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Strategic Integration of AI in Modern Data Management
The exponential growth of data from sources such as social media, IoT, and enterprise systems has catalyzed a transformative shift in data management practices. This paper explores the integration of artificial intelligence (AI), edge computing, cloud-native frameworks, and graph-based techniques to support intelligent, low-latency, and scalable data processing across complex ecosystems. It presents a comparative analysis of classical versus modern data architectures, highlighting how technologies like Graph Neural Networks (GNN4TS), reinforcement learning, and large language models (LLMs) enable more adaptive, interpretable, and automated pipelines. The study also addresses challenges in legacy system modernization, time-series modeling, and cyber threat detection while underscoring the role of AI in autonomous database management and metadata enrichment. Further, it examines critical risks - including explainability, adversarial vulnerabilities, concept drift, and privacy preservation - associated with AI-integrated data workflows. A structured overview of emerging paradigms such as neuro-symbolic AI, adaptive governance in multi-agent systems, and the potential of quantum computing provides a future-focused lens on intelligent data ecosystems. The insights presented aim to assist researchers, data engineers, and decision-makers in navigating the evolving landscape of AI-driven data management. 2025 IEEE. -
A perspective analysis on doodle art used in five educational materials /
Doodle Art are simple drawings that have concrete representational meaning in abstract shapes. A doodler intently shifts through information to generate substantial understandings. Doodle Art is one of the evolving styles that are attracting young audience especially through subject materials. Themain aim ofthe study is to understand whether doodling has emerged as a new trend in brand recall. -
Comparative Study on Gasoline and Methanol in a Twin Spark IC Engine
In search of a viable alternative to petrol and diesel, methanol, ethanol and biodiesel play an important role. Methanol and ethanol are traditional alternatives to petrol(gasoline) because of better engine performance and reduced emission of carbon monoxide, oxides of nitrogen (NOx), unburnt hydrocarbon (UBHC) and other harmful gases. This work represents the result of four sets of spark timings on engine performance and engine emissions when run on methanol and petrol. Exhaustive investigations are carried out on a variable compression ratio DTSi engine for both methanol and gasoline. Engine was run at full throttle and at a constant speed of 1600RPM. Theefficiency of the engine found to be enhanced with methanol fuel which has higher octane number and high laminar flame speed. Maximum efficiency was found to be ~25.45% and ~28.7% at compression ratio 10 for gasoline and methanol fuel, respectively. This is observed at 2624 BTDC (before top dead center) spark advance combination. Optimum compression ratio for gasoline and methanol is found to be 6.8 and 7.4, respectively, at this spark advance angle combination. Moreover, methanol fuel eventually emits lesser amount of CO, UBHC and NOx than gasoline under all experimental combinations. 2021, Springer Nature Singapore Pte Ltd. -
Enhancement of Accuracy Level in Parking Space Identification by using Machine Learning Algorithms
Parking space identification is a crucial component in the development of intelligent transportation systems and smart cities. Accurate detection of parking spaces in urban areas can significantly improve traffic management, reduce congestion, and enhance overall parking efficiency. This proposed model is focuses on enhancing the accuracy of parking space identification through the utilization of Support Vector Machine (SVM) algorithms. The proposed methodology involves the following steps. First, a dataset comprising labelled parking space images is collected and pre-processed to ensure optimal quality and consistency. Next, feature extraction techniques are applied to capture certain relevant spatial and textural information from the images in the dataset, enabling the creation of informative feature vectors. These feature vectors are then utilized to train a SVM model, which is well-known for its capability to handle complex classification tasks. To measure the effectiveness of the SVM-based approach, a comprehensive set of experiments is carried out using real-world parking data. The performance metrics is to analysis accuracy level of the parking space identification. Comparative analysis has been done by comparing the proposed SVM approach with other popular machine learning algorithmsto demonstrate the superiority. The results indicate that the SVM-based model achieves a significantly higher accuracy level in parking space identification compared to other existing algorithms. 2023 IEEE. -
Volatility-Based Stock Categorization and Risk-Informed Investment Support System
This paper presents an application that will benefit novice investors by categorizing stocks by levels of volatility so users can better understand risk by average parameters and increase factors assessed for long-term wealth generation. The offering model supports portfolio creation by enabling novice investors to choose appropriate options based on their risk tolerance, something that would be more challenging for investors with limited financial savvy under different circumstances. To develop an investment companion application that reduces emotional investing and increases strategic long-term financial decision making through effective data visualization, especially for novices. An application that uses statistics on volatility to compartmentalize stocks by low, medium and high-risk options for selection, with the ability to create and assess a portfolio based on this criteria through a virtual interface. Increased investment construction accessibility, the ability to create diverse portfolios, and a foundation for subsequent advanced investment features like notifications and trend predictive analysis. 2026 IEEE. -
Statistical Forecasting of Fat in Body Proportion Utilizing Nonlinear Anthropological Parameters and Density Evaluation
Body Fat Percentage (BFP) is an accurate body fat assessment, plays vital role in order to evaluate an individual's health status and disease risk. Traditional BFP assessments, such as dual-energy X-ray absorptiometry (DXA) and hydrostatic weighing are high in accuracy which is compromised by their cost and complexity. This research work focuses on creating a predictive BFP model using anthropometric techniques. For formulating and validating the proposed model, a benchmark dataset is used consisting of 252 samples having measures of weight, height, waist circumference (WC), hip circumference (HC), skinfold thicknesses along with air displacement plethysmography (ADP) based density estimates. For feature engineering, the most important values are selected such as body mass index, hip ratio etc., as well as logarithmic values and then the best artificial neural network model is trained. The proposed model is developed using quadratic polynomial terms with a literature-based space-cost function (r > 0.98), provided the best model with a Mean Absolute Error (MAE) of 1.5% and coefficient of determination R = 0.92 outperforming conventional works. 2025 IEEE. -
Reflections on the issues and determinants associated with women's career progression in hospitality industry at Bengaluru /
Social Sciences International Research Journal, Vol.2, Special Issue, ISSN: 2935-0544. -
Women chefs in Indian hospitality industry: Challenges and strategies /
International Multidisciplinary Research Journal, Vol.4, Issue 7, pp.117-132, ISSN No: 2231-5063. -
A study of entrepreneurial choices and challenges encountered by young graduates
India, one of the most populous countries is growing phenomenally, though the challenges of unemployment is compounding. An unique method of overcoming this issue is through motivation of college students in becoming entrepreneurs, which will not only create employment but will also reduce the pressure of gaining employment on the students. However, flexible government policies in favor of entrepreneurs will facilitate the economic development of the country. In this study, a quantitative method is used to collect the data on entrepreneurship and the changing preferences of college students. A survey (N= 209) among college students of Bengaluru, India is conducted to identify the impact of entrepreneurship on work life choices of young graduates, evaluate the emergence of entrepreneurs in influencing decisions and analyzing the differing choices of males and females in terms of entrepreneurial selections. Analysis of the collected data indicates that Indian Government policy, unskilled labor, entrepreneurial education, family background and caste are factors affecting the entrepreneurial growth rate in Bangalore. Entrepreneurship education in Bangalore is still in the early stages, thus, depriving the college students from acquiring gainful practical knowledge. The structure of a conventional learning system and lack of social experiences also affects the learning process. 2019, International Journal of Scientific and Technology Research. All rights reserved. -
Impact of COVID-19 on Delivery of Quality Hospitality Education in India
The Covid-19 pandemic caused many industries globally to undergo radical changes in their operational systems, disrupting the service delivery processes. The education industry is no exception to this phenomenon. India's higher educational institutions witnessed the immense challenge of taking the teaching process online with limited means and infrastructural support. This study aimed to assess the impact of the pandemic on the delivery of education online in India with particular reference to hospitality courses. A survey of 250 students and interview of 10 faculty members from 5 universities offering hospitality course across India showed that the online learning system is far from satisfactory and effective. Moreover, teachers need to undergo training sessions in order to improve their online teaching skills and create newer methods of imparting skills and evaluating students' performance. IJHTS -
Analyzing online food delivery industries using pythagorean fuzzy relation and composition
Food and beverages constitute a significant portion of the family expenditure, which motivates the food delivery companies in striving hard to meet the customer needs through their dynamic food delivery apps. The online food ordering system is one of the most profitable marketing strategies for restaurant businesses. The face of the restaurant industry has shifted from the traditional dine-in culture to takeaways, online ordering, and home deliveries. Digital technology and social media have a significant role in ensuring the efficiency and popularity of a food delivery app. The four essential factors for a food delivery company to satisfy the needs of the consumers in day to day life are choice of restaurants, speed of delivery, payment option and quality of service. The objective of this study is to discern and analyse these four essential factors adopted by the leading four food delivery companies and evaluate the perceptions of the consumers. The best online food delivering company is identified using Pythagorean Fuzzy Relation (PFR)and composition. The analysis concludes that Zomato food application is the best in consumers perception.The outcome of the survey is made more efficient by adopting a mathematical approach. Copyright IJHTS. -
Government Support and Policies
Government interventions play a crucial role in nurturing technopreneurship and advancing sustainability. The proposed chapter explains the significance of governmental support in bridging the financial, infrastructural, and knowledge gaps that technopreneurs face. It categorizes the types of support into financial aid, regulatory frameworks, infrastructure development, and educational programs, providing a structured overview of each. A detailed analysis of policy frameworks that foster innovation and sustainability is presented, supported with global examples such as the United States Small Business Innovation Research (SBIR) program, Israels Innovation Authority, Indias Digital India Initiative and Germanys High-Tech Strategy 2025. These examples illustrate how strategic policies can catalyse technological advancements and economic growth. The chapter further includes case studies from diverse regions, showcasing successful policy implementations and their tangible impacts. These case studies offer practical insights and best practices, demonstrating how tailored policies can create robust technopreneurial ecosystems. Finally, the chapter addresses the challenges in policy implementation and offers recommendations for future directions, emphasizing the need for adaptive, inclusive, and collaborative policy approaches. This comprehensive exploration aims to provide policymakers, academicians, and technopreneurs with valuable knowledge on leveraging government support for sustainable technopreneurial success. 2025 selection and editorial matter, Rajender Kumar, Rahul Sindhwani, Raman Kumar, Punj Lata Singh, and J. Paulo Davim. -
Social-Ecological System Framework Network
The Social-Ecological Systems Framework Network (SESFN) provides a holistic approach to understanding the complicated relationships between ecological and social systems. By integrating network analysis, SESFN unveils the dynamic interconnections and interdependencies that shape these systems, offering critical insights into governance, resilience, and adaptive capacity. This framework is a powerful tool for addressing contemporary challenges such as biodiversity conservation, resource management, and climate change. Through interdisciplinary collaboration, SESFN facilitates stakeholder engagement, combining traditional knowledge with scientific research to foster sustainable practices. The application of SESFN has established its effectiveness in promoting adaptive management and improving both ecosystem health and human well-being. As global environmental challenges deepen, SESFN emerges as a pivotal and essential framework for crafting innovative solutions to achieve sustainability and resilience across diverse social-ecological contexts. 2026 John Wiley & Sons Ltd. All rights reserved. -
BRICS VS. G7: A COMPARATIVE ANALYSIS OF ECONOMIC AND POLITICAL EFFICIENCY IN SHAPING GLOBAL ORDER
The global distribution of power is increasingly shaped by the competing influences of two major blocs: BRICS (Brazil, Russia, India, China, and South Africa) and the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States). This paper investigates how BRICS and the G7 shape the emerging multipolar global order. Using comparative analysis of key indicators: GDP, trade flows, investment patterns, diplomatic engagement, and strategic alliances. The paper examines each blocs structure and internal cohesion. The analysis underscores the G7's historical supremacy, which stems from its economic strength and political unity, in contrast to BRICS rising role as a representative for the Global South and a platform for alternative governance models. Important metrics include trade flows, investment trends, diplomatic efforts, and strategic alliances. The research also assesses the internal dynamics within each bloc, including challenges to cohesion and the effectiveness of decision-making. By comparing the advantages and drawbacks of BRICS and G7, this paper provides insights into their respective functions in a multipolar world order, evaluating their ability to promote transformative global agendas. Lastly, the paper concludes that both alliances embody divergent approaches to global governance, reflecting deeper shifts in international collaboration, competition, and the balance of power. 2025, Observare. All rights reserved. -
Perception of students and teachers on E-learning modules /
Studies suggest that e-learning has not been successful in India yet, the scope is wide given threshold of change that the country stands at and the high percentage of youth population. With E-learning Students have flexibility and an enormous range of courses at their fingertips. Also, today an important aim of institutions today is to create a knowledge base, data in the form of courses that can be accessed globally. -
Synergistic effects of NiSe2 on S-doped g-C3N4 for efficient caffeine degradation and electrocatalysis
This work focuses on the synthesis and characterization of NiSe2 on S-doped g-C3N4 to enhance the degradation of caffeine and improve the electrocatalytic performance in both HER and OER. Through a controlled synthesis method, NiSe2 was successfully anchored onto the surface of S-doped g-C3N4, leading to a significant increase in active sites and improved charge transfer. From the PXRD analysis, the crystallite sizes for the planes (210) and (311) were found to be 26 and 21 nm. Morphological analysis confirmed the uniform distribution of NiSe2 nanorod-like structures on the S-g-C3N4 nanosheets. Additionally, the composite demonstrated superior photocatalytic degradation efficiency of 96 % for caffeine under visible light irradiation by the composite, highlighting its potential application in both environmental remediation and energy conversion technologies. After the addition of hydroxyl and singlet oxygen scavengers, the degradation has been decreased to 50.3 % and 47.36 %, highlighting the potential of these radicals in the removal of caffeine. The electrochemical measurements revealed a remarkable increase in HER and OER activities of the NiSe2 on S-doped g-C3N4 composite (?128 mV and 338 mV at 10 mA cm?2 and 50 mA cm?2 respectively) compared to S-doped g-C3N4 and NiSe2 alone. This study highlights the promising role of NiSe2-S-g-C3N4 composites as multifunctional materials in addressing pressing challenges in water treatment and sustainable energy. 2025 Elsevier B.V. -
A Study and Analysis on Various Types of Agricultural Drones and its Applications
Drones are considered to be the greatest invention of mankind. Drones can be used in many areas widely. Drones can also be used in agriculture and it is called as unnamed aerial vehicles (UAV). In the traditional agriculture methods land vehicles are used to monitor various activities of the agriculture, this was consuming lot of human effort and time. Using drones in agriculture is more beneficial than using traditional methods for the activities. Usage of drones in agriculture provides a huge benefit in terms of economy and time due to their most astonishing features. In recent years many surveys have proved that drones can cover almost 10 to 15 times of the area which can be covered with traditional land based techniques. Drones can be controlled by computers according to their capacities, that is drones can be automated over some range of area, locating remote area, and even can be semi-automated. Drones can be efficiently used in agriculture for performing certain activities such as, studying weather conditions and variations, infection for the crops, land fertility and many more. Because of the efficiency of the drones they can be used in various activities of agriculture. In this paper, a detailed study has been made on various types of agricultural drones based on the feature, capacity, range as well as cost and the area of agriculture where they suit the most, and a statistical analysis about the usage of the drones in the field of agriculture. 2020 IEEE. -
The Quantification of Human Facial Expression Using Trapezoidal Fuzzy Membership Function
Fuzzy Inference System is an interesting approach. Major benefit of the FIS is, it permits the natural narration in linguistic terms of tribulations that can be resolved rather than in requisites of associations between accurate arithmetical points. This helps, handling with the complicated systems in easy way, is the major motive why fuzzy system is broadly incorporated in practice. In the present research paper, an effective approach is proposed that quantifies the human facial expression using Mamdani implication based fuzzy logic system. The recent principle engages in retrieving arithmetical values from persons face and feed them to a fuzzy classifier. Fuzzification and Defuzzification process issues trapezoidal fuzzy membership function for input as well as output. The diverse characteristic of this method is its effortlessness and maximum correctness. Experimental outcome on Image dataset depicts excellent accomplishment of the proposed methodology. In this paper, a legitimate procedure proposed for quantification of human facial expression from the features of the face by means of Mamdani type fuzzy inference system, which is proficient to set up a convenient membership association involving the various dimensions of the happy expression. Values representing features of the face are fed to a Mamdani-type fuzzy classifier. This system recognizes three levels of same happy expression namely Normal, Bit Smiley and Loud Laugh. The total output expressions for this proposed scheme is three. Another discrete element of the proposed methodology is the membership method model of expression outcome which stands on various surveys and readings of psychology. Springer Nature Singapore Pte Ltd. 2019. -
