Volume 13 | Issue 5 |

 Abstract

The role of social media in shaping voter behaviour has grown significantly in the past decade, especially in countries with high internet penetration and a growing digital population. India, the world's largest democracy, witnessed a paradigm shift in political campaigning and voter engagement during the 2014 and 2019 general elections. Social media platforms such as Facebook, Twitter, WhatsApp, and YouTube played a crucial role in shaping public discourse, mobilizing voters, and influencing electoral outcomes. This study examines the impact of social media on Indian elections, focusing on how political parties utilized digital platforms to communicate their messages, connect with the electorate, and sway voter preferences. The 2014 Indian general election marked a turning point in digital political campaigns. It was the first election where social media was extensively used for political mobilization, with the Bhartiya Janata Party (BJP) and its prime ministerial candidate, Narendra Modi, leveraging social media to reach millions of voters. Modi's campaign extensively used Twitter, Facebook, and YouTube to engage with the youth, urban voters, and first-time voters. Digital platforms facilitated direct interaction between political leaders and citizens, bypassing traditional media and allowing personalized political messaging. The 2014 elections demonstrated how a wellcoordinated social media strategy could shape voter perception, amplify political narratives, and generate mass support, ultimately contributing to the BJP's landslide victory. The 2019 general elections saw an even more sophisticated use of social media, with political parties employing data-driven strategies, artificial intelligence, and targeted digital advertising to reach specific voter demographics. The BJP further expanded its digital outreach, using WhatsApp groups to disseminate campaign messages at the grassroots level. The Congress party and regional political parties also attempted to leverage social media but struggled to match the BJP's extensive digital network. The 2019 elections highlighted the growing role of misinformation and fake news in shaping voter behaviour. The spread of manipulated content, propaganda videos, and viral misinformation through WhatsApp and Facebook became a significant concern, raising ethical questions about the role of social media in democratic processes. This study explores how social media platforms influenced voter engagement, political polarization, and public opinion formation in both elections. Through qualitative and quantitative analysis, it examines key digital campaign strategies, the role of influencers and political advertisements, and the psychological impact of targeted messaging. Additionally, the research investigates the role of social media in increasing voter turnout, particularly among young and first-time voters who consume most of their information online. Furthermore, this study discusses the challenges associated with social media-driven elections, such as the spread of misinformation, the ethical implications of micro-targeting voters, and the potential for digital manipulation of electoral processes. While social media has democratized political participation by giving a voice to ordinary citizens, it has also raised concerns about privacy, data misuse, and the authenticity of political discourse. The increasing reliance on social media as a political tool necessitates regulatory frameworks to prevent the misuse of digital platforms and ensure free and fair elections. By comparing the role of social media in India's 2014 and 2019 elections, this research highlights how digital platforms have transformed modern electoral strategies. The study underscores the need for a balanced approach to digital campaigning, where social media can be harnessed for positive voter engagement while mitigating its negative consequences. As India moves toward future elections, understanding the evolving role of social media in shaping voter behaviour will be crucial in preserving the integrity of the democratic process. This research contributes to the broader discourse on political communication and digital democracy by providing insights into the Indian electoral landscape. It also offers lessons for other democracies worldwide, where social media continues to be a dominant force in electoral politics. The findings of this study will be valuable for policymakers, political strategists, and scholars analysing the intersection of social media and electoral behaviour in the digital age.

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 Keywords

Social media, Voter behaviour, Indian elections, Digital campaigning, Political communication, Misinformation, Bharatiya Janata Party (BJP), Electoral strategies, Micro-targeting, Public opinion formation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper explores India's multifaceted challenges in teaching science at the senior secondary level across rural and urban regions. Science education is pivotal in shaping students' prospects in STEM fields, but various factors hinder its effective delivery. In rural areas, inadequate infrastructure, such as poorly equipped laboratories and the absence of qualified teachers, severely limits students' exposure to practical scientific learning. Although better resourced, urban regions face challenges related to overcrowded classrooms, insufficient individual attention, and an overemphasis on rote learning due to intense competition for university entrance exams [11]. Both rural and urban students suffer from a rigid, theory-heavy curriculum that lacks real-world relevance and fails to foster critical thinking or creativity. Socio-economic disparities further exacerbate the issue, as students from economically disadvantaged backgrounds struggle to access quality education, with rural students facing additional barriers due to poor digital infrastructure and economic hardships. Moreover, the digital divide, which widened during the COVID-19 pandemic, disproportionately affects rural students, limiting their access to online learning resources and technological tools. Gender disparities, particularly in rural areas, continue to hinder girls' participation in science education, driven by cultural norms and early dropouts. Additionally, the lack of career guidance in both regions reduces students' motivation to pursue science beyond the board exams. The paper concludes that addressing these challenges requires systemic reforms in infrastructure, teacher training, curriculum design, and equitable resource allocation to create a more inclusive and effective science education framework across India.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Science teaching, Senior secondary education, digital divide, educational frame work

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Stress scale, B.Ed. trainees, teacher education, peace education, psychometric validation

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Creative Commons Attribution 4.0 and The Open Definition

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As organizations navigate the challenges of digital transformation, the role of Business Analysts (BAs) has emerged as a vital link between strategic planning and operational execution. This study examines the measurable impact of Business Analysts on organizational performance by comparing key metrics before and after their formal integration across six major industries: Healthcare, Finance, Retail, IT/Software, Manufacturing, and Other. Using data collected from over 200 professionals, the research evaluates critical performance areas including project delivery, revenue growth, and cost efficiency. Descriptive analysis and interactive dashboards developed through Microsoft Power BI reveal notable post-implementation improvements, particularly in requirement clarity, issue identification, and resource optimization. The most significant performance gains were observed in IT/Software, Healthcare, and Finance, emphasizing the value BAs bring in aligning data-driven insights with organizational goals. By offering a structured before-and-after comparison, this study addresses a gap in existing research, which often discusses the importance of BAs without supporting data. The findings advocate for a more strategic approach to BA integration--one that includes clear role definitions, cross-department collaboration, and ongoing performance evaluation to ensure long-term business success.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Business Analysts, organizational performance, strategic alignment, data-driven decision-making, project delivery, cost efficiency, Power BI, comparative analysis

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 Abstract

"A STUDY TO ASSESS THE KNOWLEDGE, ATTITUDE AND PRACTICE ON PREVENTION OF CEREBROVASCULAR ACCIDENT AMONG HYPERTENSIVE CLIENTS AT SELECTED VILLAGE, ARAGONDA." OBJECTIVES o To assess the knowledge on Prevention of cerebro vascular accident among Hypertensive patients at selected villages, Aragonda. o To assess the Attitude on Prevention of cerebro vascular accident among Hypertensive patients at selected villages, Aragonda. o To assess the practice on Prevention of cerebro vascular accident among Hypertensive patients at selected villages, Aragonda. o To find out the association between knowledge on Prevention of cerebro vascular accident among Hypertensive patients with their selected demographic variables. o To find out the association between attitude on Prevention of cerebro vascular accident among Hypertensive patients with their selected demographic variables. o To find out the association between practice on Prevention of cerebro vascular accident among Hypertensive patients with their selected demographic variable o To provide informational booklet regarding prevention of stroke.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

"A STUDY TO ASSESS THE KNOWLEDGE, ATTITUDE AND PRACTICE ON PREVENTION OF CEREBRO VASCULAR ACCIDENT AMONG HYPERTENSIVE CLIENTS AT SELECTED VILLAGE, ARAGONDA."

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The review article explores the key success factors in leading large-scale technology initiatives, focusing on leadership styles, governance structures, risk management strategies, and stakeholder engagement approaches. By analyzing case studies across healthcare, finance, and government sectors, the review highlights how these factors contribute to the success of complex technology projects. Key lessons learned include the importance of adaptive leadership, proactive risk management, and effective stakeholder engagement throughout the project lifecycle. Additionally, the review identifies gaps in current research and outlines future directions, including the integration of emerging technologies, adaptive leadership models, cross-cultural considerations, and sustainability. This article provides valuable insights for organizations seeking to implement large-scale technology projects and offers recommendations for future research in the field.

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 Keywords

Large-Scale Technology Initiatives, Leadership Styles, Governance Structures, Risk Management, Stakeholder Engagement, Digital Transformation, Project Management, Healthcare Technology, Cloud Migration, Smart City Initiatives, Emerging Technologies, Cross-Cultural Management, Sustainability in Technology.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Suspicious Activity is predicting the body part or joint locations of a person from live camera. This project will entail detecting suspi cious human Activity fromreal-time Camera using neural networks. Human suspicious a Activity is one of the key problems in computer vision that has been studied for more than 15 years. It is important because of the sheer number of applications which can benefit from Activity detection. For example, human pose estimation is used in applications including video surveillance, animal tracking and be havior understanding, sign language detection, advanced human computer interaction, and marker less motion capturing. Low cost depth sensors have limitations like limited to indoor use, and their low resolution and noisy depth information make it difficult to esti mate humanposes from depth images. Hence, we plan to use neural networks to overcome these problems. Suspicious human activity recognition from surveillance video is an active research area of im age processing and computer vision. Through the visual surveil lance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shop ping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very dif f icult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Mostly, of the research being carried out is on images and not videos. Also, none of the papers published tries to use LR algorithm to detect suspicious activities Keywords : MachineLearning,LRAlgorithm,ComputerVision,Suspicious Activities Suspicious Activity is predicting the body part or joint locations of a person from live camera. This project will entail detecting suspi cious human Activity fromreal-time Camera using neural networks. Human suspicious a Activity is one of the key problems in computer vision that has been studied for more than 15 years. It is important because of the sheer number of applications which can benefit from Activity detection. For example, human pose estimation is used in applications including video surveillance, animal tracking and be havior understanding, sign language detection, advanced human computer interaction, and marker less motion capturing. Low cost depth sensors have limitations like limited to indoor use, and their low resolution and noisy depth information make it difficult to esti mate humanposes from depth images. Hence, we plan to use neural networks to overcome these problems. Suspicious human activity recognition from surveillance video is an active research area of im age processing and computer vision. Through the visual surveil lance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shop ping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very dif f icult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Mostly, of the research being carried out is on images and not videos. Also, none of the papers published tries to use LR algorithm to detect suspicious activities Keywords : MachineLearning,LRAlgorithm,ComputerVision,Suspicious Activities Suspicious Activity is predicting the body part or joint locations of a person from live camera. This project will entail detecting suspi cious human Activity fromreal-time Camera using neural networks. Human suspicious a Activity is one of the key problems in computer vision that has been studied for more than 15 years. It is important because of the sheer number of applications which can benefit from Activity detection. For example, human pose estimation is used in applications including video surveillance, animal tracking and be havior understanding, sign language detection, advanced human computer interaction, and marker less motion capturing. Low cost depth sensors have limitations like limited to indoor use, and their low resolution and noisy depth information make it difficult to esti mate humanposes from depth images. Hence, we plan to use neural networks to overcome these problems. Suspicious human activity recognition from surveillance video is an active research area of im age processing and computer vision. Through the visual surveil lance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shop ping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very dif f icult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Mostly, of the research being carried out is on images and not videos. Also, none of the papers published tries to use LR algorithm to detect suspicious activities Keywords : MachineLearning,LRAlgorithm,ComputerVision,Suspicious Activities Suspicious Activity is predicting the body part or joint locations of a person from live camera. This project will entail detecting suspi cious human Activity fromreal-time Camera using neural networks. Human suspicious a Activity is one of the key problems in computer vision that has been studied for more than 15 years. It is important because of the sheer number of applications which can benefit from Activity detection. For example, human pose estimation is used in applications including video surveillance, animal tracking and be havior understanding, sign language detection, advanced human computer interaction, and marker less motion capturing. Low cost depth sensors have limitations like limited to indoor use, and their low resolution and noisy depth information make it difficult to esti mate humanposes from depth images. Hence, we plan to use neural networks to overcome these problems. Suspicious human activity recognition from surveillance video is an active research area of im age processing and computer vision. Through the visual surveil lance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shop ping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very dif f icult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Mostly, of the research being carried out is on images and not videos. Also, none of the papers published tries to use LR algorithm to detect suspicious activities Suspicious Activity is predicting the body part or joint locations of a person from live camera. This project will entail detecting suspi cious human Activity fromreal-time Camera using neural networks. Human suspicious a Activity is one of the key problems in computer vision that has been studied for more than 15 years. It is important because of the sheer number of applications which can benefit from Activity detection. For example, human pose estimation is used in applications including video surveillance, animal tracking and be havior understanding, sign language detection, advanced human computer interaction, and marker less motion capturing. Low cost depth sensors have limitations like limited to indoor use, and their low resolution and noisy depth information make it difficult to esti mate humanposes from depth images. Hence, we plan to use neural networks to overcome these problems. Suspicious human activity recognition from surveillance video is an active research area of im age processing and computer vision. Through the visual surveil lance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shop ping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very dif f icult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Mostly, of the research being carried out is on images and not videos. Also, none of the papers published tries to use LR algorithm to detect suspicious activities

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

MachineLearning,LRAlgorithm,ComputerVision,Suspicious Activities

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Communication is a fundamental aspect of human interaction, and individuals with speech impairments often face significant challenges in expressing themselves effectively. This paper presents MuteTalk, a system that enables speech using gesture recognition, natural language processing (NLP), and speech synthesis. The system is divided into three core components. First, gesture recognition is implemented using OpenCV to identify specific hand gestures, which serve as input for communication. Next, the NLP module processes these recognized gestures by mapping them to predefined keywords and forming meaningful sentences based on contextual understanding. Finally, the speech synthesis module converts the structured sentences into audible speech, allowing seamless communication through a speaker system. By integrating computer vision, language processing, and speech generation, MuteTalk provides an intuitive and efficient means for non-verbal individuals to convey their thoughts. The system enhances accessibility and inclusivity, offering a practical assistive technology solution for people with speech disabilities.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Gesture Recognition, Natural Language Processing (NLP), Speech Impairments, Assistive Technology, Real-Time Communication

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Collaboration is a term commonly used in research, clinical practice, and health professions education. There are collaborations in almost every aspect of health, such as patient advocacy and health care collaborative, collaborative learning, Interprofessional collaboration in practice and in education, health care value collaborations, business collaborations, collaborative efforts in research and funding. Objectives To assess the knowledge and practice regarding Interprofessional collaboration and its importance among student nurses. To find the correlation between the knowledge and practice regarding Interprofessional collaboration and its importance among student nurses.To find the association between the knowledge and practice regarding Interprofessional collaboration and its importance with the demographic variables. Method Descriptive research design with 30 samples, selected using non probability conveience sampling technique.Results The findings of the research project revealed that in knowledge 63.3% of the samples had below average, 33.33% of the samples had average and 33.33% of the samples had above knowledge, whereas in practice 93.33% of the samples had above average, 33.33% of the samples had average and 33.33% of the samples add below average. It implies that there was no association at (p=0) of knowledge and practice with demographic variables

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

interprofessional collaboration

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study focuses on the relationship between Emotional Intelligence (EI) and Organizational Citizenship Behaviour (OCB) among employees of Zoho Corporation, located in Chennai, India. The main purpose is to understand how different aspects of emotional intelligence such as self-awareness, empathy, motivation, and social skills affect employees' willingness to go beyond their job roles and support the organization and their colleagues. The research was conducted using a descriptive design, and data was collected from 152 employees through a structured questionnaire. Statistical methods like chi-square tests and correlation analysis were used to examine the link between EI and OCB. The results show that most employees had moderate levels of emotional intelligence and were generally willing to engage in positive, voluntary behaviours at work. While some relationships between EI and OCB were not statistically significant, the overall trend suggests a positive connection between the two. The study concludes that emotional intelligence can influence organizational citizenship behaviour. It recommends that companies include emotional intelligence training in their HR practices to improve teamwork, communication, and overall workplace performance.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Emotional Intelligence, Organizational Citizenship Behaviour, Empathy, Motivation, Self-awareness, Social Skills, Workplace Performance

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Creative Commons Attribution 4.0 and The Open Definition