bluuebunny/crossref_metadata_2025 huggingface dataset:
Periodic Solution for Pair of Matrix Sylvester Volterra Integro-Dynamic System on Time Scales
Authors: CH. Harisha
Published: January 2025

In this paper, we study the periodic solution for a pair of matrix Sylvester Volterra integro-dynamic (VID) systems on time scales. We use the vectorization operator to transform the matrix Sylvester VID into the Kronecker product VID system on time scales. This approach allows us to demonstrate the conditions under which periodic solutions exist for these complex systems. By leveraging the Banach fixed point theorem, we can effectively address the intricacies involved in the periodicity of solutions within the context of time scales. We can establish new results regarding the existence of periodic solutions for the VID system on time scales through Krasnoselskii's fixed point theorem.
Image-Based Fruit and Vegetable Classification Using CNN Techniques
Authors: Deepali S. Jadhav
Published: January 2025

This paper introduces a system for identifying fruits and vegetables through a Convolutional Neural Network (CNN). The model is trained using a dataset that contains images of different fruits and vegetables, organized into sets for training, testing, and validation. It includes 36 categories, such as bananas, apples, tomatoes, and carrots. Once trained, the CNN model accurately determines whether an image represents a fruit or a vegetable. The system is deployed via Streamlit, allowing users to upload images and obtain classification results in real-time. This project highlights CNNs' potential in food identification, offering a dependable and user-friendly solution for recognizing ingredients across various applications.
Statistical Evaluation of Skin Disease Detection Models From a Pragmatic Perspective
Authors: Harshwardhan Kharpate
Published: January 2025

Alterations in the amount of melanin produced by the body are a significant contributor to the development of skin conditions. As a consequence of these abnormalities, skin diseases such as acne, psoriasis, eczema, vitiligo, ichthyosis, and seborrheic dermatitis, among others, may develop. Because all of these diseases manifest themselves in observable ways on a person's body, it is feasible to make a diagnosis of them using image processing techniques. Throughout the years, researchers have proposed a wide variety of models for the process of image processing. Some examples of these models include the convolutional neural network (CNN), the evolving fuzzy classification, the constrained-syntax genetic programming, and the Naive Bayesian classifier (NB). Nevertheless, when it comes to evaluation, these models do not take into consideration factors like regional variations, the effects of age on the skin, gender, skin tone, skin type, and so on. As a result, a study of a variety of different stratification models for skin disorders has been included in this paper. Additionally, performance measures like as accuracy, computational complexity, scalability, and other parametric adjustments have also been included. Researchers and skin care experts are welcome to make use of this information in order to determine the algorithmic combination that is most effective for a certain application. This will assist with the creation of a high-performance system for stratifying skin diseases and with the development of applications more quickly. There are also various system models accessible, such as high-performance feature extraction and selection, high-performance sickness stratification methods, high-performance skin segmentation, and so on. When doing multiparametric analysis, this article takes into consideration a variety of elements, including a person's skin tone and regional factors. These criteria are used in the process of classifying the algorithms that have been researched, and other performance indicators are evaluated. In addition, this work offers some recommendations for improving the existing models via the use of algorithmic fusion, model augmentation, incremental learning, and other similar techniques. The people who read this post can put these modifications into action to improve the functioning of the systems that are already in place for different use cases.
Skin Type Profiling through Computer Vision and Nearest Neighbor Methods for Targeted Skincare Recommendations
Authors: Yamarthi Narasimha Rao
Published: January 2025

This proposed computer vision system is an innovative solution to help in being revolutionary in dermatological diagnostics and personalized skincare. In using the latest image analysis techniques, this system picks up essential attributes related to skin type, tone as well as other skin issues like acne, pigmentation, and even fine lines. This refines the segmentation of the skin by carrying out the transformation in both HSV and YCbCr color space to accurately determine the skin, irrespective of any lighting or environmental conditions. The Fitzpatrick skin tone classification is used together with K-Nearest Neighbors (KNN) in order to have a strong difference in tone and hence be inclusive as well as accurate in results. It maps unique skin profiles against curated product databases by using one-hot encoding and cosine similarity to give actionable insights to the users. It is placed into a smooth, web-based platform that will allow real-time video and e-commerce integration, creating a friendly and accessible experience for different approach sets.
The Influence of Mathematical Problem-Solving and Critical Thinking Skills through Project-Based Flipped Classroom and Self-Regulated Learning
Authors: Hibatun Wafiroh
Published: January 2025

Introduction: In the field of mathematical education, enhancing problem-solving and critical thinking skills is essential for students to tackle complex mathematical challenges.
Objectives: This study investigates the impact of Project-Based Flipped Classroom (PBFC) and Self-Regulated Learning (SRL) on improving these cognitive abilities within mathematics. A 2 x 2 factorial design with a nonequivalent control group pretest-posttest design was used to evaluate the effects.
Methods: The participants included 64 seventh-grade students from SMP Negeri 2 Kedungpring Lamongan, divided into two groups using cluster random sampling. Data were collected through mathematical problem-solving tests and SRL-related questionnaires, with analysis performed using MANOVA.
Results: The results revealed that (1) PBFC positively influenced students' mathematical problem-solving and critical thinking skills, (2) SRL contributed significantly to the improvement of these skills, (3) the interaction between PBFC and SRL enhanced both problem-solving and critical thinking outcomes in mathematics, and (4) students who exhibited high SRL and participated in PBFC demonstrated better performance in mathematical tasks.
Conclusions: These findings highlight the effectiveness of integrating PBFC and fostering SRL to improve essential mathematical cognitive skills, with implications for teaching practices in mathematical education.
An Integrated Framework with Hybrid Anomaly Detection, Predictive Analysis, and Graph-Based Insight for Enhancing Cloud Task Scheduling Security
Authors: Bhagyashri Mankar
Published: January 2025

As cloud computing continues to revolutionize the landscape of modern IT infrastructures, ensuring the security of cloud task scheduling has become paramount. This research presents a comprehensive framework that leverages a synergy of cutting-edge technologies to address security risks associated with cloud task scheduling. The proposed framework integrates anomaly detection, predictive analysis, graph-based insights and explainable AI to enhance the task scheduling. The anomaly detection module combines diverse anomaly detection techniques, including statistical methods and deep learning models, to enhance accuracy and minimize false positives and negatives. The integration of graph-based insights capitalizes on the representation of cloud resources and their interactions as a graph. By analyzing structural changes and unusual patterns in this graph, the framework gains a holistic view of the cloud environment, enriching anomaly detection accuracy. To enhance transparency and decision-making, the framework incorporates explainable AI, providing administrators with insights into why specific anomalies are flagged. This fosters informed responses to anomalies and strengthens the human-in-the-loop element of the security strategy. In conclusion, this research advances the field of cloud task scheduling security by introducing an integrated framework that harmonizes anomaly detection, predictive analysis, graph-based insights and AI. By amalgamating these innovations, the framework empowers cloud environments to better safeguard against evolving security threats, ultimately fostering a more secure and resilient cloud ecosystem
Modeling Chronic Disease Prediction Using Statistical Machine Learning Methods
Authors: Khalid talib Othman
Published: January 2025

Chronic sicknesses, which includes diabetes, cardiovascular conditions, and cancer, pose substantial public fitness challenges worldwide because of their high occurrence and associated mortality fees. Accurate prediction of these illnesses is important for early intervention, improving affected person effects, and reducing healthcare expenses. This have a look at investigates the utility of statistical machine mastering techniques for chronic sickness prediction, leveraging patient information to develop strong predictive fashions.
The studies explores a variety of algorithms, together with logistic regression, choice timber, aid vector machines, and ensemble methods like random forests and gradient boosting. The dataset contains anonymized health information, encompassing demographic records, clinical parameters, and lifestyle elements. Feature choice techniques are employed to identify the maximum enormous predictors, enhancing model interpretability and lowering computational complexity.
The performance of each model is evaluated using metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve Cross-validation checks the generalizability and robustness of the models. The results show that ensemble methods, especially gradient versting, outperform traditional statistical methods in handling complex nonlinear relationships in the data
This study highlights the potential of machine learning in predictive healthcare management, providing tools for risk classification and personalized medical practice for the integration of Machine learning models into clinical practice enables healthcare professionals to make data-driven decisions to improve chronic disease management. Future work will focus on improving models with comprehensive data, introducing more inputs such as integrating genetic information, and addressing ethical concerns about data confidentiality and bias.
Artificial Rabbits Optimization Algorithm for PV Cell Parameter Extraction
Authors: Vimalakeerthy Devadoss
Published: January 2025

Solar energy is available in abundance and the energy obtained from solar is economical. The accurate Modelling of solar module is however more important before its installation. The manufacturers of Solar Panel would provide only few parameters in datasheet that which is not enough for modeling. Metaheuristics algorithms was applied and to obtain those few parameters of module. Artificial rabbits optimization has been applied to obtain the unknown parameters in 250Wp SVL0250P photovoltaic module at varying temperature and irradiance. A performance comparison is made with flower pollination, Jaya algorithm and it is found that Artificial rabbits optimization provides excellent results with respect to precision and convergence.
Quantifying the Performance and Trade- offs of Sustainable Investment: Triple Bottom Line Analysis using DEA Metrics
Authors: Baisakhi Mukherjee
Published: January 2025

In this era of global warming and climate change, sustainability is the key to combat the future challenges in almost every sector of the global economy. In this article we dig deep into analyzing the efforts of the benchmarked industries in the fields of ecological, humanity and ethical (GREEN) and their impacts on the economic aspects of the representing financial instruments. These days the investors are have developed interest to know about the contribution of the industries towards the sustainability of the environment, contribution towards society and how the company is monitoring their activities towards it.  This research articles attempts to analyses the efficiency of the funds by applying data envelopment analysis and concluding policy recommendations on the basis of duality programing. The articles also analyze the tradeoffs of the funds on the basis of the triple bottom line (economical, environmental and social) factors.
In conclusion, this study intends to quantify the accomplishment of the sustainable funds by applying DEA techniques and analyzing the efficiency of the funds on the triple bottom line parameters. The findings hold significance for the funds house, investors, policy makers and corporate entities that are evolving in the sustainable finance in the increasingly interconnected and socially conscious world.
An Analysis of Magnetohydrodynamic Free Convective Flow past an Infinite Vertical Porous Plate
Authors: P. Satish
Published: January 2025

The reason for this study is to find an answer for the issue of magnetohydrodynamic laminar shaky progression of an incompressible electrically leading liquid that is going beyond a limitless vertical permeable plate. This work is being finished with the goal of tracking down an answer for the issue. By looking at the effect that joule warming has on the speed and temperature profiles of a liquid stream that is coupled to a cross over attractive field, the reason for this study is to find out the impact that joule warming has on the progression of the liquid. Certain conditions of MHD stream are explored throughout the exploration. These conditions are demonstrated to be significant. These conditions are addressed mathematically to arrive at the vital outcomes. This is achieved through the use of limited distinction approximations and a PC program. Despite the way that an expansion in the joules warming boundary causes an expansion in the speed and temperature profiles that are consistently positioned close to the plate, these profiles keep on being disseminated away from the plate in a way that is steady. It is feasible to reach the determination from this that the power of the Joules warming that happens near the plate wall and at the standard affects the stream field of the MHD free convective stream. The finishes of this request, which depended on mathematical information, make plainly this peculiarity most certainly happens.
Comparison Between Cauchy's Steepest Descent and Fletcher-Reeves Methods in Solving Unconstrained Neutrosophic Nonlinear Programming Problems
Authors: G. Vanaja
Published: January 2025

The main objective of this study is to solve unconstrained single-valued Neutrosophic Nonlinear Programming Problems using Cauchy’s Steepest Descent Method(CSDM) and the Fletcher-Reeves Method(FRM). Our approach is based on new arithmetic operations and ranking on the parametric representations of Triangular Neutrosophic Numbers(TNN). We prove some important theorems for Cauchy’s Steepest Descent Method and the Fletcher-Reeves Method. Numerical examples are presented to illustrate the theory developed in this article. The outcomes of the proposed methods are compared.
A Novel CNN-RNN-LSTM Framework for Predictive Cardiovascular Diagnostics of Aortic Stenosis in a Large Scale 12-Lead ECG Dataset
Authors: Francis Densil Raj V
Published: January 2025

Aortic stenosis (AS) is a disease of the valve between the heart and aorta and may lead to heart failure if left untreated; it is one of the significant valvular heart diseases caused by the narrowing of this valve. Conventional diagnostic techniques are invasive and require resources. Machine learning and deep learning approaches for the non-invasive identification of AS were investigated using an extensive 12-lead ECG dataset of 10,646 patient records. A range of models was assessed for diagnostic performance, including Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a hybrid CNN-LSTM model, and a hybrid CNN-RNN-LSTM model. The results indicate that SVM and RF had 74% and 76% accuracy, respectively, while the CNN model improved the accuracy to 80%. The accuracy of the LSTM model was 82%, and the accuracy of the CNN-LSTM hybrid model was 85%. The most proficient model was the hybrid CNN-RNN-LSTM model, which had an accuracy of 87% and high precision, recall, and F1 scores. The promise of deep learning, especially hybrid models, for advancing non-invasive diagnostic techniques for aortic stenosis, including those that could greatly aid early detection and improve patient outcomes in a clinical setting, is highlighted by this research.
Implementing Deep Learning Models for Early Detection and Segmentation of Lung Cancer from Medical Imaging
Authors: Rajeev Dixit
Published: January 2025

Once surprisingly, lung cancer still ranks among the top causes of cancer deaths globally, early stage diagnosis is the key to increasing the survival benefits of the patient. Lung cancer diagnosis using classical methods like manual image analysis and histogram analysis is time consuming and has high possibilities of human errors. Lung cancer detection and segmentation with deep learning are promising, but they still encountered challenges of high accuracy in noisy or low-quality medical images. This study proposes an advanced deep learning framework articulated by CNNs with the integration of data augmentation methods and multiple scale segmentation for the automated detection and segmentation of lung cancer, on a dataset of CT scans. More importantly, when compared with existing techniques, it has a much higher accuracy in detecting the tumour areas, even under situations of varying quality of the images. Experimental data shows enhanced sensitivity and specificity over conventional strategies. This proposed model not only shortens the diagnostic time but it also provides uniform, trustful results which reduce the misdiagnosis occurrence. We are making strides that further advance AI tools to enhance clinical practice and we hope may improve the early detection of lung cancer and save lives.
Evaluation of Hardware Trojans in the Adder circuits
Authors: Allagadda Seetharamaraju
Published: January 2025

Exporting the design and fabrication of Integrated Circuits (ICs) presents a significant risk to our vital infrastructure since an attacker may exploit them by circumventing security measures by triggering a hardware Trojan. These malicious design changes implemented at an untrusted manufacturing site have the potential to leak nearly any confidential information from a protected system to an attacker. One of the most serious dangers to hardware security has been the hardware Trojan.  We present a summary of recent advances in Trojan detection approaches, grouped according to their relevance to various Trojan kinds. Adder has indeed been implemented effectively in integrated circuits like arithmetic circuits as well as arithmetic accelerators. However, recent research indicates that adder circuits contain security flaws as well. Nevertheless, there has been relatively little study of hardware Trojans within adder circuits. The Trojans are based on the attributes of adder circuits. The efficacy of hardware Trojans is often evaluated using adder circuit assessment methodologies. The performance characteristics of adder like power and delay are evaluated with and without hardware Trojans.
Quantitative Analysis of Tourism Dynamics in the Himalayas: Dual Perspective Study
Authors: Stanzin Padma
Published: January 2025

This study investigates significant factors which emerge as opportunities and challenges in the tourism sector of Ladakh, a Himalayan region, through a comparative analysis of perspectives from local residents and tourists. Employing advanced analytical techniques, the research identifies key factors influencing the region's tourism industry. A comprehensive review of existing literature is conducted to establish latent constructs, which are analysed using Structural Equation Modelling. The findings reveal significant determinants of local residents' support for tourism development and factors influencing tourists' intention to revisit. Comparative analysis of the significant factors that emerge from the two dataset reveal that socio-cultural aspects emerge as a critical factor for both groups, underscoring the importance of cultural sensitivity and community participation. Additionally, environmental appeal, economic benefits, social barriers, and safety concerns are identified as influential elements. These insights provide valuable guidance for policymakers to promote sustainable tourism development in the region.
A Novel case on Fuzzy Resolving Sets on Interval-valued Fuzzy Graph and its Application on Signal Processing Unit
Authors: R. Shanmugapriya
Published: January 2025

A fuzzy graph generalisation known as an interval-valued fuzzy graph (IVFG) reflects uncertainty more flexibly by representing the membership values of vertices and edges as intervals rather than fixed numbers. We have defined Fuzzy Resolving Set to interval-valued fuzzy graphs such as interval-valued fuzzy resolving set and interval-valued fuzzy super resolving set. We also derived the properties of isomorphism in this topic and explained an application based on them.
(α,β)-Metric on Cartan Space
Authors: Robin Kumar
Published: January 2025

In Finsler geometry, a Cartan space or Cartan manifold refers to a special type of geometrical structure where there is a preferred connection or curvature, often associated with a homogeneous space that carries additional symmetries. In the present research paper, we have deduced necessary and sufficient conditions under which an (α,β)-metric, K(x,ω)=α(x,ω)+ϵβ(x,ω)+2k (β^2 (x,ω))/(α(x,ω))-k^2/3  (β^4 (x,ω))/(α^3 (x,ω)), on a Cartan space admitting h-metrical d-connection becomes a locally Minkowski and conformally flat space.
A Reduction Analysis of the Weiner Index on Predicting the Boiling Point of Hydrocarbons Using Graph Technique
Authors: R. Shankar
Published: January 2025

Introduction: This study investigates the link between graph eccentricity and boiling points for nonanes and decanes, demonstrating graph theory's ability to predict the physical characteristics of organic compounds.
Objectives: Inspired by Wiener’s work on topological indices, we create a novel power formula based on graph eccentricity to predict boiling points. This provides a more straightforward technique than typical wiener index computations.
Methods: Our findings show a substantial association between graph eccentricity and boiling temperatures, demonstrating the importance of structural characteristics in predicting chemical responses.
Results: This study advances chemical graph theory by proposing an alternate approach for estimating boiling points, underlining the importance of graph-based studies in understanding molecule structures and behaviours.
Conclusions: This research examines the molecular graph’s eccentricity for 105 nonanes and decanes. Using a power formula, we discovered a substantial relationship between the boiling point and the eccentricity of the chemical structure.
A Portable Server-Based Analysis of One or More Queuing Models
Authors: Sudhakar Jyothula
Published: January 2025

The paper presents a rational approach to analyse the administrative characteristics of Programming Characterised Systems administration. The perspective of "programming characterised systems administration" is intended to improve the efficiency and display of organisational figures. The complexity of organisations nowadays that are built around this concept necessitates characterising the adequate methods for the computational work qualities. The model proposed to be used for QoS analysis is represented by a Poisson appearance process with boundary λ and administration time given by hyper-outstanding dissemination with r phases. We analyse how different bundle size distributions impact Web hub displays. Specifically, we used deterministic, Pareto, dramatic, and typical dispersion to illustrate how the bundle sizes are changeable. The parcels' seasons of manifestation in each of the four information stream scenarios followed a similar dramatic dispersion. Then, in every case, the typical parcel pausing and stay season was broken. It should be noted that regardless of the applied bundle size circulation, the mean bundle size, parcel burden, and server consumption remained essentially the same; however, the article demonstrates that the completed exhibits varied greatly. The displays were constructed with the numerical information generated for the M/G/1/∞/∞/FCFS queuing mechanism.
Influence of Xception and DenseNet121 Architectures for Plant Disease Detection
Authors: Sunil D. Kale
Published: January 2025

This paper proposes the maximum achievable solution to the diagnosis of leaf diseases by employing deep learning and image enhancement tools.
A hybrid model has been developed, with the architectures using Xception and DenseNet121, to classify leaves into four groups as healthy, diseased, rusty, and scab. The model is trained using many different datasets of plant leaf images. The result is enhanced using data augmentation techniques to improve the robustness and generalization of the model. We program and adopt a method to use the Xception and DenseNet121 models. The combination model was trained under a 10-cycle training program, which is broken down into development, maintenance, and perturbation.
Other criteria to assess our method's performance include accuracy, loss, and validation scores. The results will depict the possibility of our method for early plant diseases detection that can impact agriculture through the timely intervention process. Overall accuracy achieved is 93.70%
Our research contributes to the exploration since the use of techniques in crop management and the control of diseases may increase yields and reduce the use of pesticides, thus the extension of precision agriculture.
Multi Fuzzy Iterated Function System Consisting of Generalized Fuzzy Contraction Mapping in Fuzzy Metric Spaces
Authors: S. C. Shrivastava
Published: January 2025

The research work in this paper combines two concepts Iterated Function System and Multi Fuzzy Fractal Theory in the formulation of a new Iterated Function System, Multi Fuzzy Fractal Iterated Function System. More precisely, some uniqueness and existence of the attractor were obtained in the new multi fuzzy fractal spaces. Some illustrative examples are given that will be useful for this research purpose. From then on, our results are generalizations of some recent results provided in the literature. We define a methodology for the construction of multi fuzzy IFS in multi fuzzy fractal space consisting of a generalized contraction mapping.
Comparative Study of Functionality Isolation in Multi-Application Smart Cards
Authors: Aarya Patil
Published: January 2025

A variety of domains, including finance, government, healthcare, and transportation, extensively use multi-application smart cards due to their ability to securely manage data and execute multiple applications. In such systems, the critical challenge remains in the isolation of the application for multiple effective uses so that no application gets unauthorized access to data and resources. In general, performance and implementation restrictions affect traditional isolation mechanisms such as OS partitioning, cryptographic techniques, and hardware-based TEEs. The methodology, context-aware approach for cross-resource sharing manages access dynamically based on contextual parameters such as the state of applications, security levels, or user activity without impairing the effects of security and while maintaining adequate performance efficiency. A fair overview of the existing methodologies coupled with the enforcement of dynamic access policies underlines potential in future development for multi-application smart card environments.
Comparative Analysis of SVM Variants for GST Fraud Detection
Authors: Vivek Vyas
Published: January 2025

This study explores various Support Vector Machine (SVM) variants and provides an analytical comparison to identify their effectiveness in detecting Goods and Services Tax (GST) fraud. GST fraud poses significant challenges to regulatory authorities and businesses, necessitating robust detection methods. SVM, a powerful machine learning algorithm, offers promise in this domain due to its ability to handle complex data and nonlinear relationships. Through a comprehensive examination of SVM variants, including linear SVM, polynomial SVM, and radial basis function SVM, this study assesses their performance in GST fraud detection. Additionally, computational efficiency and scalability are investigated to gauge the practical viability of each variant. The findings contribute to advancing the understanding of SVM's applicability in fraud detection contexts and offer insights into selecting the most suitable variant for GST fraud identification. Ultimately, this research aids stakeholders, including tax authorities and businesses, in implementing effective strategies to combat fraudulent activities and uphold fiscal integrity.
StudentEmoScan: Applying Machine Learning, Problem-Solving, and Decision-Making Algorithms to Analyze Emotional Patterns and Detect Mental Health Issues in Students
Authors: Neha Beegam P E
Published: January 2025

Mental diseases can have a major impact on people's cognitive processes and behaviours, emphasising the significance of early discovery for successful treatments. This article studies how social media monitoring might help discover underlying mental health disorders by analysing expressions, writing styles, and emotional states. The study investigates the effectiveness of machine learning algorithms in Predicting mental health consequences through social media data by examining numerous mental algorithms that regulate information processing and decision-making. Different approaches are compared and evaluated using a comprehensive evaluation of existing mental state detection algorithms, to identify the most promising methods for mental health detection. The study combines insights from methodological comparisons and literature reviews to address current issues and restrictions, as well as propose future research options to overcome these hurdles.
Cyber-Physical Systems: Implementation and its Emergence with Cybersecurity and Blockchain
Authors: Ritesh Rastogi
Published: January 2025

The term "Cyber-Physical Systems" (CPS) refers to technological systems that act as a bridge between the digital and physical realms. These systems enable computer processes to interact with real-world systems in a seamless manner. On the other hand, this interconnectedness raises serious issues over their cybersecurity. The technology known as blockchain, which is characterized by its decentralized and immutable characteristics, has emerged as an indispensable instrument for addressing these concerns. The purpose of this study is to investigate the overlap between blockchain and content-based systems (CPS), with a particular emphasis on the requirement of their integration, as well as existing developments, problems, and potential future breakthroughs. In addition to this, it launches a brand-new architectural framework with the intention of improving both the operational performance and the security of CPS. In addition, the study investigates the application of predictive analytics for the purpose of real-time risk assessment and explores the potential influence that quantum-safe protocols could have on the strengthening of CPS against new threats. Embedded systems, Blockchain, Cyber Security, and Cyber Physical Systems are some of the keywords that might be used.
Rhythmic Recognition: Harnessing Deep Learning for Cutting-Edge ECG Biometric Authentication
Authors: Amit Kumar
Published: January 2025

Machine learning (ML) and deep learning (DL) are essential for analysing physiological signals, such as electrocardiograms (ECG), which can be continuously collected without requiring specific user engagement. Deep and machine learning demonstrate the ability to process large volumes of complex data with minimal human intervention, thereby contributing to their popularity in the health care sector. Electrocardiograms (ECG) represent a contemporary method of biometric verification closely linked to the distinctive characteristics of an individual's heartbeats. This study presents a convolutional neural network (CNN) model aimed at enhancing the efficiency and usability of user authentication processes. The proposed model demonstrates high accuracy and has undergone pretraining for classification across various disciplines. This was utilized for the authentication procedure following specific structural modifications. The proposed model generates an output suitable for patient authentication that is subsequently stored in a database. The model generates the patient’s output upon the registration of a new patient. This output was subsequently utilized to compute the Euclidean Distance between the current patient's output and previous outputs stored in the database, resulting in a similarity score. Convolutional Neural Networks (CNNs) have been employed to design classification models that determine the electrocardiogram (ECG) corresponding to a specific individual based on a combination of feature sets produced during the transformation phase. The results indicate that, when adequate data are available for training, deep neural networks (DNNs) can achieve greater accuracy than many alternative methods.
Exploring Multimodal Interaction Strategies for Co-Located Mixed Reality Human-Robot Collaboration
Authors: Anurag Tiwari
Published: January 2025

This paper investigates how Mixed Reality (MR) technology could enhance human-robot interaction (HRI) in the workplace. We developed a system employing a Microsoft HoloLens and a Universal Robot UR5 that allows users to execute pick-and-place tasks using two distinct approaches of interaction: heading-based (HB) selection and hand-to- finger (H2F) selection. To make it simpler and more effective, the MR interface combines easy-to-use interaction methods like voice commands and gestures with real-time visualisation. In terms of task completion time, accuracy, and user contentment, sixteen participants participated in studies demonstrating HB selection was superior than H2F. H2F performed better on exact tasks, however, which implies blended approaches could have some success. The research reveals how MR may transform things by overcoming the issues with conventional interfaces, such as being difficult to grasp and demanding much of mental effort. According to the findings, MR-enhanced HRI finds use in several spheres including industrial robots, education, and healthcare. More research will be done on how to include flexible elements like object detection and sophisticated learning models if MR systems are to operate effectively in complex and changing environments. Through connecting the actual and virtual worlds, MR technologies enable people and robots to collaborate in better, more efficient, and simpler-to-use ways.
Anti Cancer Drug Response Prediction with Machine Learning and Data Driven Approaches
Authors: V.Mahalakhsmi
Published: January 2025

This research proposes employing sophisticated machine learning approaches to improve feature selection, model performance, and guess accuracy to predict cancer treatment outcomes. Ensemble learning, SVM models, decision trees, and bootstrapped examples improve accuracy and resilience. Reduced impurity metrics discover significant features, lowering dimensions and making models simpler to interpret in huge datasets. The recommended solution outperforms others with 0.85 accuracy, 0.83 precision, 0.80 memory, and 0.81 F1 score. The AUC-ROC score of 0.87 indicates that these tests detect genuine drug reactions well. The method effectively reduces the mean absolute error to 0.30. This research highlights how vital it is to apply sophisticated machine learning algorithms to enhance drug predictions, which might impact cancer patients' choices. This strategy helps us comprehend cancer therapy changes by personalizing treatment and improving forecasts. The goal is to improve patient outcomes and advance oncology.
Local Linearization Method in Studying stationarity of SechDAR Model
Authors: Omar Saber Mustafa
Published: January 2025

This research proposes a new model for nonlinear time series called the Secant Hyperbolic Double AR model of order k, SechDAR(k), which is based on the double autoregressive model with an augmented secant Hyperbolic function.  In 2007, Ling introduced this family of double autoregressive models, with the prototype model named DAR(P), where p is the order of the model. This approach addresses data volatility that leads to heteroscedasticity. Using dynamic principles and local linear methods, this research analyzes the stability conditions of the model.   First, we approximate the previously mentioned model to a linear difference equation using a local linear strategy.   Secondly, the roots of the characteristic equation are used to assess stability. In the end, the stability conditions for the above model are applied using data showing the average monthly closing price of heating oil in US dollars from 1990 to 2024.   To evaluate the accuracy and quality of the models' fit on the actual data, we use the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC). In the application section, we use the stability condition to evaluate the stability for each order of SechDAR(k) from 1 to 6.
Leveraging Remote Sensing and AI for Smart Crop Monitoring and Management
Authors: Priya.R L
Published: January 2025

Data-driven, farmer-centric solutions are critical in the dynamic field of agriculture, which is defined by constantly shifting market needs and environmental conditions. In order to transform farming methods through the integration of cutting- edge technologies, this article introduces the comprehensive Crop Planning and Market Information System. The proposed system optimizes crop health by facilitating disease identification and providing customized cures through the use of remote sensing and AI techniques. Moreover, it makes customized crop suggestions that are in line with market trends by utilizing soil analysis and meteorological data. The system provides farmers with up-to-date market information and chances for agricultural stock launches, addressing their vital need to be informed about market conditions. It gives crop planning abilities, utilizing a choice tree set of rules. Farmers can enter their soil parameters, permitting the machine to endorse the most suitable crops for cultivation primarily based on their unique situations. The proposed system also offers disease detection functionality, leveraging Efficient Net B3 for image processing. This function allows farmers to capture photographs of their plants and accurately come across any diseases present. To enhance accessibility, the proposed mobile application features a user-friendly dashboard supporting multiple languages and provides access to agricultural training and educational programs. Ultimately, the system aims to empower farmers, improve crop yields, and drive sustainable agricultural practices in an ever-evolving world.
Visualizing and Proving the Collatz Conjecture: Ananta-Graph Approach
Authors: Vidyashree H R
Published: January 2025

The Collatz Conjecture, proposed by Lothar Collatz in 1937, is an unsolved mathematical problem often called the “3x+1 problem” or the “Hailstone sequence”. It suggests that starting with any positive integer and applying a specific process: dividing an even number by 2 or multiplying an odd number by 3 and adding 1 will eventually result in the sequence reaching the cycle 4, 2, 1 regardless of the starting value. Despite its simplicity, no one has been able to prove or disprove this conjecture for all positive numbers. The behaviour of the Collatz Conjecture can also be illustrated through a graphical representation, showing how the sequence for different starting numbers evolves before reaching the repeating cycle of 4, 2, 1. In this paper, we highlight the various graphical representations of the Collatz sequence and finally provide the proof of Collatz conjecture by plotting the Ananta-graphs.
Applications of Disease Identification in the Advancement of Intuitionistic Fuzzy Sets with Interval
Authors: K Rajesh
Published: January 2025

The Euclidean, Normalized Euclidean, Normalized Hamming, and Hamming distance measurements of IVIFSST are among the various distance measures examined in this article. The IVIFSST component plays a more important role in this case since the second type of IVIFS provides the best answer for determining the shortest distance when making a decision, even though the IFS and their extensions of membership grades as well as the non-membership grades are not constantly feasible to our fulfillment. There is a reasonable possibility that a non-zero hesitation part will exist at every evaluation point when employing this notion, especially in medical diagnosis. The IVIFSST played a crucial part in the medical field in determining reliable identification values. Lastly, we determine the shortest path between utilizing normalized hamming distance measurements over IVIFSST to assess patients and illness.
Unleashing Customization in GANs through Delineation guided Image Synthesis
Authors: M. L. Dhore
Published: January 2025

Interacting with AI systems through text alone can be challenging, especially when conveying complex visual concepts. This paper presents an innovative AI system that leverages a multi-GAN framework—integrating specialized Generative Adversarial Networks (GANs) such as Pix2Pix, SketchGAN, DCGAN, and ESRGAN—to interpret and generate high-fidelity visual content based on user sketches. By employing these GANs in a sequential pipeline, the system optimizes image synthesis quality through targeted stages, from sketch refinement to high-resolution enhancement. This structured approach enhances real-time interaction by improving image editing capabilities, enabling users to communicate more intuitively with AI. This rapid and precise visualization tool streamlines design workflows in industries like architecture and fashion, while also advancing AI towards more sophisticated, human-like intelligence that fosters creativity and production efficiency.
Automated Phishing Detection Through URL Analysis and Machine Learning
Authors: Vijaykumar
Published: January 2025

Phishing attacks are categorized as one of the greatest threats to cybersecurity. Threat, which is misinformation to make the user provide important and personal information via fake websites or emails. This paper also realizes the notion of a machine phishing detection-based learning tool aimed at classifying URLs they designated as ”phishing”, ”suspicious,” or ”safe.” Utilizing a Random Forest classifier, the system examines URL-based characteristics inclusive of URL. length, special symbols, and the usage of HTTPs to distinguish between real URLs and fake ones (or phishing URLs) with high accuracy. The model was trained and validated on a given dataset of labeled URLs, achieving 95.2% accuracy of classification higher compared to other results. For the sake of usability, the detection tool is implemented as a web application for real-time classification and the results of the classification. user-friendly interface. This is because the performance and metrics such as accuracy and speed depend on them. accuracy by using measures such as precision, recall, and F1-score. effectiveness. This paper will help to improve the level of online security. in an endeavor to provide an automated approach that deprecates dependence on minimizing human judgment and can efficiently detect cases of phishing threats.
In-Depth Exploration of Technical Indicators for Stock Market Prediction Using Machine Learning and Reinforcement Learning
Authors: Chandrayani Rokde
Published: January 2025

Introduction: This research focuses on the comprehensive exploration of technical indicators for stock market prediction, leveraging machine learning and reinforcement learning methodologies. The study aims to examine these indicators in detail, evaluate their relevance and utility, and assess their integration into predictive models. The research also investigates the efficacy of machine learning algorithms and reinforcement learning agents in forecasting stock market trends. Accurate stock market prediction is crucial in financial markets, where informed decision-making, risk management, and capital allocation depend on precise and timely forecasts. The complexity of financial markets necessitates advanced computational techniques, positioning the application of machine learning and reinforcement learning as a vital area of study.
The research involves a rigorous analysis of technical indicators, evaluating their historical performance and predictive capabilities. Machine learning models are empirically tested to determine their effectiveness in leveraging these indicators for enhanced forecast accuracy. Additionally, the study explores the innovative use of reinforcement learning agents, which autonomously navigate market complexities using historical data and reward-driven mechanisms. The findings contribute to a deeper understanding of technical indicators and provide empirical evidence for the effectiveness of machine learning and reinforcement learning models in stock market prediction.
By emphasizing the empirical nature of the study, this research offers a valuable resource for financial market practitioners and researchers aiming to harness advanced technologies for strategic decision-making. It underscores the potential of these technologies to transform investment strategies in an increasingly data-driven financial environment, marking a significant contribution to the field of financial technology
Redefining Network Resilience: A Comprehensive Metric for Wireless Communications
Authors: Prema Kandasamy
Published: January 2025

As wireless communication networks become increasingly integral to modern infrastructure, ensuring their resilience against failures and attacks is paramount. This study introduces the Resilience Connectivity Index (RCI), a novel topological metric designed to evaluate the resilience of wireless networks. Unlike existing indices, RCI integrates aspects of connectivity, redundancy, and fault tolerance to provide a comprehensive measure of network robustness. We apply RCI to a real-world wireless communication system, comparing its performance with established topological indices such as the Wiener Index, Estrada Index, and Randić Index. The results demonstrate that RCI offers superior insights into network resilience, making it a valuable tool for network design and optimization.
Nonlinear Analysis in Gaseous Dielectric Using Finite Element Method (FEM)
Authors: R Bharanidharan
Published: January 2025

Nonlinear analysis of gaseous dielectrics using Finite Element Method (FEM) is for optimizing high-voltage systems, addressing complex electric field behaviours in materials in eco-friendly alternatives. This paper discusses the mathematical background, application of GIS and circuit breakers, issues in computational modelling, and future trends such as using different dielectrics.