bluuebunny/crossref_metadata_2025 huggingface dataset:
Computational Analysis of Inventory Model for Non- Instantaneous Deterioration following Weibull Distribution with Quadratic Demand using Preservation Technology
Authors: Khyati
Published: January 2025

In this study, two different inventory models for products with quadratic demand that deteriorate slowly according to a Weibull distribution are presented and computationally analyzed. The first model investigates the conventional method without using preservation technologies while second model uses preservation technologies to lessen the consequences of deterioration. Comparing the overall inventory expenses of each model is the goal, with an emphasis on how much money can be saved by incorporating preservation technologies into inventory management procedures. The study shows, using analytical techniques and numerical simulations, that the preservation technology model effectively lowers overall costs by increasing the shelf-life of inventory items and reducing the adverse financial impact of product deterioration. The investigation verifies that spending money on preservation technologies improves inventory sustainability by cutting waste and lowers overall inventory expenditures. The present study offers significant perspectives for enterprises seeking to enhance their inventory management tactics. It highlights the concrete advantages of utilizing preservation technology to handle declining products in a demand-sensitive setting.
Machine Learning-Based Approach for ECG Analysis and Cardiac Anomaly Detection to Enhance Early Diagnosis and Treatment
Authors: Monali Gulhane
Published: January 2025

Cardiovascular diseases are still the most common cause of death in the world, so early detection methods need to get better. This study paper describes a complete machine learning-based method for analysing Electrocardiograms (ECGs) with the goal of making heart abnormal identification more accurate and faster. We used different ECG recordings as a dataset and a number of strong machine learning classifiers, such as Support Vector Machines (SVM), Random Forests, K-Nearest Neighbours (KNN), XGBoost, and LightGBM, along with new Autoencoders, to effectively find and label ECG abnormalities. The proposed method, ECG data are first normalised and noise disturbance is reduced. Next, features are extracted to find important diagnostic signs like changes in the QRS complex, PR gap, and ST segment. The models were trained and tested using these features, and the goal was to get the best detection accuracy by tweaking each model's parameters. Autoencoders were also used to pick up complex, nonlinear patterns in the ECG data. This made it easier to find small problems that older methods might miss. Findings show that while standard algorithms like SVM and Random Forests were pretty good at predicting the future, group methods like XGBoost and LightGBM were much better. It was KNN that helped prove the stability across data that hadn't been seen before. It's worth mentioning that the Autoencoders were very good at finding anomalies, especially when the ECG data was noisy. Our combined method greatly increased the number of early diagnoses, which suggests that it could be used in real life in systems that watch people's health all the time. The combining several machine learning methods for better heart health monitoring works really well, as shown in this study. This could be a useful tool for doctors to find and treat cardiovascular problems early on.
Intelligent Fertilizer Management System Using Optimized Artificial Neural Network Approach for Enhance Prediction
Authors: Bhagwan Dinkar Thorat
Published: January 2025

In agriculture, maximizing crop yields and advancing sustainable farming methods depend on efficient fertilizer management. Artificial intelligence (AI) devices have created tools in agriculture to assist farmers in obtaining precise and regulated cultivation. The right way to predict fertilizer is more important in order to satisfy farmer demands, improve yield output, and manage agricultural operations. This research uses deep learning (DL) models to provide a new introduces an innovative Automated Fertilizer Management System (AFMS) employing an Optimized Artificial Neural Network (OANN) model, enhanced through back propagation and chain rule techniques. The suggested approach makes use of sophisticated optimization techniques, including hyper parameter tuning and regularization, to refine the back propagation process and improve learning efficiency. By leveraging the chain rule for gradient computation, the model ensures accurate and efficient weight updates during training. The ANN is trained on a diverse dataset that includes soil nutrient levels and climatic conditions, specific to the Indian agricultural context. Subsequently, these characteristics are sent into an "Optimized Artificial Neural Network (OANN)" that predicts fertilizer outcomes based on information already known. In particular, the weights of OANN are adjusted using back propagation and chain rule techniques to improve the classifier's prediction accuracy.
Einstein Operators of Fuzzy Neutrosophic Soft Matrix of Type A
Authors: J.Boobalan
Published: January 2025

In this article, we define two new operators Einstein sum and Einstein product of fuzzy neutrosophic soft matrices and investigated the algebraic properties of fuzzy neutrosophic soft matrices using the max-min operators and then constructed the scalar multiplication of fuzzy neutrosophic soft matrices and investigate their algebraic properties.
The Use of Social Media as a Means of Promoting the Activities of Higher Education Institution Libraries: New Frontiers in Nonlinear Variational Inequalities
Authors: Mykhailo Poplavskyi
Published: January 2025

This study investigates the impact of social media on the visibility and outreach of libraries in higher education institutions. It analyzes the effectiveness of Facebook, Twitter, and Instagram in engaging users and promoting library services. The research identifies several strategies libraries employ, such as creating interactive content, hosting virtual events, and utilizing targeted advertising to reach specific user demographics. The article examines how libraries leverage user-generated content and feedback to enhance their online presence and foster a sense of community among patrons. The study explores the mathematical framework of Nonlinear Variational Inequalities, which provides insights into optimizing resource allocation and user engagement strategies within library systems. Thus, libraries can use concepts from this field to understand user behavior and preferences, resulting in more targeted and effective outreach efforts. The findings reveal that social media boosts user engagement and strengthens the relationship between libraries and their users, creating a more inclusive and supportive environment. This research highlights the importance of consistent and strategic social media management, emphasizing the need for libraries to develop clear objectives and metrics for success. Based on the results, the study offers recommendations for best practices in social media utilization, including the importance of regular content updates, active interaction with users, and integrating multimedia elements to enhance engagement. Libraries can enhance the promotion of their resources and services by implementing these strategies and using insights from Nonlinear Variational Inequalities, fostering a more dynamic academic community. This research underscores the transformative potential of social media as a vital tool for libraries in higher education, encouraging them to embrace innovative approaches to outreach and user engagement.
Evaluation of Continuous Sampling Plan indexed through Maximum Allowable Average Outgoing Quality
Authors: R. Shanthi
Published: January 2025

This paper introduces a result oriented designing and selection procedure for Continuous Sampling Plan type of CSP-1 indexed through Maximum Allowable Average Outgoing Quality (MAAOQ) for final products.  Expressions for performance measures such as Operating Characteristic function and MAAOQ functions are derived with using Polya distribution.It is a preferred quality index for engineers and quality controllers, when the sample size is large sufficient.
Blockchain for Academic Integrity Preventing Fraud and Enhancing Transparency in Education
Authors: Ashwani Goyal
Published: January 2025

Academic honesty is the cornerstone of educational excellence. But challenges related to fraud and record tampering remain. This article explores the application of blockchain technology as a transformative solution to enhance academic integrity in educational institutions. Blockchain's decentralized and immutable ledger provides a secure framework for managing academic credentials. This reduces fraud and ensures transparency. The study begins with an overview of current challenges in managing academic records. It highlights vulnerabilities to counterfeiting and inefficiencies in the verification process. We then propose a blockchain-based system to automate and secure certificate issuance and authentication through smart contracts to existing academic records management systems. A Proof-of-Stake consensus mechanism. It is used to balance network security and integration efficiency. This ensures that stakeholders with significant investments in the system are encouraged to act honestly. Empirical results show that blockchain systems improve data security. Increase transparency and increase efficiency of record management Performance indicators such as transaction throughput Inspection time and the efficiency of the consensus mechanism It emphasizes the system's ability to handle large volumes of data while maintaining operational integrity. This research concludes that blockchain technology offers a robust solution to contemporary challenges in academic integrity. By providing a transparent method effective and more secure academic record management. Additionally, this article suggests avenues for future research. Including scalability and integration with lifelong learning certification.
Comparative Analysis of Machine Learning Models for Optimizing Sustainable Agriculture and Farmer Profitability
Authors: Prachi Janrao
Published: January 2025

This paper presents a revolutionary AI-based platform designed to transform agricultural practices through data-driven decision-making. By addressing critical issues such as land underutilization, crop selection challenges, market volatility, and limited technological access, the platform provides actionable insights that enhance productivity and sustainability. It integrates advanced machine learning algorithms and features multilingual AI voice assistants to improve user convenience across diverse farming communities. The functionalities of the platform are examined, with a focus on its innovative tools tailored specifically for farmers. Ultimately, this research highlights the platform's potential to enhance agricultural efficiency, promote economic resilience among farmers, and contribute to the development of a more sustainable agricultural future.
An Efficient Ensemble Feature Selection-Based Segmentation and Multi-Class Classification for Heterogeneous Plant Disease and Non-Disease Images
Authors: M. Nagageetha
Published: January 2025

In recent years, the rapid advancement of computer vision and machine learning techniques has revolutionized the field of agriculture by enabling automated detection and classification of plant diseases. This work presents a comprehensive approach for the efficient segmentation and multi-class classification of heterogeneous plant disease and non-disease images. The proposed methodology combines multi-level thresholding-based filtering, feature ranking, leaf feature segmentation, and ensemble classification  to achieve high accuracy in disease recognition. In the initial stage, multi-level thresholding-based filtering is applied to enhance image quality and reduce noise, facilitating more precise disease pattern extraction. Subsequently, feature ranking methods are employed to select the most discriminative features from the pre-processed images. These features play a pivotal role in distinguishing between various plant diseases and healthy leaves. Experimental results on a multi-modal plant disease and non- disease images demonstrate the superiority of our proposed method in terms of accuracy and efficiency.
IoT-Assisted Cloud Computing Architecture for Alzheimer's Disease Detection Using LSTM Networks
Authors: Surjeet
Published: January 2025

The early diagnosis of the disease remains one of the significant challenges in healthcare, making available advanced, scalable, and efficient diagnostic solutions for the condition imperative. Conventional diagnostics are usually resource-intensive, time-consuming, and fail to be performed in real-time. In this paper we propose an IoT-assisted cloud computing architecture with the use of LSTM networks to detect Alzheimer disease accurately and in a timely manner. This integrated solution exhibits IoT-enabled wearable devices, providing us with real-time data, cloud computing that provides a robust core of processing such data, and finally, we have LSTM networks that help us analyze time-series data of our patients, whose psychometric tests, cognitive performances, and physiological signals, etc. In contrast to past approaches that depend on static data sets and centralized processing, our framework provides scalability, real-time processing, and incremental learning through distributed cloud architecture. Experimental evaluations on clinical datasets show a detection accuracy of 92% which outperforms state-of-the-art models by 15%. Moreover, the thin IoT framework of system guarantees the efficient data transfer, bringing down the latency by 30%, than the traditional architectures. This game changing technology gives medical professionals an early diagnostic tool to intervene earlier in Alzheimer’s disease, so that more personalized treatment is possible. IoT-cloud synergy in health care presents a significant paradigm shift toward sustainable health management by not just eliminating the barriers between early disease detection and patient treatment but also by offering a scalable framework to empower patients to take proactive measures that improve their health status.
ContrastNet: Unsupervised Feature Learning for EEG Signals and HSI Classification Using DCNN and Autoencoders
Authors: M. Rama
Published: January 2025

Scientific research on digital images has advanced significantly following their emergence. From earlier times to our current situation, the sizes as well as the quality of the images generated have increased substantially. Nevertheless, there are only so many outcomes that may be achieved when the data in these pictures stays in the band that is visible (RGB band). It has become necessary to obtain photos containing additional broadband information as a result. The Hyperspectral Imaging (HSI) technique was created to address this requirement. A new framework called ContrastNet is intended for unsupervised learning of features in two different domains: ECG readings and HSI. ContrastNet attempts to extract discriminative features from these complicated data types without necessitating labelled data to be trained by utilizing automatic encoders and Deep Convolutional Neural Networks (DCNNs).When it comes to EEG signals, ContrastNet picks up representative features that identify significant patterns connected to various brain activity levels. Comparably, ContrastNet collects features from HSI classification that  encode spectral data important for differentiating between various categories of interest. ContrastNet is an adaptable feature learning method that can handle various datasets and applications without requiring a large amount of labelled data by employing DCNNs and automatic encoders in an unsupervised way.
Certified Bondage Number for Circulant Graphs
Authors: G. Navamani
Published: January 2025

A certified dominating set of a graph is a dominating set in which every vertex in must either have no neighbours or at least two neighbours in  , where denotes the set of all vertices in . A certified domination number of represented by is defined as the smallest size of such a certified dominating set of . A  bondage number is defined to be the cardinality of minimum number of edges  such that ) broadened this parameter to encompass certified domination and we define  certified bondage number of a graph , to be the size of the minimum number of edges   so that ) [) that is minimum number of edges to be removed to enlarge  (reduce) the certified domination number of . In this study, we introduce this parameter and determine the certified bondage number for circulant graphs
Fractal Graph of Total Regular Fractional Domination Number
Authors: Meenakshi M
Published: January 2025

Determining the new dominance parameter on a fractal graph, called total regular domination, is the main objective of this work. Domination parameters, specifically regular and fractional domination, in fractal network designs are examined in this paper. In order to clarify the properties of a fractal graph, the authors used prior findings to calculate the fractional domination number and related metrics.Finally, mentioned how to apply this new parameter in our real life situation.
Objectives: fractal is an assemblage of precisely defined, asymmetrical, and segmentable structures. There is a relationship between each of these smaller parts.
Methods: Consider a graph G is an undirected graph that is connected. The total regular fractional dominance number of the fractal graph FG [TRFDN], which is represented as (G), is a new parameter created by this study. to determine the lowest weight when all of the vertices have fractional weights and the dominated vertices are regular.
Introduction of c' g- Closed Sets in Nano Topological Spaces and Elimination of Prodromes in Rheumatoid Arthritis Disease Through Nano Topology
Authors: K.Kavithamani
Published: January 2025

Topology being somehow very recent in nature but has got tremendous applications over almost all other fields. Theoretical or fundamental topology is a bit dry but the application part is what drives crazy once we get used. This research explores a few applications in various fields of Science and Technology, like applications to Biology, Robotics, GIS, Engineering, Computer Sciences and medical field. Topology though being a part of mathematics but it has influenced the whole world with so strong effects and incredible applications. This study introduces c*g- closed sets in nano topology and its application. This article is to apply elimination of Prodromes in the given system through c*g-nano topology and RA factors that cause rheumatoid arthritis are identified by creating basis in c*g-nano topology.
AI Powered Solutions for Deepfake Identification
Authors: Deepali Ujalambkar
Published: January 2025

The emergence of deepfake technology, primarily driven by generative adversarial networks (GANs), has introduced new challenges in the realms of media authenticity, security, and public trust. Deepfakes, which involve the manipulation of images, videos, and audio to create highly realistic but fabricated media, can be used in both benign applications and malicious scenarios, such as misinformation, identity theft, and privacy invasion. As deepfakes become more sophisticated, AI-based solutions have emerged as the primary means of identifying these manipulations. This paper explores various AI-powered techniques for detecting deepfakes, with a focus on methodologies like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. By synthesizing insights from recent studies, this paper highlights the current capabilities and limitations of these techniques, underscoring their respective advantages in handling complex and high-dimensional media data. The paper also considers the challenges of generalizability and robustness, particularly in the face of adversarial attacks aimed at evading detection systems. Through an analysis of prominent datasets and evolving algorithmic approaches, this paper provides an overview of the technological landscape in deepfake detection and suggests pathways for future research to improve detection accuracy and resilience.
An Iterative Systematic Analytical Review of Advanced Deep Learning Models for Grape Disease Detection
Authors: Prashant G. Aher
Published: January 2025

The growth in plant disease occurrence has heavily threatened global agricultural productivity. Therefore, highly efficient and automated diagnostic tools need to be developed in the current scenario. Rapid advancement in machine learning and deep learning techniques makes present reviews far from conducting comprehensive comparative evaluations of cutting-edge models especially for important crops such as grapes related to plant disease detection. These reviews inadequately address the challenges of model interpretability, computational efficiency, and adaptability to various datasets, thus opening the doors for gaps in applying such technologies in actual farm-related settings. This paper seeks to address these limitations by doing a -guided systematic review of the latest ML and DL models including CNNs, Vision Transformers (ViTs), and hybrid ensemble approaches like DenseNet121, MobileNetV3Large, and Inception-ResNet-V2. The performance is evaluated in terms of major metrics such as accuracy, precision, recall, F1-score, and computational efficiency. Some of the new architectures include dual-track networks with Swin Transformers and group shuffle residual deformable nets, among others like interpretable models LEViT and an interpretable leaf disease detector, I-LDD, which are specifically discussed in terms of new architecture and practicality benefit. In the light of impact on scalability and usability in agricultural domains, the emphases go to lightweight architectures that optimize deployment for edges and explainable frameworks improve decision-making capabilities. With this, an inclusive review proves helpful for better selections of optimum models toward the detection of grape disease as well as generalized plant pathology with precision advancements in agriculture scenarios. This work contributes to sustainable agricultural practices and enhances food security by bridging knowledge gaps and proposing scalable, efficient, and interpretable solutions.
Neural Network Aided Extended Kalman Filter for Fault Detection and Isolation in Nonlinear Control System
Authors: Vishnu Kumar Kaliappan
Published: January 2025

This article describes the presentation of neural networks for fault detection and isolation in nonlinear systems. Model-based fault detection schemes must include residual generation. The process of residual generation for nonlinear systems is often difficult because of the size of the issue or the absence of a suitable model from which the residual can be generated. To generate residuals for fault detection, this paper develops and applies neural network-based methods for nonlinear systems. Fault Detection and Isolation (FDI) is critical in many industries to ensure the safe operation of a process. Faults detection and Identification (FDI) methods are proposed to identify the type, size, position, and time of the fault. FDI are distinguished by their robustness, rapid detection, and isolation of faults. This paper compares the effectiveness of extended Kalman filters and neural network-based fault diagnosis systems. According to simulation results, the method has a faster convergence rate and a more accurate identification result than the traditional EKF algorithm.
Heat Conduction in a Square Plate Involving Multivariable H-Function
Authors: Nitesh Kumar Sahu
Published: January 2025

A rigorous mathematical paradigm incorporating the multivariable H-function is devised to elucidate heat conduction phenomena in square plates. This innovative framework facilitates the derivation of a precise temperature field model, thereby enabling a comprehensive examination of the interplay between plate geometry and heat propagation characteristics. The analytical solution is substantiated through meticulous numerical simulations, underscoring the multivariable H-function's exceptional capability in capturing intricate heat conduction dynamics under disparate boundary conditions. This investigation substantially enhances the theoretical foundations of thermal analysis in square plates, unlocking novel avenues for interdisciplinary applications in engineering and physical sciences.
Nonlinear Improved Adaptive Optimized Grey Model for Real-Time Monitoring and Control of Weld Bead Geometry in Robot-Assisted Wire Arc Additive Manufacturing
Authors: Satish Kumar Parimi
Published: January 2025

WAAM with directed energy deposition process and live monitoring of weld bead shape gives researchers and manufacturers real-time information, allowing them to regulate the metal deposition process layer by layer effectively. This work created an improved adaptive optimized grey model (IAOGM), for online monitoring of metal deposits' height and depth in each layer. The IAOGM(1,N) model takes fewer training samples and has limited information. To improve prediction accuracy, the training data was constantly updated by eliminating old data and introducing newer data. The framework's parameters comprised welding time, current, the absolute difference in current at 5-second intervals, and arc force. The best accurate weld bead height and depth estimates were obtained by calculating the root mean square error (RMSE) for various parameter combinations. The interplay of time, current, and arc force was discovered to have a considerable impact on weld bead diameters. Using these parameters, the model predicted weld bead height and depth with MAPE, RMSE, and MAE, values of 5.48, 3.32, and 7.56 respectively, when compared to experimental data. This technique enables users to properly balance process parameters and achieve desired weld bead heights without the requirement for substantial training.
WTBAN-CNN-TL: Wavelet Transform-Based Feature Extraction and Bonferroni Adjusted ANOVA-CNN with Transfer Learning for Lung X-Ray Image Classification
Authors: Kavitha S
Published: January 2025

A comprehensive approach for lung X-ray image classification is proposed, combining wavelet transform-based feature extraction with Bonferroni ANOVA feature selection and deep learning techniques. Features are initially extracted from lung X-ray images using wavelet transform, capturing crucial information across multiple scales. These features are concatenated to form a comprehensive feature set. To optimize this feature set, ANOVA is employed with Bonferroni adjusted feature selection to control the false positive rate and to identify the most relevant features, thereby reducing the dimensionality of the input data. This optimization can result in expedited training times and reduced computational resources required by the CNN. The selected features are subsequently fed into a Convolutional Neural Network (CNN) for classification. To further boost the CNN's performance, transfer learning is utilized by leveraging pre-trained models. Additionally, the performance of ANOVA-based feature selection is also compared with that of a genetic algorithm-based method. This integrated method, referred to as WTBAN-CNN-TL, aims to reduce the training times and resource requirements while improving the accuracy and efficiency of lung X-ray image classification.
Analyzing Zakat Distribution Using Nonlinear Differential Equations: Insights from Surah Al-Baqarah Verse 177 for Modern Economic Applications
Authors: Mustopa
Published: January 2025

This research aims to analyze the effective and efficient management of zakat and its impact on community welfare. Zakat, as one of the pillars of Islam, serves not only as a religious obligation but also as a social instrument to reduce poverty and enhance the quality of life. The research findings indicate that zakat distribution follows a normal distribution with a p-value of 0.155 for the Shapiro-Wilk test and 0.200 for the Kolmogorov-Smirnov test. Furthermore, the homogeneity test shows a p-value of 0.267, indicating homogeneous variances among groups. The linearity test demonstrates a Pearson correlation coefficient of 0.872 with a p-value of 0.001, confirming a significant positive relationship between zakat contributions and the economic welfare of recipients. Simulation results show zakat distribution growth from 500,000 units in the first month to 1,200,000 units after 12 months, with a high R-squared value (0.975) in model validation. This study recommends exploring additional factors influencing zakat distribution and innovations in the collection and distribution of zakat, leveraging technology to enhance efficiency and fairness in the zakat system. Thus, it is expected that zakat can contribute more significantly to economic empowerment and sustainable community welfare
An Efficient Algorithm to Enhance Confidentiality, Security and Quality of Service(QoS) for Internet of Things(IoT) Communication Network
Authors: Voore Subrahmanyam
Published: January 2025

The communication between physical devices and Internet environment provides a flexible and more secure by Internet of Things(IoT) enable Cyber Physical Systems. The cyber physical systems are providing a bridge between physical systems platform to virtual system platform for better usage of communication technology. The IoT devices are less battery life, less processing capability and small in size. These sensor devices are constrained devices are suitable for constrained network where network is un-reliable and require low bandwidth. It is essential to provide security for these sensor devices for securely data transfer is a challenging task. To solve this task as well as to fulfill various performance metrics like minimum time consumption, enhance secure communication between these IoT devices, memory usage, confidentiality rate etc. The proposed paper applying a Cryptography based Secure Encrypted & Decryption Algorithm(CSEDA) algorithm to provide security and also to minimize attacks for IoT device in a secure network communication. In the first step, the IoT sensor devices are sensed and collected real-time analog data from outer environment. This collected data will be analyzed in hidden layer with jackknife regression function. In the second step, these classified data to be forward to further hidden layer to encrypt the data will be perform by Schmidt Samoa (SS) Encryption Algorithm after that the data will transform to cloud for store & further process. In the third step, The decryption is performed by Schmidt Samo (SS) Decryption Algorithm. In the cloud the finally the original data is stored in the database for further processing. This method is providing the security of data communication and processing with less time. The real-time data is being sensed and collected by sensor devices and perform classification, processing time and memory usage. The proposed CSEDA method providing high confidentiality of the data and minimize processing time and well utility of memory space when compare with existing algorithms GRBFNN, Deep learning model, JRSSC-DASL.
Using AI to Create More Equitable and Inclusive Higher Education Learning Systems
Authors: Ruchi Bhatnagar
Published: January 2025

Ensuring inclusion and equity in the rapidly improving educational landscape is a major challenge. Integrating artificial intelligence (AI) offers transformational potential to address this gap through personalized and personalized learning experiences This study explores the interface between AI technology and higher education in 2010, and highlights how AI can enhance inclusion and equity in the education system. Cultural high school, cultural differences, continuous learning such as traditional materials, plus academic achievement inclusion and includes individualized approaches to learning. The paper examines existing AI-driven initiatives and assesses their effectiveness in preventing educational gaps and promoting diversity. Examines the ethical implications of implementing AI, addresses concerns about data privacy, algorithmic biases, and reinforcement of existing asymmetries Detailed literature and case studies build Emphasizes successful AI applications and provides best practice ethical frameworks. The research approach includes active data preprocessing, machine learning, and ethical considerations for AI-driven interventions aimed at advancing student achievement and educational equity. The findings suggest that AI can significantly improve inclusion by providing personalized learning strategies and warning systems for at-risk students, although ethics must be treated with caution solutions to the challenges of. The paper offers policy recommendations for educational institutions to create an enabling environment for the ethical and positive use of AI, with the aim of creating a future where higher education is a beacon of opportunity for all students, including those from in regardless of education system.
Comparative Analysis of Parkinson's Disease Classification Using Deep Learning Approaches Enhanced with Optimization Techniques Versus Traditional Machine Learning Models
Authors: K. Manisekharan
Published: January 2025

Parkinson's Disease (PD) is a neurodegenerative condition that presents considerable challenges in achieving accurate early diagnosis and classification. This research explores the classification of PD through the application of advanced deep learning techniques integrated with optimization methods, compared to conventional machine learning approaches. The analysis is conducted using a diverse dataset incorporating clinical, vocal, and movement-related features to ensure comprehensive evaluation. Deep learning frameworks, such as multi-layer perceptron (MLP), Long Short-Term Memory (LSDM), the proposed deep learning model namely CNN-BiGRU were enhanced using strategies like hyperparameter optimization, regularization, and advanced gradient-based optimizers to boost performance and minimize overfitting. Similarly, traditional machine learning models, including Linear Regression, Random Tree, REP Tree, and Random Forest, were implemented and tested on the same dataset. Evaluation metrics, including accuracy, precision, recall, F1-score, and the area under the curve (AUC), were used to measure and compare the performance of all models. The findings reveal that optimized deep learning models significantly surpass traditional machine learning methods in both classification accuracy and generalization. This study emphasizes the effectiveness of optimization-enhanced deep learning techniques in PD classification and their clear advantages over traditional models.
Elevating Crop Quality: AI Neural Networks for Advanced Plant Disease Management and Enhanced Food Security
Authors: Boyapati Sahithi
Published: January 2025

The present research has gone further into diagnosing plant diseases by implementing spectral imaging with the help of Convolutional Neural Networks. Therefore, it is a step beyond traditional RGB imaging and utilizes the captured spectral data of the infrared and ultraviolet spectra for the nascence and sub-clinical level detection of plant diseases. Toward this end, CNN models such as VGG16, Efficient Net, Inception V6, and ResNet-34 were retuned in such a manner that they could process this heterogeneous dataset to enhance the level of diagnostic precision that can be achieved using standard approaches. Hence, through this retuning, it was established how the proposed system improves the performance of these models in all aspects, with particular peaks through the Efficient Net and Inception V6 models, which have maximal test accuracies up to 99.93% for early detection. This has, in a pointed manner increased the potential of the proposed approach to enhancing agricultural practices through the delivery of highly accurate tools for early intervention against diseases, hence ensuring food security worldwide. These results establish new benchmarks in plant pathology diagnostics, representing state-of-the-art imaging technology and deep learning as particularly impactful tools for disease management and mitigation strategies in agriculture.
Nonlinear Decentralized Federated Localization Framework for Dynamic Vehicular Networks
Authors: B. Deena Divya Nayomi
Published: January 2025

This study introduces a Nonlinear Decentralized Federated Localization Framework (DFLF) designed for precise real-time vehicle positioning in dynamic vehicular networks. The framework addresses GNSS inaccuracies, high communication costs, and privacy challenges by integrating nonlinear machine learning techniques, including Long Short-Term Memory (LSTM) networks, Graph Convolutional Networks (GCNs), and transformer-based attention mechanisms. These models effectively capture complex temporal and spatial dependencies from GNSS, sensor, and vehicle-to-vehicle (V2V) communication data. Federated learning ensures secure, decentralized training by exchanging encrypted model updates rather than raw data. Experimental results using synthetic GNSS data and V2V interactions demonstrate significant accuracy improvements, with root mean square errors (RMSE) of 2.3 meters in urban scenarios and 1.2 meters on highways. Scalability tests with networks of up to 500 vehicles confirm the model's robustness in dense traffic environments. Privacy-preserving measures such as differential privacy and homomorphic encryption ensure secure collaboration without notable performance degradation. The framework's nonlinear modeling capability enhances localization reliability under urban canyon conditions and intermittent connectivity, making it a viable solution for decentralized vehicle positioning in real-world scenarios.
Probiotics and Ascorbic Acid Specialized Product: Production and Quality Indicators
Authors: Lobach Evgenia Yurievna
Published: January 2025

A new type of probiotic coated tablets for particular nutritional uses has been developed, intended for children and adults, including pregnant and breastfeeding women. Probiotics are live microorganisms or products cultivated from them that have a beneficial effect on the human body, mainly by improving the health of the gastrointestinal tract (GIT). Experimental and industrial samples of Probiopan dragées enriched with bifidobacteria and lactobacilli were the subject of the study. Comprehensive studies of the product intended for particular nutritional uses allowed us to establish its high quality as regulated by applicable standards, as well as the shelf life of 1 year at a temperature of 25±5ºC and a relative humidity of 75%. The obtained data indicated a fairly high stability of vitamin C, the preservation of which was 91.6%. Based on the studies conducted, the use of the product was recommended in the amount of three dragées per day to provide at least 30% of the daily requirement for vitamin C, bifidobacteria, and lactobacteria, which can serve as a factor in normalizing the microflora of the gastrointestinal tract and preventing the development of dysbacteriosis in children, adults, and women during pregnancy and breastfeeding. Probiopan dragées are also recommended for type II diabetes, because they contain no sugar. It was also found that when consuming calcium together with the developed confectionery product, the body absorbs calcium better because of the lactic acid bacteria Lactobacillus plantarum (strain 337D).
Hybrid Model for Dissolved Oxygen Prediction Using Ensemble MINE-BISRU-Attention and LightGBM-BiSRU-Attention Approaches
Authors: Alla Rajendra
Published: January 2025

Aquaculture productivity in most cases depends on the quality of water which is one of the determinants of water health. One of the most important variables needing monitoring and control is the dissolved oxygen (DO). Even with complex relationships typical in aquaculture processes, some traditional methods of prediction have more often than not been ineffective. In this work, the LightGBM-BiSRU-Attention hybrid approach is proposed which uses LightGBM for feature selection, BiSRU for sequence learning and Attention for parameter tuning. Further, an enhanced model called Ensemble MINE-BISRU-Attention is proposed which further explores the use of the Maximal Information Coefficient (MIC) for more advanced feature selection. In this study, these models were evaluated against the Kaggle water quality dataset and consistently with good accuracy. The models predicted the average mean square error with the LightGBM-BiSRU-Attention model to be 0.178 while the Ensemble MINE-BISRU-Attention further lowered the number to 0.104. This also acts as a shift towards the intelligent aquaculture systems which provide a solution to the gaps which existed in water quality prediction models.
Servo System Design for Precise Fin Tip Control in Launch Vehicles Using Electromechanical Actuators
Authors: Albert John Varghese
Published: January 2025

This study examines the development and performance of a servo mechanism for fin tip control (FTC) in a launch vehicle, aimed at accurately manipulating rocket fins to achieve efficient steering. The analysis involves both linear and nonlinear modelling of the electromechanical actuator (EMA)-based FTC system, with the goal of developing a compensation strategy based on closed-loop control requirements. These specifications target position control for roll stabilization of launch vehicle during the FTC phase. A compensation approach, incorporating a PID controller and a notch filter, is integrated to meet the system's performance criteria. Friction, particularly Coulomb friction and Stiction, is a critical factor in achieving high-precision control using the EMA. To tackle this, a friction model is developed, and system parameters are determined by reducing the discrepancy between the outputs of the plant and the model, eliminating the need for optimization. The closed-loop performance of the system is then assessed through MATLAB/SIMULINK simulations, demonstrating the ability to meet the desired performance criteria for precise fin tip control.
Some Properties of RICCI Solitons in LP-Kenmotsu Manifolds
Authors: Bidyabati Thangjam
Published: January 2025

Examining Ricci solitons in Lorentzian para-Kenmotsu manifolds is the goal of this paper. We have established that a symmetric parallel second-order covariant tensor in a Lorentzian para-Kenmotsu manifold is a constant multiple of the metric tensor. We have shown that if L_V g+2S is parallel to the Levi-Civita connection associated with g, where V is a given vector field, then (g,V,λ) is a Ricci soliton. We have observed that a Ricci soliton in a W_2-semi-symmetric Lorentzian para-Kenmotsu manifold is shrinking. Furthermore, certain curvature properties of Lorentzian para-Kenmotsu manifolds admitting Ricci solitons are studied. Finally, we have provided an example of a 3-dimensional Lorentzian para-Kenmotsu manifold.
Soret and Angle of Inclination Effects on MHD Fluid Flow Past an Upright Porous Plate
Authors: P. Naguru Meeraiah
Published: January 2025

A theoretical investigation of MHD fluid flow is carried out under the influence of thermal diffusion, chemical reaction, and various factors. The angle of inclination is also taken into account and quantitatively analyzed in this heat and mass transport analysis. The necessary governing equations are numerically resolved using the implicit finite-difference methodology of the Crank-Nicolson type. Graphs depict non-dimensional velocity, fluid temperature and concentration distributions for many fluid parameters involved, such as the joule-heating parameter, suction parameter, chemical reaction parameter, and radiation parameter. The coefficient of skin friction, Nusselt number, and Sherwood number were calculated. The concentration of the fluid increases as the Soret number increases, whereas the Sherwood number decreases.
A User Friendly Geopolymer Concrete Mix Design Procedure
Authors: Prashant G Sawarkar
Published: January 2025

Cast-in-situ is concrete is one of the preferred types of concrete construction, which is poured into the moulds to cast concrete of desired shape. India being one of the populous countries of the world, most of the cast-in-situ concrete is handled by semi-skilled labors on the site. Thus, safety of the worker is one of the prime concerns. However, most of the mix design methodology proposed for making geopolymer concrete heavily rely on the use of highly concentrated sodium hydroxide for obtaining desired strength, which is unsafe to handle. Therefore, a user-friendly mix design methodology to produce geopolymer concrete using sodium silicate (SS) as alkali reagent and ground granulated blast furnace slag (GGBS) as the binder solid is proposed. The proposed mix design methodology also evaluates the correlation between the SS/GGBS ratio and the 28 day compressive strength for developing a rational mix design. Mix design for SS/GGBS ratios of 0.35 to 0.65 is proposed in the present paper. It was found that a strength in the range of 40 to 66 N/mm2 can be produced at the end of 28 days curing period. An example explaining the step wise process of the mix design methodology has been given in this paper.
Common Fixed Point Theorem In Complex Valued Extended B-Metric Space
Authors: Jitender Kumar
Published: January 2025

In this paper, we proved a common fixed point theorem for generalized contractive type maps in complex valued extended -metric space, which generalized many results in the literature.
Fixed Points of Generalized - Geraghty Ciric -Rational Type Contraction in B- Metric Spaces
Authors: P.Harikrishna
Published: January 2025

In this paper we prove the existence and uniqueness of the fixed points generalized ciric type Geraghty rational contractions in b-metric spaces, our results extend some of the known theorems.
Analysis of Energy Efficient Differential Fault Tolerant Adders with Minimized Nonlinearities
Authors: Sarada Musala
Published: January 2025

In this paper, two 1-bit Differential fault tolerant Full Adder circuits (1b-FA)proposed focusing on reducing nonlinearities to improve performance and reliability in modern electronic systems, using Carbon Nano FETs (CNFETs).Transmission gate logic and pass transistor logic is being used in proposed adders to improve energy and fault tolerant characteristics of Full Adder. Fault detection and fault correction mechanisms are proposed in outputs of 1-bit differential full adder. The proposed fault detection can detect only single error and proposed error correction can correct it. By using Cadence Virtuoso tool, the proposed design is simulated with 32 nm CNFET technology at a supply voltage (VDD) of + 0.9V. The performance of proposed full adder designs has been comparedwithexisting designs in terms of power, delay and power-delay product (PDP). The proposed full adders supply voltage is being varied over a range and can observe the characteristics of full adder with respect to variations in supply voltage (VDD).