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Implementation of Recent Advancements in Cyber Security Practices and Laws in India
In the past few decades, a large number of scholars and experts have found that wireless connectivity technologies and systems are susceptible to many kinds of cyber attacks. Both governmental organizations and private firms are harmed by these attacks. Cybersecurity law is a complex and fascinating area of law in the age of information technology. This essay aims to outline numerous cyber hazards as well as ways to safeguard against them. In both local and international economic contexts, it is critical to establish robust regulatory and legal structures that address the growing concerns about fraud on the internet, security of information, and intellectual property protection. Additionally, it covers cybercrime's different manifestations and security in a global perspective. Due to recent technical breakthroughs and a growth in access to the internet, cyber security is now utilized to safeguard not just a person's workstation but also their own mobile devices, including tablets and mobile phones, that have grown into crucial tools for data transmission. The community of security researchers, which includes members from government, academia, and industry, must collaborate in order to comprehend the new risks facing the computer industry. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Digitization of Monuments An Impact on the Tourist Experience with Special Reference to Hampi
The cultural heritage of India offers a deep examination of the country's political and historical evolution. Historical structures and monuments are among a nation's most valuable assets and a source of pride for Indian civilization. Monuments hold significant historical importance and exert a profound emotional influence on the community. Given the deterioration of culturally significant heritage monuments caused by factors such as weather, climate change, and human activity, as well as the threats these elements pose to numerous heritage sites of national and international significance, it is imperative to prioritize the recording, preservation, and conservation of these monuments. Events of cultural significance require comprehensive digital documentation and proper recording. As demonstrated by various programs and initiatives led by Prime Minister Narendra Modi, the government is committed to enhancing the visitor experience at monuments and museums. The primary aim of the current study is to better understand how cultural heritage sites are digitized and to assess the implications of this process for enhancing the tourist experience. To address the research objectives, a survey was conducted to analyze digital requirements. The digitization of significant cultural heritage sites is vital for the long-term sustainability of the tourism industry. Many methods will be adapted as resources permit, ensuring the industry's steady growth over time. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Development of Enhance-Net Deep Learning Approach for Performance Boosting on Medical Images
Only a few clinical procedures include the use of clinical methods for the early detection, observing, evaluation, and treatment evaluation of a range of medical illnesses. Knowing the analysis of medical images in computer vision necessitates being acquainted with the core concepts and uses of deep learning and artificial neural networks. The A rapidly expanding area of study is the Deep Learning Approach (DLA) in medical image processing. DLA is often used in medical imaging to determine if an ailment is present or not. By producing speedier, more accurate results in real time, deep learning algorithms may make the jobs of radiologists and orthopaedic surgeons easier. But the standard deep learning approach has reached its efficiencies. While offering an ideal solution known as boost-Net, we study numerous optimization strategies to increase the effectiveness of deep neural networks in this research. From a selection of well-known deep learning models, Champion-Net was selected as the deep learning model. The musculoskeletal radiograph-bone classification (MURA-BC) dataset is used in this investigation. Utilizing the train and test datasets, Enhance-Net's classification precision was evaluated. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Concernment of Feature Selection Using Classification Algorithms and Developing the Web Frame for Breast Cancer Prediction
Breast cancer is invasive cancer and it is the most common cancer diagnosed in women. The survival rate of breast cancer patients is increasing due to timely detection, better empathy about the disease, and new tailored approach for the treatment. Even hormonal imbalance, environmental factors, gene mutation, and lifestyle are also the reasons for breast cancer. Stages of breast cancer majorly depend on the size of the tumor as well as the spreading of cancer to the lymph nodes. An instinctive disease detection system and computer-aided diagnosis will help the medical practitioners in early prediction of breast cancer using machine learning algorithms. In this paper, Random Forest for ranking the features by assigning the weights and selection of features using support vector machine and Nae Bayes are used. The Breast Cancer Wisconsin Dataset from the UCI Repository has been taken for examination purposes. Features selected from support vector machine and Naive Bayes have been tested by using seven different classifiers: logistic regression, random forest, K-nearest neighbor, support vector classifier, linear support vector classifier, Gaussian Naive Bayes, and decision tree. Based on the experimental results with 7030 and 8020 splits, 7030 is obtained with the best accuracy. Support vector machine with 12 features resulted in an accuracy of 97.66% and Nae Bayes with 17 features resulted in an accuracy of 96.49% with the improved results as compared to without feature selection. As support vector machine resulted with best accuracy with 12 features, by using these 12 features, web application for the prediction of breast cancer has been developed using Web framework using Python Flask, PyCharm IDE, and the instance has been executed virtually in the Amazon EC2 cloud Platform. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Exploring the Nexus of Deepfakes and VFX Technology: Unveiling Insights, Challenges, and Innovations
This research paper explores the intersection of Deepfakes and Visual Effects (VFX) technology, investigating their convergence, implications, and advancements. Deepfakes, driven by artificial intelligence algorithms, have revolutionized the creation of synthetic media, while VFX techniques have long been utilized in the film industry for various purposes. This paper delves into the technical underpinnings of both Deepfakes and traditional VFX, highlighting similarities, differences, and synergies. It examines the potential applications of Deep-fakes in VFX-driven storytelling, digital compositing, and character animation, while also addressing the ethical concerns and risks associated with their misuse. Furthermore, the paper discusses emerging trends and innovations that bridge the gap between Deepfakes and VFX technology, paving the way for new creative possibilities and challenges. Through a comprehensive analysis, this paper aims to provide valuable insights into the evolving landscape of synthetic media and its implications for the VFX industry. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Comprehensive Survey of Methods for Identifying Counterfeit Banknotes Using Image Processing and Machine Learning
The world economy is threatened by counterfeit currencies. Counterfeit currencies are often difficult, time-consuming and ineffective to identify manually. Automated methods based on image processing techniques and machine learning algorithms are helpful in detecting counterfeit notes. This survey paper reviews the current strategies on fake banknote detection using image processing techniques and machine learning algorithms. We discuss various stages of the detection process, including image acquisition, preprocessing, feature extraction and classification. Furthermore, we analyze the limitations and comparative performance of different algorithms and approaches mentioned in the literature. The survey aims to provide insights into the various methodologies, challenges and future directions in the field of fake banknote detection, facilitating the development of more robust and effective counterfeit detection systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Linking the Path to Zero Hunger: Analysing Sustainable Development Goals Within the Context of Global Sustainability
A global framework, the Sustainable Development Goals of the United Nations, are designed to tackle the most urgent global issues. SDG 2, which stands for Zero Hunger, demonstrates a robust interconnection with the remaining seventeen goals since achieving food security and improved nutrition requires an all-encompassing approach that addresses the interconnected challenges presented by poverty, health, education, gender equality, climate change, and sustainable resource management. Within this framework, the research endeavors to ascertain the interrelationships among SDG 2 and other goals and analyze the critical goals that drive the achievement of SDG 2. Furthermore, the study provides an exhaustive analysis of the positions adopted by different nations concerning SDG 2. The results indicate that the SDGs are interconnected; while SDG 2 is closely linked to several other SDGs, their respective impacts differ. Furthermore, it has been determined that policies are crucial to attaining the SDGs. Without a transformation in agri-food systems that enhances resilience and facilitates the provision of affordable, nutritious foods and healthy diets, the current state of affairs will persist. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
SMOTE-Based Sampling for Addressing Class Imbalance
Various real-world applications, including as text categorization, categorization of gender in facial recognition for medical evaluation, fraud detection, and satellites analysis of images for oil-spill monitoring, are frequently plagued by imbalanced data. The majority class is commonly the primary focus of machine learning algorithms, with the minority samples being ignored or classified in a secondary manner. Nevertheless, despite their rarity, these minority samples are very important. When it comes to classification tasks, the issue of class imbalancewhere one class is underrepresented relative to anotherpresents a significant barrier. Specialized approaches including SMOTE, ADASYN, and cost-sensitive voting classifiers have been developed to address this problem. The minority class is oversampled in these methods, synthetic samples are created adaptively, and different prices are placed on misclassification mistakes in order to solve the issue of class imbalance. As a result, rigorous assessment utilizing pertinent metrics and cost considerations are required. The efficacy of these strategies, however, depends on dataset features and problem-specific factors. Class imbalance is still a hot topic for study, and there has been constant innovation in novel methods that are adapted to certain dataset characteristics and application fields. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
The Green Evolution: Transforming Supply Chains for a Sustainable Future
The paper analyses systemic, multi-stakeholder effort required to overcome continual challenges. Proactive solutions that prioritize supplier collaboration and transparency, aided by emerging technologies and public-policy guidance, can drive the necessary sustainability transformation. The required sustainability change can be initiated by proactive solutions that place a high priority on supplier collaboration and transparency, supported by new technology and recommendations from public policy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Voluntary Carbon Markets: Bridging Climate Solutions Through Sustainable Finance
Voluntary carbon markets (VCMs) enable companies and individuals to offset their carbon footprint by acquiring carbon credits from programs that decrease or eliminate emissions, such as renewable energy or forest conservation activities. As climate change mitigation becomes more prominent on corporate and investor agendas, voluntary carbon markets provide a framework for channeling funding toward emissions-reduction operations. The threat of climate change necessitates immediate action, pushing sustainable finance to the forefront. Carbon offsetting appears as an intriguing but contentious option in this changing ecology. It enables entities to compensate for their greenhouse gas emissions by funding programs that reduce emissions elsewhere. However, worries about the integrity of some offsets remain, emphasizing the need for strong standards and verification systems. This includes aligning lending, insurance, and investment agendas with sustainability objectives such as the Paris Agreement. As the global economy undergoes structural transformations to meet climate targets, sustainable finance invests in VCM projects to direct funds towards programs that reduce carbon emissions, boost renewable energy use, and support sustainable practices. This linkage strengthens the financial sectors role in combating climate change while also promoting economic growth and resilience. Integrating VCMs into sustainable finance strategies improves transparency and accountability in carbon offsetting methods, ensuring that investments contribute to emissions reductions and environmental benefits. This conceptual study seeks to contribute to a thorough understanding of the collaboration between VCMs, sustainable finance, and climate action, emphasizing the significance of collective action in companies and individuals achieving environmental sustainability and minimizer by the effects of climate change. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Effective Methods of Waste Management Practices in Green Hotels Toward Green Brand Image: An Empirical Study
The changes in consumer tastes are a significant motivating factor for hotels to adopt environmentally friendly practices. Recently, there has been a significant focus on the perils of climate change and the significance of adopting sustainable practices. As a result, environmentalism now influences almost every consumer decision. With the increasing awareness of environmental sustainability in the hospitality industry, the options for eco-friendly hotels are expanding, providing a wider range of choices for potential customers. Thus, this study seeks to examine the efficient strategies employed by green hotels for trash management to enhance their green brand image. Customer data from hotels was gathered and examined using SPSS 25 software. The findings suggest that implementing energy efficiency measures, promoting water conservation, and adopting sustainable and environmentally conscious building practices are effective approaches to waste management that can improve a companys brand image. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Multilingual Voice-Assisted for Traffic Sign Detection and Classification in Adverse Weather Conditions
In a world where millions of people are wounded in auto accidents each year due to negligence, a lack of understanding of traffic laws, and bad weather, there is an urgent need for greater road safety. This is particularly the case in India, where a disproportionately high number of traffic accidents lead to numerous fatalities. Ignoring traffic signs raises these risks and endangers not only vehicles but also passengers and pedestrians. This project addresses the significant issue of traffic sign recognition in bad weather and offers voice-based instruction in many languages to increase road safety. Using a mix of state-of-the-art technologies, including YOLOv8 for real-time sign detection and the Google Translate API, which supports NLP tasks, this research offers a full solution. The model's remarkable precision and efficacy underscore its capacity to revolutionize traffic safety and furnish a more secure and expedient driving encounter. With the world moving towards more autonomous mobility, this study is laying the groundwork for safer and more effective driving in the future. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Forecasting Breast Cancer with Integrated Pre-trained CNN and Machine Learning Framework from CT Images
This article investigates machine learning techniques effectiveness at using computed tomography (CT) images to forecast breast cancer, hoping to expedite early identification and plan treatment. Drawing on many different machine learning models, such as CNN, SVM, VGG16, RNN and RF, we did extensive work to measure their performance distinguishing between malignant and benign breast tissue regions. The dataset includes 2,430 CT pictures, with 70% for training and 30% for testing. It has been carefully selected and prepared in order to guarantee robustness and consistency. The precision, and in-sensitivity measure the accuracy, sensitivity, specificity is used as analytic indicators to measure the models ability to predict regions of breast cancer accurately. Our findings show that the proposed CNN model achieved an accuracy of 98.75%, superior performance. Other machine learning models are also highlighted in this study, demonstrating how breast cancer can be predicted using various methods. This research will determine the forms and technologies suitable for breast cancer forecasting. Medical imaging and clinical decision-making can move forward because of this research, offering a glimpse into how integrated machine-learning systems can bring greater precision to diagnosis and prognosis. By careful experimentation and analysis, we hope to prepare people for early intervention and personalized treatment methods. This will make for improved patient outcomes in fighting breast cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Weed Recognition in Cotton Fields Through Advanced Imaging and Learning Techniques
This research investigates the efficacy of weed recognition models in cotton fields through advanced imaging and machine learning techniques. Utilizing 10 trials, the models, namely K-NN and GBM, were evaluated across multiple performance metrics. Results reveal that GBM consistently outperformed K-NN in accuracy, precision, recall, and F1 score, with average values of 0.88, 0.89, 0.86, and 0.88, respectively, compared to K-NN's averages of 0.85, 0.87, 0.82, and 0.85. Moreover, GBM exhibited higher AUC values (0.94) than K-NN (0.92) in ROC curve analysis, indicating superior discrimination ability. Additionally, k-fold cross-validation demonstrated GBM's higher mean accuracy (0.89) and F1 score (0.88) compared to K-NN (mean accuracy: 0.86, mean F1 score: 0.85). Additionally, integrating temporal data analysis could improve the models ability to detect weed growth patterns over time. Real-time monitoring capabilities and automated decision-making systems could streamline weed management practices in agricultural settings. Furthermore, expanding the study to encompass diverse geographical regions and crop types would provide valuable insights into the generalizability and robustness of the developed models. Overall, continued research in this domain holds the potential to revolutionize weed management strategies and contribute to sustainable agriculture practices. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Deep Learning-Based Dynamic Vision: Classifying Hand Gestures
In the field of hand gesture recognition, this research introduces novel approaches by utilising a variety of state-of-the-art deep learning models, including YOLOv6, YOLOv8, VGG16, VGG19, and ResNet50. Our work involved rigorous dataset annotation and preprocessing, coupled with custom data augmentation techniques tailored for real-world scenarios. The results were excellent, as YOLOv6 exhibited remarkable precision, achieving an impressive Average Precision (AP) of 97.4% and recall (AR) of 90%. Meanwhile, YOLOv8s prowess shone in specific classes, where it attained a remarkable mean Average Precision (mAP) of 89%. We further explored the capabilities of classical Convolutional Neural Networks (CNNs) such as VGG16 and VGG19. These models demonstrated solid performance with an average accuracy of 74 and 67%, respectively. Our study also explored the utilization of ResNet50, which, despite its popularity in other computer vision tasks, showed a lower accuracy of 33% in the context of hand gesture recognition. This research showcases a significant leap beyond the conventional CNN-based research in hand gesture recognition, as we integrated both object detection and image classification models into the evaluation framework. Looking ahead, our research opens doors to exploring ensemble models that synergize the strengths of YOLOv6, YOLOv8, VGG16, and VGG19, promising a harmonized performance across all classes. Moreover, we advocate for further research into transfer learning techniques, anticipating even higher accuracy levels in scenarios constrained by limited training data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
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. -
Optimizing Diabetes Prediction Models for Enhanced Health Data Processing
Diabetes prediction is crucial for early intervention and personalized treatment. This study uses a multimodal strategy, including prediction algorithms, downsampling, feature engineering, exploratory data analysis (EDA), cross-validation, and classification techniques. EDA is used to understand diabetes-specific features, while downsampling ensures fair representation of instances with and without diabetes. Classification algorithms categorize people into appropriate diabetes risk groups using machine learning. Cross-validation evaluates predictive models in various data scenarios. The study emphasizes the value of specialized methods and domain-specific expertise in diabetes prediction, emphasizing the need for accurate risk assessment in healthcare decision-making and the potential for proactive interventions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Comparative Analysis of Classification Models Using Various Feature Sets
Feature selection is a fundamental step in Machine Learning (ML) that involves choosing some input data that would enhance the model performance. The model is able to run faster using lesser computational resources while giving reasonable results. Hence, feature selection as important as selection of a good model. In this chapter the aim is to analyze how the performance of different multiclass classification algorithms is affected on different features. The algorithms used are K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Convolutional Neural Network (CNN) on the CIFAR-10 dataset. To obtain the new dataset with modified features, we use dimension reduction methods on the original dataset. The new dataset is at least 500x smaller, and we have noticed that in the best case scenarios reducing dimensions reduces the accuracy score only marginally. The SVM is the most consistent among the experimented models. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
IoT-Powered Health Monitoring System for Protecting Vital Organs Through Cloud-Based Diagnosis
The main objective of this research was the development and evaluation of an IoT- and machine learning-based health monitoring system capable of protecting patients vital organs through cloud diagnosis. This could be achieved by connecting a set of sensors, including temperature, pressure, heart rate, and oxygen sensors, to the patient and allowing them to communicate with the cloud to transmit real-time data via IoT technologies. The data could be further analyzed and predicted using cloud-based machine learning algorithms. This study investigated the performance of different machine learning models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Naive Bayes (NB), and Negative Decision Trees (DT), for the purpose of patients health prediction based on the sensor data. After experimentation and evaluation, we found that the ANN model demonstrated the best predictive ability, with an accuracy level of 99.45%. The SVM, NB, and DT models also demonstrated good performance, with the accuracies of 96.5%, 94.34%, and 91.2%, respectively. Therefore, this research demonstrated that IoT and machine learning technologies could be successfully employed in healthcare for remote patient monitoring and timely prediction. The created system allows for real-time monitoring, which enables early prediction, potentially leading to improved patient outcomes, cost savings, and higher efficiency of provided care. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Advancing Software Defect Detection and Prevention: Bridging Gaps in Early-Stage and Evolving Software Systems
Software defect prediction (SDP) is a critical method in modern software development, saving costs while ensuring the delivery of high-quality software systems. This study investigates the vital importance of SDP, focusing on its function in detecting and correcting software faults that might lead to system failures. SDP forecasts defect-proneness and optimizes software-testing processes by using software metrics such as lines of code and change information. The chapter examines the progress of SDP research since the turn of the century, emphasizing the academic emphasis on refining static characteristics and establishing efficient learning methods for building high-performance defect predictors. Recognizing the economic consequences of software flaws, particularly in major engineering projects, this chapter emphasizes the importance of SDP in limiting project failures and economic losses in the twenty-first century. Several defect prediction methods are investigated in the context of software quality, with an emphasis on ongoing attempts to prevent and discover errors early in the software development lifecycle. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.