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Automated Brain Tumor Segmentation in MRI Using AI for Improved Neurodiagnostics
Early and accurate classification of brain tumors plays a pivotal role in clinical decision-making and treatment planning. Manual methods are time-intensive and prone to variability, creating a need for robust automated solutions. This study aims to classify brain tumors from MRI scans using artificial intelligence techniques, specifically Logistic Regression (LR) and Support Vector Machines (SVM) with Radial Basis Function (RBF) kernels. The dataset, sourced from The Cancer Imaging Archive (TCIA), includes four classes: Meningioma, Glioma, Hypothalamic tumor, and No tumor. Preprocessing involved dimensionality reduction using Principal Component Analysis (PCA) to retain dominant features. Models were trained on an 80:20 train-test split, with LR achieving 99.83% training and 78.91% testing accuracy, while SVM performed better with 93.85% training and 81.88% testing accuracy. Error analysis revealed 104 misclassified samples, primarily due to structural similarity among tumor types. The findings suggest that SVM offers superior classification performance, and the study recommends further enhancement through deep learning models like Convolutional Neural Networks (CNNs) for improved diagnostic accuracy. 2025 Oriental Scientific Publishing Company. All rights reserved. -
Polycystic Ovary Syndrome Diagnosis: The Promise of Artificial Intelligence for Improved Clinical Accuracy
PCOS is an endocrine illness that affects 610% of women worldwide. It can cause a variety of symptoms, including irregular menstruation periods, ovarian cysts, and hyperandrogenism. Its lack of defined biomarkers, overlapping symptoms, and heterogeneity make diagnosis difficult. By studying hormone profiles, identifying patterns difficult to see with conventional approaches, and offering great precision and accuracy, AI and ML techniques are transforming diagnostic difficulties. Hybrid models in the list include SWISS-AdaBoost and ensemble learning techniques that have accuracies up to 98% enabling early diagnosis along with appropriate treatment strategies. Early detection by technologies such as AI will prevent significant health complications that are PCOS-related, such as infertility, type II diabetes, or cardiovascular diseases. This study depicts the transformative role of the application of AI in diagnosing cases of PCOS and highlights the possibility of facilitating clinical decision-making, reducing potential diagnostic delays, and enhancing improvements in patient outcomes. Future research should hence be directed towards the establishment of AI within healthcare with consideration of validation, reliability, and ethical considerations to maximize its use in clinical practice. 2025 Oriental Scientific Publishing Company. All rights reserved. -
Spatiotemporal analysis and intensity prediction of forest fires using cuckoo search hybrid models
Forest fire forecasting is a critical aspect of environmental conservation and ecological risk management, particularly in biodiversitysensitive areas like Uttara Kannada, India. In this research, this article suggests a new hybrid modeling ap-proach that combines Cuckoo Search Optimization (CSO) with ensemble machine learning techniques, namely Random Forest (RF) and XGBoost (XGB), for forecasting fire intensity levels. Known as CSORF and CS-XGB, the hybrid models were trained and validated against a spatiotemporally dense dataset from 2009 to 2024, with primary environmental, topographic, and anthropogenic predictors. Aside from classification modeling, spatiotemporal analyses such as Kernel Density Estimation (KDE), seasonal fire patterns, and influence studies on features were performed to determine high-risk seasons and areas. CSO was used to automate the hyperparameter tuning process for both classifiers, yielding a significant boost in performance. The CS-XGB model registered the top accuracy of 99.49%, better than CSORF's 98.99%. Feature importance testing confirmed ecological significance, and humidity, temperature, and rainfall were the top-ranked variables. The work adds a scalable and precise prediction model that can assist in early warning systems and forest manage-ment practices. Kamal Upreti et al. -
A Study on Gynecological Cancers Using Artificial Intelligence: A Revolutionary Approach
An examination of the role of artificial intelligence (AI) in ovarian, cervical, and breast cancer early detection and management is presented in this paper. Artificial intelligence (AI) can improve diagnostic accuracy, streamline treatment protocols, and facilitate personalized medicine approaches by leveraging advancements in machine learning (ML) and deep learning (DL). Various Artificial Intelligence models have demonstrated success in improving the outcomes of cancer diagnostics, including their ability to distinguish benign from malignant tumors. Technology challenges and ethical issues related to the integration of AI into clinical practice are also discussed in the review. Specifically, we want to illustrate how artificial intelligence can lead to better prognoses and reduced mortality rates for cancer patients by enhancing early detection capabilities. 2025 IEEE. -
The Evolution of Cloud Computing: A Study of Aspirational Technologies and Practical Achievements
Cloud computing has transformed the digital landscape, providing scalability, cost efficiency, and seamless access to computing resources. Yet, there is a gap between its theoretical aspirations and real-world achievements because of data privacy concerns, regulatory compliance, vendor lock-in, and performance bottlenecks. This paper critically examines these disparities through case studies, technological breakthroughs, and industry trends as a guide to understanding these impediments to cloud adoption. The study reviews the theoretical background of cloud computing, which include models related to deployment and service, and illustrates its successes in performance, reliability, and security. Persistent barriers, however, mean security and compliance are insecure while cost unpredictability remains a concern for organizations in using the maximum potential of a cloud. The study proposes strategic solutions that can serve to bridge the gap in terms of hybrid and multi-cloud adoption, AI-driven security frameworks, regulatory compliance automation, and various cost optimization techniques. Emerging trends like quantum computing, edge computing, and green cloud initiatives are shaping the future of cloud computing. By implementing these solutions, more is made out of the potential of cloud computing in securing a more efficient and sustainable digital infrastructure. 2025 IEEE. -
Big Data Analytics and Intelligent Applications for Smart and Secure Healthcare Services
The book provides a comprehensive discussion for utilizing computational models such as artificial neural networks, agent-based models, and decision field theory, for reliability engineering. It further presents optimization solutions for smart and secure healthcare services. The text showcases how to predict the failure and repair rates of healthcare subsystems using computational intelligence. This book: Explores how data-driven methodologies and advanced computational intelligence are revolutionizing the healthcare industry, promoting efficiency, accessibility, and sustainability Highlights the pivotal role that big data analytics plays in harnessing vast amounts of patient records, clinical information, and real-time medical data to provide timely insights for healthcare professionals and policymakers Discusses the integration of artificial intelligence and machine learning techniques in healthcare, with a focus on revolutionizing disease detection, treatment planning, and resource allocation Lays the foundation for developing sustainable healthcare systems that are adaptable to long-term challenges, such as population growth, emerging diseases, and resource constraints Covers computational intelligence techniques, like fuzzy logic, neural networks, and evolutionary computations, emphasizing their role in solving complex, data-driven healthcare problems Includes topics like data management, visualization, protection, and complex adaptive systems, as well as hybrid computational intelligence techniques for synergistic problem-solving strategies This volume will serve as an ideal text for senior undergraduates, graduate students, and academic researchers in fields including electrical engineering, electronics and communications engineering, computer engineering, and mathematics. 2025 selection and editorial matter, Kamal Upreti, Nishant Kumar, Mohammad Shabbir Alam, Mohammad Shahnawaz Nasir and Debabrata Samanta; individual chapters, the contributors. -
Humancomputer interaction for cognitive, emotional and learning well-being
HumanComputer Interaction (HCI) has revolutionized the way humans engage with technology, shaping cognitive, emotional, and learning experiences. This chapter explores HCI's impact on well-being, focusing on cognitive load reduction, emotional stability, and adaptive learning. HCI technologies such as AI-driven decision support, emotion-aware systems, and personalized education platforms enhance user engagement by fostering efficiency and well-being. Cognitive well-being benefits from AI-powered cognitive tools that improve memory, decision-making, and mental agility. Emotional well-being is facilitated by affect-sensitive systems, digital therapeutics, and social HCI that reduce stress and increase emotional engagement. Adaptive learning systems, gamification, and assistive technologies also ensure inclusive education by making learning more personalized and accessible to special needs students. Future HCI trends involve neuroadaptive interfaces, wearable-integrated health technology, and AI-based mental health solutions that all improve personalization and user experience. Yet ethical issues, such as data privacy, digital addiction, and algorithmic biases, need to be addressed in order to maintain responsible technology utilization. Achieving balance between digital and physical interactions is important in the preservation of general well-being. The future of HCI, where it becomes empathetic, adaptive, and ethical interfaces, is a future that sees technology enhance not just efficiency but cognitive, emotional, and learning well-being. By making ethical AI design and user-centric experiences a priority, HCI will continue to unleash human potential and develop durable, well-being-oriented technological solutions. 2026 Elsevier Inc. All rights reserved. -
Predictive modeling of mechanical behavior in waste ceramic concrete using machine learning techniques
This study identifies the critical demand for a certain approach that aims to predict and ascertain the mechanical behavior of concrete admixed with waste ceramic, a method to overcome and mitigate the related environmental challenges as it pertains to the construction field. Concrete modification with ceramic wastes has received significant attention due to its potential improvement in sustainability. The developed predictive models on waste ceramic concrete (WCC) involved the use of advanced machine learning techniques such as Artificial Neural Network (ANN) and Light Gradient Boosting Machine (LightGBM). Experimental datasets were formulated based on 5% and 20% variability of ceramic waste percentages as input variables for training and testing data for validation of the proposed model. In each case, iterative training improved model performance, with the ANN showing moderate predictability (R = 0.70 and 0.67) and LightGBM demonstrating stronger accuracy. Predictive values ranged between 1.02 MPa and 0.12 MPa for compressive and splitting tensile strengths and had R values of 0.70 and 0.67 for the ANN model, respectively. The established findings will lead to a dependable framework for assessing and improving the performance of ceramic waste-modified concrete. In this regard, these findings have reinforced the potential of machine learning in developing sustainable construction practices. This paper is of value to engineers and decision-makers within the construction industry, providing an informed choice towards environmental sustainability and better risk management. Kamal Upreti et al. -
A Comprehensive Study Using Convolutional Neural Networks as a Method for Multi-class Skin Cancer Image Classification
Skin disorders occur more frequently than other kinds of diseases. Skin diseases can be attributed to a number of aspects, like fungi, bacteria, viruses, allergies, and so on. The rapid advancement of healthcare centered around lasers and photonics has rendered it feasible to diagnose skin disorders in a more accurate and timely manner. However, the cost of such a diagnostic remains extremely limited and prohibitively expensive. As a result, the use of image processing methods is beneficial in the initial phases of designing a computerized dermatology screening system. The retrieval of characteristics is an extremely important step in classifying skin disorders. The use of computer vision may play a crucial role in the diagnosis of a variety of skin conditions using a variety of approaches. The strategy we have proposed is straightforward and quick and requires no expensive technology besides a computer and a camera. When applied to the inputs of a colored picture, the method is successful. After that, resize a portion of the image to retrieve attributes with a pretrained convolutional neural network. The attribute was then classified using the multi-class XGBoost program. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Integrated biogasification and carbon capture pathways: a system-level review of technologies, storage options, and deployment challenges
Carbon-negative energy systems that integrate bioenergy production with permanent carbon dioxide (CO2) sequestration are increasingly recognized as essential for achieving global net-zero and beyond-zero climate targets. While extensive research exists on individual components such as biogasification, carbon capture technologies, and geological storage, a coherent system-level synthesis linking these pathways remains fragmented. This review addresses this gap by providing an integrated assessment of biogasification-based carbon capture and storage (CCS) systems, with particular emphasis on techno-economic performance, capture efficiency, subsurface storage options, and deployment challenges. Following the PRISMA 2020 guidelines, 112 studies were systematically selected from an initial pool of 780 publications and analyzed to compare advanced gasification routes, emerging capture technologies, and storage strategies. The results indicate that hybrid gasificationsolid oxide fuel cell systems can achieve efficiencies of up to 55%, while cryogenic carbon capture consistently delivers CO? purities above 95% with reduced energy penalties. Supercritical water gasification and hydrothermal pathways demonstrate strong potential for wet biomass conversion, achieving hydrogen yields exceeding 1150 mmol/L and carbon efficiencies above 80%. Despite these technical advances, large-scale deployment remains constrained by high costs (USD 8001350 per tonne CO2), infrastructure limitations, and policy uncertainty. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Enhancing Diagnostic Precision in Lung Cancer Detection Using Smote-Based Balancing Techniques
Worldwide, Lung cancer is the primary cause of death from cancer, and chances of surviving are considerably raised by early detection. While traditional diagnostic approaches heavily rely on imaging and specialized infrastructure, they often fail to serve low-resource or early screening environments. In this work, based on deep learning, lightweight framework for detecting lung cancer from structured survey data is presented. The research tackles the prevalent the problem of class disparity using the Synthetic Minority Over-sampling Technique (SMOTE), enhancing the sensitivity of predictive models. A comparative evaluation was conducted across six models Logistic Regression, SVM, KNN, Naive Bayes, Random Forest, and XGBoost. Among these, Random Forest and XGBoost achieved 95% accuracy, 0.98 recall, and ROC-AUC scores of 0.9943 and 0.9835 respectively. The proposed hybrid ensemble model (Random Forest + XGBoost) outperformed all with 96% accuracy, 0.95 precision, 0.98 recall, and a ROC-AUC score of 0.9961. These findings demonstrate that the hybrid strategy is effective in providing high diagnostic precision using clinical survey data that is not imaging. 2025 IEEE. -
AQUAPHISH: Leveraging Metaheuristics and Automated Machine Learning for Precision Phishing Detection
Phishing is an ongoing and dynamic threat in the field of cybersecurity, targeting user trust to capture sensitive data through fraudulent websites. Conventional detection systems tend to use binary classification and static features, which make them less flexible to new attack paradigms. This paper seeks to design a solid and comprehensible phishing detection system that alleviates the drawbacks of binary labeling by proposing a regression-based risk scoring model. The aim is to improve accuracy, feature interpretability, and deployment in real-time settings. The new method combines Whale Optimization Algorithm (WOA) for feature selection and H2O AutoML for model creation and assessment. A filtered dataset of 10,000 phishing and normal websites is operated upon using 48 features, which are then reduced to 36 using WOA. The last models are optimized with H2O AutoML, encompassing ensemble learners, and tested on various regression metrics. Interpretability is achieved with SHAP analysis. The best model had an R of 0.9534, RMSE of 0.1079, and MSE of 0.0116, better than traditional classification-based phishing detectors. The system, with only 36 features, had training time decreased by 23.6% and inference latency reduced by ~18%, without any sacrifice in detection accuracy (98.3%). Regression-based scoring also supported adaptive threat ranking in real time. By posing phishing detection as a regression problem and integrating metaheuristic feature selection with AutoML, this work introduces a scalable and explainable framework ready for real-world deployment. The low-latency yet high-accuracy model is best suited for integration into browser-level phishing filters and cloud-based threat intelligence platforms. 2025, Interdisciplinary Publishing Academia. All rights reserved. -
Enhanced Pneumonia Detection from Chest X-rays Using Machine Learning and Deep Neural Architectures
Pneumonia is a major worldwide health concern, particularly for vulnerable groups such as babies and the elderly. Despite advances in medical imaging, diagnosing pneumonia using a chest X-ray remains difficult, due to the subtle presentation of symptoms and the variety in picture interpretation. This study utilizes modern machine learning can improve the accuracy and speed of diagnosing pneumonia using chest X-ray images. Utilizing a comprehensive dataset from the Kaggle online repository, consisting of over 5,000 annotated images, we evaluate the efficacy of various machine learning models including deep convolutional neural networks (CNN) and ensemble learning techniques. Our findings indicate that models like the Fuzzy opponent histogram filter combined with Logistic model trees (LMT) achieved the highest accuracy at 96.97%, while the deep learning-based Lenet (CNN) with LMT closely followed at 95.85%. The study aims to improve diagnostic precision, reduce interpretation discrepancies, and facilitate faster clinical decision-making by identifying the most effective machine learning approaches for real-world applications in healthcare settings. 2025 Kamal Upreti, Anju Singh, Divakar Singh, Preety Shoran, Uma Shankar, Meenakshi Yadav and Rituraj Jain. -
IT Strategies for Effective Marketing in Globally Diverse Corporate Environments
Today, organizations discern multicultural teams, dynamic consumer tastes and shrinking landscapes of competition bordering on the internet-centric global economy. This paper investigates the role of IT strategies in improving the strategic marketing within different corporate environments. An examination of IT's role in addressing marketing and management complexity in different cultural context is made. Theoretical models are reviewed and responsible global marketing practices are promoted through digital transformation to reshapes the business operations. We also illustrate IT based solutions for dealing with cross cultural communication barriers, resistance to change as well as team dynamics. In this case study and trend analysis with trends, we show how own best practices of market segments, digitalization as well as cross cultural management work together with IT to encourage agility, customer focus, and continuous learning in the organization. 2025 IEEE. -
Decentralized Data Integrity: Integrating MySQL with Blockchain for Resilient Healthcare Systems
A transformational solution to the problems created by healthcare data management is presented by the integration of MySQL and blockchain technology, centered around security, scalability, and efficiency. This paper presents MBHA MySQL-Blockchain Healthcare Architecture combining thestructured data storage, querying capabilities of MySQL with the decentralized, tamper-proof framework of blockchain. The system shows impressive performance metrics with an average API response time of 1.54 seconds for user registration and 841 milliseconds for login. The database queries and data retrieval or insertion took less than 1 millisecond, and JWT tokens were generated for authentication in less than 50 milliseconds. Conclusion Results indicate an efficient real-time system to accomplish tasks with integrity in terms of data but also with safety in operations. This architectural model, discussed above, is issues regarding data security and access with a need to provide care-collaboration needs. Scalability would then be optimized while keeping down computational overhead; in fact, work toward readiness for adoption is mainly towards being more regulatory compliant. 2025 River Publishers -
A physics-informed neural network framework for consolidation parameter prediction using controlled clay-sand mixtures
This paper introduces a novel Physics-Informed Neural Network (PINN) model for predicting the coefficient of consolidation (Cv) in high plasticity clays. The model was trained from experimental data obtained from controlled clay-sand mixtures. The input parameters include clay content, Atterberg limits, initial void ratio, compaction energy, applied pressure and consolidation characteristics like compression index (Cc) and volumetric compressibility (mv). Additional parameters like plasticity index, porosity, activity-clay interaction and compaction efficiency were derived from feature engineering. The proposed PINN model combines data-driven loss and physics-based loss into a total loss function. The physics loss includes three constraints derived from modified Kozeny-Carman equations, activity-based mineralogical relations, and compression-volume consistency. Hyperparameter optimization identified the optimal configuration: 800 epochs, learning rate 0.001, architecture [128, 64, 32], and physics loss weights distributed as 0.7, 0.25, and 0.05. Five-fold cross-validation demonstrated robust performance (R2 = 0.9903 0.0026), significantly outperforming baseline neural networks (R2 = 0.9682 0.0126, p = 0.0116) with 73.9% reduction in Root Mean Square Error (RMSE = 6.37 10-11 m/s) and 5.71% improvement in Mean Absolute Percentage Error (MAPE = 4.48%). External validation showed the PINN (R = 0.9968) substantially outperformed empirical correlations (best R2 = 0.1636) and conventional machine learning models (best R2 = 0.9878). SHapley Additive exPlanations (SHAP) interpretability analysis validated physically meaningful decision-making, with plastic limit and activity emerging as primary drivers. This framework provides a transferable, physics-consistent solution applicable across diverse clay types for foundation design and site characterization. Copyright 2026. Published by Elsevier B.V. -
Effect of glass and coir fiber on geotechnical properties of clayey soil
The use of fibers for the improvement of weak subgrade soils is beneficial as it not only acts as reinforcement but also, increases drainage, provides better workability, inexpensive and required in exiguous quantity. Available studies on clay soil reinforced are limited to a particular type of fiber, any comparative study on two or more types of fibers on same soil, provides a useful information on understanding suitability of specific type of fiber. This study deals with experimental characterization of clay soil reinforced with glass and coir fibers. California Bearing Ratio (CBR) and Unconfined Compressive Strength (UCS) tests were performed on these fiber reinforced clay samples with different percentage of glass and coir fibers. The results of these unreinforced and reinforced soils are compared. 2019 SERSC. -
Wall jet nanofluid flow with thermal energy and radiation in the presence of power-law
The effectiveness of jet flow in the energy transfer process has made it very useful in industrial applications. These flows also have higher heat transfer coefficients than traditional cooling through convection. The appliances inclusive of the jet make effective use of fluid and enhance the heat transfer rate. The contemporary article investigates the jet flow of power-law nanofluid past a moving wall. The nanofluid is formed by suspending Cu and Al2O3 nanoparticles in water. Furthermore, the jet flow is analyzed in the presence of radiation, which is further assumed to be linear, and the application of Rosseland approximation is considered to be valid. Considering these aspects, the model is designed using partial differential equations (PDE), which are then converted to a system of non-linear ordinary differential equations (ODE) by implementing certain similarity transformations. Thus, the obtained system is solved using numerical methods, and the results are discussed with the help of graphs. The significant conclusions of the analysis were that the increase in the radiation parameter contributed to the increase in the temperature of the nanofluid. The increase in the Prandtl number reported a decrease in the amount of heat absorbed by the nanofluid. 2023 Taylor & Francis Group, LLC. -
A study on Challenges of Indian Hospitality Industry and Remedies For Sustainability in the Ever Changing Market Scenario.
VOLUME NO. 3 (2013), ISSUE NO. 11 (NOVEMBER) ISSN: 231-1009