bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Intelligence Based Public Air Purifying System
Authors: Paresha M. Dudhedia
Published: January 2025

Introduction: Air pollution poses a significant threat to public health and well-being, necessitating innovative solutions to mitigate its adverse effects. This research presents an AI-based Public Air Purifying System (PAPS) designed to enhance outdoor air quality in urban environments, densely polluted locations and other similar locations. The proposed system leverages artificial intelligence algorithms to monitor, analyze, and respond dynamically to pollution levels in real-time.
Objectives: A system for real-time surveillance of air quality metrics, including particulate matter and contaminants, using IoT-enabled sensors. Develop a system that autonomously initiates and controls air purifiers in response to pollution metrics, guaranteeing energy efficiency and peak performance. Utilize AI algorithms to enhance airflow distribution for optimal pollutant elimination in various regions.
Methods: The proposed technology integrates AI-driven optimization with a sophisticated multi-stage filtration and purification process.  The prototype has a modular design with an AI system that adaptively modifies purification levels according to real-time air quality data, guaranteeing efficient and precise pollutant elimination in various settings.
Results: The show results in the air pollution ppm values before and after using the purifier. The results show the data according to the current date and time. A steady decrease in PPM values indicates continuous improvement in air quality over time. PPM values fluctuate, initially rising and then stabilizing, indicating dynamic changes in air quality following the process.
Conclusions: The AI-Based Public Air Purifying System demonstrates a unique and significant method for addressing air pollution. Its cognitive talents facilitate real-time modifications, although enhancement is required to guarantee dependability and efficacy. With further developments, this technology may significantly improve urban air quality, mitigate health hazards, and foster sustainable living in highly populated regions
Generalization of intuitionistic fuzzy ideals via (∂ ˜, ℘˜) intuitionistic Q interval-valued fuzzy ideals using regular ordered t
Authors: Ayman Hazaymeh
Published: January 2025

This paper introduces the notion of ∂˜, ℘˜ intuitionistic Q interval-valued fuzzy subsemigroup (IQVFSS), fuzzy left ideal (IQVFLI), fuzzy right ideal (IQVFRI), fuzzy lateral ideal (IQVFLATI), fuzzy ideal (IQVFI), and fuzzy bi-ideal (IQVFBI) of an ordered semigroups. Let ∂˜, ℘˜-IQVFI is a new extension of IQVFI over ternary semigroups Z . The subset ~ = [< =] represents a (∂˜, ℘˜) − IQV F SS[IQV F LI, IQV FRI, IQV F LAT I, IQV FBI] of Z if and only if every level subset ~t is an SS [LI, RI, LAIQV F, T BI] of Z for every t ∈ (∂˜, ℘˜]. A few examples can be presented to demonstrate our results.
Diophantine Kites: Rational Diagonals and Integer Area Constructions
Authors: M. Mahalakshmi
Published: January 2025

In this paper, we aim to collect all kites with certain fixed sides in integers, rational diagonals and integer areas. We develop mathematical procedure and python programming to collect all those kites. Also, experimental analyses are done by means of examples.
Cultivating Excellence: A Total Quality Management Approach to Enhancing Student Talent in Chinese Language and Literature
Authors: Ming Li
Published: January 2025

This research explores the critical factors that contribute to the enhancement of student talent quality among undergraduates in Chinese Language and Literature in Southwest China, utilizing the framework of Total Quality Management (TQM). The study adopts a quantitative methodology, employing multiple regression analysis to examine the interrelations among university management practices, facilities, teacher effectiveness, and student personal characteristics in the context of talent development. The results indicate a moderate positive correlation (R = 0.405) between the identified independent variables and student talent quality, explaining 16.4% of the variance in talent cultivation. Although the findings are statistically significant (p = 0.049), the relatively low adjusted R² value of 0.079 suggests that unmeasured factors significantly influence talent quality outcomes. This study validates multiple hypotheses, affirming the importance of effective university management, high-quality educational facilities, and skilled teaching staff in nurturing student talent. Additionally, it highlights the role of personal attributes, such as motivation and learning ability, in fostering both academic success and personal development. These insights emphasize the need for universities to adopt an integrated and holistic approach to education that encompasses robust management practices, modern infrastructure, and comprehensive support for educators and students alike. Based on the findings, practical recommendations include optimizing administrative processes, enhancing access to contemporary learning resources, and investing in ongoing professional development for teaching staff. By illustrating the complex interplay of various factors within the higher education landscape, this study contributes to existing literature and advocates for the application of TQM principles in educational practices, ultimately aiming to improve student employability and success in an increasingly competitive job market. The findings provide actionable insights for educational policymakers and practitioners seeking to advance talent development strategies in higher education.
Data-Driven Seismic Analysis of Structures Using Machine Learning
Authors: Santha kumari Kambala
Published: January 2025

Reinforced concrete shear walls are among the most important building structural components for supporting lateral loads. In spite of its significance, the inadequate safety margins of shear walls have been brought to light by post-earthquake reconnaissance and current experimental investigations. Current shear walls cannot have their failure modes quickly identified due to the absence of models based on mechanics and empirical data. To find out how shear walls fail depending on geometric configurations, material qualities, and reinforcing details, this research uses machine learning (ML), which has recently made some strides. Results from 395 experiments with shear walls of different geometric configurations make up the study's exhaustive database. In this research, the optimal prediction method was determined by evaluating eight machine learning methods, which included K Nearest Neighbours (KNN), Naive Bayes, Randomized Forest, XG Boost, Decision Tree, Ada Boost, Cat Boost and LightGBM. An exhaustive examination led to the proposal of a Random Forest based ML method in this research. When it comes to determining how shear walls break, the suggested approach is 87% accurate. According to the study, aspect ratios, bordering element reinforcement indices, and wall length to wall ratio of thickness are key factors in shear wall failure. Lastly, this research offers a data-driven categorization approach that is open-source and may be utilized by design firms worldwide. Additional experimental data that provide new insights may be easily included into the suggested method.
Deep Learning Applications in Medical Image Analysis:Enhancing Radiology with Automated Diagnostic Tools
Authors: Jihane Ben Slimane
Published: January 2025

The main objective of this study is to incorporate deep learning–based automated tools into radiological diagnostics to improve diagnostic accuracy, decrease human error with the aid of computer algorithms, and increase workflow efficiency. The main goal is to evaluate the utility of such tools for detection and classification in practical radiological workflows, helping radiologists provide even faster and more reliable diagnoses.
To establish a thorough methodology, data preprocessing, model selection, and validation were applied for a variety of medical imaging datasets such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI) etc. These datasets were then used to train deep learning models, mainly convolutional neural networks (CNNs) and transformer architectures to identify feature determinants for common diagnostic markers. Various imaging modalities are being compared based on performance metrics accuracy, sensitivity and specificity. A comparison to more conventional methods of radiology was also performed to demonstrate the relative strengths and weaknesses of deep learning models.
Findings showed that deep learning algorithms may enhance diagnostic accuracy in medical imaging, attaining higher levels of sensitivity and specificity than standard approaches in some examples. It also found that automated diagnostics could save time for image analysis, improving workflow and enabling radiologists to focus on challenging cases. Error analysis identified components requiring further development, especially on edge cases and rare disease detection where traditional and deep learning methods may work best in combination.
Enhancing Job Satisfaction and Professional Commitment: Keys to Success in the Public Sector
Authors: Ahmad Khanali
Published: January 2025

This literature review explores the relationship between job satisfaction and professional commitment among public sector employees, particularly civil servants (ASN). It synthesizes findings from studies conducted between 2010 and 2023 to identify key factors that influence job satisfaction and how these factors impact employees' commitment to their profession. Central themes include the role of leadership, work environment, organizational citizenship behavior, and emotional intelligence in shaping professional commitment. Findings reveal that a supportive work environment, strong leadership, and a healthy work-life balance significantly enhance job satisfaction, which in turn boosts professional commitment. Employees with high emotional intelligence and resilience tend to exhibit stronger commitment, even during crises, such as the COVID-19 pandemic. Furthermore, work-life conflict negatively affects job satisfaction, but employees with higher professional commitment are more likely to remain in their positions despite such challenges. The review highlights the importance of organizational policies that promote employee well-being, such as flexible working arrangements and mental health support. These measures can strengthen job satisfaction and foster long-term commitment, reducing turnover intentions. Future research should explore the long-term effects of job satisfaction on professional commitment across various public sector roles, with attention to different cultural and organizational contexts. The review emphasizes the need for holistic HR strategies to foster a resilient and committed workforce, ensuring sustained performance and employee retention in public sector organizations.
Natural Language Processing Techniques for Sentiment Analysis in Social Media
Authors: Pramod Kumar
Published: January 2025

This research has used the techniques of Natural Language Processing to explore how to do sentiment analysis on social media by using three algorithms, G-LSTM, RMDEASD, and BERT. It tested how the models perform on a different set of domains like disaster management, corporate performance, and consumer behavior for the purpose of sentiment classification. The study utilized more than 500,000 social media posts as the dataset, with accuracy rates of 88.4% for G-LSTM, 85.7% for RMDEASD, and 91.2% for BERT. Results reveal that BERT surpassed the other models in terms of accuracy and contextual understanding in aspect-based sentiment analysis. Moreover, the hybrid model G-LSTM was effective in disaster-related tweets by achieving a 92.3% accuracy in real-time sentiment classification. Comparisons with related work show that the proposed models significantly improve the accuracy and robustness of sentiment analysis over traditional machine learning methods. Challenges such as sentiment classification in low-resource languages are also addressed, providing insights on how to improve model applicability in diverse linguistic contexts. The findings add to the increasing body of research in sentiment analysis and indicate its potential applications in several industries, such as healthcare, marketing, and public opinion analysis.
Integration of Grey Wolf Optimization Framework with Convolutional Neural Network for Kidney Tumor Classification from Ct Images
Authors: R.Mary Victoria
Published: January 2025

Our society is affected by the most common disease kidney tumor (KT) in humans. The early diagnosis of KT may reduce the risk of death rates. Preventive measures can be taken to reduce the severe effects and overcome the tumor progression. Traditional methods consume time and tedious task. Deep Learning (DL) methods are now popular in constructing the systems that can detect and classify KT accurately in short duration of time. DL uses pre-trained Convolutional Neural Network (CNN) techniques to provide analysis from medical images. In this paper, Grey Wolf Optimization (GWO) algorithm optimizes CNN hyperparameters. The performance metric values for the detection models of CNN, CNN combined with particle swarm optimization (PSO), CNN combined with genetic algorithm (GA), CNN combined with Search Engine Optimization (SEO), CNN combined with Wild Horse Optimization (WHO) are compared with the propose CNN combined with GWO. The proposed model achieved promising results when the number of classes able to be predicted (K) is equal to 5.
A Novel Intrusion Detection System for Mitigating Black Hole Attacks in Wireless Networks
Authors: A. Manjula
Published: January 2025

This study introduces an innovative Intrusion Detection System (IDS) designed to mitigate black hole attacks in wireless networks, particularly in the context of the NS2 simulation platform. Black hole attacks, wherein malicious nodes deliberately drop or misroute data packets, severely disrupt network reliability and connectivity. Traditional security measures, including encryption and authentication, are insufficient against such threats. The proposed IDS integrates anomaly detection with signature-based techniques, enabling the identification of both known and novel black hole attack patterns. The system employs adaptive learning, allowing continuous enhancement of detection accuracy by analysing past network behaviours and evolving threats. Extensive simulations using the AODV routing protocol on the NS2 platform validate the system's efficiency in maintaining network integrity and performance. This IDS provides a robust Défense mechanism for wireless networks, ensuring secure and reliable communication, particularly for critical applications in IoT, smart city infrastructure, and military domains.
Graph - Based Innovations in Cryptographic Security
Authors: Slashi Leel
Published: January 2025

Cryptography focuses on securely transmitting messages from one party to another, ensuring that an unauthorized third party cannot comprehend them. To achieve this, various mathematical concepts, particularly from Graph Theory, have been incorporated into cryptographic techniques for more secure data transmission. The process of converting the original plain-text message into an unreadable cipher-text is known as encryption, while converting the cipher-text back to the original plain-text is referred to as decryption. Graph Theory is essential in numerous fields, particularly in cryptography, where its properties and straightforward matrix representation in computers make it highly valuable. In this paper the encryption and decryption process has been made more safer by using adjacent matrix of complement graph.Introduction: In today's digital communication landscape, ensuring secure data transmission is essential, and cryptography plays a vital role in safeguarding sensitive information. Graph theory, with its intricate structures and concepts, offers innovative frameworks for creating robust encryption methods. This research explores using adjacency matrices, graph complements, and disjoint graphs to develop secure cryptographic systems. By leveraging graph complements and structural complexities, these methods obscure messages, enhancing resistance to cryptographic attacks. Applications range from securing communication channels to enhancing blockchain privacy. Building on previous studies, this work demonstrates the potential of graph-theory-based cryptography in addressing modern security challenges effectively.
A Simplified Approach to Solving Balanced Generalized Quadratic Fuzzy Transportation Problems
Authors: M.Venkatachalapathy
Published: January 2025

The transportation model is a specialized linear programming problem aimed at optimizing the shipment of goods from several sources to different locations. The primary objective is to minimize total transportation costs while ensuring that the supply constraints at each source and the demand requirements at each destination, represented as quadratic fuzzy numbers, are satisfied. This paper introduces a novel approach for solving generalized quadratic fuzzy transportation problems using the Piecewise Quadratic Fuzzy Vogel’s Approximation Method (PQFVAM) and the Piecewise Quadratic Fuzzy Modified Distribution Method (PQFMODI)The proposed method is simple, relying on basic arithmetic and logical calculations with generalized quadratic fuzzy numbers, making it efficient and practical for real-world applications.
Stability and Hopf Bifurcation Analysis of Tumors with Immature and Mature Lymphocytes
Authors: M.Sridevi
Published: January 2025

The competition between immune system and tumor is highly intricate. Our aim is to develop a simple authentic mathematical model to understand the crucial factors that influence the outcomes of anti -tumor response. We are focusing on the concept that lymphocytes progress through two developing stages immature and mature lymphocytes. We formulated a new model for anti-tumor immune responses and have examined it characteristics. Steady states are found under specific conditions. Using Routh-Hurwitz criteria, local asymptotical stability of equilibria is investigated. Hopf bifurcation is analysed with a time delay as a bifurcation parameter. The length of the delay is derived to maintain stability. Analytical findings demonstrate the impact of delay on destabilizing the system and generating the periodic oscillations. The system undergoes different phases where the rate of mature lymphocytes influx progresses through many stages. This includes uncontrolled tumor growth initially, then reaching a stable state with considerable tumor presence, followed by periodic oscillations. Further progressing to a stable state with minimal tumor and ultimately attaining a stable state free of tumors. Analytical work is illustrated by numerical simulations.
A mathematical approach for Data Management Transformation: Bridging Data Lakes and Data Fabrics with Advanced Analytics
Authors: Albia Maqbool
Published: January 2025

Over the last decade, big data and advanced analytics have changed a lot, and as a result, data management has been transformed from the legacy data lake to the modern data fabric architecture. The transformation is founded on the increasing demand for seamless, real-time integration and access between disparate data sources in order to turn actionable insights more quickly. Data lakes are good at centralizing high amounts of structured and unstructured data from different sources but lack governance, scalability, and timely analytics. In contrast, data fabrics provide an integrated intelligent layer that connects diverse data environments, ensuring superior agility, better data quality, and more optimized performance. Data fabrics utilize advanced analytics and machine learning to dynamically evaluate data flows and change them accordingly, automate integration processes and help organisations get just-in-time and just-enough data driven decisions quicker. In this paper well address how organizations are embracing data fabric solutions to mitigate the weaknesses of data lakes, and deliver results like faster insights, better compliance, and more accurate predictive analytics. The value of this change is demonstrated with relevant use cases in the real-world reinforcing that it will act as a catalyst for innovation and boost operational effectiveness. These findings highlight the significance of data fabrics as the future solution for enterprises aiming for a higher competitive advantage in an emerging data-driven world.
Applying the TOPSIS Method to Assess Public Transportation Performance: A Case Study of Telangana Public Transport Service
Authors: M.Tirumala Devi
Published: January 2025

The Telangana government implemented a number of advancements in public transportation, including the Hyderabad Metro Rail, among others, to help with traffic issues in the twin cities of Hyderabad and Secunderabad. One of the chief goals of the government and the local operator has been to increase the standard of local urban transportation and thus push the envelope in terms of economy as well as the ease of commuter operation. Effective strategies are required to achieve sustainable growth in light of the complex issues related to traffic congestion and pollution. This study presents a computational framework for evaluating the consumer satisfaction of Telangana's current public transportation system in the above twin cities. The goal of the present study is how each mode of transportation performs in regard to each criterion, which can assist decision-makers and transit authorities in more efficiently allocating resources. For this it was used the additive normalization method and TOPSIS method to determine the optimal mode of transportation for a real-world case-study (Telangana, India). The results showed that metro rail performed better than other means of transportation in the given environment.
A Python Framework for Pest Management and Crop Yield prediction
Authors: Yogesh V. Gandge
Published: January 2025

In recent years, agriculture has become increasingly important as the world's population continues to grow. The demand for food grains is constantly on the rise; this exerts more pressure on farmers to maximize their crop yields. One of the biggest challenges the farmers face is managing pests and maintaining soil fertility. Farmers apply pesticides only after witnessing the pest on the crop. By this time the pest has already made an impact on the crop leading to crop loss. Traditional methods of pest management and preserving soil fertility are time-consuming and less effective.In this paper, a Python framework has been developed that can help farmers maximize their crop yield by predicting the attack of the pest on the crop and the most suitable period for spraying the pesticide.Timely and precise prediction of outbreak of pest can help in controlling the pest and improving crop yield. DM is used to forecast pest attacks. Time series data on the pest trap values is collected from the website. Here the pest considered is American Ball worm adult and the crop considered is cotton. The weekly data is collected from 1991 to 2000.On this data after preprocessing it is subjected to DM model. For prediction 10 years data is considered. ARIMA model is developed and employed on this time series data.The accuracy of the forecasting model is verified by using RMSE value. We will go through the benefits of using this framework and describe how it can make farming more efficient and productive.
Innovativeness and Firm Performance in Apparel Firms in Sri Lanka: The Moderating Role of Family Continuity
Authors: GS Nanayakkara
Published: January 2025

Purpose: Empirical evidence relating to the link between innovativeness and performance is largely unequivocal with innovativeness significantly influencing firm performance. However, the influence of the family dynamic on this relationship is not well established and few studies have been carried out that explores the influence of family context on the innovativeness –performance relationship. The objective of this article is to examine the influence of  family continuity and its relationship with innovativeness and firm performance in an under-examined industry and country context using the resource based view.
Method: The study methodology was survey based and took place in the apparel sector in Sri Lanka. The sample frame was 234 registered apparel firms with the Export Development Board (EDB) of Sri Lanka.
Findings: Family continuity is moderating the relationship between innovation and firm performance when the moderator is strong as well as weak. In situations that family continuity is low, the influence of innovation on performance is greater and significant compared to high family continuity’ circumstances. Higher family continuity weakens the impact of innovation on firm performance. Family continuity has a large effect on the endogenous variable firm performance and contributes in a very significant way to the endogenous construct, firm performance. Therefore, there is a large negative moderation effect in the model as family continuity weakens the relationship between innovation and performance
Research Limitations: The generalizability of the findings are limited by the focus on a single country setting. The study is also confined to the apparel sector and applicability of the findings to other sectors is not easily generalizable due to differences in organizational practices.
Novelty – Whilst the relationship between innovativeness and firm performance has been assessed, this is among the first theoretically sensitized studies linking resource based view, family continuity, innovativeness and firm performance in a developing country context. Prior family business empirical research has focused on Western European or US contexts and studies from developing family business country contexts are limited.
Design of a High speed Low Low-Power Latched Comparator for Medical Implants
Authors: Sharmila Vallem
Published: January 2025

The latched dynamic  comparator is a fundamental component in all ADC architectures. Thermal effects, kickback, and offset voltage influence it. The kickback noise of the latched comparator in medical implant ADCs can impact the resolution, precision, and settling period. The current study examines a latching comparator that aims to reduce kickback noise. This research presents a low-power latched comparator for medical implants functioning at 1 V. This investigation implements a comparator utilising the sampling switching approach. This method successfully minimises kick-back noise and clocking feed-through by minimising unwanted charge injections in the comparator. The comparator decreases power usage while maintaining noise levels. The comparator uses 45-nanometre CMOS technology. The proposed comparator demonstrates superior performance compared to leading comparators regarding kickback noise, power consumption, delay. The input voltage has an inverse effect on the comparator's delay. Simulations indicate that the comparator uses 31 nW at 1 V. The comparator consumes 70% less power compared with different systems. The suggested sample modification decreases kickback noise by a minimum of 18%.
Towards 6G: Next Generation Wireless Systems
Authors: Haresh L. Judal
Published: January 2025

5G will only be able to meet some of the demands of the coming technological advances in 2030 and beyond. Compared to 5G networks, sixth-generation (6G) networks are expected to introduce novel use cases and performance metrics, such as global coverage, cost efficiency, increased radio spectrum, energy intelligence, and safety. The growing global demand for ultra-high spectral efficiencies, data rates, speeds, and bandwidths in next-generation wireless networks motivates researchers to investigate the peak capabilities of massive MIMO (multiple input multiple outputs) and new technique filter bank multi-carrier (FBMC). Lower out-of-band emissions are observed in the FBMC technique compared to orthogonal frequency division multiplexing (OFDM), an essential requirement of upcoming next-generation wireless systems. This paper compares the spectral efficiency for Massive MIMO uplink in a single-cell scenario using linear detectors at the BS with perfect CSI. Arbitrarily larger SNR values are obtained with a higher number of BS antennas in Massive MIMO, which helps to increase the data rate. This paper also demonstrates how linear detectors can help to reduce the symbol error rate (SER) in a Massive MIMO. This paper demonstrates that with the same number of BS antennas and user combinations ZF detector outperforms the MRC detector.
Simulation of 5KWp Grid Connected Rooftop Solar Power Plant using PV Watts India
Authors: Jayanna Kanchikere
Published: January 2025

In grid connected rooftop solar PV system, available rooftop area on buildings is used for setting up solar power plant and DC power generated from solar photovoltaic (SPV) cells is converted to AC power by solar grid inverter and is fed to the grid during day time. In night when solar power is not sufficient, loads are served by drawing power from grid. In this paper, the Simulation analysis of 5 KWp solar photovoltaic roof top grid connected power plant at Sharad Institute of Technology college of Engineering, Ichalkaranji city is carried out using PV Watts India simulation software. The simulation results of DC energy output of PV module and AC energy output of inverter are presented. From the simulation, the annual average solar irradiation is 6.02 kWh/m2/day. Total annual DC energy output of the PV array is 8301 Kwh and the annual output of the inverter is 7922 KWh. The system losses are 14.08% and the capacity factor is 18.1%.
Cyber security and digital transformation issues in the information age
Authors: Vilas Nair
Published: January 2025

The Information Age has transformed worldwide connections and commercial operations via digital transformation, however it also presents intricate cybersecurity issues. Digital transformation fosters creativity, efficiency, and data-driven decision-making across sectors.. These vulnerabilities arise from insufficient security frameworks, advancing cyber threats, and the emergence of sophisticated cyber-attacks. The interdependent characteristics of contemporary systems exacerbate the possible consequences of breaches, jeopardizing data privacy, operational continuity, and customer confidence. Moreover, adherence to changing legal frameworks like GDPR and CCPA poses a considerable problem for organisations balancing the conflicting demands of modernization and security. Cybersecurity must be prioritized strategically, necessitating organisations to implement proactive measures like zero-trust architectures, constant threat monitoring, and comprehensive incident response plans. The integration of cybersecurity and digital transformation is crucial for developing resilient ecosystems that use technology while maintaining security. Organisations may manage risks and capitalize on the advantages of digital transformation by prioritizing staff training, using sophisticated threat detection technology, and integrating security into the foundation of digital initiatives. This abstract emphasizes the critical need to tackle cybersecurity concerns as essential to the success of digital transformation efforts in the Information Age.
An Intelligent Metaheuristic Technique Assisted Feed Forward Neural Network (FFNN) for Alzheimer Disease Classification System
Authors: C. Sudha
Published: January 2025

One of the prevalent and general kind of dementia is Alzheimer Disease (AD). Only few people detects the incidence of disease at the early stage, since there is no specific symptom. Distinct reason for cause and cure of AD is not identified. When the disease is identified at the early stage, it can be managed and treating the patient also effective. This context necessitated early identification of disease with the assistance of computational technologies. The performance of disease classification is influenced by the dimensionality, irrelevant feature selection and misclassification. In this research work, shortcomings in the existing disease identification is improved by introducing optimization and deep learning techniques. The issues in handling dimensionality is handled by Linear Discriminant Analysis (LDA) and optimal features are retrieved by Fireworks Algorithm (FA). The optimal feature are trained and disease is classified using Feed Forward Neural Network (FFNN). The classification result is investigated by comparing with the existing state-of-art classification algorithms. The proposed LDA-FA-FFNN outperforms the existing approaches and achieves 93% accuracy
An Efficient Blockchain Assisted Electronic Health Record (HER) Authentication System
Authors: K.Nandha Kumar
Published: January 2025

Present study is aimed to improve the current state of e-Health systems by implementing the blockchain in EHRs to improve security and privacy. With a growing emphasis on storing records electronically, threats to privacy from the open nature of internet connectivity, third party data and conventional understandings of security are stronger than ever. In this research, the authors present a patient-centered e-Health system that utilizes blockchain technology to achieve secure storage and information retrieval using machine learning algorithms. In this context, the proposed system built upon blockchain improves the level of interoperability while achieving reliable data protection. The symmetric and the asymmetric key methods are used and include key generation, key interchange, content and key encryption, and signatures for authentication and response. Performance evaluations show this method enhances the existing systems to deliver better data security and privacy to the healthcare consumers. This work responds to major security shortcomings of today’s e-Health solutions and outlines the framework for the portable, secure solutions of the future.
Comprehensive Analysis of Diabetes Prediction System: Machine Learning and Deep Learning
Authors: T. Nedunchezhian
Published: January 2025

Disease management and clinical diagnostics are two significant problems in the medical field and have a constructive effect on the public health system. Diabetes mellitus (DM) is a typical metabolic disease; people face an increased level of blood sugar (Persistent hyperglycemia). It has an impact on many parts of the body including the heart, kidney,eyes, foot, skin, etc. Several compey Diabetes Diagnosis Systems (DDSs) use various Machine Learning (ML) techniques for deriving useful information from clinical datasets for DDSs and the disease. However, trapping into local optimum solution, absence of privacy, missing value in input dataset, and deficiency of incremental classification are main limitations associated with conventional diabetes classification algorithms based on ML.
On Solving Generalized Quadratic Diophantine Equation Dx^2+y^2=(D+λ^2 )^n
Authors: R. Krishna
Published: January 2025

Diophantine Equations named after ancient Greek mathematician Diophantus, plays a vital role not only in number theory but also in several branches of mathematics. In this paper, we have solved specific class of quadratic Diophantine equation and obtained its complete solutions. As verification, we have solved some of its particular cases.
Integrating UX Design Principles in AI-Driven Systems: Enhancing User Experience in Emerging Technologies
Authors: Prasad Raghunath Mutkule
Published: January 2025

The paper proposes a comprehensive methodology for integrating UX design principles into AI-driven systems to enhance user experience in emerging technologies. The approach emphasizes a user-centered, iterative development process that incorporates advanced prototyping tools, continuous feedback loops, and collaboration between UX designers and AI developers. By focusing on refining user interfaces through detailed analysis of user interactions and behavior, the proposed methodology ensures that AI systems are not only functional but also intuitive and accessible. Key elements include leveraging machine learning models for personalization, utilizing natural language processing to enhance communication, and implementing recommendation systems that align closely with user needs and preferences. Data from external APIs, historical records, and real-time user behavior further support the AI engine in making informed, user-centric decisions. Continuous feedback mechanisms play a pivotal role in refining both AI algorithms and UX components, allowing the system to adapt dynamically to user feedback and evolving requirements. This methodology aims to bridge the gap between advanced AI capabilities and user-friendly design, ultimately driving higher user engagement, satisfaction, and trust in AI-driven systems.
Utilizing The Max-Flow Min-Cut Theorem: Ford-Fulkerson Algorithm for Traffic Congestion Management
Authors: Vallabh Shinde
Published: January 2025

This research paper explores the application of the Ford-Fulkerson algorithm to analyze road traffic congestion in Kharadi Bypass, Pune a rapidly urbanizing city facing severe traffic management challenges. Traffic congestion has become a significant urban transport issue, resulting from the increasing number of vehicles and insufficient road infrastructure to accommodate the growing urban population. This article deals with the identification of maximum flow in the road traffic of Pune through the Ford-Fulkerson algorithm, which is well-established in graph theory. The study identifies critical nodes from key routes and intersections within the city, thereby delineating congestion causative nodes. The analysis reflects that for Pune, low road connectivity to a large extent in demand areas exacerbates congestion with significant levels of delay faced by the users of the roads, including pedestrians and public transport users. Moreover, potential solutions arising to alleviate congestion suggest the need for improvement of infrastructure through the laying of additional roads and the optimization of existing facilities. Results From this background, it is clear how important it is to have a systemic approach to the management of traffic in an urban scenario, and how efficient strategies for flowing traffic can improve transport network efficiencies as a whole in Pune. The research will contribute both to an understanding of traffic dynamics within the city of Pune and to offering some framework for similar urban centers with congestion problems.
Iris Detection and Authentication System Using Deep Learning Techniques
Authors: Yogita Deepak Sinkar
Published: January 2025

A biometric modality for individual identification, iris recognition is very reliable because human iris patterns are stable and distinctive. An overview of a unique deep learning method for iris recognition and authentication using convolutional neural networks (CNN) is provided in this abstract. The suggested approach provides a reliable and safe means of personal verification by utilizing Convolutional neural networks to extract complex information from iris pictures. The first step in the iris identification procedure is to get a person's iris image, which is usually done with the use of specialist iris imaging equipment. To improve the clarity of the iris pattern and reduce noise, the obtained iris picture is pre-processed. To generate a uniform template for additional processing, the iris region is then separated and isolated from the remainder of the picture. Using Convolutional neural network deep learning for iris detection has several benefits, such as high accuracy, resilience to spoof attacks, and adaptability to changes in illumination and pupil dilation. Applications like financial transactions, border security, and secure access management are just a few of the areas in which technology excels.
Empirical Analysis of Healthcare Supply Chain Risk Assessment using KPIS and PLS-SEM
Authors: Gajanan Padmane
Published: January 2025

This current research uses the PLS-SEM methodology with key performance indicators to carry out the empirical analysis in healthcare supply chain risk assessment. Its purpose is to understand how different supply chain responsiveness, agility, and dependability affect overall risk management, quality, and cost in a healthcare business. The set of 25 KPIs was established and validated based on a systematic review, with data collected through an online survey among Indian healthcare supply chain specialists. In the present study, several theories about the benefits on total costs, quality, and agility related to supply chain responsiveness, agility, and cost management were examined. Hence, through the PLS-SEM analysis, the correlations' significance was confirmed, establishing how flexible supply chains and cost efficiency management contribute to quality enhancement and risk reduction within the healthcare environment.
UNET-Driven Water Body Segmentation for Enhanced Hydrologic Model Reliability and Water Resource Distribution
Authors: Archana Kadam
Published: January 2025

Accurately dividing up bodies of water is important for making hydrologic models more accurate. These models are very important for figuring out where and how water resources will be available. This study looks into how UNET-based models can be used to separate bodies of water. It does this by comparing how well three models work: CNN_UNET, EfficientNetB0_UNET, and MobileNet Lightweight UNET. The main goal is to find out how well and accurately these models work to make hydrologic model estimates better. The CNN_UNET model is the basis for this method; it provides a basic but useful framework for dividing areas of water. EfficientNetB0_UNET, on the other hand, uses advanced convolutional neural network (CNN) methods to improve the accuracy of segmentation by finding the best balance between model efficiency and complexity. Finally, the MobileNet Lightweight UNET model is used to see if it is possible to keep the model's accuracy high while reducing its processing needs. This would make it ideal for real-time use in water forecast. A dataset that showed different water body traits was used to train and test the models. The comparison of accuracy shows that the MobileNet Lightweight UNET model had the best training accuracy (81.46%) and validation accuracy (77.14%). The EfficientNetB0_UNET model, on the other hand, was only moderately accurate, with scores of 78.64% for training and 72.73% for validation. With a training accuracy of 74.61% and a validation accuracy of 71.96%, the CNN_UNET model had the worst result. The results show that the MobileNet Lightweight UNET model is the most promising for water body division in hydrologic applications. It has the best mix of accuracy and efficiency, which makes forecasts about water resources more reliable.
An Open-Source Approach to analyze eye Movements in CAD Users
Authors: Sachin S Kallurkar
Published: January 2025

This study focuses on analyzing eye-tracking data sought from mechanical engineering students to identify saccades and fixations using a Python-based algorithm for CAD software. The data was collected for eye movements using the Tobii Pro Nano eye tracker while students engaged in tasks that required intense visual and cognitive efforts with task using CAD software. The proposed algorithm processes the raw gaze data to pinpoint saccades and fixations, shedding light on students' visual patterns and cognitive load. The results highlight the algorithm's capability to differentiate various eye movements, which is crucial for assessing eye fatigue and productivity. This research offers a valuable tool for real-time analysis of visual attention and cognitive load in educational settings, enhancing our understanding of how students interact with visual tasks which affects the fatigue and productivity of the CAD users.
Disc-Cup Aware Ensemble Network (DCE-Net) for Glaucoma Detection
Authors: Deepti Sahu
Published: January 2025

Glaucoma is that serious eye disease which causes serious impairment of visual acuity and it causes permanent blindness in the advanced stages of this disease by damaging the optic nerve head. Prevention of such permanent blindness depends totally on proper detection and timely treatment of glaucoma at its early stages. To diagnose this retinal diseases, retinal fundus imaging has been used in recent years. As the careful analysis of such images is challenging, identifying the region of interest in fundus images like optic cup (OC) and optic disc (OD) is difficult because of anatomy and vascular patterns. Among all the many structural markers, the most useful in the diagnosis of glaucoma is the ratio of the OC to the OD known as the CDR. Manual testing of the fundus images is time-consuming and relies on the skill and interpretation ability of the clinician. This paper proposed an automated ensemble model DCE-Net for glaucoma detection through deep learning techniques YOLOv10 and modified DeepLabV3. The YOLOv10 object detection model was utilized for the detection of the OD and the OC, whereas the modified DeepLabV3 used for the pixel-level segmentation. The final segmented masks produced by the modified DeepLabV3 were applied to the computation of the CDR as a glaucoma indicator. This model evaluates performance on 3703 images achieved from thirteen publicly available fundus image data and obtains 98.67% accuracy and 98.84% F1 score.
Nanomaterial-Enhanced Sensors for Biomarker Detection: Improving Accuracy and Feasibility for Point-of-Care Early Detection Devices
Authors: Tanya Arora
Published: January 2025

The diagnosis at the early stage enhances the patient’s outcome, influences treatment efficacy, and reduces the cost of health care. Nanomaterial-enhanced sensors represent an important step in developing point-of-care diagnostic devices highly sensitive and specific for disease biomarkers. This paper presents an overview of the research studies on how the introduction of nanomaterials like gold nanoparticles, carbon nanotubes, graphene oxide, and quantum dots has already helped advance biosensor research by greatly improving the sensitivities and feasibility of portable point-of-care devices for the early onset and detection of diseases like cancers, cardiovascular diseases, and infectious diseases. This paper thoroughly explores the integration of diverse nanomaterials, such as gold nanoparticles, carbon nanotubes, graphene, and quantum dots, with biosensing technologies, which have proved surprisingly effective at improving biosensor performance, particularly in respect to the detection of nucleic acids, proteins, and metabolites associated with early stages of disease. We then go on to describe how nanomaterials functionalized biosensors contribute to the improvement of POC accuracies and feasibility, such as real-time, label-free, and multiplexed biomarker detection. Other challenges the field currently faces that we outline include the scalability of nanomaterials for production, the stability and biocompatibility of sensors, and considerations related to regulation. We also emphasize areas of future directions in this regard toward the elimination of barriers. Microfluidic integration with nanomaterial-enhanced biosensors is one promising avenue for creating compact, user-friendly POC devices that can be applied either in clinical settings or resource-limited venues. This article provides an overview of recent advances aimed at emphasizing how nanomaterials may revolutionize diagnostic technology in advancing early disease detection and personalized health care.