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Background: Kidney transplantation is widely recognized as the primary treatment for those with end-stage renal disease. Our goal was to provide an overview of the clinical characteristics of postrenal transplant patients (commercial vs. noncommercial) seeking care in the emergency department (ED). Methodology: The study was conducted at a leading Omani ED from 2015 to 2021; this study included all postrenal transplant patients. Various variables were categorized, coded, and analyzed. Results: A total of 55 postrenal transplant patients were included in our study. Of these, 60.0% were male (n = 33). Among the participants, 14.5% (n = 8) had undergone noncommercial renal transplants, whereas the remaining 85.5% (n = 47) had undergone commercial renal transplants. Among noncommercial postrenal transplant patients, the most common reasons for ED visits were fever (5), gastrointestinal symptoms (4), and cardiac symptoms (3). They also presented with pain at the surgical site (2) and urological symptoms such as dysuria and hematuria (2). In contrast, the most frequent clinical presentations among commercial postrenal transplant patients were cardiac symptoms (13) and pain at the surgical site (13), followed by fever (12). Noncommercial renal transplant patients had a high discharge rate of 62.5% (5) and a low admission rate of 37.5% (3). On the other hand, commercial renal transplant patients necessitated a high admission rate of 59.6% (28) and a low discharge rate of 40.4% (19). Conclusion: The majority of patients had received commercial kidney transplants, and the most common complaints upon their presentation to the ED were cardiac symptoms and pain at the surgical site. Patients undergoing commercial transplants had higher rates of admission.

Aims The younger generation of today is highly dependent on digital technologies worldwide. Studies on young children's cognitive and socio-emotional development have shown that there can be conflicting effects from using screen-based media or from exposing them to it. The study explored the relation between unsupervised use of digital screen time with student mental health and behavioral problems. Methods It was a cross sectional descriptive study approached primary and secondary school going children from grades 2–8 (age 6 to 14 years), purposively selected six schools consist of three English and Bangla medium schools from Dhaka city. A total of 420 students along with their parents were enrolled by clustered random sampling. Study explored the effect of the unsupervised screen time on student mental health and social wellbeing through semi structured questionnaires, Strength and Difficulties Questionnaire (SDQ), Pittsburgh Quality of Sleep Scale (PSQI), Spencer Children Anxiety Scale (SCAS) and Development and Wellbeing Assessment Scale (DAWBA). Results Students used various forms of digital screens for 4.6 hours every day, and 56% of them used these devices without parental supervision or monitoring. English Medium students spend significantly more time on screens on a daily average (5.5 hours) compared with students at Bangla Medium schools (3.7 hours). 21.2% students had mental health concern, this percentage was higher in the unsupervised group (56.2%) than in the supervised group (43.8%). In the unsupervised group, students experienced higher levels of emotional difficulties (15.7%), behavioral difficulties (28.3%), hyperactivity behavior difficulties (17.4%), peer relations difficulties (28.8%), and pro-social behavior difficulties (6.7%) compared with supervised group. 83.3% of students in the supervised group found higher levels of anxiety compared with the unsupervised group (16.7). In the unsupervised group, 15.4% of the students had experienced sleeping problems, compared with 14% in the supervised group. Conclusion These results suggest an impact of unsupervised screen time on the prevalence of mental health problems among students. Appropriate screen usage may be a major intervention target to improve children's mental health and wellbeing.

Inhibition of acetylcholinesterase (AChE) is a crucial target in the treatment of Alzheimer's disease (AD). Common anti-acetylcholinesterase drugs such as Galantamine, Rivastigmine, Donepezil, and Tacrine have significant inhibition potential. Due to side effects and safety concerns, we aimed to investigate a wide range of phytochemicals and structural analogues of these compounds. Compounds similar to the established drugs, and phytochemicals were investigated as potential inhibitors for AChE in treating AD. A total of 2,270 compound libraries were generated for further analysis. Initial virtual screening was performed using Pyrx software, resulting in 638 molecules showing higher binding affinities compared to positive controls Tacrine (-9.0 kcal/mol), Donepezil (-7.3 kcal/mol), Galantamine (-8.3 kcal/mol), and Rivastigmine (-6.4 kcal/mol). Subsequently, ADME properties were assessed, including blood-brain barrier permeability and Lipinski's rule of five violations, leading to 88 compounds passing the ADME analysis. Among the rivastigmine analogous, [3-(1-methylpiperidin-2-yl)phenyl] N,N-diethylcarbamate showed interaction with Tyr123, Tyr336, Tyr340, Phe337, Trp285 residues of AChE. Tacrine similar compounds, such as 4-amino-2-styrylquinoline, exhibited bindings with Tyr123, Phe337, Tyr336, Trp285, Trp85, Gly119, and Gly120 residues. A phytocompound (bisdemethoxycurcumin) showed interaction with Trp285, Tyr340, Trp85, Tyr71, and His446 residues of AChE with favourable binding. These findings underscore the potential of these compounds as novel inhibitors of AChE, offering insights into alternative therapeutic avenues for AD. A 100ns simulation analysis confirmed the stability of protein-ligand complex based on the RMSD, RMSF, ligand properties, PCA, DCCM and MMGBS parameters. The investigation suggested 3 ligands as a potent inhibitor of AChE which are [3-(1-methylpiperidin-2-yl)phenyl] N,N-diethylcarbamate, 4-Amino-2-styrylquinoline and bisdemethoxycurcumin. Furthermore, investigation, including in-vitro and in-vivo studies, is needed to validate the efficacy, safety profiles, and therapeutic potential of these compounds for AD treatment.

Hepatorenal syndrome (HRS) is a functional renal failure that develops in patients with advanced hepatic cirrhosis with ascites and in those with fulminant hepatic failure. The prevalence of HRS varies among studies but in general it is the third most common cause of acute kidney injury (AKI) in cirrhotic patients after pre-renal azotemia and acute tubular necrosis. HRS carries a grim prognosis with a mortality rate approaching 90% three months after disease diagnosis. Fortunately, different strategies have been proven to be successful in preventing HRS. Although treatment options are available, they are not universally effective in restoring renal function but they might prolong survival long enough for liver transplantation, which is the ultimate treatment. Much has been learned in the last two decades regarding the pathophysiology and management of this disease which lead to notable evolution in the HRS definition and better understanding on how best to manage HRS patients. In the current review, we will summarize the recent advancement in epidemiology, pathophysiology, and management of HRS.

Introduction. COVID-19 presents a special challenge to the kidney transplant population.

Land use changes and mounting water demands reduce freshwater inflows into estuaries, impairing estuarine ecosystems and accelerating coastal seawater intrusion. However, determining minimum river inflows for management guidelines is hampered by a lack of ecosystem-flow link data. This study describes the development of freshwater inflow guidelines for the Wami Estuary, combining scarce river flow data, hydrological modeling, inferring natural salinity regime from vegetation zonation and investigating freshwater requirements of people/wildlife. By adopting the Building Blocks Methodology, a detailed Environmental Flows Assessment was performed to know the minimum water depth/quality seasonal requirements for vegetation, terrestrial/aquatic wildlife and human communities. Water depth requirements were assessed for drought and normal rainfall years; corresponding discharges were obtained by a hydrological model (HEC-RAS) developed for the river channel upstream of estuary. Recommended flows were well within historically occurring flows. However, given the rapidly increasing water demand coupled with reduction in basin water storage due to deforestation/wetland loss, it is critical to ensure these minimum flows are present, without which essential ecosystem services (fisheries, water quality, mangrove forest resources and wildlife/tourism) will be jeopardized. The EFA process is described in painstaking detail to provide a reference for undertaking similar studies in data-poor regions worldwide.

Background: Alzheimer's disease (AD) is a neurodegenerative disease caused by the formation of amyloid-beta (Aβ) plaques and neurofibrillary tangles. Acetylcholinesterase (AChE), a serine hydrolase, is the crucial enzyme in the hydrolysis of one of the most extensively known neurotransmitters acetylcholine (ACh) that has been associated with the pathophysiology of AD. From this perspective, enzymatic inhibition of AChE activity has been an auspicious treatment strategy for AD. Different natural products extracted from various plant sources have begun to gain popularity worldwide as a potential inhibitor of acetylcholinesterase (AChEi), and thus, could be used as a therapeutic option for AD treatment. Purpose: The present study congregates and discusses the hitherto scattered data of medicinal plants used for the treatment of AD worldwide. The already existing drugs of AD have been discussed. This study aims to highlight species with assessed efficacy, usable tissues, model organisms, and extraction information, to point out species of interest for further study. A critical assessment and examination of plants were done for the potential pharmacological evaluations. Materials and methods: This review surveys all the published literature (research and review articles) between 2000 and 2021 using numerous search engines and publication houses viz. Web of Science, PubMed, ScienceDirect, Springer, Google scholar, Taylor and Francis imprints, Chemspider, Wiley and NCBI (National center for Biotechnology Information). The literature search was conducted using the following keywords in different combinations: acetylcholinesterase inhibitors, plant extract and Alzheimer disease. More than 300 studies were recovered from which 107 were considered final summarization. Latin names of the identified plant species were validated at The Plant List (2013); version 1.1.; http://www.theplantlist.org/ (accessed 15th August 2018). Results: A bulk amount of research has revealed a comprehensive inventory of plant-based acetylcholinesterase inhibitors to date. Due to the lack of promising, effective, and safe inhibitors, this broad array of phytochemicals has captured the focus of research on the inhibition-based treatment of AD. From the literature search, sixteen plant species were found to have high potency with IC50 values ranging from 0.08 µg/mL to 18.9 µg/mL. Conclusion: This review broadly discusses the existing approved drugs against AD along with natural products extracted from several plant sources with potential AChEi . Species with higher AChEi potency could be evaluated further for pharmacological application.

The main purpose of this paper is to find some fixed point results with a new approach, particularly in those cases where the existing literature remains silent. More precisely, we introduce partial completeness, f̄-orbitally completeness, a new type of contractions and many other notions. We also ensure the existence of fixed points for non-contraction maps in the class of incomplete partial b-metric spaces. We have reported some examples in support of our results.

The use of recyclable materials in road construction, protect the environment, is an influential factor in the decision-making process for road projects. As one of the most important elements in nature and an original basis for the formation of life on earth, Carbon has been used in many different industries for its high surface area and porosity. Because of asphalt binder rheology characteristics, asphalt layers tend to return to their initial conditions after traffic flow but some of these deformations return in elastic form and some not return in plastic form. In this research, the Dynamic Shear Rheometer (DSR) test was used to analyze asphalt Binder performance Grade. Carbon Black particles processed from agricultural waste recycling (walnut skin) with specific weight percentages of 3, 5, 7 and 10, were used for asphalt binder rehabilitation. According to the results, the use of Carbon Black particles derived from the recycling of agricultural waste improved asphalt binder performance Grade from PG64 to PG74 for modified asphalt binder with 7% carbon black and rheological parameters like complex modulus increased 1.7 times and phase angle decreased approximately 0.85 times.

Earnings Manipulation within GAAP same of Earnings Management, that may be Efficient or Opportunistic. But Earnings manipulation is fraud through violation of GAAP. However, Earnings Manipulation is prevented from properly evaluate the company's performance. The development of a model that can be used to help predict earnings manipulation, it can be provide possible to better evaluate the company's performance. Therefore, the aim of this paper is to adjustment coefficients of beneish model and development of a new model for prediction of earnings manipulation based on the best predictor variables. For this purpose, all manufacturing firms of Tehran Stock Exchange during the years 2002- 2012, include 990 Observation, with the discriminant and logit analysis, were studied. The results indicate that on Iran economic environment, beneish model, not able to identify earnings manipulation. the adjusted beneish model and developed models of discriminant and logit analysis approach, respectively with overall accuracy 72%, 75% and 81%, is able to identify earnings manipulator and non- manipulator firms. Also evidence showed that accounting information is useful for prediction of earnings manipulation.

Earnings management is both good and bad. Of course, alittle of earnings management can be improved information content ofaccounting. Opportunistic behavior of management with earningsmanipulation decrease information credibility and wreck earningsquality. This research is trying to identify efficient or opportunistictype of earnings management in portfolio of high and low levels ofearnings quality. For this purpose, data of 841 firm- year during 2001-2011 to estimate earnings quality and earnings managementmeasurement models and 490 firm-years to test the hypothesis wereanalyzed. Results show that earnings management in firms has a lowquality of earnings opportunity. Thus, highly earnings managementreduces the quality of earnings and artificial data.

Background: Babesia is blood-tissue parasite, which are transmitted by hard ticks from Ixodidae family. The parasite is the cause of Babesiosis among ruminants. Babesia is as one of main fatal factors among livestock in endemic regions such as Iran. The aim of this study was to investigate Babesia spp infection using microscopic and molecular methods among small ruminants in Mazandaran and Golestan provinces, northern Iran, in 2011-2012. Materials and Methods: In this cross-sectional study, 22 flocks of total 220 sheep and goats were selected from different regions of these provinces. Subsequently, ten suspected animals were selected randomly and blood sample were taken from their ear. To diagnose thick and thin smears were prepared and stained with Geimsa stain and then examined under light microscope. Standard PCR and Semi nested PCR was performed to differentiate genus of Theileria and Babesia, also identify the species of Babesia. Results: A total of 220 blood samples (160 sheep and 60 goats), 34 cases (15.4 %) had Babesia infection using microscopic. Whereas, 11 cases (5%) were found positive through Babesia spp using standard PCR and also among them, two cases were found mixed infection through Theileria spp. In addition, two microscopic negative were positive by PCR assay. Totally, using semi-nested PCR, Babesia ovis (n=10) and Babesia motasi (n=1) were detected. Conclusion: Our results showed ovine Babesiosis was common in the Iranian Northern provinces .Moreover, Babesia ovis is the main causative agent of ovine Babesiosis in this areas. The relatively high prevalence of Babesia infection in livestock indicates the epizootic stability status of babesiosis in the northern part of Iran.

Lake Tanganyika, the deepest and most voluminous lake in Africa, has warmed over the last century in response to climate change. Separate analyses of surface warming rates estimated from in situ instruments, satellites, and a paleolimnological temperature proxy (TEX86) disagree, leaving uncertainty about the thermal sensitivity of Lake Tanganyika to climate change. Here, we use a comprehensive database of in situ temperature data from the top 100 meters of the water column that span the lake's seasonal range and lateral extent to demonstrate that long-term temperature trends in Lake Tanganyika depend strongly on depth, season, and latitude. The observed spatiotemporal variation in surface warming rates accounts for small differences between warming rate estimates from in situ instruments and satellite data. However, after accounting for spatiotemporal variation in temperature and warming rates, the TEX86 paleolimnological proxy yields lower surface temperatures (1.46 °C lower on average) and faster warming rates (by a factor of three) than in situ measurements. Based on the ecology of Thaumarchaeota (the microbes whose biomolecules are involved with generating the TEX86 proxy), we offer a reinterpretation of the TEX86 data from Lake Tanganyika as the temperature of the low-oxygen zone, rather than of the lake surface temperature as has been suggested previously. Our analyses provide a thorough accounting of spatiotemporal variation in warming rates, offering strong evidence that thermal and ecological shifts observed in this massive tropical lake over the last century are robust and in step with global climate change.

Only 10% of duodenal diverticula are symptomatic. We present the case of a man who fell from a height of 6 ft, landing on his abdomen and presenting 4 h later with severe back pain and a rigid abdomen. At laparotomy, a perforated retroperitoneal duodenal diverticulum was found and repaired with an omental patch. No other injury was noted. Not only is this perforation unusual, but the absence of other injuries sustained during this minor blunt trauma makes this case unique. This case highlights the need for a high index of suspicion when managing patients with back or abdominal pain following minor trauma.

Today, speech synthesis is a part of our daily lives in computers all around the world. Central Kurdish Speech Corpus Construction is a speech corpus that is a primary data source for developing a speech system. There are still two main issues that prevent them from achieving the best possible performance, the lack of efficiency in training and analysis, and the difficulty in modelling. The biggest obstacle against text-to-speech in the Kurdish language is that there is a lack of text and speech recognition tools compounded by the fact that around 30 million people speak the Kurdish language in different countries. To address this issue, this corpus introduced a large vocabulary of Kurdish Text-to-Speech Dataset (KTTS, Gigant), including a pronunciation lexicon and speech corpus for the Central Kurdish dialect. A variety of subjects is comprised to record these sentences. The sentences are recorded in a voice recording studio by a Kurdish man who is a dubber. The goal of the speech corpus is to create a collection of sentences that accurately reflect the real data about the Central Kurdish dialect. A combination of audio and visual sources is used to record the 6,078 sentences of 12 document topics. They were recorded in a controlled environment using microphones that were not noisy. The total record duration is 13.63 h. The recorded sentences are in the ".wav" format., (© 2024 The Authors.)

Introduction: Immunohistochemistry (IHC) is crucial for breast cancer diagnosis, classification, and individualized treatment. IHC is used to measure the levels of expression of hormone receptors (estrogen and progesterone receptors), human epidermal growth factor receptor 2 (HER2), and other biomarkers, which are used to make treatment decisions and predict how well a patient will do. The evaluation of the breast cancer score on IHC slides, taking into account structural and morphological features as well as a scarcity of relevant data, is one of the most important issues in the IHC debate. Several recent studies have utilized machine learning and deep learning techniques to resolve these issues., Materials and Methods: This paper introduces a new approach for addressing the issue based on supervised deep learning. A GAN-based model is proposed for generating high-quality HER2 images and identifying and classifying HER2 levels. Using transfer learning methodologies, the original and generated images were evaluated., Results and Conclusion: All of the models have been trained and evaluated using publicly accessible and private data sets, respectively. The InceptionV3 and InceptionResNetV2 models achieved a high accuracy of 93% with the combined generated and original images used for training and testing, demonstrating the exceptional quality of the details in the synthesized images., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues., Competing Interests: he authors have declared that no competing interests exist., (Copyright: © 2023 Salih et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM 2.5 ) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM 2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM 2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM 2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM 2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM 2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM 2.5 forecasting maps., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://github.com/Tarik4Rashid4/covid19models., Competing Interests: The authors declare no conflict of interest to any party., (© 2022 The Authors. Published by Elsevier B.V.)

The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is, perhaps, the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters' stochastic tuning of ELM's supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine-cosine algorithm was utilized to tune the ELM's parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network's training time is only 0.9421 ms and the overall detection test time for 3100 images is 2.721 s., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.)

Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.

Handwriting recognition is regarded as a dynamic and inspiring topic in the exploration of pattern recognition and image processing. It has many applications including a blind reading aid, computerized reading, and processing for paper documents, making any handwritten document searchable and converting it into structural text form. High accuracy rates have been achieved by this technology when recognizing handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. However, there is not such a system for recognizing Kurdish handwriting. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic/Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive database has been created for handwritten Kurdish characters which contain more than 40 thousand images. The created database has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system the experimental results show an acceptable recognition level. The testing results reported an 83% accuracy rate, and training accuracy reported a 96% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and comparable to the similar to other languages handwriting recognition systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (Copyright © 2022 Elsevier B.V. All rights reserved.)

One of the popular metaheuristic search algorithms is Harmony Search (HS). It has been verified that HS can find solutions to optimization problems due to its balanced exploratory and convergence behavior and its simple and flexible structure. This capability makes the algorithm preferable to be applied in several real-world applications in various fields, including healthcare systems, different engineering fields, and computer science. The popularity of HS urges us to provide a comprehensive survey of the literature on HS and its variants on health systems, analyze its strengths and weaknesses, and suggest future research directions. In this review paper, the current studies and uses of harmony search are studied in four main domains. (i) The variants of HS, including its modifications and hybridization. (ii) Summary of the previous review works. (iii) Applications of HS in healthcare systems. (iv) And finally, an operational framework is proposed for the applications of HS in healthcare systems. The main contribution of this review is intended to provide a thorough examination of HS in healthcare systems while also serving as a valuable resource for prospective scholars who want to investigate or implement this method., Competing Interests: Declaration of competing interest None., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Economic load dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to economic load dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness-dependent optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness-dependent optimizer (FDO) examines the search spaces based on the searching approach of particle swarm optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have used a fitness-dependent optimizer to solve the economic load dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of the fitness-dependent optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for the fitness-dependent optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multidimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. The empirical results obtained using the enhanced variant of the fitness-dependent optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional fitness-dependent optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced fitness-dependent optimizer. Correspondingly, various standard estimations are used to prove the stability of the fitness-dependent optimizer in phases of exploitation and exploration., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Barzan Hussein Tahir et al.)

The Facebook application is used as a resource for collecting the comments of this dataset, The dataset consists of 6756 comments to create a Medical Kurdish Dataset (MKD). The samples are comments of users, which are gathered from different posts of pages (Medical, News, Economy, Education, and Sport). Six steps as a preprocessing technique are performed on the raw dataset to clean and remove noise in the comments by replacing characters. The comments (short text) are labeled for positive class (medical comment) and negative class (non-medical comment) as text classification. The percentage ratio of the negative class is 55% while the positive class is 45%., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2022 The Author(s). Published by Elsevier Inc.)

The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consisting of 1937 images from five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: (i) two-class classification (normal vs COVID-19); (ii) three-class classification (normal, COVID-19, and other CVDs), and finally, (iii) five-class classification (normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities., (© The Author(s) 2022.)

There is no doubt that new technology has become one of the crucial parts of most people's lives around the world. By and large, in this era, the Internet and the Internet of Things (IoT) have become the most indispensable parts of our lives. Recently, IoT technologies have been regarded as the most broadly used tools among other technologies. The tools and the facilities of IoT technologies within the marketplace are part of Industry 4.0. The marketplace is too regarded as a new area that can be used with IoT technologies. One of the main purposes of this paper is to highlight using IoT technologies in Industry 4.0, and the Industrial Internet of Things (IIoT) is another feature revised. This paper focuses on the value of the IoT in the industrial domain in general; it reviews the IoT and focuses on its benefits and drawbacks, and presents some of the IoT applications, such as in transportation and healthcare. In addition, the trends and facts that are related to the IoT technologies on the marketplace are reviewed. Finally, the role of IoT in telemedicine and healthcare and the benefits of IoT technologies for COVID-19 are presented as well.

The early diagnosis and the accurate separation of COVID-19 from non-COVID-19 cases based on pulmonary diffuse airspace opacities is one of the challenges facing researchers. Recently, researchers try to exploit the Deep Learning (DL) method's capability to assist clinicians and radiologists in diagnosing positive COVID-19 cases from chest X-ray images. In this approach, DL models, especially Deep Convolutional Neural Networks (DCNN), propose real-time, automated effective models to detect COVID-19 cases. However, conventional DCNNs usually use Gradient Descent-based approaches for training fully connected layers. Although GD-based Training (GBT) methods are easy to implement and fast in the process, they demand numerous manual parameter tuning to make them optimal. Besides, the GBT's procedure is inherently sequential, thereby parallelizing them with Graphics Processing Units is very difficult. Therefore, for the sake of having a real-time COVID-19 detector with parallel implementation capability, this paper proposes the use of the Whale Optimization Algorithm for training fully connected layers. The designed detector is then benchmarked on a verified dataset called COVID-Xray-5k, and the results are verified by a comparative study with classic DCNN, DUICM, and Matched Subspace classifier with Adaptive Dictionaries. The results show that the proposed model with an average accuracy of 99.06% provides 1.87% better performance than the best comparison model. The paper also considers the concept of Class Activation Map to detect the regions potentially infected by the virus. This was found to correlate with clinical results, as confirmed by experts. Although results are auspicious, further investigation is needed on a larger dataset of COVID-19 images to have a more comprehensive evaluation of accuracy rates., Competing Interests: Conflict of interestThe authors declare that there is no conflict of interest regarding the publication of this paper., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.)

With the increasing number of samples, the manual clustering of COVID-19 and medical disease data samples becomes time-consuming and requires highly skilled labour. Recently, several algorithms have been used for clustering medical datasets deterministically; however, these definitions have not been effective in grouping and analysing medical diseases. The use of evolutionary clustering algorithms may help to effectively cluster these diseases. On this presumption, we improved the current evolutionary clustering algorithm star (ECA*), called iECA*, in three manners: (i) utilising the elbow method to find the correct number of clusters; (ii) cleaning and processing data as part of iECA* to apply it to multivariate and domain-theory datasets; (iii) using iECA* for real-world applications in clustering COVID-19 and medical disease datasets. Experiments were conducted to examine the performance of iECA* against state-of-the-art algorithms using performance and validation measures (validation measures, statistical benchmarking, and performance ranking framework). The results demonstrate three primary findings. First, iECA* was more effective than other algorithms in grouping the chosen medical disease datasets according to the cluster validation criteria. Second, iECA* exhibited the lower execution time and memory consumption for clustering all the datasets, compared to the current clustering methods analysed. Third, an operational framework was proposed to rate the effectiveness of iECA* against other algorithms in the datasets analysed, and the results indicated that iECA* exhibited the best performance in clustering all medical datasets. Further research is required on real-world multi-dimensional data containing complex knowledge fields for experimental verification of iECA* compared to evolutionary algorithms., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

To collect the handwritten format of separate Kurdish characters, each character has been printed on a grid of 14 × 9 of A4 paper. Each paper is filled with only one printed character so that the volunteers know what character should be written in each paper. Then each paper has been scanned, spliced, and cropped with a macro in photoshop to make sure the same process is applied for all characters. The grids of the characters have been filled mainly by volunteers of students from multiple universities in Erbil., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2021 The Author(s). Published by Elsevier Inc.)

Background and Aim: Most of the mixed reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts., Methodology: The proposed system enhanced mean-value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the three-dimensional mean-value coordinates and improvised mean-value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discolouration artefacts around the blending region., Results: The accuracy in terms of overlay error of the proposed solution is improved from 1.01 to 0.80 mm, whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 s from 0.211 s. The processing time and the accuracy of the proposed solution are enhanced as compared to the state-of-art solution., Conclusion: Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video., (© 2021 John Wiley & Sons Ltd.)

Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the model's parameters consume much time. In this approach, the first phase comes to train a deep CNN working as a feature extractor, and the second phase comes to use Extreme Learning Machines (ELMs) for real-time detection. The main drawback of ELMs is to meet the need of a large number of hidden-layer nodes to gain a reliable and accurate detector in applying image processing since the detective performance remarkably depends on the setting of initial weights and biases. Therefore, this paper uses Chimp Optimization Algorithm (ChOA) to improve results and increase the reliability of the network while maintaining real-time capability. The designed detector is to be benchmarked on the COVID-Xray-5k and COVIDetectioNet datasets, and the results are verified by comparing it with the classic DCNN, Genetic Algorithm optimized ELM (GA-ELM), Cuckoo Search optimized ELM (CS-ELM), and Whale Optimization Algorithm optimized ELM (WOA-ELM). The proposed approach outperforms other comparative benchmarks with 98.25 % and 99.11 % as ultimate accuracy on the COVID-Xray-5k and COVIDetectioNet datasets, respectively, and it led relative error to reduce as the amount of 1.75 % and 1.01 % as compared to a convolutional CNN. More importantly, the time needed for training deep ChOA-ELM is only 0.9474 milliseconds, and the overall testing time for 3100 images is 2.937 s., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Elsevier Ltd. All rights reserved.)

Background and Aim: Image registration and alignment are the main limitations of augmented reality (AR)-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts., Methodology: Markerless image registration method was used for AR-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right-to-left direction and non-border occlusion from left-to-right direction., Results: This study has improved video precision to 0.57-0.61 mm alignment error. Furthermore, with the use of bidirectional points, that is, forward and backward directional cloud point, the iteration on image registration was decreased. This has led to improve the processing time as well. The processing time of video frames was improved to 7.4-11.74 frames per second., Conclusions: It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by the movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed AR system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels and so forth., (© 2021 John Wiley & Sons Ltd.)

This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ), expectation maximisation (EM), K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet., Competing Interests: There are no conflicts of interest declared by the researchers., (© 2021 The Authors. Published by Elsevier Inc.)

Background and Aim: Most of the Mixed Reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts., Methodology: The proposed system enhanced the mean value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the 3D mean value coordinates and improvised mean value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discoloration artefacts around the blending region RESULTS: As compared to the state of art solution, the accuracy in terms of overlay error of the proposed solution is improved from 1.01mm to 0.80mm whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 seconds from 0.211 seconds CONCLUSION: Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video. This article is protected by copyright. All rights reserved., (This article is protected by copyright. All rights reserved.)

Background and Aim: Image registration and alignment are the main limitations of augmented reality-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts., Methodology: markerless image registration method was used for Augmented reality-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right to left direction and non-border occlusion from left to right direction., Results: This study has improved video precision to 0.57 mm ∼ 0.61 mm alignment error. Furthermore, with the use of bidirectional points, i.e. Forwards and backwards directional cloud point, the iteration on image registration was decreased. This has led to improved the processing time as well. The processing time of video frames was improved to 7.4 ∼11.74 fps., Conclusions: It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed augmented reality system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels, etc. This article is protected by copyright. All rights reserved., (This article is protected by copyright. All rights reserved.)

This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2020 Aram M. Ahmed et al.)

In this data article, we present the data used to evaluate the statistical success of the backtracking search optimisation algorithm (BSA) in comparison with the other four evolutionary optimisation algorithms. The data presented in this data article is related to the research article entitles 'Operational Framework for Recent Advances in Backtracking Search Optimisation Algorithm: A Systematic Review and Performance Evaluation' [1]. Three statistical tests conducted on BSA compared to differential evolution algorithm (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly algorithm (FF). The tests are used to evaluate these mentioned algorithms and to determine which one could solve a specific optimisation problem concerning the statistical success of 16 benchmark problems taking several criteria into account. The criteria are initializing control parameters, dimension of the problems, their search space, and number of iterations needed to minimise a problem, the performance of the computer used to code the algorithms and their programming style, getting a balance on the effect of randomization, and the use of different type of optimisation problem in terms of hardness and their cohort. In addition, all the three tests include necessary statistical measures (Mean: mean-solution, S.D.: standard-deviation of mean-solution, Best: the best solution, Worst: the worst solution, Exec. Time: mean runtime in seconds, No. of succeeds: number of successful minimisation, and No. of Failure: number of failed minimisation)., (© 2019 The Author(s).)

One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm's weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2019 Chnoor M. Rahman and Tarik A. Rashid.)

The whale optimization algorithm (WOA) is a nature-inspired metaheuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This algorithm has shown its ability to solve many problems. Comprehensive surveys have been conducted about some other nature-inspired algorithms, such as ABC and PSO. Nonetheless, no survey search work has been conducted on WOA. Therefore, in this paper, a systematic and meta-analysis survey of WOA is conducted to help researchers to use it in different areas or hybridize it with other common algorithms. Thus, WOA is presented in depth in terms of algorithmic backgrounds, its characteristics, limitations, modifications, hybridizations, and applications. Next, WOA performances are presented to solve different problems. Then, the statistical results of WOA modifications and hybridizations are established and compared with the most common optimization algorithms and WOA. The survey's results indicate that WOA performs better than other common algorithms in terms of convergence speed and balancing between exploration and exploitation. WOA modifications and hybridizations also perform well compared to WOA. In addition, our investigation paves a way to present a new technique by hybridizing both WOA and BAT algorithms. The BAT algorithm is used for the exploration phase, whereas the WOA algorithm is used for the exploitation phase. Finally, statistical results obtained from WOA-BAT are very competitive and better than WOA in 16 benchmarks functions. WOA-BAT also outperforms well in 13 functions from CEC2005 and 7 functions from CEC2019.

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models., Competing Interests: The authors have declared that no competing interests exist.