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Nagaraj P,1 Deepalakshmi P,1 Romany F Mansour,2 Ahmed Almazroa3 1Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Virudhunagar, Tamil Nadu, India; 2Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Egypt; 3Department of imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Science, Riyadh, Saudi ArabiaCorrespondence: Nagaraj PDepartment of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil, Srivilliputtur, Virudhunagar, Tamil Nadu, 626126, IndiaEmail nagaraj.p@klu.ac.inPurpose: Classification of medical data is essential to determine diabetic treatment options; therefore, the objective of the study was to develop a model to classify the three diabetes type diagnoses according to multiple patient attributes.Methods: Three different datasets are used to develop a novel medical data classification model. The proposed model involved preprocessing, artificial flora algorithm (AFA)-based feature selection, and gradient boosted tree (GBT)-based classification. Then, the processing occurred in two steps, namely, format conversion and data transformation. AFA was applied for selecting features, such as demographics, vital signs, laboratory tests, medications, from the patients’ electronic health records. Lastly, the GBT-based classification model was applied for classifying the patients’ cases to type I, type II, or gestational diabetes mellitus.Results: The effectiveness of the proposed AFA-GBT model was validated using three diabetes datasets to classify patient cases into one of the three different types of diabetes. The proposed model showed a maximum average precision of 91.64%, a recall of 97.46%, an accuracy of 99.93%, an F-score of 94.19%, and a kappa of 96.61%.Conclusion: The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently.Keywords: diabetes, GBT, feature selection, artificial flora, classification

Background and Aims: The use of intrathecal opioids is associated with high risk of pruritis and this may be decreased by adding a low dose of naloxone. This study evaluated the effect of the addition of 20 μg of naloxone to fentanyl-bupivacaine mixture on the incidence of pruritis in pregnant females scheduled for cesarean section (CS)., Material and Methods: Eighty pregnant patients scheduled for CS under spinal anesthesia were randomized to receive either 10 mg of 0.5% hyperbaric bupivacaine (2 ml) plus 25 μg fentanyl (group F) or 10 mg of 0.5% hyperbaric bupivacaine (2 ml) plus 25 μg fentanyl and 20 μg naloxone (group FN). The incidence, onset, duration, site, and severity of pruritis were measured. Furthermore, the postoperative numerical rating scale (NRS) score, the total tramadol rescue analgesia, and the time for the first request of rescue analgesia were recorded., Results: Compared to the F group, the FN group showed a significant decrease in the incidence of pruritis ( P = 0.022), prolongation of the onset of pruritis ( P = 0.006), shortening of the duration of pruritis ( P = 0.029), and decrease in the severity of pruritis ( P = 0.039). Furthermore, the postoperative pain score, the rescue analgesic consumption, and the time for the first request of rescue analgesia were comparable between the two groups ( P > 0.05)., Conclusions: The addition of an ultra-low dose of naloxone (20 μg) to fentanyl-bupivacaine mixture in spinal anesthesia for pregnant females scheduled for CS significantly reduced the incidence of pruritis without having a significant effect on the postoperative analgesia., Competing Interests: There are no conflicts of interest., (Copyright: © 2024 Journal of Anaesthesiology Clinical Pharmacology.)

An improved whale optimization algorithm (SWEWOA) is presented for global optimization issues. Firstly, the sine mapping initialization strategy (SS) is used to generate the population. Secondly, the escape energy (EE) is introduced to balance the exploration and exploitation of WOA. Finally, the wormhole search (WS) strengthens the capacity for exploitation. The hybrid design effectively reinforces the optimization capability of SWEWOA. To prove the effectiveness of the design, SWEWOA is performed in two test sets, CEC 2017 and 2022, respectively. The advantage of SWEWOA is demonstrated in 26 superior comparison algorithms. Then a new feature selection method called BSWEWOA-KELM is developed based on the binary SWEWOA and kernel extreme learning machine (KELM). To verify its performance, 8 high-performance algorithms are selected and experimentally studied in 16 public datasets of different difficulty. The test results demonstrate that SWEWOA performs excellently in selecting the most valuable features for classification problems., 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., (© 2023 The Authors.)

Lupus Nephritis (LN) is a significant risk factor for morbidity and mortality in systemic lupus erythematosus, and nephropathology is still the gold standard for diagnosing LN. To assist pathologists in evaluating histopathological images of LN, a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images. This method is based on an improved Cuckoo Search (CS) algorithm that introduces a Diffusion Mechanism (DM) and an Adaptive β-Hill Climbing (AβHC) strategy called the DMCS algorithm. The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset. In addition, the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images. Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution. According to the three image quality evaluation metrics: PSNR, FSIM, and SSIM, the proposed image segmentation method performs well in image segmentation experiments. Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images., Competing Interests: Conflict of InterestThe authors declare that there is no conflict of interest regarding the publication of the article., (© Jilin University 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed algorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved optimizer with efficient value., 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., (© 2023 Elsevier Ltd. All rights reserved.)

The domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene selection is to minimize computing costs and enhance classification precision. Therefore, this article designs a new wrapper gene selection algorithm named artificial bee bare-bone hunger games search (ABHGS), which is the hunger games search (HGS) integrated with an artificial bee strategy and a Gaussian bare-bone structure to address this issue. To evaluate and validate the performance of our proposed method, ABHGS is compared to HGS and a single strategy embedded in HGS, six classic algorithms, and ten advanced algorithms on the CEC 2017 functions. The experimental results demonstrate that the bABHGS outperforms the original HGS. Compared to peers, it increases classification accuracy and decreases the number of selected features, indicating its actual engineering utility in spatial search and feature selection., (© 2023 The Author(s).)

Explainable Artificial Intelligence (XAI) is a branch of AI that mainly focuses on developing systems that provide understandable and clear explanations for their decisions. In the context of cancer diagnoses on medical imaging, an XAI technology uses advanced image analysis methods like deep learning (DL) to make a diagnosis and analyze medical images, as well as provide a clear explanation for how it arrived at its diagnoses. This includes highlighting specific areas of the image that the system recognized as indicative of cancer while also providing data on the fundamental AI algorithm and decision-making process used. The objective of XAI is to provide patients and doctors with a better understanding of the system's decision-making process and to increase transparency and trust in the diagnosis method. Therefore, this study develops an Adaptive Aquila Optimizer with Explainable Artificial Intelligence Enabled Cancer Diagnosis (AAOXAI-CD) technique on Medical Imaging. The proposed AAOXAI-CD technique intends to accomplish the effectual colorectal and osteosarcoma cancer classification process. To achieve this, the AAOXAI-CD technique initially employs the Faster SqueezeNet model for feature vector generation. As well, the hyperparameter tuning of the Faster SqueezeNet model takes place with the use of the AAO algorithm. For cancer classification, the majority weighted voting ensemble model with three DL classifiers, namely recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM). Furthermore, the AAOXAI-CD technique combines the XAI approach LIME for better understanding and explainability of the black-box method for accurate cancer detection. The simulation evaluation of the AAOXAI-CD methodology can be tested on medical cancer imaging databases, and the outcomes ensured the auspicious outcome of the AAOXAI-CD methodology than other current approaches.

Recently, autonomous systems have received considerable attention amongst research communities and academicians. Unmanned aerial vehicles (UAVs) find useful in several applications like transportation, surveillance, disaster management, and wildlife monitoring. One of the important issues in the UAV system is energy efficiency, which can be resolved by the use of clustering approaches. In addition, high resolution remote sensing images need to be classified for effective decision making using deep learning (DL) models. Though several models are available in the literature, only few approaches have focused on the clustering and classification processes in UAV networks. In this aspect, this paper designs a novel metaheuristic with an adaptive neuro-fuzzy inference system for decision making named MANFIS-DM technique on autonomous UAV systems. The proposed MANFIS-DM technique intends to effectively organize the UAV networks into clusters and then classify the images into appropriate class labels. The proposed MANFIS-DM technique encompasses two major stages namely quantum different evolution based clustering (QDE-C) technique and ANFIS based classification technique. Primarily, the QDE-C technique involves the design of a fitness function involving three parameters namely average distance, distance to UAVs, and UAV degree. Besides, the image classification model involves a set of subprocesses namely DenseNet based feature extraction, Adadelta based hyperparameter optimization, and ANFIS based classification. The design of QDE-C algorithm with classification model for autonomous UAV systems show the novelty of the work. The experimental result analysis of the MANFIS-DM method is carried out against benchmark dataset and the results ensured the enhanced performance of the MANFIS-DM technique over the other methods with the maximum accu y of 99.13%., 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 © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

In recent times, energy related issues have become challenging with the increasing size of data centers. Energy related issues problems are becoming more and more serious with the growing size of data centers. Green cloud computing (GCC) becomes a recent computing platform which aimed to handle energy utilization in cloud data centers. Load balancing is generally employed to optimize resource usage, throughput, and delay. Aiming at the reduction of energy utilization at the data centers of GCC, this paper designs an energy efficient resource scheduling using Cultural emperor penguin optimizer (CEPO) algorithm, called EERS-CEPO in GCC environment. The proposed model is aimed to distribute work load amongst several data centers or other resources and thereby avoiding overload of individual resources. The CEPO algorithm is designed based on the fusion of cultural algorithm (CA) and emperor penguin optimizer (EPO), which boosts the exploitation capabilities of EPO algorithm using the CA, shows the novelty of the work. The EERS-CEPO algorithm has derived a fitness function to optimally schedule the resources in data centers, minimize the operational and maintenance cost of the GCC, and thereby decrease the energy utilization and heat generation. To ensure the improvised performance of the EERS-CEPO algorithm, a wide range of experiments is performed and the experimental outcomes highlighted the better performance over the recent state of art techniques., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.)

Digital dermoscopy is used to identify cancer in skin lesions, and sun exposure is one of the leading causes of melanoma. It is crucial to distinguish between healthy skin and malignant lesions when using computerised lesion detection and classification. Lesion segmentation influences categorization accuracy and precision. This study introduces a novel way of classifying lesions. Hair filters, gel, bubbles, and specular reflection are all options. An improved levelling method is employed in an innovative method for detecting and removing cancerous hairs. The lesion is distinguished from the surrounding skin by the adaptive sigmoidal function; this function considers the severity of localised lesions. An improved technique for identifying a lesion from surrounding tissue is proposed in the article, followed by a classifier and available features that resulted in 94.40% accuracy and 93% success. According to research, the best method for selecting features and classifications can produce more accurate predictions before and during treatment. When the recommended strategy is put to the test using the Melanoma Skin Cancer Dataset, the recommended technique outperforms the alternative., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ragab, Choudhry, Al-Rabia, Binyamin, Aldarmahi and Mansour.)

This paper focuses on the study of Coronavirus Disease 2019 (COVID-19) X-ray image segmentation technology. We present a new multilevel image segmentation method based on the swarm intelligence algorithm (SIA) to enhance the image segmentation of COVID-19 X-rays. This paper first introduces an improved ant colony optimization algorithm, and later details the directional crossover (DX) and directional mutation (DM) strategy, XMACO. The DX strategy improves the quality of the population search, which enhances the convergence speed of the algorithm. The DM strategy increases the diversity of the population to jump out of the local optima (LO). Furthermore, we design the image segmentation model (MIS-XMACO) by incorporating two-dimensional (2D) histograms, 2D Kapur's entropy, and a nonlocal mean strategy, and we apply this model to COVID-19 X-ray image segmentation. Benchmark function experiments based on the IEEE CEC2014 and IEEE CEC2017 function sets demonstrate that XMACO has a faster convergence speed and higher convergence accuracy than competing models, and it can avoid falling into LO. Other SIAs and image segmentation models were used to ensure the validity of the experiments. The proposed MIS-XMACO model shows more stable and superior segmentation results than other models at different threshold levels by analyzing the experimental results., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Recent improvements in current technology have had a significant impact on a wide range of image processing applications, including medical imaging. Classification, detection, and segmentation are all important aspects of medical imaging technology. An enormous need exists for the segmentation of diagnostic images, which can be applied to a wide variety of medical research applications. It is important to develop an effective segmentation technique based on deep learning algorithms for optimal identification of regions of interest and rapid segmentation. To cover this gap, a pipeline for image segmentation using traditional Convolutional Neural Network (CNN) as well as introduced Swarm Intelligence (SI) for optimal identification of the desired area has been proposed. Fuzzy C-means (FCM), K-means, and improvisation of FCM with Particle Swarm Optimization (PSO), improvisation of K-means with PSO, improvisation of FCM with CNN, and improvisation of K -means with CNN are the six modules examined and evaluated. Experiments are carried out on various types of images such as Magnetic Resonance Imaging (MRI) for brain data analysis, dermoscopic for skin, microscopic for blood leukemia, and computed tomography (CT) scan images for lungs. After combining all of the datasets, we have constructed five subsets of data, each of which had a different number of images: 50, 100, 500, 1000, and 2000. Each of the models was executed and trained on the selected subset of the datasets. From the experimental analysis, it is observed that the performance of K-means with CNN is better than others and achieved 96.45% segmentation accuracy with an average time of 9.09 seconds., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Chetna Kaushal et al.)

Cyber physical system (CPS) is a network of cyber and physical elements, which interact with one another in a feedback form. CPS approves critical infrastructure and is treated as essential in day to day since it forms the basis of futuristic smart devices. An increased usage of CPSs poses security as a challenging issue and intrusion detection systems (IDS) can be applied for the identification of network intrusions. The latest advancements in the field of artificial intelligence (AI) and deep learning (DL) enables to design effective IDS models for the CPS environment. At the same time, metaheuristic algorithms can be employed as a feature selection approach in order to reduce the curse of dimensionality. With this motivation, this study develops a novel Poor and Rich Optimization with Deep Learning Model for Blockchain Enabled Intrusion Detection in CPS Environment, called PRO-DLBIDCPS technique. The proposed PRO-DLBIDCPS technique initially introduces an Adaptive Harmony Search Algorithm (AHSA) based feature selection technique for proper selection of feature subsets. For intrusion detection and classification, and attention based bi-directional gated recurrent neural network (ABi-GRNN) model is applied. In addition, the detection efficiency of the ABi-GRNN technique has been enhanced by the use of Poor and rich optimization (PRO) algorithm based hyperparameter optimizer, which resulted in enhanced intrusion detection results. Furthermore, blockchain technology is applied for enhancing security in the CPS environment. In order to demonstrate the enhanced outcomes of the PRO-DLBIDCPS technique, a wide range of simulations was carried out on benchmark dataset and the results reported the better outcomes of the PRO-DLBIDCPS technique in terms of several measures., (© 2022. The Author(s).)

The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the "HAM10000" dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model's classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Md Khairul Islam et al.)

Microvascular problems of diabetes, such as diabetic retinopathy and macular edema, can be seen in the eye's retina, and the retinal images are being used to screen for and diagnose the illness manually. Using deep learning to automate this time-consuming process might be quite beneficial. In this paper, a deep neural network, i.e., convolutional neural network, has been proposed for predicting diabetes through retinal images. Before applying the deep neural network, the dataset is preprocessed and normalised for classification. Deep neural network is constructed by using 7 layers, 5 kernels, and ReLU activation function, and MaxPooling is implemented to combine important features. Finally, the model is implemented to classify whether the retinal image belongs to a diabetic or nondiabetic class. The parameters used for evaluating the model are accuracy, precision, recall, and F1 score. The implemented model has achieved a training accuracy of more than 95%, which is much better than the other states of the art algorithms., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Mahmoud Ragab et al.)

It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model's training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Mahmoud Ragab et al.)

The COVID-19 pandemic has been a disastrous event that has elevated several psychological issues such as depression given abrupt social changes and lack of employment. At the same time, social scientists and psychologists have gained significant interest in understanding the way people express emotions and sentiments at the time of pandemics. During the rise in COVID-19 cases with stricter lockdowns, people expressed their sentiments on social media. This offers a deep understanding of human psychology during catastrophic events. By exploiting user-generated content on social media such as Twitter, people's thoughts and sentiments can be examined, which aids in introducing health intervention policies and awareness campaigns. The recent developments of natural language processing (NLP) and deep learning (DL) models have exposed noteworthy performance in sentiment analysis. With this in mind, this paper presents a new sunflower optimization with deep-learning-driven sentiment analysis and classification (SFODLD-SAC) on COVID-19 tweets. The presented SFODLD-SAC model focuses on the identification of people's sentiments during the COVID-19 pandemic. To accomplish this, the SFODLD-SAC model initially preprocesses the tweets in distinct ways such as stemming, removal of stopwords, usernames, link punctuations, and numerals. In addition, the TF-IDF model is applied for the useful extraction of features from the preprocessed data. Moreover, the cascaded recurrent neural network (CRNN) model is employed to analyze and classify sentiments. Finally, the SFO algorithm is utilized to optimally adjust the hyperparameters involved in the CRNN model. The design of the SFODLD-SAC technique with the inclusion of an SFO algorithm-based hyperparameter optimizer for analyzing people's sentiments on COVID-19 shows the novelty of this study. The simulation analysis of the SFODLD-SAC model is performed using a benchmark dataset from the Kaggle repository. Extensive, comparative results report the promising performance of the SFODLD-SAC model over recent state-of-the-art models with maximum accuracy of 99.65%.

The rapid development of technologies in biomedical research has enriched and broadened the range of medical equipment. Magnetic resonance imaging, ultrasonic imaging, and optical imaging have been discovered by diverse research communities to design multimodal systems, which is essential for biomedical applications. One of the important tools is photoacoustic multimodal imaging (PAMI) which combines the concepts of optics and ultrasonic systems. At the same time, earlier detection of breast cancer becomes essential to reduce mortality. The recent advancements of deep learning (DL) models enable detection and classification the breast cancer using biomedical images. This article introduces a novel social engineering optimization with deep transfer learning-based breast cancer detection and classification (SEODTL-BDC) model using PAI. The intention of the SEODTL-BDC technique is to detect and categorize the presence of breast cancer using ultrasound images. Primarily, bilateral filtering (BF) is applied as an image preprocessing technique to remove noise. Besides, a lightweight LEDNet model is employed for the segmentation of biomedical images. In addition, residual network (ResNet-18) model can be utilized as a feature extractor. Finally, SEO with recurrent neural network (RNN) model, named SEO-RNN classifier, is applied to allot proper class labels to the biomedical images. The performance validation of the SEODTL-BDC technique is carried out using benchmark dataset and the experimental outcomes pointed out the supremacy of the SEODTL-BDC approach over the existing methods., Competing Interests: The authors declare that they have no conflict of interest., (Copyright © 2022 Maha M. Althobaiti et al.)

This research was aimed at presenting performance of 3-dimensional input convolutional neural networks for steady-state visual evoked potential classification in a wireless EEG-based brain-computer interface system. Overall performance of a brain-computer interface system depends on information transfer rate. Parameters such as signal classification accuracy rate, signal stimulator structure, and user task completion time affect information transfer rate. In this study, we used 3 types of signal classification methods that are 1-dimensional, 2-dimensional, and 3-dimensional input convolutional neural network. According to online experiment with using 3-dimensional input convolutional neural network, we reached average classification accuracy rate and average information transfer rate as 93.75% and 58.35 bit/min, respectively. This both results significantly higher than the other methods that we used in experiments. Moreover, user task completion time was reduced with using 3-dimensional input convolutional neural network. Our proposed method is novel and state-of-art model for steady-state visual evoked potential classification., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Zeki Oralhan et al.)

An important aspect of the diagnosis procedure in daily clinical practice is the analysis of dental radiographs. This is because the dentist must interpret different types of problems related to teeth, including the tooth numbers and related diseases during the diagnostic process. For panoramic radiographs, this paper proposes a convolutional neural network (CNN) that can do multitask classification by classifying the X-ray images into three classes: cavity, filling, and implant. In this paper, convolutional neural networks are taken in the form of a NASNet model consisting of different numbers of max-pooling layers, dropout layers, and activation functions. Initially, the data will be augmented and preprocessed, and then, the construction of a multioutput model will be done. Finally, the model will compile and train the model; the evaluation parameters used for the analysis of the model are loss and the accuracy curves. The model has achieved an accuracy of greater than 96% such that it has outperformed other existing algorithms., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Abdullah S. AL-Malaise AL-Ghamdi et al.)

In this modern era, each and everything is computerized, and everyone has their own smart gadgets to communicate with others around the globe without any range limitations. Most of the communication pathways belong to smart applications, call options in smartphones, and other multiple ways, but e-mail communication is considered the main professional communication pathway, which allows business people as well as commercial and noncommercial organizations to communicate with one another or globally share some important official documents and reports. This global pathway attracts many attackers and intruders to do a scam with such innovations; in particular, the intruders generate false messages with some attractive contents and post them as e-mails to global users. This kind of unnecessary and not needed advertisement or threatening mails is considered as spam mails, which usually contain advertisements, promotions of a concern or institution, and so on. These mails are also considered or called junk mails, which will be reflected as the same category. In general, e-mails are the usual way of message delivery for business oriented as well as any official needs, but in some cases there is a necessity of transferring some voice instructions or messages to the destination via the same e-mail pathway. These kinds of voice-oriented e-mail accessing are called voice mails. The voice mail is generally composed to deliver the speech aspect instructions or information to the receiver to do some particular tasks or convey some important messages to the receiver. A voice-mail-enabled system allows users to communicate with one another based on speech input which the sender can communicate to the receiver via voice conversations, which is used to deliver voice information to the recipient. These kinds of mails are usually generated using personal computers or laptops and exchanged via general e-mail pathway, or separate paid and nonpaid mail gateways are available to deal with certain mail transactions. The above-mentioned e-mail spam is considered in many past researches and attains some solutions, but in case of voice-based e-mail aspect, there will be no options to manage such kind of security parameters. In this paper, a hybrid data processing mechanism is handled with respect to both text-enabled and voice-enabled e-mails, which is called Genetic Decision Tree Processing with Natural Language Processing (GDTPNLP). This proposed approach provides a way of identifying the e-mail spam in both textual e-mails and speech-enabled e-mails. The proposed approach of GDTPNLP provides higher spam detection rate in terms of text extraction speed, performance, cost efficiency, and accuracy. These all will be explained in detail with graphical output views in the Results and Discussion., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Safaa S. I. Ismail et al.)

Clinical Decision Support Systems (CDSS) provide an efficient way to diagnose the presence of diseases such as breast cancer using ultrasound images (USIs). Globally, breast cancer is one of the major causes of increased mortality rates among women. Computer-Aided Diagnosis (CAD) models are widely employed in the detection and classification of tumors in USIs. The CAD systems are designed in such a way that they provide recommendations to help radiologists in diagnosing breast tumors and, furthermore, in disease prognosis. The accuracy of the classification process is decided by the quality of images and the radiologist's experience. The design of Deep Learning (DL) models is found to be effective in the classification of breast cancer. In the current study, an Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification (EDLCDS-BCDC) technique was developed using USIs. The proposed EDLCDS-BCDC technique was intended to identify the existence of breast cancer using USIs. In this technique, USIs initially undergo pre-processing through two stages, namely wiener filtering and contrast enhancement. Furthermore, Chaotic Krill Herd Algorithm (CKHA) is applied with Kapur's entropy (KE) for the image segmentation process. In addition, an ensemble of three deep learning models, VGG-16, VGG-19, and SqueezeNet, is used for feature extraction. Finally, Cat Swarm Optimization (CSO) with the Multilayer Perceptron (MLP) model is utilized to classify the images based on whether breast cancer exists or not. A wide range of simulations were carried out on benchmark databases and the extensive results highlight the better outcomes of the proposed EDLCDS-BCDC technique over recent methods.

Background: For pregnant women who require an emergency cesarean section (CS), extending labor epidural analgesia as quickly as feasible to good quality anesthesia is a critical issue. This indicates the presence of functional labor epidural analgesia and reduces the need for general anesthesia. Addition of magnesium increases anesthetic and analgesic qualities of epidural anesthesia., Objectives: The purpose of this trial was to assess the role of adding magnesium sulfate (MgSO 4 ) with levobupivacaine to speed up the conversion of labor epidural analgesia into enough anesthesia for emergency CS., Methods: Fifty parturients were randomly assigned to receive 19.5 mL of levobupivacaine 0.5% with either 0.5 mL of normal saline 0.9% (Group I) or 0.5 mL of MgSO 4 10% (Group II) after receiving labor epidural analgesia. We documented the onset of block (loss of pinprick to T6), number of patients needing additional analgesia, the time needed for sensory and motor blockade to recover, and the adverse effects., Results: The frequency of patients receiving intraoperative supplements was comparable in the study groups (P = 0.491), although the onset of the block was faster in Group II than in Group I (P = 0.000*). Group II took substantially longer to recover from sensory and motor blockade than Group I (P = 0.001* and P = 0.001*, respectively). In both groups, the occurrence of adverse events was similar., Conclusions: Adding 50 mg of MgSO 4 to levobupivacaine 0.5% accelerated the epidural top, and both sensory onset and motor blocks period were prolonged as compared to levobupivacaine alone when extending epidural analgesia for emergency CS., Competing Interests: Conflict of Interests: The authors declare no conflicts of interest., (Copyright © 2022, Author(s).)

Natural computing refers to computational processes observed in nature and human-designed computing inspired by nature. In recent times, data fusion in the healthcare sector becomes a challenging issue, and it needs to be resolved. At the same time, intracerebral haemorrhage (ICH) is the injury of blood vessels on the brain cells, which is mainly liable for stroke. X-rays and computed tomography (CT) scans are widely applied for locating the haemorrhage position and size. Since manual segmentation of the CT scans by planimetry by the use of radiologists is a time-consuming process, deep learning (DL) is used to attain effective ICH diagnosis performance. This paper presents an automated intracerebral haemorrhage diagnosis using fusion-based deep learning with swarm intelligence (AICH-FDLSI) algorithm. The AICH-FDLSI model operates on four major stages namely preprocessing, image segmentation, feature extraction, and classification. To begin with, the input image is preprocessed using the median filtering (MF) technique to remove the noise present in the image. Next, the seagull optimization algorithm (SOA) with Otsu multilevel thresholding is employed for image segmentation. In addition, the fusion-based feature extraction model using the Capsule Network (CapsNet) and EfficientNet is applied to extract a useful set of features. Moreover, deer hunting optimization (DHO) algorithm is utilized for the hyperparameter optimization of the CapsNet and DenseNet models. Finally, a fuzzy support vector machine (FSVM) is applied as a classification technique to identify the different classes of ICH. A set of simulations takes place to determine the diagnostic performance of the AICH-FDLSI model using the benchmark intracranial haemorrhage data set. The experimental outcome stated that the AICH-FDLSI model has reached a proficient performance over the compared methods in a significant way., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Nada M. Alfaer et al.)

Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur's Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Maha M. Althobaiti et al.)

Recently, Internet of Things (IoT) and cloud computing environments become commonly employed in several healthcare applications by the integration of monitoring things such as sensors and medical gadgets for observing remote patients. For availing of improved healthcare services, the huge count of data generated by IoT gadgets from the medicinal field can be investigated in the CC environment rather than relying on limited processing and storage resources. At the same time, earlier identification of chronic kidney disease (CKD) becomes essential to reduce the mortality rate significantly. This study develops an ensemble of deep learning based clinical decision support systems (EDL-CDSS) for CKD diagnosis in the IoT environment. The goal of the EDL-CDSS technique is to detect and classify different stages of CKD using the medical data collected by IoT devices and benchmark repositories. In addition, the EDL-CDSS technique involves the design of Adaptive Synthetic (ADASYN) technique for outlier detection process. Moreover, an ensemble of three models, namely, deep belief network (DBN), kernel extreme learning machine (KELM), and convolutional neural network with gated recurrent unit (CNN-GRU), are performed. Finally, quasi-oppositional butterfly optimization algorithm (QOBOA) is used for the hyperparameter tuning of the DBN and CNN-GRU models. A wide range of simulations was carried out and the outcomes are studied in terms of distinct measures. A brief outcomes analysis highlighted the supremacy of the EDL-CDSS technique on exiting approaches., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Suliman A. Alsuhibany et al.)

At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence (AI) techniques including machine learning (ML), deep learning (DL), and computer vision (CV) approaches find useful for the recognition, analysis, and prediction of COVID-19. Several ML and DL techniques are trained to resolve the supervised learning issue. At the same time, the potential measure of the unsupervised learning technique is quite high. Therefore, unsupervised learning techniques can be designed in the existing DL models for proficient COVID-19 prediction. In this view, this paper introduces a novel unsupervised DL based variational autoencoder (UDL-VAE) model for COVID-19 detection and classification. The UDL-VAE model involved adaptive Wiener filtering (AWF) based preprocessing technique to enhance the image quality. Besides, Inception v4 with Adagrad technique is employed as a feature extractor and unsupervised VAE model is applied for the classification process. In order to verify the superior diagnostic performance of the UDL-VAE model, a set of experimentation was carried out to highlight the effective outcome of the UDL-VAE model. The obtained experimental values showcased the effectual results of the UDL-VAE model with the higher accuracy of 0.987 and 0.992 on the binary and multiple classes respectively., Competing Interests: All the authors of the manuscript declared that there are no potential conflicts of interest., (© 2021 Elsevier B.V. All rights reserved.)

Purpose: We conducted this study to explore the hypothesis that the addition of ketamine to levobupivacaine in ultrasound-guided Transversus Abdominis Plane (TAP) block would result in a better and prolonged duration of postoperative analgesia for patients undergoing abdominoplasty. Material and Methods . This randomized prospective study was conducted on 50 patients who were scheduled for abdominoplasty. TAP block was performed bilaterally for all patients either with levobupivacaine 0.5% 15 ml plus ketamine 0.5 mg/kg in a total volume of 20 ml in the LK group ( n  = 25) or with levobupivacaine 0.5% 15 ml plus 5 ml normal saline in a total volume of 20 ml in the L group on each side., Results: Visual analogue scale (VAS) was significantly lower in the LK group in resting condition at 6, 12, and 16 h postoperatively compared to the L group. On movement, the VAS was significantly lower at 4, 6, 8, 12, 16, and 24 h postoperatively in the LK group compared to the L group. The time for first rescue analgesia was longer in the LK group (18.7 ± 4.8 h) than that in the L group (6.5 ± 2.4 h) with the reduced total amount of rescue morphine in the LK group (1.14 ± 2.2 mg) versus the L group (5.86 ± 3.6 mg). Only six patients in the LK group requested rescue morphine, whereas nineteen patients requested rescue morphine in the L group., Conclusions: In TAP block, adding ketamine 0.5 mg/kg enhanced the analgesic efficacy of levobupivacaine 0.5% in patients undergoing abdominoplasty and reduced the required analgesics postoperatively., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2021 Radwa F. Mansour et al.)

Type 2 diabetes (T2D) is a systematic chronic metabolic condition with abnormal sugar metabolism dysfunction, and its complications are the most harmful to human beings and may be life-threatening after long-term durations. Considering the high incidence and severity at late stage, researchers have been focusing on the identification of specific biomarkers and potential drug targets for T2D at the genomic, epigenomic, and transcriptomic levels. Microbes participate in the pathogenesis of multiple metabolic diseases including diabetes. However, the related studies are still non-systematic and lack the functional exploration on identified microbes. To fill this gap between gut microbiome and diabetes study, we first introduced eggNOG database and KEGG ORTHOLOGY (KO) database for orthologous (protein/gene) annotation of microbiota. Two datasets with these annotations were employed, which were analyzed by multiple machine-learning models for identifying significant microbiota biomarkers of T2D. The powerful feature selection method, Max-Relevance and Min-Redundancy (mRMR), was first applied to the datasets, resulting in a feature list for each dataset. Then, the list was fed into the incremental feature selection (IFS), incorporating support vector machine (SVM) as the classification algorithm, to extract essential annotations and build efficient classifiers. This study not only revealed potential pathological factors for diabetes at the microbiome level but also provided us new candidates for drug development against diabetes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Zhang, Guo, Zeng, Zhang, Chen, Gamarra, Mansour, Escorcia-Gutierrez, Huang and Cai.)

Pregnancy is a complicated and long procedure during one or more offspring development inside a woman. A short period of oxygen shortage after birth is quite normal for most babies and does not threaten their health. However, if babies have to suffer from a long period of oxygen shortage, then this condition is an indication of pathological fetal intolerance, which probably causes their death. The identification of the pathological fetal intolerance from the physical oxygen shortage is one of the important clinical problems in obstetrics for a long time. The clinical syndromes typically manifest five symptoms that indicate that the baby may suffer from fetal intolerance. At present, liquid biopsy combined with high-throughput sequencing or mass spectrum techniques provides a quick approach to detect real-time alteration in the peripheral blood at multiple levels with the rapid development of molecule sequencing technologies. Gene methylation is functionally correlated with gene expression; thus, the combination of gene methylation and expression information would help in screening out the key regulators for the pathogenesis of fetal intolerance. We combined gene methylation and expression features together and screened out the optimal features, including gene expression or methylation signatures, for fetal intolerance prediction for the first time. In addition, we applied various computational methods to construct a comprehensive computational pipeline to identify the potential biomarkers for fetal intolerance dependent on the liquid biopsy samples. We set up qualitative and quantitative computational models for the prediction for fetal intolerance during pregnancy. Moreover, we provided a new prospective for the detailed pathological mechanism of fetal intolerance. This work can provide a solid foundation for further experimental research and contribute to the application of liquid biopsy in antenatal care., Competing Interests: The authors have declared that no competing interests exist.

Background: Management of difficult airway due to laryngeal mass is a major challenge to the anesthesiologists, and awake fiberoptic intubation (AFOI) would be the technique of choice., Objectives: The current study aimed at comparing the effects of administration of dexmedetomidine-propofol or ketofol for sedation during AFOI in terms of intubation conditions, hemodynamic stability, and patients and anesthesiologist's satisfaction., Methods: Eighty adult patients, 18 - 60 years old, ASA (the American Society of Anesthesiologists) physical status I-III, with difficult airway due to laryngeal mass and planned for AFOI were enrolled. Sedation was randomly given to the patients according to their assigned group by either dexmedetomidine-propofol (group D; n = 40) or ketofol (group K; n = 40). Outcome variables included time to reach Ramsay sedation scale (RSS) ≥ 3, intubation time, number of patients in need of rescue propofol, patient's discomfort score, tolerance to endoscopy and intubation, hemodynamic parameters, patients and anesthesiologist's satisfaction, and occurrence of side effects., Results: Time to reach RSS ≥ 3 and intubation time were significantly shorter, and fewer patients required rescue propofol in the K group as compared to the D group (P = 0.000*, and 0.035*, respectively). Higher discomfort score and better tolerance to endoscopy and intubation were noticed in group K than group D, but with no statistical significance (P = 0.132, 0.137, and 0.211, respectively). Patients in group D had significantly lower mean arterial pressure (MAP) and heart rate (HR) after the loading dose till five minutes after intubation (P = 0.000*). There was no significant difference in patients' satisfaction between the two groups (P = 0.687), while anesthesiologist's satisfaction was higher in group K compared with that of group D (P = 0.013*). Cough score as well as incidence of unfavorable respiratory outcomes were not significantly different between the two groups (P = 0.611, 0.348, respectively)., Conclusions: Ketofol and dexmedetomidine-propofol combination were suitable and satisfactory for AFOI. However, ketofol was more advantageous in offering faster onset of sedation, shorter intubation time, stable hemodynamic profile, as well as higher anesthesiologist's satisfaction when compared to dexmedetomidine-propofol., Competing Interests: Conflict of Interests:Authors declared no financial interests related to the material in the manuscript.

The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution. The DR model applies a multilevel optimization measure that incorporates pre-processing, adaptive-learning-based Gaussian mixture model (GMM)-based concept region segmentation, connected component-analysis-based region of interest (ROI) localization, AlexNet DNN-based highly dimensional feature extraction, principle component analysis (PCA)- and linear discriminant analysis (LDA)-based feature selection, and support-vector-machine-based classification to ensure optimal five-class DR classification. The simulation results with standard KAGGLE fundus datasets reveal that the proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy. Comparative analysis with spatial invariant feature transform (SIFT) technique (accuracy-94.40%) based DR feature extraction also confirms that AlexNet DNN-based DR outperforms SIFT-based DR., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest.

Complications caused due to diabetes mellitus result in significant microvasculature that eventually causes diabetic retinopathy (DR) that keeps on increasing with time, and eventually causes complete vision loss. Identifying subtle variations in morphological changes in retinal blood vessels, optic disk, exudates, microaneurysms, hemorrhage, etc., is complicated and requires a robust computer-aided diagnosis (CAD) system so as to enable earlier and efficient DR diagnosis practices. In the majority of the existing CAD systems, functional enhancements have been realized time and again to ensure accurate and efficient diagnosis of DR. In this survey paper, a number of existing literature presenting DR CAD systems are discussed and analyzed. Both traditional and varoius evolutionary approaches, including genetic algorithm, particle swarm optimization, ant colony optimization, bee colony optimization, etc., based DR CAD have also been studied and their respective efficiencies have been discussed. Our survey revealed that evolutionary computing methods can play a vital role for optimizing DR-CAD functional components, such as proprocessing by enhancing filters coefficient, segmentation by enriching clustering, feature extraction, feature selection, and dimensional reduction, as well as classification.

The follow-up of eight patients who were alive and disease-free for at least 12 months following completion of therapy for small cell carcinoma of the lung (SCC) is presented. One patient is alive and well. Five patients (62%), including two with acute leukemia, died of second malignancies. One patient died with late recurrence of SCC, and one patient died of an unexplained neurologic degenerative disease with dementia. It is concluded that patients with apparent cure of SCC are at high risk for serious disorders including second malignancies.