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.

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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.

Requirement Elicitation is key activity of requirement engineering and has a strong impact on design and other phases of software development life cycle. Poor requirement engineering practices lead to project failure. A sound requirement elicitation process is the foundation for the overall quality of software product. Due to criticality and high impact of this phase on overall success and failure of projects, it is very necessary to perform the requirements elicitation activities in a perfect and specific manner. The most difficult and demanding jobs during Requirement Elicitation phase is to select appropriate and specific technique from a wide array of techniques and tools. In this paper, a new approach is proposed using an artificial neural network for selection of requirement elicitation technique from a wide variety of tools and techniques that are available. The training of Neural Network is done by back propagation algorithm. The trained and resultant network can be used as a base for selection of requirement elicitation techniques.

In this review, the paper furnishes object identification's relationship with video investigation and picture understanding, it has pulled in much exploration consideration as of late. Customary item identification strategies are based on high-quality highlights and shallow teachable models. This survey paper presents one such strategy which is named as Optical Flow method. This strategy is discovered to be stronger and more effective for moving item recognition and the equivalent has been appeared by an investigation in this review paper. Applying optical stream to a picture gives stream vectors of the focus-es comparing to the moving items. Next piece of denoting the necessary moving object of interest checks to the post preparation. Post handling is the real commitment of the review paper for moving item identification issues. Their presentation effectively deteriorates by developing complex troupes which join numerous low-level picture highlights with significant level setting from object indicators and scene classifiers. With the fast advancement in profound learning, all the more useful assets, which can learn semantic, significant level, further highlights, are acquainted with address the issues existing in customary designs. These models carry on contrastingly in network design, preparing system, and advancement work, and so on In this review paper, we give an audit on pro-found learning-based item location systems. Our survey starts with a short presentation on the historical backdrop of profound learning and its agent device, in particular Convolutional Neural Network (CNN).