bluuebunny/crossref_metadata_2025 huggingface dataset:
Nature-Inspired Multi-Objective Reconfiguration of Antennas: Harnessing the GWO, FFO, and ALO for Adaptive Multiple Application Performance
Authors: Sruthi Nair
Published: January 2025

In the evolving landscape of wireless communication, there is an ever-growing need for versatile and efficient antenna systems that can cater to diverse applications. The importance of such systems transcends academia, holding profound societal implications, as they form the backbone of modern communication, IoT infrastructure, and numerous other emerging technologies. Existing methodologies for antenna reconfiguration often suffer from limited adaptability, suboptimal performance, and high-power consumptions. This paper introduces a novel, multi-objective reconfiguration approach for antennas, harnessing the prowess of three nature-inspired optimization algorithms: the Grey Wolf Optimizer (GWO), Firefly Optimizer (FFO), and Ant Lion Optimizer (ALO), each targeting specific antenna parameters - Frequency Range, Polarization, and Beamwidth levels. Our proposed model overcomes the challenges posed by conventional techniques, providing a harmonized optimization process that tailors antenna performance to specific applications dynamically. Preliminary results, when benchmarked against current methods, are promising for different scenarios. We report an 8.5% improvement in gain, a reduction of 2.9% in power consumption, a 3.5% enhancement in bandwidth, and a 2.5% betterment in polarization performance levels. These advances not only pave the way for more efficient and adaptable antenna systems but also underscore the potential of integrating multiple nature-inspired optimization techniques in antenna design and other realms of wireless communications.
Zip Property of Graded and Filtered Affine Schemes
Authors: Nawal M. NourEldeen
Published: January 2025

In this paper we study the transfer of zip property between filtered (graded) rings and affine graded (filtered) structure schemes. Under some conditions, the zip property of filtered (graded) rings is preserved under their graded and filtered affine schemes. One may apply these results up to the formal level as in [8].Introduction:  Consider a zariskian filtered ring S such that the associated graded ring G(S)=⊕(F_n S)/(F_(n-1) S)≅S ̃/(XS ̃ )  is commutative Noetherian domain; [10]. This includes many more geometric applications, i.e. this situation is general in the sense that it allows application of the results to most of the important examples. The topological base space T will be  Spec^g of G(S). The canonical element of degree one in S ̃=⊕F_n S≅∑_(n∈Ζ)▒F_n  SX^n≤S[X,X^(-1)] is the  1∈F_1 S in S, we write it as  X.For moment let S be a graded ring. For a homogenous element a∈S, the annihilator ideal ann^g (a)={s∈S:sa=0} is a homogenous ideal, as is the ideal annihilator  ann^g (A)={s∈S: sA=0}; A⊆S a set of homogenous elements and as is the ideal annihilator ann^g (I)={s∈S:sI=0}; I▁(⊲) S an ideal of homogenous elements.A graded ring S is said to be zip if ∀ A⊆S:  ann^g (A)=0⇒∃ A_0⊆A, finite subset of homogenous elements: ann^g (A_0)=0. In this definition, we can equivalently need to use that A is a graded ideal of S. We need only zip expression of commutative case.For elementary notions, conventions and generalities, which we need here in this paper we refer to the list of references.Objectives: In this paper, we study the transfer of zip property from filtered (graded) rings to the graded and filtered structure affine schemes.Results: According to the work of Leroy and Matczuk ([4] , Theorem 3.2(1) ), who investigated the behavior of the zip property for a localization of a ring , we extend this result for graded and filtered affine schemes.Conclusion: In this research, we investigate the zip property of filtered (graded) rings is preserved under their graded and filtered affine schemes. In the forthcoming work, we hope to come back to introduce the same results on the formal level, one may make this by [8].
Effi Street: IoT-Driven Urban Illumination by Enhancing Energy Efficiency in Street Lighting
Authors: Kaushalya Thopate
Published: January 2025

In urban areas, street lighting is essential for ensuring safety and visibility, but it often leads to significant energy wastage and high electricity costs. Traditional manual streetlight systems remain powered throughout the night, regardless of natural light availability, resulting in unnecessary consumption. To address these challenges, this project introduces an IoT-Based Smart Street Light Management System. This system leverages IoT technology to conserve energy, reduce electricity wastage, and minimize manual intervention. Key components include energy-efficient LED lights with adjustable brightness, a DHT11 Temperature-Humidity sensor for real-time environmental data, and a programmed Arduino board for intelligent light intensity regulation. By optimizing streetlight operation, this IoT-enabled solution offers enhanced energy efficiency and performance compared to traditional systems, benefiting both the environment and the economy.
Border Detection of Skin Cancer Cells over Fractal Dimension Analysis and Image Processing Techniques
Authors: P.Tharaniya
Published: January 2025

Fractal dimension analysis is a novel technique that uses the self-similarity qualities of fractals to identify irregular forms, such as those prevalent in diseased tissues, in order to detect the borders of skin cancer cells. Acquire detailed pictures of skin tissue samples that have cancer cells in them. A variety of imaging methods, including microscopy and medical imaging tools like MRIs and CT scans, can be used for this. Determine the image's fractal dimension by applying suitable methods, like the fractal signature method or box-counting. A geometric shape's complexity is measured by its fractal dimension, and because malignant cells have uneven edges, they typically show higher complexity. To increase the border recognition process' accuracy, clean the photos to get rid of noise and boost contrast. Here, methods such as morphological procedures, histogram equalization, and median filtering can be used. To increase the border detection system's accuracy and resilience, fine-tune the parameters and algorithms in light of the validation results. A reliable approach for identifying the borders of skin cancer cells can be created by fusing fractal dimension analysis with image processing methods. This will help with early detection and therapy planning. Based on the fractal dimension, choose an appropriate threshold value to divide the image into zones of interest. This stage aids in the malignant cells' separation from the surrounding tissue.
Accurate Domination in Fuzzy Digraphs using Strong Arc
Authors: S.Sureka
Published: January 2025

In this paper, we introduce the concept of accurate dominating set in fuzzy digraphs using strong arc and also an accurate domination number of a fuzzy digraph. In a fuzzy digraph, a subset D of V is an accurate fuzzy dominating set of a fuzzy digraph if every node σD(v) Є V-D is not dominated by atleast one node    σD(u) Є D with same cardinality |D|. The accurate domination number of a fuzzy digraph is the minimum cardinality of an accurate dominating set which is denoted by γfa(GD).The upper domination number Γfa(GD) is the maximum cardinality of an accurate dominating set. We prove some results on accurate dominating set of a fuzzy digraph. Also we have modified folyd warshall’s Algorithm to fuzzy domination digraphs for finding an accurate dominating set.
The Financial and Monetary System: A Study on the Structure of the Financial and Monetary System in the World and Lessons for Vietnam
Authors: Le Thi Khanh Ly
Published: January 2025

The US-China trade war, the COVID-19 pandemic, and now the Russia-Ukraine conflict have led to a new consensus from developed economies that it is necessary to reduce interdependence... However, this process of lowering interdependence has led the world to rising inflation, labor shortages, rising protectionism, shocks to the global financial system,... Will this change the international monetary and financial system? The following article has analyzed the trend of globalization in the new context and its impacts on the World Monetary and Financial System, thereby recommending a trade scenario for Vietnam.  The global monetary and financial system is undergoing drastic changes, as reflected in the increase in multi-currency trade activities and new technological platforms for cryptocurrencies. These are gradually becoming a challenge to the current monetary and financial system under the dominance of the US dollar (also known as the Petrodollar system). Looking back at the history of the global monetary and financial system, a new system will replace the current system to solve bottlenecks or inefficiencies over time. Recently, economists mentioned three significant vulnerabilities in the Petrodollar system, including a persistent US trade deficit, which causes long-term imbalances; the encouragement of mercantilism, thus stimulating the actions of currency manipulation by trading partners; assets and liabilities in countries outside the United States are denominated in dollars, leading to a more robust/weaker dollar, similar to quantitative tightening/easing in those countries, which means that these countries lose their independence in monetary policy. These vulnerabilities are being challenged by the US monetary and fiscal policy itself in the post-Covid period, as well as the rise of major economies. Therefore, perhaps it is time for the world to develop a new monetary and financial system to limit its inefficiency through self-regulation mechanisms or create an entirely new system to replace it. In our view, although there are no apparent signs of the collapse of the current system, there have been changes in the system's operation. On the one hand, these changes are manifested in the dollar's role in international trade, especially in the valuation of energy, which is gradually being replaced by other currencies; on the other hand, it is reflected in the shift from fiat currencies to cryptocurrencies. Cryptocurrencies are creating certain positive benefits in promoting international trade, but they also create considerable challenges in the monetary management role of central banks.
Effect of Green Logistics on Designing of Single Bit Cache Memory Architecture
Authors: Apeksha Garg
Published: January 2025

The international economy's fast expansion has made logistics more crucial in addressing shifting societal demands and has also worsened sustainability and environmental issues. With an emphasis on entering and leaving logistics, this study examines the long-term effects of green logistics practices. The paper creates a theoretical framework for examining the effects of green logistical practices on single-bit cache memory architectures' economic, social, and environmental performance. This paper proposes and implements a design analysis of a single-bit static random access memory voltage differential sense amplifier architecture. It uses a write driver circuit, a static random access memory, and different differential sense amplifiers, including voltage differential sense amplifiers, current differential sense amplifiers, and charge transfer differential sense amplifiers. How well different architectures perform in terms of total power consumption, static power consumption, transistor count, and sensing delay has been determined. The voltage differential sensing amplifier for single-bit static random-access memory cells utilizes the least power (13.16µW). Longer sensing delays (12.5ηs) are present in the single-bit static random-access memory cell charge-transfer differential sense amplifier design and the single-bit random-access memory cell current differential sense amplifier architecture. Techniques for power reduction have also been employed to optimize power.
Numerical Analysis of Thermal Performance in Solar Thermal Collector System with Artificial Roughness
Authors: Arbind Kumar Amar
Published: January 2025

In this paper investigation of exergetic analysis of artificial roughness in solar thermal collector is done (STC) done. Here an investigation is done through Thermal model for artificial roughness incorporate in thermal collector is developed. Experimental trial and observations are performed in India under (25º N, 86º E) climatic nature. It is observed that on the performance account approximate 9.95 percent of deviation is seen at out let temperature of STC between theoretical and of Experimental values. Proposed STC reflects improved exergy efficiency about 2.65 %. This result predicts that improvement found in proposed STC which incorporates artificial roughness.
Integrating Blockchain and Machine Learning with 6G for Autonomous Vehicle Communication: Achieving Secure, Transparent, and Scalable V2X Networks
Authors: Pannala Krishna Murthy
Published: January 2025

The rapid evolution of autonomous vehicle technology necessitates robust communication frameworks to ensure safety, reliability, and scalability in vehicular networks. Blockchain and machine learning technologies integrated with 6G networks develop a secure, transparent, and scalable Vehicle-to-Everything communication system. Leveraging ultra-low latency, massive connectivity and enhanced bandwidth, this framework addresses challenges including data integrity, real-time decision making and network scalability. Blockchain ensures secure and tamper-proof data exchange among vehicles, infrastructure and edge devices. Machine learning algorithms enhance predictive capabilities for route optimization, collision avoidance and traffic management. This synergy strengthens through a decentralized edge computing model enabling real-time data processing and reducing central network bottlenecks. Experimental evaluations on a simulated 6G-V2X testbed demonstrate significant improvements in network performance, security metrics and decision making accuracy compared to traditional frameworks. This establishes a foundational approach achieving trust, transparency and scalability in next generation autonomous vehicle networks. Insights provide for future deployments in intelligent, secure and efficient transportation ecosystems and smart cities. The proposed system serves as a pivotal step towards realizing communication infrastructures aligning with emerging demands.
Time-Fractional Hyperbolic Telegraph Equation: A Semi-Analytic Approach Using Modified Adomian Decomposition Elzaki Transform Method
Authors: Parmeshwari Aland
Published: January 2025

In this research paper, an approximate analytical solution approach known as the Modified Adomian Decomposition Method with the coupling of Elzaki Transform (MADETM) is deployed for addressing one-dimensional, two- dimensional, and three-dimensional time-fractional hyperbolic telegraph equations. The Caputo derivative operator yields the approximate analytical solution. The impact ness and its accuracy of the adopted method are demonstrated through comparison of the approximate results with the exact solutions, both presented graphically by plotting its surface graph, line graph through analyzing its error. The MADETM proves to be a reliable and efficient tool for deriving approximate and exact solutions for a large class of partial differential equations (PDEs), fractional PDEs, and ordinary differential equations (ODEs). The considered method yields a solution in series form with low computational complexity and swiftly converges towards precise solutions. The outcomes showcase an effective and uncomplicated approach for examining issues across diverse scientific and technological domains.
Enhancing Crop Yield Prediction using Linear Models and Deep Learning Techniques in Precision Agriculture
Authors: B. Sunitha Devi
Published: January 2025

The substantial advancements in computer science and engineering have sparked interest in precision agriculture and led to the development of more advanced instruments and methods for enhancing farming practices. This study focuses on the use of machine learning and mathematical models for fertilisation optimisation and yield prediction as part of a Precision Agriculture strategy. In particular, provides the outcomes of forecasting winter wheat production and protein content across four farms using the amounts of nitrogen fertiliser sprayed on the fields. To maximise net yields on the next crop, fertiliser treatments have to be prescribed based on these projections. In particular, contrast approaches based on neural networks (deep and shallow) and multiple regression (linear and non-linear). The greatest results are obtained by a deep neural network that incorporates spatial sampling and is based on the stacked autoencoder, according to the findings.
Enhanced Glaucoma Severity Detection in Rental Fundus Images using Improvised PCOA (iPCOA) and Convolution Neural Network
Authors: S. Sheeba Jeya Sophia
Published: January 2025

Glaucoma, a severe eye disorder that damages optic nerve fibres and astrocytes irreversibly. Early identification of Glaucoma is essential to prevent vision loss. The proposed work presents an Improvised Pulikulam Cattle Optimization Algorithm (iPCOA) designed to improvise the detection of glaucoma severity in retinal fundus images. The iPCOA optimizes Convolutional Neural Network (CNN) parameters to precisely classify images based on optic disc size and shape to distinguish between healthy and glaucomatous conditions. The proposed work is evaluated using MATLAB with diverse datasets and it achieves an accuracy above 95%, surpassing benchmarks in accuracy, sensitivity and specificity metrics which are commonly used for glaucoma diagnosis. This research introduces an innovative tool for advancing glaucoma diagnostics with promising significant contributions to ophthalmic research and clinical practice. Recent advancements in medical imaging and machine learning techniques have emphasized the importance of non-invasive approaches for early glaucoma detection, reducing the burden on healthcare providers and improving patient outcomes. The iPCOA framework not only enhances diagnostic accuracy but also offers a scalable solution adaptable to various healthcare settings, facilitating broader access to reliable glaucoma screening. By integrating these enhanced algorithmic improvements with robust validation on real-world datasets, this study establishes a foundation for future developments in computer-aided diagnostics for ocular diseases.
An Exploration of the Influence of MHD, Porosity, and Wall Transpiration on Jeffrey, Fluid Flow
Authors: P. Aparna
Published: January 2025

This work explores the complex dynamics of Jeffrey fluid flow, with a focus on the effects of wall transpiration, porosity, and magnetohydrodynamics (MHD). The goal of the study is to offer a thorough grasp of how these variables affect the flow properties of Jeffrey fluid. Additionally, the effects of porosity and MHD on fluid velocity are examined. Finding the analytical solution for the incompressible transient flow of MHD (magneto hydrodynamic) Jeffrey fluid above an accelerating porous plate is the main goal of this research work. The plate is infinite in the x-direction and produces an oscillating motion when the fluid flows over it at. Porous plates are utilized to allow both injection and suction phenomena to occur. Using the right choice of nondimensional variables makes the model's governing equation dimensionless. These nondimensional differential equations of the Jeffrey fluid are solved with the aid of perturbation techniques and Laplace transformation. Studies are conducted on how various characteristics affect velocity. Oscillating frequency (ω), ratio of relaxation to retardation time (λ1), Jeffrey fluid parameter (β), wall transpiration parameter (γ), time (t), porosity parameter (k), and magnetic parameter (Ha) are some of these characteristics. The results have ramifications for different technical and industrial situations where Jeffrey fluids are encountered and add to the corpus of knowledge on non-Newtonian fluid dynamics.
IoT Data Optimization for Creating Interfaces using Chatbots on Industrial Data
Authors: Priyanka G. Jaiswal
Published: January 2025

This research paper explores the integration of Industrial Internet of Things (IoT) data to develop an innovative AI chatbot tailored for the industrial sector. The chatbot aims to optimize resource allocation, minimize downtime through proactive predictive maintenance, enhance energy efficiency, and provide real-time data insights. By leveraging this technology, the primary objective is to drive a transformative shift in manufacturing processes, fostering greater efficiency, sustainability, and cost-effectiveness. The research underscores the transformative potential of the AI chatbot across critical domains such as resource management, predictive maintenance, energy optimization, and analytics, emphasizing its impact on industrial operations. By analyzing existing solutions and rationale behind the chosen approach, the study elucidates how our proposed chatbot solution enhances performance and addresses key imperatives for success. Through effective implementation this solution not only yields immediate benefits but also lays the groundwork for future advancements in the field.
An Interval approach to solve Fuzzy Fractional Programming Problem as a Fuzzy Linear Complementarity Problem
Authors: Jenifer D H
Published: January 2025

An optimization problem where the goal function is a proportion between two functions is known as fractional programming, and the goal is to maximize or minimize the ratio. This paper discusses a methodology to resolve Fuzzy Fractional Programming Problem as a Fuzzy Linear Complementarity Problem. The study seeks to emphasize the key characteristics, make some new observation and motivate further application in linear complementarity problem. The constraints of the Fractional Programming problem is taken as a Fuzzy Linear Programming problem and further it transformed in to Fuzzy Linear Complementarity Problem by taking various  – cut levels. Utilizing Interval numbers operations the given problem is resolved. A numerical instance is provided to show how the suggested strategy works.
Insect Identification System using Faster RCNN with ADAM optimizer Segmentation model
Authors: R. Padmavathi
Published: January 2025

Researchers have been more interested in automated insect recognition in recent years, and many different approaches have been taken to studying the practical implications of this field. When it comes to designing pest management tactics and safeguarding beneficial insects, accurate identification of the insects at play is crucial. Insect target detection has always relied heavily on artificial identification methods; however, deep learning can automatically extract characteristics for detection, solving the issue of poor detection accuracy due to subjective considerations. Here, we present our work on an improved method of insect identification using segmentation-based visual cues. The bug images were segmented using Faster RCNN with the ADAM optimizer, and the features were extracted using InceptionV3. The findings show that our suggested model outperformed competing methods in terms of accuracy and performance.
A Deep Learning Based Adaptive Model for Blocking Incoming Calls Based On The Caller's Voice Commands
Authors: Kalyani Ghuge
Published: January 2025

The proliferation of intrusive and potentially harmful incoming calls necessitates innovative solutions that transcend traditional blocking mechanisms. This research introduces a groundbreaking deep learning-powered adaptive model that revolutionizes call management through sophisticated voice command analysis. By leveraging advanced machine learning techniques, we develop a context-aware system capable of dynamically interpreting caller intent with unprecedented precision.
Our novel methodology integrates multimodal feature extraction, sentiment analysis, and reinforcement learning to create an intelligent call-blocking mechanism. Utilizing state-of-the-art convolutional and transformer-based neural architectures, we process voice commands to classify potential spam or unwanted communications with 97.6% accuracy. The proposed adaptive framework demonstrates remarkable user-centric flexibility, reducing false positive rates by 62% compared to existing rule-based systems.
The research significantly contributes to privacy protection technologies, offering a robust, real-time solution that learns and adapts to individual user preferences. By transforming call management from static filtering to dynamic, intelligent screening, we address critical challenges in telecommunications privacy and user experience.
Integrating Argonomic Method and Yield Crop Modelling with ML Baseline
Authors: V.Sujatha
Published: January 2025

Predicting agricultural yields using machine learning has been the subject of several research, most of which have concentrated on individual cases. It is possible that their data and techniques won't work with other crops or in different places. Contrarily, machine learning is not used by operating large-scale structures like MARS Crops Harvest Foretelling Systems (MCYFS). When there is a flood of newly disclosed data, ML becomes a potentially useful tool. In order to construct a machine learning foundation for huge-scale crop yield prediction, researchers integrated agronomic concepts of crop modelling with machine learning. A methodology that focusses on accuracy, modularity, and reusability forms the basis. Researchers made sure everything was accurate by using machine learning without leaking any information and by creating characteristics or predictors that could be explained in connection to the development and growth of crops. The characteristics were developed by researchers using information from the MCYFS database, which included meteorological, remote sensing, soil, and crop simulation results.
The researcher focused on a reusable and modular procedure that can accommodate various crops and nations with little configuration adjustments. Using standard input data, the method may be utilised to conduct repeated tests and produce repeatable outcomes. The outcomes act as a springboard for more improvements. In order to compare the performance, the researchers used a straightforward strategy that required no prediction skills and either projected the training set's average for 3 countries like India (IND), Spain (SP) and Ireland (IL) and 5 crops like springs barley, wheat, sunflower, potatoes and sugar beetroot). The researcher forecasted yield at the regional level in the case studies. Additionally, the researchers compared the projections with previous MCYFS forecasts and aggregated them at the national level. Across nations, the normalised RMSE (NRMSE) for the beginning season forecasts was similar. Wheat had an NRMSE of 7.88 for wheat (IND), whereas sweet beetroot had an NRMSE of 8.22 (SP). NRMSEs for potatoes, sugar beetroot and wheat, on the other hand, were double those of MCYFS. At the conclusion of the season, NRMSEs were still similar to MCYFS. Adding more information foundations, creating high analytical characteristics & testing various ML algorithms may all help to enhance the baseline. Large-scale agricultural harvest prediction using ML will be encouraged by the baseline.
Non-linear, Reinforcement Learning-based Algorithm for Real-Time MANET Configuration and Threat Prediction
Authors: K.Purnima
Published: January 2025

Mobile Ad Hoc Networks (MANETs) face challenges such as dynamic topologies, high node mobility, and increased vulnerability to attacks like black hole and DDoS. This study develops a non-linear reinforcement learning-based algorithm (DRL-MANET) to address real-time network configuration and threat prediction. The model aims to ensure efficient performance and secure operation under unpredictable conditions. The approach uses deep reinforcement learning (DRL) to optimize network decisions based on real-time feedback. An LSTM-based anomaly detection system identifies and mitigates threats by integrating detection outputs into the decision-making process. Federated learning allows decentralized model training, preserving privacy through differential privacy and blockchain mechanisms. Hierarchical clustering and adaptive updates minimize computational overhead and support scalability. Simulation results show a packet delivery rate of 97.2%, a threat detection accuracy of 96.8%, and a 7% reduction in throughput when scaling to 150 nodes. Compared to MA3DQN and EDRL, DRL-MANET demonstrates lower latency, faster recovery from node failures, and improved resource management. These findings illustrate how the model handles high traffic, variable mobility, and evolving attack scenarios. The proposed algorithm supports secure, scalable, and adaptable solutions for MANETs. The methods and results offer a practical framework for managing dynamic network environments while addressing privacy and resource constraints.
Nonlinear Optimization Framework for Educational Timetable Scheduling using Differential Evolution
Authors: Ravindra Babu.B
Published: January 2025

This research presents a nonlinear model for optimizing educational timetables using the Differential Evolution algorithm. The model manages complex scheduling tasks by considering various constraints, including teacher availability, course requirements, classroom features, and institutional rules. It processes data through feature engineering to quantify factors like time slots, teacher expertise, and course priorities. The algorithm dynamically explores multiple timetable configurations, evaluating their suitability based on hard constraints like teacher availability and classroom occupancy, and soft constraints such as minimizing idle periods and meeting preferences. Experimental analysis shows that the model improves scheduling by resolving conflicts, reducing idle hours, and utilizing classroom space more efficiently. The findings highlight a structured approach to balancing institutional needs and logistical challenges in timetable generation.
Jacobi Matrix Polynomial and its Integral Results
Authors: V.Sri Lakshmi
Published: January 2025

Introduction: Recent advancements in matrix polynomial structures associated with special functions have gained significant traction, showcasing a diverse array of applications across various engineering disciplines. This paper primarily aims to explore and derive multiple integral representations for the modified Jacobi Matrix Polynomial. Specifically, we will delve into both finite and infinite single integral representations, as well as double integral representations of the polynomial. By examining these integral forms, we seek to enhance the understanding of their mathematical properties and implications, thereby providing valuable insights that can be leveraged in practical engineering scenarios. The exploration of these representations not only enriches the theoretical framework surrounding modified Jacobi Matrix Polynomials but also highlights their potential utility in solving complex problems within the engineering domain.
Theoretical Development of Neurofuzzyzenon: A Hybrid Framework for Intuitive, Adaptive Decision-Making
Authors: R. Poornima
Published: January 2025

This article introduces the theoretical framework of Neurofuzzyzenon, an innovative hybrid approach that integrates principles from artificial neural networks, fuzzy logic systems, and Zen philosophy. Neurofuzzyzenon is proposed as an adaptive decision-making architecture that not only mimics human cognition but also incorporates elements of intuitive, holistic thinking akin to Zen practices. The fusion of these domains aims to create a system that is not only flexible and adaptive but also capable of achieving balance in complex, uncertain environments. We explore the theoretical underpinnings, potential applications, and future research directions for Neurofuzzyzenon, positing that this new paradigm could redefine how intelligent systems understand and interact with the world.
Spatial Moving Average Polynomial Model on Irregular Lattice and its Full-Likelihood Based Implementation
Authors: Lidia Khonglam
Published: January 2025

The simultaneous incorporation of a contiguity-based neighbourhood matrix of first and second order neighbors in the spatial moving average model on an irregular lattice can be specified in two different ways- as a quadratic polynomial or as a product of two linear polynomials. The present article concerns with the second order spatial moving average polynomial model of the latter form. Expression for the joint probability distribution of the response is derived together with expressions for the first and second order moments of the model using characteristic function. Model parameters are estimated using method of maximum likelihood and expression for their standard errors are obtained. In the absence of an analytical solution of the full likelihood function, optimization of the full likelihood function using Differential Evolution technique has been performed. Confidence interval for drawing inference has been constructed based on the derived standard error expression and bootstrapping method. Using likelihood ratio test statistic and bootstrap results, simultaneous confidence regions for the two spatial dependence parameters are also constructed. Implementation of the model has been executed on simulated data, and relevant comparisons made with linear models and first order spatial moving average model. The advantage of using the proposed model lies in its ability to provide statistically valid inference, as it affects producing spatial dependence of the first and the second lag orders in the residuals, insignificant.
Solution of Cauchy and Boundary Problems of Discrete Multiplicative-Poverative-Additive Derivative Third-Order Equation
Authors: Aygun Mammadzada
Published: January 2025

Introduction: Within the framework of Cauchy and boundary issues, the authors of this paper investigate the third-order discrete mixed derivative equation. For the purpose of removing these derivatives and establishing the general solution of the related discrete multiplicative-poverative-additive derivative equation, it makes use of the definitions of discrete additive, multiplicative, and poverative derivatives. There are three arbitrary constants that are dependent on this answer. The subsequent step entails finding these constants by making use of primary or boundary conditions. In order to correctly handle the Cauchy and boundary issues, the essay attempts to solve for these constants.
Objectives: To study third-order discrete mixed derivative equations in the framework of Cauchy and boundary problems, and to find a general solution through eliminating mixed derivatives with the use of discrete derivative definitions.
Methods: The problem is stated as a new discrete additive, multiplicative, and poverative derivative definitions. We formulate the general solution and identify the arbitrary constants from primary or boundary conditions.
Results: The general solution of discrete equation has successfully been obtained containing three arbitrary constants determined by the imposed boundary or Cauchy conditions.
Conclusions: This study enhances the efficient solution of third-order discrete mixed derivative equations, consolidating the utility of the discrete definitions of the derivatives for solving Cauchy and boundary problems in a systematic approach.
Inventory Optimization with Ramp-Type Demand, Time-Dependent Holding Costs, and Discounted Backorders under Inflation
Authors: Ekta Chauhan
Published: January 2025

This study presents an inventory model with a ramp-type demand pattern, where holding costs increase over time, and inflation is taken into account. The model is particularly relevant for inventories like seasonal produce and newly launched fashion items. When stock runs out, the inventory manager offers a discount to customers willing to backorder their demand. The goal is to determine the optimal ordering policy and backorder discount by minimizing the total cost across a replenishment cycle. Finally, numerical results are provided to assess how the optimal policies respond to changes in key system parameters. Graphs are used to visualize the impact of these parameters on the total inventory cost, enhancing the understanding of the model.
Superior Asymmetrical PWM AC Chopper Fed Capacitor Run Induction Motor Drive using Evaporation Based Water Cycle Algorithm
Authors: Murali Narayanamurthy
Published: January 2025

Introduction: This research presents a novel improved asymmetric pulse width modulation technique for increasing the efficiency of a capacitor-powered, pulse width modulated (PWM) AC chopper-supplied induction motor. It uses a water cycle optimization technique based on evaporation and incorporates 4 distinct pulses in each quarter cycle. It is compared with the standard method of sinusoidal pulse-width modulation. Power quality is defined by properties such as input power factor, efficiency, and total harmonic dispersion of voltage and current. Simulation results yields better response for the proposed method when compared with the conventional technique.
Objectives: The goal of the study is to minimize total harmonic distortion of voltage and total harmonic distortion of current while optimizing power factor and efficiency in order to improve power quality metrics. This paper focuses on obtaining the optimal switching angles by an evaporation-based water cycle algorithm.
Methods: The pulses in the enhanced asymmetrical pulse width modulation approach have varying widths while maintaining quarter cycle symmetry. The switching on angles and switching off angles is varied. The proposed enhanced asymmetrical pulse width modulation by evaporation-based water cycle algorithm and conventional sinusoidal pulse width modulation technique is compared.
Results: The simulation results show that performance parameters by proposed technique is better compared to conventional technique. The controlling techniques implemented for PWM AC chopper are sinusoidal pulse width modulation and enhanced asymmetrical pulse width modulation by evaporation based water cycle optimization algorithm.
Conclusions: This paper discusses novel optimization algorithm for the speed control of capacitor run induction motor with improved parameters. The performance enhancement parameters taken into account total harmonic distortion of current, total harmonic distortion of voltage, efficiency and power factor. This work can be compared with the other techniques like particle swarm optimization, cuckoo search optimization and proposed technique for the enhanced asymmetrical pulse width modulation for capacitor run induction motor.
Design and Analysis of High Speed Low Power 4T-2R finFET-Memristor based SRAM
Authors: T.Swapna Rani
Published: January 2025

Power usage management has emerged as a significant challenge in the production process due to the continued miniaturisation of CMOS technology. All standards for developing designs of non-volatile storage are going to gain through the implementation of memristors. Memristive devices have been defined by non-volatility, significant switching frequency, minimal energy consumption, and small dimensions.  FinFETs may replace conventional transistors, significantly enhancing efficiency in space and power, hence improving the design. The development of a non-volatile SRAM unit using FinFET and Memristor is suggested and compared to conventional SRAMs. Compared to current CMOS 6T SRAM and finFET 6T SRAM, the proposed SRAM exhibits significantly reduced power consumption and demonstrates speed improvements of at least 87% and 54%, respectively. Furthermore, Monte Carlo calculations have been conducted for the described 6T-SRAM architecture to enhance comprehension of the device's robustness.
Modeling Infectious Disease Spread: Differential Equations in Population Dynamics and Epidemiology
Authors: R. Vinodhini
Published: January 2025

Quantitative modelling of epidemiological diseases plays a vital role in disease dynamics and monitoring and surveillance. Differential equations provide a good way of understanding disease transmission dynamics, and the effectiveness of different mitigation measures. This paper aims at discussing the application of differential equations in studying diseases spreading, particularly with reference to compartmental models like SIR model, and extensions for their improved realism. The paper focuses on the fundamental aspects of differential equations used in population dynamics, epidemics, immunity and vaccines, and the consequences of controlling or changing different aspects of communities. By applying these two models, the paper gives case details concerning the transmission of diseases such as covid 19 and influenza. The findings offer details of disease spread and extension and will serve as a basis for further studies and eventual treatment on the subject in the health sector.
Advancing Mental Health Diagnostics Through the Fusion of Multimodal Data with CNN and LSTM Techniques
Authors: Chhaya Nayak
Published: January 2025

Multimodal assessment of mental health disorders is an important problem to solve since millions of people suffer from disorders while approaches to detect them are often time-consuming and non-objective. This work aims to develop advanced diagnostic approaches by using the additional modalities like speech, facial expression, and physiological data to include the complex and multiple signals of psychopathology. In this paper, synthetic spatial features are extracted using the Convolutional Neural Networks (CNNs) while the temporal features are adopted by utilizing the Long Short-Term Memory (LSTM) network to solve both spatial and sequential data problems. When integrating these models, we obtain a solid diagnostic system that offers more profound analysis of diverse mental health conditions. The findings also corroborate that the fusion model has higher accuracy and reliability than single modal and single technique-based approaches in different mental health conditions. What is more it not only refining diagnostics but also creates new opportunities for early detection and individual approach in immediate treatment. The work underlines how AI improves and speeds up mental health diagnostic opportunities and underlines the necessity of using a multi-modal approach for the assessment and management of patients’ conditions.
Mathematics Learning Innovation with Ethno-Based Sttep-W (Structured Think-Talk-Evaluate-Presentation-Write) Model: Efforts to Improve Students' Mathematical Connection Skills
Authors: Jaka Wijaya Kusuma
Published: January 2025

Having mathematical connection skills in learning mathematics is important, but students' mathematical connection skills are still relatively low. The results of modifying the development of the think-talk-write learning model, namely the Ethno-based STTEP-W learning model, are expected to influence students' mathematical connection skills positively. This study aims to describe the quality of the Ethno-based STTEP-W learning model and its developed devices that include valid, practical, and effective criteria for improving the mathematical connection ability of junior high school students. This research uses the research and development (R&D) method. The learning model development procedure used in this study is the development model, according to Nieveen and Plom, consists of three stages: preliminary research, prototyping phase, and assessment phase. The subjects in this study are junior high school students in grade VII and SMP Negeri 1 Tanara mathematics teachers. It is carried out in the even semester of the 2023-2024 academic year for triangular and quadrilateral subjects. The data in this development research are qualitative and quantitative. The analysis is carried out for validity, practicality, and effectiveness. The results of the study prove that the Ethno-based STTEP-W (Think-Talk-Evaluate-Presentation-Write) learning model developed is valid, practical, and effective in improving students' mathematical connection skills. The components of the STTEP-W learning model can improve students' mathematical connection skills during mathematics learning. Ethno-based mathematics learning mathematics applied in the Ethno-based STTEP-W model also shows that mathematics is not only limited to abstract concepts and formulas that are difficult to understand but also as a living and relevant tool in daily life.
Long- and Short-Term Memory Networks in Financial Market Predictions: A Study on USD Fluctuations
Authors: Himanshu Jindia
Published: January 2025

The financial market is very sensitive to changes in the USD exchange rate. People may better assess the economic condition and steer clear of financial hazards with accurate predictions of the USD rate of exchange. This study proposes a CNN-STLSTM-AM hybrid method for closing USD price forecasting. A convolutional neural network (CNN) uses the input data to extract local characters. One better model suggested in the article, Specialized Tanh Long and Short-term Memory (STLSTM), uses local characters to predict the closing value of the USD exchange rate. The feature weights are distributed through the Attention Mechanism (AM) according to the effect of the localized character on the closing price. To prohibit total discarding or retention, the output range is altered from (0, to 0.2, 0.96). This is achieved by adding the tanh (x) plus 0.2 functions to the input gate. The following neural networks are examined and compared: SVR, CNN, CNN-LSTM, CNN-LSTM-AM, LSTM, GRU-LSTM, and CNN-STLSTM-AM, all of which predict the closing price. Experiments show that CNN-STLSTM to AM is the most accurate prediction model.
An Econometric Analysis of Egypt's Cereal Production and Environmental Emissions for Food Security
Authors: Zainab Shawky El-khalifa
Published: January 2025

Agricultural production substantially contributes to human-induced greenhouse gas emissions that intensify global climate change. The increasing population, rising food demand, decreasing arable land, and declining soil fertility due to excessive fertilizer use have created a significant challenge to food security, ultimately leading to resource exploitation and environmental degradation. Egypt's economy relies significantly on the agricultural sector, which accounted for roughly 14% of the nation's total GDP in 2023; concurrently, it plays a considerable role in climate change through greenhouse gas emissions that adversely affect the environment. This research aimed to analyze the relationship between GHG emissions and the agricultural sector using the ARDL method of cointegration from 1990 to 2021. The findings revealed that the agricultural sector is the primary source of GHG emissions in Egypt. The expansion of cultivated area will lead to environmental degradation due to increased greenhouse gas emissions in the short term, while cereal production and the enlargement of land for cereals will cause elevated greenhouse gas emissions in the long term due to the utilization of traditional agricultural practices. The study recommended increasing credit to farmers and augmenting agricultural investments to establish modern, environmentally sustainable agricultural systems that enhance production and ensure food security through the application of contemporary agricultural technologies.