Volume 13 | Issue 5 |

 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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 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

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This paper introduces TextGuardianship, a Python and AI-based plagiarism detection tool that aims to provide a sound and effective means of detecting potential plagiarism. The system utilizes cutting-edge Natural Language Processing (NLP) methods and machine learning models, such as TF-IDF vectorization, cosine similarity, and semantic analysis, to scan and compare text data with high precision. Main aspects of TextGuardianship are the provision for both direct text uploading and PDF upload, allowing easy content scanning in its original format. The platform also boosts user comprehension through comprehensive reports identifying matched sources and offering similarity scores.

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Creative Commons Attribution 4.0 and The Open Definition

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Plagiarism Detection, Natural Language Processing, Text Similarity, Python, TF-IDF, AI in Education

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The revolutionary impact of Question Answering (Q&A) systemsin contemporary e-commerce. These AI solutions deliver immediate and precise answers to customer inquiries, transforming the online buying process. By eliminating repetitive customer communication, Q&A systems lower operational expenses, enhance customer happiness, and liberate human agents to handle complex issues. The analysis identifies the critical impact of Q&A systems as fundamental elements of an effective e-commerce strategy, enabling businesses to succeed in a global digital market. These technology-based tools offer fast, accurate, and personalized responses to customer queries, revolutionizing the experience of online shopping. It basically uses the machine learning and natural language processing technology which it further uses to Utilization of Q&A systems assists in creating a good brand image, giving e-commerce companies an image of being customer-centric and technology- savvy. As customers want instant access to quality information, Q&A systems become crucial for online retailing companies.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Q & A, E- commerce, customer support, Information accessibility, Information Retrieval

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Alzheimer's disease (AD), the leading cause of dementia, remains a major public health challenge. Despite extensive research, its complex genetic, molecular, and environmental underpinnings are not fully understood. This review highlights the role of bioinformatics in integrating and analyzing multi-dimensional datasets from genomics, transcriptomics, proteomics, and neuroimaging to advance AD research. We outline core AD pathologies, including amyloid-beta plaques, tau tangles, and neuroinflammation, which drive neuronal dysfunction and cognitive decline. High-throughput genomic techniques, such as genome-wide association studies and next-generation sequencing, have identified key risk factors beyond the known. Transcriptomic methods, including bulk and single-cell RNA (Ribo Nucleic Acid) sequencing, reveal gene expression dynamics and cell-specific vulnerabilities. Integrated bioinformatics approaches, such as network-based analyses, aid in biomarker discovery for early diagnosis and targeted therapy. Emerging technologies like spatial transcriptomics and multi-omics signal a promising future, reshaping AD research and advancing precision medicine.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Alzheimer's Disease, Bioinformatics, Genomics, Transcriptomics, Biomarker Discovery, Precision Medicine

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The chemical industry is a vital contributor to global economic development; however, its processes often result in significant environmental challenges. Chemical production is responsible for pollution across air, water, and land while contributing to climate change and ecosystem disturbances. This paper examines the environmental impact of chemical manufacturing, explores emerging sustainable technologies, and presents strategies for reducing ecological damage through green chemistry principles and regulatory interventions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

: Green chemistry, pollution, industrial waste management, chemical sustainability, environmental conservation.

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Creative Commons Attribution 4.0 and The Open Definition