bluuebunny/crossref_metadata_2025 huggingface dataset:
Security Protection of Industrial Cyber Security with IIoT Through Zone Function Modeling Algorithm
Authors: Basi Reddy.A
Published: January 2025

The convergence of cyber and physical domains is a burgeoning phenomenon inside conventional industrial systems, with the objective of augmenting adaptability and effectiveness in the realms of surveillance, management, and regulation. The integration of industrial cyber-physical systems (ICPSs) on a large scale is associated with an increased susceptibility to security vulnerabilities. The identification of attacks is an essential element of the initial protective barrier and carries substantial significance within the wider framework of security safeguarding. Currently, there is a lack of a data-driven approach to evaluate the risk of SCADA software for IIoT devices, despite the clear need to understand the cyber threat to urban critical infrastructure. Statistical models are developed with the purpose of determining risk metrics for SCADA systems, which may be employed to evaluate the probability of exploiting vulnerabilities connected with SCADA. We have developed a customizable schema for ranking SCADA hazards based on our study findings. The security community may employ this schema to improve their understanding of hazards particular to SCADA. In order to ascertain the effectiveness of the proposed approach, a laboratory apparatus is ultimately developed. The results suggest that the suggested approach provides a solution characterized by a notable degree of precision, while also demonstrating efficient performance in real-time situations.
The T-Generalized P - K Wright Function and its Properties
Authors: Arvind Maharshi
Published: January 2025

In this paper, we introduce t-Generalized p-k Wright function (_r^(t,p))Ψ_s^k  and discuss about its convergence condition. We obtain functional relation between t-Generalized p-k Wright function, Generalized p-k Wright function and generalized Wright function and some special cases have also been discussed. We obtain integral representation of t-Generalized p-k Wright function and discussed its properties.
Invariant Submanifolds of Generalized Sasakian-Space-Forms
Authors: Sagarika Nandy
Published: January 2025

The new characterizations of invariant submanifolds of generalized Sasakian-space forms (SSF)   in terms of their behavior with respect to the various curvature tensors are obtained in this work. By examining the connections between these submanifolds' second fundamental form σ and certain curvature tensors Wi with i = 2, 3, 4, 6, 7, we derive necessary and sufficient conditions for their geodesicity. We show that total geodesicity corresponds to the claim that tensor products vanish, Q(σ, Wi) = 0, subject to different non-degeneracy conditions for each curvature tensor. The essential characterization comes from the W6 curvature tensor, which suffices to fulfil 2n(f1−f3)≠0, and all other tensors lead to complementary constraints concerning the structural functions f1, f2, and f3. These findings offer various avenues for studying the geometric nature of invariant submanifolds and enhance our insights into the behaviour of generalized Sasakian-space-forms.
Gaps in Access to People's Business Credit Financing for MSEs in Depok, West Java Experimental Design Approach
Authors: Irma Setyawati
Published: January 2025

This research investigates the challenges encountered by micro and small enterprises (MSEs) in Depok, Indonesia, in accessing the Kredit Usaha Rakyat (KUR) program and explores how these obstacles impact poverty reduction and income distribution. Employing an experimental design, the study focuses on key factors affecting financial access, including MSEs' comprehension of KUR, business age, financial reporting practices, and the adoption of technology in marketing. A total of 147 MSEs were divided into treatment and control groups, where the treatment group received targeted interventions through Focus Group Discussions (FGDs). Data was collected both before and after the intervention to assess its impact on KUR access. The findings reveal a marked improvement in KUR access for the treatment group, particularly in terms of enhanced understanding of KUR procedures and significantly improved financial reporting capabilities. Additionally, the increased adoption of technology in marketing was found to positively influence financial access. Regression analysis demonstrates that factors such as KUR understanding and financial reporting practices have a statistically significant effect on financial accessibility, while business age exerts minimal influence. Conversely, the control group did not exhibit any substantial changes in KUR access, highlighting the efficacy of the intervention. In conclusion, this study emphasizes that structured interventions, such as FGDs, are effective in strengthening MSEs' ability to secure financing through KUR, which is pivotal for poverty alleviation and economic inclusion. The results underscore the necessity for ongoing training and support programs to sustain MSEs' growth and financial stability
The Evaluation Model of School Literacy Programme at the Mover Senior High Schools
Authors: Sapta Mupakat Tatar Purba
Published: January 2025

This study aims to evaluate the implementation of the school literacy movement (SLM) program using modified CIPP program evaluation components (Context, Input, Process, and Product) and the Discrepancy Evaluation Model. Using the qualitative approach, the focus of this research is to evaluate the implementation of the SLM at four mover senior high schools in the province of Jakarta. The results showed a gradual increase of student and teacher involvement. In the initial period, student participation was very low, but after some time, there was an increase in interest in reading, although the final results were not evenly distributed across students. Some schools succeeded in improving students’ literacy achievement while others still needed to be strengthened in implementation. At the end of the evaluation period, the SLM program at the mover schools showed significant progress. This research has a theoretical impact on the field of education management, particularly in designing the SLM management model and the establishment of rules related to the use of gadgets at the mover senior high schools in the province of Jakarta. This research also has a practical impact as a guideline in making regulations related to the SLM for the relevant education authorities.
A Non-linear analysis of Wall Putty and ConcreteCubes for Self-Cleaning usingTitanium Dioxide
Authors: A. Hemamathi
Published: January 2025

Concrete is the most common construction materials for building construction. However, in urban region the surface of concrete is contaminated due to air pollution.  Self-cleaning construction materials have the ability to clean themselves by breaking down and removing dirt, pollutants, and other contaminants without the need for human intervention. Photocatalytic materials such as titania (TiO2) and zinc oxide (ZnOgenerally possess self- cleaning properties in it and is introduced in this study. This paper reviews the Non-linear analysis ofTitania and Zinc oxide on concrete and wall putty. The optimum dosage of Titania is investigated and the its periodic absorption is noticed to check the removal of dirt from the surface of concrete. The same principle is applied on wall putty panel. The results of the study showed that TiO2 can be used upto 10%. Increase in dosage decreased the tensile strength of finished putty layer.
Efficient Edge Chromatic Number Calculation Using Python:
Authors: C. Paul Shyni
Published: January 2025

Graph coloring is one of the most important concept in graph theory and is used in many real time applications. Graph coloring includes, vertex coloring, edge coloring, region coloring etc., of graphs. Proper edge coloring means, coloring of edges of a graph such that no two adjacent edges have the same color. Edge chromatic number of a graph is the minimum number of colors required for proper edge coloring of the graph and is denoted by(G). In this article, GEC algorithm is proposed for finding the edge chromatic number of a graph with computer coding for the same using python. This proposed computer algorithm is explained with an example and it helps to understand the execution process of python to find the edge chromatic number of a graph.
Disease diagnosis in Cassava leaves using CNN design and ResNet algorithm
Authors: M L Sneha Snigdha
Published: January 2025

Introduction: Growth rate of crops is significantly lowered by illnesses that affect plants. It is impossible for anyone to eat the crops since they are tainted with various diseases. Farmers may suffer enormous losses as a consequence. Since cassava is an important food source in several countries, the financial system might be seriously damaged by the issue at hand.
Objectives: Traditional plant pathogen detection is labour-intensive and error-prone. It is not typically a dependable strategy to identify and stop the spread of plant viruses. Innovative technologies like deep learning as well as machine learning might aid in the early detection of plant diseases as an approach to get around these problems.
Methods:  The main goal of the work is to employ deep learning to image classification in order to accurately identify diseases that especially impact cassava plants.
Results: This recognition may make it possible to implement preventative measures like the specific application of chemical pesticides or confinement of contaminated crops. Each and every training and testing image comes from a rural area in the natural world. Using a specific collection of information, the simulation has been verified to ascertain its true results.
Conclusions: The installation of a precise disease identification and mitigation model has the potential to significantly increase the durability of the cassava crop, improving food production and the quality of life for millions of people who are dependent upon this valuable crop.
Normal BE-Algebras
Authors: M. Bala Prabhakar
Published: January 2025

In this present article we propounded the idea of normal BE-algebras, derived some significant properties of normal BE-algebras and obtained a set of equivalent conditions indicating when a normal BE-algebra assumes the characteristics of an involutory BE-algebra. Some sufficient conditions for a BE-algebra to become a normal BE-algebra are derived. Also, congruence relation is introduced on a normal BE-algebra.
Exploring Advanced Stability of Higher-Order Functional Equations in Neutrosophic Normed Spaces via Hyers-Ulam Methodologies
Authors: P. Agilan
Published: January 2025

In this article, focuses on examining the stability of higher-order functional equations within the framework of neutrosophic normed spaces, which incorporate elements of uncertainty and indeterminacy. By utilizing the Hyers-Ulam method, the study investigates how small perturbations in the functional equations impact their solutions. The research extends classical stability theories, such as Hyers-Ulam stability, into the neutrosophic normed space context, providing a broader understanding of how functional equations behave under uncertainty. The findings offer significant contributions to the field of functional equations and neutrosophic mathematics, opening up new pathways for applications in areas that require the handling of imprecise data.
Hybrid Deep Learning Framework for Real-Time Source Code Vulnerability Detection
Authors: Gunda Brahma Sagara
Published: January 2025

Source code vulnerabilities threaten software security, making detection essential in modern development. Traditional methods like static and dynamic analysis often fail due to high false positives and limited scalability. This work introduces a hybrid deep learning framework using CNNs, LSTMs, and code embeddings to detect vulnerabilities in real time. Incorporating Abstract Syntax Trees (ASTs) and Graph Neural Networks (GNNs), the system ensures structural representation and program semantics analysis. Integrated into CI/CD pipelines, the approach improves precision, recall, and F1-score (up to 96.25%) while reducing false positives and delays.
Applied Nonlinear Analysis of Machine Learning Models for Education Streams: A Mathematical and Statistical Perspective
Authors: Swapnil B. Mohod
Published: January 2025

The application of nonlinear analysis in machine learning (ML) has emerged as a significant approach to understanding and enhancing educational systems. This research explores the integration of nonlinear mathematical and statistical models in various education streams to identify patterns, trends, and opportunities for improvement. By leveraging ML algorithms such as neural networks, decision trees, and support vector machines, the study examines complex relationships and dynamics that linear models often fail to capture. Key areas of focus include predicting student performance, optimizing curriculum design, and identifying at-risk students. The study also investigates the role of advanced statistical techniques such as regression analysis, chaos theory, and fractals in interpreting large-scale educational data. The findings highlight how nonlinear techniques can address challenges in data heterogeneity, noise, and multidimensionality, thereby enhancing decision-making processes in educational management. This paper aims to provide a robust framework for implementing nonlinear ML techniques, offering actionable insights for educators, administrators, and policymakers.
Fault Detection in Wireless Sensor Networks Using Horse Herd Algorithm and Convolutional Neural Network with Attention Layer
Authors: Ridha Mohammed Alfoudi
Published: January 2025

Reliable and real-time detection of faults in Wireless Sensor Networks (WSNs) is significant for the ongoing flow of critical data despite being a strenuous task. In this article, we present a comprehensive WSN-suitable fault detection system as a solution to this challenge. The primary step involves a thorough pre-processing of data such as partitioning, data cleaning, reordering of sample windows, and normalization through the min-max technique. These steps are fundamental to preparing the dataset, while also assisting systematize the proposed solution. A Horse Optimization Algorithm (HOA) integrated with a Convolutional Neural Network is at the heart of the method. As such, the CNN is able to capture highly advanced spatial and temporal features embedded in the processed data, as its convolutional layers are proficient in pattern extraction. Further, hyperparameter optimization of CNN learning rates, batch sizes, and the total number of convolution layers is performed using HOA, to improve the CNN performance. WSNs can greatly benefit from this, with the proposed model identifying faults with 99.47% accuracy on the test dataset, and 99.63% accuracy on the training dataset. These results show the proposed method’s effectiveness and accuracy in addressing fault detection issues in WSNs towards enabling better stability and performance within the network.
A Single Detection And Diagnosis Algorithm For Electrical Faults in a Five-Phase.Permanent.Magnet Synchronous Motor Drive
Authors: Sabna M.
Published: January 2025

In all processing and manufacturing industries, approximately half of the operating cost is contributed to the maintenance process. Due to high reliability, and fault-tolerant capability, five-phase Permanent Magnet Synchronous Motors (5ϕ-PMSM) are commonly used in high-power and fault-tolerant applications. Early-stage detection and diagnosis of faults can reduce maintenance costs. This paper proposes a single algorithm for detecting and diagnosing electrical faults such as inter-turn short circuit faults, phase-to-phase faults, phase-to-ground to ground faults, and open circuit faults in a 5ϕ-PMSM drive. The discrete wavelet transforms and statistical parameters extract the fault features from the normalized stator currents under normal and faulty conditions. A fuzzy logic system is adopted to diagnose electrical faults and faulty phases. Since the algorithm uses normalized stator currents for fault detection and diagnosis, it can be used for detecting and diagnosing electrical faults in 5ϕ-PMSM drive with any capacity. The time of fault detection and diagnosis process is less than two cycles of stator current. Finally, the proposed algorithm is experimentally validated using Raspberry Pi.
Green Fuel Synthesis from Jatropha curcas Seed Oil: A Computational Approach using Python Programming Language
Authors: Sangeeta D Benni
Published: January 2025

The depletion of fossil fuels and environmental concerns in the recent era has gained significant attention in the production of biodiesel from non-edible seed oils as an alternative fuel. Biodiesel is one of the renewable fuel to replace fossil fuel. The synthesis of biodiesel from the seed oils is time consuming and costly. In order to predict new seed oils in biodiesel synthesis, several studies have been conducted to suggest the advantages of artificial intelligence in the field of producing green energy. The fourth industrial revolution is thought to be fuelled by artificial intelligence (AI) and machine learning algorithms, which can understand complicated issues with non-linear correlations. In the present study attempt has been made to predict the fuel properties of Jatropha curcas seed oil using python programming language. The fatty acid composition required for calculation is derived from literature. This code can be preserved and used multiple times to analyse the fuel properties which is time saving. The values obtained are well within the range of ASTM and EN standards.
Artificial Intelligence in Retail Marketing: Optimizing Product Recommendations and Customer Engagement
Authors: M. Sivasankari
Published: January 2025

Artificial Intelligence (AI) has revolutionized the retail marketing landscape by enabling businesses to optimize product recommendations and enhance customer engagement. One of the most effective AI methods in this domain is Collaborative Filtering, a machine learning technique that analyzes user behavior and preferences to generate personalized product recommendations. Leveraging tools like Amazon Personalize, retailers can deliver tailored shopping experiences, improve conversion rates, and foster customer loyalty. This approach combines advanced data analytics with real-time insights to predict consumer needs accurately and ensure seamless interactions. The integration of AI-powered solutions allows retailers to stay competitive by aligning offerings with customer preferences, thereby driving revenue growth and satisfaction.
Design and Implementation of a Bluetooth-Controlled Robotic Arm for Efficient Pick-and-Place Operations
Authors: Dilip S. Choudhari
Published: January 2025

The advancement in Robotics and Automation, particularly in IoT, has led to the development of Bluetooth-operated robotic arms. These arms perform tasks like pick and place, reducing human fatigue and error. Two control strategies, force, and motion control, enable precise operations. By using smartphones with Bluetooth connectivity, users can control the arm remotely. The Bluetooth module, like HC05, facilitates this connection. Such arms can handle hazardous materials or tasks in inaccessible areas. These arms offer flexibility and can be reprogrammed for various tasks. Designed using Arduino Uno and four servo motors, they are controlled via a mobile app, ensuring user-friendly operation. The system, modeled using CAD software and 3D printing, enhances safety and efficiency in industrial setups. It enables remote control and precise movements for tasks like handling munitions.
An Efficient Medical Image Compression using Modified Set Partitioning in Hierarchical Trees (MSPIHT)
Authors: Julian L. Webber
Published: January 2025

Set Partitioning in Hierarchical Trees (SPIHT) is an effective image compression algorithm. SPIHT is a unique approach which transmits the values of the pixels indirectly to the coordinates. The packet coders in the images present a universal transform the framework in a constriction of the filter bank schema which is attained by the Space Frequency Quantization (SFQ) and the effective compression is achieved by the Huffman coding. The proposed scheme permits transformation in the joints and alterations in the discrete magnitude of the input image. Hence, coding performance is enhanced and the image is compressed without compromising the excellence of the image. MSPIHT is an enhanced algorithm that fits the requirement of medical image compression for storage and transmission of medical data. MSPIHT improves on the standard SPIHT in the encoding operation of wavelet coefficients to provide high quality images at low bit rates important in medical applications where accuracy is paramount. The modification is aimed at changing the set partitioning strategy by minimizing memory consumption rates, thus improving the compression ratio and speed. It also may be used at telemedicine where high image compression rate is required for the quick and efficient image transfer for diagnostics and consultations. Experimental results shows that the image compression scheme is improved with the SPIHT-SFQ based Huffman Coding scheme and it outperforms the current image compression algorithms.
Deep Learning Driven Heart Disease Prediction using ECG Signal Classification
Authors: Kayam Saikumar
Published: January 2025

The non-stationary motivations of the ECG structures are significant readings to estimate the pace and rhythm of the heartbeat and therefore to estimate the health of the heart. This paper proposes a robust method for automatic ECG classification, segmenting data into two categories: variations in normal and pathological cardiovascular patterns. More specifically, the collected ECG signal is normalized, filtered to eliminate noise interference. Hence morphological attributes are formed to represent the feature of the classes to the best of their ability. ACO is incorporated into segmentation and feature selection to reduce feature vectors of abnormalities. The classification of ECG signals is made through using a Bi-Long Term Short Term Memory (Bi-LSTM) neural network because it is a network proven for sequence prediction and it is capable to capture long temporal dependence in the data. Evaluation criteria show that, relative to other proposed classifications of ECG, the presented approach reaches 90% classification accuracy. It has been established that the integration of ACO with Bi-LSTM provides improved classification of ECGs thereby making it a useful tool in clinical diagnosis and therapy monitoring.
Optimizing Gender and Age Group Classification through Spatial Domain Analysis of Fingerprint Features
Authors: Ashwini Sanjay Gaikwad
Published: January 2025

Biometric systems, particularly fingerprint recognition, have undergone significant evolution, extending beyond individual identification to encompass gender and age group recognition. This paper introduces a Spatial and Frequency Domain Fusion Approach for Gender and Age Group Recognition using fingerprints. The proposed algorithm integrates ridge segmentation, normalization, orientation, and frequency estimation, enhancing ridge features through Gabor filtering. Classification utilizing Support Vector Machines (SVM) results in robust gender and age group recognition. Application of the algorithm to both old and revised datasets demonstrate high accuracy, achieving 91.21% and 98.05%, respectively, in combined gender and age group classification. The approach addresses the limitations of existing systems, contributing to the advancement of fingerprint-based biometric technology.
Mathematical Modelling and Propagation of Infectious Diseases in Confined Areas
Authors: Srinivasarao veerla
Published: January 2025

A mathematical model for the propagation of infectious diseases has been discussed in this paper.  The model that has been examined is basically SIR model. The governing equations have been solved to the best possible solution.  The influence of critical parameters over the system has been discussed with the graphical illustrations. It  is observed that as the transmission (β) increases the sensitive population and, with the duration increasing, the vulnerable population diminishes and stays constant and is time-independent. Further, it is observed that as the rate of infection increases the susceptible population tens to zero. In addition to the above as  rate of recovered individual (R) increases the susceptible population (S) decreases. The phenomena  is an agreement with the real life situation. In addition to the above as rate of recovered individual increases the susceptible population decreases.
Improving Sentiment Classification and Fake Video Detection of YouTube Videos Using Custom Metadata and GAN Model
Authors: Shraddha Kalbhor
Published: January 2025

With the rapid increase in user-generated content, YouTube faces a significant challenge in managing the vast amounts of data, including the proliferation of fake videos published online in a short span of time. Effective sentiment analysis and fake video detection are crucial for understanding user engagement, preferences, and maintaining the platform's integrity. This research aims to improve sentiment classification and fake video detection for YouTube videos using custom metadata and Generative Adversarial Networks (GANs). The proposed methodology focuses on extracting sentiment from user comments and custom metadata associated with YouTube videos. A multi-stage process is employed that includes custom data creation, data preprocessing, feature extraction, and sentiment classification. By integrating domain-specific features and contextual information, our aim is to improve the accuracy and relevance of sentiment analysis. The proposed approach not only facilitates more efficient organization and categorization of YouTube content but also provides valuable insights for detecting fake videos, even with a limited number of user comments. Experimental evaluations on diverse datasets, including a custom dataset and the publicly available YouTube US video dataset, demonstrate significant enhancements in performance compared to existing methods. To further boost model performance, the GANs model is used which performs data balancing and leads to notable improvements in both recall and F1-score metrics, showing an increase of approximately 5% to 6% across all machine learning models. This research addresses the critical need for reliable sentiment analysis and fake video detection in the ever-expanding digital content landscape.
Quantitative Analysis of Natural Calamities for Disaster Management through Machine Learning Techniques Using Satellite Data
Authors: Nita Nimbarte
Published: January 2025

Geographic information and Remote sensing systems have been used in recent years by disaster platforms for calamity warning and preparedness for earthquakes, landslides, and floods to aid in the assessment and management of catastrophic risk. In the realm of technology, it has also received a lot of attention. It would not be possible to use sensory data without the appropriate technology for organizing enormous amounts of data and obtaining information from numerous sources, such as maps or measurement channels. This study uses machine learning techniques to locate and evaluate locations damaged by landslides, earthquakes, floods, avalanches, and wildfires. After applying filters to improve the quality of the images, the images are classed using both supervised and unsupervised methods after being segmented using a thresholding technique. To analyze devastation, images from before and after disasters are collected from MRSAC Nagpur and processed with Python-based tools, ArcGIS, ERDAS, and QGIS.
A Multiple Mechanism for Identifying Green Land Cover Using Auto Encoder in Satellite Image Processing
Authors: Ravindra S. Tambe
Published: January 2025

The automatic identification of green land cover zones in satellite images may be crucial for preserving ecological balance and guaranteeing environmental safety after deforestation. With this research, we hope to answer the demand for an automated tool that may serve as an assistive ecosystem service provider by developing a novel two-stage framework for identifying green land cover in satellite images. The framework is specifically designed to tackle the challenges related to the expanding application of automated computer vision-based frameworks for green land cover detection based on satellite imagery. Initially, distinct kinds of chromatically homogenous land cover areas in the satellite picture are segmented using a clustering approach. To isolate only green land cover clusters from other clusters that signify water bodies, arid regions, built-up areas, etc., an auto encoder model is constructed in the second stage using a range of green land cover samples. The proposed framework shows an impressive over 97.5% detection accuracy even with a small experimental dataset, which is comparable to the results of widely used Mask R-CNN based segmentation techniques.
Evaluating the Impact of Genomic and Clinical Features on Breast Cancer Prognosis Using Deep Learning Approaches
Authors: Poonam R.Dholi
Published: January 2025

Breast cancer prognosis plays a crucial role in tailoring personalized treatment plans and improving survival rates. This research evaluates the impact of genomic and clinical features on breast cancer prognosis prediction using deep learning techniques, with a specific focus on the METABRIC dataset. The study utilizes a gene expression feature set of 300, 350, and 400 features, selected using the Minimum Redundancy Maximum Relevance (mRMR) algorithm to reduce feature redundancy while retaining high-relevance predictors. A deep neural network (DNN) model was employed to assess the predictive power of the selected features. The architecture was designed to handle the integration of these genomic features efficiently, optimizing for both performance and interpretability. The research also investigates the influence of different optimizer-activation function pairings on model performance, examining how these combinations affect the training stability, convergence, and accuracy of the model. Results were evaluated using a variety of performance metrics, including accuracy, precision, recall, F1 score, and AUC-ROC, providing a comprehensive assessment of the model’s ability to predict breast cancer prognosis. Comparisons between the feature sets showed that the inclusion of additional gene expression features (400 features) slightly improved model performance, though diminishing returns were observed beyond a certain threshold. The best-performing model utilized the Adam optimizer paired with the ReLU activation function, which offered superior convergence rates and robust performance across all metrics. This study highlights the potential of combining genomic data with advanced feature selection techniques and deep learning models to improve prognosis prediction in breast cancer. The findings suggest that a careful balance of feature selection and model optimization is crucial for achieving reliable predictions, with important implications for integrating such models into clinical decision-making systems.
Advancements in Speech Recognition: A Comprehensive Survey of Machine Learning Techniques with a Focus on GAN-AE Integration
Authors: Mandar Pramod Diwakar
Published: January 2025

The utilization of speech recognition technology assumes a critical role in contemporary applications, encompassing virtual assistants and transcription services. This thorough evaluation paper examines the present state of voice recognition with a specific emphasis on machine learning methodologies, particularly the integration of Generative Adversarial Networks and Autoencoders (GAN-AE). The paper presents a comprehensive analysis of methodologies including supervised and unsupervised learning as well as deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Deep Neural Networks (DNNs). It accentuates the obstacles encountered within the field such as the robustness to noise, scarcity of data and real-time processing while proposing innovative solutions that leverage GAN-AE.  The significance of GAN-AE is emphasized through practical case studies that showcase its efficacy in various applications ranging from augmenting voice assistants to adapting to different accents and limited data scenarios. The findings underscore the potential of this technology to enhance accuracy and adaptability across diverse domains. Additionally, this document recognizes potential avenues for future exploration, proposing an investigation of the incorporation of voice recognition with other modalities as well as addressing ethical considerations regarding prejudice and privacy. In summary, this survey paper contributes to the ongoing discourse on speech recognition by providing valuable insights, novel solutions, and directions for future research, all in the pursuit of more precise and responsible technology.
Investigation of Pumped Storage Hydro Power Plant
Authors: Vaibhaw R Doifode
Published: January 2025

Designed to meet the increasing energy demand and provide a sustainable alternative for energy storage and generating, the Ghatghar Pumped Storage Plant (PSP) is a historic hydroelectric project. Operating since 2008, the plant guarantees effective and quick construction by employing an inventive dual-reservoir system with an upper and lower dam built using Roller Compacted Concrete (RCC) technology. The 250 MW facility uses cyclical water transfer to store energy during off-peak hours and produce electricity at peak demand, therefore using the ideas of pumped storage. Modern elements include Francis turbines, steel-lined pressure shafts, and an underground powerhouse with sophisticated generator units and transformer systems abound in the architecture of the facility The building of the project created major difficulties including the relocation of settlements and exact engineering of water conductor systems, tail race tunnels, and ancillary buildings. Acquired overall 320.096 hectares of land, reconciling social and environmental concerns with development demands. Faster, less expensive, and high-quality dam building was made possible by RCC technology and specialized equipment like stacker conveyors and high-pressure water blasters. During peak demand, the plant runs six hours daily producing 1.5 million units (MUs) of electricity and yearly grid contribution of 469.5 GWh. It is a vital part of energy load control as it pumps water for seven hours daily during off-peak times using more than necessary power. Beyond just its technical prowess, the Ghatghar PSP demonstrates how well sophisticated engineering, sustainable energy sources, and social responsibility are incorporated. This research emphasizes how important pumped storage facilities are to solve world energy issues, promote grid stability, and enhance renewable energy sources complementing each other. Modern energy systems are modeled after it, which strikes a mix of sustainability, economy, and community effect.
Advancing Web Security: Machine Learning-Based Attack Detection with Optimized Features
Authors: Sainath Patil
Published: January 2025

Web applications remain highly susceptible to cyberattacks despite efforts to mitigate these threats through initiatives like the OWASP Top 10. This research addresses the critical challenge of improving web attack detection by integrating advanced feature extraction techniques and machine learning (ML) models. A novel approach was developed, consisting of three main contributions: the design of a testbed attack network to simulate real-world scenarios, the application of a wrapper-based feature extraction method combining mutual information (MI) and genetic algorithms (GA) to select pertinent traffic features, and the implementation of a super learning ensemble model for robust attack detection. The feature selection method extracted features included critical traffic attributes, which significantly enhanced the model's detection capabilities. The proposed ensemble-based super learner model achieved a remarkable accuracy of 99.12% with a reduced prediction time of 125 milliseconds. Compared to conventional ML models, the proposed model demonstrated a 26% improvement in detection accuracy and a 99% reduction in prediction time, making it highly efficient for real-time web attack detection.
Face Identification based on different Convolutional Neural Network Models for Security System: A Review
Authors: Reena Sharma
Published: January 2025

Computer based authentication system is reliable and mature in technology driven world.  When this authentication is based on biometrics then it is more secure. Face is most prominent identifier in biometric system. Many challenges have come in recognition process when environment is unconstrained. In this paper, different pre trained deep learning models like: VGG16, VGG19, ResNet50, MobileNetV2 are applied to the LFW dataset for Face Recognition. These models are pre trained on ImageNet dataset and for LFW dataset, we achieved the accuracy for fine tune model 73.51%, 74.70%, 87.76%, 90.49% respectively. It has been observed that highestaccuracy has been found by the MobileNetV2which is pre trained on ImageNet dataset.
Hybrid Framework for Advanced Ocular Disease Diagnosis
Authors: Aditya Patil
Published: January 2025

Ocular diseases, a leading cause of vision impairment globally, necessitate early, accurate diagnosis for timely intervention. Conventional methods often face limitations in sensitivity and specificity.
To address this challenge, we propose a novel hybrid framework that synergistically integrates deep learning and traditional machine learning techniques. Our approach leverages the power of deep neural networks to extract intricate features from ocular images, while incorporating the robustness of traditional algorithms for enhanced classification.
The proposed framework achieved exceptional performance, surpassing existing methods with an average accuracy of 95.7% across eight common ocular diseases. This significant improvement demonstrates the potential of our approach to revolutionize ophthalmic diagnostics. Our findings offer valuable insights for future research and clinical practice, paving the way for more accurate and efficient detection and management of ocular diseases.
Coati Optimization Algorithm for Enhanced Energy Management in AC/DC Hybrid Microgrids
Authors: Saurabh V Kumar
Published: January 2025

The Coati Optimization Algorithm is introduced as a novel approach for optimizing energy management in AC/DC hybrid microgrids, addressing the increasing complexities of integrating renewable energy sources and electric vehicles. This research highlights the algorithm's capability to enhance optimal power flow by efficiently managing renewable energy uncertainties, thus ensuring stability and reliability within microgrid operation. Extensive simulations demonstrate that the enhanced coati optimization not only outperforms traditional methods in achieving energy efficiency but also reduces operational costs. The algorithm's nature-inspired mechanism allows for rapid convergence and improved solution accuracy, showcasing its adaptability to varying energy demand scenarios. This study contributes to advancing the field of smart grid technology, positioning the Coati Optimization Algorithm as a significant tool for facilitating sustainable energy management practices in future microgrid applications. Through comprehensive analysis and validation, the methodology paves the way for more resilient and efficient energy systems.
A Comparative Analysis of Large Language Models for English-to-Tamil Machine Translation with Performance Evaluation
Authors: SyedKhaleel Jageer
Published: January 2025

Machine translation is essential for cross-linguistic communication, especially for low-resource languages like Tamil, which presents challenges due to complex morphology, syntax, and cultural nuances. This study compares three advanced Large Language Models (LLMs) Claude, ChatGPT, and Gemini in translating English to Tamil, focusing on culturally sensitive, political, technical, and idiomatic content. Claude enhances coherence with prompt tuning, batch processing, and temperature control, proving effective in document-level translations. ChatGPT, using a decoder-only architecture and Reinforcement Learning from Human Feedback (RLHF), aligns translations with human cultural and contextual expectations. Gemini’s transformer-based framework integrates advanced tokenization and attention mechanisms, preserving tense, gender, and politeness markers in Tamil, excelling in idiomatic and technical translations. The study employed a diverse corpus, including poetry, technical documentation, and conversational text. Translation performance was evaluated using BLEU, BERT-based metrics, METEOR, and Translation Edit Rate (TER). Results show that Gemini ranks highest in accuracy and precision, especially for complex syntax and idioms. Claude demonstrates fluency but has a relatively high TER. ChatGPT scores well in semantic alignment, with high BERT and METEOR metrics. This analysis highlights the capabilities and limitations of each model in translating Tamil, providing insights into LLM performance for low-resource languages.
Advances in Nonlinear Dynamics: A New Approach to Chaos Theory Applications
Authors: Podila Padmaja
Published: January 2025

The study of nonlinear dynamics and chaos theory has significantly influenced our understanding of complex systems across various scientific and engineering disciplines. This paper presents a novel approach to chaos theory applications, highlighting recent advances that extend its utility beyond traditional domains. By integrating modern computational tools and mathematical frameworks, we explore new methodologies for analyzing and predicting the behavior of chaotic systems. Key contributions include the development of enhanced algorithms for chaos detection, improved modeling techniques for nonlinear systems, and the application of chaos theory in emerging fields such as quantum mechanics, artificial intelligence, and climate science. Furthermore, this research underscores the potential of chaos-based approaches in solving real-world problems, including secure communications, energy optimization, and biomedical signal processing. Our findings demonstrate that the interdisciplinary adoption of chaos theory, coupled with advancements in computational capabilities, opens new frontiers for understanding and leveraging the inherent unpredictability of complex systems.
Advancing Neurodegenerative Disorder Diagnosis: A Multiclass Approach Utilizing Machine Learning on fMRI Data
Authors: Sanskriti Gupta
Published: January 2025

This study pioneers advancements in mental health diagnostics by integrating advanced machine learning techniques with neuroimaging to enhance the accuracy of classifying depression, Alzheimer’s disease, and normal controls using fMRI data processed with SPM12 software. The methodology involves extracting deep features from fMRI data through the VGG19 model, capturing intricate patterns indicative of mental health conditions. Subsequently, three machine learning algorithms are employed for classification, with k-nearest neighbor (KNN) emerging as the top performer, achieving an impressive accuracy of 98.73%. This highlights the robustness of the chosen classifiers in managing multiclass neuroimaging data.The findings underscore the efficacy of the proposed methodology, particularly the superior accuracy of KNN in distinguishing between individuals with depression, Alzheimer’s disease, and normal controls. A thorough evaluation of the classifiers sheds light on their respective strengths and limitations, offering a deeper understanding of their clinical applicability.This research introduces a novel approach that not only advances the field but also sets new benchmarks in neuroimaging-based diagnostics, promising a transformative shift in how machine learning is applied to mental health diagnosis.
“Festival de la arcilla” como práctica ciudadana activista del departamento del Huila
Authors: Ruiz Solórzano Jaime
Published: January 2025

El siguiente texto titulado “El ‘Festival de la arcilla’ como práctica ciudadana activista del departamento del Huila” hace parte de la continuación de un proyecto de investigación que busca establecer las relaciones entre las artes visuales y la violencia política. El propósito del escrito consiste en reflexionar sobre las experiencias que han tenido algunas comunidades para dinamizar las prácticas visuales correspondientes al activismo artístico ciudadano. Como situación problemática se busca indagar sobre los modos que tales activismos se visibilizan en el sur de nuestro país. Se trata de una investigación sobre el arte contemporáneo colombiano, fundamentada en los estudios sociales; por lo cual es un proyecto de carácter interdisciplinario.  La metodología aplicada versa sobre consulta de documentos, realización de entrevistas y la revisión de fotografías. El escrito concluye que las prácticas ciudadanas activistas corresponden a otras maneras de implementar el activismo social o los movimientos que buscan contradecir los parámetros culturales establecidos. Tales prácticas involucran a las personas y grupos generalmente segregados o invisibilizados por medio de la activa participación; al tiempo, se centran en una serie de planeaciones, gestiones y acciones de tipo etnográfico, en el cual los proyectos planteados y las actividades formativas/creativas se desenvuelven en, con y para grupos comunitarios. En suma, se puede decir que las prácticas ciudadanas activistas citadas lograron la organización comunitaria y crear arte con colectividades y en territorios por fuera de los marcos artísticos de visibilización departamental y nacional.
Diagnosis of bovine viral diarrhea in Bubalus bubalis dairy farms
Authors: Gavidia Oriana , Pérez-Gil Rosaura
Published: January 2025

Bovine viral diarrhea (BVD) is a disease with worldwide distribution and endemic in most bovine populations, causing a wide range of clinical manifestations, including reproductive disorders, such as embryo mortality and abortions, and low milk production. The purpose of this research was to describe the situation of BVD in Bubalus bubalis females from one dairy farm, in Portuguesa state, Venezuela. Fifty-two 52 samples were taken in August 2022. From these, 22 were from a low milk yield group (3.35 ± 0.30 liters/day) and 30 from a high milk yield group (6.91 ± 0.82 liters). The farmer reported fluctuations in milk production, weak calves, the presence of blisters in the oral cavity and dead animals. The ELISA test was performed to detect BVD p80 antibody using the IDvet ® kit, and the positive cases were defined using a cut-off value sample/negative ≤ 40. The seropositive results for the non-structural protein p80, in general, were 55.76%. In the group of buffaloes with low milk yield, 54.54% were positive, while in the group with high milk yield, 53.33% were positive, with no significant  differences between animal groups (p>0.05). These results show a humoral immune response against potential BVD infections (regardless of the milk yield) since p80 is a non-structural protein that manifests during viral replication. Therefore, it is suggested to implement sanitary plans for the control of BVDV to minimize the economic losses associated with the disease.