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A Hybrid Deep-ensemble Decision-Support Framework for Reliable Early Breast Cancer Detection: A Cross-validated Outcome Analysis
OBJECTIVE The necessity to diagnose breast cancer early and correctly is the need to minimize the diagnostic uncertainty and unwarranted clinical procedures. This paper assesses the reliability of a hybrid deep-ensemble decision-support model in terms of diagnostic reliability, stability of outcome, and translational feasibility of the model via structured clinical data to detect early breast cancer. METHODS The Wisconsin Diagnostic Breast Cancer dataset which consisted of 569 cases of benign and malignant tumors was analyzed retrospectively. The framework proposed combines the deep learning of latent representations with stacked classification, ensemble-based feature selection, and stacked classification. Performance evaluation was performed based on sensitivity, specificity, accuracy, F1-score, and area under the curve (AUC) performed using stratified 10-fold cross-validation. The statistical stability across folds and the comparison with baseline models were determined with the help of non-parametric tests (p<0.05). RESULTS The model had good diagnostic performance with an accuracy of between 91.2-100 (Mean 96), Sensitivity of 76.2-100, good specificity value, and AUC 0.973-1.000. Variability in performance between folds was low, and statistically significant enhancement as compared to baseline classifiers were present. CONCLUSION The hybrid deep-ensemble model is highly diagnostic, has robust discriminative ability, and ultimately remains stable, which demonstrates the methodological robustness and diagnostic reliability of the proposed framework as a proof-of-concept decision-support model for early breast cancer detection, with potential translational relevance subject to further external clinical validation. 2026, Turkish Society for Radiation Oncology. -
A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing
Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the models performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPMs superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPMs effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women. 2024 by the authors. -
Sodium AlginateEngineered CaF? NPs: Surface Passivation, and Tunable Biofunctional Performance
The optimization of surface chemistry in nanomaterials is vital for enhancing their applicability in advanced healthcare sectors. This study focuses on synthesizing polymer-functionalized NPs (NPs) to improve structural stability and biological efficacy against a broad spectrum of pathogens. Herein, calcium fluoride (CaF?) and sodium alginate-functionalized CaF? (CaF?SA) NPs were synthesized to determine the impact of SA on physicochemical and optical properties. The synthesized NPs were extensively characterized using XRD, UV-Vis, DLS, FTIR, PL, electron microscopy (SEM/TEM), and XPS. Their enhanced performance is attributed to defect passivation, reduced crystallite size, and the formation of a homogeneous organic-inorganic interface through strong chemical interactions between Ca? sites and alginate functional groups. The CaF?SA NPs exhibited superior broad-spectrum antimicrobial activity compared to bare CaF? against S. aureus, S. pneumoniae (Gram-positive), K. pneumoniae, E. coli (Gram-negative) and C. albicans (fungal strains). The quantitative assessments via MIC, MBC, and CFU assays confirmed effective inhibition of CaF2-SA. These findings highlight defect modulation and polymer passivation as powerful strategies, suggesting CaF?SA NPs as promising candidates for advanced bio-interactive and healthcare applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Assessing the role of trade openness, FDI, and political stability on sustainable development: Evidence from developed and developing economies
The study tries to investigate the long run and short run relationship between trade openness (TO), political stability (PO), and FDI on sustainable development of select developed and developing nations. Time series data from 1995 to 2021 of about 25 economies-10 developed economies and 15 developing economies-was collected and analyzed using Phillips Perron Fisher panel unit root test, panel auto regressive distributed lag (PARDL) model, and panel fully modified least squares/fully modified OLS. From the result, it found that FDI and TO are positively contributing to sustainability development index (SDI) in developing countries rather than the developed countries in the long run. In addition to this, changes in the SDI score is significantly influenced by the present and past import and export activities in developed as well as developing economies in the short run. 2023, IGI Global. All rights reserved. -
Exploring the Influence Dynamism of Economic Factors on Fluctuation of Exchange Rate-An Empirical Investigation for India Using ARDL Model
The Indian Foreign Exchange Market has experienced significant changes over the past decade, due to high degree of instability of the Indian Rupee leading to its devaluation against major global currencies. Exchange rate is considered as one of crucial indicators to determine the economic growth. Volatility of exchange rate of each day is influenced by various factors such as demand and supply, Gross Domestic Product, Interest rate, employment rate, public debt, balance of payments, inflation etc. Though there are multiple causes to determine the movement of exchange rate, but still the accurate level of causation is unpredictable. Keeping this in mind, this paper tries to attempt the relationship that exists between the exchange rate and select macroeconomic factors. To analyse the extent of influence of the selected variables on the exchange rate, the research paper uses 10 years of data spanning from Jan 2013 to Nov 2022. Further, the study uses monthly data of above-mentioned variables to bring out the analysis to meet the objectives. Descriptive statistics is used to find the characteristics of the data, correlation analysis and Ordinary Least Square method is used to find the relationship and impact level select macroeconomic factors on exchange rate. Autoregressive Distributed Lag (ARDL) model is used to find if any short run and long run association exists between the variables and the exchange rate. 2023, ASERS Publishing House. All rights reserved. -
Measurement Model of CO-PO Attainment in Higher Education: A Simplified Approach
The educational system in most countries are moving toward Outcome-Based Education (OBE) which is a student-centric teaching and learning methodology. The basic idea behind the adoption of OBE model is that the graduates should possess a sound knowledge in their respective disciplines and also have global mobility and acceptance. The Outcome-Based Education (OBE) should be based on the vision and mission of the institution. The institutions should clearly spell out the learning objectives of the program and course. The Course Outcome (CO), Program Outcome (PO), Program Specific Outcome (PSO) and Program Educational Objectives (PEO) determine clearly what the students are expected to accomplish, post their course or program respectively. This study aims to provide the simplified approach on assessment, evaluation and calculating the attainment levels of students through COs and POs in a management program. To assess the CO attainment for management courses, the authors have identified the subject Entrepreneurship Development offered in the first semester from the 2018-2020 batch of 60 students from the MBA program of an autonomous institute. The Course Outcome (CO) and Program Outcome (PO) are mapped with the Continuous Internal Assessments (CIA) and Semester Exam End (SEE) and thus the attainment levels of each CO are measured. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Attention to Economic Factors and Its Response to Foreign Portfolio Investment: An Evidence from Indian Capital Market
Stock market consists of a variety of investors. Among these, Foreign Portfolio Investors (FPIs) is a key investment influx. These investments can change or fluctuate due to several macroeconomic factors which can cause a shift in the dynamics of the markets in India. This paper examines the factors influencing for foreign portfolio investment in long run as well as short run. The sample comprises of 120 monthly observations on Foreign Portfolio Investment (FPIs) and Macro economic variables such as Oil prices (OP), Gross Domestic Product (GDP), Interest Rate (IR), Exchange rate of Indian Rupee with USD (ER), Inflation (CPI), Nifty Index (NSEI), 10year Bond Prices (BP) and Index of Industrial production (IIP) over a period of 10years, spanning from January 2013 to November 2022. The study employed Autoregressive Distributed Lag model (ARDL) to establish the long run association with error correction models. The result indicates that there is long run association between the Foreign Portfolio Investment and macro-economic variables. Among this, NSEI, IIP and ER played a significant role to determine FPI investments in the long run, whereas in the short run, FPI was impacted by ER and NSEI significantly. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Autoregressive Distributed Lag Approach for Estimating the Nexus between Net Asset Value of Mutual Fund and Economic Determinants in India
India has seen a phenomenal growth in cumulative mutual fund investment from Rs 7.93 trillion in 2012 to Rs 40.38 trillion in 2022, which is more than a five-fold increase since last 10 years. Retail investors are now realizing the power of savings and Systematic Investment Plans (SIP) to build long term wealth. A financial literacy wave which is sweeping across India has projected mutual funds as a significant contributor and beneficiary of this phenomenon. The evolving economic landscape of India provides investors with excellent opportunities to capitalize on these fluctuations through systematic investment in safe investment vehicles like mutual funds. The market associated with mutual funds is always subjected to economic risks. The erratic fluctuations in macroeconomic variables can largely explain the Volatility in Net Asset Value (NAV) of equity oriented mutual fund schemes. With this background, this paper examines the impact of select macroeconomic variables on mutual funds performance in India. To analyse this, monthly observations of select macroeconomic variables, average NAV of large cap, mid cap, and small cap funds collected for a period of 10 years starting from January 2013 to November 2022. Descriptive statistics is used to probe the characteristics of the variable. In addition, correlation and ordinary least square method is applied to check the existing relationship and impact level of macroeconomic factors on NAV of select schemes. Lastly, short and long run relationship is analysed using Autoregressive Distributed Lag Model (ARDL). 2023, ASERS Publishing House. All rights reserved. -
Does Green Financing affect the Sustainable Economic Growth of Emerging Economies? Evidence from Panel ARDL Model
This study examines the nexus between green finance determinants and sustainable economic growth in Brazil, India, China, and South Africa using a panel Autoregressive Distributed Lag (ARDL) approach. These rapidly developing countries face the dual challenge of maintaining economic growth while addressing environmental sustainability. The analysis focuses on five key independent variables: Comparative Advantage in Low Carbon Technology Products, Total Trade in Low Carbon Technology Products, Trade Balance in Low Carbon Technology Products, Annual CO2 Emissions, and Lack of Coping Capacity. Short-run results indicate that Total Trade in Low Carbon Technology Products negatively affects GDP, suggesting that while green trade is expanding, it currently lacks stable, revenue-generating mechanisms. Annual CO2 Emissions and Lack of Coping Capacity positively influence GDP in the short term, reflecting continued dependence on emission-intensive industries and limited infrastructure for resilience. Comparative Advantage and Trade Balance in Low Carbon Technology Products are statistically insignificant in the short run, implying delayed economic benefits. In the long run, none of the green finance indicators show a significant relationship with GDP, possibly due to the substantial upfront investments required for green projects, which delay economic returns. The study underscores the need for strategic investments in technology, infrastructure, and governance to align economic growth with long-term sustainability goals. 2025 Sathish Pachiyappan et al., published by Oikos Institut d.o.o. -
Golden Insights: Analyzing the Influence of Economic Indicators on Sovereign Gold Bond Performance in India
India has been the leading consumers of gold with the consumption of around 774 metric tons in 2022. The demand for gold in India is majorly associated with its culture, tradition, attractiveness, and the source for financial security (GJC,n.d.)The gold market in India plays a vital role in the economy as a stable asset and hedge against inflation due to its ability to hold value over time. In order to limit the import of gold and reduce the countrys current deficit, the Indian Government introduced Sovereign Gold Bonds in 2015 as a substitute to physical gold. As SGBs export-import values are backed by Reserve Bank of India (RBI) they are considered as an inflation hedging tool. The study aims to examine the effectiveness of SGBs, in the changing economy by understanding the impact of key economic indicators Inflation Rate, Exchange Rate, Per Capita Income, Gold Prices, and GDP Growth Rateon the performance of Sovereign Gold Bonds (SGBs) in India. 36 months observations of the selected macroeconomic variables and series wise released prices are collected for a period starting from September 2021 till August 2024 for the analysis. Descriptive statistics is applied to understand the characteristics of the variables. Further, correlation and ordinary least square method is used to check the existing relationship and impact level of macroeconomic variables on SGBs. Lastly, both long run and short run relationships of these variables are analyzed using the Autoregressive Distributed Lag Model (ARDL). 2025, Iquz Galaxy Publisher. All rights reserved. -
The Macro Lens: Exploring the Impact of Macroeconomic Variables on Indias Small Cap, Mid Cap, and Large Cap Indices
Subject and Purpose of Work: This study explores the intricate relationship between key macroeconomic variables and Indias equity market segments, specifically the NIFTY Small-cap, Mid-cap, and Large-cap indices. The primary objective is to evaluate how selected macroeconomic factors influence market dynamics and investor sentiment in the Indian context. Materials and Methods: The research analyses monthly data spanning five years, from January 2019 to January 2024. The macroeconomic indicators considered include Foreign Institutional Investment (FII), Domestic Institutional Investment (DII), Consumer Price Index (CPI), Purchasing Managers Index (PMI), Treasury Bill Rate, Gold Price, and Reverse Repo Rate. Statistical techniques such as the Unit Root Test, Ordinary Least Squares (OLS), and Granger Causality Test are employed to assess the short-term and long-term impacts of these variables on market indices. Results: The findings reveal that GDP, CPI, PMI, and Gold Price exhibit no statistically significant influence on the NIFTY Small-cap, Mid-cap, or Large-cap indices, aligning with certain earlier studies. However, variables like FII, DII, Treasury Bill Rate, and Reverse Repo Rate show varying degrees of influence across the indices, highlighting the complex and segmented nature of the Indian equity market. Conclusion: These insights are valuable for investors, policymakers, and financial analysts in refining investment strategies, informing policy frameworks, and enhancing market forecasting models. The study underscores the need for continuous evaluation of macroeconomic influences to better navigate market volatility and investor behaviour. 2025 Sathish Pachiyappan et al., published by John Paul II University of Applied Sciences. -
Driving profitable business growth through economical optimization, energy management, and industrial 5.0 innovations
The chapter emphasizes the significance of economic optimization, energy efficiency, and Industrial 5.0 innovations in driving sustainable growth and profitability in today's business landscape. It highlights the strategic allocation of resources to maximize efficiency and minimize costs, using lean management principles, automation, and data analytics. Energy management is crucial for reducing operational costs and mitigating environmental impact, using renewable energy sources and smart technologies. Industrial 5.0, a new era of industrial transformation, combines automation, connectivity, and data exchange, with technologies like artificial intelligence, IoT, and blockchain. 2024, IGI Global. -
Scripts influence on reading processes and cognition: a preamble
[No abstract available] -
Editorial: Methods and applications in cognitive science
[No abstract available] -
A Multifaceted Approach at Discerning Redditors Feelings Towards ChatGPT
Generative AI platforms like ChatGPT have leapfrogged in terms of technological advancements. Traditional methods of scrutiny are not enough for assessing their technological efficacy. Understanding public sentiment and feelings towards ChatGPT is crucial for pre-empting the technologys longevity and impact while also providing a silhouette of human psychology. Social media platforms have seen tremendous growth in recent years, resulting in a surge of user-generated content. Among these platforms, Reddit stands out as a forum for users to engage in discussions on various topics, including Generative Artificial Intelligence (GAI) and chatbots. Traditional pedagogy for social media sentiment analysis and opinion mining are time consuming and resource heavy, while lacking representation. This paper provides a novice multifrontal approach that utilises and integrates various techniques for better results. The data collection and preparation are done through the Reddit API in tandem with multi-stage weighted and stratified sampling. NLP (Natural Language processing) techniques encompassing LDA (Latent Dirichlet Allocation), Topic modelling, STM (Structured Topic Modelling), sentiment analysis and emotional analysis using RoBERTa are deployed for opinion mining. To verify, substantiate and scrutinise all variables in the dataset, multiple hypothesises are tested using ANOVA, T-tests, KruskalWallis test, Chi-Square Test and MannWhitney U test. The study provides a novel contribution to the growing literature on social media sentiment analysis and has significant new implications for discerning user experience and engagement with AI chatbots like ChatGPT. 2024 Padarha et al., licensed to EAI. -
Interconnected Intelligence: Navigating Through Power Quality Checking and Control Using Smart Intelligence-Based Methods
Globally, power quality issues incur substantial costs. In the United States, power quality problems contribute to a $150 billion annual cost, covering lost productivity, equipment damage, and safety hazards. Smart intelligence-based methods can potentially cut these costs by up to 50%. In India, power quality disturbances result in a $10 billion annual cost involving equipment damage, productivity losses, and customer dissatisfaction. The adoption of smart intelligence-based power quality methods in India is projected to grow annually by 25% for the next 5years due to increasing grid demands. In todays intricate power landscape, dependable electrical systems are crucial. Power quality disturbances, including voltage variations, harmonics, and flicker, can disrupt sensitive equipment, resulting in financial losses and safety risks. Addressing these challenges, smart intelligence-based methods emerge as promising solutions. This chapter systematically explores the application of artificial intelligence, machine learning, and data analytics for elevated power quality monitoring, assessment, and regulation. Such intelligent approaches optimise power system performance, reduce downtimes, and ensure a consistent supply of high-quality electrical energy. The assimilation of smart intelligence-based methods emerges as a promising avenue to address these challenges effectively. Harnessing the capabilities of these intelligent paradigms empower power systems to attain optimal performance, curtail downtimes, and ensure a steadfast provision of high-grade electrical energy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Leveraging ensemble learning for enhanced security in credit card transaction fraudulent within smart cities for cybersecurity challenges
In the age of digital transactions, credit cards have emerged as a prevalent form of payment in smart cities. However, the surge in online transactions has heightened the challenge of accurately discerning legitimate from fraudulent activities. This paper addresses this crucial concern by introducing a pioneering system for detecting fraudulent credit card transactions, particularly within highly imbalanced datasets, in the realm of cybersecurity. This paper proposes a hybrid model to effectively manage imbalanced data and enhance the detection of fraudulent transactions. This paper emphasizes the efficacy of the hybrid approach in proficiently identifying and mitigating fraudulent activities within highly imbalanced datasets, thereby contributing to the reduction of financial losses for both merchants and customers in smart cities. As cybersecurity in smart cities evolves, this paper underscores the significance of ensemble learning and cross-validation techniques in optimizing credit card transaction analysis and fortifying the security of digital payment systems. 2024, Taru Publications. All rights reserved. -
Legal conundrums of space tourism
Private commercial space tourism carrying passengers to outer space is no longer a distant or far-fetched fantasy, rather it is at verge of becoming an affordable reality with exponential development in space technology including development of Reusable Launch Vehicle (RLV), increasing involvement of private companies like Virgin Galactic, SpaceX, Blue Origin etc. into research and funding of space tourism explorations and applications. It is also receiving huge attention from the public. These developments reflect the infinite possibilities and inevitability of space tourism in near future. However, space tourism may also pose many critical legal issues which must be addressed to ensure the consistent and sustainable development of space tourism, and to secure the rights of all stakeholders involved including operators, passengers, launching State etc. The research paper would highlight the crucial legal issues associated with the space tourism. The paper would critically analyze the efficiency of the present international space treaties in dealing with these issues. At the end, the paper would also attempt to provide few suggestions and solutions to these legal conundrums relating to space tourism. 2021 IAA -
Humour as a Moderator Between Hassles and Well-Being
Humour is a universal phenomenon that offers several physiological and psychological benefits across cultures. The objectives of this study were to examine the relationships between daily hassles, humour and well-being; and to investigate the moderating effect of humour on the relationship between hassles and well-being. A correlational design was adopted to collect data from 644 participants (men = 300, women = 344), aged between 18 and 58years using purposive and snowballing sampling techniques. The Daily Hassles Scale, Sense of Humour Questionnaire (SHQ-R) and the Personal Well-Being IndexAdult (PWI-A) were administered to the sample. The self-report measures were appropriately scored and the collective data were analyzed. Statistical analyses revealed a positive relationship between sense of humour and well-being. A negative relationship was observed between sense of humour and hassles; and between well-being and hassles. Further, sense of humour was found to be moderating the relationship between daily hassles and well-being. This study highlights the role of humour in softening the impact of hassles on the well-being of the Indian population. This strengthens the construct of humour in the context of positive psychology. The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India 2024. -
A hybrid crypto-compression model for secure brain mri image transmission
Medical image encryption is a major issue in healthcare applications where memory, energy, and computational resources are constrained. The modern technological architecture of digital healthcare systems is, in fact, insufficient to handle both the current and future requirements for data. Security has been raised to the highest priority. By meeting these conditions, the hybrid crypto-compression technique introduced in this study can be used for securing the transfer of healthcare images. The approach consists of two components. In order to construct a cutting-edge generative lossy compression system, we first combine generative adversarial networks (GANs) with oearned compression. As a result, the second phase might address this problem by using highly effective picture cryptography techniques. A randomly generated public key is subjected to the DNA technique. In this application, pseudo-random bits are produced by using a logistic chaotic map algorithm. During the substitution process, an additional layer of security is provided to boost the techniques fault resilience. Our proposed system and security investigations show that the method provides trustworthy and long-lasting encryption and several multidimensional aspects that have been discovered in various public health and healthcare issues. As a result, the recommended hybrid crypto-compression technique may significantly reduce a photos size and remain safe enough to be used for medical image encryption. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.