Volume 14 | Issue 6

 Abstract

: Early and accurate diagnosis is important since cardiovascular diseases (CVDs) are among the leading causes of death worldwide. Electrocardiography (ECG) is a useful diagnostic tool that allows physicians to detect and monitor a variety of heart disorders by observing the electrical activity of the heart. However, manually identifying ECG characteristics and classifying heartbeats is a challenging and time-consuming operation that requires a high ability level. To deal with the stated issue, we developed a novel system, named ArythmiAR, by combining Convolutional Neural Networks (CNNs) and Augmented Reality (AR) for interactive diagnosis with 3D visualisation and real-time interaction. The major features of the ArythmiAR are: 1. Deep learning ECG classification for better arrhythmia detection, 2. 3D heart modelling and assembly for better visualisation, 3. AR interface for deployment of CNN model, 4. 3D location of the heart sub-regions responsible for arrhythmia anomalies, 5. Improved 3D visualisation and interaction. In this work we explore different strategies for ECG classification, using data rebalancing techniques to improve the performance of the models. We focus on CNN and Multilayer Perceptron (MLP) models which achieved 99.07% accuracy with the MLP model and were extremely competitive on the PhysioNet MIT-BIH Arrhythmia dataset. The paper also demonstrates the use of the deep learning model for ECG categorisation in an augmented reality environment. It is an augmented rendering prototype that allows the user to identify, view and interact with specific cardiac regions causing arrhythmia. It helps doctors to better identify patients and find better ways to treat them.

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 Keywords

ECG Classification, Arrhythmia Detection, Deep learning, Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), Augmented Reality (AR), 3D Heart Visualisation, Medical Imaging, Healthcare AI, PhysioNet MIT-BIH Dataset.

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Most crime scenes contain fingerprints, which are among the most crucial pieces of identification evidence. For the creation of latent fingerprints on various surfaces, including porous, non-porous, and semi-porous surfaces, various kinds of fingerprint powders are utilized. Developing efficient, economical, and ecologically conscious methods for latent fingerprint visualization on non-porous surfaces remains challenging. In this work, off-white and grey powders derived from crab shells, an abundant marine waste material, were used to develop a novel fingerprint powder for latent fingerprint visualization. White powder from crab shells was prepared by cleaning, boiling, drying, crushing, and sieving. Grey powder from crab shells was prepared by using muffle furnace it using analytical techniques in order to assess its particle size, shape, and chemical properties. The material was tested for its viability in visualizing fingerprints on non-porous surfaces by using various deposition techniques, such as TV glass, Polished wood, Metal door, mobile glass, plastic covers. The developed fingerprints were evaluated for contrast, clarity and ridge detail. Results show that crab shell powder has good affinity with sweat and oil residues left in latent fingerprints and produces clear and well-defined ridge patterns. The results suggest that crab shell ash can be a low-cost, eco-friendly and efficient alternative for latent fingerprint visualization. This research adds to the field of forensic science, as well as demonstrates the potential to use marine waste for sustainable technology.

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 Keywords

Latent fingerprints, Crab shell Powder and Ash, Non-porous surfaces, Semi- porous surfaces, Eccrine Fingerprints, Sebaceous Fingerprint, Eco-friendly fingerprint powder.

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Emotion recognition is an increasingly important part of improving human-computer interaction. This is because emotions have a large part in how people behave towards each other and how they feel in general. Many industries want robots that can recognise and respond to emotional cues as people do. Affective agents can be useful in many fields such as education, health care, gaming, marketing, customer service, human-robot interaction and entertainment. The aim of this study is to investigate the potential of multimodal (artificial intelligence) AI to improve virtual assistants. More effective and sympathetic virtual assistants are developed using different emotion recognition techniques. The proposed approach enhances the system's emotion awareness and improves the user satisfaction with sympathetic dialogue by using written cues and facial expressions. The proposed MER (Multimodal Emotion Recognition) is effective as the FER (Facial Emotion Recognition) model reaches 71% accuracy in real-time and the TER (Textual Emotion Recognition) model reaches 59% accuracy in validation. Our lightweight architecture merges DialoGPT-based answer generation with face and text-based emotion identification for real-time inference. It is different from other multimodal emotion-aware systems in that it shows how it can work with large language models to have empathetic conversations

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 Keywords

Multimodal Emotion Recognition; Facial Emotion Recognition; Textual Emotion Analysis; Affective Computing; Human-Computer Interaction; Empathetic Virtual Assistants;

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 Abstract

Cardiovascular diseases (CVDs) are still one of the leading causes of mortality worldwide, which reveals the importance of early and precise diagnosis. ECG stands for electrocardiography and is a helpful diagnostic tool that allows doctors to identify and track different heart conditions by examining the electrical activity of the heart. However, manual identification of ECG features and classification of heartbeats is a time consuming and difficult task which requires a lot of skills. We developed a novel system named ArythmiAR to solve the above problem by integrating Convolutional Neural Network (CNN) and Augmented Reality (AR) for interactive diagnosis with 3D visualisation and real-time interaction. The main features of the ArythmiAR are: (1) deep learning ECG classification for efficient arrhythmia detection; (2) 3D heart modelling and assembly for better visualisation; (3) AR interface for deploying CNN models; (4) 3D location of the sub-regions of the heart responsible for arrhythmia anomalies; and (5) improved 3D visualisation and interaction. In this work we explore different ECG classification approaches, using data rebalancing techniques to improve the performance of the models. Our focus is on Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) models that performed very competitively on PhysioNet MIT-BIH Arrhythmia dataset and reached 99.07% accuracy with MLP model. The work also demonstrates the use of the deep learning model for ECG classification in an AR environment. It is a prototype for augmented rendering, allowing the user to locate, see and interact with specific parts of the heart that cause arrhythmia. The platform gives doctors the tools they need to make better diagnoses and create better treatment plans, improving care for all patients

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 Keywords

ECG Classification, Arrhythmia Detection, Deep Learning, Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), Augmented Reality (AR), 3D Heart Visualization, Medical Imaging, Healthcare AI, PhysioNet MIT-BIH Dataset.

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: Help desk systems are an integral part of any institution or public service environment as visitors often need instant help for navigation, enquiries and access to services. In many cases, manual help desks are not available around the clock, and visitors have to wait for a human operator. The Fixed-Place Help Desk Robot with Face Recognition and Voice Q&A for Visitor Assistance addresses this issue by providing an automated, voice activated, fixed-position assistant that can answer frequently asked questions and provide directional assistance. The Raspberry Pi 5 is the heart of the system and is the central controller for processing voice input, responses and the camera. A microphone listens to the user's speech . Speech recognition converts it into text . A response engine searches the query against a set of preprogrammed answers in the system. The generated response is then converted into speech via text to speech output and played through a speaker. At the same time, live visual monitoring is provided by a camera at the help desk location.This project is particularly useful in colleges, hospitals, offices, libraries and other public places where visitors repeatedly and frequently ask questions. The system reduces manual work load and improves response time and provides a uniform user-experience. It also shows how embedded hardware, speech technologies and human-computer interaction can be practically integrated into a compact and low-cost assistive system..

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 Keywords

Raspberry Pi 5, Fixed-Place Help Desk Robot, Face Recognition, Voice Recognition, Speech-to-Text, Text-to-Speech (TTS), Human-Machine Interaction (HMI)Raspberry Pi 5, Fixed-Place Help Desk Robot, Face Recognition, Voice Recognition, Speech-to-Text, Text-to-Speech (TTS), Human-Machine Interaction (HMI)

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 Abstract

In civil engineering, the structural integrities of bridges are very important. This is because problems with the bridge surface can make bridges unsafe, more costly to maintain, and even lead to catastrophic failure. Most traditional methods for inspecting bridges are manual, where trained personnel look for cracks, spalling, corrosion and other issues on the surfaces of. These manual inspections work to some extent, but they are time-consuming and labour-intensive and are prone to error. Moreover, manual assessments are subjective and often do not agree with each other, especially when there are multiple defects or when surface conditions are made more difficult by lighting, noise, or other environmental factors. In recent years, machine learning and deep learning have emerged as powerful tools to automate the detection of defects in civil infrastructure. Among them, convolutional neural network (CNN) based object detection models like the YOLO (You Only Look Once) family have shown a lot of promise for real-time detection tasks due to their speed and efficiency. But the models that are already out there have some issues. Many of them are designed to find only one kind of defect, making them less useful in the real world where multiple defects often happen at the same time. Standard models also lack mechanisms to focus on the most important parts of an image, making it more difficult for them to detect small, subtle or overlapping defects. Bounding box regression uses standard loss functions such as IoU or GIoU but these may not be effective with defects that are not in the shape of a box which can lead to errors in localisation . These limitations highlight the necessity of a more powerful and precise system capable of detecting various kinds of defects on bridge surfaces under different challenging and variable circumstances. We propose a YOLOv8 model with an improvement using the Convolutional Block Attention Module (CBAM) and the Wise-IoU loss function in this paper. This new model is named YOLOv8-CBAM-Wise-IoU. The CBAM module enhances the feature representation by employing channel and spatial attention. This guides the model to focus on relevant portions of the image and ignore irrelevant background noise. This is even more important for detection of small cracks, subtle corrosion or overlapping defects, that could be missed by standard detection models. The Wise-IoU loss function improves bounding box regression by dynamically adjusting the loss weight according to the object characteristics. This leads to more accurate localisation and reduces false positives. The proposed model can detect seven different types of defects on bridge surfaces simultaneously. They consist of cracks, spalling, corrosion, delamination, surface scaling, efflorescence and potholes. We collected a large set of labelled bridge pictures for training the model, and augmented it with methods such as rotation, scaling and brightness changes to make it more generalisable. We put a lot of effort into improving the training, tuning hyperparameters, performing ablation studies to evaluate the contribution of each component (YOLOv8 backbone, CBAM, and Wise-IoU) to the overall performance. Tests show that YOLOv8-CBAM-Wise-IoU performs much better than the standard YOLO models and other common methods. The model could detect 97.9% of the defects, 76% of the times it was supposed to, 58% of the times it was supposed to and 55.4% of the times it was supposed to. It was also able to detect defects in real world scenarios such as variations in lighting, complex backgrounds and non-uniform surface textures. The system also had a faster detection speed which was good for real time use and this meant it could be used for inspection systems that use drones or cameras. In summary, the YOLOv8-CBAM-Wise-IoU model is a reliable, scalable and efficient method for multi-defect detection on bridge surfaces. To solve the problems of traditional detection methods, the system adopts the attention mechanism and more advanced loss function. Enables the complete, accurate and automated monitoring of the health of structures. It could enhance preventative maintenance schedules, lower the cost of inspections and make bridges safer overall. This study adds to the development of intelligent infrastructure inspection systems, and also shows the effectiveness of combining deep learning, attention mechanisms, and optimised loss functions to address practical civil engineering applications.

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 Keywords

YOLOv8, Multi-Defect Detection, Bridge Surface Inspection, Convolutional Block Attention Module (CBAM), Wise-IoU Loss Function.

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 Abstract

The importance of emotion recognition is increasing for improving human-computer interactions. People's feelings strongly affect people's feelings and interaction with each other. In many sectors, machines that can recognise and respond to emotional cues like humans do are needed. Emotionally sensitive agents can be useful in many industries such as education, healthcare, gaming , marketing, customer service, human-robot interaction and entertainment. We investigate in this paper how to improve effectiveness and compassion of virtual assistants by upgrading them with multimodal Artificial Intelligence (AI) and a variety of emotion identification methods. The approach proposed uses written signals and facial expressions to increase the awareness of emotions of the system and to improve the satisfaction of the user by means of sympathetic dialogue. Multimodal Emotion Recognition (MER) is effective as the Facial Emotion Recognition (FER) model is 71% accurate in real time and the Textual Emotion Recognition (TER) model is 59% accurate in validation. The lightweight design allows for real-time inference and combines DialoGPT-based answer generation with textual and facial emotion recognition. This shows that it works with large language models for empathetic interaction, unlike previous multi-modal emotion-aware systems.

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 Keywords

Multimodal Emotion Recognition; Facial Emotion Recognition; Textual Emotion Analysis; Affective Computing; Human-Computer Interaction; Empathetic Virtual Assistants

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 Abstract

Contribution: This paper discusses the advantages and difficulties of the construction, implementation and assessment of MoodleBot, a large language model (LLM) chatbot, in computer science education settings. It explores possible LLM applications in LMSs (e.g. Moodle) for supporting self-regulated learning (SRL) and help-seeking behaviour. Computer science teachers find it challenging to add new features to LMSs that make the learning environment more engaging and helpful. MoodleBot solves this. MoodleBot is a platform for communication between teachers and students. Research Questions: Despite teachers' and educators' hesitation to embrace new AI technology and the problems of bias and hallucinations, this study answers two questions. RQ1: What are students' perceptions of MoodleBot as a teaching tool? (RQ2) How accurate are the MoodleBot responses and how close are they to the assigned course material? technique: This study reviews the pedagogical literature on AI-powered chatbots and applies the retrieval-augmented generation (RAG) technique in the design and data processing of MoodleBot. The technology acceptance model (TAM) examines the degree of user acceptance via factors like perceived utility and perceived ease of use. The study included 46 participants and 30 of them filled out the TAM questionnaire. Results: LLM-based chatbots such as MoodleBot can improve the teaching and learning experience significantly. The success rate of help with course-related tasks (88%) was high in this study. The positive reaction of the students is a sign of the success and practicality of AI-powered instructional tools in real life. The results show that educational chatbots can be used in courses to tailor learning and to support teachers' work but that automated fact-checking needs to be improved.

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 Keywords

Artificial Intelligence, Large Language Models, Retrieval-Augmented Generation, Learning Management Systems, AI Chatbot, Personalized Learning, Higher Education.

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 Abstract

Non-Technical Losses (NTLs), such as stealing electricity, messing with meters, making illegal connections, and billing fraud, are a big problem for modern power distribution systems because they cost a lot of money and slow things down. Old-fashioned ways of finding fraud, like manual inspections and audits based on fixed rules, don't work well, cost a lot, and can't handle the huge amounts of data that smart meters send. This paper talks about how well a Machine Learning-based Apriori Algorithm works to automatically find NTLs. The proposed system analyses three years' worth of monthly electricity usage data from about 15,000 customers. We used and compared a number of models, including Support Vector Machine (SVM), Deep Neural Network (DNN), Gradient Boosted Reinforcement Learning (GBRL), and Apriori-based association rule mining. Experimental results show that the Apriori-based model is more accurate, has a higher recall rate, is more precise, is more specific, and has a lower false positive rate than traditional methods. The system helps utility companies save money, run their businesses more efficiently, and make the smart grid more reliable.

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 Keywords

Non-Technical Losses, Electricity Theft, Smart Grid, Apriori Algorithm, Machine Learning, Fraud Detection, Support Vector Machine, Deep Learning

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 Abstract

Umbilical granuloma is the most common umbilical lesion encountered in neonates, resulting from excessive granulation tissue formation during the postnatal healing of the umbilical stump. In Ayurveda, this condition may be correlated with disorders such as Pindalika, a complication arising from improper management of the umbilical cord, and Nabhigata Arsha, characterized by abnormal tissue overgrowth at the umbilicus. 28 Days old make neonate presented with a complaint of pinkish, moist swelling on umbilicus with discharge and occasional bleeding since birth. On examination a single swelling on umbilicus cherry red in colour with serous discharge was present. It was soft, non tender and moist. Under aseptic precautions Apamarga kshara ( Alkaline preparation having Achyranthes aspera plant) was applied on umbilical granuloma. This procedure was repeated for next 2 days. At 7th day from 1st day of kshara application, it was completely healed. Umbilical granuloma was successfully treated with Kshara karma.

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 Keywords

Umbilical granuloma, Pindalika, Kshara karma, Nabhigata arsha

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The growing visibility of transgender life writing in contemporary Indian literature has created new opportunities for understanding gender identity, embodiment, and resistance from the perspective of lived experience. Transgender autobiographies challenge dominant social narratives by foregrounding voices that have historically been marginalized and excluded from mainstream literary discourse. This article examines 'From Manjunath to Manjamma: The Inspiring Life of a Transgender Folk Artist' and 'A Small Step in a Long Journey' (A Memoir by Akkai Padmashali) as significant autobiographical narratives that document the complex relationship between body, identity, and social recognition. Drawing upon theories of gender performativity, body politics, subalternity, and life writing, the study explores how the transgender body becomes a site of negotiation, regulation, and self-assertion. The article argues that autobiographical narration functions as a form of narrative resistance through which transgender individuals reclaim agency, challenge normative gender structures, and construct empowering identities. By comparatively examining the two autobiographies, the study seeks to demonstrate how embodied experiences are transformed into narratives of resilience, visibility, and social change.

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 Keywords

Transgender Autobiography, Embodiment, Narrative Resistance, Identity, Body Politics, Life Writing, Visibility.

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Plastic pollution is a global environmental threat due to its non-biodegradable nature. This study successfully demonstrates the production of bioplastic from fish scales collected from local markets. Chitosan was extracted through the 3 processes of deproteinization; demineralization, deacetylation. In this project, development of biodegradable bioplastic film made from fish scales, which has several beneficial characteristics including heat tolerance, smooth texture, transparency, soft touch, tensile strength, and it is degradable in 3 days with water and 6 days with soil. The purpose of the degradability test was to determine whether the bioplastic that were produced would break down when exposed to soil microbes. The film's production costs are low and reasonably simple. Thus, the process called recycle of waste become environmentally friendly, economical, and healthy. Also, it reduces the usage of petroleum derived product by using bioplastics instead of plastic. For all that reason's bioplastic usage increased in our life.

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Bioplastic film, Fish scales, Chitin, Chitosan, Biodegradable.

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Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language generation tasks, yet they frequently produce factually incorrect or logically inconsistent content, a phenomenon commonly referred to as 'hallucination.' Existing detection approaches predominantly operate in a post-generation setting, evaluating the complete output only after the full text has been generated. This paper proposes a novel real-time, token-level hallucination detection framework that monitors the internal probability distributions of an LLM at each decoding step during text generation. By tracking per-token entropy values and identifying anomalous uncertainty patterns, the proposed system flags high-risk tokens and segments before the hallucination is fully expressed. The framework is evaluated on GPT-2 using the TruthfulQA and HaluEval benchmark datasets, requiring only modest computational resources (standard GPU or CPU). Experimental results demonstrate that the approach achieves 82.4% precision and 79.1% recall in detecting hallucination-prone token sequences, with less than 3% computational overhead compared to standard generation. This work addresses a critical gap in existing literature and offers a lightweight, practical solution suitable for deployment in resource-constrained environments such as MCA-level research settings.

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 Keywords

Hallucination Detection, Large Language Models, Token-Level Analysis, Entropy-Based Detection, Real-Time NLP, Text Generation, GPT-2, TruthfulQA

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Abstract Housing is one of the basic necessities of human life. A safe and quality house not only improves the standards of living of individuals but also plays a significant role in promoting social and economic development. With the objective of realizing the dream of home ownership for poor and underprivileged people, the government of India introduced the PradhanMantriAwasYojana-Gramin(PMAY-G). The scheme aims to provide permanent and secure housing facilities to eligible rural households, thereby enhancing their quality of life and contributing to their overall socio-economic development. The present study aims to examine the socio-economic implications of the paradhanMantriAwasYojana -Gramin (PMAY-G) in Tamil Nadu. The data and information required for the study have been obtained from secondary sources, including reports of the ministry of Rural Development , Government of India, publications of the Government of Tamil Nadu, Censud reports, schemes documents, relevant research articles, and related books. The collected information has been analyzed and interpreted using descriptive and analytical approaches. The findings of the study reveal that the PradhanMantriAwasYojana-Gramin (PMAY-G) has brought about significant improvements in the quality of hosing, expansion of health and sanitation facilities, enhancement of employment opportunities, strengthening of the social and economic status of women, and reduction of poverty among rural households .furthermore, the scheme has contributed to improving social inclusion and enhancing the overall quality of life of rural communities. However, delays in the allocation and disbursement of funds, rising construction costs, and various administrative challenges have emerged as major factors affecting the effective implementation of the scheme. Therefore, the study emphasizes the need for appropriate policy measures and efficient implementation mechanisms to expand the benefits of the programme and to promote sustainable and equitable rural development.

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Key words : Pradhan Mantri Awas Yojana- Gramin (PMAY-G), Social Inclusion , Socio- Economic Implications, Rural Housing , Rural Development , Women's Empowerment, Poverty Alleviation, Tamil Nadu .

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Hridroga (heart diseases) are tremendously increasing in our society due to the change in the life style, diet pattern, and environmental conditions. The global burden of diseases is altering from infectious diseases to the non-communicable diseases, and now becoming the chief cause of the death in all over the world. However, as an Ayurvedic point of view in the presence of limited available literature which is also scattered, unclear, and even challenging to interpreting. Therefore, a conceptual study on Ayurvedic concept of Hridroga is required to determine whether it is sustainable in current time period. In order to achieve that goal, the current Article Aims to shed light on the idea of Hridroga using both classical and contemporary literature as sources. Furthermore, the good health is necessary for everyone, so all the section of Ayurveda can work together in the prevention of cardiovascular and related to other heart diseases. This article explains how Hridroga resembles with the symptoms of cardiovascular diseases.

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: Hridroga, Ayurveda, Hridaya, Cardiovascular Diseases, Coronary Artery Disease, Ischemic Heart Disease, Integrative Medicine

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Children's educational experiences are largely influenced by the atmosphere created within their families. The emotional climate, parental guidance, communication patterns, and learning opportunities available at home collectively contribute to their academic growth and overall personality development. As the first and most influential setting for learning, the family provides experiences, support systems, values, and resources that contribute to students' educational outcomes. During the higher secondary stage, students undergo critical developmental transitions that require a supportive and conducive home atmosphere. The present study aims to investigate the home environment of higher secondary school students in Dimapur district. The study focuses on understanding the nature and quality of home environments experienced by students and examining variations based on selected demographic variables. The findings of the study are expected to provide valuable insights for educators, parents, policymakers, and researchers in promoting positive home conditions that enhance students' educational and personal development.

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Home Environment, Higher Secondary Students, Parental Involvement, Family Environment, Adolescent Development

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Creative Commons Attribution 4.0 and The Open Definition

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This study examines the influence of sustainability awareness, environmental concern, and green product knowledge on green consumer buying behaviour in Ranchi, Jharkhand. In recent years, increasing environmental degradation, climate change, and resource depletion have led to a shift in consumer preferences towards environmentally friendly products. Sustainability awareness helps consumers understand the long-term impact of their consumption patterns, while environmental concern reflects their emotional involvement and responsibility towards environmental protection. Additionally, knowledge about green products enables consumers to differentiate between conventional and eco-friendly alternatives. The study is based on primary data collected through a structured questionnaire from 100-150 respondents in Ranchi city. The data has been analyzed using percentage analysis and basic statistical tools with the help of MS Excel. The findings indicate that sustainability awareness and environmental concern significantly influence consumers' intention to purchase green products. However, limited knowledge about green product features, higher prices, and lack of availability act as major barriers to green purchasing behaviour in semi-urban regions like Ranchi. The study concludes that promoting environmental education, increasing awareness campaigns, and improving accessibility and affordability of green products can enhance green consumer behaviour. The results are useful for policymakers, marketers, and environmental organizations aiming to encourage sustainable consumption practices and support India's environmental sustainability goals.

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 Keywords

Sustainability Awareness, Environmental Concern, Green Product Knowledge, Green Consumer Behaviour, Ranchi, Jharkhand

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Idol immersion, a significant religious practice in India, particularly during festivals such as Durga Puja and Ganesh Chaturthi, has emerged as a major environmental concern due to its adverse effects on aquatic ecosystems (Yigam & Bharti, 2025).A study on Bramha Pond, Varanasi, investigated the impact of idol immersion on water quality, analyzing parameters such as pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS), and heavy metals like Pb, Cd, and Cr .Results indicated a significant increase in pollutant levels during and after immersion, with heavy metal concentrations exceeding permissible limits (WHO, 2017). For example, Pb concentration increased from 0.120 mg/L to 0.250 mg/L, while BOD increased from 12.5 mg/L to 22.5 mg/L .The study concludes that idol immersion severely degrades water quality and disrupts aquatic ecosystems, recommending sustainable practices such as eco-friendly idols and artificial immersion tanks to mitigate environmental impacts.

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Idol immersion, Bramha Pond, Water pollution, Heavy metals, Physicochemical parameters, Phytoremediation, Varanasi

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The present research is focused on assessing the influence of online consumer reviews and ratings on pre purchasing decisions for E-commerce platforms. In today's digital world, people rarely buy anything without checking online reviews. Consumers often depend on the experiences and opinions shared by others. This research focuses on understanding how online reviews influence consumer perception and play an important role in pre-purchase decision-making in the Ranchi district. The present research is descriptive and focused on awareness level of online reviews and ratings and whether the factors influence online consumer reviews, ratings, content, and message. The study used both primary and secondary data. The Primary and secondary data were utilized to achieve the current research objectives. The main aim of this study is to explore how consumers read, understand, and trust online reviews before making a purchase. It also searches for different factors such as ratings, detailed feedback, positive and negative comments and whether the reviewer seems real or not. To collect relevant information, a survey was conducted among people from different age groups and backgrounds using a structured questionnaire. This helped in understanding real opinions and behaviours of consumers. The results of the study show that online reviews have a strong impact on buying decisions. Most consumers feel more confident about purchasing a product when they see positive reviews and high ratings. On the other hand, negative reviews can create doubt and often lead people to avoid buying the product. However, many consumers are also becoming more aware of fake or paid reviews which makes them more careful. In conclusion, online reviews have become an essential part of the modern shopping experience. Businesses should focus on maintaining honesty and encouraging genuine feedback from customers to build trust. This study highlights how important online reviews are in shaping consumer perception and helping people make better purchasing decisions.

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 Keywords

consumer perception, online reviews, e-commerce, buying behaviour, consumer Trust, purchase decision, digital word- of- mouth

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Background :Patellofemoral Pain Syndrome (PFPS) is one of the most common causes of anterior knee pain, especially among young adults. Several modifiable and non-modifiable factors such as BMI, Q-angle, and pain intensity may influence the occurrence and severity of PFPS. However, the relationship between these factors and functional disability remains inadequately explored in general populations. Aim: To determine the prevalence of PFPS in the general population and to examine the correlation between selected risk factors (BMI, Q-angle, and VAS) and functional disability assessed using the Kujala Anterior Knee Pain Scale. Methodology: A cross-sectional study was conducted on 100 participants aged 20-40 years in the physiotherapy outpatient department of a multispeciality hospital. BMI, Q-angle, and pain intensity (VAS) were recorded, along with functional disability using the Kujala Score. Data were analyzed using descriptive statistics and Karl Pearson correlation. Results: A strong negative correlation was found between Kujala Score and BMI (r = -0.94), Q-angle (r = -0.92), and VAS (r = -0.91). Higher BMI, increased Q-angle, and higher VAS scores were associated with poor knee function. Conclusion: The study demonstrates that BMI, Q-angle, and pain intensity are strongly associated with PFPS severity. Early identification and modification of these risk factors may help prevent functional decline and chronic knee pain.

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 Keywords

Patellofemoral Pain Syndrome, Kujala Anterior Knee Pain Scale, Body Mass Index, Visual Analog Scale.

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