Internet of Medical Things (IoMT) is the collection of medical devices and related applications which link the healthcare IT systems through online computer networks. In the field of diagnosis, medical image classification plays an important role in prediction and early diagnosis of critical diseases. Medical images form an indispensable part of a patient's health record which can be applied to control, handle and treat the diseases. But, classification of images is a challenging task in computer-based diagnostics. In this research article, we have introduced a improved classifier i.e., Optimal Deep Learning (DL) for classification of lung cancer, brain image, and Alzheimer's disease. The researchers proposed the Optimal Feature Selection based Medical Image Classification using DL model by incorporating preprocessing, feature selection and classification. The main goal of the paper is to derive an optimal feature selection model for effective medical image classification. To enhance the performance of the DL classifier, Oppositionbased Crow Search (OCS) algorithm is proposed. The OCS algorithm picks the optimal features from pre-processed images, here Multi-texture, grey level features were selected for the analysis. Finally, the optimal features improved the classification result and increased the accuracy, specificity and sensitivity in the diagnosis of medical images. The proposed results were implemented in MATLAB and compared with existing feature selection models and other classification approaches. The proposed model achieved the maximum performance in terms of accuracy, sensitivity and specificity being 95.22%, 86.45 % and 100% for the applied set of images.

At present times, the real-time requirement on the multiaccess healthcare monitoring system, information mining, and efficient disease diagnosis of health conditions is a difficult process. The recent advances in information technology and the internet of medical things (IoMT) have fostered extensive utilization of the smart system. A complex, 24/7 healthcare service is needed for effective and trustworthy monitoring of patients on a daily basis. To accomplish this need, edge computing and cloud platforms are highly required to satisfy the requirements of smart healthcare systems. This paper presents a new effective training scheme for the deep neural network (DNN), called ETS-DNN model in edge computing enabled IoMT system. The proposed ETS-DNN intends to facilitate timely data collection and processing to make timely decisions using the patterns that exist in the data. Initially, the IoMT devices sense the patient's data and transfer the captured data to edge computing, which executes the ETS-DNN model to diagnose it. The proposed ETS-DNN model incorporates a Hybrid Modified Water Wave Optimization (HMWWO) technique to tune the parameters of the DNN structure, which comprises of several autoencoder layers cascaded to a softmax (SM) layer. The SM classification layer is placed at the end of the DNN to perform the classification task. The HMWWO algorithm integrates the MWWO technique with limited memory Broyden-Fletcher-Goldfarb-Shannon (L-BFGS). Once the ETS-DNN model generates the report in edge computing, then it will be sent to the cloud server, which is then forwarded to the healthcare professionals, hospital database, and concerned patients. The proposed ETS-DNN model intends to facilitate timely data collection and processing to identify the patterns exist in the data. An extensive set of experimental analysis takes place and the results are investigated under several aspects. The simulation outcome pointed out the superior characteristics of the ETS-DNN model over the compared methods.

Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non-uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN) based recognition called OKM-CNN model. The proposed OKM-CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM-CNN model over other compared methods in a considerable way.

No abstract

The booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model. The parameter optimization of the LS-SVM model was performed using hybridization of Grey Wolf Optimization (GWO) with Differential Evolution (DE), called optimal GWO (OGWO) algorithm. The hybridization process is performed to eliminate the local optima problem of GWO and enhance the diversity of the population. To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and experimental analysis was performed on it to verify the predictive results on the time series. The experimental outcome was analyzed in terms of two performance measures namely Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The obtained simulation outcome infers that the OLS-SVM model yielded better predictive outcome of the return rate of financial products.

At present times, financial decisions are mainly based on the classifier technique, which is utilized to allocate a collection of observations into fixed groups. A diverse set of data classifier approaches were presented for forecasting the financial crisis of an institution using the past data. An essential process toward the design of a precise financial crisis prediction (FCP) approach comprises the choice of proper variables (features) that are related to the issues at hand. This is termed as a feature selection (FS) issue that assists to improvise the classifier results. Besides, computational intelligence techniques can be used as a classification model to determine the financial crisis of an organization. In this view, this article introduces a new FS using elephant herd optimization (EHO) with modified water wave optimization (MWWO) algorithm-based deep belief network (DBN) for FCP. The EHO algorithm is applied as a feature selector, and MWWO-DBN is utilized for the classification process. The application of the MWWO algorithm helps to tune the parameters of the DBN model, and the choice of optimal feature subset from the EHO algorithm leads to enhanced classification performance. The experimental results of the proposed model are tested against three benchmark data sets, namely AnalcatData, German Credit, and Australian Credit. The obtained simulation results indicated the superior performance of the proposed model by attaining maximum classification performance.

In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network (ResNet) based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM for COVID-19 Diagnosis. The proposed RCAL-BiLSTM model involves a series of processes namely bilateral filtering (BF) based preprocessing, RCAL-BiLSTM based feature extraction, and softmax (SM) based classification. Once the BF technique produces the preprocessed image, RCAL-BiLSTM based feature extraction process takes place using three modules, namely ResNet based feature extraction, CAL, and Bi-LSTM modules. Finally, the SM layer is applied to categorize the feature vectors into corresponding feature maps. The experimental validation of the presented RCAL-BiLSTM model is tested against Chest-X-Ray dataset and the results are determined under several aspects. The experimental outcome pointed out the superior nature of the RCAL-BiLSTM model by attaining maximum sensitivity of 93.28%, specificity of 94.61%, precision of 94.90%, accuracy of 94.88%, F-score of 93.10% and kappa value of 91.40%.

Mobile edge computing (MEC) becomes popular as it offers cloud services and functionalities to the edge devices, to enhance the quality of service (QoS) of end-users by offloading their computationally intensive tasks. At the same time, the rise in the number of internet of things (IoT) objectives poses considerable cybersecurity issues owing to the latest rise in the existence of attacks. Presently, the development of deep learning and hardware technologies offers a way to detect the present traffic condition, data offloading, and cyber-attacks in edge networks. The utilization of DL models finds helpful in several domains in which the MEC provides the decisive beneficiary of the approach for traffic prediction and attack detection since a large quantity of data generated by IoT devices enables deep models to learn better than shallow approaches. In this view, this paper presents a new DL based traffic prediction with a data offloading mechanism with cyber-attack detection (DLTPDO-CD) technique. The proposed model involves three major processes traffic prediction, data offloading, and attack detection. Initially, bidirectional long short term memory (BiLSTM) based traffic prediction to enable the proficient data offloading process. Then, the adaptive sampling cross entropy (ASCE) technique is executed to maximize the network throughput by making decisions related to offloading users to the WiFi system. Finally, a deep belief network (DBN) optimized by a barnacles mating optimizer (BMO) algorithm called BMO-DBN is applied as a detection tool for cyberattacks in MEC. Extensive simulation is carried out to ensure the proficient performance of the DLTPDO-CD model. The experimental outcome stated the superiority of the presented model over the compared methods under different dimensions.