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Multimodal artificial intelligence for early cancer detection via liquid biopsy, imaging, and clinical records
Tumours are diverse and multiscale, making it difficult for modern medicine to diagnose early cancer. Using structured clinical data, radiologic imaging features, and liquid samples, this research presents a multimodal AI framework for the early and reliable detection of cancer. The proposed approach surpasses single-modality approaches by integrating signals from various domains, including cancer genetic, anatomical, and physiological data. Using attention-based fusion, representation learning, and better preprocessing, we developed a prediction model that fine-tuned the weights of different modes. The results of the experiments demonstrated that it outperformed unimodal models on all datasets in terms of sensitivity, specificity, and generalisation. The framework has potential for screening purposes because of its ability to detect cancer at an early stage. Clinical confidence and interpretability were both boosted by the results of explainability tests, which revealed substantial feature contributions. The suggested multimodal framework outperformed unimodal baselines across all assessment cohorts with an AUC of 0.94, sensitivity of 0.91, and specificity of 0.88. Experimental results confirm multimodal fusion's clinically interpretable early cancer detection and precision oncology decision assistance. Copyright 2026. Published by Elsevier B.V. -
Sustainable Innovations in Statistics and Data Science
Description: Sustainable innovations in statistics and data science are increasingly vital in tackling complex global challenges such as climate change, public health crises, and resource management. By developing and applying advanced analytical methods, these fields enable more efficient, equitable, and informed decision-making across sectors. Integrating sustainability into data practices ensures that technologies support long-term environmental, social, and economic goals. This intersection not only enhances the accuracy and relevance of insights but also promotes ethical data use aligned with global sustainability standards. Sustainable Innovations in Statistics and Data Science brings together cutting-edge research, methodologies, and applications that address sustainability challenges across various fields. It delves into insights, techniques, and case studies that drive sustainable outcomes in environmental science, healthcare, urban planning, and other critical areas. Covering topics such as air pollution, environmental science, and urban development, this book is an excellent resource for researchers, academicians, graduate and postgraduate students, data science and statistics practitioners, policymakers, government officials, industry leaders, innovators, educators, curriculum developers, and more. Coverage: The many academic areas covered in this publication include, but are not limited to: Air Pollution Artificial Intelligence (AI) Cardiovascular Health Climate Change Corporate Social Responsibility (CSR) Data Science Environmental Science Geometric Distribution Healthcare Quality Control Smart Cities Statistics Sustainable Innovation Urban Analytics Urban Development. 2026 by IGI Global Scientific Publishing. All rights reserved. -
A Compact Workflow Model for Cloud Computing
Scheduling tasks in the cloud computing environment, particularly for data intensive applications is of great importance and interest. In this paper, we propose a new workflow model presented in a rigorous graph-Theoretic setting. In this new model, we would like to incorporate possible similarities between requisite files which are needed to complete the given set of tasks. We show that it is NP-Complete to compute the make span in this model even with oracle access to the cost of retrieving a file. 2015 IEEE. -
Evaluation of machine and deep learning models for utility mining-based stock market price predictions
Considering the extreme volatility of stock market returns and hazards, accurate price prediction has attracted the attention of both financial institutions and regulatory bodies. Stocks, due to their historically strong returns, have long been considered by investors to be an excellent asset allocation strategy. Predicting stock prices has never ceased being a hot topic of study. Many early-day economists sought to foretell future stock values. In subsequent years, as computer technology has advanced rapidly and mathematical theory has been extensively studied, it has been shown that mathematical models, like the time series model, may be very effective in predicting due to their simplicity and superiority. Over time, the time series model is put into practice. Over time, the horizon widened. Support vector machines and other ML techniques have challenges when applied to stock data because of its non-linearity. In subsequent years, thanks to advancements in deep learning, models like RNN and LSTM Neural Networks were able to analyze non-linear input, remember the sequence, and remember valuable information,Stock data forecasting cannot be done without it. 2024 Author(s). -
Carmelight Trends in Social Sector Expenditure
The Multidisciplinary National Journal, Vol-10 (1), pp. 77-96. ISSN-0975-9484 -
Nexus Between The Carbon Dioxide Emission And Economic Growth: Evidence From India
Increase in economic activities contributes to the economic growth of a country. It is evident that emerging economies have recorded higher economic growth and significant increase in coal consumption, energy consumption and electricity consumption. On the other hand, the emission of greenhouse gases (GHG) generating consequences in the atmosphere. In this context, this study tries to analyse the association between GDP per capita, FDI, population, trade openness and CO2 emissions per capita in India. The study is based on secondary data, which has been collected from the World Bank database. The time period under consideration is from 1960 to 2017. Augmented Dickey Fuller test has been used to test the unit root. VAR lag order criteria have been used for lag selection of the model. Since the variables are integrated at I (1) and I (0), the ARDL model has been used for the purpose of analysis. Furthermore, for checking the stability of the model, the CUSUM test has been used. The results show that in the long run, GDP per capita and FDI has a positive impact on CO2 emission whereas, in the short run coal consumption, FDI, GDP per capita and trade openness appears to have a significant and positive impact towards CO2 emission. 2020 - Kalpana Corporation -
Nexus Between The Carbon Dioxide Emission And Economic Growth: Evidence From India
Increase in economic activities contributes to the economic growth of a country. It is evident that emerging economies have recorded higher economic growth and significant increase in coal consumption, energy consumption and electricity consumption. On the other hand, the emission of greenhouse gases (GHG) generating consequences in the atmosphere. In this context, this study tries to analyse the association between GDP per capita, FDI, population, trade openness and CO2 emissions per capita in India. The study is based on secondary data, which has been collected from the World Bank database. The time period under consideration is from 1960 to 2017. Augmented Dickey Fuller test has been used to test the unit root. VAR lag order criteria have been used for lag selection of the model. Since the variables are integrated at I (1) and I (0), the ARDL model has been used for the purpose of analysis. Furthermore, for checking the stability of the model, the CUSUM test has been used. The results show that in the long run, GDP per capita and FDI has a positive impact on C02 emission whereas, in the short run coal consumption, FDI, GDP per capita and trade openness appears to have a significant and positive impact towards C02 emission. 2020 Kalpana Corporation. All Rights Reserved. -
An Energy, Mobility and Obstacle Aware Clustering based Intelligent Routing Protocol for FANET
The use of Flying Adhoc Networks (FANETs), also known as Unmanned Aerial Vehicles (UAVs), has increased in recent years. However, the fast movement of UAVs can lead to unreliable links and inefficient data transmission. To address this issue, the Intelligent-based Energy and Mobility-aware Clustering (IEMC) protocol has been developed, utilizing Battle Royale Optimization (BRO) for Cluster Head (CH) selection and a Deep Q-Learning (DQL)-based fast dynamic hello interval algorithm for path maintenance. Despite these advancements, FANETs still face challenges due to environmental obstacles affecting communication routes. To solve these issues, this article proposes an Intelligent-based Energy, Mobility, and Obstacle-aware Clustering (IEMOC) protocol for FANET routing. This protocol uses an intelligent Bezier route selection technique to deal with obstacles obstructing the paths of FANET nodes and a speed-based mobility prediction technique to reduce the impact of mobility during transmission. If link failure occurs due to an obstacle in the network, the IEMOC protocol selects an optimal alternative routing path via neighboring nodes based on its mobility awareness factor, link duration, network connectivity, and route availability, recovering the failed route without initiating the route discovery process. The effectiveness of the IEMOC protocol is validated through performance evaluations using the Network Simulator (NS)-2.35, and simulation results demonstrate that the IEMOC protocol outperforms conventional routing protocols in FANETs. 2025 The Authors. Published by Elsevier B.V. -
Nurturing Adult Socio-Emotional Skills and Engagement: The Transformative Power of Mentoring Program
There has been a growing interest in understanding the ways education could integrate socio-emotional learning (SEL) skills in their curriculum. This chapter explores by considering mentoring approach as a channel to foster SEL skills that would be beneficial to both adult learners and educators alike. The chapter emphasizes on the key SEL skills and also focuses on the need for higher institutions to promote adult SEL, not only for faculties but also for adult learners. Two main types of mentoring have been addressed, viz the traditional mentoring versus alternative mentoring approach. The chapter also discusses about incorporating the train-the-trainer model for mentoring. In essence, this SEL-based adult mentoring ensures that both mentees and mentors benefit. The mentees have gained self-awareness, responsible decision-making skills, relationship skills and emotional intelligence through this mentoring approach, while the mentors have acquired a sense of accomplishment and fulfillment that promotes their emotional intelligence and decision-making skills. 2026 by IGI Global Scientific Publishing. All rights reserved. -
EEG Emotion Recognition Using PSO-Based Feature Selection and Convolutional Neural Networks
EEG signals have become a promising source for emotion recognition due to their ability to capture the brain's electrical activity connected with different emotional conditions. In this work, a novel approach is proposed that integrates Particle Swarm Optimization (PSO)-based feature selection with Convolutional Neural Networks (CNNs) for improved EEG emotion classification. The method with the preprocessing of a notch filter to eliminate noise and enhance the quality of the EEG signals. Key features, including Magnitude Squared Coherence Estimate (MSCE) and Power Spectral Density (PSD), are extracted to capture essential frequency-domain information. PSO is employed to optimize the selection of features, reducing dimensionality while preserving the most relevant and informative attributes for emotion recognition. The optimized feature was subsequently passed to a CNN classifier, which improves the model's capability to accurately differentiate between different emotional states. This study is implemented using Python software to analyze emotion, and the effectiveness of the proposed approach is assessed using the EEG Brainwave dataset. Experimental results demonstrate that the proposed approach delivers an accuracy of 92.6% and a precision of 91%, highlighting its effectiveness in real-time, high-precision emotion recognition from EEG data. 2025 IEEE. -
Unveiling Green Supply Chain Practices: A Bibliometric Analysis and Unfolding Emerging Trends
Supply chain management is a multi-dimensional approach. Growing eco-consciousness has forced businesses to optimize operations and incorporate green practices across all the stages of supply chain in manufacturing and service sectors. Reviewing the past research literature propels us to understand its current and future prospects. Employing a systematic analysis, this research explores the intellectual structure of green supply chain practices and their connection to performance outcomes in various industries. This study covers a systematic literature review, content analysis, and bibliometric analysis on green supply chain management using VosViewer. It utilizes a PRISMA-guided screening method for identification, screening, eligibility and inclusion of literature from the literature available since 1999. The bibliometric analysis reveals key contributors, thematic clusters, prevailing theoretical frameworks, and emerging research trends in the domain of green supply chain management. China, followed by the United States and the United Kingdom, emerged as leading contributors to research in this area, driven by rapid economic growth, heightened environmental concerns, and well-established academic and industrial infrastructures. The study identifies eight thematic clusters within green supply chain management, including the triple bottom line, circular economy, and carbon emissions. The most highly cited papers within these clusters were examined for their methodologies, tools, and key findings, highlighting the prominent theories utilized in this field. Moreover, the research discusses how advanced technologies such as AI, blockchain, and big data analytics are poised to transform supply chains by enhancing decision-making and mitigating risks, thus playing a pivotal role in the future of green supply chain management. Copyright 2024 CA Rajkiran, Shaeril Michel Almeida. -
Polycystic ovary syndrome: An exploration of unmarried women's knowledge and attitudes
Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder among women of reproductive age and a chief cause of subfertility attributed to ovulation. Besides, lack of knowledge about PCOS, its treatment, and lifestyle changes influence the prognosis. The present qualitative inquiry investigates the knowledge and attitudes of unmarried women towards the syndrome, associated treatment, and necessary lifestyle changes in the fight against the same. A total of 15 participants with PCOS were selected using purposive sampling (n from southern parts of India viz. Kerala and Tamil Nadu states. The telephonic interviews were conducted in late November and early December 2020. He conventional content analysis emerged with six major themes. The themes capsulated women's knowledge, causes, complications and risk factors, treatment of PCOS their perceived importance of health promotive behaviours such as physical activity, sleep patterns, and perceived support from society. The importance of diet, exercise and a healthy lifestyle were additional relevant factors stressed by the respondents. Although the medicines helped participants attain regular menstrual cycles, they also had side effects reported in the discussion. Few respondents reported that they lacked the necessary awareness of PCOS when diagnosed at a younger age. The study enhances the understanding of PCOS from a qualitative approach that has cultural relevance apart from pertinent clinical and lifestyle implications. 2022 The Author(s) -
Enhanced Jaya Optimization Algorithm with Deep Learning Assisted Oral Cancer Diagnosis on IoT Healthcare Systems
Recently, healthcare systems integrate the power of deep learning (DL) models with the connectivity and data processing capabilities of the Internet of Things (IoT) to enhance the early recognition and diagnosis of disease. Oral cancer diagnosis comprises the detection of cancerous or pre-cancerous abrasions in the oral cavity. Timely identification is essential for successful treatment and enhanced prognosis. Here is an overview of the key aspects of oral cancer diagnosis. One potential benefit of utilizing DL for oral cancer detection is that it analyses huge counts of data fast and accurately, and it could not need clear programming of the rules for recognizing abnormalities. This can create the procedure of detecting oral cancer more effective and efficient. Thus, the study presents an Enhanced Jaya Optimization Algorithm with Deep Learning Based Oral Cancer Classification (EJOADL-OCC) method. The presented EJOADL-OCC method aims to classify and detect the existence of oral cancer accurately and effectively. To accomplish this, the presented EJOADL-OCC method initially exploits median filtering for the noise elimination. Next, the feature vector generation process is performed by the residual network (ResNetv2) model with EJOA as a hyperparameter optimizer. For accurate classification of oral cancer, a continuously restricted Boltzmann machine with a deep belief network (CRBM-DBN) model. The simulated validation of the EJOADL-OCC algorithm is tested by the series of simulations and the outcome demonstrates its supremacy over present DL approaches. 2024, American Scientific Publishing Group (ASPG). All rights reserved. -
Relation between electricity consumption and economic growth in Karnataka, India: An aggregate and sector-wise analysis
Karnataka is a highly progressive and rapidly growing state in India, with huge potential for industrial growth, however, it grapples with power deficits and other problems in electricity sector, which make it a good case study for Indian electricity sector. Given the importance of electricity in the urbanisation and growth process, the paper analyses the electricity consumption trend in Karnataka, examine its causality with economic growth at aggregate and sectoral levels using Granger causality test, and forecast the future electricity consumption applying Holt-Winters smoothening (no seasonality) technique. The general trend reflects higher consumption by the agricultural consumers, compared to the revenue-generating 'Industries' and 'Commercial' categories, mainly due to the policy of de-metering and providing 'free' power to agricultural consumers since late 1980s. The Granger causality tests reveal that there is no causality relation (neutrality hypothesis) between electricity consumption and economic growth in Karnataka, for total, agricultural and industrial consumption. This basically stems from the inaccurate measurements of agricultural consumption, higher dependence on captive generation, and poor quality grid supply. Finally, electricity consumption is predicted to be around 69,347 GW h by 2019?20. Future policies should focus on universal metering, reducing cross-subsidization, supplying good quality and reliable power to all sectors, and economical planning of resource-mix to achieve adequate, productive and efficient electricity consumption. 2020 Elsevier Inc. -
English to Hindi Translation System Using Hybrid Techniques
Good communication is critical for overcoming cultural and linguistic divides in today's internationalized society. An essential communication component is the Translation of written materials, primarily academic papers, from one language into another. This abstract focuses on the research involved in translating academic publications from Hindi to English. Translating Hindi academic papers into English is naturally hard due to the significant linguistic and cultural differences between the two languages. The proposed work provided an analytical analysis of various models used in language translation, including the seq-to-seq model, MT5, and LSTM, with the help of BLEU score, Learning rate, and average loss. MT5 model outshines others in terms of an average loss of 4.75; meanwhile, LSTM has an average loss of 5.56, and the seq-to-seq model has an average loss of 6.09, implying weaker Translation. 2024 IEEE. -
Interface improvement and multiscale assessment of recycled concrete aggregates with epoxy resin polymer
Recycled concrete aggregate (RCA) exhibits challenges like weak bonding, high porosity, and inferior strength compared to natural aggregates. This study evaluates the effect of epoxy resin polymer treatment on RCA on enhancing compressive and split tensile strengths in concrete, replacing natural aggregates with untreated RCA (UTRAC) and treated RCA (ERTAC) at 25%, 50%, 75%, and 100% levels. The tests were conducted at 3, 7, and 28 days. UTRAC showed reductions of up to 26.32% in compressive strength and 35.38% in tensile strength at 100% replacement; ERTAC outperformed control concrete (CC) with gains of up to 26.32% in compressive strength (at 25%) and 122.73% in tensile strength (at 100%), identifying 25% as the optimum replacement ratio. SEM and XRD analyses confirmed improved particle packing, reduced porosity, and stronger interfacial transition zones (ITZ) in ERTAC. The Author(s) 2026. -
Female Political Representation and Economic Development in India: An Empirical Analysis
Recent years have seen an enhanced focus on women's roles in politics, with research increasingly showing that having a more significant gender representation in decision-making roles can significantly impact economic growth. This chapter delves into how women's political involvement, economic advancement, and gender equality have evolved in India over twenty years from 2000 to 2020, using a time series analysis. The study uses vector autoregression (VAR) analysis to examine how political representation of female, participation rate of labour force (LFPR), and health investment affect the Gender Development Index (GDI). The model diagnostics successfully demonstrated stationarity, non-serial correlation, and the lack of homoscedasticity. The analysis highlights that Female LFPR and GDI are positively related, whereas health expenditure and GDI are negative. Female labour market participation improves GDI, whereas females consistently receive less healthcare expenditure than males, leading to a negative relationship between health expenditure and GDI. Importantly, it is observed that labour market participation has a more substantial effect on GDI than political representation or health investments. This shows that greater female labour force participation is more critical in gender equality than increased political representation or healthcare spending. Highlighting the necessity for policies tailored to women, the chapter argues that these measures are critical for enhancing LFPR and boosting GDI and societal progress. The chapter contributes to the gender discourses in political participation and the empowerment of female, proposing a strategy to improve women's contribution to the labour market, leading higher GDI and, as a result, a more equitable society. 2026 selection and editorial matter, Hebatallah Adam and Abul Hasnat Monjurul Kabir; individual chapters, the contributors. All rights reserved. -
Investigation of the correlation between optical and ?-ray flux variations in the blazar Ton 599
The correlation between optical and ?-ray flux variations in blazars reveals a complex behaviour. In this study, we present our analysis of the connection between changes in optical and ?-ray emissions in the blazar Ton 599 over a span of approximately 15 yr, from 2008 August to 2023 March. Ton 599 reached its highest flux state across the entire electromagnetic spectrum during the second week of 2023 January. To investigate the connection between changes in optical and ?-ray flux, we have designated five specific time periods, labelled as epochs A, B, C, D, and E. During periods B, C, D, and E, the source exhibited optical flares, while it was in its quiescent state during period A. The ?-ray counterparts to these optical flares are present during periods B, C, and E; however, during period D, the ?-ray counterpart is either weak or absent. We conducted a broad-band spectral energy distribution (SED) fitting by employing a one-zone leptonic emission model for these epochs. The SED analysis unveiled that the optical-ultraviolet emission primarily emanated from the accretion disc in quiescent period A, whereas synchrotron radiation from the jet dominated during periods B, C, D, and E. Diverse correlated patterns in the variations of optical and ?-ray emissions, like correlated optical and ?-ray flares, could be accounted for by changes in factors such as the magnetic field, bulk Lorentz factor, and electron density. On the other hand, an orphan optical flare could result from increased magnetic field and bulk Lorentz factor. 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Study of correlation between optical flux and polarization variations in BL Lac objects
Polarized radiation from blazars is one key piece of evidence for synchrotron radiation at low energy, which also shows variations. We present here our results on the correlation analysis between optical flux and polarization degree (PD) variations in a sample of 11 BL Lac objects using ?10 yr of data from the Steward Observatory. We carried out the analysis on long-term (?several months) as well as on short-term time-scales (?several days). On long-term time-scales, for about 85 per cent of the observing cycles, we found no correlation between optical flux and PD. On short-term time-scales, we found a total of 58 epochs with a significant correlation between optical flux and PD, where both positive and negative correlation were observed. In addition, we also found a significant correlation between optical flux and ?-ray flux variations on long-term time-scales in 11 per cent of the observing cycles. The observed PD variations in our study cannot be explained by changes in the power-law spectral index of the relativistic electrons in the jets. The shock-in-jet scenario is favoured for the correlation between optical flux and PD, whereas the anticorrelation can be explained by the presence of multizone emission regions. The varying correlated behaviour can also be explained by the enhanced optical flux caused by the newly developed radio knots in the jets and their magnetic field alignment with the large-scale jet magnetic field. 2022 The Author(s).