Volume 13 | Issue 6 |

 Abstract

This paper presents an Android-based healthcare application designed to enhance accessibility to medical services for patients and healthcare providers. The application allows users to book appointments, maintain digital health records, receive medication reminders, and consult doctors remotely. It aims to simplify the interaction between patients and healthcare professionals, especially in remote or underserved areas. The system leverages mobile technology to provide a user-friendly interface, real-time updates, and secure data handling. This solution promotes efficiency, reduces paperwork, and supports digital transformation in the healthcare sector.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Android Application, Healthcare, Firebase, Patient Management, Telemedicine.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

: Personalized media experiences are rapidly evolving from static, preference-based models to dynamic, context-aware systems that respond in real-time to users' emotional states and activities. In this paper, we present a novel, integrated pipeline that fuses real-time facial emotion detection (captured via webcam) and offline activity recognition (analyzing uploaded video files) to drive a contextual song recommendation engine. The system comprises three tightly coupled modules: a Kivy-based GUI application leveraging OpenCV and DeepFace for low-latency facial affect analysis; a Flask web service for user management, video ingestion, and recommendation logic; and an offline video processor employing an Ultralytics YOLOv5 model fine-tuned for "running" and "sleeping" activities. We detail data collection and annotation procedures, model architectures and training regimes, algorithmic pseudocode, deployment via container orchestration, and front-end integration. Quantitative evaluation demonstrates 87-90% accuracy in seven-class emotion classification, 90.1% mAP in two-class activity detection, and round-trip latencies under 100 ms for emotion feedback. A user study with thirty participants reports 92% satisfaction with recommendation relevance and 4.6/5 mean perceived utility. Compared to standalone emotion- or activity-based recommenders, our unified approach yields a 25% uplift in personalization metrics. We conclude by mapping future research avenues: expanding affective and activity taxonomies, reinforcement-learning driven playlist adaptation, multimodal sensor fusion, and on-device inference for privacy.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

: Convolutional Neural Networks, Facial Expression Recognition, Activity-Based Learning, Machine Learning, Emotion Identification, Mood-Based Music Recommendation, Personalized Audio Experience.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Oral diseases have a significant impact on the quality of life of children. Early exposure to irritants in the infant's environment (e.g., bacteria or sugars) can cause oral problems. Many synthetic compounds have strong antimicrobial activity and consequently are widely utilized in pediatric medicine, they may have side effects such as the disruption of the natural micro-flora, leading to microbial resistance. These aspects thus suggest the need for studies and the development of alternative antimicrobials. Potable plant extracts have been widely used as therapeutic agents in oral health, with an important number of active components. The antimicrobial activities of these agents have been tested side by side with conventional antibiotic treatments. Furthermore, the introduction of plant-derived antimicrobials is receiving a growing interest from the pharmaceutical industry because of their effectiveness and increased safety margin as compared to their synthetic analogues. Plant-based antimicrobials hold promise for improving pediatric oral health by providing safe and effective alternatives to synthetic agents. However, further research and development are necessary to fully realize their potential.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Antimicrobial agents, alternative antimicrobials, plant extracts, Microbial Ecology, Antimicrobial Resistance, Flavonoids, Terpenoids, Alkaloids

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In today's world, the role of language has evolved from merely a communication tool to a powerful asset in the soft power arsenal. Attempts to globalize a language can be beneficial for effectively deploying other soft power instruments, since language is the vehicle of culture, ideas, and vision. It thereby contributes to economic and political influence in an increasingly globalized world. In this paper, based on Joseph Nye's theory of soft power, I will explore the role of language as a constituent of soft power, with a focus on examining the competence of the Hindi language as a soft power tool and its potential influence in China.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Soft-power, Language, Hindi, China

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper examines the integration of Artificial Intelligence (AI) in the retail and supply chain sectors across India and globally. Drawing from 40 research studies, it delves into AI applications in customer experience, inventory and demand forecasting, logistics optimization, and sustainability initiatives. It highlights benefits like improved efficiency and personalization, while also addressing challenges such as data quality, ethics, and costs. The paper includes case studies and concludes with actionable recommendations for leveraging AI in modern retail and logistics.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Retail, Supply Chain, Customer Experience, Demand Forecasting, Sustainability, Automation, Predictive Analytics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The legalization of cannabis for medical purposes is an increasingly global trend, supported by a growing body of scientific evidence demonstrating its therapeutic efficacy for a variety of conditions. Concurrently, many prescribers have voiced concerns that this increased utilization may lead to the development of cannabis use disorder in patients. While cannabis use disorder has been extensively studied in recreational users, with findings often extrapolated to medical cannabis patients, research specifically addressing dependence on medical cannabis remains limited, and standardized methodologies for assessing this phenomenon are lacking. This article presents a narrative review of existing research, aiming to determine the relevance and applicability of concerns regarding dependence in recreational cannabis users to patients prescribed medical cannabis. The review focuses on key factors related to medical cannabis and dependence, including the influence of dosage, potency, cannabinoid composition, pharmacokinetics, administration route, frequency of use, and the crucial role of set and setting. Significant differences between medical and recreational cannabis use are highlighted, underscoring the difficulties inherent in extrapolating data from recreational use studies. Given the numerous unanswered questions surrounding the potential for dependence arising from medical cannabis use, it is imperative that these issues be addressed to effectively minimize potential harms. This review culminates in seven recommendations designed to enhance the safety of medical cannabis prescribing practices. It is anticipated that this review will contribute to a deeper understanding of the complexities surrounding medical cannabis dependence.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Medical cannabis, Dependence, Recreational cannabis use, Dosage, Potency, Prescribing practices, Cannabinoid composition, Pharmacokinetics, Administration route, Frequency of use, Cannabis use disorder, Harm reduction, Recommendations, Narrative review.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Accurate and rapid location of fault in distribution network is of great significance to improve the reliability of power supply in distribution network. At present, the fault location of distribution management system main station requires high data quality of line terminals, and there are problems such as poor fault tolerance and low accuracy of fault location, and it is not suitable for fault location of multi-point simultaneous fault.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fault location, Distribution network, Power supply reliability, Distribution Management System (DMS), Data quality, Line terminals, Fault tolerance, Location accuracy.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

: Heart disease remains one of the leading causes of mortality globally, necessitating the development of early and accurate diagnostic tools. This project focuses on predicting the likelihood of heart attacks using various machine learning (ML) algorithms. A publicly available clinical dataset, including features such as age, gender, chest pain type, blood pressure, cholesterol, and ECG results, is used for training and evaluation. The dataset undergoes preprocessing steps including data cleaning, normalization, and feature encoding. Supervised learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree, and Random Forest are implemented and compared based on performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The Random Forest algorithm outperformed others in terms of accuracy and generalization ability. The system is integrated into an Android application using Firebase as a backend service, enabling real-time user interaction and prediction delivery. The study demonstrates that ensemble learning methods offer robust and interpretable solutions for heart disease prediction, which can support clinical decision-making and preventive care. Future enhancements may include integration with wearable devices and deployment in real-time hospital environments.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Heart Disease Prediction, Machine Learning, Random Forest, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Clinical Data, Android Application, Firebase Integration, Healthcare Analytics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Migrant garment workers in Tirupur, Tamil Nadu, constitute the backbone of the region's thriving knitwear industry, which significantly contributes to India's export economy. However, these workers often face numerous challenges, including low wages, poor working conditions, long working hours, and substandard living arrangements. Despite the economic contributions of these workers, there is limited access to a comprehensive social support system that can address their needs. This paper examines the existing social support mechanisms available to migrant garment workers in Tirupur, focusing on labor rights, healthcare, housing, and welfare schemes. Through a combination of qualitative and quantitative research, the study identifies the gaps in the current support system and highlights the urgent need for robust policy interventions. The paper aims to explore the extent of support from government, employers, and non-governmental organizations (NGOs) and suggests strategies to enhance social protection and improve the overall welfare of migrant workers in the garment sector.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

SOCIAL SUPPORT SYSTEM FOR MIGRANT GARMENTS WORKERS IN TIRUPUR

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The increasing resistance of mosquitoes to synthetic repellents has led to the growing interest in herbal alternatives. This study aims to formulate and evaluate a herbal mosquito repellent cone using natural ingredients with mosquito-repelling properties. A combination of essential oils from plants such as citronella, eucalyptus, and lemongrass, known for their insect-repellent activity, was used to create the formulation. The repellent cones were developed by incorporating herbal extracts into a base material, and their physical characteristics, such as texture, burning rate, and repellent efficiency, were evaluated. The repellent efficacy was tested in controlled environments, measuring the reduction in mosquito landing and biting rates. Additionally, the stability of the repellent over time and under varying environmental conditions was assessed. The results suggest that the herbal mosquito repellent cone is an effective, eco-friendly alternative to conventional synthetic repellents, with significant mosquito deterrent properties. The formulation proved to be safe, biodegradable, and cost-effective, making it a viable option for widespread use.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Herbal mosquito repellent, formulation, evaluation, essential oils, citronella, eucalyptus, lemongrass, cone, insect-repellent, eco-friendly, mosquito deterrent, biodegradable, natural alternative.

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Creative Commons Attribution 4.0 and The Open Definition