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568 results on '"P. Suneetha"'

63. Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set

Author

Anton, Simon Duque, Kanoor, Suneetha, Fraunholz, Daniel, and Schotten, Hans Dieter

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

In the context of the Industrial Internet of Things, communication technology, originally used in home and office environments, is introduced into industrial applications. Commercial off-the-shelf products, as well as unified and well-established communication protocols make this technology easy to integrate and use. Furthermore, productivity is increased in comparison to classic industrial control by making systems easier to manage, set up and configure. Unfortunately, most attack surfaces of home and office environments are introduced into industrial applications as well, which usually have very few security mechanisms in place. Over the last years, several technologies tackling that issue have been researched. In this work, machine learning-based anomaly detection algorithms are employed to find malicious traffic in a synthetically generated data set of Modbus/TCP communication of a fictitious industrial scenario. The applied algorithms are Support Vector Machine (SVM), Random Forest, k-nearest neighbour and k-means clustering. Due to the synthetic data set, supervised learning is possible. Support Vector Machine and k-nearest neighbour perform well with different data sets, while k-nearest neighbour and k-means clustering do not perform satisfactorily., Comment: This is a preprint of a work published in the Proceedings of the 13th International Conference on Availability, Reliability and Security (ARES 2018)

Published
2019
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77. Modified Apriori Graph Algorithm for Frequent Pattern Mining

Author

Yuvraj, Pritish and R, Suneetha K.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Databases
Abstract

Web Usage Mining is an application of Data Mining Techniques to discover interesting usage patterns from web data in order to understand and better serve the needs of web-based applications. The paper proposes an algorithm for finding these usage patterns using a modified version of Apriori Algorithm called Apriori-Graph. These rules will help service providers to predict, which web pages, the user is likely to visit next. This will optimize the website in terms of efficiency, bandwidth and will have positive economic benefits for them. The proposed Apriori Graph Algorithm O((V)(E)) works faster compared to the existing Apriori Algorithm and is well suitable for real-time application.

Published
2018

82. DRR Dhan 58, a Seedling Stage Salinity Tolerant NIL of Improved Samba Mahsuri Shows Superior Performance in Multi-location Trials

Author

Rekha, G., Abhilash Kumar, V., Gokulan, C. G., Koushik, M. B. V. N., Laxmi Prasanna, B., Kulkarni, Swapnil, Aleena, D., Harika, G., Hajira, S. K., Pranathi, K., Punniakoti, E., Kale, R. R., Dilip Kumar, T., Ayyappa, D., Anila, M., Sinha, Pragya, Manohara, K. K., Padmavathi, G., Subba Rao, L. V., Laha, G. S., Srinivas Prasad, M. S., Fiyaz, R. A., Suneetha, K., Balachandran, S. M., Patel, Hitendra Kumar, Sonti, Ramesh V., Senguttuvel, P., and Sundaram, R. M.

Published
2022
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91. Optimal Kernel-based Extreme Learning and Multi-objective Function-aided Task Scheduling for Solving Load Balancing Problems in Cloud Environment

Author

Gugulothu, Ravi, Thommandru, Vijaya Saradhi, and Bulla, Suneetha

Abstract

Workload balancing in cloud computing is not yet resolved, particularly considering Infrastructure as a Service (IaaS) in the cloud network. The problem of being underloaded or overloaded should not occur at the time of the server or host accessing the cloud which may lead to create system crash problem. Thus, to resolve these existing problems, an efficient task scheduling algorithm is required for distributing the tasks over the entire feasible resources, which is termed load balancing. The load balancing approach assures that the entire Virtual Machines (VMs) are utilized appropriately. So, it is highly essential to develop a load-balancing model in a cloud environment based on machine learning and optimization strategies. Here, the computing and networking data is utilized for the analysis to observe the traffic as well as performance patterns. The acquired data is offered to the machine learning decision to select the right server by predicting the performance effectively by employing an Optimal Kernel-based Extreme Learning Machine (OK-ELM) and their parameter is tuned by the developed hybrid approach Population Size-based Mud Ring Tunicate Swarm Algorithm (PS-MRTSA). Further, effective scheduling is performed to resolve the load balancing issues by employing the developed model MR-TSA. Here, the developed approach effectively resolves the multi-objective constraints such as Response time, Resource cost, and energy consumption. Thus, the recommended load balancing model securesan enhanced performance rate than the traditional approaches over several experimental analyses.

Published
2025
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92. Cash vs. food assistance to improve adherence to antiretroviral therapy among HIV-infected adults in Tanzania

Author

McCoy, Sandra I, Njau, Prosper F, Fahey, Carolyn, Kapologwe, Ntuli, Kadiyala, Suneetha, Jewell, Nicholas P, Dow, William H, and Padian, Nancy S

Subjects
Infectious Diseases, Pediatric, Prevention, Behavioral and Social Science, Clinical Research, Pediatric AIDS, Clinical Trials and Supportive Activities, HIV/AIDS, Zero Hunger, Adult, Anti-Retroviral Agents, Food Assistance, HIV Infections, Humans, Medication Adherence, Remuneration, Tanzania, adherence, cash transfers, food assistance, food security, HIV infection, impact evaluation, retention, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Virology
Abstract

ObjectiveWe evaluated the effectiveness of short-term cash and food assistance to improve adherence to antiretroviral therapy (ART) and retention in care among people living with HIV in Tanzania.MethodsAt three clinics, 805 participants were randomized to three groups in a 3 : 3 : 1 ratio, stratified by site : nutrition assessment and counseling (NAC) and cash transfers (∼$11/month, n = 347), NAC and food baskets (n = 345), and NAC-only (comparison group, n = 113, clinicaltrials.gov NCT01957917). Eligible people living with HIV were at least 18 years, initiated ART 90 days or less prior, and food insecure. Cash or food was provided for 6 or less consecutive months, conditional on visit attendance. The primary outcome was medication possession ratio (MPR ≥ 95%) at 6 months. Secondary outcomes were appointment attendance and loss to follow-up (LTFU) at 6 and 12 months.ResultsThe primary intent-to-treat analysis included 800 participants. Achievement of MPR ≥ 95% at 6 months was higher in the NAC + cash group compared with NAC-only (85.0 vs. 63.4%), a 21.6 percentage point difference [95% confidence interval (CI): 9.8, 33.4, P

Published
2017

96. Unleashing Creativity in Linguistic Discourses through Advertising Skills

Author

Suneetha, Y.

Abstract

Creative fluency is positively correlated with the quantity and quality of talk as well. The fluency gives an indication of the learner's ability to cope with real-time communication. This paper makes a correlative study on nurturing narrative tasks through advertising skills. English advertising exploits from the high adaptability of the English language. English enables the creators of advertisements to use word puns, figurative language, and to mix individual styles and types of texts. The aim of this paper is to explore what is linguistically creative in everyday interactions by analyzing advertising language. The present study has been done by comparing 100 advertising slogans of Indian Brands and International Brands to analyze language of advertising from linguistic aspects. Further, it provides examples to describe the most commonly used linguistic devices and figures of speech in advertising as a base for creative discourses.

Published
2015

97. Trauma Group Therapy with African American Children and Adolescents: A 30-Plus Year Content Analysis

Author

Manyam, Suneetha B. and Davis, Terah L.

Abstract

Group therapy plays a prominent role in trauma healing through resiliency for African American children and adolescents. Understanding how groups can facilitate change for this population is crucial to become a culturally competent counselor. Further, studying the best practices of group work is essential to better serving African Americans. Through a content analysis of scientific literature from 1980 to 2018 on trauma and group work with African American children and adolescents, authors explore the common themes that emerged. Counseling implications from an advocacy and multicultural perspective along with future recommendations are discussed at the end.

Published
2020
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