Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K-Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used.

The thyroid nodule is one of the endocrine issues caused by an irregular cell development. This rate of survival can be improved by earlier nodule detection. Accordingly, the accurate recognition of the nodule is of the utmost importance in providing powerful results in building the survival rate. The reduction in the accuracy of manual or semiautomatic segmentation methods for thyroid nodule detection is due to many factors, basically, the lack of experience of the sonographer and latency of operation. Most lesion regions in ultrasound images are homogeneous. Therefore, the value of entropy in these regions is high compared to its neighbours. Based on this criterion, a novel procedure for automatically selecting the seed point in thyroid nodule images is proposed. The proposed system consists of three components: neutrosophic image enhancement and speckle reduction to reduce speckle noise and automatic seed selection algorithm extracted from the centre of candidate block in ultrasound thyroid images based on the principle that most of its Higher Order Spectra Entropies (HOSE) from Radon Transform (RT) at different angles are within the range between average and maximum entropies, and the region growing image segmentation is applied with the constant threshold. The performance of proposed automatic segmentation method is compared with other methods in terms of calculating, True Positive (TP) value (96.44 ± 3.01%), False Positive (FP) value (3.55 ± 1.45%), Dice Coefficient (DC) value (92.24 ± 6.47%), Similarity Index (SI) (80.57 ± 1.06%), and Hausdroff Distance (HD) (0.42 ± 0.24 pixels). The proposed system can be considered as an added value to the malignancy diagnosis in thyroid nodule by an endocrinologist.

<p class="Abstract">The amount of data processed and stored in the cloud is growing dramatically. The traditional storage devices at both hardware and software levels cannot meet the requirement of the cloud. This fact motivates the need for a platform which can handle this problem. Hadoop is a deployed platform proposed to overcome this big data problem which often uses MapReduce architecture to process vast amounts of data of the cloud system. Hadoop has no strategy to assure the safety and confidentiality of the files saved inside the Hadoop distributed File system(HDFS). In the cloud, the protection of sensitive data is a critical issue in which data encryption schemes plays avital rule. This research proposes a hybrid system between two well-known asymmetric key cryptosystems (RSA, and Paillier) to encrypt the files stored in HDFS. Thus before saving data in HDFS, the proposed cryptosystem is utilized for encrypting the data. Each user of the cloud might upload files in two ways, non-safe or secure. The hybrid system shows higher computational complexity and less latency in comparison to the RSA cryptosystem alone.</p>

Developing a confident Hadoop essentially a cloud computing is an essential challenge as the cloud. The protection policy can be utilized during various cloud services such as Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS) and also can support most requirements in cloud computing. This event motivates the need of a policy which will control these challenges. Hadoop may be a used policy recommended to beat this big data problem which usually utilizes MapReduce design to arrange huge amounts of information of the cloud system. Hadoop has no policy to ensure the privacy and protection of the files saved within the Hadoop Distributed File System (HDFS). Within the cloud, the safety of sensitive data may be a significant problem within which encryption schemes play an avital rule. This paper proposes a hybrid method between pair well-known asymmetric key cryptosystems (RSA and Rabin) to cipher the files saved in HDFS. Therefore, before storing data in HDFS, the proposed cryptosystem is employed to cipher the information. In the proposed system, the user of the cloud might upload files in two ways, secure or non-secure. The hybrid method presents more powerful computational complexity and smaller latency as compared to the RSA cryptosystem alone.

Novel Coronavirus disease 2019 (COVID-19) is a type of pandemic viruses that cause respiratory tract infection in humans. The clinical imaging of Chest X-Ray (CXR) by Computer Aided Diagnosis (CAD) plays an important role to identify the patients who infected by COVID-19. The objective of this paper presents a Computer Aided Diagnosis (CAD) method for automatically classify 110 frontal CXR images of contagious people according to Normal and COVID-19 infection. The proposed method contains of four phases: image enhancement, feature extraction, feature selection and classification. Gaussian filter is performed to de-noise the images and Adaptive Histogram Equalization (AHE) for image enhancement in pre-processing step for better decision-making process. Local Binary Pattern (LBP) features set are extracted from the dataset. Binary Particle Swarm Optimization (BPSO) is considered to select the clinically relevant features and developing the robust model. The successive features are fed to Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. The experimental results show that the system robustness in classification COVID-19 from Normal images with average accuracy 94.6%, sensitivity 96.2% and specificity 93%.