology develops, academics' interest in Chinese character recognition has continuously increased, as it significantly affects social and economic development. Recent development in this area is promising. …

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Application Of A Gene Modular Approach For Clinical Phenotype Genotype Association And Sepsis Prediction Using Machine Learning In Meningococcal Sepsis, Asrar Rashid, Arif R. Anwary, Feras Al-Obeidat, Joe Brierley, Mohammed Uddin, Hoda Alkhzaimi, Amrita Sarpal, Mohammed Toufiq, Zainab A. Malik, Raziya Kadwa, Praveen Khilnani, M. Guftar Shaikh, Govind Benakatti, Javed Sharief, Syed Ahmed Zaki, Abdulrahman Zeyada, Ahmed Al-Dubai, Wael Hafez, Amir Hussain

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Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition …

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More On Complex Hesitant Fuzzy Graphs, Abdulazeez Alkouri, Eman A. Abuhijleh, Ghada Alafifi, Eman Almuhur, Fadi M.A. Al-Zubi

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Correctly determining a company’s market worth during an entire year or a certain period presents a difficulty to decision-makers. In the case of the merger of companies, the need performs heavier when both the companies’ owners are attracted to establishing a fair price at the optimal time to merge. The effectiveness of representing, connecting and manipulating both uncertainty and periodicity information becomes highly required. Hence, study and nhance some properties and conditions of the algebraic structure of complex hesitant fuzzy graphs. Therefore, the degree of composition between two complex hesitant fuzzy graphs is proposed. Also, the formal definitions of union, …

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Dynamic Data Sample Selection And Scheduling In Edge Federated Learning, Mohamed Adel Serhani, Haftay Gebreslasie Abreha, Asadullah Tariq, Mohammad Hayajneh, Yang Xu, Kadhim Hayawi

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Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It enables distributed learning to train on cross-device data, achieving efficient performance, and ensuring data privacy. In the era of Big Data, the Internet of Things (IoT), and data streaming, challenges such as monitoring and management remain unresolved. Edge IoT devices produce and stream huge amounts of sample sources, which can incur significant processing, computation, and storage costs during local updates using all data samples. Many research initiatives have improved the algorithm for FL in homogeneous networks. However, in the typical distributed learning application scenario, data is generated …

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Forecasting Networks Links With Laplace Characteristic And Geographical Information In Complex Networks, Muhammad Wasim, Feras Al-Obeidat, Fernando Moreira, Haji Gul, Adnan Amin

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Forecasting links in a network is a crucial task in various applications such as social networks, internet traffic management, and data mining. Many studies on forecasting links in social networks and on other networks have been conducted over the last decade. In this paper, we propose a novel method based on graph Laplacian eigenmaps for predicting the geographic location of nodes in complex networks. Our method utilizes the adjacency matrix of the network and generates a scoring matrix that captures the similarity between nodes in terms of their geographic location. By transforming the distance matrices into score matrices using exponential …

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Explainable Machine Learning For Evapotranspiration Prediction, Bamory Koné, Rima Grati, Bassem Bouaziz, Khouloud Boukadi

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Full-Text Articles in Physical Sciences and Mathematics

Offline Handwritten Chinese Character Using Convolutional Neural Network: State-Of-The-Art Methods, Yingna Zhong, Kauthar Mohd Daud, Ain Najiha Binti Mohamad Nor, Richard Adeyemi Ikuesan, Kohbalan Moorthy

All Works

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More On Complex Hesitant Fuzzy Graphs, Abdulazeez Alkouri, Eman A. Abuhijleh, Ghada Alafifi, Eman Almuhur, Fadi M.A. Al-Zubi

All Works

Dynamic Data Sample Selection And Scheduling In Edge Federated Learning, Mohamed Adel Serhani, Haftay Gebreslasie Abreha, Asadullah Tariq, Mohammad Hayajneh, Yang Xu, Kadhim Hayawi

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Forecasting Networks Links With Laplace Characteristic And Geographical Information In Complex Networks, Muhammad Wasim, Feras Al-Obeidat, Fernando Moreira, Haji Gul, Adnan Amin

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Explainable Machine Learning For Evapotranspiration Prediction, Bamory Koné, Rima Grati, Bassem Bouaziz, Khouloud Boukadi

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