Enhanced cervical precancerous lesions detection and classification using Archimedes Optimization Algorithm with transfer learning.

Cervical cancer (CC) ranks as the fourth most common form of cancer affecting women, manifesting in the cervix. CC is caused by the Human papillomavirus (HPV) infection and is eradicated by vaccinating women from an early age. However, limited medical facilities present a significant challenge in mid- or low-income countries. It can improve the survivability rate and be successfully treated if the CC is detected at earlier stages. Current technological improvements allow for cost-effective, more sensitive, and rapid screening and treatment measures for CC. DL techniques are widely adopted for the automated detection of CC. DL techniques and architectures are used to detect CC and provide higher detection performance. This study offers the design of Enhanced Cervical Precancerous Lesions Detection and Classification using the Archimedes Optimization Algorithm with Transfer Learning (CPLDC-AOATL) algorithm. The CPLDC-AOATL algorithm aims to diagnose cervical cancer using medical images. At the preliminary stage, the CPLDC-AOATL technique involves a bilateral filtering (BF) technique to eliminate the noise in the input images. Besides, the CPLDC-AOATL technique applies the Inception-ResNetv2 model for the feature extraction process, and the use of AOA chose the hyperparameters. The CPLDC-AOATL technique involves a bidirectional long short-term memory (BiLSTM) model for the cancer detection process. The experimental outcome of the CPLDC-AOATL technique emphasized the superior accuracy outcome of 99.53% over other existing approaches under a benchmark dataset.

Vertical alveolar bone augmentation of atrophied posterior mandibular regions with simultaneous dental implant placement using allogeneic bone rings vs autogenous bone rings: a randomized controlled clinical trial.

OBJECTIVE: Ideal implant placement in atrophied posterior mandibular regions is challenging due to surgical difficulties and anatomical limitations. This study aimed to evaluate the use of allogeneic bone rings for vertical augmentation of atrophied posterior mandibular regions with simultaneous implants compared to autogenous bone rings, while avoiding donor site morbidity. METHOD AND MATERIALS: A total of 24 vertically atrophied posterior mandibular segments (in 14 patients) were equally randomized into a study group in which mineralized freeze-dried allogeneic bone rings were used, and a control group in which autogenous bone rings with prepared implant osteotomies were harvested from the chin and used. All augmentation sites were prepared before inserting the bone rings. Implants were simultaneously inserted, fixing the bone rings into the native bone. All patients were clinically assessed after 1 week, 2 weeks, and 1 month. Crestal bone level was radiographically assessed after 1 week, 6 months, and 3 months of prosthetic loading. RESULTS: None of the 24 bone rings showed signs of implant or graft failure. There was no significant difference in the crestal bone level between the groups. CONCLUSION: Allogeneic bone rings can be a viable alternative to autogenous bone rings in augmenting the posterior aspect of the mandible, mitigating the concerns associated with donor site complications.

Nanozyme as detector and remediator to environmental pollutants: between current situation and future prospective.

The term "nanozyme" refers to a nanomaterial possessing enzymatic capabilities, and in recent years, the field of nanozymes has experienced rapid advancement. Nanozymes offer distinct advantages over natural enzymes, including ease of production, cost-effectiveness, prolonged storage capabilities, and exceptional environmental stability. In this review, we provide a concise overview of various common applications of nanozymes, encompassing the detection and removal of pollutants such as pathogens, toxic ions, pesticides, phenols, organic contaminants, air pollution, and antibiotic residues. Furthermore, our focus is directed towards the potential challenges and future developments within the realm of nanozymes. The burgeoning applications of nanozymes in bioscience and technology have kindled significant interest in research in this domain, and it is anticipated that nanozymes will soon become a topic of explosive discussion.

Binary Hunter-Prey Optimization with Machine Learning-Based Cybersecurity Solution on Internet of Things Environment.

Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth of Internet technology. Phishing becomes a common threat to Internet users, where the attacker aims to fraudulently extract confidential data of the system or user by using websites, fictitious emails, etc. Due to the dramatic growth in IoT devices, hackers target IoT gadgets, including smart cars, security cameras, and so on, and perpetrate phishing attacks to gain control over the vulnerable device for malicious purposes. These scams have been increasing and advancing over the last few years. To resolve these problems, this paper presents a binary Hunter-prey optimization with a machine learning-based phishing attack detection (BHPO-MLPAD) method in the IoT environment. The BHPO-MLPAD technique can find phishing attacks through feature selection and classification. In the presented BHPO-MLPAD technique, the BHPO algorithm primarily chooses an optimal subset of features. The cascaded forward neural network (CFNN) model is employed for phishing attack detection. To adjust the parameter values of the CFNN model, the variable step fruit fly optimization (VFFO) algorithm is utilized. The performance assessment of the BHPO-MLPAD method takes place on the benchmark dataset. The results inferred the betterment of the BHPO-MLPAD technique over compared approaches in different evaluation measures.

Green "turn-off" luminescent nanosensors for the sensitive determination of desperately fluorescent antibacterial antiviral agent and its metabolite in various matrices.

Nitazoxanide (NTX) is an antimicrobial drug that was used for the treatment of various protozoa. However, during the coronavirus pandemic, NTX has been redirected for the treatment of such virus that primarily infect the respiratory tract system. NTX is now used as a broad-spectrum antiviral agent. In this study, a highly sensitive and green spectrofluorometric method was developed to detect NTX in various dosage forms and its metabolite, tizoxanide (TX), in human plasma samples using nitrogen and sulfur co-doped carbon quantum dots nanosensors (C-dots). A simple and eco-friendly hydrothermal method was used to synthetize water soluble C-dots from citric acid and l-cysteine. After excitation at 345 nm, the luminescence intensity was measured at 416 nm. Quenching of C-dots luminescence occurred upon the addition of NTX and was proportional to NTX concentration. Assessment of the quenching mechanism was performed to prove that inner filter effect is the underlying molecular mechanism of NTX quenching accomplished. After optimizing all experimental parameters, the analytical procedure was evaluated and validated using the ICH guidelines. The method linearity, detection and quantification limits of NTX were 15 × 10-3-15.00 µg/mL, 56.00 × 10-4 and 15 × 10-3 µg/mL, respectively. The proposed method was applied for the determination of NTX in its commercial pharmaceutical products; Nanazoxid® oral suspension and tablets. The obtained % recovery, relative standard deviation and % relative error were satisfactory. Comparison with other reported spectrofluorimetric methods revealed the superior sensitivity of the proposed method. Such high sensitivity permitted the selective determination of TX, the main metabolite of NTX, in human plasma samples making this study the first spectrofluorimetric method in literature that determine TX in human plasma samples. Moreover, the method greenness was assessed using both Eco-Scale and AGREE approaches to prove the superiority of the proposed method greenness over other previously published spectrofluorimetric methods for the analysis of NTX and its metabolite, TX, in various dosage forms and in human plasma samples.

Outcomes Of Mesh Fixation Versus Non Fixation In Laparoscopic Transabdominal Preperitoneal Inguinal Hernia Repair: A Randomized Clinical Study.

Objectives: The present study aimed to compare the results of laparoscopic transabdominal preperitoneal (TAPP) inguinal hernia repair with and without mesh fixation regarding postoperative pain, recurrence, operative time, and complications. METHODS: This randomized controlled clinical trial included 100 patients who underwent TAPP inguinal hernia with mesh fixation (group A) or a fixation-free procedure (group B) for early onset inguinal hernia at the General Surgery Department, Kafrelsheikh University Hospital, from January 2021 to June 2022. RESULTS: The parameters for pain assessment (NRS) in the first week (mean 7 (5 - 8)), the first month (mean 3 (1 - 5)), and after three months(mean 0 - (70% of patients), (mean 1- (30% of patients) were significantly higher in the fixation group (p<0.001). The fixation group had significantly more operative time than non fixation, with a mean (69.34±13.55, 60.92±10.18) respectively. Recurrence rate and postoperative complications did not show any significant difference between the studied groups. CONCLUSIONS: Mesh non-fixation for laparoscopic TAPP hernia repair is safe, practical, and effective with minimal postoperative pain and no increased risk of recurrence.

Intelligent Energy-Aware Thermal Exchange Optimization with Deep Learning Model for IoT-Enabled Smart Healthcare.

In recent years, Internet of Things (IoT) and advanced sensor technologies have gained considerable interest in linking different medical devices, patients, and healthcare professionals to improve the quality of medical services in a cost-effective manner. The evolution of the smart healthcare sector has considerably enhanced patient safety, accessibility, and operational competence while minimizing the costs incurred in healthcare services. In this background, the current study develops intelligent energy-aware thermal exchange optimization with deep learning (IEA-TEODL) model for IoT-enabled smart healthcare. The aim of the proposed IEA-TOEDL technique is to group the IoT devices into clusters and make decisions in the smart healthcare sector. The proposed IEA-TEODL technique constructs clusters using the energy-aware chaotic thermal exchange optimization-based clustering (EACTEO-C) scheme. In addition, the disease diagnosis model also intends to classify the collected healthcare data as either presence or absence of the disease. To accomplish this, the proposed IEA-TODL technique involves several subprocesses such as preprocessing, K-medoid clustering-based outlier removal, multihead attention bidirectional long short-term memory (MHA-BLSTM), and weighted salp swarm algorithm (WSSA). The utilization of outlier removal and WSSA-based hyperparameter tuning process assist in achieving enhanced classification outcomes. In order to demonstrate the enhanced outcomes of the IEA-TEODL approach, a wide range of simulations was conducted against benchmark datasets. The simulation results inferred the enhanced outcomes of the IEA-TEODL technique over recent techniques under distinct evaluation metrics.

Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection.

An early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomes and effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. The purpose of the study is to detect LCC effectually in histopathological images. To execute this, the BERTL-HIALCCD method follows the concepts of computer vision (CV) and transfer learning for accurate LCC detection. When using the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER system. For the effectual recognition of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. Finally, the coati optimization algorithm (COA) is exploited for the parameter choice of the DCRNN approach. For examining the efficacy of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images. The experimental outcomes demonstrate that the combination of AER and COA algorithms attain an improved performance in cancer detection over the compared models.

Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing.

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 ß-Hill Climbing (AßHC) 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.

Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images.

Diagnostic and predictive models of disease have been growing rapidly due to developments in the field of healthcare. Accurate and early diagnosis of COVID-19 is an underlying process for controlling the spread of this deadly disease and its death rates. The chest radiology (CT) scan is an effective device for the diagnosis and earlier management of COVID-19, meanwhile, the virus mainly targets the respiratory system. Chest X-ray (CXR) images are extremely helpful in the effective diagnosis of COVID-19 due to their rapid outcomes, cost-effectiveness, and availability. Although the radiological image-based diagnosis method seems faster and accomplishes a better recognition rate in the early phase of the epidemic, it requires healthcare experts to interpret the images. Thus, Artificial Intelligence (AI) technologies, such as the deep learning (DL) model, play an integral part in developing automated diagnosis process using CXR images. Therefore, this study designs a sine cosine optimization with DL-based disease detection and classification (SCODL-DDC) for COVID-19 on CXR images. The proposed SCODL-DDC technique examines the CXR images to identify and classify the occurrence of COVID-19. In particular, the SCODL-DDC technique uses the EfficientNet model for feature vector generation, and its hyperparameters can be adjusted by the SCO algorithm. Furthermore, the quantum neural network (QNN) model can be employed for an accurate COVID-19 classification process. Finally, the equilibrium optimizer (EO) is exploited for optimum parameter selection of the QNN model, showing the novelty of the work. The experimental results of the SCODL-DDC method exhibit the superior performance of the SCODL-DDC technique over other approaches.

A Machine Learning Approach for Monitoring and Classifying Healthcare Data-A Case of Emergency Department of KSA Hospitals.

The Emergency Departments (EDs), in hospitals located in a few important areas in Saudi Arabia, experience a heavy inflow of patients due to viral illnesses, pandemics, and even on a few special occasions events such as Hajj or Umrah, when pilgrims travel from one region to another with severe disease conditions. Apart from the EDs, it is critical to monitor the movements of patients from EDs to other wards inside the hospital or in the region. This is to track the spread of viral illnesses that require more attention. In this scenario, Machine Learning (ML) algorithms can be used to classify the data into many classes and track the target audience. The current research article presents a Machine Learning-based Medical Data Monitoring and Classification Model for the EDs of the KSA hospitals and is named MLMDMC-ED technique. The most important aim of the proposed MLMDMC-ED technique is to monitor and track the patient's visits to the EDs, the treatment given to them based on the Canadian Emergency Department Triage and Acuity Scale (CTAS), and their Length Of Stay (LOS) in the hospital, based on their treatment requirements. A patient's clinical history is crucial in terms of making decisions during health emergencies or pandemics. So, the data should be processed so that it can be classified and visualized in different formats using the ML technique. The current research work aims at extracting the textual features from the patients' data using the metaheuristic Non-Defeatable Genetic Algorithm II (NSGA II). The data, collected from the hospitals, are classified using the Graph Convolutional Network (GCN) model. Grey Wolf Optimizer (GWO) is exploited for fine-tuning the parameters to optimize the performance of the GCN model. The proposed MLMDMC-ED technique was experimentally validated on the healthcare data and the outcomes indicated the improvements of the MLMDMC-ED technique over other models with a maximum accuracy of 91.87%.

Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging.

Prostate cancer (PCa) is becoming one of the most frequently occurring cancers among men and causes an even greater number of deaths. Due to the complexity of tumor masses, radiologists find it difficult to identify PCa accurately. Over the years, several PCa-detecting methods have been formulated, but these methods cannot identify cancer efficiently. Artificial Intelligence (AI) has both information technologies that simulate natural or biological phenomena and human intelligence in addressing issues. AI technologies have been broadly implemented in the healthcare domain, including 3D printing, disease diagnosis, health monitoring, hospital scheduling, clinical decision support, classification and prediction, and medical data analysis. These applications significantly boost the cost-effectiveness and accuracy of healthcare services. This article introduces an Archimedes Optimization Algorithm with Deep Learning-based Prostate Cancer Classification (AOADLB-P2C) model on MRI images. The presented AOADLB-P2C model examines MRI images for the identification of PCa. To accomplish this, the AOADLB-P2C model performs pre-processing in two stages: adaptive median filtering (AMF)-based noise removal and contrast enhancement. Additionally, the presented AOADLB-P2C model extracts features via a densely connected network (DenseNet-161) model with a root-mean-square propagation (RMSProp) optimizer. Finally, the presented AOADLB-P2C model classifies PCa using the AOA with a least-squares support vector machine (LS-SVM) method. The simulation values of the presented AOADLB-P2C model are tested using a benchmark MRI dataset. The comparative experimental results demonstrate the improvements of the AOADLB-P2C model over other recent approaches.

Pharmacological Studies on the Antidiabetic, Antioxidant, and Antimicrobial Efficacies of Commiphora myrrha Resin in Streptozotocin-Induced Diabetes in Rats: A Preclinical Study.

Results: The aqueous extracts of MAE were phytochemically analyzed, and the results revealed the presence of high concentrations of tannins, sterols, and isoprenoids (terpenoids), while steroids and flavonoids were found in moderate concentrations. The plant extract showed promising inhibition of the growth of gram-positive and gram-negative pathogens. It also showed that MAE has potential antihyperglycemic and antioxidant activities. Microscopic examination of the pancreas showed degenerative changes and atrophy associated with dilatation of the exocrine ducts in the STZ-induced diabetic rats, while the treatment revealed that the Langerhans islets were close to normal without any histopathological alteration. Conclusion: The present results suggested that an aqueous extract of MAE could be considered an efficient antidiabetic, antioxidant, and antimicrobial treatment in the future.

4-(5-Amino-pyrazol-1-yl)benzenesulfonamide derivatives as novel multi-target anti-inflammatory agents endowed with inhibitory activity against COX-2, 5-LOX and carbonic anhydrase: Design, synthesis, and biological assessments.

In the current medical era, the single target inhibition paradigm of drug discovery has given way to the multi-target design concept. As the most intricate pathological process, inflammation gives rise to a variety of diseases. There are several drawbacks to the single target anti-inflammatory drugs currently available. Herein, we present the design and synthesis of a novel series of 4-(5-amino-pyrazol-1-yl)benzenesulfonamide derivatives (7a-j) with COX-2, 5-LOX and carbonic anhydrase (CA) inhibitory activities as potential multi-target anti-inflammatory agents. The pharmacophoric 4-(pyrazol-1-yl)benzenesulfonamide moiety in Celecoxib was used as the core scaffold and different substituted phenyl and 2-thienyl tails were grafted via a hydrazone linker to enhance inhibitory activity against hCA IX and XII isoforms, yielding target pyrazoles 7a-j. All reported pyrazoles were evaluated for their inhibitory activity against COX-1, COX-2, and 5-LOX. Pyrazoles 7a, 7b, and 7j showed the best inhibitory activities against the COX-2 isozyme (IC50 = 49, 60 and 60 nM, respectively) and against 5-LOX (IC50 = 2.4, 1.9, and 2.5 µM, respectively) with excellent SI indices (COX-1/COX-2) of 212.24, 208.33, and 158.33, respectively. In addition, the inhibitory activities of pyrazoles 7a-j were evaluated against four different hCA isoforms I, II, IX, and XII. Both transmembrane hCA IX and XII isoforms were potently inhibited by pyrazoles 7a-j with KI values in the nanomolar range; 13.0-82.1 nM and 5.8-62.0 nM, respectively. Furthermore, pyrazoles 7a and 7b with the highest COX-2 activity and selectivity indices were evaluated in vivo for their analgesic, anti-inflammatory, and ulcerogenic activities. The serum level of the inflammatory mediators was then measured in order to confirm the anti-inflammatory activities of pyrazoles 7a and 7b.

A novel metaheuristics with adaptive neuro-fuzzy inference system for decision making on autonomous unmanned aerial vehicle systems.

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 accuy of 99.13%.

Early and accurate detection of melanoma skin cancer using hybrid level set approach.

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.

Automated COVID-19 Classification Using Heap-Based Optimization with the Deep Transfer Learning Model.

The outbreak of the COVID-19 pandemic necessitates prompt identification of affected persons to restrict the spread of the COVID-19 epidemic. Radiological imaging such as computed tomography (CT) and chest X-rays (CXR) is considered an effective way to diagnose COVID-19. However, it needs an expert's knowledge and consumes more time. At the same time, artificial intelligence (AI) and medical images are discovered to be helpful in effectively assessing and providing treatment for COVID-19 infected patients. In particular, deep learning (DL) models act as a vital part of a high-performance classification model for COVID-19 recognition on CXR images. This study develops a heap-based optimization with the deep transfer learning model for detection and classification (HBODTL-DC) of COVID-19. The proposed HBODTL-DC system majorly focuses on the identification of COVID-19 on CXR images. To do so, the presented HBODTL-DC model initially exploits the Gabor filtering (GF) technique to enhance the image quality. In addition, the HBO algorithm with a neural architecture search network (NasNet) large model is employed for the extraction of feature vectors. Finally, Elman Neural Network (ENN) model gets the feature vectors as input and categorizes the CXR images into distinct classes. The experimental validation of the HBODTL-DC model takes place on the benchmark CXR image dataset from the Kaggle repository, and the outcomes are checked in numerous dimensions. The experimental outcomes stated the supremacy of the HBODTL-DC model over recent approaches with a maximum accuracy of 0.9992.

Enhanced Gravitational Search Optimization with Hybrid Deep Learning Model for COVID-19 Diagnosis on Epidemiology Data.

Effective screening provides efficient and quick diagnoses of COVID-19 and could alleviate related problems in the health care system. A prediction model that combines multiple features to assess contamination risks was established in the hope of supporting healthcare workers worldwide in triaging patients, particularly in situations with limited health care resources. Furthermore, a lack of diagnosis kits and asymptomatic cases can lead to missed or delayed diagnoses, exposing visitors, medical staff, and patients to 2019-nCoV contamination. Non-clinical techniques including data mining, expert systems, machine learning, and other artificial intelligence technologies have a crucial role to play in containment and diagnosis in the COVID-19 outbreak. This study developed Enhanced Gravitational Search Optimization with a Hybrid Deep Learning Model (EGSO-HDLM) for COVID-19 diagnoses using epidemiology data. The major aim of designing the EGSO-HDLM model was the identification and classification of COVID-19 using epidemiology data. In order to examine the epidemiology data, the EGSO-HDLM model employed a hybrid convolutional neural network with a gated recurrent unit based fusion (HCNN-GRUF) model. In addition, the hyperparameter optimization of the HCNN-GRUF model was improved by the use of the EGSO algorithm, which was derived by including the concepts of cat map and the traditional GSO algorithm. The design of the EGSO algorithm helps in reducing the ergodic problem, avoiding premature convergence, and enhancing algorithm efficiency. To demonstrate the better performance of the EGSO-HDLM model, experimental validation on a benchmark dataset was performed. The simulation results ensured the enhanced performance of the EGSO-HDLM model over recent approaches.

Prediction of Diabetes through Retinal Images Using Deep Neural Network.

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.

Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model.

Osteosarcoma is a kind of bone cancer which generally starts to develop in the lengthy bones in the legs and arms. Because of an increase in occurrence of cancer and patient-specific treatment options, the detection and classification of cancer becomes a difficult process. The manual recognition of osteosarcoma necessitates expert knowledge and is time consuming. An earlier identification of osteosarcoma can reduce the death rate. With the development of new technologies, automated detection models can be exploited for medical image classification, thereby decreasing the expert's reliance and resulting in timely identification. In recent times, an amount of Computer-Aided Detection (CAD) systems are available in the literature for the segmentation and detection of osteosarcoma using medicinal images. In this view, this research work develops a wind driven optimization with deep transfer learning enabled osteosarcoma detection and classification (WDODTL-ODC) method. The presented WDODTL-ODC model intends to determine the presence of osteosarcoma in the biomedical images. To accomplish this, the osteosarcoma model involves Gaussian filtering (GF) based on pre-processing and contrast enhancement techniques. In addition, deep transfer learning using a SqueezNet model is utilized as a featured extractor. At last, the Wind Driven Optimization (WDO) algorithm with a deep-stacked sparse auto-encoder (DSSAE) is employed for the classification process. The simulation outcome demonstrated that the WDODTL-ODC technique outperformed the existing models in the detection of osteosarcoma on biomedical images.