Volume 12 | Issue 10 |

 Abstract

Poly Cystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age, resulting from insulin resistance and compensatory hyperinsulinemia. This imbalance affects the ovaries leading to the formation of small cysts. Incidence varies between 8-10%, more common amongst infertile women. In present era due to sedentary life style, lack of physical activities, excessive stress and anxiety worsens the prognosis of the diseases. These are a few signs and symptoms associated with the condition- PCOS. Weight gain Irregular menstrual cycle, Facial hair growth, male pattern baldness, Acne which refuses to heal, pigmentation around the neck region and infertility. Darkening of the skin. Dark patches of skin can form in body creases like those on the neck, in the groin, and under the breasts. Hormone changes can trigger headaches in some women. Rising in prevalence of PCOS has become a great cause of health concern as it is increasing the incidences of infertility and metabolic syndrome i.e. insulin resistance diabetes and dyslipidaemia at a younger age. According to Ayurveda the PCOS involves Kapha and Vata Dosha, Meda, Mamsa, Rakta Dhatus. In general, it is considered as Bahudoshavastha, for which Ayurveda can play important role. Especially Panchakarma can have better results which includes Vamana, Virechana, Anuvasana, Niruha & Nasya. Panchkarma may be better line of treatment for PCOS as it works on correcting the underlying root cause along with symptoms of the diseases. And along with shamana drugs it can give better results.

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 Keywords

Polycystic ovarian syndrome (PCOS), Bahudoshavastha, Panchkarma, Acanthosis nigricans, Hirsutism

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research presents a hybridized econometric and machine learning framework for forecasting inflation in India and evaluating its repercussions on financial markets. By combining traditional methods like Vector Autoregression (VAR) with machine learning models such as Random Forest and Long Short-Term Memory (LSTM) networks, we improve the accuracy of inflation forecasts. The study reveals that machine learning methods outperform traditional models in capturing non-linear relationships and extended temporal dependencies, which are crucial for emerging markets like India. In addition, the research examines the ripple effects of inflation on market returns, exchange rates, and interest rates, contributing novel insights into the dynamic interactions between inflationary trends and financial market volatility. These findings have important implications for both policymakers and investors, especially in managing inflation risks.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Inflation Forecasting, Machine Learning, Financial Markets, VAR, LSTM, Random Forest, Inflation Volatility.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study explored the impact of parenting style and conflict resolution style on relationship satisfaction among 100 students. Using the Perceived Parenting Style Scale (PPSS), Conflict Management Style Quiz (CMSQ), and Relationship Satisfaction Scale (RSAT), the research found significant relationships between parenting style, conflict resolution style, and relationship satisfaction. These findings highlight the importance of parenting and conflict resolution styles in shaping relationship satisfaction among students and help to provide strategies to improve the Mental health and successful adulthood of the students.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

parenting style, conflict resolution style, relationship satisfaction

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The construction industry is inherently hazardous, necessitating innovative solutions to enhance worker safety. Recent studies highlight the transformative potential of smart helmet technology in this domain. J. Smith et al. (2023) demonstrate how smart helmets improve situational awareness by providing real-time data on environmental hazards and facilitating immediate communication among workers. L. Chen et al. (2023) focus on the real-time monitoring capabilities of these devices, emphasizing their role in tracking vital signs and environmental conditions to mitigate risks. T. Zhang et al. (2023) explore the integration of augmented reality, which enhances task execution by overlaying critical information directly within a worker's field of view. Additionally, A. O'Connor et al. (2023) discuss the positive impact of smart helmets on safety culture, fostering a proactive approach to hazard reporting and safety practices. Finally, M. Torres et al. (2023) address the future of smart helmets, outlining both the innovative advancements and challenges, such as cost and data privacy, that must be addressed to facilitate broader adoption. Collectively, these studies underscore the importance of smart helmets in redefining safety standards in construction, highlighting their potential to significantly reduce workplace accidents and enhance overall safety culture.

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 Keywords

Smart helmets, worker safety, real-time monitoring, augmented reality, construction industry, safety culture, IoT technology, hazard detection, occupational safety, innovative solutions.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research focuses on developing sustainable fish feed practices in Muzaffarpur, Bihar, addressing the increasing demand for fish in global food production. Aquaculture, a key player in providing high-quality protein, heavily relies on marine resources, particularly fish meal and fish oil. These are not sustainable for long-term use, and the growing demand for fish products poses a significant challenge to the sustainability of aquaculture. The objectives of the research include analyzing current fish feed practices, evaluating the sustainability of existing feed sources, and identifying alternative, locally-sourced protein options for fish feed, such as agricultural by-products and plant-based proteins. The research follows a comprehensive methodology including literature review, fieldwork, interviews with fish farmers, and laboratory analysis of fish feed samples. Preliminary findings indicate a substantial reliance on commercial fish feed, which presents sustainability challenges. Small-scale field trials have been initiated, testing alternative feed sources with active participation from local fish farmers. Stakeholder engagement is emphasized, aiming to promote sustainable fish feed practices. Future research will focus on expanding field trials, conducting more detailed laboratory analyses, and enhancing stakeholder involvement. This study contributes to the development of sustainable practices in aquaculture, with implications for environmental preservation and the long-term viability of the industry in Bihar.

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 Keywords

Sustainable Fish Feed, Aquaculture, Fish Meal Alternatives, Muzaffarpur, Bihar, Agricultural By-products, Plant-based Proteins, Fish Nutrition, Aquaculture Sustainability, Commercial Fish Feed, Omega-3 Fatty Acids, Marine Resource Dependency, Fish Farming Practices, Environmental Impact, Protein Sources in Aquaculture, Alternative Fish Feed Solutions, Stakeholder Engagement, Field Trials, Fish Feed Laboratory Analysis, Local Fish Farming.

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Creative Commons Attribution 4.0 and The Open Definition

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Women's safety remains a critical issue globally, with alarming rates of harassment, molestation, and assault reported across various demographics. Despite historical respect for women in society, contemporary challenges persist, with estimates indicating that 35lifetime. To address this pressing issue, this work presents the development of a smart wearable device aimed at enhancing women's safety through the Internet of Things (IoT). The proposed device is designed for daily use and activates in emergencies to provide real-time protection. Key features include GPS tracking, which allows for continuous location monitoring via a dedicated Android application, and an integrated camera module that captures and transmits photos to the app during dangerous situations. Additionally, the device is equipped with an SOS alert system to attract attention from nearby individuals, and it sends SMS notifications to family members and local police stations when activated. This innovative wearable device aims to provide a comprehensive solution to the safety challenges faced by women and improve their overall protection in critical situations.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Internet of Things (IoT), GPS Tracking, SMS Notification System, Wearable Technology.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Recent advancements in Natural Language Processing (NLP) have greatly enhanced ChatBot capabilities, enabling more seamless and natural user interactions. This paper presents the development and implementation of a Java-based ChatBot utilizing NLP techniques, showcased through a simple Swing application. The ChatBot engages with users by answering their queries and offering assistance on a variety of topics. The ChatBot employs key NLP components such as tokenization, part-of-speech tagging, and sentiment analysis to understand user input and generate suitable responses. Its architecture combines rule-based logic with random selection to handle a wide range of inputs, leveraging Java's robustness and scalability alongside powerful NLP libraries. Key features of the ChatBot include: The ChatBot extracts key entities and identifies user intents to interpret inputs. Personalized responses are provided based on user queries and predefined patterns. The paper also addresses challenges such as managing ambiguous inputs and ensuring response relevance. These outcomes emphasize the potential of combining Java and NLP to produce smart ChatBots that deliver efficient, customized user experiences for applications like customer service and personal support.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

NLP, ChatBot, tokenization, sentiment analysis, part-of-speech tagging

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The analysis of Superstore sales data is critical for understanding the dynamics of retail performance and developing effective business strategies. This paper presents a detailed review of Superstore's transactional data spanning multiple years, focusing on identifying sales trends, profitability drivers, and regional performance patterns. Using a combination of exploratory data analysis(EDA), statistical-modeling, and machine-learning techniques, the study evaluates the impact of key variables such as product category, discount rates, shipping costs, and customer demographics on sales outcomes. The research highlights significant trends, including seasonal spikes in demand, regional disparities in product preferences, and the effect of discounting on profit margins. For example, categories like technology and furniture exhibit higher sales volumes but are sensitive to discounts, affecting profitability. Moreover, the analysis reveals that while discounts increase sales temporarily, they do not always contribute to long-term profitability, suggesting the need for a balanced discounting strategy. Geographical analysis also shows that regions with higher shipping costs have reduced profitability, emphasizing the importance of optimizing supply chain logistics. The study further delves into customer segmentation to identify high-value customers and assess their purchasing behaviors. Techniques like RFM (Recency, Frequency, and Monetary) analysis are employed to group customers based on their purchase patterns, providing insights for targeted marketing initiatives. Machine learning models, including regression analysis and clustering algorithms, are applied to forecast sales and classify customers, allowing for data-driven decision-making.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Superstore Sales Analysis, Retail Performance, Profitability, Customer Segmentation, Discount Strategies.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This systematic review examines the effectiveness of Garbhasanskar--a traditional Indian approach focusing on holistic maternal well-being--as a viable intervention for reducing antenatal depression among urban Indian women. Antenatal depression (AND) is a rapidly growing mental health concern. Untreated AND has severe consequences for both fetus and mother. These include altered fetal development, neurodevelopmental issues, pre-term and low birth weights for the babies; for the mothers there is higher risk of postpartum depression, increased risk of physical illnesses like diabetes, more risk of maladaptive behaviours. Modern psychological interventions often face challenges such as accessibility, cultural stigma, and a lack of integration with traditional practices. For the review, empirical studies from 2010 to 2024 were extracted using databases like Google Scholar and PubMed. 6 studies met the inclusion criteria. Data was synthesized using narrative synthesis to examine the impact of practices like yoga, mantra chanting, prenatal education, mindfulness, and nutritional guidance, on AND. Findings indicate that women engaged in these had a notable reduction in AND.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

antenatal depression, Garbhasanskar, garbh sanskar, urban India, traditional interventions, maternal health, systematic review

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Bhakti Kavya Ki Mahan Vidhushi Meerabai

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Bhakti Kavya Ki Mahan Vidhushi Meerabai

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Creative Commons Attribution 4.0 and The Open Definition