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Analyzing the Diagnosis and Treatment of Astrocytoma, Oligodendroglioma, and Glioblastoma: A Systematic Review
Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has had a substantial impact on a variety of fields, including healthcare, neuro-oncology, and precision medicine. In recent years, the availability of large-scale labeled datasets has allowed AI-driven advances in glioma detection, classification, and prognosis prediction. However, issues remain in assuring model generalizability, interpretability, and real-world clinical application. One of the most significant disadvantages is the underrepresentation of rare glioma subtypes, which prevents appropriate classification and therapy optimization. This study thoroughly assesses AI-based approaches for glioma classification, survival prediction, and biomarker discovery. A comprehensive survey of ML and DL models published between 2015 and 2024 has been conducted, evaluating radiomics-based tumor detection, multi-omics data integration, and AI-assisted decision-making frameworks. The review investigates the usefulness of convolutional neural networks CNNs, support vector machines SVMs, ensemble learning, and hybrid AI architectures, focusing on classification accuracy, sensitivity, and clinical applicability. Despite these advances, AI-driven glioma research faces challenges such as dataset consistency, clinical validation gaps, and a scarcity of explainable AI (XAI) frameworks. This paper offers a comparative analysis of artificial intelligence approaches assessing their strengths, constraints, and clinical relevance in glioma diagnosis and prognosis prediction in order to solve these challenges. The results underscore artificial intelligences revolutionary capacity in redefining glioma diagnosis, enhancing accuracy, and shaping the future of personalized treatment, thereby integrating computational progress with clinical neuro-oncology. Glioma diagnosis, deep learning, astrocytoma, oligodendroglioma, glioblastoma. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Comprehensive Review on Using Electronic Waste as a Construction Material
The study explores the use of electronic waste (E-waste) as a sustainable alternative in a variety of construction applications, addressing the rising global issue of E-waste management. E-waste, consisting of both metallic and non-metallic components, contains valuable and hazardous materials, which can lead to environmental damage, if improperly managed. The study demonstrates different methods for incorporating E-waste into bituminous mixes, high-strength concrete, and other composite materials. Research shows that, using E-waste improves the strength as well as durability properties of concrete. It acts as a practical alternative for disposing of E-waste and promotes the use of sustainable building methods. Furthermore, the potential of employing E-waste in the construction of flexible pavements is reviewed, which demonstrates positive results in improving the mechanical characteristics of asphalt mixtures. The study also highlights how E-waste can help with sustainable building methods by cutting down on landfill waste, preserving natural resources, and lowering carbon footprints. The results show that the use of E-waste not only offers a more suitable choice than traditional materials but also helps to lessen pollution from solid waste, which is consistent with worldwide efforts to improve environmental sustainability. Overall, this study shows that E-waste is a useful material for the construction, providing a creative waste management strategy that is in line with worldwide sustainability objectives. The results indicate that E-waste can be an important source of eco-friendly building materials, reducing environmental pollution and encouraging responsible resource utilization, if it is treated and integrated properly. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Construction Waste: Key Causes and Reduction Approaches
The construction industry is a significant contributor to global waste, with Construction and Demolition (C&D) waste comprising a substantial portion. This paper investigates the key causes and reduction approaches. Key sources of waste include material offcuts, packaging, and unused materials due to excess procurement or design changes. Factors that exacerbate waste include inadequate project planning, poor site management, and insufficient worker training. Economic factors often favor new materials over recycled ones due to cost and time concerns. Rapid urbanization and redevelopment further escalate C&D waste as older buildings are demolished for new construction. Technological advancements and innovative methods, such as prefabrication and modular construction, have the potential to reduce waste generation. However, traditional construction practices and resistance to change impede widespread adoption. This paper highlights the urgent need for integrated waste management strategies, emphasizing the roles of policy, education, and technology in reducing C&D waste. Sustainable practices, such as using recycled materials, improving on-site waste segregation, and adopting circular economy principles, are crucial for minimizing the environmental impact of construction activities. Addressing these factors is essential for achieving sustainability in the construction industry and supporting global environmental conservation efforts. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
An Overview of Material Waste Management in Construction Projects
Wastage of construction materials has long been a persistent issue within construction projects. The improper planning and management of construction materials during the performance of construction activities is a critical issue that negatively impacts the performance of construction projects. Encouraging sustainable waste management involves minimizing waste generation and promoting the reuse, recycling, and recovery of resources. This paper provides a broad overview of construction waste minimization and management, as well as mitigation factors for sustainable construction waste management. It integrates sustainability principles into waste management practices, including the adoption of a waste management hierarchy to advance environmental friendliness within the building industry. Also this delves into the significance of material waste, taking into account its environmental, economic, and social repercussions. It identifies various sources of material waste across the construction lifecycle, shedding light on the factors contributing to waste generation and inefficiencies. It evaluates existing practices and strategies utilized for waste minimization and management, encompassing approaches like reuse, recycling, and disposal. It emphasizes the crucial need to tackle material waste in construction projects to foster sustainability and optimize resource utilization in the built environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Exploring REITs in Indian Context: A Modern Avenue for Real Estate Investment
This paper explores how Real Estate Investment Trusts (REITs) are conceived and are poised for growth in Indias financial domain. REITs bring the opportunity for ordinary investors to invest in real estate based income generating assets with the added advantages of liquidity, transparency and control of SEBI regulation (2019). A closer look at how Indias listed REITs have performed to date, and what impact they have had on capital markets, investor preference and real estate development (Sharma and Iyer 2021). The results suggest that REITs may contribute in attracting additional capital to the real estate industry and The results indicate that REITs can significantly facilitate capital flow into the real estate industry and democratize property investment in India. The article emphasizes future prospects and policy concerns vital for the evolution of the REIT sector in India through worldwide benchmarking and empirical analysis (World Bank 2020). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Early Detection of Cyber Threats in EVCS Using Machine Learning: A Focus on Reconnaissance Attacks
There is a significant rise in electric vehicle adoption and robust and secure electric vehicle charging station infrastructure to meet this increasing demand. However, advanced technology is vulnerable to several cyber threats. Primarily starting with reconnaissance attacks, attackers gather information about the system to plan greater attacks. This can further lead to several kinds of attacks such as Denial of Service and Host Attacks where the attacker can bypass firewalls, create false traffic and disrupt service for the users. Thus, it is important to detect and prevent these attacks at an early stage. This paper presents a robust machine learning model in order to detect reconnaissance attacks. To the best of our knowledge, there have not been enough studies that focus on specific attack categories for early detection of cyber threats. The ensemble model used in the study demonstrates an impressive accuracy of 97.71% with a good balance between precision and recall. Moreover, variables related to power consumption which are harder to manipulate are used as features. This approach contributes towards more secure EVCS, fosters user trust and promotes adoption of electric vehicles at large. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Statistical Learning inPharmacovigilance: A Data-Driven Approach to AI-Enhanced Drug Safety Monitoring
Pharmacovigilance is transforming at warp speed in response to big data and advanced analytical techniques. This paper will provide an overview of where pharmacovigilance currently stands by focusing on integrating artificial intelligence (AI), machine learning (ML) and real-world data (RWD) in order to improve drug safety monitoring. These new methods are increasingly supplementing traditional ones which serve as their base. The purpose of this survey is to assess how effective they are, point out the major challenges standing in their way as well as offer recommendations for future research. In conclusion, although AI and ML could prove helpful especially with handling large volume and complexity of datasets, there is a need for tackling data quality, integration issues and regulatory acceptance concerns first. Standardized methodologies should be worked out and collaboration among all stakeholders encouraged so as to maximize the pharmacovigilance benefits that can come from these technologies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Optimized Fake News Detection in Social Networks Using Boosting Algorithms andMachine Learning Classifiers
Rising incidence of fake news on social media has turned verifying information into an imperative issue; hence, fact-checking information is becoming an important task. The traditional machine learning-based models like Logistic Regression, Nae Bayes, Support Vector Machines, and Random Forest suffer from the high-dimensional textual data, and the model may not yield optimal results in fake news detection classification. This paper suggests a better detection framework incorporating Gradient Boosting, CatBoost, and AdaBoost, along with Multinomial Nae Bayes for comparative study. This research uses TF-IDF vectorization and advanced text preprocessing, such as stopword removal, tokenization, and feature engineering,are done for better classification accuracy. The research was carried out on public dataset, including the Fake Job Posting dataset of Kaggle, to ensure model flexibility. The findings show remarkable performance enhancement with CatBoost posting the best accuracy of 98.23% and an ROC-AUC score of 0.9739, surpassing traditional models. A statistical significance test (t-test) validates the improvements as significant. Results have shown that ensemble-based approaches perform well in handling imbalanced and high-dimensional text data, and they should be generalizable to real-world fake news detection tasks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Optimizing Fraud Detection Systems in Credit Card Transactions Using Machine Learning Techniques
Rapid e-commerce services and emerging technologies have grown to use credit card usage as a widespread way of effecting payments, thereby increasing bank transaction volume. It is, therefore, equally increasing fraudulent activitiesthus showing the critical need for fraud detection methods development. Class-weighting hyperparameters are studied and applied to handle class imbalance between fraudulent and legitimate transaction classes. We mainly use Bayesian optimization for these hyperparameters tuning with consideration of unbalanced data problems. The key components of our method involve weight-tuning as a preprocessing step and using the extreme gradient boosting [XGBoost] algorithm to enhance further the light gradient boosting machine [LightGBM] based on an ensemble voting process. Moreover, we use deep learning for hyperparameter tuning with special consideration given to our introduced weight-tuning approach. Experiments on real-world datasets demonstrate the efficiency of our strategies. We follow recall-based metrics and the widely used ROC-AUC scores for the unbalanced datasets, which are more appropriate for measuring the model performance. All the algorithms are compared based on fivefold cross-validation, while the majority voting ensemble method is applied to evaluate the combined performance of the algorithms. The previous results prove that LightGBM and XGBoost perform best, with optimal performances obtained at ROC-AUC scores of 0.95, precision of 0.79, recall of 0.80, and an F1 score of 0.79. Further, deep learning with Bayesian Optimization achieves the ROC-AUC scores of 0.908, precision of 0.96, recall of 0.82, F1 score of 0.88, and Accuracy of 0.9996all of which were significant improvements over the previous approaches. This paper presents Bayesian-optimized LightGBM for fraud detection, where it improves accuracy and efficiently tunes hyperparameters. The main novelty here is integrating Bayesian Optimization into dynamically enhancing model performance for handling class imbalance and reducing false detections. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Deep Learning Approach to Phishing Detection Using BiLSTM with an Attention Mechanism
Phishing sites are a serious cyber threat as they trick users into revealing sensitive personal data. Conventional detection techniques, including rule-based systems and blocklists, cannot cope with changing phishing tactics. In this paper, a new approach to phishing detection is introduced using a Bidirectional Long Short-Term Memory (BiLSTM) network with an attention mechanism. The suggested model learns and examines URL-based features and identifies forward and backward relationships in data, enhancing classification accuracy. A 30,000 URL-tagged dataset is utilized to train the model, which is then optimized with the help of methods like sequence tokenization, embedding layers, dropout regularization, and class weight balancing to counter data imbalance issues. The BiLSTM layer processes sequential information about URLs in a bidirectional manner, whereas the attention mechanism applies weights to important features differently to ensure the model pays attention to the most critical elements of phishing URLs. The model was tested based on standard performance metrics and has attained an astounding accuracy of 99.22%, precision of 99.1%, recall of 99.3%, and an F1-score of 99.2%, surpassing the traditional approach like Logistic Regression. The model indicates good generalization ability and is possible to be applied in real-time in web security systems. In the future, the use of dynamic data analysis and large datasets will be applied to improve further the detection efficiency and responsiveness against the new emerging phishing attacks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Decoding the Impact of Social Media on IPL Player Retention Using Sentiment Analysis and Ensemble Learning
This research query contrasted the use of player performance measures and fan sentiment rating scores in predicting player retention in the Indian Premier League (IPL). With the use of quantitative variables such as batting averages and bowling economies and qualitative variables including sentiment and visibility scores in over 1000 Reddit comments and posts, the research utilizes machine learning algorithms such as the Balanced Random Forest Classifier and Easy Ensemble Learning algorithms for enhancing decision-making on retention. In contrast to earlier approaches, which have largely disregarded the role of retention decision-making and qualitative data use in this context, the present study closes this gap by combining fan sentiment measures with visibility. The findings show that although conventional metrics hold, public opinion and sentiment have increasingly become factors in retention policy, particularly in the recent past. The highest performing model, which is a blend of both qualitative and quantitative traits, has an overall accuracy of 88%. The study finds the shift toward a more holistic, integrated approach, with the focus being on the data-driven nature of IPL team management for marketability, off-field pertinence, and on-field performance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
The Role of Prescriptive Analytics on Product Availability Towards Improved Customer Loyalty in Quick Commerce
Quick commerce (Q-commerce) has transformed retail by enabling ultra-fast deliveries, requiring optimised product assortment and inventory management. While traditional e-commerce offers a broad product range and competitive pricing, its delivery limitations led to Q-commerces emergence, ensuring fulfilment within 30min to a few hours. This study applies prescriptive analytics, machine learning and optimisation algorithms to enhance decision-making in Q-commerce. Advanced forecasting models, such as LSTM networks, improved demand forecasting with a Mean Absolute Error (MAE) of 0.25 and Root Mean Square Error (RMSE) of 0.35, reducing inventory costs by 10%. Linear programming optimised product mix, increasing sales by 15%. LSTM demonstrated high accuracy in predicting demand patterns, ensuring the availability of high-demand products while minimising overstock. Market Basket Analysis (MBA) revealed significant product associations, streamlining fulfilment centre operations and enhancing cross-selling strategies. Market Basket Analysis (MBA) using the Apriori algorithm identified key product associations, reducing picking times by 20% and boosting order value by 12%, contributing to a 15% rise in overall sales. Personalised recommendation systems using collaborative and content-based filtering increased conversion rates by 20% and customer retention by 15%. Despite these advancements, challenges in computational feasibility and synthetic data applicability persist. Future research should focus on real-time analytics and adaptive inventory strategies to enhance scalability and efficiency. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Exploring Research Trends and Publications in Gift-Giving Behavior: A Bibliometric Analysis Using Data Mining Techniques
Gift-giving is universal human behavior. It has been extensively studied for its cultural, psychological, and economic significance. This study aims to showcase the efforts of various researchers and the journals that share their work with readers. It looks at publication trends, areas of research, and research patterns based on bibliographic analysis using data mining. 86 articles from Scopus were analyzed. Various techniques like three-field plot, bibliographic coupling of documents, term co-occurrence maps, bar charts, line graphs were used. The findings reveal an increasing trend in publications and citations. The leading contributions are from the United States, United Kingdom, and Hong Kong. Notable emerging topics include virtual gifting, impression management, joint gift-giving, and the influence of live-streaming platforms. Term co-occurrence and bibliographic coupling analyses identified major areas of focus, such as consumer behavior, cultural values, and peer dynamics. These insights demonstrate the evolution of gift-giving studies, particularly the shift toward online and digital contexts. These findings will help future researchers by providing them with a comprehensive overview of the current state of research in Gift-Giving behavior. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Advancing Intrusion Detection Using Deep Learning: A Hybrid Approach
Intrusion detection systems (IDSs) are vital for securing networks against evolving cyberthreats. Traditional machine learning models often struggle with complex network traffic and imbalanced attack patterns. This study proposes an advanced ensemble model integrating ANN, LSTM, random forest, and LightGBM to enhance detection accuracy and robustness. Evaluations on the KDD99 dataset demonstrate that the ensemble model outperforms standalone ANN-LSTM models, achieving 92.4% accuracy, 97.4% precision, 87.1% recall, and a 91.9% F1 score. Hybrid models also showed significant improvements, with Nadam optimization yielding an F1 score of 93.10% for ANN-LSTM-random forest and Adam optimization achieving 93.30% for ANN-LSTM-LightGBM. By addressing data imbalance and improving attack pattern detection, this approach provides a scalable, efficient solution for real-time intrusion detection with superior performance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Lung Cancer Classification from CT-Scan Images Using an Enhanced VGG16 Model
Lung cancer has been one of the most common and deadly types of cancer around the globe, for which early detection is quite crucial for patient survival. In this research work, a deep learning-based method for four-class classification of chest CT-scan images, such as Squamous Cell Carcinoma, Large Cell Carcinoma, Adenocarcinoma, and Normal, is presented. With a modified VGG16 architecture, adding Squeeze-and-Excitation (SE) blocks and residual connections, the enhanced SERES_VGG16 model enhances feature representation and classification accuracy. The dataset we used here contains preprocessed chest CT-scan images divided into a training set, validation set, and test set. It is trained with augmentation techniques in the data to improve generalization. Its performance is evaluated using measures of standard performances, such as F1-score, recall, precision, accuracy and confusion matrices. The model achieved over 95% accuracy, class-wise precision ranging from 94 to 99%, recall ranging from 88 to 99%, F1-score from 93 to 96%. The presented approach reached over 95% accuracy on the test set and can be a trusted second opinion for radiologists to assist with early and accurate lung cancer subtype classification. However, this study is constrained by the small size of the dataset and the lack of other clinical parameters like genetic information. Future studies will concentrate on expanding the dataset and integrating multi-modal clinical information for enhanced robustness. This work in this study justifies the importance of deep learning in the classification of the medical images and points out further ways toward improving automated diagnostic systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
E-Commerce and Consumer Trust Impact of Industry 4.0 on MSME Sales and Business Practices in India
The role of Industry 4.0 in business practices and consumer preference is gaining high importance in Indias MSME economy. This study examines if e-commerce implementation has influenced MSMEs sales volume, which encouraged them to shift from offline to online business. This study suggests global regulatory norms to promote e-commerce practices in consumer markets. In order to study this issue, data from 407 respondents were collected and processed using advanced statistical software IBM SPSS and AMOS, paying special attention to the inter-relation between Industry 4.0 interventions and consumer behavior. Advanced statistical software, including Structural Equation Modeling and path analysis, describe how Industry 4.0 influences company practices, consumer confidence, and sales in the MSME economy. Advanced research demonstrates high inter-relation between Industry 4.0-initiated improvement and consumer confidence, and they demonstrate insights into complexity about how technological innovations influence corporate operations and consumer attitudes. Findings of this study demand stringent regulations that enhance effective standards and consumer psycho-logical well-being in e-commerce. This study contributes to the building block of effective utilization of e-commerce in Indias fast-evolving industry, and it stresses the top priority for comprehensive frameworks that address the challenges emerging from advanced technologies. Firms can navigate complexity in e-commerce interactions better by acknowledging the implications and establishing trust. Lastly, this study highlights the key role of e-commerce in shaping consumer behavior and demands global regulatory norms to make e-commerce practices in consumer markets effective and sustainable. Findings provide a road map to policymakers and firms to frame and implement policies that enhance customer confidence and encourage long-term prosperity in the MSME economy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Exploratory Analysis of Anthropometric and Demographic Factors Influencing PCOS: A Study on BMI, Weight, and Waist Ratio
Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting women of reproductive age, often leading to hormonal imbalances, obesity, and an increased risk of metabolic and cardiovascular diseases. This study examines the impact of PCOS on anthropometric and demographic variables such as Body Mass Index (BMI), weight, height, and waist ratio, using a comprehensive dataset. By comparing these factors between individuals with and without PCOS, the study aims to identify significant differences and correlations, thereby contributing to a deeper understanding of PCOS and informing clinical management strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Unveiling thePower ofBayesian Optimization: Methods, Insights, andApplications
Bayesian optimization (BO) has emerged as a popular approach for optimizing expensive black-box functions, which are common in modern machine learning, scientific research, and industrial design. This paper provides a comprehensive review of the recent advances in Bayesian optimization techniques, addressing new methodological developments such as multi-fidelity optimization, transfer learning, and neural network surrogates. Additionally, we explore the increasing role of BO in complex, high-dimensional, and multi-objective optimization problems, as well as its application in various fields like hyperparameter tuning, reinforcement learning, and neural architecture search. The goal of this review is to offer both theoretical insights and practical guidelines to researchers and practitioners working in areas where BO is a suitable tool. Finally, we discuss key challenges and propose directions for future research in the rapidly evolving field of Bayesian optimization. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Trajectories forSpace Missions: Bridging Tradition andInnovation
Spacecraft trajectory optimization has always been a determining factor in successful space missions as it should be precise and efficient in automatically exploiting new opportunities present in the complex and dynamic environment. Traditional optimization algorithms cannot meet the increasing demand for fast computation, adaptation ability, or overcoming real-time constraints. A recently developed technique called reinforcement learning is quite promising in dealing with such issues by proposing innovative solutions for trajectory optimization. This paper surveys cutting-edge reinforcement learning solutions for optimizing spacecraft trajectory problems. Comprehensive and pragmatic analysis based on different aspects of currently available solutions, and concise reports are generated to get the latest update on this field, as well as provide reference on designing future-related solutions. The survey suggests that more efforts from the research field should be spent on reinforcement learning solutions especially when applied in the real mission scenario because there are still many challenges unattended by the community that were pointed out before being delivered at the end-user level. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Advancements in Automated Spine Disorder Detection Using CT Scans: A Decade of Progress (20142024)
Automated spine disorder detection has transformed a lot in the last decade, from classic segmentation techniques to advanced deep learning models. Remarkable developments can be noticed in this field, especially in developing hybrid architectures combining CNNs with LSTM networks to increase diagnostic accuracy. Recent implementations reach an accuracy of up to 97.46% and a precision of 99.72%, highlighting the achievement of impressive performance metrics by modern systems in detecting spinal deformity. Integrating U-net architectures for detecting accurate cervical spine fracture and developing two-tier detection pipelines which efficiently balance specificity and sensitivity are significant innovations. Early approaches concentrated on detecting basic anatomical features, and the latest methods comprise advanced deep learning models for comprehensive analysis. From traditional segmentation tasks to managing complicated challenges and iterative random walks, the field of automated spine disorder detection has improved significantly. However, issues regarding data standardization and model generalization persist, despite this growth. Future research should focus on the development of more robust, system-independent frameworks that are capable of handling various imaging conditions and patient populations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.