bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing Passport Verification Processes with Secure QR Code Technology
Authors: Vikas H
Published: January 2025

The main concern of today can be the quality and the security of the data. One of the new methods of the security system to resolve this can be the utilization of the QR codes, which can reduce the larger misinterpretation of the data and enhance the security, considering this research article promotes a method to encrypt the data to a QR code, which can avoid the manipulation of the data. The preferred QR code is encrypted using a secret key and will not be shared to the unauthorized personals, the usual and finest method of encryption is incorporated, which is TTJSA algorithm. The quick response system is used to encrypt the data, in to the embedded system to a form of unique style of information in the documents, the original data can be encrypted and can be verified as competing to the original data. As the system is unique and flexible to certain extent, the same can be used for the many other applications as well, this can always avoid the theft of the data from the hackers as the encryption and the decryption used are highly secure.
Design and Simulation of Subsonic Frequency, Frequency-Selective Circuit Using Active Inductor
Authors: Ramesh Krishna
Published: January 2025

Designing very low frequency (VLF) tuning circuits poses a significant challenge due to the impractical size of inductors required at these frequencies. As the frequency decreases, the inductance value needed becomes larger, making it difficult to implement physically. To address this issue, this paper presents an alternative approach that replaces the traditional passive inductive component (L) with a simulated inductor. The simulated inductor, designed specifically for VLF applications, offers a practical solution to overcome the limitations of using large inductors. The design of the simulated inductor is explained in detail, and its application in VLF tuning circuits are demonstrated. By replacing physical inductors with simulated counterparts, the circuit's size is significantly reduced, while maintaining the desired frequency-selective characteristics. The simulation of the designed circuit is carried out using PSPICE, an analog simulation software. The performance of the simulated inductor in tuning circuits are evaluated, and the results are graphically represented using Python. These simulations provide valuable insights into the effectiveness and accuracy of the proposed method in replicating the behaviour of large inductors at very low frequencies. The results confirm that simulated inductors offer a viable solution for VLF circuit design, reducing component size without sacrificing performance. This approach is expected to benefit various applications where size constraints and frequency selectivity are critical.
AI/ML Based Detection of Unknown Compromises on ICT Devices
Authors: Manikrao Dhore
Published: January 2025

The growing dependence on online communication and increased cyberattacks need more reliable techniques for detecting network anomalies. While successful, traditional signature-based detection systems have trouble detecting zero-day attacks and cannot analyse encrypted traffic, which today makes up over half of all internet traffic. The goal of the machine learning-based method for network anomaly detection in this research is to find malicious activities in encrypted and unencrypted network traffic. This research uses machine learning methods to detect abnormal behaviours that suggest possible security breaches. Several classification methods were investigated and tested using datasets that were made available to the public. Even in encrypted network environments, the results demonstrate that machine learning techniques, especially anomaly-based detection, can improve detection accuracy and lower false positives.
The proposed model's novelty lies in its integration of tailored feature selection with attack-specific and unified datasets, ensuring a comprehensive approach to identifying network anomalies.
Minkowski and conformal flatness of Cartan space using (α,β)-metric
Authors: Rinka
Published: January 2025

The purpose of the present paper is to study the Minkowski and conformal flatness nature of Cartan space under the condition of h-metrical d-connection of the Cartan space using (α,β)-metric K(x,ω)=α(x,ω)+(β^(n+1) (x,ω))/(α^n (x,ω)).
Distance-Based Robust Fuzzy Topological Mathematical Modelling for Spider Networks in Edge Detection Techniques
Authors: E. Jayanthi
Published: January 2025

Abstract: Introduction: Topological indices are numerical numbers used to represent a graph and describe its characteristics. In this method to find the distance based fuzzy topological spider network and it is implemented in robust regression. It will give the new proposed robust fuzzy topological model (RFTM). This model connected with the concept of image processing specially in edge detection techniques and verify our proposed model performance.
Objectives: Distance-based fuzzy topological indices are widely used in chemistry for quantitatively describing molecular structure and correlating it with various physicochemical properties. These indices are derived from the molecular graph, where atoms are represented as nodes and bonds as edges. There are several distances based topological indices, as well as a diverse set of distance-based molecular structure descriptors. Structure-based topological descriptors of chemical networks allow us to predict physicochemical attributes and bioactivities of compounds using QSAR/QSPR approaches. Topological indices are numerical numbers used to represent a graph and describe its characteristics. A robust fuzzy graph is a concept that integrates the robustness of graph structures with the flexibility and uncertainty modelling provided by fuzzy graph theory. It is used in applications where the system's topology or relationships among elements (nodes) are prone to noise, uncertainty, or partial truth, and there is a need for a reliable representation and analysis despite these uncertainties.
Methods: To find the distance based fuzzy topological spider network and it is implemented in robust regression. It will give the new proposed robust fuzzy topological model (RFTM). This model connected with the concept of image processing specially in edge detection techniques and verify our proposed model performance.
Results: In this paper, we provide analytical formulas for the Szeged index, edge Szeged index, edge vertex Szeged index, and PI index of spider networks and Augmented spider networks. Our proposed result also used in the concept of edge detection techniques.
Conclusions: The discrete characteristics of spider networks and augmented spider networks are determined using distance-based topological indices. We determined the analytical formulations for Wiener, Szeged, and their numerous versions, as well as PI of spider networks and augmented spider networks. The approach we utilized to calculate the topological networks is useful for evaluating the topological indices of various interconnection networks.The proposed model RFTM perform well compared to existing model. Also, it is the maximum no of true edges detected and reduced the number of false edges. The TLS MATLAB toolbox was used to simulate the previously provided data. Depending on the sample size, the data is generated randomly. Furthermore, the threshold settings in the toolbox in the image are set to two.
Theoretical Analysis of Nonlinear Reaction-diffusion Equation on Immobilised Alpha-Chymotrypsin in Acetonitrile Solvent
Authors: A. Dorathy Cathrine
Published: January 2025

The study focuses on immobilised Alpha-chymotrypsin operating under kinetic constraints. This model incorporates equations representing the inherent kinetics of four distinct reactions, along with the experimentally analysed physical properties of three different support materials, to estimate the immobilised biocatalyst's initial activity and nucleophile selectivity. Approximate analytical solutions of acyl donor and nucleophile substrate concentrations under steady-state conditions are derived concerning all diffusion parameters using Akbari Ganji and homotopy perturbation methods.  We examine our analytical results with numerical data obtained using MATLAB software to assess accuracy. It is shown that the derived analytical results closely match the numerical results. The model reliably replicates experimentally observed trends in initial rate and nucleophile selectivity across varying enzyme loadings.
A Robust Pre-processing Framework for ROI Extraction in Knee Osteoarthritis X-Ray Analysis
Authors: Ravindra D. Kale
Published: January 2025

Medical image processing depends much on pre-processing, particularly in cases of precise categorisation and prediction—like those involving the diagnosis of osteoarthritis (OA) in the knee. This work presents a rapid and simple approach to get the area of interest (ROI) from knee X-ray images such that the Kellgren and Lawrence (KL) technique may be used for accurate grading. By means of a planned approach including Gaussian blurring, thresholding, and sophisticated statistical and wavelet-based algorithms for finetuning characteristics, the system addresses issues like noise, poor contrast, and uneven illumination. The recommended approach divides the knee joint, eliminates regions not required, and enhances the view of crucial diagnostic elements like the gap between the upper and lower knee bones. It makes separating ROIs for all five KL grades—from healthy knees to severe OA cases—simple. With more than 8,000 X-ray images in the Kaggle knee OA dataset, the research ensures system dependability and applicability in various contexts. The method significantly increases the precision of ROI segmentation, thereby enhancing the feature extraction for next classification projects. While eliminating uncertainty, the separated ROIs maintain crucial characteristics and help to distinctly differentiate KL grades. Though it might function even better with future developments like adding more sophisticated brightness control and noise reduction techniques, the framework performs effectively in many different image settings. The foundation for automated OA detection is laid by this study. It closes the distance between the limitations of imaging and clinical requirements. It also guides the direction of medical image processing forward.
Blockchain-Based Nonlinear Analysis for Data Auditing Management: Principles and Applications
Authors: M. Sri Lakshmi
Published: January 2025

This document introduces the "Blockchain-Based Nonlinear Analysis for Data Auditing Management: Principles and Applications," a framework designed to transform data management strategies through increased transparency, security, and efficiency. Utilizing the immutable and decentralized characteristics of blockchain technology, this framework ensures the integrity of data and provides traceability across diverse sectors. It presents a secure method for the recording and auditing of data transactions, protecting against unauthorized alterations and increasing trust among stakeholders. The framework is versatile, with potential applications in various domains, yet its efficacy is particularly demonstrated through a theoretical case study in the organ donation and transplantation field. Here, it tackles prevalent challenges such as consent verification, organ matching, and the documentation of transplant outcomes, ensuring a process that is both transparent and secure. The framework's adaptability and scalability position it as an essential tool for enhancing data accuracy, auditability, and operational transparency across numerous data management contexts. The principles behind the framework and highlights its practical applications, underscoring its role in improving data management practices globally.
A Two-Stage Enhanced Genetic Algorithm for the Multi-Depot Vehicle Routing Problem in Epidemic Logistics Management
Authors: Aji Thomas
Published: January 2025

Effective epidemic logistics management is essential to curbing infectious disease spread, mitigating outbreaks, and saving the lives of several infected people through the distribution of life saving medicines. Here we explore how best to distribute medical supplies during an out- break. During any epidemic outbreak, the problem is to distribute material in the least possible time. This research attempts to optimize medical supply distribution during epidemic outbreaks as a key priority. The problem of distribution of essential medical aid during an epidemic break has been converted into a Multi-Depot Vehicle Routing Problem (MDVRP). Our proposed solution relies on a two-stage enhanced Genetic Algorithm (GA), which incorporates domain specific knowledge with advanced GA techniques to find near optimal solutions efficiently. At first, GA is used on MDVRP with objectives such as minimizing total distance and time while optimizing resource allocation, ultimately producing an initial set of vehicle routes to serve affected people from multiple points across a region. To further improve solution quality, the second stage of an algorithm is introduced; during this phase, each demand node location in the solution group is treated like a subdepot and GA is used to find the optimal route optimizing routing purposes. Compared to more traditional methods, our two-stage enhanced GA is evaluated against several realistic epidemic logistics scenarios and shows significant improvements in solution quality, robustness, and computational efficiency, ultimately saving the lives of those needing immediate healthcare aid during epidemics.
Approximation of Fixed Points via Picard-Abbas Hybrid Iteration Scheme for p-Quasi-Nonexpansive Multivalued Mappings
Authors: Manbhalang Chyne
Published: January 2025

We present results on stability and convergence for the Picard-Abbas iteration scheme for ρ-quasi-nonexpansive multivalued mappings within modular function spaces. Furthermore, we demonstrate an application of the iteration scheme in differential equations.
An Evacuation Vehicle's Shortest Toll-free Routing across Smart Cities using the Fuzzy Weighted Graph Technique
Authors: Angel Benjamin
Published: January 2025

Rapid technical improvements and rising consumer demands are the two challenges facing in the transport sector. India's growing economy has raised demand for infrastructure and transport services. Maharashtra, while being a sizable city, has inadequate public transportation infrastructure and poor pathway blockades. In smart cities, an integrated public transportation network optimizes time, cost, comfort, safety, accessibility, and convenience for citizens through incorporating many forms of transportation. The fuzzy weighted graph technique is applied in this article to address the demands of the shortest distance with toll-free route accessibility for paramedics and private shuttles to major events in the smart cities of Maharashtra. This initiative will persuade the government to levy a fine on the public for individuals who use the shortest distance designated only for special vehicles.
Deep Custom Transfer Learning Models for Recognizing Human Activities via Video Surveillance
Authors: Saurabh Gupta
Published: January 2025

The use of video surveillance for human activity recognition (HAR) in inpatient rehabilitation, activity recognition, or mobile health monitoring has grown in popularity recently. Before using it on new users, a HAR classifier is often trained offline with known users. If the activity patterns of new users differ from those in the training data, the accuracy of this method for them can be subpar. Because of the high cost of computing and the lengthy training period for new users, it is impractical to start from scratch when building mobile applications. The 2DCNNLSTM, Transfer 2DCNNLSTM, LRCN, or Transfer LRCN were proposed in this paper as deep learning and transfer learning models for recognizing human activities via video surveillance. The Transfer LRCN scored 100 for Training Accuracy and 69.39 for Validation Accuracy, respectively. The lowest Validation Loss of 0.16 and the Lowest Training Loss of 0.001 was obtained by Transfer LRCN, respectively. The 2DCNNLSTM has a 98.34 lowest training accuracy and a 47.62 lowest validation accuracy.
A Robust Regression Analysis of Factors Affecting Diesel and Petrol Price
Authors: P.Anandhi
Published: January 2025

Introduction: A lot of factors influence how much crude oil and gasoline prices fluctuate. Various crises, including war and political unrest, the economic and financial crisis, terrorist actions, and natural catastrophes, have had a significant impact on crude oil and gas prices throughout the last several decades. The robust regression analysis approach is beneficial for investigating the influence of the explanatory variable’s diesel and petrol prices. Statistics reveal that some variables, events, and crises have a significant impact on diesel and gasoline prices. To reject the least significant components and proceed to additional regression analysis on the most impacted variables to minimize the number of variables, the ordinary least squares approach was used. The study model explains how these components interact and aids in price forecasting to assist the economy and avoid undesirable scenarios.
Objectives: To identify and understand the key factors influencing the fluctuations in diesel and petrol prices, including supply and demand, crude oil prices, government taxes, and currency exchange rates. To analyse historical and current trends in diesel and petrol prices, enabling predictions about future price movements.
Methods: Robust Regression Analysis for Identify relationships between fuel prices and influencing factors and Correlation Analysis for Measure the degree to which two or more variables. The Trend Charts to use line graphs or bar charts to visualize price changes over time and Forecasting Models to use machine learning algorithms to predict future fuel prices based on historical data.
Results: The following 19 years of gasoline and diesel prices in India served as the foundation for the research project "Forecasting Model for Petrol and Diesel Price" (December 2001–December 2019). To achieve its objectives, this study evaluated secondary data gathered from multiple secondary sources and used a variety of statistical approaches, including trend analysis, regression analysis, and one-way ANOVA. Gasoline shortages are likely to remain, denying fuel to those in most need at any costs, while wealthy countries compete for limited supply.
Conclusions: In summary, the prices of natural gas and crude oil have fluctuated throughout time and will continue to do so. Natural gas and crude oil prices are influenced by a variety of variables, including large environmental disasters, the status of the economy, and the adoption of new technologies. The multiple regression establishes a predictive relationship between variables for both dependent and independent variables. Using data and historical records, the multiple regression technique found and indicated a link between factors that significantly influenced natural gas and crude oil prices.
Fixed Point Results in G Metric Space via Α-Series
Authors: K. Varalakshmi
Published: January 2025

Introduction: Mustafa and sims [1] introduced the concept of G-metric space in 2005. Afterwards, Mustafa et al and many authors [3]-[19] obtained some common fixed-point theorems, coupled and tripled fixed point results for mappings satisfying different contractive conditions in G metric space. In this study we prove fixed point results in G metric space via α-series by using some conditions that are a sequence of a mappings and a self-mapping.
Objectives: To show tripled fixed-point theorems and common fixed point theorems by using sequence of mappings and self a self-mapping via α-series and shown an example which supports the main result.
Methods: In recent study the authors worked on fixed point results by using different contractions such as Suzuki type contraction, Rational type contraction, cyclic contraction, F-contraction, Mier keeler contraction, (ψ, ϕ)-weakly contractive mappings and Integral type contractions etc. in G-metric spaces. In this study we proved fixed point results in G metric space via α-series by using sequence of mappings.
Results: Obtained Unique common fixed point and tripled fixed point results in G metric spcace via α-series.
Conclusion: In this study we present unique tripled fixed-point and common fixed-point results for a sequence of mappings and a self-mapping in G metric space via α-series and discussed corollary with supporting example.
New Kinds of Open Sets in Intuitionistic Interval-Valued Fuzzy Topological Space
Authors: S.Sivaraja
Published: January 2025

In an intuitionistic interval valued fuzzy topological space (inshort IIVFTS) a new sort of open sets termed intuitionistic interval valued fuzzy minimum open(resp.maximal open) have been researched.
Contra M-continuous Maps in Pythagorean Fuzzy Topological Spaces
Authors: B. Vijayalakshmi
Published: January 2025

In this paper, we introduce and investigate Pythagorean fuzzy contra -continuous maps in Pythagorean fuzzy topological spaces and also discuss about some properties and characterization of Pythagorean fuzzy contra -irresolute maps.
A Nonlinear Analytical Approach for Video Compression in Telecommunication
Authors: Sanket B. Kasturiwala
Published: January 2025

Introduction: The shift from analog to digital video has revolutionized communication by offering noise resistance, clarity, ease of transmission, and interactive user experiences. Digital video supports greater content capacity, catering to diverse viewer preferences and enhancing multimedia consumption. A nonlinear analytical approach for video compression techniques, such as transform coding, predictive coding, motion compensation, and entropy coding, play a vital role in applications like video conferencing and live streaming, enabling real-time transmission while efficiently utilizing network resources and storage. The integration of these technologies fosters a dynamic, high-quality multimedia ecosystem
Objectives: Research and development in nonlinear video compression objectives focus on reducing video file sizes without compromising quality by leveraging redundancies and characteristics of video data. This is vital objectives for applications like video streaming, conferencing, digital television, and multimedia distribution, driving improvements in storage efficiency, bandwidth utilization, and user experience.
Methods: Existing methodologies for block matching motion estimation, such as FS, TSS, NTSS, DS, and ETSS, have shown limitations in video compression. To address this, a novel "Nonlinear approach" combining the Hexagonal Search (HS) and Efficient Three Step Search (ETSS) algorithms has been proposed. This technique aims to reduce redundancy, minimize search points, and improve video compression efficiency. It also delves into the complexities of motion estimation and compensation, providing insights from the conceptualization of video sequences to the implementation of motion-compensated coding techniques.
Results: All the parameters such as Sum of Absolute Difference (SAD), Mean of Absolute Difference (MAD), Mean Square Error (MSE), Peak-Signal to Noise Ratio (PSNR) and Compression Ratio are applied for the statistical performance study. Nonlinear approach gives appreciable results as compared with the existing methodologies. Video quality as also assessed through various nonlinear compression techniques give better results.             
Conclusions: A Nonlinear approach to block matching motion estimation has been introduced, integrating the strengths of Efficient Three-Step Search (ETSS) and Hexagonal Search (HS). This method delivers superior results while requiring fewer search points than traditional algorithms such as TSS, FS, NTSS, and 4SS. Hexagonal Search (HS) also outperforms Diamond Search (DS) and Cross Diamond Search (CDS) in efficiency. Rigorous testing using the Akiyo.avi standard database confirms the Nonlinear approach's effectiveness, particularly in reducing computational complexity, making it a more advanced motion compensation technique compared to ETSS.
Comprehensive Numerical Analysis of Fluid Flow Characteristics in a Lid-Driven Cavity with a Central Obstacle: Flow Physics
Authors: Dalmalka Praveen Kumar
Published: January 2025

Boundary layer flows and vortex dynamics are critical in understanding fluid motion in confined geometries. The lid-driven cavity (LDC) flow is a classical benchmark for analysing fluid behaviour under varying conditions, especially when obstacles are introduced. While past studies have explored primary and secondary vortex structures, the modulation of enstrophy and palinstrophy caused by obstacles has been underexplored. This study aims to bridge that gap by systematically investigating the effect of Reynolds numbers and obstacle aspect ratios on flow properties in a 2D LDC. To analyse the influence of Reynolds number (Re) and obstacle Aspect ratio (AR) on enstrophy (Z) and palinstrophy (P). To evaluate the enhancement in vortex dynamics and flow topology caused by introducing square obstacles at various aspect ratios within the cavity. To identify optimal configurations for improving fluid transport and mixing efficiency. An unsteady, laminar, incompressible flow of Newtonian fluid was simulated by the Finite volume-based Fractional Step Method. Square obstacles with aspect ratios AR=0L,0.4L, 0.5L, and 0.6L were placed at the centre of the cavity with numerical study for a range of Re=10,100,400, and 1000. The interaction between AR and Re dictates vortex dynamics in LDC flows. Higher AR disrupts flow symmetry through sharp-edge interactions, while increased Re elevates inertial effects, causing vortex splitting, secondary vortex formation, and improved fluid mixing. Obstacles boost Z and P by up to 53.08% and 50.85%, with the most significant increases for AR = 0.6L at Re = 1000 compared to a cavity without an obstacle. This suggests stronger vorticity gradients and enhanced mixing due to AR = 0.6L, as the obstacle occupies more of the cavity, restricting flow and creating stronger shear zones near the walls and the obstacle. At high Re, inertial forces dominate, leading to stronger vortex shedding, and more pronounced vorticity changes near sharp edges. Increasing Re from 10 to 1000 for AR = 0.6L raises Z and P by approximately 36.34% and 43.55%, illustrating a shift from viscous-dominated laminar to inertia-dominated flow, enhancing vortex strength, sharpening vorticity gradients, and improving mixing efficiency. The interplay between AR and Re dictates vortex dynamics, with higher AR disrupting symmetry and higher Re amplifying inertial effects, leading to vortex splitting, enhanced secondary vortices, and improved mixing. The increasing obstacle height amplifies Z and P, leading to improved fluid mixing and vortex dynamics, serving as effective tools for flow control and mixing enhancement in industrial and engineering applications.
Using GeoGebra and SageMath to Assist Engineering Students in Tracing Different Curves and Solving Numerical Methods
Authors: Bhavna
Published: January 2025

Recently, there has been a growing emphasis on the use of technology and software in the educational process. The purpose of this article is to show readers how to use the software Geogebra and SageMath to trace the different curve and solving numerical methods. This application serves as a helpful tool for instructors and students for projects, training, lectures, and other purposes in addition to being a computer environment. The author discusses numerical techniques, how to make certain graphical representations in this application, and how to trace any curve (Parametric, Polar, and Cartesian) using Geogebra and SageMath.
Traditional techniques of tracing the curve and solving numerical procedures, such as the Gauss-Seidal and Gauss-Jordan methods, might be time-consuming. For this reason, tools like Geogebra and SageMath can make calculations for students, teachers, engineers, and other professionals go more quickly.
In an effort to add to the specialized literature, the author developed fresh examples of abstract usage of GeoGebra and SageMath. The article notes the need for the education system to integrate blended learning and teaching methods, which combine computer programs with traditional teaching methods to improve the teaching of mathematics. It recommends using GeoGebra, SageMath programs for assisted instruction in the teaching and learning of mathematics, specifically for cases of numerical methods and Tracing the curve, respectively.
Estimation and Comparison of Seismic Hazard Parameters for National Capital Region of India: using an Updated Earthquake Catalogue
Authors: M. Madhusudhan Reddy
Published: January 2025

Delhi, situated in seismic zone class IV with a peak ground acceleration of 0.24 g, is highly prone to seismic activity. This study characterizes the seismicity of the Delhi region (28.70° N and 77.10° E) by considering a radial extent of 350 km around New Delhi. Earthquake data were collected from various sources and literature, resulting in a homogeneous earthquake catalogue with 574 events ranging from 3.0 Mw to 8.6 Mw, spanning the period from 1800 to 2024. Completeness analysis of the catalogue was conducted using the CUVI and Stepp's methods, estimating completeness periods for four magnitude groups: 3.0 ≤ Mw ≤ 3.99, 4.0 ≤ Mw ≤ 4.99, 5.0 ≤ Mw ≤ 5.99, and Mw ≥ 6.0. Seismic source details, including active faults, faults, and fault tectonics, were digitized using ArcGIS software, resulting in a seismotectonic map featuring four active faults (Pawalgarh Fault, Kotabag Fault, Yamuna Tear, and Kala Amb Tear Fault), 95 fault tectonics, and 3,175 minor and major faults. Seismic hazard parameters such as a-value, b-value, λm, Mmax, and magnitude completeness were computed using standard methods. The a-values ranged from 3.13 to 4.12, and the b-value ranged from 0.65 to 0.83. The Mc value, determined using the Z-map in MATLAB, was found to be 3.5 Mw. This study is significant as it provides a comprehensive and updated catalogue that can be utilized for future hazard analysis of Delhi. The findings of this research will aid in the development of more accurate seismic hazard assessments and preparedness strategies for the National Capital Region, ensuring better mitigation of potential seismic risks.
MRI-Based Brain Tumor Detection Using Convolutional Neural Networks and Machine Learning Techniques
Authors: Vivek Agrawal
Published: January 2025

Brain tumors are extreme and doubtlessly fatal sicknesses, and early and correct analysis are critical to enhance affected person effects. Magnetic resonance imaging, or MRI, is an critical tool for early detection and screening of mind cancer. Brain tumor detection has turn out to be more correct in recent years due to the combination of system gaining knowledge of (ML) and convolutional neural network (CNN).This paper offers a focused analysis the use of A complete evaluate of CNN-ML methods on mind tumor detection for How an awful lot of ML-CNN-based strategies in identification Effective are most appropriate patterns We seek methods, statistics sorts, and business selections focusing constraints which are stated collectively This insight presents informed hints for algorithms and techniques to enhance brain tumor detection. This review objectives to make a contribution to the development of early detection and scientific control, and focuses on the transformative position of CNN and ML in advanced medical imaging strategies.
Efficiency of Hotels Using Data Envelopment Analysis in Dar es Salaam, Tanzania
Authors: Lilian J. Kishimbo
Published: January 2025

This paper aims to systematically investigate the most efficient hotel(s) for benchmarking in Dar es Salaam, Tanzania. This study employed systematic Data Envelopment Analysis (DEA) to measure the relative efficiency of 50 different hotels in Dar es Salaam. The study characterized the relative efficiency using three outputs (i.e. room revenue, food and revenue and other revenue) and four inputs (number of employees, operational costs and number of guest rooms).The results revealed that 24 relatively efficient hotels could be used for benchmarking, whereas 26 had inefficient management. As an avenue for future research, this study used a dataset of one year. However, it is recommended that future studies consider a broader period of data sets e.g. 2 - 5 years, to capture the efficiency change over time. Businesses in Tanzania are skeptical about sharing their data as it is considered confidential; thus, managers and hoteliers are encouraged to be optimistic about the results implications. To improve efficiency, Tanzanian hotels must re-think marketing strategies and enlarge their seasonal activities to ensure more efficiency. To our understanding, this is the first paper to systematically report on the efficiency of city and resort hotels in Tanzania. Thus, the study has set the benchmark results and comparable methodology for future hotel efficiency studies.
A Dependable Multistage Interconnection Network Topology and Fault-Tolerance
Authors: D.Deepakraj
Published: January 2025

Interconnecting networks will become more and more popular as computer networks spread. To facilitate communication across processes in various networks, this paper examines the challenges associated with connecting heterogeneous computer networks. The course includes a thorough overview of pertinent literature. The degree of network interconnectedness, global addressing and routing strategies, and the roles played by interfaces and gateways between networks are covered. Developing larger supercomputers and more dependable parallel computing systems has garnered significant attention and effort. Multistage interconnection networks (MINs) are frequently utilized as interconnection mediums in supercomputer environments because of their fault tolerance, low cost, minimal transmission delay, and self-routing capabilities, enhanced by the growing number of nodes in the system. In distributed and parallel systems intended to provide quick and effective communication amongst high-capacity processors, MINs are frequently employed as switching fabrics.
The Impact of Knowledge Management Strategies on Organizational Productivity in the Retail Sector at Jiangxi Province in China
Authors: Luo Zhuo
Published: January 2025

This study explores the impact of knowledge management (KM) strategies on organizational productivity in the Chinese retail sector, specifically focusing on Jiangxi Province. While KM practices are increasingly recognized as critical for enhancing organizational performance globally, their adoption in China’s retail industry has been relatively slow. This research aims to bridge this gap by identifying the key factors influencing the successful implementation of KM strategies, such as organizational culture, leadership, technological infrastructure, and employee engagement. It further examines how these factors collectively contribute to improving organizational productivity in the context of China’s rapidly evolving retail sector. Drawing on a combination of qualitative and quantitative methods, this study provides empirical evidence on the role of KM strategies in optimizing operational efficiency, fostering innovation, and boosting overall competitiveness within retail businesses. The findings suggest that organizations with robust KM structures and procedures, alongside a culture that promotes knowledge sharing, are better positioned to adapt to the dynamic market demands and drive productivity. Additionally, leadership support and the integration of advanced technological tools are crucial for the effective application of KM practices. This research contributes to the growing body of literature on KM by offering new insights into its practical applications in the Chinese retail context and providing actionable recommendations for policymakers, business leaders, and retail organizations seeking to improve their productivity and competitiveness through enhanced KM practices.
Enhancing Talent Quality in Chinese Language and Literature: Insights from Total Quality Management
Authors: Ming Li
Published: January 2025

This study investigates the factors influencing student talent quality improvement among undergraduate students in Chinese Language and Literature in Southwest China, framed within the Total Quality Management (TQM) perspective. With a focus on institutional and individual components, the research employs a quantitative methodology utilizing multiple regression analysis to explore the interplay of university management practices, facilities, teacher attributes, and student personal factors on talent development. The findings reveal a moderate positive correlation (R = 0.405) between the independent variables and student talent quality, accounting for 16.4% of the variance in talent cultivation. While statistically significant (p = 0.049), the low adjusted R² value of 0.079 indicates that other unmeasured factors play a crucial role in influencing talent quality. The study supports multiple hypotheses, confirming that effective university management, high-quality facilities, and competent teachers significantly contribute to the cultivation of student talent. Additionally, student personal attributes, such as motivation and learning ability, emerge as essential elements in enhancing both talent development and personal growth. These insights underscore the necessity for universities to adopt a holistic approach that integrates robust management systems, enhanced infrastructure, and comprehensive support for both educators and students. Based on the findings, practical recommendations are provided, including streamlining administrative processes, improving access to modern learning resources, and investing in continuous professional development for teachers. This study contributes to the existing literature by highlighting the intricate relationships between various factors in a higher education context, particularly within the aspect of Chinese Language and Literature. By emphasizing the importance of TQM principles, it advocates for a systematic enhancement of educational practices to ensure the success of students and, ultimately, improve employability outcomes in an increasingly competitive job market. The research findings serve as a valuable resource for educational policymakers and practitioners aiming to refine their strategies for talent development in higher education.
Enhancing Sales Performance in Henan’s Media Industry through the Synergy of Digital Marketing and AI
Authors: Zhao Weiguang
Published: January 2025

This study examines how digital marketing and artificial intelligence (AI) can improve sales performance in the media industry of Henan Province, China. As the market undergoes rapid digital transformation, understanding the role of these technologies in driving sales is crucial for maintaining competitiveness. The research explores three main areas: the impact of digital marketing and AI on sales performance, employee perceptions of these technologies, and the interaction between digital marketing and AI in enhancing sales outcomes. Data was gathered from 240 participants using a structured questionnaire distributed via JotForm and popular Chinese social media platforms. The demographic analysis revealed that 74.2% of respondents were female, with 48.3% aged between 30-39 years. Various statistical methods, including normality tests, descriptive analysis, T-tests, and bivariate correlation, were applied to ensure the reliability and validity of the data. The normality test indicated well-distributed data, while descriptive analysis showed high mean scores, reflecting a strong understanding of the survey items. The T-test found no significant difference in the impact of digital marketing and AI on sales performance, suggesting equal contributions of both technologies. The bivariate correlation revealed a moderate positive relationship between digital marketing and AI, highlighting their complementary roles in boosting sales. A Cronbach’s Alpha value of 0.94 confirmed the high internal consistency of the data. The study concludes that integrating digital marketing and AI into a unified strategy is critical for media organizations to optimize their sales performance. It underscores the need for innovation and healthy competition within media networks to drive digital transformation. Future research should investigate the relationship between innovative marketing strategies and sales performance in non-Western contexts.
Text-Based Airline Sentiments Analysis Using Deep Learning Ensemble Optimization
Authors: Abha Kiran Rajpoot
Published: January 2025

Text Sentiment Analysis is one of the foundational pillars of sentiment analysis. There hasn't been any research done yet that makes the use of the combined power of modern models and contextual information present in texts, nor has it been studied how contextual information may be controlled in any effective manner. It has been demonstrated that the model proposed in this paper can be used to accurately and efficiently classify sentiments for a given topic. The system is able to capture all of the content contained within the text by combining rule-based methods and deep learning models. In a study undertaken by our group, it has been found that building an embedded representation and attention mechanism is effective in the processing of valence shifting cases. Our proposed mechanism also combines custom-designed rules along with domain-specific sentiment dictionaries. The proposed method has been tested using three datasets and has shown superior performance to other methods, regardless of its higher cost as compared to other methods. PPD (Probability Proportion Difference) is used in this study to pick characteristics that regard each term to be part of a class list. It's a quick and easy way to remove irrelevant words from a feature vector. Categorical Probability Proportion Difference (CPPD) and Probability Proportion Difference (PPD) are used to choose features in a suggested method (CPD). In order to identify features that effectively distinguish between classes and deliver the necessary outcomes, CPPD features selection methods can be used the comparison of the proposed methods' performance with that of the CPD method and the Information Gain (IG) approach shows that all three methods are comparable in their ability to detect sentiment. Two standard datasets (each containing a number of movie reviews and a number of book reviews) have been used as data sources for testing suggested feature selection methods. A study conducted by the authors of this article suggests that the CPPD feature selection method proposed in this article performs better for sentiment classification than other methods. The proposed model is fast and uses the Virgin America Airlines reviews dataset for training and testing. The Monkey learn model achieved 75% precision, 91% recall, and 89% of accuracy, showing the model capabilities in real time Text sentiment analysis.
Enhancing the Productivity of Construction Process Plants by Implementation of Innovative TPM Techniques
Authors: Permindur Singh
Published: January 2025

Total Productive Maintenance (TPM) is a manufacturing strategy aimed at enhancing equipment efficiency over its entire lifecycle by involving and motivating all employees. In the context of globalization, companies face intense competition, and to remain competitive, manufacturing sectors have embraced advanced maintenance practices that allow them to stay competitive on a global stage. Similarly, the construction industry strives to meet international standards in infrastructure development, recognizing its key role in driving a country's economic growth. One of the major challenges faced by the construction sector today is the integration of technology, which offers significant opportunities to improve the quality of construction projects. Construction equipment, being a crucial part of the construction process, requires careful management and optimization. By adopting modern technological innovations, the construction industry can enhance project efficiency, improve outcomes, and support overall economic growth.
Enhanced Design of Ethernet to HDMI Accelerators and IP Subsystems for IoT Systems
Authors: Ipseeta Nanda
Published: January 2025

The fast headway in computerized hardware has given rise to Framework System on Chip (SoC) innovation, empowering the integration of different reusable Mental Intellectual Property (IP) components, processors, memory components, and transport designs into a single chip. SoCs are progressively connected in different divisions due to their capacity to coordinated numerous functionalities, counting Web of Things (WoT) capabilities, onto a single stage. As the request for complex, multi-functional gadgets develop, plan complexity, control administration, and space limitations gotten to be significant. A various levelled plan approach, emphasizing the reuse of pre-designed and confirmed IP squares, decreases advancement costs and time. Apparatuses like the Xilinx Vivado IP Integrator encourage this by permitting creators to consistently coordinated IPs. This paper investigates an SoC engineering outlined for High-Definition interactive Media Interface (HDMI) and Ethernet flag handling, which utilizes an FPGA framework to store and transmit video information over long separations through an Ethernet organize. This plan underpins IoT gadget network and can show video on numerous screens utilizing HDMI whereas guaranteeing flag astuteness and high-quality yield.
The Role of IT Factors and IT Governance in E-commerce Adoption and Performance among Chinese Manufacturing SMEs
Authors: Zhang Min
Published: January 2025

This study investigates the key IT factors influencing the adoption of E-commerce platforms in small and medium-sized manufacturing enterprises (SMEs) in China, and examines their subsequent effect on organizational performance and efficiency. The research also assesses the moderating role of IT governance in shaping these relationships. Utilizing a framework based on well-established technology adoption models, such as the Diffusion of Innovations (DOI), Technology Acceptance Model (TAM), and the Technology-Organization-Environment (TOE), the study identifies critical factors including relative advantage, compatibility, simplicity, IT knowledge, information security, and network security. The findings highlight the significant role of IT governance in maximizing the benefits of E-commerce adoption, demonstrating that strong IT governance practices can optimize technological investments and ensure alignment with business objectives. Empirical results suggest that the implementation of E-commerce platforms positively impacts organizational performance, especially through features such as direct payment systems, search functionalities for buyers and sellers, and enhanced marketing capabilities. Recommendations for both E-commerce developers and users emphasize the importance of secure, compatible, and innovative solutions that integrate seamlessly with existing organizational systems. Additionally, the study underscores the need for government support and tailored E-commerce solutions to accelerate adoption among SMEs. This research contributes to the body of knowledge on E-commerce adoption by providing empirical evidence on the factors that influence it and their impact on organizational performance, offering insights to enhance competitiveness and operational efficiency in China's manufacturing sector.