Objective : Diabetic Retinopathy (DR) is a retinal disease that can cause damage to blood vessels in the eye, that is the major cause of impaired vision or blindness, if not treated early. Manual detection of diabetic retinopathy is time-consuming and prone to human error due to the complex structure of the eye. Methods & Results : various automatic techniques have been proposed to detect diabetic retinopathy from fundus images. However, these techniques are limited in their ability to capture the complex features underlying diabetic retinopathy, particularly in the early stages. In this study, we propose a novel approach to detect diabetic retinopathy using a convolutional neural network (CNN) model. The proposed model extracts features using two different deep learning (DL) models, Resnet50 and Inceptionv3, and concatenates them before feeding them into the CNN for classification. The proposed model is evaluated on a publicly available dataset of fundus images. The experimental results demonstrate that the proposed CNN model achieves higher accuracy, sensitivity, specificity, precision, and f1 score compared to state-of-the-art methods, with respective scores of 96.85%, 99.28%, 98.92%, 96.46%, and 98.65%.

Data preprocessing is crucial in the Machine Learning pipeline because the models’ learning ability directly affects the quality of data and the underlying information acquired from this stage. Nevertheless, surprisingly, there are many alternatives for each transformation task, which makes an inexperienced user overwhelmed. A simple Python-based Auto-preprocessing architecture for Automated Machine Learning is developed to offer automated, interactive, and data-driven support to help the users perform data preprocessing tasks efficiently. The suggested method provides valuable insights into a dataset and can handle standard data preprocessing tasks adeptly. Initially, it detects the data problem and presents it to the end-user using compelling visualizations. Then, it recommends the most effective data cleaning and preparation method to the user after evaluating the state-of-the-art candidate techniques. For evaluation, the proposed architecture is employed on ten different and diverse datasets for automatic data preprocessing before passing it to an ML algorithm. The results are then compared with the results generated by the same ML algorithm but implemented on manually preprocessed data. The results have shown that not only did this approach make the whole process uncomplicated and facile, but it was also able to improve the performance of the model significantly.

Recent technological advancement in wireless communication has led to the invention of wireless body area networks (WBANs), a cutting-edge technology in healthcare applications. WBANs interconnect with intelligent and miniaturized biomedical sensor nodes placed on human body to an unattended monitoring of physiological parameters of the patient. These sensors are equipped with limited resources in terms of computation, storage, and battery power. The data communication in WBANs is a resource hungry process, especially in terms of energy. One of the most significant challenges in this network is to design energy efficient next-hop node selection framework. Therefore, this paper presents a green communication framework focusing on an energy aware link efficient routing approach for WBANs (ELR-W). Firstly, a link efficiency-oriented network model is presented considering beaconing information and network initialization process. Secondly, a path cost calculation model is derived focusing on energy aware link efficiency. A complete operational framework ELR-W is developed considering energy aware next-hop link selection by utilizing the network and path cost model. The comparative performance evaluation attests the energy-oriented benefit of the proposed framework as compared to the state-of-the-art techniques. It reveals a significant enhancement in body area networking in terms of various energy-oriented metrics under medical environments.

The Internet of Things (IoT) permits billions of objects to communicate with each other. With the rapid growth of its applications, it faces various challenges such as high latency, low processing and storage capacity, and network failure. For this purpose, the fog computing paradigm is associated with IoT to bring the cloud computing paradigm closer to the IoT devices. Thus, it extends the computing, storage, and networking facilities of the cloud computing paradigm toward the edge of the network. However, the fog-based IoT network processes data and stores it locally at IoT devices rather than sending it to the cloud. Further, it also provides a faster response as compared to the cloud. Unfortunately, the characteristics of fog-based IoT networks arise traditional real-time security challenges, which may increase severe concern to the end-users. However, this paper aims to focus on fog-based IoT communication, targeting real-time security challenges. In this paper, we review the layered architecture of fog-based IoT networks along working of IoT applications under the fog computing paradigm. Furthermore, we demonstrate real-time security challenges as well as review the existing possible solutions used to address the real-time security challenges. In the end, the research challenges are discussed along with the future research directions in the fog-based IoT networks that should be addressed by the research community.

Due to the advancement in industry 4.0, cybercrimes in internet-based business applications are rapidly increasing. To encounter such attacks, the intruders use various tools and techniques such as Phishing, Malware, SQL Injection, Ransomware, Cross-Site Scripting (XSS), Denial of Service (DoS), Session Hijacking, and Credential Reuse. Phishing is most commonly in practice in the current digital era. Phishing is a fraudulent attempt to obtain sensitive information from deceiving users. These types of cyber-attacks are used to harm individuals or entire businesses depending on the targeted objective of the attacker. To combat these attacks, the research community provides various phishing detection techniques. In this article, we classify state-of-the-art fishing detection techniques in five major categories, that are, (1) Data Mining, Deep Learning & Machine Learning-Based Approach, (2) Search Engine-Based Approach, (3) URL Scan-Based Approach, (4) Blacklisting-Whitelisting Approach, and (5) Visual Similarity-Based Approach. Further, a comparative analysis is provided within each category concerning their strength and weaknesses. Moreover, the article provides phishing prevention best practices to the readers to avoid such attacks.