Browsing by Author "Sayyad Samee"

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Brain Inspired Visual Effects and Animation Psychological Computing Impact in Indian Television Advertisement Pre and Post 2000s
(IOS Press BV, 2021) Gondi Surender Dhanunjay; Pranjal Singh; Sayyad Samee; K. Vengatesan; Abhishek Kumar; Achintya Singhal
Technology in its immense boom in the last decade has made us aware of a lot of ways to increase consumer potential and engagement with different products in various spheres and aspects of production. Taking this idea forward, the main idea of this study is to identify the major visual effects facets being used and how they contributed towards consumer engagement. In this regard, a pilot study was done and then questionnaire has been prepared which was completed by 369 participants between the age group 18-60 years. Hence the main aim of this work is to use statistical data to understand how the last decade has proved beneficial for the Advertising industry through the use of visual effects Statistical analysis is used to interpret the data. © 2021 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
Data mining technique based critical disease prediction in medical field
(IOS Press BV, 2020) J. Preetha; S. Raju; Abhishek Kumar; Sayyad Samee; R. Vengatesan
In the present days' deaths because of some critical disease has become a significant issue in the medical field. Data mining is one of the significant territories of research that is famous in wellbeing associations. Data mining has a functioning job for finding new patterns and examples in the healthcare association which is valuable for every one of the gatherings related to this field. The medical dataset has heterogeneous data as numbers, content, and pictures that can be mined to convey an assortment of helpful data for the physicians. The examples picked up from the medical data can be helpful for the physicians to find diseases, foresee the survivability of the patients after disease, the seriousness of diseases and so forth. The focal point of this paper is to break down the utilization of data mining in medical space and a portion of the systems utilized in critical disease prediction. We have completely reviewed many research papers of data mining identified with some critical disease prediction. © 2020 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
Intrusion detection framework using efficient spectral clustering technique
(IOS Press BV, 2020) K. Vengatesan; Abhishek Kumar; K. Harish Eknath; Sayyad Samee; Rajiv Vincent; V.D.Ambeth Kumar
Developing cyber-security threats are an industrious test for system managers and security specialists as new malware is persistently cleared. Attackers may search for vulnerabilities in commercial items or execute advanced surveillance crusades to comprehend an objective's network and assemble data on security items like firewalls and intrusion detection/avoidance systems (network or host-based). Numerous new assaults will in general be changes of existing ones. In such a situation, rule-based systems neglect to detect the assault, despite the fact that there are minor contrasts in conditions/credits between rules to distinguish the new and existing assault. To detect these distinctions the IDS must have the option to disconnect the subset of conditions that are valid and foresee the feasible conditions (not the same as the first) that must be watched. We have given various techniques to detect intrusions (or anomalies) which are dissipated consistently and structure little clusters of irregular data. To improve the clustering results, the dissipated anomalies are detected and expelled before agent clusters are framed utilizing SC (spectral clustering). For assessment, a manufactured and genuine data set are utilized and our outcomes show that the utilization of SC (spectral clustering) is a promising way to deal with the advancement of an Intrusion Detection System. © 2020 The authors and IOS Press.