A Green Inventory Model for Growing Items with Mortality and Permissible Delay in Payment
Carbon emission reduction; Delay in payment; Environment; Growing items; Inventory model
With rapid industrial growth, environmental concerns have become increasingly important. The expanding market and rising greenhouse gas emissions are significantly contributing to environmental degradation, pushing the Air Quality Index to high levels. Simultaneously, the growing global population is driving up the demand for livestock, which heavily relies on natural resources. This paper proposes an inventory model that incorporates key environmental factors, including carbon emission reduction through optimal investment, payment delays and mortality. The model aims to determine the optimal solution while taking into account the environmental impacts. An analytical discussion on the concavity of the objective function in relation to the decision variable is included. The paper outlines a solution methodology to obtain the optimal result, supported by a numerical example. Sensitivity analysis reveals that the selling price and investment in carbon emission reduction are the most influential parameters. 2025 IEEE.
2025 7th International Conference on Information Systems and Computer Networks, ISCON 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ISCON65210.2025.11341741 https://www.scopus.com/pages/publications/105033356895?origin=resultslist
Khileri P., School of Sciences Christ (Deemed to Be University), Bangalore, India; Sharma A., School of Sciences Christ (Deemed to Be University), Bangalore, India
Enhanced Digital Image Watermarking Using 3-Level Discrete Wavelet Transform (DWT)
Discrete Wavelet Transform (DWT); Watermark and Statistical measure
This study compares the algorithm's performance to that of the DWT level 1 and level 2 techniques while proposing a digital picture watermarking technology using a 3-step Discrete Wavelet Transform (DWT). The suggested method uses alpha blending to overlay a multibit watermark into the frequency subband of the lower cover image. The watermark's appearance is controlled by the blending scale. For uniformity, watermark extraction uses the same scale factor. The 3-stage DWT approach is superior because the algorithm performs well for various scaling factors that are obtained in relation to statistical characteristics connected to the Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). 2025 IEEE.
https://doi.org/10.1109/ISCON65210.2025.11341523 https://www.scopus.com/pages/publications/105033350155?origin=resultslist
Neetha S.S., Christ University, Department of Computer Science, Bangalore, India; Sah S., School of Computing Science and Engineering, Sharda University, India; Deepa S., Christ University, Department of Computer Science, Bangalore, India
Relationship Between Interpersonal Conflict, Stress and Burnout in Nurses using ML Classification
burnout; interpersonal conflict; machine learning; nurse; stress
Nurses often face challenges due to heavy workloads, ambiguous roles, and hierarchical pressures. This makes them particularly susceptible to stress and burnout. These factors impair their mental health, productivity, and interpersonal dynamics, resulting in emotional exhaustion, dissatisfaction, and high turnover rates. Therefore, the association between interpersonal conflict, stress, and burnout, among nurses is examined in this study. Data were collected from 636 registered nurses in Karnataka. An Artificial Neural Network was used to classify nurses on low/high interpersonal conflict levels based on their stress/burnout levels. It was found that stress and burnout strongly predict interpersonal conflicts. Factors like impaired judgment and altered behavior contribute heavily to these challenges. AI and IoT can be used to manage stress and burnout. Predictive AI analytics can monitor physiological and behavioral indicators, which can help provide an early intervention. 2025 IEEE.
Pai, Sangeetha; Saha, Saibal Kumar; Jha, Ajeya
Proceedings of 2025 3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025;pp.1346-1351
https://doi.org/10.1109/ISACC65211.2025.10969439 https://www.scopus.com/pages/publications/105005167586?origin=resultslist
Pai S., Manipal Academy of Higher Education, Department of Commerce, Karnataka, Manipal, India; Saha S.K., School of Business, Management Christ University, Bangalore, India; Jha A., Sikkim Manipal Institute of Technology, Department of Management Studies, Sikkim, India
Revolutionizing Legal Intelligence: Advances in Neural Networks and Language Models for Legal NLP
BERT; Contract Clause Extraction; GPT; Large Language Models (LLMs); Legal Document Classification; Legal NLP; Legal Text Summarization; Machine Learning; Named Entity Recognition (NER); Neural Networks; Transformer Models
As the legal field continues to generate vast amounts of complex text, from contracts to court rulings, machine learning and natural language processing (NLP) techniques have emerged as valuable tools to help analyze and organize this data. In this paper, a number of state-of-the-art models will be reviewed and evaluated, including transformer models like BERT, GPT, and T5, and neural network models such as LSTM and CNN-RNN hybrids. These were then tested for the legal tasks of document classification, text summarization, and entity recognition. Some of the metrics used for evaluation include Accuracy, F1-Score, ROUGE, and BLEU. Advanced models, in particular large language models (LLMs), outperform the traditional methods by a large margin since they capture the niceties of legal language and structure much more completely. Meanwhile, high-quality legal datasets remain scarce, legalese remains incomprehensible to most, and the models are still relatively unexplainable. In sum, these challenges clearly call for future research in terms of data augmentation, explainable AI techniques, and more robust training methods that would allow AI-powered tools to be integrated much more effectively within lawyers' workflows to support them in their decision-making processes. 2025 IEEE.
Noronha, Raoul Samuel; Alenchery, Alex Stanley; Deepa, S.; Jayapriya, J.; Vinay, M.
Proceedings of 2025 3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025;pp.608-616
https://doi.org/10.1109/ISACC65211.2025.10969245 https://www.scopus.com/pages/publications/105005212651?origin=resultslist
Noronha R.S., Christ University, Department of Computer Science, Bengaluru, India; Alenchery A.S., Christ University, Department of Computer Science, Bengaluru, India; Deepa S., Christ University, Department of Computer Science, Bengaluru, India; Jayapriya J., Christ University, Department of Computer Science, Bengaluru, India; Vinay M., Christ University, Department of Computer Science, Bengaluru, India
IoT Based Bus Identification and Distance Notifier for the Visually Impaired
auditory feedback; Bus identification; distance notifier; GPS; raspberry Pi; RFID
Public transport is a major obstacle for the visually impaired, and it tends to limit their independence and mobility. To overcome this problem, the current project presents an IoT-based bus identification and distance notification system that is meant to offer real-time support and improve the traveling experience of the visually impaired commuters. The system uses a Raspberry Pi controller in combination with an RFID reader and several RFID cards, each one of which is designated for a particular bus, to detect oncoming vehicles. A GPS unit monitors the position of the bus at all times, so it is possible to calculate exact distances to any of the stops. For additional convenience, the system includes three push-button switches programmed to three predetermined bus stops. Users may pick their destination stop, and the system will offer auditory feedback in terms of distance to the selected destination. Feedback is offered by an earphone, providing hands-free use and receiving instructions without visual interaction. The integration of RFID-based bus identification, GPS location, and voice guidance provides smooth real-time information, less reliant on outside help. The system increases mobility confidence through accurate, timely, and convenient information, enabling blind travelers to make use of public transport independently. Through the combination of IoT technology and assistive technologies, the project enables enhanced accessibility and inclusion in urban mobility systems. 2025 IEEE.
Mathew, Bettina S.; Chundru, Aishwarya; Gaur, Himanshi; Bindu Madavi, K.P.; Krishna Sowjanya, K.
International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity, ISAC3 2025;
https://doi.org/10.1109/ISAC364032.2025.11156546 https://www.scopus.com/pages/publications/105018914293?origin=resultslist
Mathew B.S., Christ University, Department of CSE, Bengaluru, India; Chundru A., Christ University, Department of CSE, Bengaluru, India; Gaur H., Christ University, Department of CSE, Bengaluru, India; Bindu Madavi K.P., Christ University, Department of CSE, Bengaluru, India; Krishna Sowjanya K., CMR Institute of Technology, Department of CSE, Bengaluru, India
Advanced Machine Learning Framework for Precision Rainfall Prediction for Jharkhand, India
Crop Yield; Ensembled; Phase Expansion; Precipitation Index Prediction
Jharkhand, characterized by a substantial agricultural population predominantly reliant on rain-fed agriculture, faces significant challenges due to the erratic nature of precipitation. The study uses meteorological variables and historical rainfall data from the Jharkhand Space Agency Centre (JSAC) to predict rainfall with precision and resilience. Three supervised machine learning algorithms, Random Forest, K-Nearest Neighbour (KNN), and Ridge Regression, are employed to evaluate their performance across monsoon and non-monsoon periods. A novel algorithm is proposed for Jharkhand, offering better modularity and accuracy to predict the Rain Index. The results show the efficacy of these algorithms in capturing the temporal variability of rainfall in Jharkhand. The ensemble modeling model obtained an MSE score of 0.457, providing valuable insights into the viability and competence of machine learning algorithms for rainfall estimation. This research offers a valuable scope for policymakers, researchers, and stakeholders to formulate sustainable strategies to address climate variability and its impact on rain-fed agriculture. The study contributes significantly to meteorological research and offers valuable insights for policymakers, researchers, and stakeholders. 2025 IEEE.
Patnaik, Manorama; Kundu, Roumo; Bareen, Firdaus; Kundu, Tannisha; Raza, Shahid; Rawani, Rajesh
https://doi.org/10.1109/ISAC364032.2025.11156230 https://www.scopus.com/pages/publications/105018905342?origin=resultslist
Patnaik M., Amity University Jharkhand, Amity Institute of Information Technology, Ranchi, India; Kundu R., ECT-Global VT India BLR, SAP Labs BLR, Bangalore, India; Bareen F., Amity University Jharkhand, Amity Institute of Information Technology, Ranchi, India; Kundu T., Amity University Jharkhand, Amity Institute of Information Technology, Ranchi, India; Raza S., Christ ((Deemed to Be) University, Department of Computer Science, Bangalore, India; Rawani R., Jharkhand Space Agency Centre, Ranchi, India
Financing Green Startups: A Blockchain-Powered Approach
blockchain; crowdfunding; decentralized finance (DEFI); environmental impact; green bonds; green startups; smart contracts; sustainable finance; tokenization
Green startups routinely encounter difficulty in obtaining financing owing to the high funding needs to launch their business, and the imprecise market acceptance and future returns of those businesses, rendering them unacceptable to traditional latter-day funding methods. The funding modality available today fails to provide the needed transparency, flexibility and accessibility to encourage ventures based on green projects. This paper develops a funding model based in blockchain for green startups, that employs tokenization, decentralised finance (DeFi) and smart contracts with automaticity, as an efficient way of funding to provide secure, traceable funding structures that make funds available related to the performance of the venture. We present a conceptual model showing the responsibilities of startups, funders and smart contracts within a de-centralised funding ecosystem. Various case studies such as Power Ledger and WePower are investigated in order to validate the practical relevance of the model. Our research indicates how blockchain mechanisms can heighten trust, enhance liquidity, and automate funding that is linked to impact. This paper contributes to future work on scalable platforms that are both regulation compliant and also provide a fit between Blockchain infrastructure and the unique requirements of sustainable innovation. 2025 IEEE.
Srinivas, D.; Giri, Simon; Raina, Kartikeya; Singh, Hardev; Udayai, Kirti; Ahmad, Vasim
Proceedings - 2025 5th International Conference on Internet of Things: Smart Innovation and Usage, IoT-SIU 2025;
https://doi.org/10.1109/IOT-SIU65919.2025.11402723 https://www.scopus.com/pages/publications/105035838983?origin=resultslist
Srinivas D., SR University, School of Business, Telangana, Warangal, India; Giri S., Christ University, Department of Commerce, Bangalore, India; Raina K., Graphic Era (Deemed to be University), Department of Management Studies, India; Singh H., Uttaranchal Institute of Management, Uttaranchal University, Dehradun, India; Udayai K., Graphic Era (Deemed to be University), School of Management Studies, Uttarakhand, Dehradun, India; Ahmad V., Uttaranchal Institute of Management, Uttaranchal University, Dehradun, India
Analyzing Uzbekistan's Export Trends: A Data-Driven Study on Annual Export Volume by Country
Economic Policy; Exports; International Markets; SDMX Data; Trade Analysis; Uzbekistan
Uzbekistan's export sector plays a crucial role in economic development, contributing to GDP growth and international trade partnerships. This study analyzes annual export volume by country using the SDMX dataset, highlighting key trends, regional trading partners, and sectoral contributions. The research employs time-series analysis, trade flow modeling, and export diversification measures to provide insights into Uzbekistan's evolving trade structure. The results indicate shifting export dynamics influenced by geopolitical factors, regional economic policies, and trade agreements. Policy recommendations focus on enhancing market diversification, improving export efficiency, and fostering trade competitiveness. 2025 IEEE.
Jagadish, Sripelli; Verma, Goldy; Agarwal, Prerna; Shrivastava, Pranav; Kaur, Baljinder; Anand, Garima
Proceedings of the 2025 International Conference on Technology Enabled Economic Changes, InTech 2025;pp.1566-1571
https://doi.org/10.1109/InTech64186.2025.11198417 https://www.scopus.com/pages/publications/105024858810?origin=resultslist
Jagadish S., School of Computer Science and Artificial Intelligence, Sr University, Telangana, Warangal, 506371, India; Verma G., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Agarwal P., School of Computer Science and Engineering and Technology, Bennett University, Greater Noida, India; Shrivastava P., Galgotia College of Engineering and Technology, Department of Computer Sciences, Greater Noida, India; Kaur B., Lovely Professional University, Department of Computer Science and Engineering, Punjab, Phagwara, India; Anand G., School of Sciences, Christ University, Bangalore, India
Analysis of Market Communication and Informatization Services: A Data-Driven Study Based on SDMX Statistics
Digital Services; Economic Analysis; Informatization; Market Communication; SDMX Data
The market communication and informatization services sector plays a crucial role in modern economic development, facilitating digital transformation and connectivity. This study leverages official statistical data to analyze trends, growth patterns, and the economic impact of communication services in Uzbekistan. Using the SDMX dataset, we evaluate sectoral contributions, regional disparities, and the role of technological advancements. The findings provide insights into investment efficiency and policy recommendations for sustainable sectoral growth. 2025 IEEE.
Kumar, Komuravelly Sudheer; Shanvi; Agarwal, Prerna; Shrivastava, Pranav; Rakhra, Manik; Anand, Garima
Proceedings of the 2025 International Conference on Technology Enabled Economic Changes, InTech 2025;pp.1560-1565
https://doi.org/10.1109/InTech64186.2025.11198363 https://www.scopus.com/pages/publications/105024918563?origin=resultslist
Kumar K.S., School of Computer Science and Artificial Intelligence, Sr University, Telangana, Warangal, 506371, India; Shanvi, University Institute of Engineering and Technology, Chitkara University, Punjab, India; Agarwal P., School of Computer Science and Engineering and Technology, Bennett University, Greater Noida, India; Shrivastava P., Galgotia College of Engineering and Technology, Department of Computer Sciences, Greater Noida, India; Rakhra M., Lovely Professional University, Department of Computer Science and Engineering, Punjab, Phagwara, India; Anand G., School of Sciences, Christ University, Bangalore, India
Analyzing Wholesale Trade Volume in Uzbekistan: A Data-Driven Study of Internal Trade Dynamics
Internal Trade; Market Trends; SDMX Data; Time-Series Analysis; Uzbekistan; Wholesale Trade
Wholesale trade as a segment of internal trade possesses a great potential for shaping supply chain management, and market conditions in Uzbekistan. This paper discusses monthly changes of the wholesale trade volume regarding its main issues, seasonal fluctuations, and regional diversification using the SDMX dataset. Using timeseries analysis, trend decomposition, and correlation modeling, this paper aims at determining the effects of the economic policy, consumer demand factors, and trade restrictions in the wholesale trade sector in Uzbekistan. It also reveals seasonality in the trade, trade heterogeneity across regions, and impact of a shock in the market, which is very relevant and useful for policymakers and key players in the trade business. 2025 IEEE.
Yamsani, Nagendar; Kant, Vishnu; Agarwal, Prerna; Shrivastava, Pranav; Singh, Amanpreet; Anand, Garima
Proceedings of the 2025 International Conference on Technology Enabled Economic Changes, InTech 2025;pp.1572-1576
https://doi.org/10.1109/InTech64186.2025.11198230 https://www.scopus.com/pages/publications/105024878241?origin=resultslist
Yamsani N., School of Computer Science and Artificial Intelligence, Sr University, Telangana, Warangal, 506371, India; Kant V., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Agarwal P., School of Computer Science and Engineering and Technology, Bennett University, Greater Noida, India; Shrivastava P., Galgotia College of Engineering and Technology, Department of Computer Sciences, Greater Noida, India; Singh A., School of Computer Application, Lovely Professional University, Punjab, India; Anand G., School of Sciences, Christ University, Bangalore, India
From Producer to Consumer: AI-Blockchain Integration for Sustainable Supply Chain Tracking and Optimization
AI; Anomaly detection; Autoencoders; Blockchain technology; Classification; Forest; Isolation; Optimization; Supply chain; SVM
The blockchain is transforming the way supply chains operate. It is a decentralized peer-to-peer system that ensures safe and transparent data interchange. Because there is no central authority, blockchain technology can't be hacked; the data are dispersed throughout numerous nodes. Therefore, more openness, safety, and resistance to tampering are assured as opposed to conventional centralized database systems that depend on a single authority to manage all the data. This would ensure the immutability of the transaction log, which accurately traces the products from origin to destination. The supply chain refers to the transportation and distribution of goods when transactions are verified in real time and integrated into a secure, cryptographic ledger. This decentralized framework provides better tracking of various manufacturers, reduced chances of fraud, and greater trust among the participants. The early understanding based on tracking will definitely helps the stakeholders to improve the blockchain activities, reliability, it will minimize risks, and enhances efficiency. This will parallelly help those who are involved within supply chain management giving high accountability and seamlessness in the movement of details. 2025 IEEE.
Abhay Bhaskaran, M.T.; Joseph, Akash Varughese; Thomas, Romal; Kavitha, S.
Proceedings of the 2025 International Conference on Technology Enabled Economic Changes, InTech 2025;pp.489-496
https://doi.org/10.1109/InTech64186.2025.11198226 https://www.scopus.com/pages/publications/105024920196?origin=resultslist
Abhay Bhaskaran M.T., Christ University, Department of Computer Science, Bangalore, India; Joseph A.V., Christ University, Department of Computer Science, Bangalore, India; Thomas R., Christ University, Department of Computer Science, Bangalore, India; Kavitha S., Christ University, Department of Computer Science, Bangalore, India
Towards Smarter Warehouse Layouts: Simulation-Driven Insights on Congestion and Forklift Flow Patterns
Behavioral Flow Modeling; Discrete Event Simulation; Forklift Logistics; Replication-Based Analysis; Warehouse Slotting Optimization
In high-mix, make-to-order warehouse environments, slotting decisions in constrained warehouse settings affect flow dynamics, yet their behavioral implications remain underexplored. This study employs discrete event simulation (DES) using the software FlexSim to evaluate three slotting strategies: baseline reflecting current operations, a benchmark informed by heuristic frequency-based clustering, and a proposed layout based on a learned configuration within a forklift-operated warehouse characterized by narrow aisles, unidirectional traffic, and spatial contention. Instead of emphasizing throughput alone, the study takes a closer look at how forklifts operate on a day-to-day basis, specifically how their time is divided between active movement, waiting due to blockages, and idling with no task assigned. Each strategy was tested over 20 simulation replications. Notably, the proposed layout cut blocked time by more than 30% and allowed forklifts to remain idle (and ready) more often, without reducing overall utilization. These patterns held consistently across runs. Statistical analysis confirmed that the differences in forklift behavior were significant, and Levenes tests showed that performance didnt become more erratic. These findings demonstrate that the improvements are systematic, not random. The work presents a simulation-based method for diagnosing layout effectiveness by looking at behavior, not just outputs, connecting slotting choices to real operational flow and system stability. This approach supports more resilient warehouse designs in settings with limited space and high product mix. 2025 IEEE.
El Baz, Hicham; Parag Jose, C.; Bhat, Vinayambika S.; Wang, Yong
Proceedings of 2025 International Conference on Intelligent Systems and Pioneering Innovations in Robotics and Electric Mobility: Transforming Mobility and Automation Through Intelligent Engineering, INSPIRE 2025;pp.578-582
https://doi.org/10.1109/INSPIRE67328.2025.11300596 https://www.scopus.com/pages/publications/105032428880?origin=resultslist
El Baz H., School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, United States; Parag Jose C., Department of Electrical and Electronics Engineering, Christ University, Karnataka, India; Bhat V.S., Department of Electronics & Communication Engineering, Mangalore Institute of Technology & Engineering, Karnataka, India; Wang Y., School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, United States
Co Adaptation Vs Signal Altering Regularization Layer in Deep Learning: A Trade Off Analysis via Node Redundancy and Transfer Learning
Deep learning; Gaussian Dropout; Node redundancy; Overfitting; RDrop; Regularization; Regularization Layer; Transfer learning
This study investigates the trade-off between incorporating regularization layers-such as Dropout, R-Drop, and Gaussian Dropout-and the phenomenon of co-adaptation in deep learning models. While regularization is designed to enhance generalization by disrupting hidden layer activations and reducing overfitting, it may also introduce node redundancy, potentially diminishing the model's capacity to learn efficiently. Conversely, co-adaptation, though often considered undesirable, may help preserve beneficial internal representations that contribute to learning generalizable data patterns-particularly in transfer learning scenarios-where regularization may inadvertently hinder such learning. Using the CIFAR-10 dataset, this study conducts an empirical analysis of how various regularization strategies influence neuron redundancy and downstream transfer performance. The results indicate that, although regularization effectively controls overfitting, excessive distortion in hidden representations can impair the model's ability to generalize across tasks. These findings provide insights into the need for balanced regularization strategies that maintain useful structure while minimizing detrimental redundancy. 2025 IEEE.
INDISCON 2025 - IEEE 6th India Council International Subsections Conference, Proceedings;
https://doi.org/10.1109/INDISCON66021.2025.11252043 https://www.scopus.com/pages/publications/105030165687?origin=resultslist
Omathil G.G., Christ University, Department of Computer Science, Bangalore, India; Sreeja C.S., Christ University, Department of Computer Science, Bangalore, India
Blockchain and Social Networking in the Age of Privacy: A New Approach to Data Protection and Transparency
Blockchain; Data Protection; Privacy; Social Networking; Transparency
Online social networks (OSNs) have become a significant area of application due to the rapid increase in online interactions. However, the unauthorized exposure of users' private information can lead to severe repercussions, including risks to users' personal safety. Privacy concerns in OSNs have garnered widespread attention. While some research efforts have attempted to address these privacy challenges in recent years, they often overlook the need to maintain essential social network functionalities including data access, retrieval and sharing. Consequently, ensuring the protection of sensitive information for delivering privacy-preserving and efficient services for social network remains a complex challenge. 2025 IEEE.
Manoharan, Geetha; Padhiary, Mrutyunjay; Satyanarayana, Pamarthi; Joseph, Christabell; Misra, Utkarsh; Soni, Mukesh
2025 6th International Conference for Emerging Technology, INCET 2025;
https://doi.org/10.1109/INCET64471.2025.11139890 https://www.scopus.com/pages/publications/105016779349?origin=resultslist
Manoharan G., Sr University, School of Business, Telangana, Warangal, India; Padhiary M., Assam University, Triguna Sen School of Technology, Department of Agricultural Engineering, Assam, Silchar, 788011, India; Satyanarayana P., Vel Tech University, School of Law, Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology, Tamil Nadu, India; Joseph C., Christ University, School of Law, Bangalore, India; Misra U., Graphic Era Hill University, Haldwani Campus, India; Soni M., Lovely Professional University, Division of Research and Development, Phagwara, India
Dynamic Load Balancing in Cloud Computing Using Memetic Algorithm for Better Response Time
Cloud Computing; Load Balancing; Memetic Algorithm
Load balancing is a critical aspect of distributed computing systems, involving the distribution of workload across multiple resources to ensure optimal utilization and performance. In today's digital landscape, where the demand for online services continues to grow exponentially, efficient load balancing mechanisms are indispensable for maintaining high availability, scalability, and reliability of web applications, cloud services, and network infrastructures. The paper implements a load balancing solution using a Memetic Algorithm, an amalgamated optimization method that integrates elements of genetic algorithms with local search methods. By leveraging the principles of evolutionary computation and problem-specific knowledge, memetic algorithms offer a promising approach to addressing the complex optimization and the hurdles connected with the load distribution across the systems. The proposed algorithm is compared with the existing algorithms and proved to be slightly better than the traditional algorithm in terms of response time. Experimental results show that the proposed approach reduces response time by 0.26 seconds. 2025 IEEE.
Krishna Sowjanya, K.; Bindu Madavi, K.P.; Khater, Gaurav; Jacob, Aaron; Ahmad, Emaduddin Shamim
Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025;
https://doi.org/10.1109/InC465408.2025.11256447 https://www.scopus.com/pages/publications/105030337115?origin=resultslist
Krishna Sowjanya K., CMR Institute of Technology, Dept. of CSE, Karnataka, Bengaluru, India; Bindu Madavi K.P., Christ University, Dept. of Computer Science and Engineering, Karnataka, Bengaluru, India; Khater G., CMR Institute of Technology, Dept. of CSE, Karnataka, Bengaluru, India; Jacob A., CMR Institute of Technology, Dept. of CSE, Karnataka, Bengaluru, India; Ahmad E.S., CMR Institute of Technology, Dept. of CSE, Karnataka, Bengaluru, India
A Hybrid Clustering Approach for Enhanced Classification Efficiency in Data Analytics
Clustering; Evaluation Metrics; Gaussian Mixture Model; Integration; K-means; Visualization
Clustering is a fundamental technique in data analytics that groups data points with similar characteristics into clusters. It is crucial for uncovering hidden patterns, trends, and structures in datasets. Clustering reduces the complexity of large datasets by summarizing data into representative clusters. This simplification makes it easier to analyze and interpret data, especially when dealing with high-dimensional datasets. By identifying meaningful groups, clustering provides actionable insights that supports decision-making. For instance, businesses can make concrete decisions about product recommendations, pricing strategies, or resource allocation based on cluster analysis. The approach described in the paper offers an efficient method for combining K-means and Gaussian Mixture Model (GMM) clustering techniques. The method combines two wellknown clustering techniques, K-means and GMM, to leverage their respective strengths. K-means is known for its simplicity and efficiency, while GMM can model complex data distributions with varying covariance structures. Instead of directly integrating the results of K-means and GMM, the approach uses a simplified averaging technique to converge the cluster labels obtained independently from both methods. This suggests that the method may involve assigning weights to the cluster labels obtained from K-means and GMM and then averaging them to obtain final cluster assignments. Overall, this approach presents a promising direction for combining K-means and GMM clustering techniques, offering a streamlined integration process that simplifies the consideration of varying covariance types in GMM. The effectiveness of the method is evaluated through empirical studies and comparisons with existing clustering approaches. 2025 IEEE.
Mannooparambil, Joseph Mathew; Saju, Roshan Koshy; Varghese, Rohan Thomas; Reena, Melbin J
https://doi.org/10.1109/InC465408.2025.11256425 https://www.scopus.com/pages/publications/105030344478?origin=resultslist
Mannooparambil J.M., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Saju R.K., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Varghese R.T., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Reena M.J., Christ University, Department of Computer Science and Engineering, Bengaluru, India
Analyzing Technology Ecosystem Business Models: A Predictive Modelling Approach
Customer Retention; Ecosystem Business Models; Machine Learning; Predictive Modeling
In the rapidly changing landscape of technology, companies are devoting an increasing amount of their resources to developing product ecosystems that collaborate to deliver enhanced consumer experiences and strengthen their business models. As opposed to traditional standalone solutions, these ecosystems are intended to facilitate everyday tasks, increase user engagement, and provide seamless integration, all of which ensure a steady stream of revenue and dedicated customer base. This analysis provides an overview of the many ecosystem models that are now transforming the technology industry. An examination of ecosystems that help businesses maintain long-term revenue sustainability and high customer retention rates is provided by the model analysis, along with insights into how ecosystems may enhance user experience by being more connected, straightforward, and user-friendly. Technology ecosystems' quantitative effects are lacking, which makes it difficult to comprehend how they affect long-term revenue sustainability and customer retention. It is challenging to understand how technological ecosystems impact long-term revenue sustainability and customer retention due to the lack of measurable consequences. Through the use of multiple linear regression, this study illustrates the ecosystem business models' long-term revenue and customer retention. The study visualized the relationships of the technology ecosystem with an accuracy of 90-99%. This shows how to measure ecosystem impact and gives firms data-driven insights to improve their ecosystem initiatives. 2025 IEEE.
Chebbi, Nandan Shrinivas; Mahashabde, Roshan Balaji; Shrinivas, Bharath Masthi; Makam, Prashasth Kishore; Kumar, Sandeep; Kiran, V.
https://doi.org/10.1109/InC465408.2025.11256413 https://www.scopus.com/pages/publications/105030320346?origin=resultslist
Chebbi N.S., RV College of Engineering, Department of ECE, Bengaluru, India; Mahashabde R.B., RV College of Engineering, Department of ECE, Bengaluru, India; Shrinivas B.M., RV College of Engineering, Department of ECE, Bengaluru, India; Makam P.K., RV College of Engineering, Department of ECE, Bengaluru, India; Kumar S., Christ University, Department of AI, ML and DS, Bengaluru, India; Kiran V., RV College of Engineering, Department of ECE, Bengaluru, India
Machine Learning Approaches for Predicting Player Position in Football
Gradient Boosting; KNN; Logistic Regression; Random Forest; Support Vector Machine; Voting Classifier; XG Boost
This paper presents a comparative study of different machine learning algorithms, including K- Nearest Neighbor (KNN), Random Forest, Gradient Boosting, XG Boost, Support Vector Machine, Voting Classifier and Logistic regression to develop a Player Position Prediction System in football. Initially, the study utilized a modified dataset containing 18434 records, focusing on simplicity for ease of analysis. Through experimentation, it was found that Logistic regression provided a strong balance between efficiency and scalability, making them ideal for rapid decision-making in environments with limited features. In contrast, Support Vector Machine, XGboost and voting classifier excelled in offering more detailed, feature-rich analyses, which are particularly beneficial when handling complex data. Building on these findings, the plan is to apply the same algorithms to improve the system's overall accuracy and efficiency. By leveraging the strengths of each approach, the aim is to create a scalable, effective recommendation system tailored for real-world applications in the car industry. This study highlights the importance of choosing the right algorithm based on the tradeoffs between computational efficiency and the depth of analysis required in recommendation systems. 2025 IEEE.
Tyagi, Tamanna; Naveen, J.; Rao, B Satyam; Dhabuwala, Prince
https://doi.org/10.1109/InC465408.2025.11256390 https://www.scopus.com/pages/publications/105030339993?origin=resultslist
Tyagi T., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India; Naveen J., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India; Rao B.S., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India; Dhabuwala P., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, India
Comparative Study of AI Models for Automated Tuberculosis Detection Using Image Processing Techniques
chronic infectious disease; detection of tuberculosis (TB); Digital image processing techniques; Medical practice; Mycobacterium tuberculosis
Tuberculosis is a critical global health issue, particularly in resource-limited regions where early and accurate diagnosis is important and is in need so that the treatment is effective and the control transmission is controlled. The known diagnostic methods, such as sputum smear microscopy and nucleic acid amplification tests are costly, time-consuming, and require trained professionals. Due this in some cases it is inaccessible in many regions. Deep learning-based automated TB detection offers a promising alternative by enhancing diagnostic efficiency through medical imaging analysis. This study presents a comparative evaluation of five deep learning models, InceptionResNetV2, DenseNet, VGG16, ANN, and a custom CNN, trained on a dataset of 3,008 chest radiograph images, evenly distributed between TB-positive and normal cases. The dataset underwent advanced preprocessing techniques, pixel normalization, and data augmentation. The hyperparameter tuning process was applied, which optimized the learning rates, dropout rates, convolutional filter sizes, and batch sizes to enhance model performance. The models were assessed using accuracy, precision, recall, F1-score, sensitivity, specificity.. Experimental results indicated that the custom CNN achieved the highest classification accuracy (99.51). The superior performance of the custom CNN over other models is attributed to optimized feature extraction, effective preprocessing, and structured hyperparameter tuning. A comparative analysis with previous studies highlights how this approach mitigates dataset limitations and improves model interpretability, and the potential of AI-driven TB detection, enhancing future diagnostic efficiency by improving model generalizability and deployment in real-world healthcare settings. 2025 IEEE.
Khakha, Asim Yash; Poonia, Ramesh Chandra
https://doi.org/10.1109/InC465408.2025.11256377 https://www.scopus.com/pages/publications/105030338937?origin=resultslist
Khakha A.Y., Christ University, Department of Computer Science, Delhi NCR, India; Poonia R.C., Christ University, Department of Computer Science, Delhi NCR, India
A Software Package for Detecting Anomalies in User Authentication
Abnormal Behavior; Artificial Intelligence; Audit Log; Deep Learning; Machine Learning; Neural Networks
Anomaly detection is a very important tool for various applications such as intrusion detection, fraud, malfunction, system health monitoring and event detection in IoT devices. Recently, user authentication has become an extremely popular topic in information security research environments. The definition of user authentication is formulated as the process of verifying the identity declared by the user for a system object. Authentication is a method used to distinguish between true or false authentication requests. There are many methods used to authenticate a user that can identify valid users in protected resources. This article discusses various methods for analyzing abnormal user behavior in information systems, namely such methods as machine learning, neural networks, hybrid methods. Based on the analysis of system logs in the Astra Linux operating system, a software package has been developed to identify anomalies when trying to authenticate users. 2025 IEEE.
Lapina, Maria; Mary Anita, E.A.; Vechkanov, Alexey; Tokmakova, Marina; Lapin, Vitalii
https://doi.org/10.1109/InC465408.2025.11256363 https://www.scopus.com/pages/publications/105030339337?origin=resultslist
Lapina M., North Caucasian Federal University, Faculty of Mathematics and Computer Science named after prof. N. I. Chervyakov, Stavropol, Russian Federation; Mary Anita E.A., Christ University, Depatament of Computer Science, Bangalore, India; Vechkanov A., North Caucasian Federal University, Faculty of Mathematics and Computer Science named after prof. N. I. Chervyakov, Stavropol, Russian Federation; Tokmakova M., North Caucasian Federal University, Faculty of Mathematics and Computer Science named after prof. N. I. Chervyakov, Stavropol, Russian Federation; Lapin V., North Caucasian Federal University, Faculty of Mathematics and Computer Science named after prof. N. I. Chervyakov, Stavropol, Russian Federation