Volume 13 | Issue 1

 Abstract

The dairy sector in India is contributing significantly to the national economy. India is the largest producer and consumer of milk in the world. Dairy farming is becoming more possible because of data-driven approaches used in Dairy sector. Dairy farming is one of the leading component of agricultural activities in almost all parts of the world including India. India is the world's largest producer of dairy products by volume and has the world's largest dairy animal population. Demand of milk is continuously increasing due to increasing population of the world. Dairy farming has been an important part of the agricultural scenario for thousands of years. India being a predominantly agrarian economy has about 70 per cent of its population living in villages, where livestock play a crucial role in the socio-economic life. Livestock provide high-quality foods such as milk, cheese, butter, ghee, etc. India is not only one of the top producers of milk in the world, but also the largest consumer of milk and milk products in the world. Due to the shortfall in supply, we have to import significant amounts of milk products to meet internal demand. Dairy farming is a form of agriculture that is dedicated to the production of milk and dairy products from the care and feeding of cattle, mainly dairy cows. Dairy farmers focus on managing the health, welfare, and milk production of animals to obtain the highest quantity and quality of milk possible.

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 Keywords

Dairy Farming in India and Abroad, Dairy Sector, Animal Husbandry, Specific Guidelines, Review of Literature, Findings and Suggestions.

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Advancements in machine learning, particularly convolutional autoencoders (CAEs) and generative adversarial networks (GANs), have revolutionized fingerprint recreation, enabling precise and efficient reconstruction even from low-quality or partial data. This paper reviews key contributions from recent literature, highlighting convolutional neural network (CNN) architectures, multi-loss optimization techniques, and sparse encoding methods to enhance reconstruction accuracy. By synthesizing insights from these studies, the review discusses challenges such as dataset quality, adversarial robustness, and scalability, offering a roadmap for future developments in fingerprint reconstruction using machine learning.

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 Keywords

Fingerprint reconstruction, convolutional autoencoders, deep learning, biometrics, latent fingerprint analysis, biometric security, image reconstruction, ridge patterns, minutiae patterns, low-quality fingerprint enhancement, forensic fingerprint reconstruction, machine learning.

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Indian diaspora plays an important role for accelerate social and cultural development. If we look at the world culture, one thing is can be noticed is the Indian Diaspora that incorporates unique force in some veneration. The incorporation of the British Empire in India can be linked to the continuation of modern Indian Diaspora all over the world. If we look back to nineteenth century, India concurred labour was taken over to the British colonies in different parts of the world. Countries like Fiji, Mauritius, Guyana, Trinidad, Surinam, Malaysia, South Africa, Sri Lanka and so on leave the foot prints of Indian immigration in a peculiar way. Over two million Indian people fought on behalf of the empire in various wars fought abroad. Moreover in Boer War and the two World Wars and some remained behind to claim the land on which they had fought as their own. In the early part of 20th century many traders from Gujarat left for East Africa in large numbers s if it's an emulation of the ancestors. In this paper an attempt will be undertaken to reveal the social development and the relation of Indian Diaspora from colonial period to post independence period.

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 Keywords

Diaspora, India and Development

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The banking sector is the backbone and key component of the financial system that drives economic development. As a key driver of economic growth, the bank supports economic activity and meets the financial needs of all sections of society. This study employs ratios and trends to evaluate the banks financial performance in terms of capital adequacy, asset quality, earning capacity, management efficiency, and liquidity. We found that the majority of the selected banks are complying with Basel minimum capital requirement norms and earning adequately. However, it is further found that as banks earning increased non-performing asset component in bank books also increased.

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 Keywords

Basel Norms, Earning capacity, Financial position, Capital adequacy, NPA

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This paper serves as a primer on the mechanics of stock price movements, examining the factors behind price fluctuations. It highlights how autoregressive models can help explain these changes and distinguishes between stationary and non-stationary processes in stock prices. The paper also explores how market shocks influence prices. In efficient markets, new information is quickly reflected in stock prices, making them difficult to predict due to the presence of a unit root. However, investor emotions such as fear, greed, and overconfidence can lead to irrational behavior, causing mispricing and the formation of price bubbles. These psychological factors can create patterns of herd behavior that, while seemingly random, can be analyzed using technical tools like Moving Averages and the Relative Strength Index (RSI). Ultimately, the paper shows that while stock prices may appear unpredictable, human emotions and collective behavior generate recurring patterns that can be studied and used for forecasting.

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Stock Prices, Autoregression, Random Walk, Efficient Market Hypothesis, Behavioural Finance, Leading Indicators, Lagging Indicators, Volatility

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The climate change is one of the very typical issues, its impact now considered as common global issue by all the countries and looking for best controlling ways in view of future unpredictable situations. Though the problem recognised a few decades earlier, its present situational impact attracting immediate attention. Apart from human beings witnessing the adverse effects, all the plants, animals and other living beings are also not an exception. Several reports suggest climate change mostly depends on three important factors, population, environment, and utility of resources in diversified ways. Lead termination of green belts also reported as vital reason. Reports evidenced that huge variation found in average earth atmosphere prior to industrial era. East and West Godavri districts in the state of Andhra Pradesh in India have significant global identity because of its rich heritage, culture, climatic conditions, and as a profound supplier of food materials. These areas are very famous for agriculture and the quality of many products like rice, sugar cane, coconut. The aqua food cultivation made these districts wealthy enough. However, lot of changes are being observed in the typical maintenance of agriculture and other sustainment due to significant changes in seasonal weather conditions due to climate change. In view of this, it is also found at cases that people are migrating to other places for their sustainment. Typical investigations with important surveys carried out for the identification of observable impact of climate change in these areas and people. Possible scientific suggestions for mitigation of the issues were made.

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Godavari Districts, Andhra Pradesh, India, Agri and Aqua Culture, Climate Change, Impact on Sustainment, Scientific Suggestions.

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Method development and validation are critical components of analytical chemistry, particularly for UV-Visible (UV-Vis) spectroscopy and High-Performance Liquid Chromatography (HPLC). These techniques are widely used for the qualitative and quantitative analysis of pharmaceuticals, environmental samples, and food products. Over the past decade, advancements have focused on improving accuracy, precision, robustness, and sensitivity in analytical methods. For UV-Vis spectroscopy, the emphasis has been on optimizing wavelength selection, solvent compatibility, and linearity in absorbance-concentration relationships. Enhanced chemometric approaches and advanced software tools have streamlined method development, reducing time and improving reliability. In HPLC, method development has centered around optimizing chromatographic conditions such as mobile phase composition, flow rate, column selection, and detection methods. Novel stationary phases, gradient elution techniques, and the integration of mass spectrometry have expanded HPLC's applicability. Validation strategies, guided by ICH (International Council for Harmonisation) guidelines, ensure the reliability of methods through rigorous testing of parameters like accuracy, precision, linearity, limit of detection, and limit of quantification

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Method development, Validation, Analytical method, UV spectroscopy, High-Performance Liquid Chromatography (HPLC), Quantitative analysis, Qualitative analysis, Assay, Chromatographic separation, Analytical techniques.

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 Abstract

"Marriage: A Historical Survey"

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"Marriage: A Historical Survey"

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Facial gesture-based cursor control presents a novel, hands-free interaction system that leverages computer vision and machine learning to translate facial expressions into cursor movements. This system utilizes algorithms such as Convolutional Neural Networks (CNNs) to identify facial landmarks, while classifiers like Support Vector Machines (SVMs) or k-Nearest Neighbors (k-NN) interpret gestures, including blinking, smiling, and raising eyebrows, to execute actions like cursor navigation, clicks, and scrolling. Additionally, integrated voice commands provide a seamless way to articulate mouse actions, enhancing the system's functionality. By analyzing real-time video input, this technology delivers an intuitive and accessible interface, enabling gesture-driven control for individuals with mobility challenges and offering an innovative alternative for hands-free interaction.

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Computer Vision, Machine Learning, Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Support Vector Machines (SVMs), Voice commands, Hands-free interaction

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The Office Prediction Project aims to predict the likelihood of a person working in an office setting. This review paper provides an overview of the existing literature on office prediction, highlighting the key challenges, methodologies, and results. We also identify gaps in current research and propose future directions for the project. The film industry invests heavily in producing and marketing movies, making accurate box office revenue predictions crucial for minimizing financial risks. This literature review examines the application of linear regression models in machine learning for predicting box office revenue. A comprehensive analysis of existing studies reveals that linear regression models can effectively forecast box office performance using variables such as production budget, genre, release date, and social media buzz. The review discusses the strengths and limitations of linear regression models in this context, including issues related to data quality, feature selection, and model interpretability. The findings of this review provide insights for film industry stakeholders seeking to optimize production and marketing strategies using data-driven approaches. Future research directions are identified, including the exploration of ensemble methods and deep learning techniques to improve prediction accuracy.

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Box office revenue prediction, Linear regression, Machine learning, film industry, data-driven decision making.

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This study investigates the impact of porous inserts in forced convection systems on heat transfer enhancements. ANSYS Fluent 2021 is used to study the effects of varying porous layer thickness and Reynolds number on pressure drop and heat transfer rates in pipes. Porous media of varying radius ratios of R = 0.8, 0.6, 0.4, and 0.2 were analyzed under constant temperature conditions. The results from the study found that increasing the thickness of the porous medium generally enhances the peak axial velocity, shifts it closer to the pipe wall, and improves overall heat conduction efficiency. A significant temperature gradient near the pipe wall was observed, indicating an increase in the Nusselt number and overall heat transfer efficiency. Also, there exists an optimal porous thickness beyond which no significant increase in the Nusselt number occurs. The study found that the influence of porosity on heat transfer is less pronounced compared to that of porous layer thickness. The Performance Evaluation Criteria (PEC) indicates that porous media inserts substantially enhance the overall heat transfer.

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Porous Media, Porous Layer Thickness, Thermal Dispersion, Nusselt number

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Biomedical waste (BMW) management is critical for hospital hygiene and preventing the spread of infections. Housekeeping staff, who handle and dispose of biomedical waste, must possess adequate knowledge of proper management practices. This study aimed to assess the level of knowledge regarding BMW management among housekeeping staff in selected hospitals of Ujjain City, Madhya Pradesh, and to identify gaps that need to be addressed through training. Methods: A quantitative research approach using a quasi-experimental pre-test and post-test design was employed. A total of 300 housekeeping staff from various hospitals in Ujjain were selected through stratified random sampling. Data were collected using a structured questionnaire covering demographic details, general knowledge, waste segregation, handling procedures, legal knowledge, and hospital-specific practices. Knowledge levels were evaluated before and after the intervention (training program). Data were analyzed using descriptive and inferential statistics. Results: The pre-test results showed that 20% of participants had good knowledge, 50% had moderate knowledge, and 30% had poor knowledge. Following the intervention, the percentage of participants with good knowledge increased to 50%, while those with poor knowledge decreased to 10%. The t-value was 24.02, with a p-value of < 0.05, confirming that the training significantly improved the participants' knowledge.Conclusion:The study demonstrated that training significantly enhanced the biomedical waste management knowledge of housekeeping staff. However, challenges remain in consistent waste segregation and PPE usage, which require ongoing training and support. The findings underscore the importance of regular educational interventions to improve compliance with BMW management practices, ensuring better hospital hygiene and reduced health risks.

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Biomedical Waste Management, Housekeeping Staff, Hospital Hygiene, Training, Waste Segregation, PPE, Knowledge Assessment, Ujjain City.

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This study investigates the development of advanced feature engineering tools and techniques to uncover potential associations between polycystic ovary syndrome (PCOS) and gynecological cancers, such as ovarian cancer. By leveraging variables derived from ultrasound imaging and metabolic data, this research establishes a systematic approach to feature extraction, transformation, and selection for predictive modelling. Key ultrasound variables, such as follicle count and ovarian volume, are integrated with metabolic indicators, including glucose levels and hormonal profiles, to construct a comprehensive dataset. These features are then processed using methodologies like recursive feature elimination, correlation analysis, and principal component analysis (PCA) to identify the most significant predictors. Machine learning models, including logistic regression and random forests, are trained on the engineered features to evaluate the predictive accuracy and robustness of the approach. The results highlight the pivotal role of combining multimodal datasets in achieving high predictive performance, with random forests achieving an F1-score of 0.87. Furthermore, this research emphasizes the importance of feature engineering in medical diagnostics, offering insights into the complex interrelations between PCOS and ovarian cancer. The findings advocate for continued advancements in data integration and model development to support personalized healthcare interventions.

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PCOD/PCOS, Gynecological cancer, Machine Learning, Deep Learning

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Abstract--The rapid advancement of technology, particularly machine learning (ML) and the Internet of Things (IoT), is revolutionizing agriculture by optimizing crop production and ensuring sustainability. This paper presents a web-based platform developed to assist farmers in selecting suitable crops, predicting fertilizer requirements, and diagnosing plant diseases. The platform uses data from Kaggle, ML models implemented in Flask, and ReactJS for the frontend. Farmers can input NPK values and city names to receive insights on temperature, humidity, and fertilizer requirements, while also benefiting from disease prediction through image uploads and SMS alerts. The system aims to improve farming practices, promote sustainability, and enhance productivity [3], [4].

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Keywords--Machine Learning, Precision Agriculture, ReactJS, Flask, Crop Prediction, Disease Detection, SMS Alerts, Fertilizer Recommendation

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'Causality' and 'freedom' are central to discussions in Philosophy of action. Causality and freedom have been addressed from ontological as well as ethical perspectives. Ontologically, causality refers to the dynamics of changes signifying the way there is continual success of cause and effect. Needless to say that cause and effect are relative terms .That which is a cause in relation to the effect can be seen as the effect in relation to its antecedent which is the cause. The relationship between cause and effect is one of necessity such that given the cause effect can be predicted in advance and given the effect the cause can be inferred retrospectively. Causal necessity lives no room for human freedom, Therefore moral judgments cannot be passed on the causal process in the state of nature. 'Freedom' on the other hand is a moral concept. Freedom of will makes sense only in human domain, because human beings are endowed with autonomy of will. Given the circumstances, different individual are free to act differently. Causality and freedom do not go hand in hand. State of nature is understood and explained in terms of causal laws whereas it is the freedom of will which defines the distinctiveness of human action. The distinction between action and event is fundamental. Event is causally determined whereas action is determined by free will. In other words an event is caused whereas action is willed. Morality makes sense only in the domain of human actions, because human agents hare autonomy of will. The 'will' can be used and abused as well on account of which the moral judgments are rendered meaningful. The dissertation is an exercise to a undertake analysis of the notion of 'causality' and 'freedom' in Buddhist Philosophy.

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Causality, Freedom, Determinism, Samsara, Nirvana, Law of karma, suffering.

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Ayurvedic medicine is an ancient system that originated in India thousands of years ago and has been used for prevention and treatment of heart disease with the support of cardiovascular health. Heart-related illnesses are mainly caused by imbalances in the three doshas, and treating CVDs can be difficult when trying to restore their balance. Historically, herbal treatments have been used to cure a wide range of illnesses in many traditional medical systems, demonstrating their importance in human healthcare. Various Ayurvedic herbs like Ashwagandha, Arjuna, and Guggulu are scrutinized for their roles in circulating blood, lowering cholesterol, and controlling blood pressure. But a review of clinical studies and case reports indicates that if used in conjunction with conventional therapies, Ayurvedic treatments may promote heart health. More research and clinical trials are necessary to comprehend their efficacy in contemporary cardiology. The paper notes that integrating ayurvedic medicine with conventional treatments may provide comprehensive management of heart disease.

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Ayurveda, Heart Disease, Cardiovascular Health, Herbal Medicine, Arjuna, Ashwagandha, Guggulu.

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Various essential elements are crucial for the survival and well-being of human beings. other beings for their survival, agriculture plays a crucial role in the development of the economy of India. The major impediment to food security is population growth resulting in increasing demand for food. To increase the supply, farmers need to grow more on the same land. Tech can help farmers produce more through crop yield prediction. 1. The primary goal of this. paper is to predict 3. Applying crop yield. rainfall, crop, meteorological conditions, area, production, and yield variables, which have threatened the long-term sustainability of agriculture (Mahmood et al., 2017). Crop yield forecasting: a decision-making tool that employs machine learning and deep learning techniques to assist in making informed choices regarding agricultural production

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Deep Learning, Machine Learning.

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In light of the growing importance of environmental sustainability, the transition to renewable energy sources like solar energy has become crucial. Solar products, which harness solar energy for various applications, hold significant potential in addressing energy needs while minimizing environmental impact. However, the widespread adoption of these products is heavily influenced by consumer awareness and understanding of their benefits and functionality. This study examines how demographic variables such as age, monthly family income, and educational qualification impact consumer awareness of solar products. Using a descriptive and exploratory approach, the research gathered data from 120 participants in Haryana through a structured questionnaire. The analysis, which includes Chi-square tests and descriptive statistics, reveals significant associations between consumer awareness and demographic factors. Younger consumers, those with higher income, and individuals with higher educational qualifications are more likely to be informed about solar products. The findings emphasize the need for targeted education and outreach to enhance awareness, promoting the adoption of solar energy solutions for a more sustainable future.

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Exploring, Awareness, Consumers, Solar, and Products.

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The protection of personal identification numbers and passwords is a significant barrier for cybersecurity. Deceptive login pages soliciting personal information deceive billions of users daily. A variety of nefarious approaches are employed to deceive individuals into accessing harmful websites, such as phishing emails, clickjacking, malware, SQL injection, session hijacking, man-in-the-middle attacks, denial of service, and cross-site scripting. The offender creates a fraudulent yet convincingly comparable website to deceive victims into divulging their credentials. Researchers have offered many security solutions to mitigate these vulnerabilities; however, these methods are both ineffective and susceptible to error. We introduce and implement a client-side defence system that employs machine learning to detect phishing attempts and recognise fraudulent web sites. Our machine learning algorithm serves as a proof of concept for the Google Chrome plugin PhishCatcher, which classifies URLs as either trustworthy or suspicious. The random forest classifier evaluates a login page for authenticity after acquiring four web properties. The precision and validity of the extension were evaluated on multiple real-world web applications. The findings exhibited a precision and accuracy rate of 98.5% when evaluated on 400 authentic URLs and 400 identified phishing URLs. We assessed the latency of our technique using forty phishing URLs. We improved Random Forest by integrating XGBOOST, a technique that evaluates datasets through forest trees or ensembles of estimators to optimise features more efficiently and achieve superior accuracy.

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IOT devices, Support Vector Machine (SVM) and Random Forest (RF).

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This theoretical conceptualization evaluated the importance of green leadership in bringing about a sustainable change in the educational institutions particularly focusing on the psychological factors influencing the stakeholder behaviour. This framework discusses the Theory of Planned Behaviour, Value-Belief-Norm theory, and Social Identity Theory. This paper also identifies the key characteristics of green leaders that include sustainable knowledge, visionary thinking, effective communication and emotional intelligence. Simultaneously, the challenges are also identified that are faced by the green leaders in implementing the sustainable change.

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Green leaders, sustainable change, psychological factors, stakeholders, educational institutions

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Creative Commons Attribution 4.0 and The Open Definition