In this work girl child security system using a GPS tracker in lOT with Thingspeak (Group 1) is going to be designed and compared with the girl child security device using fuzzy classifier (Group 2).: This system ensures the safety and security of the girl child by continuous monitoring of the location details of the girl child. 10 samples were taken for each group. The P¡0.005 signifies the performance of the system. Girl child security system using a GPS tracker in lOT with Thingspeak (Group 1) having mean accuracy 0.4, Standard deviation 0.516 and standard error 0.1633 and the Girl child security device using fuzzy classifier (Group 2) mean accuracy 0.2, Standard deviation 0.422 and standard error 0.133. The significance P¡O.05 shows the goodness in the performance. The girl child security system using a GPS tracker in lOT with Thingspeak significantly performs better than the girl child security device using fuzzy classifier.
In today's fast-growing world, we all are bounded by time and need our things as fast as possible on our tables. So, is the case of tech-domain. The world is viewing a shift in the digital world. It is highly visible that various fields are extending their business from physical to virtual world. An instance for that is that fruits are now not only available in a physical market but can be bought through online platforms. And this shift of the digital world is making the business environment more competitive. Therefore, to be more rapid, effective the older technologies of integration need modification. Hence, the rapid change in older integration technologies are seen nowadays. SOA (service-oriented architecture) does provide theoretical instructions but it rarely meets the expectations when used practically. Therefore, today's companies have to be up to date to provide a smooth and hurdle-less experience to their users. Also, it's a known fact that this digital transformation is not a product of only one technology or applications. It requires multiple technologies and applications to go hand in hand to meet all the requirements of the user. In order to do so, they must bring data from disparate sources to multiple audiences, such as customers, suppliers, and employees, securely, and at scale. That is why now connectivity is seen as an executive concern. But still there are organizations who don't consider connectivity as an important part to lead their whole firm onto the path of success. Earlier approaches were made for fewer endpoints and slower delivery expectations. That's why we need new point to point integrations. IT leaders then must meet two seemingly contradictory goals: they must ensure stability and control over core systems of record, while enabling innovation and rapid iteration of the applications that access those systems of record. This is the challenge now variously referred to as bi - modal or two-speed IT. Existing connectivity approaches are not fit for these new challenges. And using SOAP-based web services technology to implement SOA proved to be a heavyweight approach that was ill-suited then and even more ill-suited now for today's mobile use cases. This research paper highly focuses on the new approaches towards connectivity of multiple technologies and their integration. API - led connectivity approach meets all today's connectivity needs. This research paper would be providing an outline of the core of this approach, implementation's challenges and discuss how IT leaders can realize this vision in their own organizations.
The smart devices intercommunication concept has gained an enormous increase in fame and use with the constant development and research in communication networking, especially in the lives of people who live in highly developed areas. In this concept, a huge number of daily used smart applications are users in exchange of data being collected by smart devices, this may lead to the security and privacy issues. Normal user might not take observation on weakness and threats that initiate by advance technologies. Main aim of the paper it to provide clear image of lOT, attacks that can happen on HAN and there possible solutions to overcome those attacks in future integrated on a single platform. The HAN devices provides the users with continent and comfortable life reducing their physical efforts, by determining the behavioral activities of the.
The article discusses the possibility of political organizations utilizing deepfake technologies. It is observed that deepfakes can affect all levels of public and political life and contribute to the development of several problems, including reputational risks for celebrities and ordinary citizens, the growth of organized crime, and social stability and national security concerns. The sophistication of deepfake technology (DT) has increased significantly. Cybercriminals can now modify sounds, images, and movies to scam individuals and organizations. This growing threat to international institutions and individuals demands our attention. This article discusses deepfakes, their societal benefits, and how deception technology works. The hazards deepfakes pose to enterprises, governments, and legal systems worldwide are highlighted. In addition, the paper will examine potential solutions for deepfakes and end with future research goals. The authors conclude by discussing potential threats, prospects, and key pathsfor state regulation of this content within the framework of broader political and legal instruments to combat the spread of disinformation and fake news.
The objective of the work is Car Loan Forecasting Using an Extreme Logistic Regression algorithm with novel credal sets and K-Nearest Neighbors algorithm. Machine Learning Techniques is an emerging research field that can be used for predictions. When compared to the Extreme Logistic Regression algorithm with novel credal sets, and the K-Nearest neighbors algorithm uses a framework for vehicle loan prediction to predict loan default from banks while clients are facing significant challenges. This research study considers two groups such as an Extreme Logistic Regression algorithm with novel credal sets and K-Nearest Neighbors algorithm. The sample size considered for each of the algorithms is 20. The accuracy, precision, and recall of Logistic Regression Algorithm with Novel Credal Set produce 73.86%, 17.75% and 24.73% respectively in Car Loan Forecasting on the dataset used whereas the Accuracy, Precision, and Recall of K-Nearest Neighbors algorithm produces 67.86%, 16.67%, and 21.86% respectively Car Loan Forecasting default. It shows the statistical significance of (p ¿ 0.001) from the independent sample T-test. When comparing with accuracy, precision, and recall, an Extreme Logistic Regression algorithm outperformed the K-Nearest Neighbors algorithm approach sub-stantially.
