Daily Archives: October 3, 2026

Authors: Monika Jaiswal

Abstract: The present study examines the effectiveness of recruitment metrics in the IT sector in Delhi NCR, focusing on their role in improving hiring efficiency, recruitment decisions, quality of hire, consistent recruitment practices, and employee retention. Recruitment metrics such as time to recruit, quality of recruitment, cost per recruitment, offer acceptance ratio, and employee retention provide organisations with measurable information for evaluating recruitment performance. A quantitative research approach was adopted using a descriptive and analytical research design. Primary data were collected through a structured questionnaire using a five-point Likert scale from 450 respondents associated with the IT sector in Delhi NCR. The collected data were analysed using SPSS and Microsoft Excel through frequency, percentage, mean, standard deviation, ANOVA, regression coefficients, Spearman’s rho, and Chi-square analysis. The findings indicate that quality of recruitment was considered the most important recruitment metric by 153 respondents, followed by time to recruit with 117 respondents. Hiring efficiency recorded the highest mean score of 3.97, while quality-of-hire recorded a mean of 3.94. The ANOVA results showed a statistically significant model (F = 26.393, p = 0.002), and all four examined predictors demonstrated significant positive relationships with recruitment effectiveness. The study concludes that systematic application of recruitment metrics can strengthen recruitment efficiency, quality, consistency, and employee retention in the IT sector in Delhi NCR

DOI: http://doi.org/10.5281/zenodo.23121148

Published by: vikaspatanker

Authors: Karupothu Bharath, Assistant Professor T Swapna Sridevi

Abstract: Cloud computing has become a fundamental part of modern digital infrastructure, enabling organizations to deploy applications, store data, and deliver services across highly distributed environments. However, the dynamic nature of cloud systems also creates complex security challenges, including abnormal network activity, unauthorized access, misconfigurations, and emerging vulnerabilities. Traditional security mechanisms often depend on predefined rules and known attack patterns, making them less effective when dealing with previously unseen or continuously evolving threats. This paper presents a machine learning-based approach for strengthening cloud security by combining anomaly detection and vulnerability classification. The proposed framework uses an Autoencoder to identify unusual patterns in cloud-related data and XGBoost to classify detected security events and potential vulnerabilities. The system processes security-relevant information such as network traffic, system logs, user activity, and historical vulnerability records through data preprocessing and feature extraction stages before applying the machine learning models. By combining unsupervised anomaly detection with supervised classification, the framework is designed to provide a more adaptive approach to identifying both familiar and potentially unknown security threats. The proposed solution is implemented as a web-based security framework using Python and Django, providing mechanisms for data management, model evaluation, prediction, and security monitoring. It is intended to reduce dependence on manual analysis while supporting faster identification of suspicious activities and potential vulnerabilities. The framework also provides a foundation for continuous model improvement as new security data becomes available. Overall, the proposed approach demonstrates how machine learning can be integrated into cloud security workflows to support proactive vulnerability detection and improve the adaptability of security monitoring in distributed environments.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue5.153

Published by: vikaspatanker

Authors: Pilli Satya Sri, M Sujana Priya Darshini

Abstract: Finding missing persons is a challenging task that often requires significant time, manual investigation, and coordination between authorities and the public. This paper presents an AI-assisted missing-person identification system that uses face recognition and machine learning to support the search and identification process. The proposed system allows authorized personnel to register missing-person cases by storing details such as name, age, contact information, location, and photographs in a centralized database. The system also provides a user interface through which the public can submit photographs of unidentified or potentially missing persons along with relevant information. The submitted images are processed and compared with the registered missing-person photographs using facial feature matching techniques. When a suitable match is identified, the system provides the corresponding case information and supports notification to the concerned user or authority. The application is implemented using Python, PyQt5, PostgreSQL, and machine learning-based face recognition techniques, providing an integrated platform for case management, image matching, and search assistance. The proposed system aims to reduce manual effort, improve the speed of image-based identification, and support police and public participation in locating missing persons.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue5.152

Published by: vikaspatanker

Authors: Prof. Eswara Rao. P, Dr. Viswanadh Kumar. G

Abstract: Vernacular architecture and climate are engaged in a happy marriage in any indigenously developed settlement. We documented and analyzed a vernacular Palle or village settlement such as Ramanapalli, Chennur which are located in tropical zone of hot and semi-arid climatic Kadapa region of Andhra Pradesh. Palle is a rural settlement in Kadapa district of Rayalaseema, India. It is particularly significant for architectural studies because it has been documented as an example of climate-sensitive vernacular architecture in the Rayalaseema region. The Palle form and structure come from many years of evolution and sharing knowledge. This shows how different physical and non-physical forces like weather, land, religion, culture, money, old building methods, and how people organize themselves have shaped them. These are made up of small groups of homes with thick walls, heavy roofs, small windows, and narrow streets. In villages like Chennur, Ramanapalle, and Rachinayapalle, there are different types of houses, known locally as ‘Chowda or Dante Middelu’. The designs of these houses are usually rectangular or square, built using traditional methods that take into account the local climate. They are well suited to meet the everyday needs of the people living there. The people living in these houses adjust their daily activities to fit the natural qualities of the space around them. The house is designed and built in a way that responds to the hot and dry climate. This type of design is a form of passive architecture that uses local materials, thick walls, small windows, traditional flat roof with high thermal mass, and small vents near the roof but below it. These thick walls and heavy roof help absorb and slow down the transfer of heat. The small windows are placed carefully to reduce the amount of direct sunlight entering. However, the 'House form of Chowda Middelu' in Kadapa district is vanishing because of various reasons in recent years. Previously, this type of architecture and the way people lived were very responsive to the climate. But now, these traditional designs and habits are slowly changing into ones that don't take the climate or local surroundings into account. This research suggests the need for a set of guidelines that can help balance modern development with the traditional building methods used in the area.

DOI: http://doi.org/10.5281/zenodo.23118495

Published by: vikaspatanker

Authors: Mayanja Shafique

Abstract: The study aimed at exploring Coventry University students’ perceptions of factors that influenced them to make healthy or unhealthy dietary choices. A qualitative approach was used to explore students’ perceptions of factors that influenced their dietary choices. Methods and Analysis- Five participants all Coventry University students were recruited for this study using purposive sampling method. One to one semi-structured interviews were conducted to explore students’ perceptions of factors influencing their dietary choices. Students were interviewed using open-ended questions to capture a broader perspective of the phenomena of interest. Interviews were manually transcribed, and an inductive thematic analytical method was used to analyse data which lead to the identification of three major themes. Results- The results indicate that three major themes emerged which included concept and perspectives of healthy and unhealthy dietary choices, perceptions of factors influencing students’ dietary choices and solutions to healthier choices. Concepts and perspectives indicated that students understood the meaning of healthy and unhealthy dietary choices as well as expressing how they understood the concepts. The students also expressed their perceptions of factors that influenced them to make dietary choices which includes socio-economic, lifestyle factors, social and community networks and environmental factors. These include among cost, Time, knowledge, health, Availability, Accessibility, location and weather changes. However, the most influential factors were cost, time, family and health. Possible recommendations were suggested to promote healthier choices including proper planning, advertisement of healthier foods, Eating when necessary and personal responsibility.

DOI: https://doi.org/10.5281/zenodo.23117855

Published by: vikaspatanker