Financial Risk Prediction for Agricultural Enterprises Using Intelligent Modeling and Dynamic State Analysis

Agricultural enterprises have financial uncertainties due to market volatility, climate disruptions, and changes in policies; therefore, farming operations must use timely and accurate forecasts, as they are particularly vulnerable to external economic shocks and environmental variability. Standard forecasting methods usually cannot capture nonlinear dependencies and dynamic shifts in risk profiles; therefore, there is a need to consider intelligent, adaptive systems. Research proposes a novel financial risk prediction model using the Sooty Tern Optimization Algorithm Attention-Based Long Short-Term Memory (STOA-Att-LSTM). Financial risk data were collected, which included agricultural enterprise financial records, national weather databases, and commodity market indices. To ensure data integrity and modelling efficiency, two essential pre-processing techniques were employed. Handling missing values was performed using linear interpolation to reconstruct incomplete sequences, particularly in time-series financial and climatic data, to standardize variables, facilitating efficient model training and convergence. The STOA algorithm was used to optimize the hyper-parameters of the Att-LSTM model, enhancing its generalization and predictive accuracy. The attention mechanism enabled the model to dynamically focus on critical time-dependent features influencing financial risk. Dynamic state analysis further strengthened the framework by capturing temporal shifts in enterprise conditions. Model evaluation using Python-based implementation of error metrics and classification accuracy (0.9899) showed better results compared to traditional and baseline deep learning (DL) models. The proposed framework offers a robust, adaptive tool for proactive financial risk assessment in agricultural enterprises, supporting sustainable decision-making in uncertain environments

Community based Sustainable Tourism Development - A tool for fostering and promoting peace: A case study of Odisha, India.

Much has been analyzed, discussed and written about tourism, its interdisciplinary approach and multidimensional concepts, but one of the most significant roles that the tourism has been playing for a while now and which has not come into the limelight is the fostering and promotion of peace. Tourism is regarded as one of the most pioneering sectors, and has not only made socio economic contributions but also created a harmonious platform for all to exchange, share, and understand each other better in order to gain co-operation, mutual understanding, a sense of belonging, and integrity. In the backdrop of a peaceful society where justice, equality, human rights and prosperity stand firm, this is in part due to the remarkable contributions of tourism in assimilating people into a common platform of thought. Society and its people have witnessed socio economic development, the creation of equal opportunities for everyone to live in, and sustainability which to a greater extent has been achieved due to the catalytic nature of tourism which in turn fosters and promotes a peaceful existence. This paper emphasizes and explores the role of tourism in enhancing peace through community based sustainable tourism development by interconnecting the environment, the local community, the tourists, and other important aspects .The paper cites some of the key examples of Odisha in terms of its community participation and their involvement in sustainable development initiatives leading to the harmonious inter-existence between locals and visitors

Leveraging AI-Driven Health Informatics for Predictive Analytics in Chronic Disease Management

People are getting long-term illnesses like diabetes, heart disease, and high blood pressure more and more often. Because of this, it\u27s even more important to find better ways to handle these situations and move quickly when they happen.  Using AI-powered health informatics in predictive analytics seems like a good way to improve the quality of care and patient outcomes when dealing with long-term illnesses.  This study looks at how AI models, like machine learning algorithms, predictive modelling, and data-driven analytics, can change how long-term illnesses are watched, identified, and treated.  By looking at a lot of data from smart tech, medical pictures, and electronic health records (EHRs), AI systems can find patterns and guess how a disease will get worse before the symptoms show up.  By finding high-risk patients early on, these insights can help healthcare workers make the best use of resources, give more personalised care, and cut costs. Using AI in health technology also makes it easier to make systems that can keep an eye on people with long-term illnesses in real time. These systems can keep an eye on vital signs, living factors, and drug compliance all the time.  This can help people get help right away, which can cut down on problems and hospital stays.  AI technologies can also help automate repetitive chores like data filing, medical support, and decision-making, which frees up healthcare workers to spend more time caring for patients directly.  However, using AI to handle chronic diseases can be hard because of issues with data protection, the need for uniform data forms, and making sure that AI models can be understood and held accountable.  At the end of the paper, the future uses of AI in managing chronic diseases are talked about. It is emphasized that healthcare workers, data scientists, and lawmakers need to keep researching and working together to get the most out of AI-driven health informatics

Multiple ocean parameter-based potential fishing zone (PFZ) location generation and validation in the Western Bay of Bengal

A new conceptual framework based on satellite data, including chlorophyll (CHL), sea surface temperature (SST) fronts, relative winds, current vectors, Ekman transport, and eddies, has been developed to identify potential fishing zones (PFZ) in the Bay of Bengal (BoB). The framework aims to provide persistent forecasts, even under cloudy conditions, based on feature propagation. The validation of the PFZ was carried out using fish catch data collected by the Fishery Survey of India (FSI) between 2016 and 2018. Hooking rates (HR) from longlines and catch per unit effort (CPUE) from trawl nets were used to analyse the data points in hook rate categories (1.0–3.0 and > 3.0) and CPUE categories (50–100 kg and > 100 kg) and interpret them with the PFZ maps. The analysis showed that the high fish catch locations were consistent with persisting features in the BoB, such as high chlorophyll patches, SST fronts, and cyclonic eddies. The high fish catch locations based on hook rate and high CPUE were found to be collocated with the high chlorophyll persisting features and thermal gradients in the BoB. The regression analysis shows that availability of the food (CHL) had the strongest correlation with fish catch, followed by the comfort condition (fronts and eddies)

Causality between Service Quality and Customers’ Satisfaction Across Star Hotels

Background: To win the race of hares, one has to become a leopard performing in faster and pro-active manners than others to win the competition. But to come first and remain ahead of others for longer durations, demands quality initiatives in business processes and therefore the topic on quality in service operations beards paramount importance. Objectives: On this note, we have tried to assess the prevailing service quality of various star category hotels in the state of Odisha in India where the renowned SERVQUAL scale is employed to map the difference of opinions between the guests visiting the hotels. Materials &amp; Methods: A total of 395 numbers of interviews were conducted through convenient sampling process and the data was captured through the use of a structured questionnaire. The collected data were assessed through the use of descriptive statistics that includes percentage calculations, cross tabulations, t-test, analysis of variance, correlation coefficient, and multiple regression analysis, etc. Results: The results revealed sorry state of affairs at the selected hotels where positive gap scores were obtained between the expectations and perceptions of the customers. Based upon the findings, curtain corrective measures are proposed in the study for enhancing the qualitative parameters of the hotels. Epilogue: Survival of the fittest is the word that describes the market scenario. The entities that remain fit, agile, anticipatory, proactive, caring, and most importantly quality conscious can only win the race with flying colours. Originality: This study analyzes the quality of various services initiatives prevailing at the star hotels in the state of Odisha in India for which our study can certainly help in generating different guidelines to ensure the optimal levels of quality as desired by the guests at the hotels which will help in development of a long term and sustainable quality initiative in the sector compared to international standards.</jats:p

The Role of Influencer Marketing on Consumer Buying Decision

The purpose of this research is to identify how influencer marketing can influence buying behavior of the consumers. To find out the same, primary data collection method was followed in this research work. Sample size was 66 however, 50 complete responses were received. From the responses it was found that most some participants directly buy products recommended by influencers while most of them check reviews about that product before buying it. Some consumers compare recommended products with other products before buying and some consumers check the price before buying. Therefore, from this information it can be concluded that influencers can positively influence buying behavior of the consumers. However, several factors affect this. For example, higher number of followers ensures that an influencer influences consumer. However, if influencers are paid for product promotion, then it cannot influence consumer buying behavior. On the other hand, area of expertise of the influencers also plays a major role in influencing buying behavior of the consumers. Besides, if an influencer is from a same country with the followers, then the influencers can easily influence their buying behavior.Keywords: Influencer marketing, consumer buying behavior, consumer-buying process, buying behavior of followers</jats:p

Hybrid Multi-User Based Cloud Data Security for Medical Decision Learning Patterns

Machine learning plays a vital role in the real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also these cloud based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers in order to store medical records or machine learning patterns for decision making. Due to high computational memory and time, these cloud systems require an efficient data security framework in order to provide strong data access control among the multiple users. In this paper, a hybrid cloud data security framework is developed to improve the data security on the large machine learning patterns in real-time cloud computing environment. This work is implemented in two phases, data replication phase and multi-user data access security phase. Initially, machine decision patterns are replicated among the multiple servers for data recovering phase. In the multi-access cloud data security framework, a hybrid multi-access key based data encryption and decryption model is implemented on the large machine learning medical patterns for data recovery and security process. Experimental results proved that the present two-phase data recovering and security framework has better computational efficiency than the conventional approaches on large medical decision patterns.</jats:p

Impact of Influencer Market on Consumers of the Tourism Industry

This research has focused on the concept of influencer marketing and its impact on consumers of the travel and tourism industry. The goal was to find out whether travel and tourism companies through influencer marketing can attract consumers in this post-COVID situation or not. For this purpose, empirical data were collected from fifty social media users who are following at least one travel influencer on any social media site. From their responses collected by sharing survey questionnaire, it was found that most of the participants are not influenced by social media influencers in terms of deciding where they would travel.</jats:p