Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal IJoICT (International Journal on Information and Communication Technology) Indian Journal of Forensic Medicine & Toxicology
Karthikeyan
Unknown Affiliation
Computer Science & IT Public Health

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Familiarity and Desire to Perceive Medical Education Courses among School Students in India- A Pilot Study Nirmal Famila Bettie; Keerthi Narayan.V; Rathika Rai; Mugesh Raj; Karthikeyan; Mohamed Azarutheen
Indian Journal of Forensic Medicine & Toxicology Vol. 15 No. 2 (2021): Indian Journal of Forensic Medicine & Toxicology
Publisher : Institute of Medico-legal Publications Pvt Ltd

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37506/ijfmt.v15i2.14346

Abstract

The number of dentist serving the rural areas is relatively low and the practice of seeking general physiciansfor dental treatments can still be evident in areas where dental services are not accessible. But it is unclearwhether students from suburban regions prefer to pursue dentistry as their first choice of preference. Hence,the present study was aimed to find the interest in dentistry among the aspiring school students, to evaluate ifthe existing exams remain a challenge, from choosing a medical course, and also to assess if the emergenceof COVID-19 has actually increased the popularity of traditional medicines. A survey questionnaire with16 questions was shared through online media to 200 school students both in urban and suburban regionsafter validation for this pilot study. After consent to participate, the responses collected were tabulated andstatistically analyzed. It was observed that only 64% (urban) and 73% (suburban) were aware of dentistrywith a slight increase in student’s choice for traditional medicine courses over allopathic medicine (40%urban, 48% suburban) post pandemic. Within the limitations of this study it is evident that school studentsare less aware of various dentistry courses, and access to training for the exams can motivate the number ofstudents seeking admission to dentistry courses.
Enhancing Cybersecurity Against DDOS Attacks Evaluating Supervised Machine Learning Techniques Janaki; Karthikeyan
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.964

Abstract

An individual or group launches a cyber attack when they intentionally try to get into another person's or group's computer system. Typically, the goal of an attacker is to gain an advantage by interfering with the victim's network. Now that COVID-19 has wreaked havoc on businesses throughout the world, it's cybercriminals' ideal storm. When it comes to cyber threats, Distributed Denial-Of-Service attacks (DDoS) are the most common and dangerous for corporate networks, apps, and services. Distributed denial of service attacks aim to flood a server, service, or network with malicious traffic in an effort to interrupt regular traffic. Financial losses, decreased productivity, damaged brands, worse credit and insurance ratings, damaged relationships with suppliers and customers, and IT budget overruns are all possible outcomes. Developing Network Intrusion Detection Systems (NIDSs) that can reliably foretell DDoS attacks is an urgent issue. This study used the CICDDoS2019 dataset to assess supervised Machine Learning (ML) methods. The machine learning algorithms that were assessed include AdaBoost, Naïve Bayes, MLP-ANN, Random Forest, and SVM. We use the assessment metrics: Area Under the Curve (AUC), Accuracy, F-measure, Precision, and Recall. This study demonstrates that of the algorithms tested, AdaBoost shows the highest promise in detecting DDoS attacks
Co-Authors Janaki Keerthi Narayan.V Mohamed Azarutheen Mugesh Raj Nirmal Famila Bettie Rathika Rai