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Deep Learning-Based Consolidated Disease Classification in Health Data Management
Healthcare data management is critical for ensuring comprehensive and high-quality medical treatment. Sensitive patient data management has a potential new option thanks to blockchain technology. However, existing blockchain-based healthcare data management systems face challenges in scalability, integration, and regulatory compliance. To address these issues, a novel blockchain-based healthcare data management system has been proposed to provide a secure, decentralized, and interoperable platform for managing sensitive patients medical information. Proposed approach involves collecting comprehensive health measurements from patients using wearable sensors and ensuring the security and integrity of patient data through robust user verification protocols. Artificial Neural Networks (ANNs) are employed to consolidate disease symptoms, enhancing the efficiency and accuracy of data analysis. The results and comparative analysis showcase the efficiency of the proposed method in terms of precision, accuracy, recall, search accuracy and F1-score. The accuracy of the proposed method is improved by 12.9%, 6.07%, and 14.28% when related to the existing ACTION-EHR, BSDMF, and BlockMedCare techniques respectively. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Food Security and Its Impact on Society: Cases of Developing World
Food security is a matter of concern in the twenty-first century as is evident from the importance given to it in the United Nations Sustainable Development Goals. Despite attempts to address food scarcity concerns at global conventions such as the World Food Summit of 1996, food remains scarce. Scholars further suggest that though food scarcity is a global issue, its roots and impact is local. Consequently, a study of food must study the major challenges that converge to undermine food security worldwide including conflicts, climate change, global policies and in recent times even the Covid 19 pandemic. However, at a fundamental level food scarcity is the by-product of not just a legacy of past failures to build more just, sustainable, and resilient food systems, but rather a by-product of our inability to be responsible and sustainable consumers. This chapter highlights that despite surplus food production, developing nations often face food insecurity owing to the diversion of food towards developed nations. These nations, instead of sharing global resources (including food and agricultural labour), often contribute towards the global food crisis. Moreover, some of these developed nations engage in an industrialised system of food produc-tion which might meet the nations food requirements but are not sustainable modes of production and pose a serious threat to the environment. Nevertheless, the indis-cretions of the developed nations affect the developing nations economically as well as socially. As social outcasts, marginalised communities and individuals within the developing world are worst affected. As a result, this chapter offers insight into the social struggle brought on by inaccessibility to food. The chapter further suggests that addressing concerns of food security is not only a matter of addressing the inequalities manifest in the production, distribution and consumption of food but also learning to be responsible and sustainable consumers. Simply stated, the chapter recommends connecting SDG 2 with SDG 12. This chapter would also include the position of India in GHI, the Ukraine crisis and its aftermath in various developing countries, the earthquake in Turkey and how it affects the food security, and a few instances from Africa to highlight the concepts of food security and its correlation with sustainability of any society. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Sustainable consumption: Barriers and catalysts for green purchase
This chapter examines factors influencing green purchasing in both developed and developing nations, addressing catalysts and barriers. Key obstacles include price sensitivity, socioeconomic disparities, lack of standardized eco- labeling laws, and ingrained cultural beliefs. It highlights the roles of government regulations, corporate social responsibility (CSR), environmental awareness, and sustainable packaging in shaping consumer behavior. The study explores knowledge gaps, financial limitations, and cultural influences that affect sustainable choices. Strategies such as eco- labeling, tiered pricing, and financial incentives are analyzed, alongside CSR's impact on consumer trust. Emerging trends like post- COVID green consumption, blockchain for eco- label transparency, AI- driven marketing, and circular economy practices are also discussed. Ultimately, the chapter proposes an integrated framework combining technological, cultural, and economic perspectives to enhance sustainability. 2025, IGI Global Scientific Publishing. All rights reserved. -
Artificial Intelligence in Fostering Sustainable Development
Sustainable development is vital to mankind. The world is finding a growing effort of Artificial Intelligence (AI) towards sustainability, and we made an attempt to address the development in sustainability using AI systems. Sustainable development has three pillars of sustainability (i.e. social, economic, environment), and as such, the pillars of sustainable AI. The entire Life cycle of AI products can foster change in the movement of sustainability from which greater integrity and social justice can be achieved. Sustainable AI helps us to address the whole socio-technical system more than AI applications. This paper tried to address the positive impacts of AI on sustainable indicators in terms of Environmental, Societal and Economy factors. This paper is prepared to make readers, policymakers, AI ethicists and AI developers to inspire and connect with the environment for the current and future generations as there are few AI costs to be made compatible with the environment. 2023 American Institute of Physics Inc.. All rights reserved. -
Unveiling the Future: Exploring Stock Price Prediction in the Finance Sector through Machine Learning and Deep Learning - A Comprehensive Bibliometric Analysis
The investigation of predicting share prices is a captivating and beneficial area of study within the realm of economic research. precise projections and findings can potentially benefit shareholders by reducing the risk of making suboptimal investment selections. The objective of this investigation is to examine the present state of research pertaining to the prognostication of share price predictions through the utilization of Machine Learning (ML) and Deep Learning techniques. The present study examined the existing body of scientific works on methods involving DL and ML in the context of predicting the value of stocks. This study presents a comprehensive overview of research trends, methodologies, and applications in a particular field by conducting a bibliometric analysis of publications indexed in the Scopus database. Drawing from the presented data, recommendations for optimal methodologies can be formulated. The data was visually represented through the utilization of the R programming language and Vos Viewer software. The investigation additionally discerns the primary authors, institutions, and nations that are making contributions to this particular field of research. The outcomes of this investigation possess the potential to guide future research trajectories and offer significant perspectives for professionals and policymakers who are keen on utilizing machine learning and deep learning in the financial sector. 2024 IEEE. -
Development and Validation of a Framework to Identify High Potential Employees in Organizations
The present study aimed to develop and validate a multidimensional framework for identifying high-potential employees (hi-pots) to support succession planning and leadership development in organizations. A sequential exploratory mixed-methods approach was employed. In the qualitative phase, semi-structured interviews with seven organizational leaders were conducted to identify key traits and behaviors of hi-pots. The subsequent quantitative phase involved 276 managerial employees who responded to a newly developed measurement scale. The data were subjected to statistical validation to reinforce the proposed model. The validated framework comprises three dimensions: Foundation, growth, and career. The foundation dimension includes inherent traits such as optimism and sociability. The growth dimension, encompassing promotability, adaptability, and proactiveness, showed the strongest predictive power for leadership readiness. The career dimension involves performance-based competencies like technical proficiency and time management. Among these, the growth dimension emerged as the most influential for leadership potential. Organizations can utilize this framework for structured talent identification, improving leadership pipelines, and strategic HR planning. 2026 Econjournals. All rights reserved. -
A Novel Approach to Packet Dropping and Malicious Attack Detection using Ensemble Techniques
Packet-dropping attacks interrupt data transfer while damaging security protocols, which create a threat to wireless Sensor Networks and Mobile Ad Hoc networks. This paper examines packet-dropping detection methods as well as security attack identification since these threats represent significant risks to networks such as Wireless sensor networks and Mobile Ad Hoc Networks. The research paper utilized a dataset from Kaggle for network traffic analysis, which classified packets through their behaviors as either abnormal or normal. The detection employed a stacking classifier with logistic regression as the meta-classifier and Support Vector Machine, Gradient Boosting, and K-Nearest Neighbour as its main constituents. The analysis model showed high detection rates for packet-dropping incidents, reaching 93.5%, and for malicious attacks, reaching 98.2%, based on the experimental test results. The obtained data shows that stacking models show stable reliability levels above traditional approaches. Ensemble learning proves effective for discovering cyber threats through results that reduce the number of incorrect detections. The stacking classifier functions as a dependable framework for developing security measures required to protect computer networks from modern-day threats. 2025 IEEE. -
A Study on the Impact of Intervention Program for the Care Givers of People with Head and Neck Cancer
Head and neck cancer patients experienced profound psychosocial and functional abilities because of the location of the disease and treatment. It hampers their activities of daily living making them dependant on the caregivers. Subsequently caregivers have different needs especially during the initial phase of hospitalization because they are unfamiliar to the whole process, unprepared for the new task and new role and lack the necessary knowledge and skills in care giving. Thus they face a number of problems. Hence this study aimed to understand their needs, develop psycho educational intervention program based on it and assess its feasibility. It was taken up because of limited number of Indian studies and increase in the number of incidences owing to the changing life style. Quasi experimental research design and sequential mixed research design was used. The variables taken for the study were Caregiver burden and distress. Zarit burden interview schedule and Caregiver Self Assessment Questionnaire was used to collect data from 30 caregivers of head and neck cancer patient before and after the Psycho educational intervention program was delivered. Paired sample t test and Cohen s d tests were used for data analysis. The effect size for burden and distress was 2.01 and 1.91 respectively. Findings showed that the intervention program significantly reduced the level of burden -
Thermorheological and magnetorheological effects on Marangoni-Ferroconvection with internal heat generation
Marangoni convectiveinstability in a ferromagnetic fluid layer in the presence of a spatial heat sourceand viscosity variation is examined by means of the classical linear stability analysis. The higher order Rayleigh-Ritz technique is used to compute the critical Marangoni number. The effective viscosity of the ferromagnetic liquid is taken to be a quadratic function of both the temperature and magnetic field strength. It is shown that the ferromagnetic fluid is significantly influenced by the effect of viscosity variation and is more prone to instability in the presence of heat source compared to that when viscosity is constant. On comparing the corresponding results of heat source and heat sink it is found that heat sink works in tandem with the effect of viscosity variation if magnetic field dependence of viscosity dominates over temperature dependence. If the temperature dependence of viscosity dominates, the effects of viscosity variation and heat sink are mutually antagonistic. Published under licence by IOP Publishing Ltd. -
Bard-Taylor ferroconvection with time-dependent sinusoidal boundary temperatures
The combined effect of centrifugal acceleration and time-varying boundary temperatures on the onset of convective instability of a rotating magnetic fluid layer is investigated by means of the regular perturbation method. A perturbation expansion in terms of the amplitude of applied temperature field is implemented to effectively deal with the effects of temperature modulation. The criterion for the threshold is established based on the condition of stationary instability manifesting prior to oscillatory convection. The modulated critical Rayleigh number is computed in terms of Prandtl number, magnetic parameters, Taylor number and the frequency of thermal modulation. It is shown that subcritical motion exists only for symmetric excitation and the destabilizing effect of magnetic mechanism is perceived only for asymmetric and bottom wall excitations. It is also delineated that, for bottom wall modulation, rotation tends to stabilize the system at low frequencies and the opposite is true for moderate and large frequencies. Furthermore, it is established that, notwithstanding the type of thermal excitation, the modulation mechanism attenuates the influences of both magnetic stresses and rotation for moderate and large frequencies. Published under licence by IOP Publishing Ltd. -
On the influencing facets of infant mortality in Karnataka: A study based on birth orders
The infant mortality rate (IMR) is used to assess the overall physical health of any community. Reducing this and spreading awareness among people can improve the well-being of society. In India, IMR is high due to the complex and challenging health policies and increased population, but various socio-economic and demographic factors play a significant role in determining the infant mortality rate. This study majorly focuses on identifying the factors influencing infant mortality, and a model has been proposed to estimate the likelihood of an infant's survival in Karnataka. For the empirical analysis, data has been taken from the National Family Health Survey-4 (2015-16), India. It is found that mothers' education and female literacy are the most significant factors affecting the IMR irrespective of the birth order. It is also found that the various socio-economic and demographic factors do not have a significant influence on the survival status of an infant as the birth order increases. Other factors like preceding birth interval, wealth index, caste, and religion also influence infant mortality. Hence, it is suggested that parents should have access to quality education and health facilities near their place of residence to reduce infant mortality at each order of birth. 2025 Author(s). -
Adoption and Usage of Artificial Intelligence in Food Processing Industries
In recent years, technological changes and advancements have forced Fast Moving Consumer Goods (FMCG) industries, especially food processing, to redesign their functionality. This includes the integration of technologies like Artificial Intelligence (AI) to enhance performance. Future trends in the food processing industry will be shaped by sustainability, efficiency, traceability, wellness, safety, hygiene, health, and newlinetransparency. Food processing industries are compelled to embrace digitalization in the newlinecurrent era of globalization and digital transformation. AI encompasses programs, newlinealgorithms, robotics, drones, data mining, cloud computing, sensors, driver-less newlinevehicles, the internet of things, digital platforms, and machines, representing a new newlinelevel of intelligence. AI aims to replicate human reasoning and problem-solving newlinecapabilities, leading to task automation, increased efficiency, and reduced human newlineeffort. The growth of AI is reshaping the food processing industry, with potential newlineapplications spanning from cultivation, supply chain management, storage and safety, newlineHuman Resource Management (HRM), and Customer Relationship Management (CRM). Integrating and adopting AI in food processing can address unique challenges and offer substantial benefits across these functions. While large-scale food processing newlineindustries have made significant progress in adopting AI systems, small and mediumscale food industries are also integrating AI technology. The current research study employs a quantitative research methodology and obtained data from 320 small and medium-scale food processing industries employees in the city of Bengaluru. The primary surveyed data were analyzed using the Structural Equation Modeling (SEM) approach through AMOS 26. The research used the UTAUT 2 model to measure the usage and adoption of Artificial Intelligence (AI) among the employees of small and medium-scale food processing industries. -
Digitalizing Sustainable Trade Corridors: A Multi-Layered Big Data Analytics Framework for Green Trade Reform
The contemporary period of rapid digital transformation has resulted in global trading routes changing from using physical means to intelligent ecosystems. One of the main ideas of this chapter is the conceptual framework that combines the power of the Internet of Things (IoT) and Big Data Analytics (BDA) to totally change the customs procedures as well as the supply chain management. However, the use of technology has only been applied in some parts of the cross- border trade sector which has created information silos and caused slow operations. This research is dedicated to showing the advantages of coupling the instantaneous IoT sensor data with powerful BDA workflows in risk reduction, and- increased transparency, and even promoting regional integration. The chapter offers a practical guide for policymakers and logistics professionals who want to connect the technical aspect of digital customs reforms with the ethical and sustainable implementation. 2026 by IGI Global Scientific Publishing. -
Unfolding the aggression and locus of control paradigm in sportspersons and non-sportspersons
The present study investigated Aggression and Locus of Control on Combat Sports Persons, Non-Combat Sports Persons, and Non-Sports Persons. In this study, a sample of 240 individuals (80 Combat sports, 80 Non-Combat Sports & 80 Non-Sportspersons) was used through purposive sampling. The tools administered were the Buss and Perry Aggression Questionnaire by Arnold H. Buss and Mark Perry and Rotters Locus of Control Scale by Julian Rotter respectively. The objective of the study was to investigate Aggression and Locus of Control in males and females from Combat, Non-Combat, and Non-Sports persons. This research also aims to explore the relationship between Aggression and Locus of Control. Mean, t-test, F-value (ANOVA), and correlation have been computed over SPSS-16. Results suggest that males from Combat have higher Aggression than people from non-sports and non-combat sports. There is also a significant difference between non-sports persons and sports people over the Locus of Control, sports persons showed internal locus of control compared to non-sports persons who were higher on external locus of control. The result also indicates a significant relationship between the anger dimension of the Aggression and Locus of Control. 2025 ARD Asociaci Espala. -
Unfolding the aggression and locus of control paradigm in sportspersons and non-sportspersons
The present study investigated Aggression and Locus of Control on Combat Sports Persons, Non-Combat Sports Persons, and Non-Sports Persons. In this study, a sample of 240 individuals (80 Combat sports, 80 Non-Combat Sports & 80 Non-Sportspersons) was used through purposive sampling. The tools administered were the Buss and Perry Aggression Questionnaire by Arnold H. Buss and Mark Perry and Rotters Locus of Control Scale by Julian Rotter respectively. The objective of the study was to investigate Aggression and Locus of Control in males and females from Combat, Non-Combat, and Non-Sports persons. This research also aims to explore the relationship between Aggression and Locus of Control. Mean, t-test, F-value (ANOVA), and correlation have been computed over SPSS-16. Results suggest that males from Combat have higher Aggression than people from non-sports and non-combat sports. There is also a significant difference between non-sports persons and sports people over the Locus of Control, sports persons showed internal locus of control compared to non-sports persons who were higher on external locus of control. The result also indicates a significant relationship between the anger dimension of the Aggression and Locus of Control. 2025 ARD Asociaci Espala. -
An Efficient Compressive Data Collection Scheme for Wireless Sensor Networks
The Compressive Data Collection (CDC) scheme is an efficient data-acquiring method that uses compressive sensing to decrease the bulk of data transmitted. Most existing schemes are modeled as Non-Uniform Sparse Random Projection (NSRP), and an NSRP-based estimator is used. These models cannot deal with anomaly readings that deviate from their standards and norms. Therefore, we provide a new CDC strategy in this study that uses an opportunistic estimator and routing. Initially, neighbor nodes are identified using the covariance function following the Gaussian process regression, and the data transfer to the neighbor node is done using the compressive sensing technique. Compressed data are then projected by using conventional random projection. Finally, the sample required to retrieve data is estimated using margin-free and maximum likelihood estimators. Results show that the sample needed to retrieve the data is less in the proposed scheme. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A self-cooperative trust scheme against black hole attacks in vehicular ad hoc networks
The main objective of the Vehicular Adhoc NETwork (VANET) is to provide secure communications for the vehicles in the network without fixed infrastructures. It inherits all the properties of the MANET. Achieving reliable routing to avoid various routing attacks is the major concern in the vehicular network. Routing attacks degrade the performance of the network. Black hole attack is one of the routing attacks, which drops the data packets without forwarding them to the destination vehicle. Different routing schemes are proposed to provide security against these attacks, which still have security issues. Hence a new self-cooperative trust scheme is proposed in this paper, to detect single as well as collaborative black hole attackers in the network. Two processes: self-detection and cooperative detection, are used to detect attackers in the network. Results show that the proposed scheme has better performance in terms of throughput, PDR and delay. Copyright 2021 Inderscience Enterprises Ltd. -
An Enhanced Secure Message Authentication Protocol for Internet of Vehicles
Internet of Vehicles (IoV) aims to transform the driving experience to the next level by ensuring communication with other vehicles, pedestrians handheld devices, Road Side Units (RSU), and other sensors used in the smart city environment. It integrates the benefits of the Internet of Things (IoT) and Vehicular Adhoc NETwork (VANET) to offer a safe and comfortable driving environment. Since communication is established through insecure channels, IoV is prone to various security attacks. Hence, there is a need for an authentication mechanism to ensure secure communications. For VANET, various authentication techniques are available, but they are not suitable for IoV due to their high computation overhead. Hence, we suggest a novel secure authentication scheme for IoV, which ensures authentication, conditional privacy preservation, message integrity, traceability, and unlinkability. It also provides security against various attacks. The proposed scheme performs better with less computation, communication, and storage overhead. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Employing Artificial Intelligence and Automation in Sustainable Development Research
The rise of artificial intelligence (AI) and its increasingly widespread influence across various sectors necessitates an evaluation of its impact on the attainment of Sustainable Development Goals (SDGs). Industrialized nations have reaped the benefits of 21st-century technologies, particularly automation, which has fundamentally transformed the manufacturing and industrial production processes. The next evolutionary phase in automation is the advent of AI, characterized by machines and systems demonstrating intelligence, not only capable of performing tasks but also collaborating synergistically with humans and the environment. Among its myriad contributions, AI is poised to enhance development, foster sustainable resource utilization, and facilitate effective waste management. Intelligent systems are set to reshape various domains, including transportation, precision agriculture, biodiversity conservation, environmental modeling, public health, construction, manufacturing, and initiatives aimed at fostering prosperity on Earth. These systems will possess the ability to perceive, analyze situations, and responsively react to real-time cues such as human gestures, facial expressions, and the movement of pedestrians crossing busy streets. This research delves into the intricate relationship between AI systems and the objectives of sustainable development (SD). 2025 by Apple Academic Press, Inc. -
Employing Artificial Intelligence and Automation in Sustainable Development Research
The rise of artificial intelligence (AI) and its increasingly widespread influence across various sectors necessitates an evaluation of its impact on the attainment of Sustainable Development Goals (SDGs). Industrialized nations have reaped the benefits of 21st-century technologies, particularly automation, which has fundamentally transformed the manufacturing and industrial production processes. The next evolutionary phase in automation is the advent of AI, characterized by machines and systems demonstrating intelligence, not only capable of performing tasks but also collaborating synergistically with humans and the environment. Among its myriad contributions, AI is poised to enhance development, foster sustainable resource utilization, and facilitate effective waste management. Intelligent systems are set to reshape various domains, including transportation, precision agriculture, biodiversity conservation, environmental modeling, public health, construction, manufacturing, and initiatives aimed at fostering prosperity on Earth. These systems will possess the ability to perceive, analyze situations, and responsively react to real-time cues such as human gestures, facial expressions, and the movement of pedestrians crossing busy streets. This research delves into the intricate relationship between AI systems and the objectives of sustainable development (SD). 2025 by Apple Academic Press, Inc.