bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Emitter Prediction in Heavy Duty Vehicles Using Recurrent Convolutional Neural Network
Authors: N. Saikiran
Published: January 2025

Predicting emissions from Heavy Duty Vehicles (HDV) accurately is crucial for environmental monitoring and regulatory compliance. These vehicles, particularly diesel trucks, significantly contribute to urban air pollution, Particulate Matter (PM), and Nitrogen Oxide (NOx) emissions. Effective vehicle emissions control and detection are crucial for protecting public health. Regular vehicle inspections typically conducted every six months or annually, aim to identify and filter out high-emission vehicles at testing stations. This research compares the performance of Faster Mask Recurrent Convolutional Neural Networks (FMRCNNs) to existing in predicting vehicle emissions, aiming to improve prediction accuracy and enable more effective monitoring and control. This study presents a novel approach using FMRCNN that leverages convolutional layers for spatial feature extraction and recurrent layers for modeling temporal dependencies. The FMRCNN model is thoroughly evaluated against CNN and LSTM models using a comprehensive dataset of heavy-duty vehicle emission measurements. The results show that the FMRCNN model significantly outperforms CNN and LSTM models in prediction accuracy and computational efficiency, making it a superior choice for emission prediction tasks. These findings demonstrate the potential of FMRCNN to improve emission predictions for HDV, providing a more accurate and reliable tool for environmental monitoring and helping to ensure compliance with regulatory standards. FMRCNN can reduce harmful emissions and promote a cleaner environment by enhancing emission prediction capabilities.
Forward-Backward System for Fuzzy Doubly Stochastic Differential Equations
Authors: Falah H. Sarhan
Published: January 2025

Our main goal in this work is to study a general class of forward-backward fuzzy double stochastic differential equations systems ( in short FBFDSDEs ) in a precise manner, not relaying the equations on the solution process only, but rather on their law. By verifying the existence and uniqueness of the solution to the forward equation of the general system FBFDSDEs as well as the backward equation under Lipschitz conditions, we prove the existence of the maximum solution of the general system FBFDSDEs under monotonic continuity conditions.
Deep Learning-Driven Multi-Modality Image Fusion: Enhancing MRI and CT Registration and Diagnostic Performance
Authors: Amita Jajoo
Published: January 2025

Improving the accuracy of diagnosis and treatment planning depends much on multimodality medical imaging, particularly integrating MRI and CT images. This work aims to build a quick and accurate image registration and fusion system capable of merging beneficial elements from MRI and CT images. Preprocessing actions like Gaussian smoothing, contrast enhancement, and normalisation helped to guarantee acceptable quality of the sources for alignment. Phase correlation and Fourier shift was used for registration; then weighted average was used for merging. Quantitative metrics like PSNR, SSIM, and entropy revealed improved structural similarity and information abundance in the combined images. Furthermore improving the edge features and reducing the noise by using the UNET deep learning model produced diagnostically superior results. The findings revealed that the framework could achieve appropriate integration by keeping the sharpness of structures from CT and the specifics of soft tissues from MRI. Although the technique is effective, additional research on more sophisticated fusion techniques and tiny alignment issues has to be done. This work offers a consistent approach for multi-modality imaging, therefore supporting therapeutic applications requiring good view of all interior components.
Predicting Mental Disorders with Nonlinear Variational Inequalities: Insights from Diverse Online Social Network Datasets
Authors: Sushant.A.Patinge
Published: January 2025

Introduction: Online social networks provide a wealth of data for predicting mental disorders through the analysis of user-generated content, network connections, and behavioural patterns. This research explores deep learning and machine learning techniques to develop accurate and scalable predictive models, offering promising opportunities for early detection, intervention, and personalized support in mental health care. By reviewing current research, challenges, and various deep learning architectures, the paper highlights the advancements and future directions in utilizing these techniques for mental disorder prediction.
Coincidence Point Theorem on Parametric Cone Metric Space for AJ's Pair
Authors: S.Dhivya
Published: January 2025

The article is based on the coincidence point theory especially in parametric cone metric space. Some coincidence point theorems of contractive mappings are proved by omitting the normality conditions.
The Stochastic Analysis of a Diabetic Person's Blood Glucose Levels During Treatment
Authors: T.Mahalakshmi
Published: January 2025

This paper assumes a diabetic person with n stages and the absorption stage is a controlled stage. Blood glucose level of a patient with a damaged pancreas may pass through the fair control stage, poor control stage, and very poor control stage several times before it reaches the controlled stage during the treatment. Blood glucose level can identify from any one of the n stages and it can be moved to other stages easily. This may form number of cycles of fair control stage to poor control stage to fair control stage based on the changes in the blood glucose level of the patient. This paper provides two types of models. In model 1, r to s to r cycle, and in model 2 u to v to w to u cycle. This paper obtains the probability generating function of the number of cycles before the glucose level is controlled and their numerical illustrations are provided.                                                 
Introduction: In model 1,Blood glucose level of a patient with damaged pancreas may pass through fair control level (say stage r) and poor control level (say stage s) several times, between them before the glucose level reaches controlled level due to the treatment. This may form number of cycles of fair control level to poor control level to fair control level based on the changes in the blood glucose level of the patient.  This is taken as one r-s-r cycle. In model 2, Blood glucose level of a patient with damaged pancreas may pass through fair control level (say stage u) ,poor control level (say stage v)and very poor control level (say stage w) several times, between them before the glucose level reaches controlled level due to the treatment. This may form number of cycles of fair control level to poor control level to very poor control level to fair control level based on the changes in the blood glucose level of the patient. This is taken as one u-v-w-u cycle. For more details on such deficiency and disorder, one may refer to  [3], [4], [5]. Using the Mathematical approach given in [1], [8], [9], and [10], we study the case of a diabetic person.
Results: The results of  model 1 in section 2 and model 2 in section 3. In this sections are derive the probability generating function, mean and variance of number of cycles occur in our model during treatment time.
Conclusions:   In Model 1, if a diabetic person start the treatment in fair control stage then the person can reduce the expected value of number of r-s-r cycle. In Model 2, when the transition probability of controlled blood glucose level (absorption stage) is high, the expected value of r-s-r cycle is decrease
New Aspects of Fourth-order Differential Subordination and Superordination via Meromorphic Multivalent Functions with a Novel Integral Operator
Authors: Sara Falih Makttoof
Published: January 2025

In this paper , by using a new integral operator   of meromorphic multivalent functions, we obtain some applications for highr-order (fourth- order) differential subordination and superordination result ,Also we obtain several sandwich-type results..
Multimodal Deep Learning Framework for Intelligent Video Querying and Graph Recognition in Educational Content Analysis
Authors: Y. N. Thakare
Published: January 2025

Video content has become increasingly complex, presenting challenges in extracting and analyzing multi-modal information. Traditional search mechanisms fall short in comprehensively processing and interpreting multimedia resources. VIDIWISE addresses these challenges through an innovative approach integrating multiple advanced technologies. The system leverages deep learning models like ResNet18 for visual feature extraction, Whisper for audio transcription, and machine learning techniques for keyframe detection and text recognition. By employing computer vision algorithms, the system performs OCR on video frames, clusters visual features, and generates a comprehensive, searchable transcript that combines textual and visual insights. The proposed system transforms video content analysis by creating a unified approach that extracts and correlates information across different modalities. Through intelligent processing techniques like feature clustering, timestamp-based segmentation, and semantic analysis, VIDIWISE enables precise and efficient video content retrieval.
An Enhanced Euler's Methods Based on Heronian and Cubic Means for Numerically Solving Fuzzy Differential Equations
Authors: A. Hari Ganesh
Published: January 2025

Introduction: In recent years, fuzzy differential equations have become an increasingly popular topic. In engineering and scientific fields, the study of numerical solutions for fuzzy differential equations is crucial. For the purpose of solving fuzzy initial value problems, several numerical approaches have been devised.
Objectives: This attempt aims to create enhanced Euler's methods for numerically solving fuzzy differential equations to generate approximate solutions for situations too complex to be expressed precisely.
Methods: Euler's Method, Modified Euler's Method, and Improved Euler's Method using Heronian Mean and Cubic Mean are some of the enhanced Euler's techniques that this research seeks to provide. Utilising Zadeh's extension principle for fuzzy sets, we adapt Euler's traditional methods to tackle this dependency issue inside a fuzzy framework.
Results: A numerical example contrasting the proposed methodologies with the conventional Euler's Method is presented. The efficacy of the proposed approaches is demonstrated by numerical solutions and their geometric representations. Moreover, the numerical example illustrates the satisfactory precision of the upgraded Euler methods. 
Conclusion: This research proposal aims to address first-order differential equations through two innovative methods: the Heronian Mean and Cubic Mean of enhanced Euler's approaches for fuzzy primary value. The proposed methods produce excellent outcomes and are efficient, precise, and user-friendly.
Comparing Machine Learning Algorithms for Predicting Cryptocurrency Prices: An Analytical Study
Authors: Pappula Ashok
Published: January 2025

Introduction: Due to advances in financial technology, cryptocurrency is a new asset with outstanding academic potential. Price volatility and dynamism make Cryptocurrency prediction difficult.
Objectives: This paper presents three Recurrent Neural Network (RNN) methods for predicting Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) prices to predict the price of Cryptocurrency.
Methods: This study uses two Machine learning algorithms, Random Forest (RF) and Gradient Boosting (GB), to predict the prices of Bitcoin (BTC), Binance Coin (BNB), and Ethereum. (ETH)
 Results: Two machine learning algorithms predict BTC, ETH, and BNB prices. The model's accuracy was assessed using performance metrics. We followed this by comparing the actual and expected prices of the models. The GB algorithm surpassed its competitor, attaining MAPE values of 2.884169154 for BNB3.612352523 for ETH and 4.593971246 for BTC.
Conclusion: The prices of cryptocurrency swing regularly. Since the cryptocurrency market is nonlinear, time series data is challenging to evaluate when making price predictions. Machine learning algorithms are used in many financial and economic activities. Investors, researchers, and professionals need cryptocurrency value forecasts. In this study, GB and RF algorithms are used to predict the prices of BTC, ETH, and BNB
Coefficient Estimates and Fekete-Szegö Inequality for a Certain New Subclass of Bi-Univalent Functions by Using Generalized Operator with Bernoulli Polynomials
Authors: Muhammed Salih Muhammed
Published: January 2025

In this study, we have delineated a novel subclass  of  bi-univalent functions in the unit disk and established on the coefficients  also by using Bernoulli polynomials. Moreover, the Fekete-Szegö problem within this subclass has been resolved.
A Study On Vibrations of Nanorod using Timoshenko Beam Theory Under the Influence of Magnetic Field
Authors: Siva Priyanka Alaganandhan
Published: January 2025

The transverse vibrations of a thin graphene rod under the influence of a magnetic field are investigated. An analytical method is developed to solve the frequencies of the transversely vibrating graphene rod. Explicit results are obtained using Timoshenko beam theory as a function of wavenumber and phase velocity and numerical results are performed. It is observed that when the difference in magnetic field strength is minor, the influence of beam slenderness on frequencies becomes magnified. Finally, it is found that the hardness or softening impact of the beam model can be controlled by varying the field strength.
Building a Sustainable Future: Understanding Generation Z's Purchasing Intention Toward Green Insurance
Authors: Sharon Elsen Abraham
Published: January 2025

Introduction: Green insurance involves offering insurance products and services that foster environmental sustainability. These policies aim to incentivize eco-conscious practices by providing coverage for environmentally responsible initiatives, such as renewable energy projects, energy-efficient technologies, and sustainable behaviors. As concerns over environmental conservation grow, particularly among younger generations, interest in green insurance is steadily increasing.
Objective: The purpose of this study is to identify the variables influencing Generation Z's intention to purchase green insurance in South India. towards green insurance in South India.
Methods: A survey questionnaire was given to a sample of Generation Z customers in the area in order to gather data for the study. The study focused on four key variables green attitude, perceived effectiveness, subjective norms, and willingness to pay. To create and validate a purchasing intention model, the study uses exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM).
Results: Results revealed that green attitude, perceived effectiveness, subjective norms, and willingness to pay are having positive impact on green purchase intention of Gen Z. Our findings not only provide new insights into the green purchase intention of Generation Z but also offer practical implications for marketing and policy-making.
Conclusion:  These insights are vital for insurance companies looking to tailor their products and marketing strategies to better appeal to Generation Z, thereby fostering the growth of the green insurance market in India.
Radiative Flow and Heat Transfer of Chemically Reacted Casson Nanofluid Over a Wedge Under the Influence of MHD and Viscous Dissipation
Authors: Ch. Vittal
Published: January 2025

The effect of thermal radiation, viscous dissipation with magnetohydrodynamic and chemically reacted Casson Nano fluid flow across a moving wedge and convective boundary condition in existence of internal heat generation/absorption is studied, the obtained results are presented. The simple governing partial differential equations are altered into non-linear ordinary differential equations through employing suitable similarity transformations. The obtained non-linear ordinary differential equations are effectively elucidated numerically through Keller-Box method by use of MATLAB software. The variation of the pertinent constraints on velocity, temperature and nanoparticle volume fraction are explored through graphs. The graphs of skin friction coefficient, Nussult number and Sherwood number are plotted as a function of physical parameters for different values. The present results are well matched with the previous reported work. The present work demonstrates the nanoparticle volume fraction profile decrease by enhance of chemical reaction, Brownian motion, Schmidt parameter values and increases with increment in thermophoresis parameter values.
Pure and Weakly Pure Elements in Lattice Modules
Authors: Santosh Mitkari
Published: January 2025

This study concerns with investigation of Pure and Weakly pure elements of  lattice modules. An element N of M is called pure, if aN = N ∧ a1M , for each a of L. An element K of M is called weakly pure, if aN = N ∧ a1M , for each idempotent element a of L. Also, this study obtains the relation between pure, idempotent and multiplication elements of lattice modules.
Prime Labeling of Bull Graph
Authors: M. Ganeshan
Published: January 2025

Let G be a graph. A bijection f:V→ {1,2,…..|V|} is called a prime labeling [3] if for each edge  e=uv in E, we have GCD{ f(u),f(v)}=1. A graph that admits a prime labeling is said to be a prime graph. In this paper we show that bull graph  admits Prime labeling in the context of variety graph operations namely duplication of vertex, fusion of vertices and Switching in Bull graph.
Locally Harmonious Chromatic Number of Certain Tree-Structured Networks
Authors: M. Siva
Published: January 2025

Graph coloring is one of the oldest and best-known problems of graph theory. The locally harmonious coloring of G is a proper vertex coloring in which adjacent edges receive different color pairs [3]. In another way, all the vertices in N [v] receive different colors for all v in G. The minimum number of colors required to obtain a locally harmonious coloring of a graph G is called the locally harmonious chromatic number of G and is denoted by h1(G). In this paper, we investigate the locally harmonious chromatic number of slim tree, hypertree, shuffle hypertree and l-complete binary tree.
Nonlinear PESTLE Framework-Based Sentiment Analysis Using Machine Learning for Root Cause Identification
Authors: Sudarshan S. Sonawane
Published: January 2025

Sentiment analysis has gained significance in understanding the intricate dynamics of public sentiment in contemporary social media and news communication. This research proposes a nonlinear sentiment analysis model that integrates the PESTLE framework with machine learning techniques to identify and quantify the influence of external factors—political, economic, social, technological, legal, and environmental—on sentiment patterns. The model employs a Support Vector Machine (SVM) classifier with nonlinear kernel transformations to capture complex relationships between sentiment triggers and influencing factors. Experimental results demonstrate the efficacy of the nonlinear approach in correlating sentiment classifications with their intrinsic influences, offering a more robust understanding of root causes. Future work will explore advanced nonlinear classifiers and additional external factors for enhanced real-world applicability.
Machine Learning with IoT Based Email Phishing Detection
Authors: Choppa Ananda Kumar Reddy
Published: January 2025

Email is a popular method for communicating for individuals as well as professionals. Email is routinely used to send sensitive personal data such as passwords for accounts, credit report specifics, and banking information. As a result, cybercriminals may profit from the information, making them lucrative. Con artists utilise phishing to act as trusted sources in order to gain confidential information from people. By employing false pretences, the sender or a phishing email might deceive you into exposing personal information. This work approaches the detection of compromised emails as a classification issue, and it demonstrates how machine learning algorithms are utilized to determine whether emails are phished or not. The SVM classifier has a maximum accuracy of 0.998 percent in classifying emails.
An Ellustrative Research on Challenges and Opportunities Faced Due To Evolving Legal Landscape of Online Gaming in India
Authors: Garima Dhaka
Published: January 2025

The legal framework for online games in India is dynamic, although its evolution is still impeded by a lack of proper central or nationwide legislation. As the market introduces new games, the regulatory regime adjusts, but only at a place that lags behind the pace of growth of the industry. Online gaming in India may face the challenges of the evolving legal landscape but also offers ample opportunities. Since India is home to one of the world's largest online gaming markets, it is imperative that the current regulatory framework, which encompasses player safety and responsible gaming, be revised in order to guarantee the sector's overall prosperity. Some forms of online gaming sites may be impacted by the Rules, 2021 for IT (Intermediary Guidelines and Digital Media Ethics Code), which are examined in this article. At the same time, it delves into recent judicial rulings that have clarified, as they pertain to internet gaming, the distinction between skill games and games of chance. Concerns such as preserving transparency, reducing malpractice, and treating addiction-related difficulties are addressed in this research, which sheds light on the improvements in regulatory activities in India by relying on national experiences. The study goes on to talk about how responsible gaming campaigns, industry standards, and self-regulation will shape the laws that govern online gambling in the future. The goal is to create a long-lasting regulatory structure that can adjust to new player safety standards, encourage responsible gambling, and help the India sector expand. Through this analysis, the study hopes to contribute to the discussion on effective regulation that balances innovation, safety, and market growth within the online gaming ecosystem.
GQF-PSO Algorithm for Temp Control Loop in Process Control
Authors: Jotiram Deshmukh
Published: January 2025

In the real time of process optimization, the "Gaussian Quantum Firefly-PSO Algorithm for PID Controlled Temperature Loop of Process Plant" is an innovative method. Proportional-Integral-Derivative (PID) management of the temperature loop, firefly-inspired optimization, and quantum computing are all combined in this innovative technique. This algorithm offers the possibility of accurate and efficient optimization of individual loops by including Gaussian distributions and quantum behaviors into the firefly-PSO framework. This would guarantee improved performance, stability, and safety in intricate industrial processes. This introduction highlights the potential of this novel technique by summarizing the convergence of process control, firefly-inspired optimization, and quantum computing.
The Opticode: A User-Centric Tool for Enhancing Software Efficiency and Minimizing Errors Through Dead Code Elimination and Loop Invariant Code Motion Techniques
Authors: Tulshihar Patil
Published: January 2025

Introduction: This article introduces OptiCode, a complex software tool that uses loop invariant code mobility and dead code reduction, among other advanced code optimization techniques, to improve code efficiency and decrease compile time. Using Loop Invariant Code Motion (LICM) and Abstract Syntax Trees (ASTs) for precise code analysis, OptiCode efficiently detects and eliminates redundant code, as well as optimizes loop structures by removing 4.87% of dead code with an efficiency of 5.38. OptiCode outperforms other apps in comparison, as seen by the considerable compile time savings and excellent efficiency ratings that it achieves. Objectives: To remove the unused code and elements affecting the efficacy of the code.
Methods: Source code is passed as an input then the lexer performs the tokenization. Tokenized words are processed by the parser to assess the syntax of the code. Customized Abstract Syntax Tree removes the dead code, and the data is passed to Customized Loop invariant code Motion which optimize the looping structure in the code. At last, the optimized code is generated.
Results: OptiCode outperformed Taskapp (3.89), Agilla (3.67), and Rfmtoleds (3.45) with its greatest efficiency rating of 5.38 on a 10-point scale in our comparison research. The 731 lines of code in the OptiCode codebase include 150 lines of dead code and 57 variables that aren't used
Conclusions: The code is optimized to save space and time. Performance increases as number of lines increases.
The Impact of Psychological Capital on the Creative Capacity of Students in Project-Based Learning Courses at Universities in Vietnam
Authors: Ho Thi Hien
Published: January 2025

Creativity and innovation capacity is a key factor in promoting sustainable development across all aspects, and for universities, this becomes even more significant. This study aims to examine the impact relationships of psychological capital on students' creative capacity in project-based learning modules at universities in Vietnam. Using a quantitative research method through exploratory factor analysis (EFA) and structural equation modeling (SEM), the study involves a sample of 468 students who are currently studying at universities in Vietnam and have either directly engaged with or previously experienced project-based learning environments. The results of the study provide valuable contributions both scientifically and practically. Although psychological capital was found to have no direct effect on students' creative capacity, it was found to have an indirect impact through two mediating factors: satisfaction level and environmental adaptability. Additionally, both satisfaction and adaptability were shown to have a positive effect on students' learning motivation. Based on the research findings, the authors propose several recommendations to enhance psychological capital, thereby improving students' creative capacity in project-based learning courses at universities in Vietnam.
Complex Tangent Trigonometric Approach Applied to (γ, τ )-Rung Fuzzy Set using Weighted Averaging, Geometric Operators and its Extension
Authors: Raed Hatamleh
Published: January 2025

This paper presents a new method that generates complex tangent trigonometric (γ, τ )-rung fuzzy sets. Thisarticle will deal with averaging, geometric, generalized weighted averaging, generalized weighted geometricusing complex tangent trigonometric (γ, τ )-rung fuzzy set. We used an aggregating model to get the weightedaverage and geometric. Several sets with significant characteristics will be further studied using the algebraicapproaches.
Geometrical Abstraction on VAN Hiele Levels Among Mathematics Undergraduates
Authors: Asdar Ahmad
Published: January 2025

This research aims to analyze the geometrical abstraction on van Hiele levels among Mathematics Undergraduates at The State University of Makassar by utilizing an explanatory sequential mixed methods approach. Research subjects are selected by using a purposive sampling method from each van Hiele level to analyze their geometrical abstractions through geometrical abstraction tests and interviews. Data analysis includes: 1) statistical analysis, 2) qualitative data collection, 3) thematic analysis, 4) result interpretation, and 5) source triangulation. NVivo 12 software assists in coding qualitative data. Results indicate 27.7% of undergraduates are at the visualization level, 37.2% at the analysis level, 14.1% at the informal deduction level, 1.0% at the formal deduction level, and 2.6% at the rigor level. On the visualization level, abstraction involves understanding geometry concepts contextually. On the analysis level, it involves using concepts in complex contexts. On the informal deduction level, students connect geometry concepts, while on the formal deduction level, they deepen understanding through mathematical concepts. Furthermore, on the rigor level, students construct and connect various mathematical concepts broadly. This study also found higher van Hiele levels lead to more comprehensive geometrical abstraction. Future studies are recommended to explore the relationship between van Hiele levels and geometrical abstraction.
Enhancing IoEV Efficiency: Smart Charging Stations and Battery Management Solutions
Authors: Supriya Arun Wadekar
Published: January 2025

The proliferation of electric vehicles (EVs) necessitates a robust infrastructure for efficient charging and battery management. This research work focuses on the and aims to enhance the charging processes, optimize charging station operations, and improve battery management using advanced machine learning techniques. The study proposes a comprehensive framework integrating IoT and machine learning to monitor, analyze, and predict charging needs, station availability, and battery health in real-time. By leveraging predictive analytics, the research seeks to minimize charging times, prevent overloading of charging stations, and extend battery lifespan. The findings from this study will contribute to the development of a smarter, more efficient, and sustainable EV ecosystem, addressing the critical challenges of scalability, reliability, and user convenience in the growing field of electric transportation. This research work explores the integration of IoT and machine learning to create a smart ecosystem for electric vehicles (EVs). Focusing on charging, charging stations, and battery management, the study develops predictive models to optimize charging schedules, reduce wait times at stations, and enhance battery longevity. The proposed system aims to provide real-time insights and automation, improving the efficiency and user experience of EV charging networks. By addressing key challenges in EV infrastructure, this research contributes to the advancement of sustainable and intelligent transportation solutions.
A Type 2 Fuzzy Logic Model to Predict the Force During the Turning Process
Authors: Kumar Devapogu
Published: January 2025

This study introduces a compensatory fuzzy logic system for modelling turning processes by combining the Gaussian mixture model (GMM) with an intervals-based type 2 fuzzy logical system. To simulate the turning process, an IT2FLS is first evoked. The input variables of this model are mapped to the force of cutting and surface quality. The second step is to fix the error residuals by using the GMM within the IT2FLS framework. Most models are built on the premise that the error is normally distributed, which is where the concept for such an inclusion comes from. During its development, the GMM takes stochastic, un-delayed behaviours into account while also refining the derived rules The compensating fuzzy logic system can effectively manage uncertainties, predict the force of cutting and surface quality, and provide users with a comprehensive understanding of the turning processes, according to experimental validation.
Deep Reinforcement Learning for Traffic Flow Optimization in Urban Planning
Authors: Amar Bharatrao Deshmukh
Published: January 2025

Traffic jam becomes a major problem on a worldwide scale these days. The number of vehicles has risen considerably, but the capacity of roadways and transportation infrastructure to manage the additional congestion has not yet increased proportionately. Public health and society are suffering as a result of the rise in road congestion and pollution caused by traffic. In order to minimize traffic congestion, this research develops a revolutionary reinforcement learning (RL)-based approach. Researcher have created a novel deep Q network (DQN) and seamlessly incorporated it into the system. The applied DQL model cut the length of traffic waits by 50% and made each lane more appealing by 10%. The outcomes show that the approach is successful at establishing strict standards for reducing traffic. The proposed system shows that that RL can efficiently control traffic traffic snarl-up in cities quickly without compromise in the environmentally friendly nature. Added on utilizing RL may considerably ease traffic snarl-up in cities.
Real Time Monitoring and Control of Pump by Deep Reinforcement Learning
Authors: Paromita Goswami
Published: January 2025

Flood prevention is aided by rainwater pumping stations situated close to cities or agricultural regions. These stations activate a suitable number of pumps, each with a different capacity, in response to the actual amount of rainwater that is falling. Unfortunately, effective control is typically lacking when rule-based pump operations are used in isolation to monitor basin water levels. Diminishing the number of switches that is on or off for the pumps at rainfall stations is just as important as keeping the maximum water level low to avoid floods. Pump switch frequency reduction reduces maintenance expenses by lowering the chance of mechanical failure. This paper presents a method for operating rainwater pumping stations in real-time utilizing Deep Reinforcement Learning (DRL). The goal is to meet all of these operational requirements at the same time, using only data that is presently noticeable like inflow, rainfall, place of storage amount, water level basin and outflow. It was trained using simulated rainfall data created using the Huff technique with different return periods and durations. Experiments were carried out using the Storm Water Management Model (SWMM), which was set up to mimic the rainwater pumping station. Next, the suggested DRL model's efficiency was contrasted with the station's present rule-based pump operation.
A Multimodal and Interpretable Deep Learning Framework for Early Detection of Blood Cancer Using Clinical Data
Authors: G. Chinna Pullaiah
Published: January 2025

Diagnosing blood cancer at early stages presents challenges due to limitations in methods that rely on single data sources, which often fail to provide comprehensive insights. The study introduces a Multimodal and Interpretable Deep Learning Framework (MIDLF) that combines patient symptoms, imaging, and laboratory results to predict blood cancer with explainable outputs. The framework integrates a graph-based model for encoding symptoms, a hybrid Vision Transformer (ViT) and Convolutional Neural Network (CNN) for imaging analysis, and an attention-driven module for laboratory data. A gated attention mechanism fuses these inputs, capturing relationships across modalities. Preprocessing includes graph-based embeddings for symptoms, self-supervised learning for unlabeled imaging data, and generative models to address missing laboratory values. Evaluation used the BIOBANK dataset, which includes multimodal clinical data. The framework achieved an accuracy of 94.8%, a recall of 95.2%, and an AUC-ROC of 97.1%. These results were benchmarked against Cell Scoring Neural Network (CSNN) and Three-Stage Feature Selection with Twice-Competitional Ensemble Learning Method (TSFS-TCEM), demonstrating improved prediction consistency across metrics. Clinician reviews validated feature importance outputs from SHAP and Layer-wise Relevance Propagation, showing alignment with clinical reasoning. This framework addresses challenges in integrating diverse clinical data and explaining model predictions. Results suggest that combining symptoms, imaging, and laboratory data enables more informed diagnostic predictions, facilitating early detection of blood cancer.
Hybrid Framework for Twitter Data Sentiment Classification using MLP-Deep Learning
Authors: L. Sudha Rani
Published: January 2025

Organizations collect data on user opinions from social media, enabling research into trends and behavior. Classifying text by identifying sentiments within content is complex due to varying expressions and contexts. Sentence-level analysis, often employed by traditional approaches, overlooks key aspects hidden in the data. A hybrid framework integrating Twitter-based feature selection with sentiment classification using a multi-layer perceptron (MLP) model is introduced in this paper. Techniques such as Support Vector classifiers and Random Forests are examined to enhance the accuracy of classification. Baseline models are compared with the proposed framework, which demonstrates reliable precision and improved classification in experiments across several datasets. The structured hybrid approach directly addresses challenges that arise when analyzing content from social media platforms.
Pythagorean Neutrosophic Cubic Set
Authors: Berna Joyce L
Published: January 2025

The purpose of this work is to broaden the definition of Neutrosophic Cubic Set (NCS) and Pythagorean Cubic Set (PCS) to Pythagorean Neutrosophic Cubic Set (PNCS). Related qualities are examined and the concepts of T-external, I-external, and F-external Pythagorean Neutrosophic Cubic Set (PNCS) and T-internal, I-internal, and F-internal PNCS are conveyed.
Mathematical Models: Unlocking Solution to Two Biological Frontiers
Authors: Bevara Kondala Rao
Published: January 2025

In this paper, we study two different biological problems by using mathematical formulations. In the first problem, our study is based on hyperprolactinemia of non-cycling African elephant which is not associated with hyperestrogenism. There is no distinguishable pattern in the moderated and noticed hypoprolactiemic group, and moderate group is in base line. Time-based profile is absorbed in cyclic females, and elevations observed during follicular phase. In mathematical model, Weibull distribution is used. Medical results are analysed with corresponding mathematical model.In the second problem, a four-parameter generalized log-logistic distribution is introduced, using a quadrature rank transmutation map to create a transmuted four-parameter log-logistic distribution. The reliability function for the four distributions is calculated. The standard model applies to the concentration of prolactin in dairy cows, with continuous probability and cumulative distribution functions for the four-parameter log-logistic distributions. Finally, the implementation corresponds to the probability distribution, with results closely tied to medical reports.
Homotopy Analysis Method for the Approximate Solution of the SIRC Epidemic Model
Authors: S. Geethamalini
Published: January 2025

This paper investigates the Homotopy Analysis Method (HAM) as a means of approximating a solution to the SIRC epidemic model. This method allows the solution of the governing differential equation to be found as an infinite series with easily calculated components. The HAM uses an auxiliary parameter and a straightforward way to regulate and alter the region where the infinite series solution converges. The outcomes are displayed, and with just six terms, an extremely precise approximation solution can be achieved.
Some Fixed Point Theorems Using Φ_p Operator
Authors: K Dinesh
Published: January 2025

This article's goal is to use the Φp operator to prove a few fixed point theorems in complete metric space. Furthermore, we investigate whether fixed points for self mappings that meet the requirements of rational expression exist and are unique in a complete metric space. Our findings expand upon and generalize a great deal of previously published research.
An Integrated Approach to Optimize Risk in SCM using Blockchain and Machine Learning Techniques
Authors: Ritesh Kumar Singh
Published: January 2025

In the dynamic field of supply chain management (SCM), identifying and mitigating risks is crucial for sustaining resilience and efficiency. Conventional risk management strategies frequently inadequately handle the complexities and uncertainties intrinsic to contemporary supply networks. This study investigates a hybrid approach that combines blockchain technology with machine learning to improve risk detection and management in supply chain management. Blockchain provides a decentralised and transparent system for monitoring supply chain operations, guaranteeing data integrity and transparency. Machine learning enhances this process by examining extensive amounts of past and present information to discern trends, forecast future dangers, and propose mitigation solutions. The suggested system utilises blockchain's inviolability and machine learning's predictive powers to tackle significant difficulties including identifying fraud, demand forecasting, supplier assessment, and disruption prediction. Case studies and quantitative assessments illustrate the efficacy of the hybrid method in mitigating vulnerabilities and enhancing decision-making. This research enhances current understanding in digital supply chain management and offers practical insights for practitioners aiming to implement novel technology to alleviate supply chain hazards. This hybrid system utilises the immutable characteristics of blockchain to provide safe and open data storage, while machine learning models derive actionable insights, hence improving productivity and making decisions. The experiment demonstrates four machine learning models and we achieved 97% accuracy for two model for Random Forest and Support Vector Machine that outperform from other models.
Power Dominator Equitable Coloring for some Standard Graphs
Authors: G. Navamani
Published: January 2025

A power dominator coloring of a graph is a proper coloring where each vertex in power dominates at least one complete color class. The power dominator chromatic number of is represented by . A graph is said to be equitably -colorable if it can be properly colored with colors such that the size of any two color classes of is differ by at most one, i.e, and represents an equitable chromatic number of . The power dominator equitable coloring of a graph is a proper - colorable if each vertex of power dominates each and every vertex of some color class for which . In this paper, we obtain the power dominator equitable chromatic number for some standard graphs.
Applications of Discrete Mathematics in Computer Science: Algorithms, Graph Theory, and Beyond
Authors: R. Vinodhini
Published: January 2025

Discrete mathematics has a central importance in the fundamental parts of the theory of computers. From the development of powerful algorithms to graph theory and optimization of networks discrete mathematics plays a role in the analysis and solution of computational problems. This paper aims to look at how discrete mathematics encompasses algorithms and graph theory within today’s real-world problems. As such, the study looks at how the concept of calculus in sets, relations, combinatory, and logic as fundamental blocks towards algorithm development or optimization forms the basis for growth within the field of computer science. Thus, discussing the recent changes and case studies the paper demonstrates that discrete mathematics is indeed a vital domain in computational research and its advancements’ applicability in such areas of study as data structures, network analysis, and computational complexity.
The Influence of Competence, Integrity, and Compensation on Internal Audit Units Performance
Authors: Mukodas Arif Subekti
Published: January 2025

Internal audit units (SPI) at State Islamic Universities face significant challenges in ensuring effective and accountable governance. These challenges arise from limited technical competence, inconsistent integrity practices, and a lack of equitable compensation systems. This study aims to examine the influence of competence, integrity, and compensation on SPI performance, focusing on their direct and mediated effects. The novelty of this research lies in exploring the limited role of compensation compared to intrinsic factors like competence and integrity in enhancing internal audit functions. A quantitative research design was employed, involving 120 SPI personnel from various universities. Data were collected through structured surveys and analyzed using Structural Equation Modeling (SEM). Results showed that competence and integrity positively and significantly impact SPI performance, with competence being the most dominant factor. Integrity also mediates the relationship between competence and performance. However, compensation did not have a significant direct or mediating effect on performance, indicating the greater importance of intrinsic motivators. The findings emphasize the need for improving SPI competence through technical training and fostering a culture of integrity via ethical programs. Compensation should still be fair but should not be the primary focus. These results provide actionable insights for policymakers and educational leaders to enhance the effectiveness of internal audit units in higher education.
Fast Generation of K-Secluded Vertex Sets for Fuzzy Graph Partitioning Subject to Forbidden Induced Subgraphs
Authors: V. Vijayalakshmi
Published: January 2025

To deal with difficulties arising from partitioning the fuzzy graph and its connectivity where forbidden induced subgraphs are a raised consideration, this work introduces the concept of k-secluded vertex sets in graph theory. A vertex set is defined to be k-secluded every piece in its open neighborhood contains at most k vertices. This definition generalizes the classical definition of important separators to provide more specific method to find minimal separators subject to various restrictions. Our analysis provides evidence that proves that the quantity of connected vertex sets of cardinality k that contain a set SS and are disjoint from a set T is at most 4k. This result extends previous bounds and highlights the possibility of the efficient enumeration of such sets. We further generalize this notion to graphs not containing a particular finite set F of forbidden induced subgraphs and prove that in this case the number of maximal subgraphs remains between 2O(k) hence computable. The main contribution of our work is to propose a new enumeration strategy for identifying all MVS (k) in a fuzzy graph. This algorithm greatly improves the efficiency of solving the Connected k-Secluded F-Free Subgraph problem for which the current accuracy is 70% while its improved algorithm provides 92% accuracy. The work constitutes a significant achievement in new methods of fuzzy graph partitioning, algorithms for counting, and research on crucial separators under various constraints for application in network analysis and algorithmic graph theories.
Early 2024 Research on Recent Advances in the Diagnosis of Breast Cancer Leveraging Deep Learning Techniques
Authors: Ishita Rana
Published: January 2025

Breast Cancer (BrC) is still a serious worldwide health issue, requiring innovative methods of early detection to enhance patient outcomes. If BrC is detected and treated early, there is a strong chance that the patient will recover. In order to predict the growing of cancer cells using medical imaging modalities, a number of researchers have developed automated DL-based approaches that are effective and accurate. There are currently very few review studies that provide an overview of some of the existing research on BrC diagnosis. Emerging architectures and modalities in the diagnosis of BrC, however, were not covered by this research. This review canters on the developing deep learning (DL) architectures for the detection of BrC. However, were unable to address new modalities and architectures in the diagnosis of BrC. The developing DL architectures for BrC detection are the main topic of this review. The survey that follows outlines current DL based architectures, evaluates the advantages and disadvantages of previous research, looks at the datasets that have been used, and goes over image pre-processing methods. This study supports ongoing efforts by the global healthcare community to improve BrC outcomes by utilizing state-of-the-art technology to enable timely and effective detection. It also presents research directions for future researchers, challenges, and performance metrics and results.