Design, synthesis, in vitro, and in silico anti-α-glucosidase assays of N-phenylacetamide-1,2,3-triazole-indole-2-carboxamide derivatives as new anti-diabetic agents.

In this work, a novel series of N-phenylacetamide-1,2,3-triazole-indole-2-carboxamide derivatives 5a-n were designed by consideration of the potent α-glucosidase inhibitors containing indole and carboxamide-1,2,3-triazole-N-phenylacetamide moieties. These compounds were synthesized by click reaction and evaluated against yeast α-glucosidase. All the newly title compounds demonstrated superior potency when compared with acarbose as a standard inhibitor. Particularly, compound 5k possessed the best inhibitory activity against α-glucosidase with around a 28-fold improvement in the inhibition effect in comparison standard inhibitor. This compound showed a competitive type of inhibition in the kinetics. The molecular docking and dynamics demonstrated that compound 5k with a favorable binding energy well occupied the active site of α-glucosidase.

DRPSO:A multi-strategy fusion particle swarm optimization algorithm with a replacement mechanisms for colon cancer pathology image segmentation.

Colon adenocarcinoma (COAD) is a type of colon cancers with a high mortality rate. Its early symptoms are not obvious, and its late stage is accompanied by various complications that seriously endanger patients' lives. To assist in the early diagnosis of COAD and improve the detection efficiency of COAD, this paper proposes a multi-level threshold image segmentation (MIS) method based on an enhanced particle swarm algorithm for segmenting COAD images. Firstly, this paper proposes a multi-strategy fusion particle swarm optimization algorithm (DRPSO) with a replacement mechanism. The non-linear inertia weight and sine-cosine learning factors in DRPSO help balance the exploration and exploitation phases of the algorithm. The population reorganization strategy incorporating MGO enhances population diversity and effectively prevents the algorithm from stagnating prematurely. The mutation-based final replacement mechanism enhances the algorithm's ability to escape local optima and helps the algorithm to obtain highly accurate solutions. In addition, comparison experiments on the CEC2020 and CEC2022 test sets show that DRPSO outperforms other state-of-the-art algorithms in terms of convergence accuracy and speed. Secondly, by combining the non-local mean 2D histogram and 2D Renyi entropy, this paper proposes a DRPSO algorithm based MIS method, which is successfully applied to the segments the COAD pathology image problem. The results of segmentation experiments show that the above method obtains relatively higher quality segmented images with superior performance metrics: PSNR = 23.556, SSIM = 0.825, and FSIM = 0.922. In conclusion, the MIS method based on the DRPSO algorithm shows great potential in assisting COAD diagnosis and in pathology image segmentation.

An enhanced Coati Optimization Algorithm for global optimization and feature selection in EEG emotion recognition.

Emotion recognition based on Electroencephalography (EEG) signals has garnered significant attention across diverse domains including healthcare, education, information sharing, and gaming, among others. Despite its potential, the absence of a standardized feature set poses a challenge in efficiently classifying various emotions. Addressing the issue of high dimensionality, this paper introduces an advanced variant of the Coati Optimization Algorithm (COA), called eCOA for global optimization and selecting the best subset of EEG features for emotion recognition. Specifically, COA suffers from local optima and imbalanced exploitation abilities as other metaheuristic methods. The proposed eCOA incorporates the COA and RUNge Kutta Optimizer (RUN) algorithms. The Scale Factor (SF) and Enhanced Solution Quality (ESQ) mechanism from RUN are applied to resolve the raised shortcomings of COA. The proposed eCOA algorithm has been extensively evaluated using the CEC'22 test suite and two EEG emotion recognition datasets, DEAP and DREAMER. Furthermore, the eCOA is applied for binary and multi-class classification of emotions in the dimensions of valence, arousal, and dominance using a multi-layer perceptron neural network (MLPNN). The experimental results revealed that the eCOA algorithm has more powerful search capabilities than the original COA and seven well-known counterpart methods related to statistical, convergence, and diversity measures. Furthermore, eCOA can efficiently support feature selection to find the best EEG features to maximize performance on four quadratic emotion classification problems compared to the methods of its counterparts. The suggested method obtains a classification accuracy of 85.17% and 95.21% in the binary classification of low and high arousal emotions in two public datasets: DEAP and DREAMER, respectively, which are 5.58% and 8.98% superior to existing approaches working on the same datasets for different subjects, respectively.

Accurate multilevel thresholding image segmentation via oppositional Snake Optimization algorithm: Real cases with liver disease.

Liver-related diseases significantly contribute to global mortality rates. Accurate segmentation of liver disease from CT scans is essential for early diagnosis and treatment selection, particularly in computer-aided diagnosis (CAD) systems. To address challenges posed by inconsistent liver presence and unclear boundaries, an enhanced Snake Optimization (SO) algorithm is proposed that integrates with opposition-based learning (OBL) called (SO-OBL), proving effective in global optimization and multilevel image segmentation. Experiments using CEC'2022 test functions compare SO-OBL with eleven recent and state-of-the-art metaheuristic algorithms, demonstrating its superior performance. Additionally, an advanced liver disease segmentation model based on SO-OBL incorporates an optimized multilevel thresholding technique, leveraging Otsu's function. Notable segmentation metric results, including FSIM = 0.947, SSIM = 0.941, PSNR = 24.876, MSE = 236.88, and execution time = 0.281, underscore the model's efficiency and potential for accurate diagnosis in CAD systems.

Application of modified artificial hummingbird algorithm in optimal power flow and generation capacity in power networks considering renewable energy sources.

Today's electrical power system is a complicated network that is expanding rapidly. The power transmission lines are more heavily loaded than ever before, which causes a host of problems like increased power losses, unstable voltage, and line overloads. Real and reactive power can be optimized by placing energy resources at appropriate locations. Congested networks benefit from this to reduce losses and enhance voltage profiles. Hence, the optimal power flow problem (OPF) is crucial for power system planning. As a result, electricity system operators can meet electricity demands efficiently and ensure the reliability of the power systems. The classical OPF problem ignores network emissions when dealing with thermal generators with limited fuel. Renewable energy sources are becoming more popular due to their sustainability, abundance, and environmental benefits. This paper examines modified IEEE-30 bus and IEEE-118 bus systems as case studies. Integrating renewable energy sources into the grid can negatively affect its performance without adequate planning. In this study, control variables were optimized to minimize fuel cost, real power losses, emission cost, and voltage deviation. It also met operating constraints, with and without renewable energy. This solution can be further enhanced by the placement of distributed generators (DGs). A modified Artificial Hummingbird Algorithm (mAHA) is presented here as an innovative and improved optimizer. In mAHA, local escape operator (LEO) and opposition-based learning (OBL) are integrated into the basic Artificial Hummingbird Algorithm (AHA). An improved version of AHA, mAHA, seeks to improve search efficiency and overcome limitations. With the CEC'2020 test suite, the mAHA has been compared to several other meta-heuristics for addressing global optimization challenges. To test the algorithm's feasibility, standard and modified test systems were used to solve the OPF problem. To assess the effectiveness of mAHA, the results were compared to those of seven other global optimization algorithms. According to simulation results, the proposed algorithm minimized the cost function and provided convergent solutions.

Mitigation of Benzene-Induced Haematotoxicity in Sprague Dawley Rats through Plant-Extract-Loaded Silica Nanobeads.

Benzene, a potent carcinogen, is known to cause acute myeloid leukaemia. While chemotherapy is commonly used for cancer treatment, its side effects have prompted scientists to explore natural products that can mitigate the haematotoxic effects induced by chemicals. One area of interest is nano-theragnostics, which aims to enhance the therapeutic potential of natural products. This study aimed to enhance the effects of methanolic extracts from Ocimum basilicum, Rosemarinus officinalis, and Thymus vulgaris by loading them onto silica nanobeads (SNBs) for targeted delivery to mitigate the benzene-induced haematotoxic effects. The SNBs, 48 nm in diameter, were prepared using a chemical method and were then loaded with the plant extracts. The plant-extract-loaded SNBs were then coated with carboxymethyl cellulose (CMC). The modified SNBs were characterized using various techniques such as scanning electron microscopy (SEM), X-ray diffraction (XRD), UV-visible spectroscopy, and Fourier transform infrared (FTIR) spectroscopy. The developed plant-extract-loaded and CMC-modified SNBs were administered intravenously to benzene-exposed rats, and haematological and histopathological profiling was conducted. Rats exposed to benzene showed increased liver and spleen weight, which was mitigated by the plant-extract-loaded SNBs. The differential white blood cell (WBC) count was higher in rats with benzene-induced haematotoxicity, but this count decreased significantly in rats treated with plant-extract-loaded SNBs. Additionally, blast cells observed in benzene-exposed rats were not found in rats treated with plant-extract-loaded SNBs. The SNBs facilitated targeted drug delivery of the three selected medicinal herbs at low doses. These results suggest that SNBs have promising potential as targeted drug delivery agents to mitigate haematotoxic effects induced by benzene in rats.

Liver Cancer Algorithm: A novel bio-inspired optimizer.

This paper introduces a new bio-inspired optimization algorithm named the Liver Cancer Algorithm (LCA), which mimics the liver tumor growth and takeover process. It uses an evolutionary search approach that simulates the behavior of liver tumors when taking over the liver organ. The tumor's ability to replicate and spread to other organs inspires the algorithm. LCA algorithm is developed using genetic operators and a Random Opposition-Based Learning (ROBL) strategy to efficiently balance local and global searches and explore the search space. The algorithm's efficiency is tested on the IEEE Congress of Evolutionary Computation in 2020 (CEC'2020) benchmark functions and compared to seven widely used metaheuristic algorithms, including Genetic Algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE), Adaptive Guided Differential Evolution Algorithm (AGDE), Improved Multi-Operator Differential Evolution (IMODE), Harris Hawks Optimization (HHO), Runge-Kutta Optimization Algorithm (RUN), weIghted meaN oF vectOrs (INFO), and Coronavirus Herd Immunity Optimizer (CHIO). The statistical results of the convergence curve, boxplot, parameter space, and qualitative metrics show that the LCA algorithm performs competitively compared to well-known algorithms. Moreover, the versatility of the LCA algorithm extends beyond mathematical benchmark problems. It was also successfully applied to tackle the feature selection problem and optimize the support vector machine for various biomedical data classifications, resulting in the creation of the LCA-SVM model. The LCA-SVM model was evaluated in a total of twelve datasets, among which the MonoAmine Oxidase (MAO) dataset stood out, showing the highest performance compared to the other datasets. In particular, the LCA-SVM model achieved an impressive accuracy of 98.704% on the MAO dataset. This outstanding result demonstrates the efficacy and potential of the LCA-SVM approach in handling complex datasets and producing highly accurate predictions. The experimental results indicate that the LCA algorithm surpasses other methods to solve mathematical benchmark problems and feature selection.

Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification.

Background: The examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning. A theoretical and practical issue. However, methods based on deep learning (DL) have greatly helped blood cell classification. Materials and Methods: Images of blood cells in a microscopic smear were collected from GitHub, a public source that uses the MIT license. An end-to-end computer-aided diagnosis (CAD) system for leukocytes has been created and implemented as part of this study. The introduced system comprises image preprocessing and enhancement, image segmentation, feature extraction and selection, and WBC classification. By combining the DenseNet-161 and the cyclical learning rate (CLR), we contribute an approach that speeds up hyperparameter optimization. We also offer the one-cycle technique to rapidly optimize all hyperparameters of DL models to boost training performance. Results: The dataset has been split into two sets: approximately 80% of the data (9,966 images) for the training set and 20% (2,487 images) for the validation set. The validation set has 623, 620, 620, and 624 eosinophil, lymphocyte, monocyte, and neutrophil images, whereas the training set has 2,497, 2,483, 2,487, and 2,499, respectively. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set. Conclusion: Using a combination of the recently developed pretrained convolutional neural network (CNN), DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of WBCs for leukemia detection. The proposed method is more accurate compared to the state of the art.

Dynamic Coati Optimization Algorithm for Biomedical Classification Tasks.

Medical datasets are primarily made up of numerous pointless and redundant elements in a collection of patient records. None of these characteristics are necessary for a medical decision-making process. Conversely, a large amount of data leads to increased dimensionality and decreased classifier performance in terms of machine learning. Numerous approaches have recently been put out to address this issue, and the results indicate that feature selection can be a successful remedy. To meet the various needs of input patterns, medical diagnostic tasks typically involve learning a suitable categorization model. The k-Nearest Neighbors algorithm (kNN) classifier's classification performance is typically decreased by the input variables' abundance of irrelevant features. To simplify the kNN classifier, essential attributes of the input variables have been searched using the feature selection approach. This paper presents the Coati Optimization Algorithm (DCOA) in a dynamic form as a feature selection technique where each iteration of the optimization process involves the introduction of a different feature. We enhance the exploration and exploitation capability of DCOA by employing dynamic opposing candidate solutions. The most impressive feature of DCOA is that it does not require any preparatory parameter fine-tuning to the most popular metaheuristic algorithms. The CEC'22 test suite and nine medical datasets with various dimension sizes were used to evaluate the performance of the original COA and the proposed dynamic version. The statistical results were validated using the Bonferroni-Dunn test and Kendall's W test and showed the superiority of DCOA over seven well-known metaheuristic algorithms with an overall accuracy of 89.7%, a feature selection of 24%, a sensitivity of 93.35% a specificity of 96.81%, and a precision of 93.90%.

Boosted federated learning based on improved Particle Swarm Optimization for healthcare IoT devices.

As healthcare data becomes increasingly available from various sources, including clinical institutions, patients, insurance companies, and pharmaceutical industries, machine learning (ML) services are becoming more significant in healthcare-facing domains. Therefore, it is imperative to ensure the integrity and reliability of ML models to maintain the quality of healthcare services. Particularly due to the growing need for privacy and security, healthcare data has resulted in each Internet of Things (IoT) device being treated as an independent source of data, isolated from other devices. Moreover, the limited computational and communication capabilities of wearable healthcare devices hinder the applicability of traditional ML. Federated Learning (FL) is a paradigm that maintains data privacy by storing only learned models on a server and advances with data from scattered clients, making it ideal for healthcare applications where patient data must be safeguarded. The potential of FL to transform healthcare is significant, as it can enable the development of new ML-powered applications that can enhance the quality of care, lower costs, and improve patient outcomes. However, the accuracy of current Federated Learning aggregation methods suffers greatly in unstable network situations due to the high volume of weights transmitted and received. To address this issue, we propose an alternative approach to Federated Average (FedAvg) that updates the global model by gathering score values from learned models primarily utilized in Federated Learning, using an improved version of Particle Swarm Optimization (PSO) called FedImpPSO. This approach boosts the robustness of the algorithm in erratic network conditions. To further enhance the speed and efficiency of data exchange within a network, we modify the format of the data clients send to servers using the FedImpPSO method. The proposed approach is evaluated using the CIFAR-10 and CIFAR-100 datasets and a Convolutional Neural Network (CNN). We found that it yielded an average accuracy improvement of 8.14% over FedAvg and 2.5% over Federated PSO (FedPSO). This study evaluates the use of FedImpPSO in healthcare by training a deep-learning model over two case studies to evaluate the effectiveness of our approach in healthcare. The first case study involves the classification of COVID-19 using public datasets (Ultrasound and X-ray) and achieved an F1-measure of 77.90% and 92.16%, respectively. The second case study was conducted over the cardiovascular dataset, where our proposed FedImpPSO achieves 91.18% and 92% accuracy in predicting the existence of heart diseases. As a result, our approach demonstrates the effectiveness of using FedImpPSO to improve the accuracy and robustness of Federated Learning in unstable network conditions and has potential applications in healthcare and other domains where data privacy is critical.

Seasonal fluctuations and diversity of Ingoldian mycobiota in two water bodies receiving different effluents at Assiut Governorate (Upper Egypt).

In the current study, fifty-eight Ingoldain fungal species assignable to forty-one genera were recovered from two water bodies receiving the treated sewage and the effluents of oils and soaps factory at Assiut Governorate (Upper Egypt), of which Anguillospora, Amniculicola, Flagellospora, and Mycocentrospora were the most prevalent genera. The most widespread identified species were Anguillospora furtive, Amniculicola longissima and Flagellospora fusarioides. Forty-three species were identified for the first time in Egypt. The most Ingoldain taxa were estimated for El-Zinnar canal, with the highest recorded taxa in winter. Whereas, the highest dominance of Ingoldian fungi was estimated for the El-Ibrahimia canal. The highest Simpson and Shannon diversity indexes were estimated for El-Zinnar canal samples recording 0.9683 and 3.741, respectively. The poorest water sites with Ingoldian fungi were those exposed directly to either treated sewage or industrial effluents, with which relatively higher values of water conductivity, cations and anions. Water temperature was the main abiotic factor driving the seasonal occurrence of Ingoldian fungi. It is interesting to isolate some Ingoldian fungal species from the stressful water sites receiving the effluents which provide valuable insights regarding their adaptation, predictive and putative role as bioindicators and their potentiality in pollutants degradation, organic decomposition, and transformation of xenobiotic compounds.

Artificial intelligence for waste management in smart cities: a review.

The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

An efficient multilevel image thresholding method based on improved heap-based optimizer.

Image segmentation is the process of separating pixels of an image into multiple classes, enabling the analysis of objects in the image. Multilevel thresholding (MTH) is a method used to perform this task, and the problem is to obtain an optimal threshold that properly segments each image. Methods such as the Kapur entropy or the Otsu method, which can be used as objective functions to determine the optimal threshold, are efficient in determining the best threshold for bi-level thresholding; however, they are not effective for MTH due to their high computational cost. This paper integrates an efficient method for MTH image segmentation called the heap-based optimizer (HBO) with opposition-based learning termed improved heap-based optimizer (IHBO) to solve the problem of high computational cost for MTH and overcome the weaknesses of the original HBO. The IHBO was proposed to improve the convergence rate and local search efficiency of search agents of the basic HBO, the IHBO is applied to solve the problem of MTH using the Otsu and Kapur methods as objective functions. The performance of the IHBO-based method was evaluated on the CEC'2020 test suite and compared against seven well-known metaheuristic algorithms including the basic HBO, salp swarm algorithm, moth flame optimization, gray wolf optimization, sine cosine algorithm, harmony search optimization, and electromagnetism optimization. The experimental results revealed that the proposed IHBO algorithm outperformed the counterparts in terms of the fitness values as well as other performance indicators, such as the structural similarity index (SSIM), feature similarity index (FSIM), peak signal-to-noise ratio. Therefore, the IHBO algorithm was found to be superior to other segmentation methods for MTH image segmentation.

An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation.

Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effective in determining the optimal multi-level thresholding for image segmentation. Therefore, an efficient version of the search and rescue optimization algorithm (SAR) based on opposition-based learning (OBL) is proposed in this paper to segment blood-cell images and solve problems of multi-level thresholding. The SAR algorithm is one of the most popular meta-heuristic algorithms (MHs) that mimics humans' exploration behavior during search and rescue operations. The SAR algorithm, which utilizes the OBL technique to enhance the algorithm's ability to jump out of the local optimum and enhance its search efficiency, is termed mSAR. A set of experiments is applied to evaluate the performance of mSAR, solve the problem of multi-level thresholding for image segmentation, and demonstrate the impact of combining the OBL technique with the original SAR for improving solution quality and accelerating convergence speed. The effectiveness of the proposed mSAR is evaluated against other competing algorithms, including the L'evy flight distribution (LFD), Harris hawks optimization (HHO), sine cosine algorithm (SCA), equilibrium optimizer (EO), gravitational search algorithm (GSA), arithmetic optimization algorithm (AOA), and the original SAR. Furthermore, a set of experiments for multi-level thresholding image segmentation is performed to prove the superiority of the proposed mSAR using fuzzy entropy and the Otsu method as two objective functions over a set of benchmark images with different numbers of thresholds based on a set of evaluation matrices. Finally, analysis of the experiments' outcomes indicates that the mSAR algorithm is highly efficient in terms of the quality of the segmented image and feature conservation, compared with the other competing algorithms.

A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification.

It is crucial to accurately categorize cancers using microarray data. Researchers have employed a variety of computational intelligence approaches to analyze gene expression data. It is believed that the most difficult part of the problem of cancer diagnosis is determining which genes are informative. Therefore, selecting genes to study as a starting point for cancer classification is common practice. We offer a novel approach that combines the Runge Kutta optimizer (RUN) with a support vector machine (SVM) as the classifier to select the significant genes in the detection of cancer tissues. As a means of dealing with the high dimensionality that characterizes microarray datasets, the preprocessing stage of the ReliefF method is implemented. The proposed RUN-SVM approach is tested on binary-class microarray datasets (Breast2 and Prostate) and multi-class microarray datasets in order to assess its efficacy (i.e., Brain Tumor1, Brain Tumor2, Breast3, and Lung Cancer). Based on the experimental results obtained from analyzing six different cancer gene expression datasets, the proposed RUN-SVM approach was found to statistically beat the other competing algorithms due to its innovative search technique.

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation.

The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed tomography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self-adaptive parameter and a mathematical principle based on the Fibonacci approach method to achieve a higher level of accuracy in the classification of COVID-19 CT images. The proposed Es-MFO algorithm is evaluated using nineteen different basic benchmark functions, thirty and fifty dimensional IEEE CEC'2017 test functions, and compared the proficiency with a variety of other fundamental optimization techniques as well as MFO variants. Moreover, the suggested Es-MFO algorithm's robustness and durability has been evaluated with tests including the Friedman rank test and the Wilcoxon rank test, as well as a convergence analysis and a diversity analysis. Furthermore, the proposed Es-MFO algorithm resolves three CEC2020 engineering design problems to examine the problem-solving ability of the proposed method. The proposed Es-MFO algorithm is then used to solve the COVID-19 CT image segmentation problem using multi-level thresholding with the help of Otsu's method. Comparison results of the suggested Es-MFO with basic and MFO variants proved the superiority of the newly developed algorithm.

Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques.

Heart disease remains the major cause of death, despite recent improvements in prediction and prevention. Risk factor identification is the main step in diagnosing and preventing heart disease. Automatically detecting risk factors for heart disease in clinical notes can help with disease progression modeling and clinical decision-making. Many studies have attempted to detect risk factors for heart disease, but none have identified all risk factors. These studies have proposed hybrid systems that combine knowledge-driven and data-driven techniques, based on dictionaries, rules, and machine learning methods that require significant human effort. The National Center for Informatics for Integrating Biology and Beyond (i2b2) proposed a clinical natural language processing (NLP) challenge in 2014, with a track (track2) focused on detecting risk factors for heart disease risk factors in clinical notes over time. Clinical narratives provide a wealth of information that can be extracted using NLP and Deep Learning techniques. The objective of this paper is to improve on previous work in this area as part of the 2014 i2b2 challenge by identifying tags and attributes relevant to disease diagnosis, risk factors, and medications by providing advanced techniques of using stacked word embeddings. The i2b2 heart disease risk factors challenge dataset has shown significant improvement by using the approach of stacking embeddings, which combines various embeddings. Our model achieved an F1 score of 93.66% by using BERT and character embeddings (CHARACTER-BERT Embedding) stacking. The proposed model has significant results compared to all other models and systems that we developed for the 2014 i2b2 challenge.

Optimized deep learning architecture for brain tumor classification using improved Hunger Games Search Algorithm.

One of the worst diseases is a brain tumor, which is defined by abnormal development of synapses in the brain. Early detection of brain tumors is essential for improving prognosis, and classifying tumors is a vital step in the disease's treatment. Different classification strategies using deep learning have been presented for the diagnosis of brain tumors. However, several challenges exist, such as the need for a competent specialist in classifying brain cancers by deep learning models and the problem of building the most precise deep learning model for categorizing brain tumors. We propose an evolved and highly efficient model based on deep learning and improved metaheuristic algorithms to address these challenges. Specifically, we develop an optimized residual learning architecture for classifying multiple brain tumors and propose an improved variant of the Hunger Games Search algorithm (I-HGS) based on combining two enhancing strategies: Local Escaping Operator (LEO) and Brownian motion. These two strategies balance solution diversity and convergence speed, boosting the optimization performance and staying away from the local optima. First, we have evaluated the I-HGS algorithm on the IEEE Congress on Evolutionary Computation held in 2020 (CEC'2020) test functions, demonstrating that I-HGS outperformed the basic HGS and other popular algorithms regarding statistical convergence, and various measures. The suggested model is then applied to the optimization of the hyperparameters of the Residual Network 50 (ResNet50) model (I-HGS-ResNet50) for brain cancer identification, proving its overall efficacy. We utilize several publicly available, gold-standard datasets of brain MRI images. The proposed I-HGS-ResNet50 model is compared with other existing studies as well as with other deep learning architectures, including Visual Geometry Group 16-layer (VGG16), MobileNet, and Densely Connected Convolutional Network 201 (DenseNet201). The experiments demonstrated that the proposed I-HGS-ResNet50 model surpasses the previous studies and other well-known deep learning models. I-HGS-ResNet50 acquired an accuracy of 99.89%, 99.72%, and 99.88% for the three datasets. These results efficiently prove the potential of the proposed I-HGS-ResNet50 model for accurate brain tumor classification.

Safety Survey on Lone Working Magnetic Resonance Imaging Technologists in Saudi Arabia.

PURPOSE: The American College of Radiology (ACR) requires MR personnel not to work alone due to the increased risk of safety issues such as projectiles, aggressive patients, and technologist fatigue. As a result, we intend to assess the current safety of lone-working MRI technologists in MRI departments in Saudi Arabia. MATERIALS AND METHODS: A cross-sectional study using a self-report questionnaire was conducted in 88 Saudi hospitals. RESULTS: A response rate of 64% (174/270) was obtained among the 270 MRI technologists which were identified. The study discovered that 86% of MRI technologists had prior experience working alone. In terms of MRI safety training, 63% of MRI technologists received such training. A question about lone MRI workers' awareness of the ACR's recommendations revealed that 38% were unaware of such recommendations. Furthermore, 22% were misinformed, believing that working alone in an MRI unit is optional or depends on the individual's desire to work alone. Working alone has the primary consequence of being statistically significantly associated with projectile/object-related accidents/mistakes (p = 0.03). CONCLUSION: Saudi Arabian MRI technologists have extensive experience working alone without supervision. Most MRI technologists are unaware of lone working regulations, which has raised concerns about accidents/mistakes. There is a need for MRI safety training and adequate practical experience to raise awareness of MRI safety regulations and policies related to lone working among departments and MRI workers.

A modified weighted mean of vectors optimizer for Chronic Kidney disease classification.

Chronic kidney Disease (CKD), also known as chronic renal disease, is an illness that affects the majority of adults and is defined by a progressive decrease in kidney function over time, particularly in those with diabetes and high blood pressure. Metaheuristic (MH) algorithms based machine learning classifiers have become reliable for medical treatment. The weIghted meaN oF vectOrs (INFO) is a recently developed MH but suffers from a fall into local optimal and slow convergence speed. Therefore, to improve INFO, a modified INFO (mINFO) with two enhancement strategies has been developed. The developed variant utilizes the Opposition-Based Learning (OBL) to improve the local search ability to avoid trapping into the local optimum, and the Dynamic Candidate Solution (DCS) is used to overcome the premature convergence problem in INFO and achieve the appropriate balance between exploration and exploitation ability. The performance of the proposed mINFO based on the k-Nearest Neighbor (kNN) classifier is evaluated on the complex CEC'22 test suite and applied to predict Chronic Kidney Disease (CKD) on datasets extracted from UCI. The statistical results revealed the superiority of mINFO compared with several well-known MH algorithms, including the Harris Hawks Optimization (HHO), the Hunger Games Search (HGS) algorithm, the Moth-Flame Optimization (MFO) algorithm, the Whale Optimization Algorithm (WOA), the Sine Cosine Algorithm (SCA), the Gradient-Based Optimizer (GBO), and the original INFO algorithm. According to our knowledge, this paper is the first of its sort to try employing the proposed mINFO for solving the CEC'22 test suite. Furthermore, the experimental results of mINFO-kNN for classifying two CKD datasets demonstrated its superiority with an overall classification accuracy of 93.17% on two CKD datasets over other competitors.