This study aimed to assess environmental risks using data extraction techniques. It focused on geophysical and biological factors and addressed the urgent need for effective risk management strategies to reduce soil erosion, water pollution, and air quality deterioration. A comprehensive dataset was created through the systematic collection of geophysical and biological data including temperature, soil composition, and biological abundance index. It used equipment such as satellite sensors and mountain transmitting stations. Various statistical tools used include decision trees and random forest algorithms. They were used to analyze data and identify important environmental risk factors. The results showed some interesting insights, revealing that the Neural Network has an accuracy of 95.5%, and the Random Forest algorithm predicts risk with an accuracy of 92.0%. It analyzed the classification of environmental hazard zones and identified high-risk zones, such as Zone A, which contains 10,000 people affected by erosion and Zone D, 20,000 people who were at risk from soil contamination. The study concludes that social media can significantly improve understanding and management of environmental risks. Additionally, it provides a useful framework for decision-makers and stakeholders to promote sustainable environmental practices.

The production function defines the output of a firm, industry, or economy based on various combinations of inputs. This study focuses on estimating the production function of India's manufacturing sector and determining the relationship between its inputs and outputs. To predict the output, measured as Gross Value Added (GVA), the study applies the Cobb Douglas Production Function (CDPF) along with several deep learning techniques, including Feedforward, Recurrent, Long Short-Term Memory (LSTM), and Bidirectional LSTM networks. Model performance is evaluated using metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the Cobb Douglas Production Function outperforms the deep learning models in predicting GVA for the organized manufacturing sector in India.

This paper focuses on the design, analysis, and fabrication of the composite body panel structure used to enclose the BAJA vehicle's roll cage structure. Traditionally, this composite structure was created to replace expensive aluminium panels while also replacing acrylic and polycarbonate panels, which are weaker and can be just as expensive as composite panels. Composite panels can be made from fibreglass, carbon fibre, wood-plastic composite, etc. In this study, we shall begin with the material selection followed by the design calculations, software-based analysis, and practical applications of the composite panels along with the manufacturing methodology.

There are numerous proceedings that take place within an actual computer network, and one of them is the monitoring of the network traffic in real-time with the added function of anomaly detection. This research focuses on the use of machine learning to improve these capabilities as stated in the following section. In the context of the current study, the emphasis is made of building powerful anomaly detection models that would be capable to work in real life by defining network and potential threats on their own due to their machine learning capabilities. Furthermore, the study gives a detailed analysis of the more complex methods like feature selection in addition to dimensionality reduction for enhancing the abilities of machine learning algorithms in the management of big data samples for world-wide network traffic. Furthermore, the presented research focuses on the application of definitions of edge computing paradigms to facilitate decentralized processes of the identification of anomalies, thereby enhancing the sensitivity and response time of essential networks. Thus, the research objectives are to address the aforelisted challenges and generate insights into constructing better network security frameworks to prevent and respond to future threats in a precise and effective mechanism.

Social media platforms have seen a significant increase in the number of users and content in recent years. Owing to the increased usage of these platforms, incidents of teasing, provocation—both positive and negative—and harassment, and community attacks have increased tremendously. There is an urgent need to automatically identify such content or tweets that can hamper the well-being of an individual or society. Analyzing social media messages from Twitter and Facebook has become the focus of sentiment analysis in recent years, which formerly focused on online product evaluations. Sentiment analysis is used in a wide range of fields besides product reviews, including harassment, stock markets, elections, disasters, and software engineering. After the tweets have been preprocessed, the extracted features are categorized using classifiers like decision trees, logistic regression, multinomial nave Bayes, support vector machines, random forests, and Bernoulli nave Bayes, as well as deep learning techniques like recurrent neural network (RNN) models, long short-term memory (LSTM) models, bidirectional long short-term memory (BiLSTM) models, and convolutional neural network (CNN) model for sentiment analysis. In this paper, different techniques are compared to classify Twitter tweets into three categories: “positive,” “negative,” and “neutral.” We proposed a novel data-balancing technique for text classification. A text classification technique is proposed for analyzing textual data using the Generative Pretrained Transformer model owing to its contextual understanding and more realistic data generation capability. Comparative analysis of different Machine learning and Deep learning models are performed with and without data balancing. The experiments show that the accuracy and F1-measure of the Twitter sentiment classification classifier are improved. The proposed ensemble has outperformed and achieved an accuracy of 90%, precision of 88%, and 81% F1 score.

Diabetes and its complications are a major cause of morbidity and mortality in India, and the prevalence of type 2 diabetes is on the rise. This calls for an assessment of the economic burden of the disease. The research design is exploratory and descriptive. The required information was collected using a structured questionnaire and interview method. The sampling criterion for this study was purposeful sampling. The study sample was composed of 60 diabetes patients from Kannamanaickanur Village and was conducted from January 2023 to March 2023. The prime objective of the study is to find out the Socio-economic profile of the diabetic, to estimate the direct cost, and indirect costs and to suggest the adoption of effective measures for the surveillance, prevention, and control of diabetes, to reduce the economic burden of diabetics. The data were entered in SPSS version 20.0 and percentage analysis was applied to interpret the Socio-economic and clinical profile of the diabetic patients. The Direct cost and indirect costs incurred by diabetic patients are estimated by descriptive statistical tools. The chi-squar. e test is applied to find out the association between the clinical factors and the cost of treating Diabetes. ANOVA is used to analyze the relationship between the age and total cost. The factors influencing the cost of treating Diabetes and the economic burden of Diabetic patients in the Kannamanaickanur Village have been analyzed by multiple regression model. The high out-of-pocket expenditures and income loss due to diabetes are likely to have long-term consequences for households, particularly those with low income and no health insurance.

In the rapidly evolving landscape of enterprise network management, artificial intelligence (AI) is emerging as a transformative force. This paper, titled "Revolutionizing Enterprise Network Management: The Role of AI-Driven Solutions in Modern Computer Networking," delves into the significant impact of AI technologies on the efficiency, security, and scalability of enterprise networks. By integrating AI-driven solutions, organizations can achieve unprecedented levels of automation, predictive maintenance, and real-time anomaly detection, thus enhancing overall network performance. This study provides a comprehensive analysis of the latest AI techniques employed in network management, including machine learning algorithms, neural networks, and advanced data analytics. Through case studies and empirical data, we demonstrate how AI enhances network security, reduces downtime, and optimizes resource allocation. Our findings suggest that the adoption of AI in network management not only improves operational efficiency but also offers a competitive advantage in the digital economy. Keywords: AI-driven network management, enterprise network security, machine learning in networking, predictive maintenance, network automation, real-time anomaly detection, computer networking, digital transformation.