The complexity of brain tissue requires skillful technicians and expert medical doctors to manually analyze and diagnose Glioma brain tumors using multiple Magnetic Resonance (MR) images with multiple modalities. Unfortunately, manual diagnosis suffers from its lengthy process, as well as elevated cost. With this type of cancerous disease, early detection will increase the chances of suitable medical procedures leading to either a full recovery or the prolongation of the patient’s life. This has increased the efforts to automate the detection and diagnosis process without human intervention, allowing the detection of multiple types of tumors from MR images. This research paper proposes a multi-class Glioma tumor classification technique using the proposed deep-learning-based features with the Support Vector Machine (SVM) classifier. A deep convolution neural network is used to extract features of the MR images, which are then fed to an SVM classifier. With the proposed technique, a 96.19% accuracy was achieved for the HGG Glioma type while considering the FLAIR modality and a 95.46% for the LGG Glioma tumor type while considering the T2 modality for the classification of four Glioma classes (Edema, Necrosis, Enhancing, and Non-enhancing). The accuracies achieved using the proposed method were higher than those reported by similar methods in the extant literature using the same BraTS dataset. In addition, the accuracy results obtained in this work are better than those achieved by the GoogleNet and LeNet pre-trained models on the same dataset.

One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis of this disease involves observing X-ray images of the knee area and classifying it under five grades using the Kellgren–Lawrence (KL) system. This requires the physician’s expertise, suitable experience, and a lot of time, and even after that the diagnosis can be prone to errors. Therefore, researchers in the ML/DL domain have employed the capabilities of deep neural network (DNN) models to identify and classify KOA images in an automated, faster, and accurate manner. To this end, we propose the application of six pretrained DNN models, namely, VGG16, VGG19, ResNet101, MobileNetV2, InceptionResNetV2, and DenseNet121 for KOA diagnosis using images obtained from the Osteoarthritis Initiative (OAI) dataset. More specifically, we perform two types of classification, namely, a binary classification, which detects the presence or absence of KOA and secondly, classifying the severity of KOA in a three-class classification. For a comparative analysis, we experiment on three datasets (Dataset I, Dataset II, and Dataset III) with five, two, and three classes of KOA images, respectively. We achieved maximum classification accuracies of 69%, 83%, and 89%, respectively, with the ResNet101 DNN model. Our results show an improved performance from the existing work in the literature.

It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized personnel and is time consuming and very costly. On the other hand, automatic diagnosis would allow for real-time diagnosis without human intervention resulting in reduced costs. Therefore, the objective of this research is to propose a novel optimized Deep Learning (DL) approach for the automatic classification and diagnosis of COVID-19 pneumonia using X-ray images. For this purpose, a publicly available dataset of chest X-rays on Kaggle was used in this study. The dataset was developed over three stages in a quest to have a unified COVID-19 entities dataset available for researchers. The dataset consists of 21,165 anterior-to-posterior and posterior-to-anterior chest X-ray images classified as: Normal (48%), COVID-19 (17%), Lung Opacity (28%) and Viral Pneumonia (6%). Data Augmentation was also applied to increase the dataset size to enhance the reliability of results by preventing overfitting. An optimized DL approach is implemented in which chest X-ray images go through a three-stage process. Image Enhancement is performed in the first stage, followed by Data Augmentation stage and in the final stage the results are fed to the Transfer Learning algorithms (AlexNet, GoogleNet, VGG16, VGG19, and DenseNet) where the images are classified and diagnosed. Extensive experiments were performed under various scenarios, which led to achieving the highest classification accuracy of 95.63% through the application of VGG16 transfer learning algorithm on the augmented enhanced dataset with freeze weights. This accuracy was found to be better as compared to the results reported by other methods in the recent literature. Thus, the proposed approach proved superior in performance as compared with that of other similar approaches in the extant literature, and it made a valuable contribution to the body of knowledge. Although the results achieved so far are promising, further work is planned to correlate the results of the proposed approach with clinical observations to further enhance the efficiency and accuracy of COVID-19 diagnosis.

The threat of malware in the Internet of Things (IoT) is ever-present given that many IoT systems today rely on the Android operating system. There has been a consistent rise in Android malware recently, with new variants adopting sophisticated detection avoidance techniques, including various forms of obfuscation. Hence, there is a need to improve the effectiveness of Android malware detection as obfuscation becomes more prevalent in the wild. In this article, we present a novel approach for obfuscated malware detection in IoT Android applications based on the visualization of app executables with Markov images. The app images are trained using a convolutional neural network (CNN) to detect obfuscated malware and for the identification of the obfuscation type. We evaluate the performance of the proposed system by experimenting with four different classification models using 12000 Android applications. The CNN model created to distinguish between malware and benign apps obtained an accuracy of 99.41%. The model for identifying obfuscated malware from benign applications obtained 99.65% accuracy while the model created to identify obfuscated malware from non-obfuscated malware yielded an accuracy of 99.81%. The model for classifying obfuscated malware into 14 different obfuscation categories obtained an accuracy of 99.67%. These results show that CNN models trained from Markov images generated using application byte code can be highly effective for obfuscated malware detection and classification. Moreover, our proposed system provides a more sustainable and cost-effective method for obfuscated malware detection compared to the manual feature-engineering-based approaches that are more prevalent in the current literature.

Malicious botnet applications have become a serious threat and are increasingly incorporating sophisticated detection avoidance techniques. Hence, there is a need for more effective mitigation approaches to combat the rise of Android botnets. Although the use of Machine Learning to detect botnets has been a focus of recent research efforts, several challenges remain. To overcome the limitations of using hand-crafted features for Machine-Learning-based detection, in this paper, we propose a novel mobile botnet detection system based on features extracted from images and a manifest file. The scheme employs a Histogram of Oriented Gradients and byte histograms obtained from images representing the app executable and combines these with features derived from the manifest files. Feature selection is then applied to utilize the best features for classification with Machine-Learning algorithms. The proposed system was evaluated using the ISCX botnet dataset, and the experimental results demonstrate its effectiveness with F1 scores ranging from 0.923 to 0.96 using popular Machine-Learning algorithms. Furthermore, with the Extra Trees model, up to 97.5% overall accuracy was obtained using an 80:20 train–test split, and 96% overall accuracy was obtained using 10-fold cross validation.

A democratic election is a crucial event in any country. Therefore, the government of the country is concerned with creating more competitive and fairer elections. This paper discusses the survey and scope of Blockchain technology adoptions in conducting elections. A distributed digital ledger is used in the Blockchain technology that is utilized for recording transactions happening between two parties. Ledger conducts this processing in an efficient and effective manner with latest secure mechanism of encryption algorithms. Therefore, the data stored in several blocks in each transaction is secure, transparent, and tamper-proof, which ultimately improves the transparency and voter confidentiality. This paper demonstrates how the benefits of the Blockchain technology such as immutability, transparency and end-to-end verifiability can be utilized by the national governments around the world to ensure fair democratic elections. In short, we aim to present a rigorous mechanism of a Blockchain based e-voting system, its efficiency based on different consensus algorithms and the overall progress and analysis based on some critical parameters to anticipate the feasibility of the successful implementation of the proposed e-voting system.