Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.

Synergistic Swarm Optimization Algorithm

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

Residual Attention Deep SVDD for COVID-19 Diagnosis Using CT Scans

COVID-19 is the common name of the disease caused by the novel coronavirus(2019-nCoV)that appeared in Wuhan,China in 2019.Discovering the infected people is the most important factor in the fight against the disease.The gold-standard test to diagnose COVID-19 is polymerase chain reaction(PCR),but it takes 5–6 h and,in the early stages of infection,may produce false-negative results.Examining Computed Tomography(CT)images to diagnose patients infected with COVID-19 has become an urgent necessity.In this study,we propose a residual attention deep support vector data description SVDD(RADSVDD)approach to diagnose COVID-19.It is a novel approach combining residual attention with deep support vector data description(DSVDD)to classify the CT images.To the best of our knowledge,we are the first to combine residual attention with DSVDD in general,and specifically in the diagnosis of COVID-19.Combining attention with DSVDD naively may cause model collapse.Attention in the proposed RADSVDD guides the network during training and enables quick learning,residual connectivity prevents vanishing gradients.Our approach consists of three models,each model is devoted to recognizing one certain disease and classifying other diseases as anomalies.These models learn in an end-to-end fashion.The proposed approach attained high performance in classifying CT images into intact,COVID-19,and non-COVID-19 pneumonia.To evaluate the proposed approach,we created a dataset from published datasets and had it assessed by an experienced radiologist.The proposed approach achieved high performance,with the normal model attained sensitivity(0.96–0.98),specificity(0.97–0.99),F1-score(0.97–0.98),and area under the receiver operator curve(AUC)0.99;the COVID-19 model attained sensitivity(0.97–0.98),specificity(0.97–0.99),F1-score(0.97–0.99),and AUC 0.99;and the non-COVID pneumoniamodel attained sensitivity(0.97–1),specificity(0.98–0.99),F1-score(0.97–0.99),and AUC 0.99.

A Novel Lung Cancer Detection Method Using Wavelet Decomposition and Convolutional Neural Network

Computerized tomography (CT) scan is the only screening test recommended by doctors to look for lung cancer. Convolutional neural networks (CNNs) have recently proven their ability to successfully classify medical images. Due to its strong compactness property, the Discrete Wavelet transform (DWT) has been commonly used in image feature extraction applications. This paper presents a novel technique for the classification of Lung cancer in Computerized Tomography (CT) scans using Wavelets to find discriminative features in the CT images and CNN to classify the extracted features. Experimental results prove that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.5%.

Leadership,Knowledge Management,and Human Capital Development

This research highlights the need to develop a framework for leadership,human capital development,and knowledge management by reviewing existing literature in the field of research.The main aim of this research is to propose a model which supports the relationship between leadership(servant leadership,transformational leadership)and human capital development.The study also proposes that knowledge management(knowledge sharing,knowledge acquisition)will moderate the relationship between leadership(servant leadership,transformational leadership)and human capital development.A set of propositions that represent an empirically-driven research agenda,and also describe the relationships between the focal variables are presented to enhance audience’s understanding within a business context.

Brain Tumor Classification in Magnetic Resonance Images Using Deep Learning and Wavelet Transform

A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign (noncancerous) or malignant (cancerous). Conventional diagnosis of a brain tumor by the radiologist is done by examining a set of images produced by magnetic resonance imaging (MRI). Many computer-aided detection (CAD) systems have been developed in order to help the radiologists reach their goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents a novel CAD technique for the classification of brain tumors in MRI images. The proposed system extracts features from the brain MRI images by utilizing the strong energy compactness property exhibited by the Discrete Wavelet Transform (DWT). The Wavelet features are then applied to a CNN to classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.3%.

Multi-Mode Frequency Reconfigurable Conformal Antenna for Modern Electronic Systems

The article presents a miniaturized monopole antenna dedicated to modern flexible electronic systems.The antenna combines three fundamental properties in a single structure.Firstly,it is characterized by a compact size compared to the state-of-the-art literature with an overall size of 18×18×0.254 mm3,secondly,the proposed antenna integrates the reconfigurability function of frequency,produced by means of a Positive-IntrinsicNegative(PIN)diode introduced into the radiating element.Thus,the antenna is able to switch between different frequencies and different modes,making it suitable to meet the ever-changing demands of communication systems.third,the antenna is equipped by the property of flexibility.In fact,a conformability test is performed and has demonstrated the stability of the antenna performance under normal and bending conditions.Finally,in order to demonstrate the potential of the proposed antenna,a comparison between the simulated and measured results is made and turned out to be a strong agreement,making the antenna an excellent candidate for future miniaturized rigid and conformal devices.

An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling

Timetabling problem is among the most difficult operational tasks and is an important step in raising industrial productivity,capability,and capacity.Such tasks are usually tackled using metaheuristics techniques that provide an intelligent way of suggesting solutions or decision-making.Swarm intelligence techniques including Particle Swarm Optimization(PSO)have proved to be effective examples.Different recent experiments showed that the PSO algorithm is reliable for timetabling in many applications such as educational and personnel timetabling,machine scheduling,etc.However,having an optimal solution is extremely challenging but having a sub-optimal solution using heuristics or metaheuristics is guaranteed.This research paper seeks the enhancement of the PSO algorithm for an efficient timetabling task.This algorithm aims at generating a feasible timetable within a reasonable time.This enhanced version is a hybrid dynamic adaptive PSO algorithm that is tested on a round-robin tournament known as ITC2021 which is dedicated to sports timetabling.The competition includes several soft and hard constraints to be satisfied in order to build a feasible or sub-optimal timetable.It consists of three categories of complexities,namely early,test,and middle instances.Results showed that the proposed dynamic adaptive PSO has obtained feasible timetables for almost all of the instances.The feasibility is measured by minimizing the violation of hard constraints to zero.The performance of the dynamic adaptive PSO is evaluated by the consumed computational time to produce a solution of feasible timetable,consistency,and robustness.The dynamic adaptive PSO showed a robust and consistent performance in producing a diversity of timetables in a reasonable computational time.

Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm

Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine learning.The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres.Artificial Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of randomness.However,ABC has some weaknesses,such as balancing exploration and exploitation.To improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is used.Furthermore,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of AC-MeanABC.The experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.

Hybrid Modified Chimp Optimization Algorithm and Reinforcement Learning for Global Numeric Optimization

Chimp Optimization Algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various do-mains.However,ChOA suffers from the weakness of the local search technique which leads to a loss of diversity,getting stuck in a local minimum,and procuring premature convergence.In response to these defects,this paper proposes an improved ChOA algorithm based on using Opposition-based learning(OBL)to enhance the choice of better solutions,written as OChOA.Then,utilizing Reinforcement Learning(RL)to improve the local research technique of OChOA,called RLOChOA.This way effectively avoids the algorithm falling into local optimum.The performance of the proposed RLOChOA algorithm is evaluated using the Friedman rank test on a set of CEC 2015 and CEC 2017 benchmark functions problems and a set of CEC 2011 real-world problems.Numerical results and statistical experiments show that RLOChOA provides better solution quality,convergence accuracy and stability compared with other state-of-the-art algorithms.

Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called RSA-SSA.The proposed method introduces a better search space to find the optimal solution at each iteration.However,we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds.The obtained solutions by the proposed method are represented using the image histogram.The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level.The performance measure for the proposed method is valid by detecting fitness function,structural similarity index,peak signal-to-noise ratio,and Friedman ranking test.Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA.The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.

Recent Advances of Chimp Optimization Algorithm:Variants and Applications

Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence methods.It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial search.Also,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable convergence.Therefore,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short time.Thus,in this review paper,several research publications using ChOA have been overviewed and summarized.Initially,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual framework.The main operations of ChOA are procedurally discussed,and the theoretical foundation is described.Furthermore,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled versions.The main applications of ChOA are also thoroughly described.The applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,etc.Evaluation of ChOA is also provided.The review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and clustering.As well,it is wealthy in research on health,environment,and public safety.Also,it will aid those who are interested by providing them with potential future research.

Erratum to:Recent Advances of Chimp Optimization Algorithm:Variants and Applications

Erratum to:J Bionic Eng https://doi.org/10.1007/s42235-023-00414-1.In this article the statement in the Funding information section was incorrectly given as‘22UQU4361183DSR03’and should have read‘23UQU4361183DSR03’.