This paper provides an extensive and complete survey on the process of detecting and preventing various types of IoT‐based security attacks. It is designed for software developers, researchers, and practitioners in the Internet of Things field who aim to understand the process of detecting and preventing these attacks. For each entry identified from the list, a brief description is provided along with references where more information can be found. However, We surveyed the current state‐of‐the‐art IoT security solutions and focused on four main aspects: (1) handpicking representative attacks, (2) identifying potential solutions, (3) performing a threat analysis for each attack and solution, and (4) ranking solutions according to the threats they overcome. By adopting this framework, we identified five main categories of defense mechanisms: distributed denial of service detection/prevention, default password protection, encryption mechanisms, intrusion detection/prevention, and anomaly detection. These solutions are relatively mature in terms of utility and usability. However, the security analysis is conducted only concerning specific attacks, which may or may not be relevant to real‐world deployment. Appropriate IoT security solutions should incorporate threat modeling while considering other factors such as resource consumption and implementation effort. Overall, evaluation of IoT security solutions is arduous due to the complexity of IoT OSes, heterogeneous IoT devices (e.g., various hardware platforms), limited availability of open‐source codebases, and restrictive policies towards intellectual property disclosure. In addition, we note that there remains a lack of studies that perform a systematic evaluation of the state‐of‐the‐art in terms of both frameworks/methodologies and mechanisms proposed.

The advanced technology Internet of Things (IoT) visualizes a worldwide, that is, internally connected, networks of smart physical entities. IoT is a promising technology used in several applications including disaster management. In disaster management, the role of IoT is so important and ubiquitous and could be life-saving. This article describes the role of IoT in disaster management. More precisely, it presents IoT-based disaster management for different kind of disasters with a comparison between some solutions that are available in the market. It shows an implementation of some examples of the application of IoT such as early-warning system for fire detection and earthquake and represents some approaches talking about the application, IoT architecture, and focusing of the study on different disasters. This study could be a good guide to stakeholder about the use of IoT technology to secure their smart cities’ infrastructure and to manage disaster and reduce risks.

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.

Malaria is a contagious disease that affects millions of lives every year. Traditional diagnosis of malaria in laboratory requires an experienced person and careful inspection to discriminate healthy and infected red blood cells (RBCs). It is also very time-consuming and may produce inaccurate reports due to human errors. Cognitive computing and deep learning algorithms simulate human intelligence to make better human decisions in applications like sentiment analysis, speech recognition, face detection, disease detection, and prediction. Due to the advancement of cognitive computing and machine learning techniques, they are now widely used to detect and predict early disease symptoms in healthcare field. With the early prediction results, healthcare professionals can provide better decisions for patient diagnosis and treatment. Machine learning algorithms also aid the humans to process huge and complex medical datasets and then analyze them into clinical insights. This paper looks for leveraging deep learning algorithms for detecting a deadly disease, malaria, for mobile healthcare solution of patients building an effective mobile system. The objective of this paper is to show how deep learning architecture such as convolutional neural network (CNN) which can be useful in real-time malaria detection effectively and accurately from input images and to reduce manual labor with a mobile application. To this end, we evaluate the performance of a custom CNN model using a cyclical stochastic gradient descent (SGD) optimizer with an automatic learning rate finder and obtain an accuracy of 97.30% in classifying healthy and infected cell images with a high degree of precision and sensitivity. This outcome of the paper will facilitate microscopy diagnosis of malaria to a mobile application so that reliability of the treatment and lack of medical expertise can be solved.