Volume 12 | Issue 7 |

 Abstract

This paper explores the evolutionary trajectory of security mechanisms in Mobile Ad-hoc Networks (MANETs), charting the progress from traditional solutions to contemporary approaches. The review encompasses the challenges posed by the dynamic and decentralized nature of MANETs, emphasizing the need for robust security protocols. It delves into early encryption techniques and authentication methods, highlighting their limitations in addressing modern threats. The abstract then navigates through the emergence of intrusion detection systems and cryptographic advancements, outlining their contributions to enhancing network security. Furthermore, it explores recent innovations such as machine learning and fuzzy logic integration, illustrating their potential in mitigating complex security challenges. By examining the historical development and current state of security mechanisms in MANETs, this abstract provides insights into the evolving strategies that researchers employ to fortify these networks against a spectrum of threats, fostering a deeper understanding of the dynamic field of mobile ad-hoc network security.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Security protocols, Intrusion detection, Trust management, Routing security, Key management.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Employee retention is a business-management term referring to efforts by employers to retain current employees in their workforce. Effective employee retention is a systematic effort by employers to create and foster an environment that encourages current employees to remain employed by having policies and practices in place that address their diverse needs. Also of concern are the costs of employee turnover (including hiring costs, training costs and productivity loss). Replacement costs usually are 2.5 times the salary of the individual. The costs associated with turnover may include lost customers and business and damaged morale. In addition there are the hard costs of time spent in screening, verifying credentials, references, interviewing, hiring and training the new employee.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Employee Retention, HR Practices, Strategies, Employee Attrition.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The present research work unfurls the knowledge, attitude and practice of waste disposal by rural households. The rural households have knowledge and attitude regarding proper waste disposal but environment friendly practices of waste disposals have turned out to be insufficient and inadequate. The study has found out that there is significant correlation between Knowledge and attitude regarding waste disposal of the households, but the correlation between practice and knowledge and practice and attitude is comparatively weaker. The study further found out that the association between knowledge and practice and attitude and practice is not visible among the rural households. To promote sustainable housing government should initiate constant awareness classes and consecutive follow ups should be undertaken on waste disposal of the rural households.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Waste disposal, Rural households, Knowledge, Attitude, Practice, Household waste, Sustainable housing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This literature review carefully explores the important factors shaping the success of Over-The-Top (OTT) platforms in the dynamic digital media landscape. Thoroughly analysing key elements--Accessibility and Device Compatibility, Freemium Models, Content Variety and Quality, Cross Promotion and Bundled Services, Targeted Marketing and Personalization, Partnerships and Collaborations, and Social Media Engagement--the study provides a comprehensive understanding of the intricate OTT ecosystem. In the ever-evolving OTT landscape, ensuring seamless user experiences across diverse devices becomes imperative, with Accessibility and Device Compatibility playing a pivotal role. Freemium Models, balancing free and premium content, strategically contribute to user acquisition and retention. The critical determinants of Content Variety and Quality significantly influence viewer satisfaction and loyalty. Strategic considerations like Cross Promotion and Bundled Services enhance user engagement by capitalizing on synergies between different content offerings. Further refining user experiences, Targeted Marketing and Personalization tailor content recommendations to individual preferences. The enhancing of OTT platforms' reach is achieved through Partnerships and Collaborations, fostering a broader audience base. Social Media Engagement emerges as a dynamic force, facilitating user interaction, feedback, and content discovery. This exhaustive literature review offers valuable insights for stakeholders in the OTT landscape, providing a guide for strategic decision-making to optimize success in managing the competitive digital media environment effectively.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

OTT Platforms, Digital Media Landscape, Factors, Strategic Decision-Making, Competitive Digital Media Environment.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Ovarian cancer remains a strong foe in the arena of women's health, ranking as one of the major causes of cancer-related death, especially when not discovered early. The current diagnostic landscape is heavily reliant on a multifaceted approach involving surgical interventions, ancestral lineage assessments, imaging techniques such as ultrasound and CT-Scans, and specialized blood tests such as CA125, all of which aim to differentiate between benign and malignant ovarian tumors. Early identification of ovarian cancer is critical, and developing machine learning tools provide potential prospects. The ability of machine learning to comprehend complicated data and provide accurate forecasts has the potential to revolutionize the diagnosis and therapy of ovarian cancer. Notably, numerous machine learning algorithms, like as Naive Bayes and Simple Regression, have demonstrate and their diagnostic capability in the diagnosis of ovarian cancer, with accuracies of 89.25% and 88.17%, respectively, across multiple repositories. The continuing study's major goal is to investigate and use various machine learning algorithms in the detection of ovarian cancer. This study aims to demonstrate a wide range of machine learning approaches designed for the early and accurate detection of both malignant and benign tumors linked with ovarian cancer. The investigation seeks not only to improve diagnosis accuracy, but also to shorten the procedure, potentially improving the efficacy of early interventions and personalized treatment paths in the field of ovarian cancer management.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Ovarian Cancer detection

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Men and women are far from equality. Women are lagging behind men in all the fields, be in economic status, political or social status. This inequality between men and women is so wide that, the Equality in gender has become a global challenge. To bring equality special opportunity has to be provided particularly to women to uplift their status. For this issue to be addressed can be with the help of an essential tool of gender budgeting. Gender Responsive Budgeting (GRB) help Advance Gender Equality and women empowerment through Fiscal Policy. It is a technique to promote the gender equality and resolve the gender gaps. This study examines the concept, strategies, impacts, and challenges of GRB. The Present paper aims to provide insights into how GRB can effectively promote women's empowerment, social inclusion, and economic development.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Key words: Gender Responsive Budgeting, Women, Empowerment, Impact, Challenges.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper utilizes data mining techniques for the deduction of lung cancer, a critical step in early diagnosis and treatment planning. Leveraging advanced algorithms, including decision trees and clustering methods, significant factors influencing lung cancer occurrence are identified from a comprehensive dataset. By analyzing patient demographics, lifestyle factors, and medical history, predictive models are developed to accurately classify individuals at risk. The study aims to enhance early detection efforts, potentially reducing mortality rates and healthcare burdens associated with lung cancer. Overall, the research contributes to the advancement of preventive healthcare strategies through effective data analysis and mining techniques.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Lung Cancer, Data Mining, Early Detection, Predictive Modeling, Healthcare.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper explores the significance and methods of effectively utilizing historical data in software testing defect prediction. With the growing complexity of software systems, predicting and preventing defects has become paramount in ensuring software quality. Leveraging historical data, such as past defects and testing outcomes, can provide valuable insights into potential vulnerabilities and areas of improvement. The abstract delves into various approaches and techniques employed in harnessing historical data for defect prediction, including machine learning algorithms, statistical analysis, and data mining methodologies. Furthermore, it investigates the challenges and limitations associated with utilizing historical data in software testing, such as data quality issues, feature selection, and model validation. By synthesizing existing research findings and methodologies, this literature survey aims to provide a comprehensive understanding of how historical data can be effectively leveraged to enhance software testing defect prediction strategies, ultimately leading to improved software quality and reliability.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Software testing, Defect prediction, Software maintenance, data analysis, Regression analysis, Predictive modeling, Feature selection, Data mining

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The purpose of this study is to assess the variation in productivity of industry workers with a better understanding of the impacts of adverse ambient conditions viz. humid or dry atmosphere, thermal stress, poor illumination, excessive noise or poor ventilation. Once the impact of ambient conditions on the overall organizational productivity may be understood, the working area comfort for the workers may be more emphasized. Environmental parameters have been measured both during day and night time, and wherever required, Time Weighted Average (TWA) have been implied to obtain accurate results. Productivity models were further used to analyze the collected data. The model results demonstrated that poor environmental parameters decrease worker's productivity, whereas ambient quality comfort at workplace could have resulted in an improved productivity.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

productivity, industry workers, Environmental parameters, workplace

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study evaluates various feature selection techniques for Music Emotion Recognition (MER) by leveraging Mel Frequency Cepstral Coefficients (MFCC) and implementing both Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). Experiments were carried out using a labeled dataset of music samples categorized into five distinct emotions. The impact of various MFCC configurations on SVM-based and CNN-based MER performance was analyzed. Results provide insights into optimal MFCC parameter selection for improved accuracy in MER systems. This research contributes to advancing the field of MER and provides guidelines for enhancing emotion classification in music. Furthermore, the proposed research demonstrates the importance of considering the emotional nuances present in music by utilizing a diverse dataset with multiple emotion categories. By encompassing emotions such as Devotional, Happy, Romantic, Party, and Sad, our study captures a wide range of emotional states expressed through music. This comprehensive approach enables a more thorough understanding of the complexities involved in music emotion recognition and enhances the applicability of the findings in real-world scenarios. The results of this research can lay the groundwork for creating more precise and resilient MER systems, which could enhance fields such as music recommendation, affective computing, and interactive music experiences

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

MFCC (Mel Frequency, Cepstral Coefficients), SVM (Support Vector Machine), CNN (Convolutional Neural Network)

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Creative Commons Attribution 4.0 and The Open Definition