Volume 12 | Issue 7 |

 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.

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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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 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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 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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 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

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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

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 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

 Abstract

Human resources is the most important factor of an organization. Labourers get wages in exchange for their services. The inefficiency and weakness of labourers directly affect the productivity of the organization even if all other factors of production are favorable. Labor welfare is an important element of the organization in creating a good environment for work. Welfare measures are important to reduce absenteeism and increase efficiency. Working and living conditions of the workers are greatly increased by providing adequate welfare measures. Welfare measures aim to provide various housing schemes, medical benefits, proper and quality education and recreation facilities to the worker's families. All these measures help the families in raising their standards of living. This makes workers pay more attention to work and thus increase their productivity. The main aim of this study is to collect information about the welfare measures provided to the employees and to assess the opinion of employees on whether they are satisfied with the welfare facilities provided by the organization. The data was collected from 100 workers and percentage, chi square test was applied for analysis.

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Creative Commons Attribution 4.0 and The Open Definition

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Labour welfare, oil palm plantation workers, satisfaction

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Creative Commons Attribution 4.0 and The Open Definition

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Cryptography is the practice and study of techniques for securing communication and data in the presence of adversaries. It involves creating written or generated codes that allow information to be kept secret. Cryptography transforms readable data (plaintext) into an unreadable format (ciphertext) through encryption, and converts it back into readable format through decryption. In this paper, we swap internal bits of both the plaintext and the key using different techniques usually a bit circle swapping, and then perform an XOR operation on both to generate the Ciphertext.

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 Keywords

Cryptography, Plaintext, Circle Swap, XOR, Key Generation, Cipher text

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Creative Commons Attribution 4.0 and The Open Definition

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Liver disease is a serious health concern worldwide, and accurate prediction of liver disease is crucial for timely intervention and treatment. In this paper, research proposes a feature selection method using Adaptive Lasso for liver data to enhance the prediction accuracy of liver disease. Adaptive Lasso effectively selects relevant features from a large pool of potential predictors by incorporating penalty terms. Experimental results demonstrate that research proposed method achieves superior performance in liver disease prediction compared to traditional feature selection techniques. The selected features provide valuable insights into the underlying factors contributing to liver disease, enabling more targeted and effective healthcare interventions. This research contributes to the field of liver disease prediction and highlights the importance of feature selection for improved healthcare outcomes.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Liver disease, feature selection, Adaptive Lasso, prediction accuracy, healthcare interventions

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Creative Commons Attribution 4.0 and The Open Definition