Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival. Deep learning algorithms for skin cancer detection have become popular in recent years. A novel framework based on deep learning is proposed in this study for the multiclassification of skin cancer types such as Melanoma, Melanocytic Nevi, Basal Cell Carcinoma and Benign Keratosis. The proposed model is named as SCDNet which combines Vgg16 with convolutional neural networks (CNN) for the classification of different types of skin cancer. Moreover, the accuracy of the proposed method is also compared with the four state-of-the-art pre-trained classifiers in the medical domain named Resnet 50, Inception v3, AlexNet and Vgg19. The performance of the proposed SCDNet classifier, as well as the four state-of-the-art classifiers, is evaluated using the ISIC 2019 dataset. The accuracy rate of the proposed SDCNet is 96.91% for the multiclassification of skin cancer whereas, the accuracy rates for Resnet 50, Alexnet, Vgg19 and Inception-v3 are 95.21%, 93.14%, 94.25% and 92.54%, respectively. The results showed that the proposed SCDNet performed better than the competing classifiers.

Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for the early diagnosis of tumors, and AI is the primary focus of researchers in the diagnosis of brain tumors. This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods. The primary objective is to explore the performance of deep and federated learning methods and evaluate their accuracy in the diagnosis process. A systematic literature review is provided, discussing the open issues and challenges, which are likely to guide future researchers working in the field of brain tumor diagnosis.

The medical sciences are facing a major problem with the auto-detection of disease due to the fast growth in population density. Intelligent systems assist medical professionals in early disease detection and also help to provide consistent treatment that reduces the mortality rate. Skin cancer is considered to be the deadliest and most severe kind of cancer. Medical professionals utilize dermoscopy images to make a manual diagnosis of skin cancer. This method is labor-intensive and time-consuming and demands a considerable level of expertise. Automated detection methods are necessary for the early detection of skin cancer. The occurrence of hair and air bubbles in dermoscopic images affects the diagnosis of skin cancer. This research aims to classify eight different types of skin cancer, namely actinic keratosis (AKs), dermatofibroma (DFa), melanoma (MELa), basal cell carcinoma (BCCa), squamous cell carcinoma (SCCa), melanocytic nevus (MNi), vascular lesion (VASn), and benign keratosis (BKs). In this study, we propose SNC_Net, which integrates features derived from dermoscopic images through deep learning (DL) models and handcrafted (HC) feature extraction methods with the aim of improving the performance of the classifier. A convolutional neural network (CNN) is employed for classification. Dermoscopy images from the publicly accessible ISIC 2019 dataset for skin cancer detection is utilized to train and validate the model. The performance of the proposed model is compared with four baseline models, namely EfficientNetB0 (B1), MobileNetV2 (B2), DenseNet-121 (B3), and ResNet-101 (B4), and six state-of-the-art (SOTA) classifiers. With an accuracy of 97.81%, a precision of 98.31%, a recall of 97.89%, and an F1 score of 98.10%, the proposed model outperformed the SOTA classifiers as well as the four baseline models. Moreover, an Ablation study is also performed on the proposed method to validate its performance. The proposed method therefore assists dermatologists and other medical professionals in early skin cancer detection.

Contribution: Recently, real-time data warehousing (DWH) and big data streaming have become ubiquitous due to the fact that a number of business organizations are gearing up to gain competitive advantage. The capability of organizing big data in efficient manner to reach a business decision empowers data warehousing in terms of real-time stream processing. A systematic literature review for real-time stream processing systems is presented in this paper which rigorously look at the recent developments and challenges of real-time stream processing systems and can serve as a guide for the implementation of real-time stream processing framework for all shapes of data streams. Background: Published surveys and reviews either cover papers focusing on stream analysis in applications other than real-time DWH or focusing on extraction, transformation, loading (ETL) challenges for traditional DWH. This systematic review attempts to answer four specific research questions. Research Questions: 1)Which are the relevant publication channels for real-time stream processing research? 2) Which challenges have been faced during implementation of real-time stream processing? 3) Which approaches/tools have been reported to address challenges introduced at ETL stage while processing real-time stream for real-time DWH? 4) What evidence have been reported while addressing different challenges for processing real-time stream? Methodology: A systematic literature was conducted to compile studies related to publication channels targeting real-time stream processing/joins challenges and developments. Following a formal protocol, semi-automatic and manual searches were performed for work from 2011 to 2020 excluding research in traditional data warehousing. Of 679,547 papers selected for data extraction, 74 were retained after quality assessment. Findings: This systematic literature highlights implementation challenges along with developed approaches for real-time DWH and big data stream processing systems and provides their comparisons. This study found that there exists various algorithms for implementing real-time join processing at ETL stage for structured data whereas less work for un-structured data is found in this subject matter.

The rapid expansion of the Web of Things (WoT) and the Internet of Things (IoT) has raised security issues, with Denial of Service (DoS) attacks becoming increasingly prevalent. So, the aim of this study is to identify the security concerns in the four architectural layers of the Web of Things, particularly DoS attacks. For this study, existing literature are identified using search queries, and approximately 80 of relevant primary papers published in the recent decade are obtained after a thorough review which helps in addressing our research questions. After finding the relevant primary studies, we applied strict quality evaluation criteria to verify that all studies are evaluated. In addition, a taxonomy of deep learning (DL) techniques is presented on the basis of literature analysis conducted in this research, which is then used to characterize the various security concerns that occur in IoT and WoT systems. The study also examines which DL approaches are used to detect DoS/DDoS attacks in IoT and WoT. Our findings indicate that the optimal form of Intrusion Detection System (IDS) for dealing with DoS attacks is a hybrid IDS, which uses both the signature-based and the anomaly-based IDS. Moreover, DL techniques such as, CNNs and LSTMs, produced excellent results but are still in the development stage in terms of scalability and practical use. This review further highlights the present state of security mechanisms and sets the basis for future research, with an emphasis on refining DL-based techniques and improving the scalability and adaptability of security systems for WoT networks.

With the continuing global pandemic of coronavirus (COVID-19) sickness, it is critical to seek diagnostic approaches that are both effective and rapid to limit the number of people infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The results of recent research suggest that radiological images include important information related to COVID-19 and other chest diseases. As a result, the use of deep learning (DL) to assist in the automated diagnosis of chest diseases may prove useful as a diagnostic tool in the future. In this study, we propose a novel fusion model of hand-crafted features with deep convolutional neural networks (DCNNs) for classifying ten different chest diseases such as COVID-19, lung cancer (LC), atelectasis (ATE), consolidation lung (COL), tuberculosis (TB), pneumothorax (PNET), edema (EDE), pneumonia (PNEU), pleural thickening (PLT), and normal using chest X-rays (CXR). The method that has been suggested is split down into three distinct parts. The first step involves utilizing the Info-MGAN network to perform segmentation on the raw CXR data to construct lung images of ten different chest diseases. In the second step, the segmented lung images are fed into a novel pipeline that extracts discriminatory features by using hand-crafted techniques such as SURF and ORB, and then these extracted features are fused to the trained DCNNs. At last, various machine learning (ML) models have been used as the last layer of the DCNN models for the classification of chest diseases. Comparison is made between the performance of various proposed architectures for classification, all of which integrate DCNNs, key point extraction methods, and ML models. We were able to attain a classification accuracy of 98.20% for testing by utilizing the VGG-19 model with a softmax layer in conjunction with the ORB technique. Screening for COVID-19 and other lung ailments can be accomplished using the method that has been proposed. The robustness of the model was further confirmed by statistical analyses of the datasets using McNemar’s and ANOVA tests respectively.