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Tailored Garment Recommendations Using Computer Vision and Machine Learning
In the realm of online shopping, despite the many advancements made in this specific field, the dilemma of finding the best clothing items that are right fit and meet the style preference of a consumer and avoid returns is yet to be completely solved. This problem has often resulted in customer dissatisfaction. The proposed system intends to deal with this problem by analyzing the users body size and suggesting the best fitting garment that suits their size and style with the help of advanced computer vision and machine learning technologies. This approach not only provides an improved online shopping experience with personalized recommendations, it furthermore contributes toward reducing the environmental impact caused by the return of ill-fitting clothing hence promoting sustainability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Robot-Assisted Children-Centric Strategies in the Hotel Industry: Enhancing Parental Attraction and Sales Growth
The hotel industry is undergoing a profound transformation, shaped by shifting consumer preferences and technological innovations. An intriguing trend within this transformation is the growing adoption of robotic technology to elevate the guest experience. Of particular interest is the emergence of child-centric robots designed to engage and educate young guests. These robots have the potential to significantly influence parental attraction to hotels, a factor that bears substantial implications for hotel sales and profitability. This research delves into the phenomenon of Robot-Assisted Child-Centric Strategies in the Hotel Industry and its impact on increasing parental attraction and, in turn, driving sales growth. It explores how hotels strategically integrate child-centric robots to create distinctive and engaging experiences for families. These robots offer interactive concierge services, in-room companions, educational support, and entertainment, enriching childrens stays and allowing parents to relax. The study investigates the technological innovations and capabilities of these robots, the strategies hotels employ to seamlessly incorporate them, and their impact on parental attraction. Employing a mixed-methods approach, including surveys, interviews, and data analysis, it uncovers the drivers behind the adoption of child-centric strategies and their correlation with sales growth. Ultimately, this research reveals how child-centric robots can revolutionize the hotel industry by attracting families and enhancing guest experiences. It provides valuable insights for hoteliers seeking to leverage this trend, helping them find the delicate balance between guest satisfaction and profitability in a competitive market. Child-centric robots offer a promising avenue for hotels, providing unforgettable stays for families while boosting sales and profitability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
The Role of Social Media in Shaping Gen Zs Awareness in Understanding Social Issues
Many social movements (such as #Black Lives Matter Movement, #MeToo Movement and #EndAcidSale Movement) have gained momentum through social media, but there is a research gap in understanding Generation Zs individuals participation and engagement in these social movements and also the awareness they have regarding social issues through social media. This study aims to understand how social media engagement affects Gen Zs understanding of social issues and how the perceptions of Gen Z regarding social issues is influenced by it. It is crucial to look into Gen Zs engagement in these social movements and also the awareness they get regarding social issues through social media. Algorithms tend to dictate the content that reaches the users; therefore, we want to analyse whether social media algorithms is effective in ensuring Gen Z social media users get an understanding of different viewpoints regarding the social issue. Understanding Gen Zs engagement in social activism through social media requires an in depth analysis on the factors that motivate them to do so. These factors can include their belief that it would lead to a systematic change, a sense of community they get when they engage with others on social media about social issues, pressure from their social circle to engage with social media content related to social issues and the desire to be seen as socially conscious by their peers. This study also aims to find out whether this has an effect on their mental health. The findings of the study show that social media content engagement moderates the relationship between social issue awareness and both real-world activism and mental health among Gen Z. Increased social issue awareness and social media content engagement lead to higher activism, but excessive engagement is found to negatively impact mental health among Gen Z. Results from the study indicates that respondents believe in the effectiveness of social media activism but also report feeling stressed, anxious, and overwhelmed by social issue-related content. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Technological Innovation in Digital Payments: A Survey of Trends, Challenges, and Opportunities
This paper critically examines the impact of technological innovations, particularly in the areas of financial technology (FinTech), artificial intelligence (AI), and machine learning (ML), on digital payments. The aim is to analyze how these advances have transformed traditional payment systems to improve transaction efficiency, reduce costs, and allow real-time analysis, making them essential components of the modern financial ecosystem. In addition, the study explores the crucial role of digital payments in promoting financial inclusion, especially in regions where the banking infrastructure is underdeveloped while addressing the current challenges in rural and remote areas. The article highlights the importance of robust cybersecurity measures, adaptive regulatory frameworks, and digital inclusion policies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Adoption of Facial Recognition System for Secured Mobility: Challenges and Future Implications of Using Digi Yatra as a Facial Recognition System
With the incorporation of facial recognition technology into the air travel journey, it is going to alter the passenger experiencetherefore, contactless, efficient, and the least painful processes will be formed. In this regard, through this paper, The authors explore into the Digi Yatra adoption, a biometric verification system newly implemented in Indian airports, where Aadhaar-based identity verification and advanced facial recognition make journeys easy and seamless for passengers. However, Digi Yatra introduces severe challenges for issues concerning the protection of data, regulation compliance, and public trust, though it enhances the operational efficiency of the operation and security. Using the Privacy by Design framework in conjunction with blockchain technology will help Digi Yatra to erase privacy concerns and foster a user-centric approach. This paper brings out the transformational impact that Digi Yatra would have on the aviation sector along with the potential of being a benchmark for other global biometric systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
AIs Role in Semantic Segmentation for Data-Driven 3D Models of Heritage Structures
Using point cloud data from laser scanning and photogrammetry to create three-dimensional models with scan-to-BIM processes has become increasingly common in heritage conservation. During the processing of point clouds, semantically segmenting data can translate captured spatial information into intelligent data structures, enabling classified, accurate, data-driven digital models of heritage structures. Subsequently, digital models are utilized for analytical tasks like structural tests, energy optimization, etc. Artificial Intelligence (AI) has become a promising solution for automating Three-Dimensional Point Cloud Semantic Segmentation (3DPCSS), enabling a faster and more accurate composition of parametric objects within 3D modeling and management systems. However, implementing 3DPCSS solely with AI presents various technical and theoretical challenges. The geometrical complexities inherent in historical structures often necessitate manual segmentation processes or oversimplified representations that miss the unique characteristics of the building. Therefore, selecting an appropriate AI framework for 3DPCSS is essential to ensure accurate results. Multiple factors determine algorithms selection, making it challenging to categorize universal solutions. The paper highlights the key factors: 1) Data collecting tools and technologies, 2) Types of the dataset, 3) Complexity of geometrical elements, and 4) Computational tasks. AI frameworks are typically selected based on the suitability and significance of these factors relative to the projects intent. Very few studies talk about the choices of algorithms. This papers significant contribution is recognizing trends in effective data acquisition strategies through a case study in India. Additionally, it identifies state-of-the-art AI models from the past decade based on a systematic literature study. The paper infers the extensive use and advancement of hybrid approaches tailored to multi-modal data types and the specific needs of heritage projects. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
ResFruitGrader: Leveraging Residual Networks for Advanced Fruit Quality Grading Systems
The rising agricultural industrys requirement for effective sorting and grading procedures has increased the demand for automated and precise fruit quality assessment in recent years. This study aims to attain high classification accuracy by investigating the use of Convolutional Neural Networks for fruit quality identification. As customers place a higher value on fresh and wholesome options, the agriculture and food industries must meet rising demands for premium produce. Fruit quality must be guaranteed since it directly affects consumer happiness and the profitability of the sector. Preprocessing methods, CNN model creation, training, and evaluation utilizing cutting-edge deep learning techniques comprise the methodology applied in our study. The research demonstrates the CNN-based methods stability and dependability in identifying a range of quality attributes, such as fruit imperfections, size, color, and maturity. The suggested CNN architecture performs remarkably well, recognizing fruit quality parameters with a 99.5% accuracy rate by utilizing a collection of various fruit photos. A promising path for improving efficiency and accuracy in fruit quality assessment within the agricultural industry is provided by the researchs insights into the transferability and scalability of the developed model for practical applications in automated fruit sorting systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Reinforcement Learning for Early Detection and Intervention of Sepsis with Graph-Based Personalized Treatment Recommendations
Early detection and treatment of sepsis, a condition that can become fatal through the bodys response to infection, can enhance patient life. This paper explores how reinforcement learning can be applied to the early detection and treatment of sepsis, along with its novel features, which include personalized treatment recommendations and graph-based representations using Graph Neural Networks (GNNs). Moreover, domain adaptation and transfer learning strategies make the model applicable in a wide range of clinical contexts. The RL model is therefore designed to identify early warning signs and give prompt, individualized answers to avoid major repercussions. To ensure wide application, the RL model was trained using an enormous dataset of patient vitals, lab results, and clinical notes from numerous centers. It is already proven in real-life clinical situations that this model can improve patient outcomes and the quality of clinical decisions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Reinforcement Learning for Early Detection and Intervention of Sepsis with Graph-Based Personalized Treatment Recommendations
Early detection and treatment of sepsis, a condition that can become fatal through the bodys response to infection, can enhance patient life. This paper explores how reinforcement learning can be applied to the early detection and treatment of sepsis, along with its novel features, which include personalized treatment recommendations and graph-based representations using Graph Neural Networks (GNNs). Moreover, domain adaptation and transfer learning strategies make the model applicable in a wide range of clinical contexts. The RL model is therefore designed to identify early warning signs and give prompt, individualized answers to avoid major repercussions. To ensure wide application, the RL model was trained using an enormous dataset of patient vitals, lab results, and clinical notes from numerous centers. It is already proven in real-life clinical situations that this model can improve patient outcomes and the quality of clinical decisions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Detecting and Countering Misinformation Through NLP-Based Approach for Fake News Detection
The rapid expansion of digital media and the seamless transmission of information have raised serious concerns about the widespread dissemination of misinformation and fake news. Combatting this issue requires robust and effective techniques that can accurately detect and classify fake news. Natural language processing (NLP) approaches have emerged as powerful tools in this endeavor, leveraging advanced text classification algorithms to identify and counteract misinformation. This study includes NLP approaches for countering misinformation through text classification, with a specific focus on fake news detection. Leveraging natural language processing techniques, the project implements a text classification pipeline for identifying and distinguishing between genuine and fake news. The pipeline encompasses essential NLP steps such as tokenization and stop word removal. Traditional machine learning algorithms, such as the gradient boosting classifier, CatBoost classifier, random forest classifier, AdaBoost classifier, logistic regression, and SVM linear kernel are trained using the transformed data to classify news articles. This study explores feature engineering techniques and model evaluation to enhance the classification performance. Experimental results indicate the effectiveness of The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Smart People Counting System by Enhancing Accuracy and Affordability with YOLOv5 and Cloud-Based Integration
Considering that all moving objects are humans, much of the work in data is based on recognizing and tracking moving objects. In this work, we present a method for counting peoples faces. Even though we use the face mask, the deep learning-based YOLOv5 algorithm and Faster R-CNN allow us to recognize the face. We do a very good job of counting people. To make the calculation more accurate, we introduced a new type of intelligent small scale computing system consisting of cheaper hardware and user-friendly cloud computing software. These findings show that intelligent computing systems can realize human vision. Additionally, by combining inexpensive hardware with cloud-based software, the planning process becomes more transparent and cost-effective. Finally, the web application allows users to view the number of authorized and unauthorized users. Based on the results obtained from this method, the deep learning YOLOv5 algorithm is used to identify and match human images to increase security, and thanks to cloud storage, users can easily view all calculated results, increasing the accuracy by 98.53%. Owing to the truth that most of the secure watches cannot be able to check each and each individual The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Privacy-Integrity Aware Efficient Workflow Scheduler for Edge-Cloud Platform
Scientific workflow execution in edge-cloud platform have attained in-creased attention, due to reduction in overall makespan assuring workflow deadlines. The workflow task is composed of diverse subtasks which are either executed in edge and the cloud; they are prone to security risk. Any loss of security breach will result to privacy and data integrity issues. Thus, providing security and meeting workflow execution strict deadlines becomes extremely difficult. The current workflow scheduling methods failed to assure both privacy and integrity together under edge-cloud computing platform. In addressing the research security and efficiency issues, this article introduced a novel approach namely Privacy-Integrity Aware Efficient Workflow Scheduler (PIAEWS) for edge-cloud platform. The PIAEWS introduces a novel trust metrics to assure only authenticated node takes part communication and consensus model to assure data integrity without compromising on user privacy constraint. The PIAEWS improves makespan and reduces overall energy consumption by assuring both security and performance together when executing genome sequencing workflow application in edge-cloud platform. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Organizational Factors Impacting the Retail Industry's Adoption of Blockchain Technology
Indian retail is undergoing a transformation due to rapid digitization and shifting consumer preferences. Blockchain technology is changing retail chain management by boosting efficiency, security, and transparency. This technology can also alter Indian retail operations by enabling verified transactions and improving inventory management to build consumer trust. Blockchain technology adoption in retail depends on organizational readiness, technological knowledge, and top management support. This study examines organizational aspects affecting blockchain adoption in retail and develops and validates a model for organizational characteristics affecting blockchain adoption. Thus, the study examines three key blockchain adoption intention constructs of blockchain knowledge, organizational readiness, and organizational support. Retailers received surveys online and the data was analyzed using SEM. The study supports that organizational management support (p?=?0.017) and organizational readiness (p?=?0.008) are significant precursors to the intention to adopt this technology. The study concludes that organizations must support and improve their readiness for modern technology with the top management's cooperation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Facial Emotion Recognition Augmented with CNNs and Face Detection: Toward Emotive Emoji Synthesis
Emotion recognition is a crucial component with broad applications in technology and healthcare industries specifically in humancomputer interaction. To improve emotion recognition accuracy, this research introduces an innovative technique that integrates face detection with Convolutional Neural Networks (CNNs). Using the Fer2013 dataset, the approach consists of carefully identifying faces in images as a preprocessing step, followed by training a CNN network to identify emotions and create corresponding emojis. After conducting extensive testing and assessment, it is determined that after employing a face detection algorithm the suggested framework is effective in both correctly identifying emotions and producing visually appealing emojis. This helps to create an interface for emotional communication that is more user-friendly and captivating. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
An Improved AI-Based Low Latency Data Transmission in 5G Communication Systems
This paper devised an advanced artificial intelligence (AI) solution for ultra-low latency data transmission in 5G networks. With increasing data rates and lower latency required in 5G networks, efficient methods for transmitting the maximum amount of data are necessary. We have developed an approach that uses AI algorithms so that data transmission can be done more optimally and help reduce latency, providing better overall performance. Our approach consists of several steps, in which we predict the traffic patterns using machine learning techniques in step 1 and allocate network resources accordingly. That helps reduce network congestion and speeds up data transmission. We also introduce deep learning algorithms to adjust the transmission parameters according to network conditions, reducing latency. We simulate our algorithm in 5G network scenarios to assess its performance. The comparison of the results shows that a very low latency was achieved for this design over the earlier methods. Our developed AI-based improved solution provides a potential key to low latency data transmission in 5G communication systems. Integrating AI methods makes the system not only perform better but also be able to adapt more easily when network conditions change. The next steps are to explore the improvements of algorithms and implement them practically in 5G networks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Efficient Intrusion Detection through Class Balancing and Feature Selection: A Case Study with SVM
Intrusion Detection Systems are of paramount importance in network security. However, in real-world scenarios, they always suffer from the challenge of class imbalance, which is dominated by normal traffic. This paper presents a novel approach to enhancing the performance of IDS by proposing a hybrid of the Random Under sampling technique with the univariate feature selection technique, SelectKBest, for handling both problems of class imbalance and high dimensionality. This model was hence tried on the Bot-IoT dataset, which is a real-world IoT network traffic representation. The SVM classifier, which has been trained with the resampled and feature-selected data, showcased 95% balanced accuracy for both normal and malicious traffic detection. The combination of RUS and SelectKBest, apart from reducing overfitting, ensured the retention of the most relevant features and thereby made the IDS model robust. It can practically enhance the performance of IDS in an imbalanced and high-dimensional dataset by providing a balanced, efficient, and precise detecting mechanism. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Machine Learning Research Methods for Identifying Inaccurate Content
Social media, especially when disseminating news, is a valuable information resource. The paper presents methods for detecting fake news, comparing their effectiveness, identifying existing problems, and describes the vectors of further development of this research area. The paper begins with a description of the relevance of the Fake News problem, which clearly describes the negative impact of false news on all spheres of human life. The following is a description of methods for detecting false news, starting from the usual rules of text analysis and ending with complex ML algorithms. In this paper, a comparative analysis of detection methods is carried out, which is based on criteria of efficiency and accuracy. The author identifies the main problems of existing methods related to data quality, changing Fake News formats and the difficulties of automatically determining the reliability of information. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Optimizing Phishing Email Classification Through Scalable Feature Extraction Using MapReduce
A bag of features (BOF) may be made using either map reduction techniques or a combination of a thesaurus and domain knowledge. This research presents the BOFMR (Bag of Features using MapReduce) and BOFWT (Bag of Features with Weighted Terms) algorithms, a scalable and efficient technique for processing large email datasets and generating feature vectors based on pre-defined characteristics. The outcomes from using both BOFs on identical datasets are compared. The algorithm leverages the parallel processing capabilities of the MapReduce framework to handle the extensive data, ensuring performance and scalability. When creating a bag of words from a training dataset, the BOFMR technique is useful. The map-reduce technique will help to create a bag of features faster even in case of a larger chunk of data. In this experiment, as data size was limited, the performance of map reduce was not measured. In another BOFWT approach, the building of BOF with domain knowledge by using the word thesaurus was a challenge. The experimental result shows that the results of BOFWT are nearer to the output of BOFMR, and both algorithms show the highest accuracy among other methods. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Feature Fusion Classification for Emotional Intelligence Using Peripheral Signals
Real-time emotion identification is an innovation in the field of humancomputer interaction, which is an essential and challenging task. The existing studies methods for identifying emotions include face, audio, and physiological signals. The study aims to develop a model for emotion classification to identify and interpret human emotions through skin temperature, respiration, and plethysmography. The study also includes analyzing and interpreting emotional states through ensemble models. The classification is based on the frequency domain signal components extracted using the Fast Fourier Transform (FFT), such as amplitude and frequency, to identify emotional states. Ensemble-based machine learning algorithms such as XGBoost and LGBM achieved the highest accuracy in classifying various emotional states. The study involves unimodal and ensemble methods to analyze the signals. The comparative classification rate of unimodal results with ensemble shows that it is the highest at 85.99%, achieved for sad emotions by XGBoost. Fusing modules like respiration, skin temperature, and plethysmography maintains the accuracy level for all four emotions. The unimodal temperature has the highest accuracy of 86.1% for calm, whereas the fusion model has maintained accuracy for all the emotional states. The feature amplitude is the most promising feature for the classification method, which attains an average of 83.2% for XGBoost. The applications enhance user experiences and contribute valuable help in psychology, mental health care, and HumanComputer Interaction. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Investigating Risk Factors for Enhanced Portfolio Performance: An AI Approach for Indian Midcap Market Analysis
This research investigates the potential of machine learning (ML) for constructing portfolios that outperform human-based management, specifically focusing on the Indian midcap market. The study compares AI-based portfolio compositions, optimised using various risk measures, to the holdings of top midcap mutual funds. In this research, the top five midcap mutual funds sectoral distributions, portfolio compositions, and AI-generated portfolios are examined. According to the research, there is significant performance potential in the AI-generated portfolio, particularly when taking shorter investment horizons into account. Portfolios that maximise the Sharpe ratio produced the best returns throughout the course of the test period for four out of the six sectors, according to the research statistics. Additionally, in order to shed light on the effectiveness and possible advantages of our strategy, our study compares the suggested technique to existing investing strategies that concentrate on particular corporations as well as well-established market benchmarks. The research shows that, particularly when taking shorter investment horizons into account, the AI-generated portfolio has great performance potential. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.