Conference Papers
- Title
- Conference Papers
Collection Items
-
Lightweight Sybil attack detection framework for wireless sensor network with cluster topology
The development of communication and networking technology has made it possible for wireless sensor networks to play a significant role in many fields. Wireless sensor networks are vulnerable to a variety of security threats because of their remote hostile features. The Sybil attack, which generates several identities to gain access to wireless sensor networks, is one such devastating but simple to spread exploit. This Paper proposes a novel identity and trust-based system to ensure protection against Sybil attacks. Analysis of the RSSI and location parameter increases the accuracy. It recognises the attackers and broadcasts information about them to all adjacent sensor nodes. Additionally, it offers other crucial security features. 2025 Author(s). -
A Bibliometric Analysis of Industry 4.0 and Health-Care Services
A key moment in health care is marked by the Fourth Industrial Revolution, commonly referred to as Industry 4.0. This transformation, driven by the convergence of digital technologies with automation and data driving processes, has led to a paradigm shift in how health care is provided. The integration of the emerging technologies in Industry 4.0, such as Internet of Things, Artificial Intelligence, Big Data Analytics and Advanced Robots, are revolutionizing patient care, improving resource allocation and shaping research's landscape. To learn more about the ever-evolving relationship between Industry 4.0 and health care, this research paper begins with a bibliographic analysis. In this interdisciplinary convergence, our bibliometric analyses serve as a lens through which we can see the key trends, research areas and influential players. The review of literature highlights the profound impact of Industry 4.0 on health care, revealing that Internet of Things technologies for real-time patient tracking, proliferation of artificial intelligence in medical diagnosis and transforming power of big data Analytics are changing health care decision making. Methodologically, we leverage bibliometrics as a quantitative analytical tool, drawing on citation counts, bibliographic coupling, and keyword co-occurrence analysis. The data for this analysis, which covered the period 20152023, was carefully collected from Scopus database. The analysis of the information reveals that, particularly from 2018 onwards, there has been a significant increase in publications concerning Industry 4.0 and health care. In this research landscape India has emerged as a strong contributor, with countries such as the United States and Italy making significant progress. Publication trends and bibliographic coupling among countries and sources shed light on collaborative networks and research focus. The emergence of machine learning, artificial intelligence and data analysis as important themes is illustrated by a co-occurrence analysis of keywords that elucidates evolving research interests. In the complicated terrain of health care converging with Industry 4.0, this research paper serves as a compass. The report highlights this convergence's transformative potential, highlighting the pivotal role that bibliometrics analysis must play in determining future research areas in adopting Industry 4.0 in the health-care sector. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Assessing Player Interaction for a Social Networking Cooperative Educational Game
Cooperative interaction in educational games, designed to stimulate teamwork, joint creativity and knowledge sharing, also carries potential security threats. One of the key dangers is data leakage. Player interaction involves the exchange of information, and in case of insufficient protection of the system, confidential data, such as personal information, game progress results or individual strategies, may become available to unauthorized persons. This may result in misuse of information, damage to reputation and violation of player privacy. The impact on the game space is also a threat. By interacting, players can change the game world, for example, by entering incorrect data, moving objects to an inappropriate location, or modifying the rules of the game. This can lead to a violation of the balance of the game, incorrect results and a deterioration in the learning effect. Substitution or falsification of game elements is no less dangerous. Attackers can introduce fake elements into the game space, for example, incorrect reviews, changed rules or incorrect data. This can lead to incorrect conclusions, distort learning outcomes, and undermine confidence in the game. In addition, the use of interaction tools can become an object of attack. Attackers can hack and modify tools, such as communication platforms or data storage systems. This can lead to data theft, incorrect operation of tools and malfunction during the game. It is shown that formal descriptions of the choice of a game strategy can exist in a game. Indicators that are essential for cooperative interaction are determined, and examples of their calculation for the case with remote interaction through a social network are given. The article contains information about collaborations, which can be used to assess and choose the direction of development in projects that use game cooperative strategies to implement tasks other than training. The project highlights aspects of cooperative interaction that affect the formation of game strategies in an educational project. Of particular interest are projects in which a social network is the tool and medium of interaction. The objectives of the project are to identify easy-to-use indicators that show the features of cooperative interaction within an educational game. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Predicting Stock Market Trends: Machine Learning Approaches of a Possible Uptrend or Downtrend
This paper delves into a statistical analysis of the stock market, emphasizing the significance of accuracy in stock predictions. Large data sets can be handled by machine learning algorithms, which can also forecast outcomes based on past data and spot intricate patterns in financial data. They assist control risks, automate decision-making procedures, and adjust to changing circumstances. Multi-source data can be combined by ML models to provide a comprehensive picture of market circumstances. They can manage intricate, nonlinear interactions, provide impartial analysis, and lessen human bias. Models are able to adjust to shifting market conditions through ongoing learning and retraining. They must, however, exercise caution when deploying models in real-world situations and ensure that they are validated. Although machine learning has advantages for stock market analysis, it must be carefully evaluated for dangers and validated before being used in practical situations. The traditional machine learning model, Logistic Regression has been used in order to predict stock prices. It focuses on binary classification based on the trend of the stock. Through the model training and evaluation and additional analysis done on the results, this research contributes towards obtaining predictions and studying reasons of a possible uptrend or downtrend to further assist companies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Pre-Service and In-Service Teachers Perceptions of Using Virtual Reality Tools in Teaching
This paper explores pre-service and in-service teachers perceptions of virtual reality (VR) technology as a teaching and learning tool in the classroom in India. The study aimed to answer four research questions, including the adoption rate of VR technology among teachers, their confidence levels in teaching using VR technologies compared to digital technologies, attitudes towards using VR technology, and the usefulness of different uses of VR technology. The survey conducted among 102 teachers found limited adoption of VR technology, lower confidence levels in using it, but willingness to use it in the future. The paper recommends providing adequate training and support to increase teachers confidence in using VR technology in their teaching practices. The study also suggests that strategies to promote VR technology should consider gender differences in attitudes towards it. Overall, the research concludes that teachers view VR technology as having potential benefits for learning and teaching across various uses. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Flipped Classroom Strategy in Online Teaching: Challenges Faced by Higher Education Teachers
Flipped classroom model has gained increasing interest among university teachers in recent years [1] (Stohr et al.). The reason for its popularity is attributed to its bearing on Vygotskys constructivism theory and for the student centered approach [2] (Ziling Xu et al.). Countries in the world are affected by COVID-19 including India. Hence higher education institutes have begun their online classes. Flipped classroom teaching has been quite prevalent in Indian higher education recently. Online class initiation from higher education institutes in India has pushed faculty members to teach online and faculty have begun flipped classroom teaching online. Flipped classroom teaching in online differs from the face to face mode. There are challenges and issues while using flipped classroom in online mode by the faculty members of higher education. This leads to the present study to find out the challenges of flipped classroom teaching in online mode by teachers of higher education. The present study adopted qualitative research method. Structured interviews and focus group discussion were conducted to answer the research question. Study was able to discuss the challenges of flipped classroom in online mode. These challenges are to be dealt with by the stake holders to bring teaching efficacy. The Author(s), under exclusive license to Springer Nature Switzerland AG 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. -
Gamification and Game-Based Learning: A Systematic Review and Comparative Analysis
In the modern world, characterized by the rapid development of technology and digitalization of almost all spheres of life, it is necessary to keep up with the times and gradually introduce information technology into our lives. This will allow us to remain competitive in a changing world, take advantage of new opportunities and improve our quality of life. It is important to understand that information technology is not just a fashion trend, but a necessary tool for successful development and progress. The paper examines the very concept of gamification, the main methods of introducing gamification into education, highlights the advantages of learning with the addition of gamification, and also works on comparing learning with and without gamification elements. The introduction of game elements into the educational process helps to improve the perception of educational material, as well as increase the level of motivation of the students themselves. It is worth noting that the learning process with the addition of game elements helps to improve attention, develop logical thinking, as well as analyze various situations. Gamification can be viewed from several angles. For a teacher, this teaching method will help to capture the attention of children, which will help create a working atmosphere in the classroom. And for students, gamification is a great opportunity to explore really important topics in game mode. They will have an increased interest in learning, which will have a beneficial effect on their further academic performance and learning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Cognitive Engagement Scale (CES) in an Online Environment: Construction and Validation
Researchers have demonstrated linkages between active engagement of students with learning material and greater learning gains. Cognitive engagement is a significant component of educational experience. Understanding the challenges associated with cognitive engagement and measuring cognitive engagement in a MOOC environment is challenging. It is the need of the hour with online learning being equivalent to classroom learning in todays dynamic academic environment. The present study aims to construct cognitive engagement scale (CES) to measure the cognitive engagement of learners who sign up for the massive open online courses (MOOC). The aim of this study is dual-fold: firstly, to conceptualize the cognitive dimension of learner engagement within MOOCs, and secondly, to construct a theoretically informed scale for assessing cognitive engagement in online environments. Study presents a detailed process of the scale development, which included item generation, item evaluation, pilot testing, testing psychometric properties of the scale, and scale refinement. The researchers crafted the initial questionnaire drawing from both existing literature and personal insights. Subject matter experts then validated the items within the questionnaire and ensured its reliability through a pilot study, where it was administered to a sample of 100 participants The final version of the scale captures the four dimensions of cognitive engagement: Passive receiving, active manipulating, constructive generating, and interactive dialoguing. The present study contributes to the growing literature on cognitive engagement and adds to the existing literature of MOOC engagement scale with focus on cognitive engagement exclusively. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
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. -
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. -
Impact of Homophily on Patient Empowerment: A Study of Online Patient Support Groups
Internet facility has led to emergence of patient support groups. These have gained prominence as these fulfils important benefits to patients. One such benefit is patient empowerment. These online groups provide opportunity to patients to interact with similar ailments and predicaments and who can understand the pain and discomfort felt by the patient. This provides validation for the patient and patients experiences. How does this homophily impacts patient empowerment? This question has been explored in this study. The methodology is based on an online survey of patients visiting such online platforms. In all 701 patients provided the data. Independent variable (homophily) and dependent variable (patient empowerment) have been measured using a 7-point Likert scale. Findings provide that both are weakly correlated, but this correlation is significant. Regression analysis led to a regression model that is fit statistically. This provides basis to encourage patients to visit online support groups. 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. -
Quantitative Structure-Activity Relationship Modeling for the Prediction of Fish Toxicity Lethal Concentration on Fathead Minnow
As there has been a rise in the usage of in silico approaches, for assessing the risks of harmful chemicals upon animals, more researchers focus on the utilization of Quantitative Structure Activity Relationship models. A number of machine learning algorithms link molecular descriptors that can infer chemical structural properties associated with their corresponding biological activity. Efficient and comprehensive computational methods which can process huge set of heterogeneous chemical datasets are in demand. In this context, this study establishes the usage of various machine learning algorithms in predicting the acute aquatic toxicity of diverse chemicals on Fathead Minnow (Pimephales promelas). Sample drive approach is employed on the train set for binning the data so that they can be located in a domain space having more similar chemicals, instead of using the dataset that covers a wide range of chemicals at the entirety. Here, bin wise best learning model and subset of features that are minimally required for the classification are found for further ease. Several regression methods are employed to find the estimation of toxicity LC50 value by adopting several statistical measures and hence bin wise strategies are determined. Through experimentation, it is evident that the proposed model surpasses the other existing models by providing an R2 of 0.8473 with RMSE 0.3035 which is comparable. Hence, the proposed model is competent for estimating the toxicity in new and unseen chemical. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Advanced Materials for Next-Generation Energy Storage Devices: A Focus on Efficiency and Cost Reduction
The increasing demand for efficient and cost-effective energy storage systems has pushed extensive research into improved materials for next-generation energy storage devices. This study discusses the crucial significance of material advances in boosting the performance and reducing the costs of storage technologies such as batteries and supercapacitors. Conventional energy storage systems face limits in energy density, charge or discharge rates, and scalability, which impede their broad implementation. Advanced materials, including nanomaterials, solid-state electrolytes, and innovative electrode compounds, offer solutions to these difficulties by enhancing energy efficiency, power output, and overall longevity. Additionally, the use of plentiful and low-cost materials, such as sodium-ion and aluminium-based compounds, presents prospects for significant cost savings. This research analyzes current trends, issues in material manufacturing, and future perspectives for energy storage systems, concentrating on balancing efficiency improvements with cost-effectiveness to enable the rising integration of renewable energy sources. The development of these materials is important to creating sustainable, scalable, and economical energy storage systems for the future. The Authors, published by EDP Sciences. -
Evaluation of machine and deep learning models for utility mining-based stock market price predictions
Considering the extreme volatility of stock market returns and hazards, accurate price prediction has attracted the attention of both financial institutions and regulatory bodies. Stocks, due to their historically strong returns, have long been considered by investors to be an excellent asset allocation strategy. Predicting stock prices has never ceased being a hot topic of study. Many early-day economists sought to foretell future stock values. In subsequent years, as computer technology has advanced rapidly and mathematical theory has been extensively studied, it has been shown that mathematical models, like the time series model, may be very effective in predicting due to their simplicity and superiority. Over time, the time series model is put into practice. Over time, the horizon widened. Support vector machines and other ML techniques have challenges when applied to stock data because of its non-linearity. In subsequent years, thanks to advancements in deep learning, models like RNN and LSTM Neural Networks were able to analyze non-linear input, remember the sequence, and remember valuable information,Stock data forecasting cannot be done without it. 2024 Author(s). -
Financial analytical usage of cloud and appropriateness of cloud computing for certain small and medium-sized enterprises
The term "cloud computing"refers to a novel approach of providing useful ICTs to consumers over the internet on an as-needed and pay-per-usage basis. Businesses may streamline internal processes, increase contact with customers, and expand their market reach with the aid of cloud computing, which provides convenient and inexpensive access to cutting-edge information and communication technologies. Developing economies like India's present unique problems for small and medium-sized businesses (SMEs), such as a lack of funding, an inadequate workforce, and inadequate information and communication technology (ICT) use. Various advantages offered by current information and communication technology solutions are unavailable to SMEs because of these limitations. If small and medium-sized enterprises (SMEs) are seeking to enhance their internal operations, communication with customers and business partners, and market reach using current information and communication technology (ICT) solutions, cloud computing might be a good fit for them. Therefore, SMEs are particularly well-served by cloud computing. Companies with a lack of capital, personnel, or other resources to deploy and use appropriate ICTs may greatly benefit from cloud computing, and the public cloud in particular. 2024 Author(s). -
Energy Management System for EV Charging Infrastructure
The increasing adoption of electric vehicles (EVs) has led to a significant rise in the demand for efficient and sustainable charging infrastructure. Managing the energy supply to meet this growing demand while ensuring grid stability presents a critical challenge. This paper presents an energy management system designed for electric vehicle charging infrastructure that balances demand and supply in real time. The proposed system dynamically allocates available power to connected EVs based on their charging demands and the total power available, ensuring optimal utilization of energy resources. By simulating various scenarios, the system demonstrates its capability to prevent overloading, efficiently distribute power, and prioritize critical energy needs. The results of the simulation show that the system can effectively manage power distribution, reduce peak load impact, and enhance the reliability of EV charging networks. This approach offers a scalable and adaptable solution for integrating EVs into the existing power grid, contributing to the development of smart and sustainable transportation systems. The Authors, published by EDP Sciences. -
On near-perfect numbers with five prime factors
Let n be a positive integer and ?(n) the sum of all the positive divisors of n. We call n a near-perfect number with redundant divisor d if ?(n) = 2n + d. Let n be an odd near-perfect number of the form n = pa11 ? pa22 ? pa33 ? pa44 ? pa55 where pis are odd primes and ais (1 ? i ? 5) are positive integers. In this article, we prove that 3 | n and one of 5, 7, 11 | n. We also show that there exists no odd near-perfect number when n = 3a1 ? 7a2 ? pa33 ? pa44 ? pa55 with p3 ? {17, 19} and when n = 3a1 ? 11a2 ? pa33 ? pa44 ? pa55 Mathematical and Computational Sciences - Proceedings of the ICRTMPCS International Conference 2023.All rights reserved. -
Machine Learning Algorithms for Predictive Maintenance in Hybrid Renewable Energy Microgrid Systems
The rapid expansion of hybrid renewable energy microgrid systems presents new challenges in maintaining system reliability and performance. This paper explores the application of machine learning algorithms for predictive maintenance in such systems, focusing on the early detection of potential failures to optimize operational efficiency and reduce downtime. By integrating real-time data from solar, wind, and storage components, the proposed models predict the remaining useful life (RUL) of critical components. The results demonstrate significant improvements in predictive accuracy, offering a robust solution for enhancing the reliability and longevity of renewable energy microgrids. The Authors, published by EDP Sciences.
Collection Tree
- Faculty Publications
- Conference Papers