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Internet of Things and Indian Banking Industry: Applications and Challenges
Technological advancements have changed the way people think about how they operate. Various new approaches to work and processes have been identified after adopting various technological changes. This paper discusses various changes adopted by the finance and banking sector with the help of various successful changes in the banking industry. The banking sector, where human-to-human interaction was the core of operating technology, has made it machine-to-machine. Various significant and recent changes have been discussed, followed by some challenges faced by this industry even after successfully adopting technology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Automate Threat Detection and Analysis Through Intelligent Data Mining Techniques for Network Traffic and Cybersecurity
Today, we are constantly surrounded by vast amounts of data, a trend that is expected to grow significantly over the next decade. The abundance of data presents challenges for thorough analysis and extraction of valuable insights buried within unstructured information. Advanced tools like data mining are crucial in uncovering this useful information and making full use of it. In light of the increasing number of security threats in networks, there is a need for robust security solutions. While traditional network security measures have been primarily managed locally, concerns about internet-based security have grown due to heightened computer usage leading to cybercriminal activities previously limited to physical intrusions. A threat intelligence program aims to enhance analytical and preventive capabilities by acquiring knowledge about potential or existing threats based on evidence. As most devices are interconnected with the Internet, many organizations prioritize cybersecurity as they acknowledge the vulnerabilities arising from this connectivityproviding opportunities for cyber-attacks. Effective threat intelligence concerning network traffic necessitates a comprehensive understanding supported by thoughtful representation techniques. This paper proposes an extensive exploration of various machine learning methods aimed at identifying weaknesses in detecting invasive activity using different approaches and evaluating their performance against the KDD 99 benchmark dataset. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Lights and Shadows in Autodesk 3ds Max: Methods and Features
Using the 3ds Max software, this paper describes the intricate modelling process of implying shadows onto various objects to make it look more realistic. In this article, Various Shadow types and controls are used in order to demonstrate the functions of shadows in Autodesk 3ds Max. The paper helps the reader understand the nature of the lights and shadows in a computer-generated environment and its implementation in the real-world situations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Employee Social Experiences and Performance Management Systems: A Brain-Friendly Approach in Organizations
In the wake of the COVID-19 pandemic, organizations are undergoing significant changes that call for more effective people management strategies. With new competitive challenges and constantly evolving environments, modern organizations need toadapt to ensure their continued success. One valuable tool for achieving this goal is a performance management system. However, it is crucial to update the theories and techniques of this system to meet the current demands. This study explores how the performance management system affects employees social experiences from a neuroscience perspective. Using a quantitative approach, the study gathered information from 268 employees across various industries in India, considering ten performance management criteria and evaluating social experience components using the SCARF model based on neuroscience. The results reveal that specific aspects of the performance appraisal system significantly impact employees social experiences. Based on their understanding of the link between different factors in the performance appraisal process and the quality of social experience, researchers recommend a performance appraisal model that promotes a brain-friendly work environment. These findings are especially relevant to managers and organizations, as they offer valuable insights into critical factors to consider when planning and implementing performance appraisal systems in modern workplaces. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Multicriteria Decision-Making Approach to Building Resilience Along the Indian Medical Equipment Supply Chain
The presence of risks that lead to potential disruptions is evident along the Indian medical equipment supply chain. Identifying and prioritising the supply chain risks is pivotal in enhancing supply chain resilience, surplus, and sustainability. This study uses multicriteria Decision-Making to prioritise supply chain risks in the Indian medical equipment industry. Unstructured interviews were conducted with industry experts from six medical equipment firms to identify supply chain risks. The identified risks were prioritised using the Analytic Hierarchy Process (AHP), Fuzzy AHP, and Analytic Network Process (ANP). AHP outlines the relative importance and ranks the risks. Finally, a simulation using ANP ranks the risks under different circumstances, considering the magnitude of impact and frequency of occurrence. A total of nine iterations were run to obtain a generalised rank for the identified supply chain risks under a combination of different scenarios of risk magnitude and frequency. The AHP results indicated that the industry experts considered inventory management risks as the most significant factor, followed by digitalisation and technological infrastructure. The Fuzzy AHP results revealed the triangulated weights in the same rank which was used to reiterate the findings from the AHP results with added dynamics in the form of the nearest neighbouring values. The ANP iterations revealed that supply and demand uncertainties must be managed first amidst any given risk scenario, followed by inventory and technological risks. The originality of this study is that the ANP results derived from nine iterations provide an overall decision matrix that can be generalised across the Indian medical equipment sector. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Machine Learning for Mental Health: A Sentiment Analysis Approach for Detecting Depressive Tendencies on LinkedIn During Layoffs Using RoBERTa
In the present corporate set-up, layoffs are an unfortunate yet common occurrence. Such occurrences lead to loss of job security and can have direconsequences on an individual's mental health, leading to depression. Depression was a global health concern well before the current downsizing came into the picture. These trying times have acted as a catalyst for this illness that affects not just mental health but all aspects of an individuals life. The study investigates the use of sentiment analysis on LinkedIn data to identify and examine depressive tendencies among victims of layoffs. Web-scraped information was taken from LinkedIn profiles of individuals affected directly or indirectly by layoffs. RoBERTa, a transformers model, is used to classify people as depressed or not by evaluating sentiment and emotional cues. A comparison between four machine learning algorithms- Decision Tree, Logistic Regression, SVM, and Nae Bayes is drawn to check their ability to detect depression. The SVM classifier performed best with an accuracy of 95.59% and 83.52% with the CountVectorizer and TF-IDF feature selection methods, respectively. Sentiment analysis aids in this research by examining the melancholic undertones in the words and phrases used in texts authored by people affected by layoffs directly or indirectly. The knowledge gained from this research can significantly affect corporate initiatives, mental health services, and human resource practices during such challenging times. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Victimizing Cyberbullying Mental Illness Through Social Media
With the exponential increase of technologies and the growth of social media users, bullying takes different methods to reach its targets. Cyberbullying has been emerging lately in the form of bullying through voice memos, videos, and most frequently in the form of text messages. Bullies might use the rich and expansive environment that social networks offer to target their victims with their attacks. Cyberbullying has an especially negative impact on younger generations, who value social affirmation above everything. Studies have found a clear link between cyberbullying and suicidal ideation, particularly in teens. This concerning trend needs the development of efficient ways for identifying and combating cyberbullying, thereby protecting young lives. Many techniques can be used to identify the bullies linguistic patterns and create a detection model that will automatically identify instances of cyberbullying and whether it can lead to the happening of suicide or not. This project proposes a hybrid model of BiLSTM and EmoBERTa for detecting cyberbullying and checking the possibilities of it leading to the happenings of suicide. The dataset was run on different models and the proposed model yields the best average performance. By putting such detecting mechanisms in place, we can make the Internet safer. Early detection of cyberbullying enables intervention, which protects vulnerable people and may avoid disasters. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Bone Abnormality Detection Using RMSprop Optimizer in VGG16
The advent of deep learning has revolutionized medical imaging, enhancing diagnostic precision, treatment planning, and patient care. This study leverages deep learning, specifically employing the VGG16 model optimized with RMSprop, to automate bone abnormality detection. Methodologically, the research encompasses data acquisition, preprocessing, and model training with RMSprop optimization. Results highlight the efficacy of this approach, showcasing RMSprops ability to detect various bone abnormalities. These findings underscore deep learnings potential in medical imaging, emphasizing its applicability beyond bone abnormality detection. The study illuminates the transformative impact of RMSprop-optimized deep learning models in medical imaging, promising advancements in automated diagnosis and treatment planning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Real-Time Stress Monitoring Using IoT Wearable Sensors and Machine Learning
This research explores the potential of Internet of Things (IoT)-enabled wearable sensors in conjunction with machine learning techniques for real-time, non-invasive stress monitoring in women, using physiological data indicative of stress states. An IoT Wearables Dataset for Womens Safety: Stress Detection and Analysis was sourced from IEEE Data port and was used to train four supervised machine learning models, namely, random forest, support vector machines (SVM), gradient boosting, and logistic regression to classify individuals into categories of stressed and unstressed based on a predefined threshold of 0.35. The random forest algorithm attained the highest accuracy of 88.5% in categorizing stress, demonstrating reliable capabilities in identifying stress indicators from wearable sensor data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Real-Time Stress Monitoring Using IoT Wearable Sensors and Machine Learning
This research explores the potential of Internet of Things (IoT)-enabled wearable sensors in conjunction with machine learning techniques for real-time, non-invasive stress monitoring in women, using physiological data indicative of stress states. An IoT Wearables Dataset for Womens Safety: Stress Detection and Analysis was sourced from IEEE Data port and was used to train four supervised machine learning models, namely, random forest, support vector machines (SVM), gradient boosting, and logistic regression to classify individuals into categories of stressed and unstressed based on a predefined threshold of 0.35. The random forest algorithm attained the highest accuracy of 88.5% in categorizing stress, demonstrating reliable capabilities in identifying stress indicators from wearable sensor data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Predicting Financial Distress in India: A Deep Learning Approach
The present study examines the efficacy of deep learning models in predicting financial distress in India. For this purpose, the study employs three distinct architectures: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Conventional Neural Network (CNN) models. Utilizing data from companies that filed for bankruptcy under the Insolvency and Bankruptcy Code 2016 for the period of 20162023, the study adopts a balanced sample approach to categorize them into distressed and non-distressed groups. Nineteen financial variables are utilized to predict financial distress. Python is used as the programming language, and Jupyter Notebook facilitates algorithm development. The findings reveal that the LSTM model, when compared to RNN and CNN, achieved 91% accuracy using parameters such as 8 LSTM units with tanh activation and a dense layer with sigmoid activation function, a batch size of 10, 50 epochs, RMSprop optimizer, and binary cross-entropy loss were used. The study suggests that deep learning presents a novel approach that can enhance performance in financial distress prediction studies. This study is believed to be the first to utilize deep learning models for financial distress prediction in India based on single-year data, offering valuable insights for financial institutions and investors seeking more effective risk management strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Predicting Financial Distress in India: A Deep Learning Approach
The present study examines the efficacy of deep learning models in predicting financial distress in India. For this purpose, the study employs three distinct architectures: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Conventional Neural Network (CNN) models. Utilizing data from companies that filed for bankruptcy under the Insolvency and Bankruptcy Code 2016 for the period of 20162023, the study adopts a balanced sample approach to categorize them into distressed and non-distressed groups. Nineteen financial variables are utilized to predict financial distress. Python is used as the programming language, and Jupyter Notebook facilitates algorithm development. The findings reveal that the LSTM model, when compared to RNN and CNN, achieved 91% accuracy using parameters such as 8 LSTM units with tanh activation and a dense layer with sigmoid activation function, a batch size of 10, 50 epochs, RMSprop optimizer, and binary cross-entropy loss were used. The study suggests that deep learning presents a novel approach that can enhance performance in financial distress prediction studies. This study is believed to be the first to utilize deep learning models for financial distress prediction in India based on single-year data, offering valuable insights for financial institutions and investors seeking more effective risk management strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Prioritisation of Challenges in OTT Video Platforms: A Multi-criteria Decision-Making Approach
Over-the-top (OTT) video platforms have emerged as the preferred choice for on-demand entertainment. In this ever-changing landscape, OTT video platforms face many challenges to being relevant in the market. Identifying and prioritizing challenges is pivotal for the sustainable growth of OTT platforms. This paper aims to comprehensively examine and prioritize the challenges of the OTT video platform. The challenges are identified through an extensive literature review and unstructured interviews with six OTT industrial experts. The importance of each challenge is measured based on the analytical hierarchy process (AHP) to develop a hierarchy of those challenges. The AHP analysis results indicated customer retention as the most significant challenge, followed by content, customer experience, infrastructure, and bandwidth. The study is subjective to the experts opinions and available literature regarding the OTT platforms. The insights gleaned from this research are poised to offer substantial value to digital platform operators, media professionals, and managers. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Prioritisation of Challenges in OTT Video Platforms: A Multi-criteria Decision-Making Approach
Over-the-top (OTT) video platforms have emerged as the preferred choice for on-demand entertainment. In this ever-changing landscape, OTT video platforms face many challenges to being relevant in the market. Identifying and prioritizing challenges is pivotal for the sustainable growth of OTT platforms. This paper aims to comprehensively examine and prioritize the challenges of the OTT video platform. The challenges are identified through an extensive literature review and unstructured interviews with six OTT industrial experts. The importance of each challenge is measured based on the analytical hierarchy process (AHP) to develop a hierarchy of those challenges. The AHP analysis results indicated customer retention as the most significant challenge, followed by content, customer experience, infrastructure, and bandwidth. The study is subjective to the experts opinions and available literature regarding the OTT platforms. The insights gleaned from this research are poised to offer substantial value to digital platform operators, media professionals, and managers. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Diagnostic Accuracy for Autism with BRCNet: A Novel Approach for Brain Region Segmentation and Classification Using Deep Learning
In the quest to enhance the diagnostic accuracy of neural disorders, particularly autism, this paper presents a novel approach for brain region classification using advanced machine learning techniques. The study utilizes the ABIDE and AAL116 atlas datasets, focusing on segmenting and classifying brain regions from resting-state functional MRI (rs-fMRI) images. We propose a three-stage process. In the first stage, data collection and preprocessing are conducted, where rs-fMRI images are preprocessed into SPM12-NIfTI format. The second stage involves the segmentation of brain regions using a Regularized VNet, resulting in the extraction of AAL116 brain region images, which are then split into training, testing, and validation sets. In the third stage, we introduce a custom-designed BRCNet (Brain Region Classification Network), which discriminates between Autism and Normal classes. Our segmentation methods are rigorously evaluated using metrics such as Dice Score, Recall, and Precision, with the proposed method achieving a Dice Score of 0.985, Recall of 0.962, and Precision of 0.991, surpassing other tested methods like UNet, Active Contour, and Binary Unit. For classification, various methods, including Support Vector Machines (SVM), Decision Trees (DT), and Neural Networks like ResNet, are compared. Our findings demonstrate that ResNet achieves an exemplary performance with an Accuracy of 97.5%, Sensitivity of 96.2%, Specificity of 97.1%, Precision of 97%, and an F-Measure of 97.93%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Deploying Deep Learning in Real-Time for Lung Cancer Diagnosis via Medical Imaging
In this research, deep learning models were used to diagnose lung cancer automatically using hospital image data. A dataset with 3,400 lung cancer images from online repositories and hospital archives was used for model training and evaluation. After preprocessing and feature extraction, various deep learning architectures such as VGG-16, CNN, ResNet and RNN were adopted in this study. The VGG-16 model had the highest accuracy rate of 96.86%, showing strong performance. This rate of accuracy is actually higher than their accuracy of 91%. These results serve to highlight the impressive accuracy achieved by our study relative to prior research. By accurately and effectively altering lung cancer diagnosis into a process entirely reliant on algorithms, deep learning models show promise for their potential. Diagnostic tools should be able to catch cancer early and accurately, identify the present type and classification for tumors. For all its promise, limitations such as dataset size and generalizability mean that clinical trials will be needed for further validation. Focus should turn toward this as the direction of future research in order to enhance model robustness and applicability against challenges. This research allows us to better the well-being of patients and reduce the burden of lung cancer through timely intervention and personalized treatment strategies by making use of advanced techniques in medical diagnostics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
RehabPal: Automated Physical Therapy Assistance
This innovative research project transforms automated physical therapy support by combining 2D and 3D pose assessment approaches with tailored feedback systems. Utilizing Mediapipe technology, the apparatus attentively observes patients movements in a two-dimensional space and gives realtime data regarding their gait, exercise compliance, and range of motion. Accurate data on joint angles and body segment alignment are provided by the system's sophisticated 3D posture estimation algorithms, which enhance tracking precision. The system incorporates customized feedback systems that include individual patients goals, progress, and conditions and delivery and reward schemes. By increasing user engagement and adherence, the integration of gamification elements has the potential to revolutionize automated physical therapy support. This comprehensive approach aims to enhance patients quality of life while simultaneously enhancing long-term rehabilitation outcomes by providing more conveniently accessible, affordably priced, and specially designed therapies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Multi-objective Deep Reinforcement Learning Approach for Multiple -Input/Multiple-output Routing in WSN
The Wireless Sensor Network (WSN) is a network of numerous devices that are interconnected via the internet and significantly impact the network. However, despite their significant applications WSNs face challenges related to network security energy levels and information transmission delays. To address these challenges, a method utilizing Multi-Objective Deep Reinforcement Learning (DRL) has been proposed. The proposed method aims to maximize energy utilization in the network by efficiently managing covered and uncovered cluster network routing. The performance of energy transmission is enhanced through the use of the Markov Decision Process model based on multi-objective DRL combined with training the network using Deep Q Network (DQN) to reduce network energy consumption. Training the network with multiple objectives may pose challenges requiring more samples and leading to higher sample complexity, which can be a limiting factor in real-world applications. Despite this, the proposed multi-objective DRL method demonstrates high performance compared to existing methods such as Particle Swarm Optimization (PSO) and Convolutional Neural Network (CNN). Specifically, multi-Objective DRL method yields superior results, achieving an energy consumption of 42J, Packet Delivery Ratio (PDR) of 90%, and an End-To-End Delay (ETED) of 45 S. These outcomes outperform existing methods in the context of WSNs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
An Enhanced Whale Optimization Algorithm for Task Scheduling in Cloud Computing
Task Scheduling is the significant challenge in the environment of Cloud Computing (CC) and has attention in numerous researchers in recent years with respect to attain cost effective computation and improve resource utilization. The existing algorithms has limitations of role and selection criteria of inertia weight was not considered. In this research, Enhanced Whale Optimization Algorithm (EWOA) is proposed for maximize effectiveness of task scheduling in CC. An inertia weight is implemented in WOA algorithm that enhances the convergence and accuracy of algorithm that helps in task scheduling effectiveness. The performance of proposed technique is estimated with performance measure of Makespan (ms), execution time (s) and resource utilization (%). The proposed method attained less execution time of 2304, 2537, 2765, 2983 and 3016s for 200, 400, 600, 800 and 1000 number of tasks. The proposed method attained the superior results when compared with other existing algorithms like Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
2D Photonic Crystal for the Detection of Infectious Virus and Bacterial Diseases
In this paper, photonic crystal (PhC) sensor for analysis and modelling for viral and bacterial detection is proposed. Optical biosensors detect cancer, Bacillus cereus, malaria, typhoid, tuberculosis, etc. Optical biosensors work by shifting the peak resonance wavelength with modest refractive index changes. Because viral pathogens rapidly mutate and replicate in the human cell nucleus, sensors that offer accurate results for viral and bacterial diseases in seconds are in high demand. Hence, optical biosensors provide fast, sensitive results. The sensor detects influenza H1N1, hepatitis B (HBV), and typhoid, respectively. A maximum sensitivity of 443.33nm/RIU with a quality factor of 1309 is obtained. Simulations are performed using finite-difference time-domain (FDTD). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.