Abstract

n The Internet of things (IoT) represents the next phase of the digital world that is expanding rapidly and transforming the lives of customers. While the Internet does not usually extend beyond the electronic world, connected objects represent the extension of internet of things and places with the adaptations of IOT in personalized services like online banking. Contactless payment technologies are demanding convenient and personalized services in IoT base banking application which provide high quality fast response to their customer anywhere, anytime. Customers are always expecting the highest level of digital security from their banks where machine to machine connectivity helps the organization to collect mass data and exchange of data with the help of sensors and numerous opportunities in banking sector. Banks always need to convert IoT into profitable data which increases their market share and gives better services to their customers. In this study, we would like to find some of the frauds in the banking sector and proposed framework.n

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15. Code for billion. (2017). Evolution of Internet of Things(IoT). [Online]. Blogger. Available from: https://codeforbillion.blogspot.com/2017/10/evolution-of-internet-of-thingsiot.html
16. Bajracharya A, Harvey B, Rawat DB. Recent Advances in Cybersecurity and Fraud Detection in Financial Services: A Survey. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC). 2023 Mar 8; 0368–0374.
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19. Nicola M, Alsafi Z, Sohrabi C, Kerwan A, Al-Jabir A, Iosifidis C, Agha M, Agha R. The socio-economic implications of the coronavirus pandemic (COVID-19): A review. Int J Surg. 2020 Jun 1; 78: 185–93.
20. Bellavista P, Berrocal J, Corradi A, Das SK, Foschini L, Al Jawarneh IM, Zanni A. How Fog Computing Can Support Latency/Reliability‐sensitive IoT Applications: An Overview and a Taxonomy of State‐of‐the‐art Solutions. In: Fog Computing: Theory and Practice. Wiley; 2020 May 5; 139–213.
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26. Khanboubi F, Boulmakoul A, Tabaa M. Impact of digital trends using IoT on banking processes. Procedia Comput Sci. 2019 Jan 1; 151: 77–84.
27. Singh C, Pattanayak D, Dixit D, Antony K, Agarwala M, Kant R, Mukunda S, Nayak S, Makked S, Singh T, Mathur V. Frauds in the Indian banking industry. IIM Bangalore Research Paper. 2016 Mar 2(505).
28. Capgemini. (2015 Mar 4). Enterprise Fraud Management: How Banks Need to Adapt. Enterprise Fraud Management: How Banks Need to Adapt. [Online]. Slideshare.net. Available from: https://www.slideshare.net/capgemini/fraud-management-in-banking-pov

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Abstract

n Solid waste is a major concern for government bodies and is a consequence of development, modernization, and urbanization. It refers to anything that is not a gas or liquid and poses a serious threat to the natural environment. Solid waste is a type of refuse that can accumulate in a certain area for an extended period of time if not properly managed. The waste can persist in the environment until it is removed, incinerated, or carried away by natural forces such as water. Solid waste needs to be disposed of appropriately to prevent environmental pollution and threats to human and animal health. In order to lessen the harmful effects of solid waste on the environment and human health, proper waste management is crucial. This makes it a significant matter in front of sustainable development. Reusing, reducing, and recycling waste is essential for a sustainable society. Waste collection has always been a problem in developing countries because of inadequate infrastructure, and inappropriate route planning. Artificial intelligence can help protect people from hazardous waste. For countering this very issue, we introduce to you our approach towards Solid waste Collection. A Mobile App which gives you a platform to register your complaints and further segregates those complaints on the basis of the severity. The solution focuses on enhancing the current process of municipal bodies and without changing their current work flow. The functionalities of actually giving the validation in the hands of the user makes the solution unique and gives government bodies complete data for tracking the worker as well. Other advancement which we provide countering the existing system is directly connecting the user with the cleanliness workers and hence gives us a working efficiency and removes the middle layer which connects them. Overall performances of our users are well organized and maintained.n

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1. Mohanty SP, Choppali U, Kougianos E. Everything you wanted to know about smart cities: The Internet of things is the backbone. IEEE Consum Electron Mag. 2020 Jul; 5(3): 60–70.
2. Yorozu Y, Hirano M, Oka K, Tagawa Y. Electron spectroscopy studies on magneto-optical media and plastic substrate interface. IEEE Transl J Magn Japan. 1987 Aug; 2(8): 740–741. (Digests 9th Annual Conf. Magnetics Japan. 1982; 301).
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4. Borozdukhin A, Dolinina O, Pechenkin V. Approach to the garbage collection in the “Smart Clean City” project. In2016 4th IEEE International Colloquium on Information Science and Technology (CiSt) 2016 Oct 24 (pp. 918–922). IEEE. .
5. Dolinina O, Pechenkin V, Tarasova V. Dynamic graph visualization approaches for social networks in educational organization. Vestnik SSTU 4 (62), 239–242 (2011). Saratov.
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7. Toppeta D. The Smart City Vision: How Innovation and ICT Can Build Smart, “Livable”, Sustainable Cities. The Innovation Knowledge Foundation. 2010; 5: 1–9. Available from http://www.intaaivn.org/images/cc/Urbanism/background%20documents/Toppet a_Report_005_2010.pdf
8. Kumar N, Swamy C, Nagadarshini K. Efficient Garbage Disposal Management in Metropolitan Cities Using VANETs. J Clean Energy Technol. 2014 Jul; 2(3): 258–262.
9. Badii C, Bellini P, Cenni D, Difino A, Nesi P, Paolucci M. Analysis and assessment of a knowledge based smart city architecture providing service apis. Future Gener Comput Syst. 2017; 75: 14–29.
10. Borozdukhin A, Dolinina O, Pechenkin V. Approach to the garbage collection in the smart clean city project. In Information Science and Technology (CiSt), 2016 4th IEEE International Colloquium on. 2016; 918–922.
11. Carlos BL, Rengifo A, Escobar M. Automatic waste classification using computer vision as an application in colombian high schools. In6th Latin-American Conference on Networked and Electronic Media (LACNEM 2015) 2015 Sep 23 (pp. 1–5). IET.
12. Buck NT, While A. Competitive urbanism and the limits to smart city innovation: The uk future cities initiative. Urban Stud. 2017; 54(2): 13–43.

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Abstract

n This research focuses on the effects of online proctoring in facilitating the online test procedure for prospects, as well as how these tools affect engagement and learnability, as well as a retrospective investigation of other online examinations now in demand and a comparison of both. As a researcher, we learned that online proctoring is essential not only for a smooth examination process, but also for the cautious management of a student’s career. This study was conducted as a result of the trends in the field of online examinations, which necessitates mentorship for students so that they remain aware of the importance of the knowledge they require for their careers and provide an honest exam. This is also the motivation for this study; for each student to perform well in exams, they must acquire knowledge; and for that, strict proctoring is necessary. As a result, it is reasonable to conclude that the capacity to efficiently proctor an online exam is a necessary feature for future scalability in the education and assessment fields. Human proctoring is also an expensive and time-consuming option. We offer a method that does automatic online proctoring without the need for human intervention in this research.n

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1. Hollister KK, Berenson ML. Proctored versus unproctored online exams: Studying the impact of exam environment on student performance. Decision Sciences Journal of Innovative Education. 2009 Jan;7(1):271–94.
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7. iTech India. (2019). Pros and Cons of Open Source vs Proprietary Software. [Online]. Available from: https://itechindia.co/blog/the-pros-and-cons-of-open-source-vs-proprietary-software/
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n In recent years, both the development of Windows application clients and the uses of smart mobile phones have increased significantly. As the number of Windows application users continues to grow, there is a rise in malicious individuals who develop harmful Windows applications with the intent of unlawfully obtaining confidential information and engaging in fraudulent activities. These applications are designed to target vulnerable areas such as mobile banking and digital wallets, aiming to deceive users and misuse their sensitive data. There are so many malicious software, tools, and programmers that are available. However, it is essential to establish a system that is capable and effective for identifying and thwarting freshly developed dangerous programmes written by hackers or programmers. This system should be able to recognise and react to sophisticated threats in an efficient manner. The purpose of this study is to identify fraudulent Windows apps using machine learning techniques.n

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1. Karbab EB, Debbabi M, Derhab A, Mouheb D. MalDozer: Automatic framework for android malware detection using deep learning. Digit Investig. 2018 Mar 1; 24: S48–59.
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n Nowadays, pattern-based image retrieval algorithms are gaining popularity just because of their uniqueness. There are several issues in the previously proposed systems. The proposed system resolves issues highlighted in the literature. Our proposed system is tested on two image datasets ILIDS and PETS 2006. LTDP provides good results as compared to LBP in baggage detection on two classes that either bag is present or not in an image because LTDP works on finding the difference between adjacent neighbors and magnitude pattern which is either 0 or 1 which means either bag is present or not. In addition to LTDP patterns, HOG transformation has also been used for better feature extraction results. The results obtained through ANN are 90% whereas SVM depicts 50% accuracy; and through classification learner, 75% accuracy is obtained.n

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1. Khanam T, Deb K. Baggage Recognition in Occluded Environment using Boosting Technique. KSII Trans Internet Inf Syst (TIIS). 2017; 11(11): 5436–5458.
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11. Wahyono, Jo KH. Detecting and recognizing LED dot matrix text in natural scene images. InEmerging Intelligent Computing Technology and Applications: 9th International Conference, ICIC 2013, Nanning, China, July 28-31, 2013. Proceedings 9 2013 (pp. 153–158). Springer Berlin Heidelberg.
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