iosr-JCE   Volume-24 ~ Issue-2 ~ Mar. - Apr. 2022

Abstract:Owing to the symbolic progress in Information Technology (IT), there is a growing awareness that Cloud Computing will soon emerge as the fifth utility. Cloud Computing is the rising model and is regarded more valuable than the conventional models of Computing. Cloud Computing is a collateral, effective, spread, adaptive and compliant models which refer to providing applications, resources (hardware, software, storage and services) in implicit data centers via Internet. Resources in it are dynamic and scalable......

Key Word: National institute of Standards and Technology (NIST), Denial of Service (DoS), Testing as a Service (TaaS), Quality of Service (QoS).

[1]. Balamurugan B. Gnana Shivitha N, Monisha V, Saranya V, "Survey of access control models for Cloud based real time applications",International Conference on Innovation Information in Computing Technologies Chennai, India 2015.
[2]. Chen-Fang Tsai, Shin-Li Lu, Jen-Hsiang Chen, " A New Quality Chart to Improve the Efficiency of Cloud Resource Management", IEEE 12th International Conference on e- Business Engineering, 2015.
[3]. Danilo Ardagna, Giuliano Casale, Michele Ciavotta, Juan F Perez and Weikun Wang, "Quality-of-service in cloud computing: modelling techniques and their applications", Journal of Internet Services and Applications, 2014.
[4]. Te-Shun Chou, "Security threats on Cloud Computing vulnerabilities", International Journal of Computer Science and Information Technology( IJCSIT), 2013.
[5]. Amin, Abolfazl, Mahdi, "Research challenges and prospective business impacts of Cloud Computing", 7th IEEE Conference on Intelligent Data Acquisition and advanced computing systems, 2013

Abstract: Fari is a neuropsychological recovery system that provides solutions to support early detection, diagnosis, and quality of life for patients with Alzheimer's disease and their caregivers. The artificial intelligent system effectively registers and interprets signs of potential dementia, to give proper care long before getting out of hand. It is based on the seven areas of cognitive function which have been summarized in a tabulated form. As people age, they get affected by Alzheimer's disease which involves memory loss and major disorientation. There is a great need to assist the patient age healthy with ICTs to improve monitoring and schedule. There is a table entitled the seven.....

Key Word: Android, Alzheimer's disease, Fari, Health care, IOS, MRI, Older people, Web API, Machine Learning.

[1]. Abbott, R., Orr, N., McGill, P., Whear, R., & Bethel, A. (2019). How do "robopets" impact the health and well-being of residents in care homes? A systematic review of qualitative and quantitative evidence.
[2]. Ambagtsheer, R., Shafiabady, N., Dent, E., Seiboth, C., & Beilby, J. (2020). The application of artificial intelligence(AI) techniques to identify frailty within a residential aged care administrative data set. Int J Med Inform.
[3]. Clinic, M. (2021). Alzheimer's disease.
[4]. Constantino, G., Falavigna, G., Solbiati, M., Casagranda, I., & Sun, B. (2017). Neural networks as a tool to predict syncope risk in the Emergency Department Europace.
[5]. Davis, S., Lasko, T., Chen, G., Siew, E., & Matheny, M. (2017). ncbi. Retrieved from Calibration drift in regression and machine learning models for acute kidney injury: httpd://www.ncbi.nlm.nih.gov/pubmed/28379439

Abstract: The primary medium access control (MAC) technique of 802.11 (Wireless Local Area Networks) is called distributed coordination function (DCF). DCF is a carrier sense multiple access with collision avoidance (CSMA/CA) scheme with binary exponential backoff (BEB), where a node increases its contention window CW upon every packet collision. The performance of an IEEE 802.11 network depends on the operation of this backoff mechanism......

Key Word: 802.11; DCF; CSMA/CA; Contention Window; Backoff; Throughput; RTS/CTS.

[1]. Alberto Leon-Garcia & Indra Widjaja, Communication Networks: Fundamental Concepts and Key Architectures, Second Edition.
[2]. James F. Kurose & Keith W. Ross, Computer Networking, A Top-Down Approach, Fifth Edition.
[3]. Giuseppe Bianchi, "Performance analysis of the IEEE 802.11 distributed coordination function", IEEE Journal on Selected Areas in Communications VOL. 18, NO. 3, MARCH 2000.

Abstract: Background: Object tracking in video surveillance is of great interest for many researchers, which is an important application and emerging research area in machine learning and image processing. Detecting object is a process to find the presence of objects with a bounding box and categories or forms of the placed objects in a picture. This paper provides a review on tracking techniques, their categorization into different types and focuses on important and useful tracking techniques. We review general strategies under literature survey on different techniques and finally state the analysis of possible research directions.

Key Word: Machine Learning; Object Detection; OpenCV; Vehicle Detection

[1]. Wei Hou, Hoekyung Jung, Dongsheng Xia- "Video road vehicle detection and tracking based on OpenCV"-International ConferenceIEEE – 2020
[2]. Apeksha P Kulkarni, Vishwanath P Baligar- "Real Time Vehicle Detection, Tracking and Counting Using Raspberry-Pi"- Proceedings of the Second International Conference on Innovative Mechanisms for Industry Applications (ICIMIA 2020)
[3]. Valanukonda Lakshmi Padmini, Dr. G. Krishna Kishore, Ponnuru Durgamalleswarao, Parasa Teja Sree –"Real Time Automatic Detection of Motorcyclists With and Without a Safety Helmet"- Proceedings of the International Conference on Smart Electronicsand Communication (ICOSEC 2020)
[4]. Neelam Dwivedia,∗ , Dushyant Kumar Singhb, Dharmender Singh Kushwahab-"An Approach for Unattended Object Detection through Contour Formation using Background Subtraction"- Third International Conference on Computing and Network Communications (2020)
[5]. Suraiya Parveen, Javeria Shah- "A Motion Detection System in Python and Opencv"- Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV 2021)

Abstract: The digital divide all over the world cannot be measured by the level of technical know-how alone. Many people get into the hype of new and trending technology, from Smart cars to Smart homes, syncing work, entertainment, and seeming security to a network that is one click away. This study is an attempt to expound on the extant features of digital knowledge, with respect to the security consciousness of tech users. The conglomeration of various IT hardware and software to form a "Smart effect" in homes, medical fields, campuses, and various enterprises is digitalization at its peak, as there are no boundaries between man, his machines, and his environment. Despite the comfort.......

Keywords: IoT, Smart Network, Vulnerability, Malware, Wireless Sensor Network, Microcontroller, Microprocessor, Espionage

[1] I. Butun, P. Österberg, & H. S.-I. C. S., and undefined 2019, "Security of the Internet of Things: Vulnerabilities, attacks, and countermeasures," ieeexplore.ieee.org, p. 1, 2019.
[2] "How to Secure Wireless Sensor Networks - Tech Briefs." https://www.techbriefs.com/component/content/article/tb/supplements/st/features/articles/28527 (accessed Feb. 28, 2022).
[3] "The Top 10 Sensor Types in IoT and their Applications." https://www.nabto.com/iot-sensor-types/ (accessed Feb. 28, 2022).
[4] P. Sethi and S. R. Sarangi, "Internet of Things: Architectures, Protocols, and Applications," J. Electr. Comput. Eng., vol. 2017, 2017, doi: 10.1155/2017/9324035.
[5] B. C. Patel, G. R. Sinha, and N. Goel, "Introduction to sensors," Adv. Mod. Sensors, Nov. 2020, doi: 10.1088/978-0-7503-2707-7CH1.

Abstract: The rapid growth of Women's violence to monitor human activity demands such a system that recognizes the violence and suspicious events automatically. Abnormal and violent action detection has become an active research area of computer vision and image processing to attract new researchers. The relevant literature presents different techniques for the detection and study of such activities from the tag-based image retrieval system proposed in recent years. This research study reviews various state-of-the-art techniques of violence study. Here propose the detection and recognition system of Women's violence and how image tag retrieval is useful for that. The survey shows the system is useful.....

Keywords: Violence Detection (VD), violent behavior, SVM, Action and Activity Recognition(AAR), Data Mining, Time Series Algorithm, Crime prediction(CP), clustering.

[1]. Muhammad Ramzan et.al. A Review on state-of-the-art Violence Detection Techniques EEE Access (Volume: 7) Date of Publication: 30 July 2019Electronic ISSN: 2169-3536 Page(s): 107560 – 107575 DOI: 10.1109/ACCESS.2019.2932114 S. Lavanyaa, D. Akila Crime against Women (CAW) Analysis and Prediction in Tamilnadu Police Using Data Mining Techniques International Journal of Recent Technology and Engineering (IJRTE)ISSN: 2277-3878, Volume-7, Issue-5C, February 2019 Pages-261-265.
[2]. Michela Leone1et.al Social network analysis to characterizewomen victims of violenceLeone et al. BMC Public Health (2019) 19:494 https://doi.org/10.1186/s12889-019-6797-y pages-1-11
[3]. Peipei Zhouet.et.al Violence detection in surveillance video using low-levelfeaturesPublished: October 3, 2018 https://doi.org/10.1371/journal.pone.0203668.
[4]. Donghyeon Wonet.al.Protest Activity Detection and Perceived Violence Estimation from Social Media Images25th ACM International Conference on Multimedia 2017arXiv:1709.06204v1 [cs.MM] 18 Sep 2017ACM ISBN 978-1-4503-4906-2/17/10.
[5]. Rishabh Singh,,PrathameshChuriet.al K-means Clustering Analysis of Crimes on Indian WomenJournal of Cybersecurity and Information Management (JCIM) Vol. 4, No. 1, PP. 5-25, 2020DOI: 10.5281/zenodo.3909955

Abstract: The aim of this Paper is to unravel the matter of modeling video behavior recorded in surveillance videosto be used in online normal behavior recognition and anomaly detection applications. With none manualmarking of the training data collection, a replacement architecture is made for automated behavior profiling andonline anomaly sampling/detection. The subsequent are the core components of the framework supported discrete scene event detection ,a compact and efficient behavior representation method is developed. Modeling each pattern employing a Dynamic Bayesian Network is employed to gauge the similarities betweenbehavior patterns (DBN). A completely unique spectral clustering algorithm supported based on unsupervised model selection and have selection....

Keywords: Dynamic Bayesian Network, Anomaly, CNN, Adaptivevideoconversion

[1]. AbidMehmood et al.," Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks", April 2021.
[2]. Andrew D. et al., "Adaptive Video Compression ForVideo Surveillance Applications" IEEE conference June 2011
[3]. Oluwatoyin P. et al., "Video-Based Abnormal Human Behaviour Recognition—A Review" IEEE conferenceApril 2016
[4]. B Ravi Kiran, et al., "An overview of deep learning-based methods for unsupervised and semi-supervised anomaly detection in videos" arXiv:1801.03149v2 [cs.CV] 30 Jan 2018
[5]. Yan Zhang, et al., "Temporal Human Action Segmentation via Dynamic Clustering" arXiv:1803.05790v2 [cs.CV] 18 Mar 2018

Abstract: Cloud computing is fast growing with high responding to technology. It has lifted the importance of the computer to the entire world. Cloud computing is also defined as Internet-based computing, whereby information, resources, and software, are provided and shared to terminals and portable devices on-demand, just like phone networks. Cloud computing can also be classified as the product or combination of grid computing, distributed computing, parallel computing, and ubiquitous handheld devices. It aims to build and forecast sophisticated service......

[1]. Article in International Journal of Cloud Computing and Services Science (IJ-CLOSER) May 2013
[2]. CloudConsulting (2014) cloud storage overview Retrieved july 11, 2014 from http//www.cloudconsulting.com/storage/
[3]. eLearning Guide (2002) : The eLearning Guide's: Learning solutions, practical applications of Technology for learning.
[4]. Oxford English Dictionary (2012) model 7th ed. Oxford: oxford university press.
[5]. OWASP(2014) cloud Top Ten Security Risks. Retrieved September 11, 2014 from http//www.owasp.org/index.php/category:OWASP_cloud_%E2%80%90%_10_project

Abstract: The Internet of Things (IoT) is an organization of actual items that are inserted with sensors, programming, and organization network to empower them to gather and trade data. IoT can possibly change small cities in various ways, including working on open wellbeing, lessening energy utilization, and expanding productivity in taxpayer supported organizations. The engineering of IoT in small cities can shift contingent upon the particular necessities of the city. Be that as it may, the essential layers framed above are commonly present in all IoT organizations. One of the critical difficulties in conveying IoT in small cities is the expense of framework. The expense of sensors, passages, and organize network can be a critical boundary for small cities. In any case, there are various ways of diminishing the expense of IoT foundation, like utilizing open source programming and shared framework. Another test is the absence of specialized aptitude in small cities. Numerous small cities don't have the in that frame of mind to send and deal with an IoT foundation. Notwithstanding, there are various organizations that can give counseling and execution administrations to assist small cities with conveying IoT.

KEYWORDS: Small, Cities, IoT

[1]. S. Thorat, G. McQueen, and P. T. Luzunaris, "The role of optimal design and application of heat tracing systems to improve the energy conservation in petrochemical facilities," IEEE Trans. Industry Application, vol. 50, pp. 163-173, Jan/Feb. 2014.
[2]. L. Zhao, I. Matsuo, F, Salehi, Y, Zhou and W. Lee, "Development of a real-time web-based power monitoring system for the petrochemical facilities," in Proc. 2018 IEEE 54th I&CPS. Conf.
[3]. K. Malmedal; P. K. Sen and J. Candelaria, Electrical Energy and the Petro-Chemical Industry: Where are we going?. IEEE 58th Annual Petroleum and Chemical Industry Conf., September 2019, PCIC- 01.
[4]. T. Wu, S. Shieh, S. Jang, and C. Liu, "Optimal energy management integration for a petrochemical plant under considerations of uncertain power supplies," IEEE Trans. Power Syst., vol. 20, no. 3, pp. 1431–1439, Aug. 2015.
[5]. Industrial cogeneration/CHP applications. [Online]. Available: http://www.cogeneration.org/chp- primer/industrial-cogeneration-chp-applications