bluuebunny/crossref_metadata_2025 huggingface dataset:
Facial Gesture Recognition with Human Computer Interaction for Physically Impaired
Authors: Anjusha Pimpalshende
Published: November 2024

Modern technologies have succeeded in reducing human effort and risks in doing many activities. As physical disability is a barrier for computer interaction based on electronic devices, modern advancements like human-computer interaction (HCI) have proven to be a dynamic field of study in the last few decades which is a major milestone to uphold the lives of the physically impaired in terms of utilizing technology. In this study we projected a method that provides assistance to the physically impaired people for communicating with these techniques. We accomplished this task by considering the facial gestures, assuming that the disability of the physically challenged having no limbs. Distinguished facial gestures, like eye-blinks, mouth open/close, and head position, are captured and fed as input by a camera as a video stream to the operating system. For example, consider a television where a normal human being can perform various operations in it by a remote control with his/her arms but imagine a physically challenged person who cannot interact with the television without human intervention. We used HCI in the form of facial gestures to establish an interaction with the television and physically impaired participants. We incorporated eye-blink as a gesture to switch on/off, mouth open/close to intimate the Operating system to start/stop taking input commands, and the Position of the head is monitored as the input command and is used to alter the volume of the television. An interactive model that recognizes all of these facial gestures provides a great support for the physically impaired people to interact with the systems.
K-Means Clustering Algorithm for exudates segmentation in fundus images for the diagnosis of diabetic retinopathy
Authors: Minakshee Chandankhede
Published: November 2024

Diabetic retinopathy currently remains one of the major causes of blindness or loss of vision in people. Automatic detection of diabetic retinopathy has been one of the challenging research fields in recent history. Exudates, optic disc, hemorrhages, cotton wool spots, and microaneurysms can slow the development of an illness if diagnosed early and with adequate treatment. The monitoring and identification process of the lesions associated with diabetic retinopathy remain very tedious tasks loaded with repetition and are prone to errors. It is in this regard that this research proposes a new methodology for the diagnosis of diabetic retinopathy in an automated way. First of all, EYEPACS-1 and APTOS 2019 retinal images datasets are obtained. After fundus image quality collection, some enhancements are made through adaptive histogram equalization and the Haar discrete wavelet transform with the support of filtering based on a Gaussian matched kernel. Further, the fovea and blood-retinal vessels are eliminated; through the K-means clustering segmentation algorithm, the areas of an optic disc are highlighted by the Gaussian blur approach. Color histogram features are then extracted from these segmented areas before being fed into a deep learning model, CNN, to classify healthy subjects versus those affected by DR with severity grading. According to the simulation findings, the accuracy posted by the proposed model was 97.25% against the EYEPACS-1 dataset and 96.85% against the APTOS 2019 datasets & samples.
High Performance Computing for Large Scale Civil Engineering Simulations
Authors: Uday Shankar Patil
Published: November 2024

High performance Computing (HPC) may be a key portion of progressing large-scale models in respectful designing. It makes modeling complicated buildings and frameworks more precise and effective than ever some time recently. This paper looks at how HPC can be utilized in respectful designing models, particularly how it can be utilized to arrange, analyze, and move forward enormous ventures. We see at diverse HPC plans, such as dispersed computing, cloud-based arrangements, and how they can be utilized for fluid-structure intelligent, auxiliary flow, and geotechnical ponders. The think about appears how HPC makes it simpler to work with exceptionally expansive datasets and complicated models, like those required for considering seismic tremors, figuring out how much wind loads an region, and keeping an eye on the wellbeing of foundation. Engineers can do more precise real-time models, move forward plans, and foresee how buildings will carry on in cruel circumstances by utilizing high-performance strategies and adaptable computer apparatuses. The study moreover talks almost the problems and restrictions that come with HPC, just like the require for specialized program, the tall costs, and issues with overseeing information. A few thoughts are given for getting around these issues, such as making equipment more productive, making advance on parallel strategies, and utilizing open-source HPC devices. Proposed strategy appears how HPC can change the field of respectful designing through case thinks about and real-life cases. The ponder appears how it can offer assistance make building ventures more secure, more ecologically neighborly, and superior at what they do. The final portions of the paper talks almost future study objectives and patterns. It stresses the significance of proceeding to come up with unused thoughts in arrange to completely utilize HPC for respectful building errands.
Enhancing Social Network Analysis using Graph Neural Networks
Authors: Madhavi M. Kulkarni
Published: November 2024

Social Network Analysis (SNA) may be a key apparatus for figuring out how individuals in social systems interface and relate to each other. Most of the time, chart hypothesis, factual models, and machine learning are utilized in conventional SNA strategies. Be that as it may, these strategies have inconvenience finding complex designs in huge, changing, and assorted systems. Chart Neural Systems (GNNs) are a modern and solid way to progress SNA. They learn models straight from graph-structured information, which makes them exceptionally great at assignments like finding communities, classifying hubs, and foreseeing joins. This think about looks into how GNNs can be utilized to form SNA way better. In specific, conversation approximately how GNN plans like Chart Attention Networks (GATs), Chart Convolutional Systems (GCNs), and GraphSAGE can be utilized to induce both nearby and worldwide structure information from social systems. By utilizing profound learning to combine information from a node's neighbors, GNNs make wealthy include embeddings that keep critical social forms like how impact spreads, how communities are organized, and how solid connections are. GNNs can too handle the sparsity and commotion that are common in social systems well, which makes inquire about more dependable. Conversation almost how combining GNNs with common SNA measurements (like centrality and clustering coefficients) can make organize patterns easier to get it and clarify. By utilizing GNNs on real-life social arrange data, appear that they are more precise at making forecasts and can be utilized on a bigger scale than conventional SNA strategies. The ponder looks at the computing challenges and trade-offs of utilizing GNNs in huge social systems. It talks almost issues like overfitting, show complexity, and being able to get it the models. GNNs are a huge step forward for SNA since they offer assistance us get it social frameworks and connections in a more complex way. Their utilize opens up other ways to see at complicated social occasions, which makes a difference individuals make way better choices in zones like promoting, criticism frameworks, and the spread of data.
Optimizing Blockchain Protocols with Algorithmic Game Theory
Authors: Nitin M. Shivale
Published: November 2024

Because blockchain technology is changing so quickly, different algorithms have been created to make sure that independent systems are safe, scalable, and efficient. However, as blockchain networks get bigger, protocol efficiency problems get harder to solve. This study looks at how blockchain technology and algorithmic game theory can be used together to solve these problems and suggests a new way to make blockchain systems work better. A part of mathematics called algorithmic game theory studies strategic exchanges where the results depend on the actions of many players. It can instruct us a part almost how to create blockchain frameworks work superior. Able to consider and move forward distinctive parts of convention execution, such as agreement forms, exchange preparing, and network security, by considering of blockchain clients as coherent specialists with their possess objectives. The primary portion of the paper talks approximately essential blockchain conventions, like Confirmation of Work (PoW) and Verification of Stake (PoS), and how they have issues with scaling and proficiency. At that point, we conversation around a few imperative thoughts from algorithmic amusement hypothesis, like Nash equilibrium, component plan, and sell off hypothesis. We appear how these thoughts can be used in blockchain systems. We see at how different game-theoretic strategies can alter how individuals act, make the most excellent utilize of assets, and boost the full execution of systems utilizing complex numerical models and re-enactments. A huge portion of the study is around how game-theoretic forms can be utilized to create assention strategies superior. For illustration, we see at how motivating force structures can be made so that the objectives of mineworkers or validators are adjusted with those of the organize. This brings down the chance of awful behaviour and makes the convention more solid.
Subring in Graph Theory
Authors: Nada A Laabi
Published: November 2024

In our study, we delved into the intricacies of graph theory by exploring the properties of subrings within various types of graphs. By focusing on prime graphs and simple graphs, we unraveled the complex relationship between subring-prime graphs. Additionally, we delved into the concept of homomorphism within both simple subring graphs and prime subring graphs, adding depth to our analysis.
Comparison and Enhancement in Cluster Based Algorithms in MANET
Authors: Manish Devendra Chawhan
Published: November 2024

Building a dynamic MANET network based on mesh technology to improve wireless communication QoS is the main objective of this project. It will be interesting to see how the service performs on mobile ad hoc networks, which are wireless networks without infrastructure management or pre-existing content. The rapid development of mobile, real-time, and multimedia technologies has increased the need for effective programming that completely supports all energy usages. This article compares three algorithms—LEACH, HEED, and MODLEACH—using a variety of comparison techniques. There is a significant amount of difference between the energy levels in MOD-LEACH which is 0.0374092J ,0.0270755J and 0.0270759J in LEACH and HEED algorithms. We can also check MOD-LEACH's dead node count as only 15 as compared to dead nodes in LEACH and HEED which are 34 and 47 respectively. Less dead nodes suggest that MOD-LEACH has the majority of living nodes in the cluster. For the packet loss parameter, MOD-LEACH loses the least number of packets ranging from 120-140 which is less that the ranges 145-165 and above 150 in HEED and LEACH algorithms.
Design of an Integrated Method for Optimizing Electron Beam Lithography Using Genetic Algorithms, Particle Swarm Optimization, and Grey Wolf Optimizer
Authors: Vinay H. Keswania
Published: November 2024

With the ever-increasing demands of advanced manufacturing for precise micro-and nanoscale patterning, optimization of the EBL process is in urgent need. The current optimization methods involve combinations such as GA with GWO or PSO with GWO, which are plagued by poor exploration-exploitation trade-offs and hence converge to suboptimal solutions or insufficient refinement of the solution. The above challenges are overcome by an innovative Adaptive Wolf-Driven Swarm Evolution approach, synergizing the strengths of GA, PSO, and GWO for the optimization process of EBL. The generation of a diversified population of solutions from GA is the starting of AWDSE ensuring wide explorations in the search space. Further, a role-based classification using GWO classifies solutions hierarchically into different roles: Alpha, Beta, Gamma, Delta. Solutions at higher ranks (Alpha, Beta) are then refined by PSO-based updates that exploit the search space by updating the solutions, while the solutions ranked lower (Gamma, Delta) are subjected to GA-driven crossover and mutation operations to maintain diversity and exploration. The adaptive switching between the evolutionary operations of GA and particle updates of PSO may definitely be driven by the leadership dynamics of GWO, which allows more intensive balancing of diversification-intensification, thus allowing improved convergence precision and velocity. Experimental results prove that AWDSE is able to obtain an improvement of about 18% reduction of critical dimensions with a shrinkage in delay time amounting to 12%, outperforming traditional approaches like GA-GWO and PSO-GWO. This advance underlines the possibility of AWDSE to significantly enhance EBL efficiency and accuracy, with vistas toward ever finer and faster ways of nanomanufacturing process.
Periodic Nature in General System and Cellular Automata
Authors: L Praveen Kumar
Published: November 2024

Introduction: In this research paper we have discussed the various periodic points in general systems and established relation among them by adopting strong conditions to them so we can relate smoothly. Additionally, we developed the relationship with the dynamical systems and derived periodic points in Tent maps and circular maps in general systems. We established the fundamental characteristics of cellular automata in general systems, such as continuity. Furthermore, we covered periodic points in additive CAs with radius zero and the inverse of a periodic point in additive cellular automata.
Objectives: To be familiar with the relationships between different periodic points in a general system, general system examples, and the contrast between the periodic behavior of a dynamical system and a new system. Explaining periodic nature in additive form and defining cellular automata in a new framework.
Methods: We employed fundamental notions of dynamical systems and the definition of cellular automata in a new system.
Results and Conclusion: The relationships between different periodic points in a general system, periodic points in a new system, the periodic nature of additive cellular automata in the new system, and its basic characteristics were all revealed to us.
Indoor Farming: Comparative Study Based on Internet of Things –A Fuzzy SWARA-TODIM Approach
Authors: R. Seema
Published: November 2024

Introduction: Indoor farming has emerged as a sustainable solution to meet the increasing demand for fresh produce while minimizing environmental impact.
This study aims to optimize a sustainable integrated vertical farming (SIVF) system by integrating some indoor farming methods in a complex multi-criteria decision-making challenge.
Objectives: Develop an artificial intelligence approach for monitoring and analyzing data from automated indoor farming systems.
The optimized SIVF system was designed with AI sensors to create a modern agriculture.
Methods: This study integrates SWARA and TODIM methods to address the MCDM problem with unknown weights.
The proposed method was solved in two processes: PyCuFN and PyFN environments for conduct a comparative study based on a real life MCDM problem.
Results: The results show a significant reduction in water consumption and energy usage in the optimized indoor farming system.
The comparison between two processes reveals minimal impact on the overall decision values and rank orders, indicating the robustness of the proposed method.
Conclusions: The study demonstrates the effectiveness of the SWARA-TODIM method and AI sensors in optimizing the SIVF system. The proposed method's ability to be solved in different environments adds to its flexibility and applicability in various contexts.
Generalization of ZZ Transform and Its Applications
Authors: Sara Falih Maktoof
Published: November 2024

ZZ integral transformation was presented via Zain UI A. Zafar in year (2016), [1,6], In this research paper, a generalized form of ZZ integral transform has been proposed, the general transform is introduced as a new integral transform called the " general ZZ integral Transform". The general ZZ Transform has been studied and proven for some fundamental functions It's applicability and ability to evaluate the exact solution have been proven via utilizing the transformation in solving n^th order ordinary differential equations.
Subclass of Harmonic Univalent Functions Connected with Hypergeometric Functions
Authors: Katterapalle Sridevi
Published: November 2024

In this paper, we introduce and define a new subclass of harmonic univalent functions involving  hypergeometric functions which are sense-preserving in the open unit disc. Further we obtain the coefficient bounds, extreme points, convex combination, growth bounds, distortion bounds and integral operator for this class.
CNN-Based Voice Recognition by Self-Governing Robots to Improve Computer-Human Communication
Authors: Ben Sujin. B
Published: November 2024

Robots must have the ability to recognize human emotions to engage with individuals and to plan their movements autonomously. Nonverbal signals encompassing pitch, loudness, spectrum, and speech speed are successful methods for transmitting emotions to most individuals. Provided this situation, a machine could have the capability of qualified figure out emotions by deploying the traits of spoken communication, and these potentially propagate vital information concerning the speaker's emotional state. More precisely, a combination of numerous facial action units can be employed to describe a human's emotion. In this paper, we propose a deep Convolutional Neural Network-based system that can identify sentiments in real-time and with a high accuracy rate. This investigation establishes an entirely novel speech-emotion detection system founded on Convolutional Neural Networks (CNNs). With the support of a top-tier GPU, a model is developed and nourished raw speech from a specific set for training, classification, and testing purposes.  We further analyze the speech data and incorporate the information from the visual and audio sources to enhance the recognition system's accuracy. The benefits of the proposed method for emotion identification and the implications of blending visual and aural suggestions are made clear by the experimental results. Convolutional neural networks (CNNs) are taught on grayscale images from the softmax dataset in the present work. To acquire the best accuracy, we experimented with different baselines and max pooling layers, finally acquiring 89.98% accuracy. Dropout is one approach we have used to ward off overfitting.
Mitigating DDoS Attacks: A Machine Learning Approach for Enhanced Detection and Response
Authors: S.Aruna Deepthi
Published: November 2024

Distributed Denial of Service (DDoS) assaults continue to be among the most dangerous risks on the Internet. With the advances in equipment for spotting and mitigating these attacks, crackers have improved their skills in originating new DDoS attack types with the intent of cloning normal traffic behavior therefore becoming silently powerful. The so-called low-rate DoS assaults are a portion of these effective DDoS attack types that aim to archive limited network traffic. This paper proposes a machine learning algorithm for the mitigation of DDoS outbreaks in the application layer. Our scheme seeks to increase the exactness and efficacy of DDoS attack detection by employing the robustness of machine learning procedures which include neural networks and support vector machines, in combination with superior feature engineering and real-time monitoring. Our outcomes show that, of the four Machine Learning algorithms, Maximum Learning Performance (MLP) results in the best sorting marks. Particularly MLP leads to an F1-score of 98.04% for legitimate traffic, 99.30% for attack traffic on emulated movement, and an F1-score of 99.87% for target traffic and 99.95% for legitimate transportation on real traffic. When it concerned the procedure of distinguishing emulated traffic using FL, MLP, and EC, we were capable of gaining an F1-score of 98.80% for malware traffic and 99.60% for valid movement; but, when it related to real traffic, we were managed to obtain an F1-score of 100% for the assault traffic and 100% for normal traffic.
Novel Catalyst Formulations for Enhanced Fuel Cell Efficiency
Authors: Naveen Jain
Published: November 2024

The quest for enhanced fuel cell efficiency is pivotal in advancing sustainable energy solutions. This paper investigates novel catalyst formulations aimed at improving the performance and longevity of fuel cells. Traditional catalysts, primarily based on precious metals such as platinum, present challenges related to cost and resource availability. In response, this study explores non-precious metal catalysts (NPMCs), composite materials, and nanostructured catalysts, which have shown promising results in recent research. Through rigorous experimental methods, including synthesis and characterization techniques, we evaluate the catalytic activity and efficiency of these novel formulations. The findings demonstrate significant improvements in power output and operational durability compared to conventional catalysts. Mechanistic insights into the reaction dynamics reveal how these new materials enhance performance metrics such as current density and voltage output. An economic analysis highlights the potential for scalability and cost-effectiveness of these innovative catalysts in commercial applications. This research underscores the critical role of catalyst design in optimizing fuel cell technology and sets the stage for future explorations aimed at overcoming existing limitations in the field. By leveraging advanced materials and formulations, we aim to contribute to the development of next-generation fuel cells with enhanced efficiency and practicality.
Performance Analysis of ML and DL Models: Impact of Linear and Non-Linear Optimizers on Model Efficiency
Authors: G. Amulya
Published: November 2024

Introduction: Emphasizing the effect of linear and non-linear optimizers on the performance of machine learning (ML) and deep learning (DL) models, this work addresses the CIFAR-10 image classification task. The work explores over multiple data sets how optimizers influence model behavior and efficiency.
Objectives: With a view on five primary assessment metrics—accuracy, precision, recall, F1-score, and AUC the main purpose is to evaluate on SVM and CNN models the performance of linear optimizers (gradient descent, SGD) and non-linear optimizers (Adam, RMSProp).
Methods: The study offers fair and complete comparison by way of a consistent evaluation strategy. Analyzing how different optimizer influences model convergence rates, computational cost, and training stability takes front stage in the experimental setting. Designed on CIFAR-10, optimizers are used on ML and DL models whose performance on all metrics is noted.
Results: Results show that non-linear optimizers especially Adam much increase CNN model performance by means of faster convergence, higher classification accuracy, and better model stability. While helpful for simpler models, linear optimizers such SGD show slower convergence and limited adaptation with more complicated data.
Conclusions: This study provides direction on selecting appropriate optimizers depending on model complexity, job needs, and computing restrictions coupled with important information on optimizing model training in image classification and other areas.
Health Monitoring and Prediction Using Non-invasive Sensors in IoT Environment
Authors: Govindappagari Bhuvaneswari
Published: November 2024

Introduction: IoT's blend with non-invasive sensors has made health state prediction possible and constant real-time monitoring transformed healthcare. Using photoplethysmography (PPG) and electrocardiograms (ECG), this device evaluates important parameters including heart rate, blood oxygen saturation, and blood pressure. As healthcare systems face increasing demands, this technology provides a patient-centered and efficient method for long-term health tracking—needed for early intervention and management of chronic diseases.
Objectives: The main goal is to evaluate using time-series data from PPG and ECG sensors the efficiency of an IoT-enabled health monitoring system coupled with machine learning models in predicting health conditions including arrhythmias and hypoxia.
Methods: This system detects events based on temporal pattern recognition by use of Long Short-Term Memory (LSTM) networks and Random Forest classifiers. Noise reduction is preprocessed and wearable device data is labeled for training of machine learning models. The design combines wearable sensors, a smartphone app, and cloud-based analysis for continuous monitoring and real-time input.
Results: Experimental data shows that the LSTM model attained a 95% accuracy while the Random Forest classifier attained a 92.5% accuracy. Both models displayed remarkable accuracy and recall even while the LSTM model excels in identifying anomalies in time-series data and the Random Forest classifier skillfully detects health events depending on certain criteria.
Conclusions: This study demonstrates the capability of IoT-based health monitoring systems to enhance patient outcomes through continuous, non-invasive monitoring and predictive analytics. Despite its potential, difficulties persist, including data security, sensor reliability, and energy efficiency, necessitating additional research for broad use and clinical integration.
The Collection of Various βws – Closed Sets in Topological Spaces
Authors: S. Saranya
Published: November 2024

This research presents an innovative class of Beta weakly semi-closed sets namely, The collection of various βws – closed sets in Topological spaces.  Throughout this paper, βws-Semi closure sets, βws-Interior  and βws – Neighbourhood sets were examined to get the fundamental facts in the Beta weakly semi-closed sets. In this paper, the notion of countable βws-semi- closed sets and βws-Interior in TS were explored and βws – Neighbourhood (βws-nbd) in TS were also studied to get results. Here, many characterizations were obtained along with some of their feature results.
Non-Singular Convoluted Matrix Collaborative with Filtering and Sampling Techniques
Authors: S Nirmala Devi
Published: November 2024

Due to the comprehensive and accessible knowledge they provide, social media platforms are developed as prominent technologies. The community strategy remains as a repository of millions of individuals for numerous application, include evaluations concerning health, services preferences investigation, and numerous others. And use this information, social media network personalized recommendation algorithms allow the user to interactively choose their alternatives via inter networks. It makes reasonable because content - based recommendation model should indeed be adequately arranged to work out the enormous quantity of information that users of social media has contributed in recent decades. In order to examine huge quantities of information (i.e., big data) efficiently, the findings suggest a beginning for something like a scientific and adaptable classification algorithm. The Fried and Function f Matrix Collaboration Recommend (F-FMCR) approach, which would be founded on a distribution personalized recommendation system that employs inter technologies for social networking sites, provides recommendations to those who use the network about some of the other individuals who have their objectives. An entity paradigm has evolved that is appropriate for assessing different concepts. Using sampling methods and Non - singular Matrix Collaborative Filtering methods, this architecture contains three elements (i.e., agencies) entitled tweeting collections agent, information retrieval agent, and networked recommendation systems agent. Here on Sentiment 140 collection, significant experiments were carried out, as well as the findings are evaluated with the present prediction models. Findings from experiments indicate demonstrated, when compared to current methodologies, the developed F-FMCR approach is superior in terms of recommended effectiveness, recommendation time, and average absolute inaccuracy.
A Two-Phase Methodology leveraging X-DenseNet for Multi-Organ Segmentation in Abdominal CT Scans
Authors: Harinder Kaur
Published: November 2024

Introduction: Multi organ segmentation (MOS) is crucial for various medical applications such as disease diagnosis, treatment planning, and surgery. Manual segmentation of CT organs is inefficient, laborious and time-consuming, necessitating the development of automated techniques. To address these challenges, automatic techniques based on deep learning models are explored in this field. These techniques require improvements to deal with patient-to-patient variability in organ size, location, and shape. Despite the transformative potential of deep learning techniques, research studies often inadequately address low contrast and overlapping structures within CT scans. This paper proposes a novel two phase methodology to integrate deep learning models and image enhancement strategies.  
Methods: Our framework contributes in two stages named (a) Optimized Contrast Limited Adaptive Histogram Equalization-Weighted Grey Wolf Optimization (optiCLAHE-Weighted GWO), where the organs contrast is enhanced by employing weighted GWO for CLAHE clip limit selection (b) X-DenseNet architecture, where segmented regions of interest are produced with X-DenseNet model from the enhanced CT.
Results: For validation of proposed multi organ segmentation (PMOS) technique, experimentation and comparative analysis with existing models has been conducted on FLARE 22 challenge dataset. The Dice Score (DSC) of liver, aorta and spleen for proposed Multi organ segmentation (PMOS) approach is 90.4%, 99.38% and 98.33% respectively. The mean of Precision (Pre), Accuracy (Acc), F-score and DSC of the proposed approach are 95.05%, 95.55%, 95.29% and 96.06% respectively.
Image Classification with Hypertensive Angle Disease Detection with Geometric Local Derivative Pre-Processing
Authors: Anand Rajasekaran
Published: November 2024

It is recognized as conjunctivitis stands as that of the second-greatest source of disability. Early detection and management of ophthalmology are essential for preventing disease owing to the benign character of loss of vision mostly in early stages of the illness and the permanent condition of vision in later stages. Straight retinal inspection, commercial digital images, laser scanners spectroscopy, scanning infrared additional factor and confocal tomography (OCT) images can all is used it to identify hypertension. These findings suggest using a Vertical Harmonic oscillator Dynamic Hinge Supporting Machine Classifier (Embedded systems) to predict glaucoma. There really are three stages to the Repository system. All those are ophthalmology identification, extraction and classification, and processing. The retina source images first were analyzed using a Geometric Local Derivative Pre - processor architecture to extract the major characteristics required for early diagnosis. The precompiled images then are submitted to Harmonic oscillator Discontinuous Single - input single Probability distribution Extraction Of features to identify significant characteristics with the sensitivity for disease prediction. Lastly, Inter Hinge Gradient Boosting Retinal Identification employs the derived features to detect glaucoma early and effectively. The Retina Mri image datasets was utilized in Simulation experiments to examine the effectiveness of the suggested method, embedded systems. Computational complexity, sensitivities, and correctness performance criteria must be looked at with respect to different Optical image quantities.
A Nonlinear Analysis of Carbon Pricing Mechanisms: Comparing the Economic Impacts of Carbon Tax and Emissions Trading Schemes in Developing Economies
Authors: Tianqi Xiong
Published: November 2024

This study evaluates the relative effectiveness of Carbon Tax and Emission Trading Schemes (ETS), for developing economies, in promoting economic growth as well as emission reductions. A systematic literature review (SLR) analyzing 36 studies is employed for this purpose. Findings reveal that Carbon Tax brings temporary economic deceleration but fuels long-term sustainability with clearer direct impacts. ETS also achieves the same objectives with greater flexibility thereby offsetting the negative impacts of Carbon Pricing via price volatility. Revenue recycling is a key tool to address economic disbenefits. This study sheds an urgent light on the imperatives for region-specific policy designs and forthcoming hybrid mechanisms as well as longitudinal socioeconomic effect investigation among underrepresented developing regions.
Design of a Decentralized Authentication and Off-Chain Data Management Protocol for VANETs Using Blockchain
Authors: Misbah Kousar
Published: November 2024

In the evolving landscape of Intelligent Transportation Systems (ITS), the need for secure and reliable data sharing is more critical than ever. As ITS increasingly rely on the exchange of sensitive information, challenges related to data privacy, security, and the integrity of communication have emerged. Traditional approaches to securing vehicular networks often depend on centralized Trusted Third Parties (TTP) and cloud-based infrastructure, which introduce vulnerabilities such as single points of failure and potential latency issues. This paper proposes a novel decentralized architecture for Vehicular Ad-Hoc Networks (VANETs) that enhances security by eliminating the dependency on centralized cloud servers. The proposed protocol leverages blockchain technology to facilitate secure off-chain storage of event data and implements a decentralized authentication mechanism. This approach ensures that the privacy and integrity of shared information are maintained without compromising the efficiency of the system.
A Novel Method for the Fuzzy Survival and Fuzzy Hazard Models of the Corticosterone Effect
Authors: Senthilkumar P
Published: November 2024

To ascertain the significance of corticosterone release scores, theoretical research was conducted. We offer formulas for fuzzy Two-Parameter Weibull distributions, fuzzy Survival and Hazard functions, and related alpha-cut sets. We showed that, using fuzzy survival and hazard models based on two-parameter Weibull distributions, the smaller fuzzy survival model for the impact of corticosterone release increases and the higher fuzzy hazard model for the impact of corticosterone release decreases as the experiment termination alpha value increases. When the alpha value of the test's termination grows, the greater fuzzy survival scenario for the effect of corticosterone release decreases while the smaller fuzzy hazard models with the same effect increase.
Statistical Model Comparison for the National Stock Exchange (NSE) Stock Price Prediction
Authors: Prachi Gupta
Published: November 2024

The stock market is one of the most versatile sectors in the financial system, and the stock market plays an important role in economic development. It is also a platform for trading various securities and derivatives without barriers. This paper focuses on the comparison of different types of time series models to predict the stock prices of five companies by using machine learning techniques.
Logic Density in Gabor Frame Structures
Authors: Manal Yagoub Ahmed Juma
Published: November 2024

The Gabor system G(g,1+ε,ε-1)={e^2iπ(ε-1)nt g(t-(1+ε)m):m,n∈Z}is examined with respect to its frame property, uunder reasonable oversampling conditions, that is, when (ε+1,ε-1∈Q).
An appropriate "rational" counterpart of the Ron-Shen Gramian is developed, establishing that for every peculiar pane function (g), The structure: G(g,ε+1,ε-1)  fails to produce a frame if  ε^2=(2n-1)/(n-1).
A particular focus is placed on the initial Hermite function, h_1=te^(-πt^2 ).
Hamacher Operations on Interval Valued Picture Fuzzy Matrices
Authors: P. Rajarajeswari
Published: November 2024

Picture Fuzzy Sets (PFS) extend fuzzy sets and intuitionistic fuzzy sets, offering a more robust framework for handling ambiguous, uncertain, and imprecise data. Interval-valued Picture Fuzzy Set represent membership, neutral, and non-membership degrees as intervals, providing a flexible solution for situations where precise values are difficult to determine. This paper introduces novel Hamacher operations for interval-valued picture fuzzy matrices, building upon existing Hamacher operations for Picture Fuzzy matrices. We develop and examine scalar multiplication and exponentiation operations using these new Hamacher operations. This research advances the development of sophisticated medical diagnosis systems, leading to improved patient outcomes and more accurate decision-making.
Secondary k-Kernel Symmetric Interval Valued Intuitionistic Neutrosophic Fuzzy Matrices
Authors: M.Anandhkumar
Published: November 2024

We propose the idea of secondary symmetric k-kernels with interval-valued intuitionistic neutrosophic fuzzy matrices (IVINFM) like an EP matrix within the complex field. The notion of secondary kernel IVINFM and k-kernel symmetric (KS) IVINFM is presented by providing examples. We also illustrate the graphical representation of KS adjacency IVINFM and incidence IVINFM. We found every isomorphic IVINFM and non-isomorphic IVINFM graph to be k-KS IVINFM. However, the reverse shall not be the case. The definition of k-symmetric IVINFM as k- KS IVINFM, but the reverse is not the case. The characteristics of secondary kernels IVINFM, which are symmetric IVINFM, have been explored in this research using examples. The relationship between s-k KS IVINFM, symmetric s- KS IVINFM, and KS IVINFM, and the KS IVINFMs are examined. We identify the required and sufficient criteria for the KS IVINFM and s-k KS IVINFM. The comparable requirements for the various g-inverses that make up an s-k KS IVINFM are shown. The generalized inverses of a KS IVINFM A, which correspond to the sets A{1,2}, A{1, 2 ,3} and A{1,2, 4} are described.
Analysis of Construction Project Cost and Time Using the Earned Value Method
Authors: I Gusti Agung Ananda Putra
Published: November 2024

This study aims to analyze the cost and time performance of the renovation project of Arwana Restaurant The Laguna Resort and Spa Nusa Dua by applying the Earned Value method. The data used in this study include the Cost Budget Plan (RAB), project implementation schedule, weekly project progress reports, and project expenditure reports. The calculation is done by calculating the value of ACWP (Actual Cost of Work Performed), BCWS (Budgeted Cost of Work Schedule), BCWP ( Budgeted Cost of Work Performed ), CV ( Cost Variance ), SV ( Schedule Variance ), CPI (Cost Performance Index), SPI (Schedule Performance Index), ETC (Estimate to Complete), ETS (Estimate Time to Complete), EAC (Estimate at Completion), and EAS (Estimate at Schedule). The results of the analysis show that at the beginning of the project the performance was good, but other things began to decline from the 11th week to the 23rd week. At the end of the project, performance improved again as indicated by a CPI value of 1.07 and an SPI of 1 indicating lower costs and time according to plan. The estimated total final project cost (EAC) of Rp2,947,878,984.57 was lower than the Cost Budget Plan (RAB) of Rp3,143,852,000, resulting in a profit of Rp195,973,015.43. The estimated total final project time (EAS) was in accordance with the planned schedule, which was 27 weeks. In conclusion, overall the project performance was quite good even though it had declined in the middle of the project implementation.
Multichannel Data Acquisition and Monitoring System Utilizing Modbus Protocol with IIoT Automation
Authors: Vijaya N. Aher
Published: November 2024

This paper introduces a multi-channel data acquisition and monitoring system that integrates Modbus communication protocol and IIoT automation to improve business. The system is designed to capture and monitor multiple parameter data simultaneously, providing accurate and fast measurements of variables such as temperature, humidity, and pressure in various applications. The system uses the widely used Modbus protocol, known for its reliability in industrial automation, to ensure seamless communication between interconnected devices and thus promote interoperability in more diverse aspects of the network environment. Working with IIoT automation, the system not only facilitates data collection, but also helps increase intelligence to focus on business efficiency. Its multi-channel architecture supports data obtained from eight different channels, making it adaptable to complex business processes or research operations that require high precision and scalability. In addition, the system is designed to provide a powerful and flexible solution to meet today's system business needs by prioritizing functionality and ease of integration. The ability to interpret effectively demonstrates its importance as a reliable tool for instant collection and monitoring of information in an environment where accuracy and speed are important.
Trends and Growth of Productivity of Agricultural Sector using Statistics Through Machine Learning in Assam
Authors: Smrita Borthakur
Published: November 2024

The study of trend and growth of agricultural sector by applying statistical techniques through machine learning analysis has become a powerful tool in in the long run. period, In this study Trend Analysis have been done using fitting of curves of the types, Exponential, Modified Exponential, Gompertz and Power Curves by taking secondary data for a period of 1993to 2023. The data have been collected from the official records of Directorate of Economics and Statistics Assam. Trend analysis have been done using Mann-Kendall test and Sen’s slope estimator. From the growth models modified exponential is adjudged by higher ????2 and lowest MSE and lowest AIC with significant t values.
Simulation of Launch Pad Atomization for Military Weapon System
Authors: Vijaya N. Aher
Published: November 2024

This study aims to optimize three critical parameters (inclination angle, hook lifting, and sparking) for a missile launching system by combining precise mathematical methods, IoT concepts, and advanced sensor technologies. The system uses an Inertial Navigation System (INS) in conjunction with gyro and proximity sensors to ensure accurate target interception by carefully determining the optimal launch angle. The Internet of Things concept is integrated effortlessly, with a clever coordinating WiFi networking, weight estimation, and sensor data processing for real-time cloud data flow.
Security protocols protect the integrity of the data, and a reliable power supply system guarantees reliable operation. The project incorporates both Hot Start and Cold Start functionalities, enabling dependable launches and prompt adaptations to dynamic changes. Flexible igniting mechanisms such as Chemical, Electrical, and Electro-Chemical systems are available, and reliable and secure missile launches are prioritized with safety features like Spark Damping Inner Tube Coating and Moisture-Resistant igniting. All things considered, this project prioritizes security, dependability, and adaptability while addressing critical elements required for a sophisticated and efficient missile launching system.
Overview of Artificial Neural Network-Based Solution for Ordinary and Partial Differential Equations by Feed Forward Method Using Python
Authors: Muppidi Maruthi
Published: November 2024

In the modern era, models such as Neural Networks are increasingly used to deal with ordinary and partial differential equations (ODEs-PDEs) because of their related complexity. The main aim of the paper is to focus on Artificial Neural Networks as a method to solve these equations since it is possible to approximate complex nonlinear relationships with great accuracy and adaptively reduce computational costs, thus enhancing the accuracy of solutions within highly dimensional and nonlinear problems. The traditional methods are not applicable to partial differential equations due to the problem of nonlinearity and high dimensionality. However, the Feed Forward method implemented in Python is quite a powerful alternative that can easily approximate complicated functions and very well have a hold on nonlinear equations. This research paper contains differential equations as nonlinear and linear equations and Feed Forward neural network models for both solutions of ODEs and PDEs. Python enables the integration of AI-based algorithms for the enhancement of more accurate and efficient results. This work compares the existing solutions based on ANNs and further highlights the possible advantages of using ANNs in ordinary as well as complex differential equations problems. The results indicate that ANNs have immense potential for further developing computational algorithms applicable to solving ODEs and PDEs, particularly in real-time applications that demand high precision and adaptability.
Elegant Fuzzy Labeling of Graphs
Authors: Nithya Sree
Published: November 2024

Fuzzy graphs and fuzzy labelings are crucial in modeling real-time systems by accommodating varying degrees of information precision. In this paper, we introduce a novel fuzzy labeling called Elegant Fuzzy Labeling. We prove that a simple graph admits Elegant Fuzzy Labeling if and only if it admits elegant labeling. Furthermore, we prove that while a simple graph that admits Elegant Fuzzy Labeling also admits fuzzy labeling, but not conversely. We identify specific classes of simple graphs that admit Elegant Fuzzy Labeling and explore practical application of this new labeling approach.
Formulation of Impulsive Differential Equations with Time-Dependent Continuous Delay
Authors: Venkatachalapathi Uday
Published: November 2024

Research in impulsive delay differential equations has been undergoing some exciting growth in recent times. This to a large extent can be attributed to the quest by mathematicians in particular and the science community as a whole to unveil nature the way it truly is. The realization that differential equations, in general, and indeed impulsive delay differential equations are very important models for describing the true state of several real-life processes/phenomena may have been the tunic. One can attest that in most human processes or natural phenomena, the present state is most often affected significantly by their past state and those that were thought of as continuous may indeed undergo abrupt change at several points or even be stochastic. In this study, a special strictly ascending continuous delay is constructed for a class of system of impulsive differential equations. It is demonstrated that even though the dynamics of the system and the delay have ideal continuity properties, the right side may not even have limits at some points due to the impact of past impulses in the present. The integral equivalence of the formulated system of equations is also obtained via a scheme similar to that of Perron by making use of certain assumptions.