Nowadays, there is a growing need for Internet of Things (IoT)-based mobile healthcare applications that help to predict diseases. In recent years, several people have been diagnosed with diabetes, and according to World Health Organization (WHO), diabetes affects 346 million individuals worldwide. Therefore, we propose a noninvasive self-care system based on the IoT and machine learning (ML) that analyses blood sugar and other key indicators to predict diabetes early. The main purpose of this work is to develop enhanced diabetes management applications which help in patient monitoring and technology-assisted decision-making. The proposed hybrid ensemble ML model predicts diabetes mellitus by combining both bagging and boosting methods. An online IoT-based application and offline questionnaire with 15 questions about health, family history, and lifestyle were used to recruit a total of 10221 people for the study. For both datasets, the experimental findings suggest that our proposed model outperforms state-of-the-art techniques.

In hilly areas, landslides can occur due to natural factors such as heavy rainfall, earthquakes, moisture in soil, or man-made factors like unplanned constructions. Landslides can be disastrous leading to a huge loss of property and lives which can be avoided using automatic prediction. Recently, machine learning algorithms have been applied to automatically identify landslides. Numerous feature extraction and classification-based approaches have been implemented on satellite images for semiautomatic detection and prediction of landslides. However, limited research has been done on fully automatic detection with acceptable accuracy. The most challenging task in the classification and prediction of landslides from satellite images is to find an appropriate database for training and yield highly accurate testing results. The primary agenda of a comprehensive study of various techniques used for the detection and classification of landslides using satellite images is to identify the research gap. The secondary objective aims to propose a prototype of novel approach for the same task. Fifty papers based on machine learning and deep learning algorithms from reputed journals are considered for analysis. This article summarizes the performances of different classification techniques from recent literature followed by comparison and discussion with respect to accuracy. Based on the gap identified an effective prototype of the landslide classification approach is proposed. A slightly modified version of the deep learning model ResNet-101 is proposed which yields an accuracy of 96.88% when tested on an augmented Beijing dataset of 770 satellite images. The article also offers the researchers the latest status, overview, and potential avenues of machine and deep learning algorithms for landslide detection. The techniques discussed will serve as a valuable resource for identifying research gaps, guiding new researchers, and fostering innovative exploration in the field of landslide classification using satellite images.

Botnets are conglomerations of traded PCs (bots) that are remotely controlled by its originator (botmaster) under a command-and-control (C&C) foundation. Botnets are the making dangers against cutting edge security. They are the key vehicles for several Internet assaults, for example, spam, distributed denial-of-service (DDoS) attack, rebate distortion, malware spreading, and phishing. This review paper depicts the botnet examined in three domains: preview of botnets, observation, and analysis of botnets, apart from keeping track of them and protecting against them too. We have also attempted to the various ways to indicate differing countermeasures to the botnet dangers and propose future heading for botnet affirmation look into a consolidated report on the energy investigation and future headings for botnet break down are also been presented in this paper.

Introduction: as technology usage grows at an exponential rate, cybersecurity has become a primary concern. Cyber threats have become increasingly advanced and specific, posing a severe risk to individuals, businesses, and even governments. The growing complexity and sophistication of cyber-attacks are posing serious challenges to traditional cybersecurity methods. As a result, machine learning (ML) techniques have emerged as a promising solution for detecting and preventing these attacks. Aim: this research paper offers an extensive examination of diverse machine learning algorithms that have the potential to enhance the intelligence and overall functionality of applications. Methods: the main focus of this study is to present the core principles of distinct machine learning methods and demonstrate their versatile applications in various practical fields such as cybersecurity systems, smart cities, healthcare, e-commerce, and agriculture. By exploring these applications, this paper contributes to the understanding of how machine learning techniques can be effectively employed across different domains. The article then explores the current and future prospects of ML in cybersecurity. Results: this paper highlights the growing importance of ML in cybersecurity and the increasing demand for skilled professionals who can develop and implement ML-based solutions. Conclusion: overall, the present article presents a thorough examination of the role of machine learning (ML) in cybersecurity, as well as its current and future prospects. It can be a valuable source of information for researchers, who seek to grasp the potential of ML in enhancing cybersecurity