bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Health Education With Puzzle On Dengue Hemorrhagic Fever's Prevention Behavior of School Aged
Authors: Lestari Ratna , Putri Fadila Septiana , Yusriana
Published: August 2024

Demam Berdarah Dengue merupakan penyakit infeksi yang disebabkan  oleh  virus  dengue  yang  ditularkan  melalui  gigitan  nyamuk Aedes aegypti.  Sekolah menjadi  sasaran dalam upaya pencegahan DBD, dikarenakan sekolah menjadi tempat yang berpotensi untuk terjadinyapenyebarandan penularan DBD. Upaya pencegahan DBD disekolah, diberikanmelalui pendidikan kesehatan pencegahan DBD menggunakan media puzzle. Penelitian ini bertujuan untuk mengetahui ada pengaruh tingkat pengetahuan, sikap, dantindakan pencegahan   DBD   sebelum   dan   sesudah   diberikan   pendidikan   kesehatan menggunakan media puzzle pada anak kelas IV & V Di SD N Sewon 1 Bantul Yogyakarta. Penelitian  ini  merupakan  penelitian  kuantitatif  dengan  desain  Pre Eksperimen pendekatan one group Pre test - Post test. Sampel dalam penelitian ini adalah anak kelas IV & V yang belum pernah mengalami DBD sejumlah 52 siswa dengan pengambilan   sampel   menggunakan   teknik   total  sampling.   Tingkat pengetahuan,   sikap,   dan   tindakan   pencegahan   DBD   diukur   menggunakan kuesioner    yang   diberikan   sebelum   dan   sesudah      pendidikan   kesehatan menggunakan media puzzle. Hasil  analisis  data  menggunakan  uji  Mc  Nemar,  didapatkan  tingkat pengetahuan pencegahan DBD dengan nilai p 0.013(p<0,05), sikap dengan nilai p 0,000 (p<0,05), tindakan p 0,002 (p=0,05) artinya adanya pengaruh pendidikan kesehatan menggunakan media puzzle terhadap tingkat pengetahuan, sikap, dan tindakan pencegahan DBD. Diharapkan sekolah memberikan edukasi kesehatan dengan topik yang berbeda menggunakan media puzzle.
IOT Based Induction Motor Health Surveillance System by Detecting Vibrations and Monitoring Temperature
Authors: Kaushalya Thopate
Published: August 2024

To extend the security, lifetime, and vitality effectiveness of engines, the energetic behavior of the turning shaft must be examined. Shaft vibrations must be checked amid operation in arrange to optimize rotor plan and certify engine execution. Moreover, the energetic behavior of the rotor must be assessed. Shaft vibrations must be measured amid operation in arrange to optimize rotor plan and approve numerical models. Be that as it may, measuring the shaft's vibration amplitude and recurrence may be a troublesome issue for metrology since the shaft turns and noncontact estimations are essential. To fathom this issue, we show a estimation framework comprising of vibration sensor SW-420 and NodeMCU ESP8266 must be mounted on engine.  These sensors simultaneously measure the vibrations produced as well as heat produced by motor. Temperature sensor DHT11 is also used to track the heat produced by the motor so that it would not exceed the range given for prolong usage of motor. The NodeMCU ESP8266 connects to a ThingSpeak server through Wi-Fi to transmit the values in real-time the server stores and displays the data on a graphical dashboard, allowing users to monitor vibration and temperature. The project demonstrates the feasibility of low-cost and efficient air quality monitoring using readily available hardware and software tools. The results of the experiments show that the developed motor surveillance can provide accurate and reliable measurements of various amplitude, frequency, celius, thus contributing to environmental sustainability and public health.
Stability and Bifurcation in a Second-Order Difference System
Authors: Smitha Mary Mathew
Published: August 2024

In the paper, we examine the system
                                                  
                                                                                             (1)
                                               
where  are positive real numbers and the initial conditions  are arbitrary nonnegative numbers. We investigate the persistence, boundedness, convergence, invariance, and global asymptotic character of the positive solutions of (1). Bifurcation diagrams are then plotted to visualize the periodic character.
Ethnomathematics in Aashtimki: A Study on Tharu's Culture
Authors: Parshuram Chaudhary
Published: August 2024

Tharu is one of the indigenous communities having their distinctive culture and practices. For doing daily tasks, they have developed their own mathematical conceptions and ideas. Ethnomathematics is the study of mathematical concepts and knowledge that are applied by a specific group of people but are typically not covered in the formal curriculum. This study is intended to explore the mathematical concepts hidden in the Aashtimki painting that Tharu's people celebrate. This study used the observation and documentation analysis method to collect data for this aim. The painting for Aashtimki have established a large degree of local knowledge that is primarily based on intuition, estimation, observation, and practice that has been passed down from generation to generation and is deeply rooted in their local culture. Emic ethnomodeling is used to investigate the mathematical elements embedded into the construction of Aashtimki painting in Tharu's community. The findings of the study revealed that Aashtimki painting have sophisticated mathematical ideas and knowledge and they used the concepts of reflection, rotation, translation and dilation of transformation implicitly.
Second Hankel Inequality for Certain Common Subclass of Classes of Starlike and Convex Functions
Authors: Gurmeet Singh
Published: August 2024

In this paper we will define a new class S* Csin (r) which is subordinate to function 1+sin z, we will find the Fekete - Szegö inequality for this class along with Fekete - Szegö inequality of the functions of this class defined S* Csin (r,Θ) through Poisson distribution. Further we have solved the second Hankel determinant of this new class.
Innovative Energy Prediction using Kolmogorov-Arnold Networks (KAN) and Liquid Neural Networks (LNN) for Smart Grids
Authors: Harish Morwani
Published: August 2024

The efficient operation and management of smart grids rely heavily on accurate energy consumption prediction. In this paper, we propose a novel approach for predicting energy consumption in smart grids that combines the strengths of Kolmogorov-Arnold Networks (KANs) and Liquid Neural Networks (LNNs). To deal with complex, time-varying energy consumption patterns, our hybrid model combines KANs' robust function approximation capabilities with LNNs' dynamic adaptively. KANs provide a solid framework for modelling complex, high-dimensional systems by combining multivariate functions into univariate functions. In addition, LNNs provide dynamic adaptability through their continuous and differentiable activation functions, which mimic the fluidity of liquids, making them particularly adept at dealing with time-dependent data and changing patterns. Our methodology combines the strengths of KANs and LNNs to create a hybrid model that can capture complex dependencies and temporal variations in energy usage data.
Forecasting Short-Term Wind Speed using ARIMA, and Convolution Neural Network Models at International Airports in Saudi Arabia
Authors: Lamiaa Abdul-jabbar Dawod
Published: August 2024

This study aimed to predict the maximum wind speed (MWS) at three international airports of Saudi Arabia, such as King Abdulaziz International Airport (KAIA), King Fahd International Airport (KFIA), and King Khalid International Airport (KKIA). For this end, we utilized advanced machine learning (ML) and Statistical techniques, including Artificial Neural Network (ANN), Convolution Neural Network (CNN), and Autoregressive Integrated Moving Average (ARIMA) models, and the daily maximum wind speed data during 2021-2023. To develop the models, we effectively employed the average mutual information (AMI) for assessing the suitable input variables to predict the maximum wind speed in KKIA, KFIA, and KAIA. The MWS forecasting models of three airports were constructed using the training subset (80%, spanning from 2021 to 2023 May) and testing subset (20%, between 2023 May and 2023 Dec). The results showed that the accuracy of the findings has been improved by implementing the proposed CNN models. Compared to the ANN, and ARIMA models, the CNN algorithm showed its remarkable potential as a high-level model in accurately estimating KKIA, KFIA, and KAIA max wind speed values and exhibiting superior generalization capability and minimal variance.
Revolutionizing Electrochemical kinetics: Homogeneous Redox Catalysis unveiled through an innovative mathematical framework using the Homotopy Perturbation Method
Authors: Saranya.K
Published: August 2024

Introduction: In Redox homogeneous catalysis the catalyst couple merely plays the role of an electron in contrast with chemical catalysis. The mathematical modelling of homogeneous redox catalysis of electrochemical reactions for the EC scheme is described by the system of second order non-linear differential equations. This model was initially designed using the classical differential equations and it is extended to the Caputo fractional derivative (FDE’s) of order in particularly sequential fractional case. The aim is to obtain approximate analytical solutions of the system of  FDE’s using Homotopy Perturbation method (HPM) and analyze the impact of various parameters with different order of . In addition, the solution of the sequential dynamic equations yields the solution of the corresponding integer-order differential equations as a special case.
Encryption and Decryption using Hill Cipher and Integral Transforms
Authors: Gobburi Rekha
Published: August 2024

We know that Hill Cipher is one of the regular methods that is used in cryptography for encryption and decryption of data using modular arithmetic under different modulus. Moreover, we have seen results using some integral transforms like Laplace transforms, Aboodh transforms and many more. This paper extracts the result of dual encryption and decryption with a combination of Hill Cipher and few integral transforms.
Introduction: In many circumstances, the sender wants to keep the message private from the public or from unauthorized users. Here, we combine Hill Cipher with Laplace and Kamal transforms to encrypt the message and apply their inverse transforms to decrypt it. In contrast to cipher text, which is a coded version of plaintext, the original message written by the user is referred to as plaintext.
Objectives: In this paper, we focus on two stages of encryption and two stages of decryption under modulo 255.
Methods: Using Hill Cipher with different transformation techniques, we have encrypted and decrypted the data. One is Hill Cipher with Laplace transform technique while the other is Hill Cipher with Kamal transform technique.
Results: In this paper, we see that the original text is transformed to cipher text which could be retrievable using some techniques.
Conclusions: This is a novel method that uses dual techniques to safeguard data: first, it encrypts the encrypted cipher text, and then it decrypts it again. The data is protected all along the way because the key that is transmitted to the recipient is very big and difficult to crack.
Higher Dimensional Triangular and Square Numbers
Authors: J. Choi
Published: August 2024

Triangular numbers and square numbers are traditionally defined in two-dimensional space. In this paper, we generalize these numbers to higher dimensions and explore their properties using a coordinate system to conceptualize spaces beyond three dimensions. By generalizing triangular numbers, we establish and prove a notable combinatorial identity and recursive relationship between triangular numbers of different dimensions. Key applications discussed include network optimization and geometric partitioning. The paper concludes by finding the ratio between generalized triangular and square numbers, which geometrically corresponds to the volume ratio of a tetrahedron to a d-dimensional cube.
A Study On Job Change Decision Making Of Peoples By Multi Objective Optimization Technique In Neutrosophic Environment
Authors: C. Jayabharathi
Published: August 2024

Introduction: The article explores the employment preferences of specific area workers in Tamil Nadu.  A person's choice of job refers to the profession that best suits him or her. In this competitive environment, changing careers can be a difficult option for professionals.
Objectives:  If expectations and experience on the job don't match,  which causes dissatisfaction, frustration, unhappiness, subpar work output and the need to look for another job.  This study discusses the career choices made by particular area workers in the Virudhachalam and Cuddalore district.
Methods: The integrated methodology in the Neutrosophic fuzzy set was used to conduct this investigation. Here, Neutrosophic entropy plays a role in determining how the constraints (parameters) are weighted. Least Eigen Neutrosophic Fuzzy Set (LENFS) and Greatest Eigen Neutrosophic Fuzzy Set (GENFS) were used to evaluate the problem's objective. For the problem, there are six objectives with respect to the six constraints that have been determined.
Results: We can figure out the range of satisfied, abstain, and unsatisfied levels for the objectives in relation to the restrictions using the values derived by GENFS and LENFS.
Conclusions: We would learn from the survey that individuals are pleased and would desire to work for the government. In addition, the survey found Neutrosophic technology to be highly beneficial.
Improved Methodology for Breast Cancer Prediction through Integration of Hard Voting Ensemble Classifier on WDBC Data Set
Authors: Archana Singh
Published: August 2024

Introduction: Disease that is prevalent and highly fatal is the breast cancer disease and it affects many people in the world. To effectively prevent the fatality rate caused by breast cancer, tools need to be developed that are capable of early diagnosis and efficient treatment. Researchers and medical experts across the world have pointed out several diagnostic techniques for this sickness; however, higher enhancement of such present methods is still needed to enhance a perfect and effective diagnosis of this disease.
Objective: It’s an objective of this research to establish quick and precise forecasts of breast cancer, which is estimated to rank second as a leading killer of women globally.
Methodology: In this paper, we provide a methodology based on hard voting ensemble classifier that combines three machine learning algorithms: logistic regression, support vector machine and decision tree to diagnose the kind of breast cancer, whether benign or malignant. The proposed model’s performance is evaluated in this study using the Wisconsin Diagnostic Breast Cancer dataset (WDBC), with random oversampling (ROS) being used to balance the dataset and Standard Scaler being used for feature scaling.
Results: The suggested approach achieved an accuracy of 0.9825, a precision of 0.9859, a recall of 0.9859, F1 score of 0.9859and AUC of .9813. Using a 10-fold cross validation it obtained a mean accuracy of .9738.
Conclusions: The suggested approach yielded superior results after the individual classifier and many acknowledged existing works are directly compared with the results.
The Bounds of Energies of Rough Complemented Graph
Authors: B.Praba
Published: August 2024

The main objective of this paper is to study the various energies and their bounds of the Rough complemented graph corresponding to the given Rough semiring. In this paper, for a given approximation space I=(U,R) where U is the nonempty finite set of objects and R is an equivalence relation on U, the Rough semiring (T,∆,∇) is taken for study. The Rough complemented graph of T  denoted by GRC(T) is a graph whose vertices are V(GRC(T))={RS(Y)|Y∈〖℘(E)〗^1 }   be the set of equivalence classes induced by I and two distinct vertices RS(X) and RS(Y) are adjacent iff RS(X)∇RS(Y)=RS(∅). Note that there will be 2^n-2  vertices in GRC(T). Also Randic, seidel, minimum dominating, maximal independent and dominating energies of GRC(T) are obtained, the lower and upper bounds of these energies are also established. These energies are obtained through Python programming, and a bar diagram is used to conduct a comparative study for various values of n. All the illustrated concepts are explained with suitable examples.
On Beta Generalized Star Pre-I-Closed Sets in Ideal Topological Spaces
Authors: S. Gowri
Published: August 2024

The purpose of this paper is to define the new idea Beta generalized star pre-closed sets, a new class of closed and open sets in topological spaces, and to examine some of its characteristics using few examples. In addition, we define Beta generalized star pre-I-closed sets, a new class of closed and open sets in ideal topological spaces and discuss through their characteristics.
Performance Analysis of Biserial Servers Connected with a Main Server by Fuzzy Ordering Approach
Authors: Syamala. P
Published: August 2024

This article presents some appraises for biserial servers connected with a main server by fuzzy ordering approach. Here we have three servers, so that three queues are formed in front of the servers. The patrons were presented along with poisson stream to the queues, and the rate of service was observed by exponential stream. The customers may get the services either from server 1(or 2), to server 2 (or 1), (biserial) then Main server or from the servers 1 or 2 to (biserial) Main server. So we get more possibilities from this situation. We consider the arrival, service and possibility rates as the fuzzy parameters. By virtue of our proposed ordering approach, we shift the fuzzy rates to crisp numerals. We detect the measures expected waiting duration in the system (E(W)) and the expected aggregate of customers in the system (E(N)) of biserial servers connected with a main server with fuzzy nature.
An Approach to Develop Cerebra-Vascular-Haemato-Cardiac Detector using Machine Learning Techniques
Authors: Shambhu Nath Saha
Published: August 2024

The "Design of Cerebra-Vascular-Haemato-Cardiac Detector using Machine Learning Approaches" project is a groundbreaking initiative that seeks to revolutionize medical diagnostics by integrating advanced machine learning techniques. This multifaceted project encompasses the development of a comprehensive detector capable of analysing cerebrovascular, haematological, and cardiac data to provide accurate and timely health assessments. The integration of diverse data modalities and the creation of a real-time detection system will contribute to early disease detection and improved patient outcomes. [1][2][3]
Cerebrovascular stroke and heart attack stand as prominent causes of disability and global mortality, underscoring the critical need for early detection to facilitate effective treatment. Traditional diagnostic systems for Electroencephalograph (EEG) and Electrocardiograph (ECG) are non-wearable due to their unwieldy instruments, posing a significant challenge. In response, we propose a Cerebra-Vascular-Haemato-Cardiac (CVHC) detector designed to detect strokes and cardiac arrests. The key elements of the CVHC detector include EEG and ECG, with additional integration of an oxygen sensor and blood pressure sensor. This comprehensive system aims to identify anomalies in the human body associated with strokes, cardiac arrests, respiratory issues, and heat strokes. Leveraging Machine Learning, the CVHC device provides immediate emergency alerts.
The signals originating from ECG, EEG, blood pressure sensor, and oxygen sensor undergo processing through a simulator, Discrete Wavelet Transformation (DWT), and Convolutional Neural Network (CNN). This processing involves convolution, noise reduction, and wavelet differentiation of EEG and ECG signals, ultimately amplifying them to attain usable voltage. The proposed system is adept at detecting Ischemic Stroke, Haemorrhagic Stroke, and Cardiopulmonary Arrest by comparing the acquired signals with predefined patterns of normal brain and heart activities. This innovative CVHC solution thus addresses the limitations of traditional diagnostic instruments, offering a wearable and efficient alternative for early detection of life-threatening conditions.
Nonlinear Dynamics in Distributed Ledger Blockchain and analysis using Statistical Perspective
Authors: Purnendu Bikash Acharjee
Published: August 2024

More and more in healthcare is blockchain technology applied for safe and open data storage. Still, it is understudied how deeply regression analysis combined with nonlinear dynamics into distributed ledger systems performs. This kind of approach may help to increase data transfer efficiency and help storage management in blockchain systems. Data speed and storage efficiency restrictions make current blockchain systems difficult to handle for large amounts of healthcare data. Conventional methods find poor data retrieval and transfer due to the great complexity and nonlinear characteristics of healthcare data. Combining nonlinear dynamics with deep regression analysis, this paper proposes a fresh approach for maximizing data transfer and storage in blockchain systems. Inspired by nonlinear dynamics ideas, a deep regression model aimed at maximizing block storage and forecast data transmission requirements was assessed on a simulated healthcare dataset using a distributed ledger system with 1,000 blocks and a 500 GB total dataset size. Performance criteria covered transmission efficiency and storage consumption. The proposed technique improved data transmission efficiency by thirty percent over current techniques. Another clear improvement was using storage; block size needs fell 25%. The best model, according to numerical research, lowered an average transmission time from 120 to 84 minutes and storage overhead from 200 to 150 GB.
Evaluating Star Vertex Cochromatic Number in Prism, Sunlet and Derived Graphs
Authors: R. Sabitha
Published: August 2024

In this discussion, the star cochromatic number q is found for the following graphs: Prism Graph q[Y_m], Line Graph of Prism Graph q[〖L(Y〗_m)], Middle Graph of Prism Graph q[〖M(Y〗_m)], Sunlet Graph q[S_m], Line Graph of Sunlet Graph q[L(S_m)], Middle Graph of Sunlet Graph q[M(S_m)].
Prime Graceful Chromatic Number of Diverse Graphs
Authors: M. Keerthika
Published: August 2024

In this paper, prime graceful coloring is introduced which aims to incorporate principles of prime and graceful coloring. The prime graceful coloring of star, path, cycle, friendship, pan and bistar graphs are proposed. This new approach is related to the prime graceful chromatic number which represent the fewest colors needed to color any graph adhering to the principles of prime graceful coloring. Also, the efficiency of new coloring are analyzed with the examples.
Innovations in Geospatial Data Analysis: Applied Nonlinear Analysis, Remote Sensing, AI, and GIS for Environmental Sustainability
Authors: C. Sudha
Published: August 2024

Abstract: Water mapping plays a pivotal role in sustainable water resource management, particularly in the context of escalating climate change impacts. This study addresses the critical need for accurate water level predictions by introducing an innovative ensemble machine learning (ML) approach. Motivated by the increasing importance of ML and mathematical models in geospatial data analysis, the research papers from the diverse database, revealing a research gap that includes the absence of standardized methodologies and the exploration of diverse ensemble methods. Leveraging the EuroSat dataset for land use and land cover classification, the proposed ensemble model combines Principal Component Analysis, Genetic Algorithm, Gradient Boosted Decision Trees, Frequency Ratio, Deep Neural Network, and Shannon's Entropy. Demonstrating superior accuracy at 98.5%, Precision at 90.4%, and recall at 92%, EnsembleML emerges as a robust solution, emphasizing the advantage of ensemble techniques for comprehensive water mapping in the face of environmental challenges.
Elzaki Transform Homotopy Analysis Techniques for Solving Fractional (2+1)-D and (3+1)-D Nonlinear Schrodinger Equations
Authors: Sandeep Sharma
Published: August 2024

In this research, New homotopy analysis method for solving the fractional (2+1) D and (3+1) D non-linear Schrödinger equations by Elzaki. To solve these equations , the Elzaki transform is applied jointly to the Homotopy analysis method (HAM). This has proved efficient in tackling fractional calculus and nonlinear dynamics since correct solutions are offered and they converge at a faster rate. The accuracy of the proposed technique has been corroborated by analyzing various examples for which the latter were used for solving high-dimensional non-linear Schrodinger equation, which indicates that the technique is quite resilient as well as efficient; thus, making it an effective tool in theoretical physics and other applied sciences.
Bipolar Vague α Generalized Continuous Mappings in Topological Spaces
Authors: F. Prishka
Published: August 2024

In this paper we have introduced bipolar vague α generalized continuous mappings in topological spaces and investigated some of their properties. Also, we have provided some characterization of bipolar vague α generalized continuous mappings in topological spaces.
Reducing Loss for Brain Tumour Detection and Classification in MRI using Deep Learning Techniques
Authors: Venkata Ramana Saddi
Published: August 2024

The signs of a brain tumour might be general or specific. The typical symptom is brought on by a tumour pressing against the brain or spinal cord. When the tumour has impacted a certain section of the brain and that area is not working correctly. Identification of brain tumours was extremely difficult and complicated due to the location, kind, shape, and size of the tumour in the brain. Brain tumour diagnosis is difficult since the tumor's size and resolution cannot be clearly evaluated in the early stages of growth. However, if a tumour is found and diagnosed early in the stage of tumour growth, there is a good chance that the patient will be treated. As a result, early tumour diagnosis is essential for successful treatment. To solve this problem, we suggested an automated method that can more help detect and classify three different types of brain tumours on MRI using deep learning techniques, faster RCNN+ResNet50.
Thermal Radiation Effects of Convective Flow of a Hybrid Nanofluid with Different Base Fluids in Heat Transfer Over a Stretching Sheet
Authors: R. Lakshmi
Published: August 2024

The study investigates a two-dimensional flow with uniform heat transfer processes in one direction, focusing on velocity profiles, temperature distribution, heat transfer characteristics, and potential effects of parameters on flow behavior of a hybrid nanofluid over a stretching sheet. Nanofluids are engineered colloidal suspensions of nanoparticles in a base fluid. In your case, TiO2 and Cu nanoparticles are dispersed in a base fluid consisting of water (H2O) and sodium citrate (NaC6H9O7). This mixture is known as a hybrid nanofluid. Additionally, consideration is given to the components of the magnetic field and nonlinear thermal radiation. The Runge-Kutta fourth-order method is a numerical technique used to solve ordinary differential equations, offering a balance between accuracy and computational efficiency, by simplification of partial differential equations. The results illustrate that significant parameter such as the magnetic parameter, nanoparticles of solid volume fractions, Radiation parameter significantly influence momentum and thermal profiles. These analyses protest that raising the Radiation parameter grounds an increase in the hybrid nanofluid temperature. Moreover, the Casson parameter has decreased the velocity profile in TiO2 - Cu/ sodium alginate hybrid nanofluids. Nonlinear thermal radiation effects become more pronounced at high temperatures. Studying their impact in hybrid nanofluids is essential for applications in high-temperature environments, such as in industrial processes or electronic devices.
Multilingual Handwritten Recognition using DNN
Authors: Thenarasi V
Published: August 2024

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a Personal Digital Assistant (PDA), in postal addresses on envelopes, in amounts in bank cheques, in handwritten fields in forms, etc. However the script-independent methodology for multilingual Offline Handwriting Recognition (OHR) becomes a very difficult task, since the multilingual methods have different characters and words. Prediction of script-independent methodology reduces accuracy rate of the OHR method. To overcome this problem, new OHR of Tamil and English is transduced into electronic data. It majorly focuses on the removal of noises and word, character segmentation methods with higher recognition rate. The images which are scanned may also contain noises. Image denoising steps consists of binarization, noise elimination, and size normalization. Words and characters segmentation are performed by using Particle Swarm Optimization (PSO) algorithm. Then those segmented samples are used for the next step which is feature extraction. Finally, word recognition is performed by using the deep neural network classifier.
Humanitarian Food Logistics: An Inventory Model for World Food Program (WFP) Operations in Boko Haram-Controlled Areas
Authors: Buba M.T. Hambagda
Published: August 2024

This paper examines the critical inventory management situation for the World Food Program (WFP) under the stronghold of Boko Haram terrorists. However, inventory management under such a manmade disaster and the arduous task required additional costs of transportation services for the foodstuff to the various destinations that had to involve military assistance as a backup with a significant increase in holding/carrying costs and the ordering cost. The safety of the humanitarian workers (WFP) and the foodstuff consignment are paramount. Considering this scenario, an attempt has been made to formulate an inventory model for foodstuffs with a constant deterioration rate and seasonal demand rate to optimize the performance of the World Food Program's relief supply chain.
Soil Health Intelligence System using Multispectral Imaging and Advanced Deep Learning Techniques (SHIDS-ADLT)
Authors: B. Dhanalakshmi
Published: August 2024

The Soil Health Intelligence System using Multispectral Imaging and Advanced Deep Learning Techniques (SHIDS-ADLT) is a cutting-edge solution designed to revolutionize the assessment and management of soil health. By leveraging the power of multispectral imaging, this system captures high-resolution data across various wavelengths, providing a comprehensive view of soil properties. Advanced deep learning algorithms are then applied to analyze this data, identifying patterns and insights that are not discernible through traditional methods. This integration of multispectral imaging with deep learning enhances the accuracy and efficiency of soil health monitoring, enabling precise identification of nutrient deficiencies, soil contamination, and other critical parameters that affect agricultural productivity.SHIDS-ADLT offers a scalable and user-friendly platform for farmers, agronomists, and researchers, facilitating informed decision-making and sustainable agricultural practices. The system’s ability to provide real-time analysis and actionable recommendations ensures that soil health is maintained at optimal levels, promoting higher crop yields and reducing the reliance on chemical fertilizers. Moreover, the continuous monitoring capabilities of SHIDS-ADLT help in early detection of soil degradation, allowing for timely interventions. This innovative approach to soil health management represents a significant advancement in agricultural technology, supporting the goal of achieving food security and environmental sustainability.
An Improved Mathematical Model by Applying Machine Learning Algorithms for Identifying Various Medicinal Plants and Raw Materials
Authors: Srinivasa Babu Kasturi
Published: August 2024

The utilization of medicinal plants holds significant promise for healthcare and pharmaceutical applications. However, the accurate identification of diverse plant species or raw materials can be challenging, necessitating innovative solutions. This project presents a novel approach for the   Recognizable proof of Therapeutic Plants or Crude Materials through Picture Preparing, utilizing progressed Machine Learning Calculations. with a wealthy legacy of flower differing qualities, is well-known for its medicinal plant resources, but distinguishing evidence is one of the most challenging challenges in Ayurvedic Pharmaceutics. Some unrefined drugs are sold under the same name on the marketplace, causing confusion and misidentification. Indeed, collectors and dealers are not entirely aware of the distinct morphological appearance or differentiating qualities of the numerous substances due to their frequent and geographical accessibility, as well as comparable properties. In this technique, a computer capable of differentiating diverse medicinal plants/raw materials using Image Preparation Using Distinctive Machine Learning Calculations would be quite beneficial. It will be helpful at every stage of the supply chain of crude cloth being used within the framework. Herre the picture preparing strategy is considered as the most strategy for classifying diverse plants of diverse characteristics or particular parcels or locales of the plant clears out which can be at that point recognized through picture handling. Distinguishing proof of plants through plant takes off on the premise of their shape, colour, and surface highlights utilizing computerized picture handling methods for classifying the plant species utilizing diverse machine learning calculations. This investigates work incorporates checking on numerous pictures preparing strategies to utilize machine learning to recognize different plants utilizing it take off highlight within the shape of a picture...
Enhancing Flight Delay Prediction and Classification Using a Hybrid Bi-LSTM: Machine Learning
Authors: Sivakumar Depuru
Published: August 2024

The flight delay prediction system plays a vital role in the airlines which helps to predict the delay of flight in real time. Airlines must estimate flight delays accurately because the findings can be used to boost customer satisfaction and airline agency profits. This research introduces a flight delay prediction framework employing the Bidirectional Long Short-Time Memory (Bi-LSTM) model. To capture the long-term dependencies in the sequential data LSTM works as a hybrid component. Overcoming the difficulties faced by the airline industries mainly focusing on flight on-time performance and the weather data through the confusion Matrix The results show that the BiLSTM model improves the LSTM model and achieves the maximum accuracy. The BiLSTM model effectively utilizes both forward and backward hidden sequences. The outcomes demonstrate the Hybrid BiLSTM approach's potential as a useful instrument for enhancing flight delay management in the aviation sector.
Construction of 3D Human Model from 2D Image using PyTorch and Blender
Authors: Ch. Prathima
Published: August 2024

In this study, we present our innovative approach to generating 3D models from 2D images, employing PyTorch, Blender, Python, and OpenCV. Our method integrates image pre-processing techniques, a pre-trained model, and rendering capabilities to produce high-fidelity 3D representations. Initially, the input images, stripped of backgrounds using OpenCV, undergo pre-processing steps to enhance features relevant for 3D reconstruction, such as edge detection and depth estimation. We utilize PyTorch, a deep learning framework, to implement a leading edge model trained on large-scale 3D datasets, enabling accurate conversion of 2D imagery into 3D structures. Subsequently, the generated 3D model is imported into Blender, a powerful 3D modeling software, for refinement and further enhancements. Python scripts facilitate the integration between PyTorch, OpenCV, and Blender, streamlining our workflow for seamless data exchange and processing. Finally, the reconstructed 3D model, generated from input images devoid of backgrounds, is rendered as a video in MP4 format, showcasing its dynamic attributes and spatial properties. This comprehensive methodology offers a robust framework for generating realistic 3D models from background-removed 2D images, with broad applications in computer vision, virtual reality, and digital content creation.
Detection of CKD Status using Feature Selection Approach based on Random Forest Classifier
Authors: Shibi Mathai
Published: August 2024

Chronic kidney disease (CKD) is a serious condition that can last a lifetime occurs on by either impaired kidney function or kidney cancer. With CKD, the kidneys are unable to filter blood properly or have totally stopped functioning, which results in the accumulation of toxins in the bloodstream and ultimately killing the patient. Early detection of CKD is probably impossible, and saving a patient's life in the last stages of CKD is extremely challenging. High variance frequently impedes clinical decision-making in the prognosis of chronic disorders, resulting in ambiguity and unfavorable outcomes, particularly in situations like CKD. Early identification and suitable treatment can raise this risk. Techniques for Machine Learning (ML) have become important instruments for improving clinical decision-making and lowering unpredictability. However, because they rely on a small number of biological characteristics, current approaches for CKD identification frequently lack accuracy. This study investigates creatinine levels by assessing estimated Glomerular Filtration Rate (eGFR) and Blood Urea Nitrogen (BUN) derived from serum creatinine through indirect measurement using the dataset's available attributes. The dataset comprises 28 attributes, including "Gender," and encompasses a total of 523 records. This research work suggests label encoder, hot encoder, standard scaler, and iterative imputation for missing values as preprocessing techniques to solve issues in medical datasets. The Boruta method is used for feature selection, and ML algorithms are utilized to create the model. This prediction analysis involved eGFR and BUN computing values for assist in providing an accurate classification of CKD and non-CKD status using proposed Boruta Feature Selection (BFS) technique with Ensemble Based Random Forest Classifier (ERFC). Moreover, the performance evaluation of proposed BFS-ERFC model is compared with Radom Weighted Optimization (RWO) with Neural Network (NN), RWO with Logistic Regression (LR) and ERFC for evaluating the patient's medical records to determine the improved classification status of CKD.
BlockFlyLEACH: Enhanced Energy Efficient for Dynamic Clustering in WSNs
Authors: N. Muthumani
Published: August 2024

The exponential growth of Wireless Sensor Networks (WSNs) has underscored the need for efficient clustering techniques to enhance network performance and prolong sensor node lifespans. This paper presents BlockFlyLEACH, an innovative approach that integrates the Enhanced Firefly Algorithm with the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with Blockchain, to achieve dynamic clustering in WSNs. The proposed method utilizes the firefly algorithm’s optimization capabilities to determine optimal cluster head positions and enhance LEACH's energy efficiency. BlockFlyLEACH aims to balance energy consumption across sensor nodes, reduce data transmission delays, and improve overall network reliability. Simulation results demonstrate significant improvements in network longevity, energy consumption, and data packet delivery rates compared to traditional LEACH and other existing clustering algorithms. This research contributes to the ongoing efforts to develop robust and adaptive WSN clustering protocols that can efficiently handle the dynamic and heterogeneous nature of real-world sensor networks.
Multi-Party Key Agreement Protocols using Conjugacy Search Problem in Five Dimensional Discrete Heisenberg Group
Authors: N. Sundarakannan
Published: August 2024

The secrete way of communicating information between entities is the science known as cryptography. In order to communicate the message through an insecure medium the communicating entities need a cryptographic keys. The process of getting a common shared key is the Key agreement Protocol. This paper proposes a multi party key agreement protocol which provides a common key between the communicating parties using conjugacy Search Problem (CSP). To implement this protocol a platform group is required. The five dimensional Discrete Heisenberg Group(DHG) is chosen as the platform group and the conjugacy search problem (CSP) taken from combinatorial group theory acts as the one way function. As a particular case a five party KAP with numerical computation is provided. In general, multiparty CSP with k communicating parties can get a common key in k-1 rounds.
Mathematical Models of Nonlinear Phenomena in Wireless Security
Authors: Milindkumar Namdevrao Dandale
Published: August 2024

The proliferation of malicious components seriously threatens network integrity and performance in wireless security. Conventional methods of solving this problem might not be able to adequately depict the complexity of nonlinear interactions and dynamics. This paper offers a new mathematical model combining nonlinear fractional objective functions inside artificial neural networks (ANNs), hence improving the detection and control of malicious node propagation. Background research indicates that conventional approaches struggle to handle the irregular nature of hostile behavior and its consequences on network stability. We present a deep ANN framework to integrate fractional calculus, which predicts the nonlinear dynamics of malicious node spread, so improving detection accuracy and reaction strategies. The fractional objective function enables the model to better adapt to real-time network changes by considering the complicated, time-dependent character of attack propagation. Our approach seems to be successful experimentally; false positives are reduced and detection rates are considerably raised. Our strategy especially improved detection accuracy by 20% above baseline methods and lowered false positive rates by 15%. These results show how effectively nonlinear fractional models could enhance security of wireless networks. In addressing the issues generated by hostile nodes in dynamic wireless systems, the suggested method represents a significant development.
Nonlinear Dynamics in Cryptographic Systems for Image Encryption: A Mathematical Modeling Approach
Authors: S. Zulaikha Beevi
Published: August 2024

Strong image encryption techniques are vital in recent years given the exponential growth of digital media and the accompanying rise in cyberthreats. High-dimensional data such as images questions conventional cryptographic methods in handling. Nonlinear dynamics offer a good replacement by means of chaotic systems to attain high degrees of security and computational efficiency. This work proposes a novel nonlinear dynamic model image encryption technique using which cryptographic security is enhanced. The fundamental flaw of linear cryptographic methods to differential and linear cryptanalysis is mostly fixed here. Our method introduces considerable uncertainty and sensitivity to initial conditions by including nonlinear transformations and chaotic maps, so improving security. Using a two-dimensional logistic map to produce encryption keys collectively together with a diffusion and permutation process guarantees pixel-level obfuscation in the proposed model. Important sensitivity and entropy values show the success of our approach in experimental results. Our method particularly achieves an average entropy of 7.9992 and a correlation coefficient of -0.0013 between adjacent pixels, so indicating a considerable increase in randomness and security relative to present methods.
Relation Between the Fractional Domination Number of Some Graphs and their Line Graphs
Authors: Mahesh Sarada
Published: August 2024

Let G is the simple connected graph with order n and its line graph noted as L(G). Let the fractional dominating number denoted by γ_(f ) (G)  of graph G and the upper fractional dominating number Г_(f ) (G). In this study we have obtained union and join on〖  γ〗_(f ) (G)  and γ_(f ) (L(G))  exploring the graphs for upper fractional domination number including Cycle, complete, Star, Bi-Star graph, wheel graph, Cubic graph, Graphs of Cartesian product like (K_2  ×〖 P〗_(n )), (K_3  ×〖 P〗_(n )) and (Cm×Cn) with consideration of the computational complexity. We have taken parameters related to       fractional domination in line graphs towards generalization. The goal of this paper is to provide a generalized results of sum 〖 γ〗_(f ) (G)+γ_(f ) (L(G)),  Г_(f ) (G)+Г_(f ) L(G) and product 〖 γ〗_(f ) (G)*γ_(f ) (L(G)),  Г_(f ) (G)*Г_(f ) L(G) for some specific graph classes.
Double Encrypted Symmetric Cryptosystem Using adjacency matrix and Linear Feedback Shift Register (LFSR)
Authors: MPR Murthy
Published: August 2024

Owing to an extensive and indiscriminate usage of social communicating networks nowadays, transfer of sensitive data genuine people has become hardship and Herculean task. Message encryption is the only tool to safeguard the original information that helps to protect the data from cyber-attacks in its journey via public channel. Graph theory is one of the important techniques used in the encryption process especially in block ciphers. Abundant research has been done in the field of cryptography using several graph theory concepts. This article explains a symmetric block cipher employing the concept of an adjacency matrix in an undirected graph. Here each block is encrypted at two different stages and three rounds at each stage. The first stage of encryption is done with an adjacency matrix and second stage using simple logical XOR operation in a special pattern. In this technique the adjacency matrix is public, the sender and receiver derive new matrices for encryption/decryption from the adjacency matrix using Linear Feedback Shift Register LFSR. LFSR polynomial acts as a secret key which is the agreement between the communicating parties.
Dampak Layanan terhadap Kepuasan Wisatawan di Tempat Wisata Gunung Kelud, Kabupaten Kediri
Authors: Sabna Fatmala Sari  , Supriyadi Edouard Aryadi , Kombang Haryadi Hananto
Published: August 2024

Penelitian ini bertujuan untuk mengidentifikasi tingkat pelayanan dan persepsi wisatawan terhadap kualitas layanan di Kawasan Wisata Gunung Kelud Kabupaten Keidiri. Penelitian ini menggunakan Pendekatan kuantitatif deskriptif dengan pengumpulan dan analisis data yang menggunakan angka kuantitatif. Lokasi penelitian tersebut adalah di Kecamatan Ngancar Kabupaten Kediri, Jawa Timur. Populasi penelitian tersebut pengunjung Objek Wisata Gunung Kelud, dengan total kunjungan pada tahun 2022 sebanyak 129.703 orang. Sampel dipilih menggunakan Purposive Sampling melalui Google Form kepada responden yang telah mengunjungi objek wisata tersebut. Jumlah sampel ditentukan dengan rumus Slovin. Instrumen penelitian berupa kuisioner dengan skala Likert untuk mengukur sikap dan persepsi wisatawan terhadap layanan di Wisata Gunung Kelud. Data dikumpulkan melalui observasi, dokumentasi, dan kuisioner kemudian dianalisis menggunakan uji asumsi klasik, regresi sederhana, dan uji t. Hasil penelitian menunjukkan bahwa terdapat hubungan yang signifikan antara Pelayanan Wisata dan Kepuasan Wisatawan di Wisata Gunung Kelud. Hasil analisis menunjukkan bahwa sebesar 61% dari tingkat kepuasan wisatawan, ini berarti bahwa pelayanan wisata memiliki kontribusi yang sangat besar terhadap Kepuasan Wisatawan di Kawasan Wisata Gunung Kelud. Namun, terdapat 39% sisanya yang dipengaruhi oleh faktor-faktor lain yang tidak diteliti dalam penelitian ini.