ICSEMT September 2025 Proceeding

Exploring Innovative Methods And Algorithms To Achieve Graceful Labelling For Different Classes Of Trees

Authors: Noor jahan Fatima, Dr sarabjit kaur

Abstract: A very common area in combinatorial mathematics, whose study cuts across many other subjects, including network theory, telecommunications, and optimization, is in graph labelling. Among various kinds of graph labelling, the kind known as graceful labelling has been of much interest to mathematicians, and in recent years has been developed as well into a practical field. Graceful labelling The unique and integer labels of the vertices of a tree make such an assignment graceful labelling when the absolute difference between the labels of adjacent vertices is unique. Despite the high number of studies on the problem since it was introduced by Golomb in 1972, algorithms have to date remained limited in scope, to subsets of trees, often binary or caterpillar trees, or are intolerably slow to run on larger and irregular trees. More recent approaches, such as heuristic-based algorithms, and machine learning approaches produce good results but are not sufficiently scalable or robust to use widely. This study will overcome such shortcomings with novel techniques that combine the greedy strategies, dynamic programming, and neural network-based techniques to arrive at an efficient and robustly applicable and generalised graceful labelling of various tree classes. The technique is to develop new algorithms and provide their performance analysis on a benchmark tree as well as recertifying the performance against the state of the art methods in terms of computation complexity. Moreover, practical tests are carried out with references to the actual network optimization cases coupled with resource allocation. The research will focus on closing the gap between theory and practice thus developing practical solutions that would be scalable, efficient, and resistant to failures. These results are expected to find use both in furthering the theoretical study of graph theory and in more practical application of the concept, providing methods that increase computational and real-world applicability of graceful labelling.

DOI: http://doi.org/10.5281/zenodo.16980687

Impact Of Nuclear Deformation On Structural Parameters, Energy Levels, And Quadrupole Moments Of 152Sm, 238U, And 240Pu Nuclei

Authors: Suresh Kumar, Rajni Devi, Shashikant Sheoran, Vandana Mahlawat

Abstract: This study explores the structural characteristics of selected deformed nuclei using theoretical frameworks, focusing on their shapes, energy levels, and quadrupole moments. Nuclear deformation results from the complex interplay between shell effects and the strong nuclear force, causing deviations from spherical symmetry. Advanced models, including the Nilsson model, Hartree-Fock-Bogoliubov (HFB) theory, and collective models, are employed to examine the influence of deformation on nuclear structure. The key findings emphasize the role of deformation in shaping rotational spectra and intrinsic quadrupole moments, with significant results for nuclei such as 152Sm, 238U, and 240Pu. Calculations of quadrupole deformation parameters and energy levels show strong agreement with experimental data, validating the theoretical approaches. The study also investigates the stabilization of heavy nuclei through deformation, which redistributes charge density and mitigates Coulomb repulsion. These findings have important applications in nuclear astrophysics, particularly in the rapid neutron capture process (r-process), as well as in nuclear technology, where insights into deformed nuclei contribute to isotope development and reactor design. A deeper understanding of deformed nuclei in this research advances both fundamental nuclear physics and practical applications.

DOI: http://doi.org/10.5281/zenodo.16980892

Theoretical Investigation Of Deformed Nuclei: Impacts On Nuclear Stability And Excitation Phenomena

Authors: Suresh kumar, Dr Vandana

Abstract: Deformed nuclei, characterized by deviations from spherical symmetry, exhibit unique structural properties that are critical to understanding nuclear stability, reaction dynamics, and excitation phenomena. This theoretical study investigates the structural properties of selected deformed nuclei using advanced nuclear models and computational approaches. Employing Density Functional Theory (DFT) with Skyrme and Gogny interactions, alongside Hartree-Fock-Bogoliubov (HFB) calculations, the research analyses deformation effects on nuclear binding energy, charge distributions, and level densities. Transitional and neutron-rich nuclei are emphasized to explore the evolution of deformation, triaxiality, and nuclear softness. The results reveal significant impacts of deformation on nuclear moment of inertia and energy spectra, particularly in rare-earth and actinide regions. The inclusion of triaxiality further enhances the accuracy of predictions for level densities and excitation spectra. Comparisons with experimental data from gamma-ray spectroscopy and Coulomb excitation validate the robustness of the theoretical frameworks employed. This study addresses key gaps in understanding nuclear deformation, particularly for isotopic chains near the neutron drip line and transitional regions. The findings provide critical insights for refining existing nuclear models and guiding future experimental investigations. Furthermore, this research highlights the importance of incorporating pairing correlations and deformation effects to predict properties of nuclei far from stability. The study contributes to the broader understanding of nuclear structure and its applications in nuclear energy, astrophysics, and particle physics. These findings underscore the role of theoretical models in complementing experimental efforts and advancing nuclear physics research.

DOI: http://doi.org/10.5281/zenodo.16980969

Deep Learning-Driven Cyber Security Framework For Cloud Computing

Authors: Ms. Rajani, Associate Professor Dr. Jyoti

Abstract: Cloud computing has revolutionized data storage, processing, and service delivery, but its dynamic and distributed nature introduces significant cyber security challenges, including data breaches, malware attacks, insider threats, and distributed denial-of-service (DDoS) attacks. Traditional security mechanisms often fail to provide real-time detection and adaptive protection against increasingly sophisticated cyber threats. Deep learning (DL), a subset of artificial intelligence, offers powerful capabilities for identifying complex patterns, detecting anomalies, and predicting potential attacks in large-scale cloud environments. This paper explores the role of deep learning in enhancing cyber security for cloud computing by reviewing DL-based models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, and generative adversarial networks (GANs). It highlights their application in intrusion detection systems (IDS), malware classification, phishing detection, and threat intelligence. Furthermore, the paper discusses implementation challenges, including data privacy, model interpretability, and computational cost, and proposes future directions for integrating DL with emerging technologies like edge computing and federated learning to achieve robust, scalable, and proactive cloud security solutions

SCALABLE AND EFFICIENT APPROACHES TO GRACEFUL LABELLING IN GRAPH THEORY

Authors: Noor jahan Fatima, Dr sarabjit kaur

Abstract: Graceful labelling is a fundamental problem in graph theory with significant applications in communication networks, coding theory, VLSI circuit design, and combinatorial optimization. The graceful tree conjecture, proposed by Rosa in 1967, asserts that every tree can be assigned a graceful labelling, yet a general proof or counterexample remains elusive. Traditional constructive techniques and exhaustive searches have established results for specific graph families, but scalability challenges persist when dealing with larger instances. This paper investigates scalable and efficient approaches to graceful labelling by integrating constructive methods with heuristic and optimization-based strategies. We explore hybrid approaches that combine deterministic recursive labelling with stochastic metaheuristics such as genetic algorithms, simulated annealing, and tabu search. Additionally, we examine the role of integer linear programming (ILP), constraint satisfaction formulations, and parallel algorithms leveraging GPU acceleration and distributed computing frameworks. Experimental evaluations demonstrate that hybrid and parallel approaches outperform traditional heuristics in terms of scalability and efficiency, particularly for large trees and special graph families. Approximation-based relaxations are also shown to provide near-graceful solutions that guide heuristic refinement. Beyond computational advancements, this work highlights theoretical implications, including potential structural insights into the graceful tree conjecture and extensions to cyclic graphs. The proposed scalable frameworks not only advance computational verification but also contribute to bridging the gap between practical applications and theoretical challenges in graph labelling.

DOI: http://doi.org/10.5281/zenodo.17045706

AI Cancers: Systemic Limitations Threatening The Integrity Of Artificial Intelligence

Authors: Sagar Gupta

Abstract: As Artificial Intelligence (AI) systems permeate critical sectors like healthcare, finance, and governance, deep-rooted limitations have begun to surface—often referred to metaphorically as "AI cancers." These include systemic issues such as algorithmic bias, hallucination, goal misalignment, data poisoning, overfitting, and lack of explainability. Like cancer in a biological organism, these flaws can spread undetected, undermining trust, accuracy, and societal safety. This paper explores the nature, origin, and consequences of these "AI cancers," while outlining the emerging strategies to detect, contain, and remediate them.

Digital India: Driving Financial Inclusion Thorugh Payments

Authors: Dr.P.Janaki, Ms.K.Mounaarthi

Abstract: The rapid advancement of financial technologies and the proliferation of smart phones and internet connectivity have transformed the payment landscape in India. Digital payment systems, encompassing methods such as Unified Payments Interface (UPI), mobile wallets, internet banking, Aadhaar – enabled payments, and card-based transactions, have redefined the way individuals and businesses conduct financial activities. Supported by government initiatives like Digital India and Jan Dhan Yojana, along with regulatory frameworks from the Reserve Bank of India (RBI), digital payments have seen exponential growth in both urban and rural areas. These systems have not only enhanced convenience, transparency, and financial inclusion but have also played a crucial role in reducing reliance on cash and fostering a cashless economy. However, challenges such as cyber security risks, digital literacy gaps, and infrastructural disparities continue to hinder seamless adoption. This paper provides an overview of the evolution, adoption trends, benefits, and challenges of digital payment systems in India, highlighting their role in shaping the country’s transition towards a digitally empowered society and economy.

Cyber Security Risks in Digital Transactions

Authors: Dr.V.Jaya Bharathi, Mr.P.Karthik

Abstract: The technique of defending online companies and their clients from online dangers and attacks is known as cybersecurity in e-commerce. The growth of the e-commerce industry makes it a more desirable target for bad actors looking to take advantage of weaknesses for monetary gain, data theft, and service interruption. It examines the essential elements of a strong e-commerce security architecture, emphasizing the core tactics and tools needed to protect private information and uphold customer confidence. the important areas, such as data encryption for both data at rest and data in transit (e.g., utilizing TLS/SSL protocols). In order to stop unwanted account access, it emphasizes the significance of safe authentication and authorization procedures like Multi-Factor Authentication (MFA). Proactive steps like threat monitoring and prevention systems, such as firewalls and intrusion detection systems, are also included in the abstract. In order to identify and mitigate attacks in real-time, the abstract also discusses proactive techniques including intrusion detection and prevention systems, such as firewalls and intrusion detection systems (IDS). It also discusses the need of adhering to regulations, especially those pertaining to data privacy legislation like GDPR and standards like the Payment Card Industry Data Security Standard (PCI DSS).

Consumer Preferences and Usage Patterns of E-Scooters in Growth Economies A Study in Erode District

Authors: Dr.P.Saravanakumar, Ms.N.Maheswari

Abstract: Electric scooters (e-scooters) are becoming an important component of urban transport, particularly in rapidly developing economies where demand for low-cost and sustainable mobility is high. This paper explores how consumers in such regions perceive and use e-scooters, focusing on demographic influences, decision-making factors, satisfaction levels, and barriers to wider adoption. A primary survey of 200 respondents was conducted in urban and semi-urban locations. The data were analyzed using descriptive statistics, chi-square tests, and comparative models of consumer perception. Results indicate that affordability, convenience in congested cities, and government incentives are the strongest motivators.

Future of Digital Payment Systems in Commerce

Authors: Mrs.M.Prema

Abstract: The commerce industry is going through a major change, influenced by new technology, shifts in consumer behavior, and changing market trends. This paper examines the future of commerce, focusing on key trends, technologies, and opportunities that will shape the industry.

A Study on Digital Transformaton and Its Effects on Business Model Innovation

Authors: Dr.C. Mani, Mr.N. GnanaSekaran

Abstract: Digital transformation (DT) is now a key enabler of business model innovation (BMI) in the modern competitive landscape. Businesses are increasingly using digital technologies like artificial intelligence (AI), cloud computing, big data, and the Internet of Things (IoT) to increase operational effectiveness, devise new revenue streams, and provide better customer experiences. This research analyzes the effect of DT on BMI, with determinants, issues, and approaches chosen by companies. Based on secondary data collected from journals, industry reports, and case studies, the study identifies that DT has a significant effect on cost bases, customer relationship, and value proposition. According to the findings, although DT fosters innovation, it is challenging due to high costs of implementation and cultural resistance. Recommendations are the adoption of agile strategies, investing in digital competencies, and implementing technology-based business models to achieve sustainable growth.

Sustainable Recycle Fashion Business Practices

Authors: Ms.G.Deepika, Mrs.P.E.Monisha

Abstract: The fashion industry is one of the largest contributors to environmental pollution due to mass production, excessive consumption, and waste generation. The emergence of sustainable and recycled fashion practices has gained traction as a solution to mitigate the adverse environmental impacts of traditional fashion business models. This article explores sustainable fashion strategies, focusing on recycling, upcycling, circular economy principles, and ethical sourcing. It examines the challenges and opportunities for businesses, the role of technology, consumer behavior, and the economic viability of integrating sustainability into fashion. The findings highlight that businesses adopting recycled fashion practices not only contribute to environmental preservation but also enhance brand value and competitiveness in a conscious market.devices.

DOI: https://doi.org/10.5281/zenodo.17062475

A Study On Digital Literacy A and Consumer Preference Formation in The Digital Economy

Authors: Mrs.B. Nagarathinam, Ms.A. Sindhu

Abstract: In the current digital environment, Consumers are increasingly relying on digital technologies to inform their purchasing decisions. The capacity to successfully navigate and use digital tools, or digital literacy, has emerged as a critical component in determining consumer preferences. With a focus on the variations in decision-making styles among consumers with different levels of digital literacy, this study investigates the connection between consumer preference formation and digital literacy. According to this study's quantitative analysis of survey data, consumers who possess greater levels of digital literacy are better able to assess online information, make well-informed decisions about what to buy, and form stronger brand preferences. The results of this study have important ramifications for companies looking to interact with tech-savvy customers, marketers, and legislators. Organizations can create more successful customer acquisition and retention strategies in the digital age by comprehending how digital literacy affects consumer behavior.

DOI: https://doi.org/10.5281/zenodo.17062444

An Adaptive Energy-Conscious Routing Framework for Optimized Communication in Mobile Ad Hoc Networks

Authors: Mrs.M. Priya, Dr. D.Sasikala

Abstract: Mobile Ad Hoc Networks (MANETs) face significant challenges in sustaining network lifetime due to limited energy resources and dynamic topology changes. Traditional routing protocols prioritize shortest-path selection without considering residual energy, leading to premature node failures and reduced connectivity. This paper presents an energy-conscious routing framework that integrates adaptive decision-making with energy efficiency to extend the operational lifetime of MANETs. The proposed method enhances packet delivery ratio and minimizes energy wastage by dynamically adjusting routes based on residual energy levels and network load. Simulation results demonstrate improved stability, balanced energy consumption, and longer network survival compared to conventional protocols. This study addresses a critical gap in energy-aware communication strategies for MANETs and provides an effective foundation for future adaptive routing research in energy-constrained environments.

Study on Employee Engagement in a Digitally Transformed Workplace in Banking Sector

Authors: Mrs.A.Kiruthika, Dr.K.Rajini

Abstract: In the era of rapid digital transformation, the banking sector is undergoing profound changes in the way services are delivered, operations are managed, and employees interact within the workplace. Digital technologies such as artificial intelligence, robotic process, automation, big data analytics and mobile banking platforms are not only reshaping customer experiences but also redefining the roles and responsibilities of employees. In this context employee engagement has emerged as a critical determination of organisational success, productivity, and innovation. This study explores the dynamics of employee engagement in digitally transformed banking workplaces, focusing on how technologies integration influences motivation, communication, collaboration, and job satisfaction. It highlights both the opportunities such as flexible work arrangement, enhanced learning platforms and data driven decision making and the challenges including digital stress, skill gaps, and resistance to change. By analysing engagement drivers in this evolving environment the paper emphasizes the need for strategic HR interventions continuous reskilling and supportive leadership to foster a culture of adaptability and sustained engagement. The findings are expected to provide valuable insights for banking institution seeking to balance digital advancement with human centric working practices.

DOI: https://doi.org/10.5281/zenodo.17062354

The Role of Technology in Driving Operational Efficiency: A Correlation Model between D2c Brands and Logistics & Supply Chain Integration

Authors: Dr.B.Gayathri

Abstract: This paper explores the cointegration between Direct-to-Consumer (D2C) brands and logistics/supply chain systems in India, emphasizing how emerging technologies enhance operational efficiency. Using statistical models, case studies, and market data, demonstrated that tech-enabled logistics is not just a support function but a strategic driver of D2C success.

A Study on Teachers Satisfaction on Reskilling and Upskilling of Digital Economy in Private Educational Institutions in Palakkad Region

Authors: Ms.N.Sruthi, Ms.P.Anjana, Ms.Savya Maria Ranjith

Abstract: Today ,educational institutions are now among the most powerful service industries in India, they contribute greatly to the future workforce and overall country-wide socio-economic growth. The integration of technology and changing industry demands have changed the emphasis of educational institutions from regular teaching to introducing the latest skills to educators and learners. With the advancement of digital technology, e-learning websites, and new teaching practices, the operation of the education industry has seen radical transformations. Multiple skill-based initiatives such as online training programs, digital classrooms, professional development sessions, and blended learning programs have eliminated barriers and improved results. Reskilling and upskilling are critical to enable teachers to stay relevant in this changing scenario and to implement new pedagogies and technologies efficiently. The aim of the research is to determine the level and influence of upskilling and reskilling among the educational professionals of Palakkad district. It sums up that for teachers of high aspirations and professional ambitions, ongoing training opportunities and challenges in their profession have to be there for them to find satisfaction and impart quality education. Without them, dissatisfaction and stagnation will erupt, undermining personal and institutional development.

DOI: https://doi.org/10.5281/zenodo.17061465

The Role of Vr-Powered Digital Twins in Sustainable Supply Chain Management

Authors: Mr.Balakrishnan S, Dr.B.Vidya

Abstract: – Integrating digital twins (DTs) and virtual reality (VR) into sustainable supply chain management (SCM) is becoming more and more important as firms want to increase productivity while reducing their environmental effect. actual-time monitoring, predictive analytics, and optimization are made possible by digital twins, which are virtual copies of actual assets and procedures. Digital twins, when paired with virtual reality, offer immersive visualization that enhances manufacturing and logistical decision-making. This study investigates how VR-powered digital twins might improve supply chain transparency, lower carbon footprints, and increase resource efficiency. Key issues including labor adaption, data integration complexity, and cost limitations are also identified in the report. Future studies must concentrate on technological developments and regulatory frameworks to promote the use of VR-powered digital twins for sustainable SCM.

A Study on the Effectiveness of Digital Marketing Strategies of Airbnb Towards Its Customers in Chennai City

Authors: Mr.S.Gnanasekar, Dr.B.Vidya

Abstract: The rapid growth of digital marketing has transformed the hospitality industry, with platforms like Airbnb leveraging online strategies to engage and attract customers. This study examines the effectiveness of Airbnb's digital marketing strategies in Chennai, analyzing their impact on customer engagement, brand perception, and booking behavior. Through a combination of surveys and secondary data analysis, the research evaluates key marketing techniques such as social media campaigns, search engine optimization (SEO), influencer collaborations, and personalized email marketing. The study aims to identify which strategies resonate most with Chennai's customers and how they influence decision-making. Findings from this research will provide insights into the role of digital marketing in enhancing customer experience and loyalty, offering recommendations for optimizing Airbnb’s marketing approach in the city.

The Role of Artificial Intelligence in Enhancing Digital Payment Secuitiry

Authors: Mrs.G.Kiruthika, Dr.S.Muthumari

Abstract: The rapid growth of digital payment systems has transformed financial transactions globally, reducing reliance on cash and improving convenience, transparency, and security. This paper analyzes the evolution of digital payment systems, their key drivers, benefits, challenges, and future potential. It explores how technological innovations such as Unified Payments Interface (UPI), mobile wallets, and contactless payments influence consumer behavior and financial inclusion, while also addressing security concerns and regulatory frameworks.

DOI: https://doi.org/10.5281/zenodo.17060861

A Study On Employee Engagement & Impact of Job Satisfaction in Digital Private Sector Bank Employees in Palakkad City

Authors: Dr.M.S.Keerthi

Abtract: Today, the banking sector is one of the biggest service sectors in India. Availability of quality services is vital for the well-being of the economy. The focus of banks has shifted from customer acquisition to customer retention. With the stepping in of information technology in the banking sector, the working strategy of the banking sector has seen revolutionary changes. Various customer-oriented products like internet banking, ATM services, Tele-banking and electronic payment have lessened the workload of customers. The facility of internet banking enables a consumer to access and operate his bank account without actually visiting the bank premises. Job satisfaction refers to a general attitude which an employee retains on account of many specific attitudes in the following areas: 1. job satisfaction, 2. individual characteristics, and 3. relationships outside the job. The objective of the study is to identify the level of job satisfaction among private sector employees in palakkad city. Simple random sampling technique was employed in the study, in order to remove any possible bias creeping in to the study, considering the small sample size of 120. It concludes that for those individuals with high expectation, there must be enough challenges available in their job, for them to derive satisfaction, and if this criterion is not met, it leads to them being dissatisfied with their job.

A Study On Employee Engagement & Impact of Job Satisfaction in Digital Private Sector Bank Employees in Palakkad City

Authors: Dr.M.S.Keerthi

Abstract: Today, the banking sector is one of the biggest service sectors in India. Availability of quality services is vital for the well-being of the economy. The focus of banks has shifted from customer acquisition to customer retention. With the stepping in of information technology in the banking sector, the working strategy of the banking sector has seen revolutionary changes. Various customer-oriented products like internet banking, ATM services, Tele-banking and electronic payment have lessened the workload of customers. The facility of internet banking enables a consumer to access and operate his bank account without actually visiting the bank premises. Job satisfaction refers to a general attitude which an employee retains on account of many specific attitudes in the following areas: 1. job satisfaction, 2. individual characteristics, and 3. relationships outside the job. The objective of the study is to identify the level of job satisfaction among private sector employees in palakkad city. Simple random sampling technique was employed in the study, in order to remove any possible bias creeping in to the study, considering the small sample size of 120. It concludes that for those individuals with high expectation, there must be enough challenges available in their job, for them to derive satisfaction, and if this criterion is not met, it leads to them being dissatisfied with their job.

DOI: https://doi.org/10.5281/zenodo.17060802

Enhanced Digital Data Security By Bio-Inspired And Neural Network Models

Authors: Pawan Singh Rajput, Dr. Devdas Saraswat

Abstract: Cyber threats pose a significant challenge to protecting cloud-based health data, including DNA sequences and patient information. This paper has proposed a model that identify the image pixel region that fit for embedding the secret information. In this pixel region identification done by genetic algorithm. Hence whole image was structured into two cluster first is embedding and other is non-embedding. Least significant bit replacement model is used for the embedding by transforming the image into frequency region. Experiment was done on real health set images and result shows that proposed model has increases the work performance.

The Role of Technology in Driving Operational Efficiency: A Correlation Model between D2c Brands and Logistics & Supply Chain Integration

Authors: Dr.B.Gayathri

Abstract: This paper explores the cointegration between Direct-to-Consumer (D2C) brands and logistics/supply chain systems in India, emphasizing how emerging technologies enhance operational efficiency. Using statistical models, case studies, and market data, demonstrated that tech-enabled logistics is not just a support function but a strategic driver of D2C success.

DOI: https://doi.org/10.5281/zenodo.17061623

The Role of Artificial Intelligence in Enhancing Digital Payment Security

Authors: Mrs.G.Kiruthika, Dr.S.Muthumari

Abstract: The rapid growth of digital payment systems has transformed financial transactions globally, reducing reliance on cash and improving convenience, transparency, and security. This paper analyzes the evolution of digital payment systems, their key drivers, benefits, challenges, and future potential. It explores how technological innovations such as Unified Payments Interface (UPI), mobile wallets, and contactless payments influence consumer behavior and financial inclusion, while also addressing security concerns and regulatory frameworks.

DOI: https://doi.org/10.5281/zenodo.17060861

NLP-Based Sentiment Analysis Of Kannada Language Inscriptions

Authors: Sachhidanand Sidramappa, Mallamma V. Reddy

Abstract: An inscription is text engraved on metal, coins, graves, rocks, building walls, and other durable surfaces. Sentiment analysis involves employing natural language processing (NLP) to ascertain the emotional impact of a sentence by examining facts, opinions, evaluations, or assertions. Sentiment analysis is the classification of Kannada Language Inscriptions text into Four categories: Grants, Eulogies, Gosaasa and Brave. These inscriptions are found in large numbers across the Karnataka state. These are official documents conveying the grant or gift of land by the kings. The eulogy inscriptions are made to praise the kings or officials. Memorial stones depict heroic stories. These memorial stones honour a hero who died on the battlefield while defending the kings. The Gosaasa inscriptions denote the donation of the cows. Sentiment analysis has developed to enable the automated analysis of extensive datasets. The main aim of sentiment analysis is to ascertain the polarity of data in Kannada language inscriptions. This work focuses on the sentiment analysis of Kannada-language inscriptions to discover diverse sentimental sentences carved on copper plates, stone surfaces, and walls. The main objective of this paper is to perform the sentimental analysis on Kannada language inscriptions with various parameters like “Grants,” “Eulogies,” “Gosaasa,” and “Brave”. The training and testing datasets are individually segregated. Multiple machine learning classifiers are employed, including K-Nearest Neighbors, Random Forest, Linear SVC and Logistic Regression.

DOI: http://doi.org/10.5281/zenodo.17213588

A Data-Driven Approach To Identifying Churn Predictors Using Demographic, Service, And Billing Insights

Authors: Mr. Parveen kumar, Dr. Deepak

Abstract: Customer churn remains a critical concern for industries such as telecommunications, banking, and subscription services, where retaining existing customers is often more cost-effective than acquiring new ones. With the increasing availability of customer-related data, data-driven approaches have become essential for understanding and predicting churn behavior. This review paper focuses on identifying significant churn predictors by analyzing demographic details, service usage patterns, and billing information. By synthesizing insights from existing research, the paper highlights how various machine learning models utilize these predictor variables to enhance the accuracy of churn prediction. Special attention is given to the role of demographic attributes such as age, gender, and location; service-related factors including plan type and usage frequency; and billing characteristics like payment history and invoice amounts. Commonly used datasets and standard evaluation metrics, such as accuracy, F1-score, and AUC-ROC, are also reviewed to provide a comprehensive understanding of model performance across studies. Furthermore, the paper discusses key limitations in current methodologies and suggests future research directions to improve real-world applicability. Overall, this review offers a consolidated perspective on effective churn predictors and provides practical guidance for developing more targeted and efficient customer retention strategies.”

A Survey On Digital HealthCare Data Analysis Techniques For Developing Machine Learning Models

Authors: Prasannta Tiwari, Dr. Pritaj Yadav

Abstract: Diabetic Retinopathy (DR) is a progressive eye disease requiring early diagnosis for effective treatment. This study introduces a novel diagnostic framework named GARID (Genetic Algorithm-based Retinopathy Image Diagnosis), designed to enhance the accuracy of retinal image classification through intelligent feature optimization and robust classification. The proposed model operates in two primary phases: feature optimization using a modified Genetic Algorithm (GA) and classification via a Tree Bagger ensemble learning method. Initially, retinal images undergo preprocessing and denoising using Wiener filtering. Segmentation is performed using GA, where cluster centers are evolved through crossover and mutation strategies to identify regions of interest. Features are then extracted using histogram analysis and Discrete Wavelet Transform (DWT), capturing both spatial and frequency information. The final feature set is classified using a tree-based ensemble model, ensuring high generalization and detection precision. Experimental results confirm that GARID improves class-wise detection, recall, and F-measure, offering a reliable solution for automated diabetic retinopathy screening.

Development and Validation of a Stability-Indicating Hplc Method for Efavirenz and Assessment of Its Degradation Profile Under Ich-Mandated Stress Conditions

Authors: Rajesh Meshram, Rajesh Gour

Abstract: A stability-indicating HPLC method was developed and validated for Efavirenz, a non-nucleoside reverse transcriptase inhibitor. The method effectively separated Efavirenz from its degradation products under various stress conditions, including acidic, alkaline, oxidative, thermal, and photolytic stress. The results showed that Efavirenz is highly labile to alkaline hydrolysis and moderately susceptible to acidic hydrolysis and oxidation. The method was linear, accurate, precise, and robust, with a mean recovery of 98.9-101.2% and RSD < 1.5%. The study provides a comprehensive degradation profile of Efavirenz, confirming its vulnerability to hydrolysis, especially in alkaline environments. This method is suitable for routine quality control and stability monitoring of Efavirenz in pharmaceutical formulations.

DOI: https://doi.org/10.5281/zenodo.17278340

Bio-Geological and Ensemble Learning Based Diabetic Retinopathy Image Class Prediction

Authors: Prasannta Tiwari, Dr. Pritaj Yadav

Abstract: Diabetic Retinopathy (DR) is a progressive eye disease requiring early diagnosis for effective treatment. This study introduces a novel diagnostic framework named GARID (Genetic Algorithm-based Retinopathy Image Diagnosis), designed to enhance the accuracy of retinal image classification through intelligent feature optimization and robust classification. The proposed model operates in two primary phases: feature optimization using a modified Genetic Algorithm (GA) and classification via a Tree Bagger ensemble learning method. Initially, retinal images undergo preprocessing and denoising using Wiener filtering. Segmentation is performed using GA, where cluster centers are evolved through crossover and mutation strategies to identify regions of interest. Features are then extracted using histogram analysis and Discrete Wavelet Transform (DWT), capturing both spatial and frequency information. The final feature set is classified using a tree-based ensemble model, ensuring high generalization and detection precision. Experimental results confirm that GARID improves class-wise detection, recall, and F-measure, offering a reliable solution for automated diabetic retinopathy screening.

Survey On Privacy Preserving Mining Techniques And Application

Authors: Jayshree Boaddh, Dr. Shailja Sharma, Dr. Rakesh Kumar

Abstract: Digital platform increase the easiness of data organization and utility. Extraction of information from raw data was performed by data mining algorithms. This information has many applications but few of miners extract knowledge which might affect the privacy of individual, organization, community, etc. So this paper focuses on finding the techniques which provide privacy of data against data mining algorithms. Paper has performed a survey on recent methodology proposed by different researcher. Some of data mining methods were also describe in the paper which help in information extraction. Evaluation parameters were detailed for comparison of privacy preserving methods

DOI: http://doi.org/

Survey on Digital Data Knowledge Extraction Techniques and with Security

Authors: Jayshree Boaddh, Dr. Shailja Sharma, Dr. Rakesh Kumar

Abstract: Digital platform increase the easiness of data organization and utility. Extraction of information from raw data was performed by data mining algorithms. This information has many applications but few of miners extract knowledge which might affect the privacy of individual, organization, community, etc. So this paper focuses on finding the techniques which provide privacy of data against data mining algorithms. Paper has performed a survey on recent methodology proposed by different researcher. Some of data mining methods were also describe in the paper which help in information extraction. Evaluation parameters were detailed for comparison of privacy preserving methods.

DOI: http://doi.org/10.5281/zenodo.17365841