bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimized Computational Intelligence with Association Rules for a Collaborative Consumer Product Recommendation System
Authors: R. Renukadevi
Published: January 2025

In essence, a manufactured goods suggestion is a filtering method that tries to predict and exhibit the goods that an end user is likely to want to purchase. Product suggestions are product listings that are tailored to each individual visitor to a website based on information about them, their preferences, and/or the preferences of other like buyers. Recommendation systems have gained favor recently as a solution to the problem of information overload. They achieve this by showing consumers the products that are most pertinent to them based on a huge quantity of information. Through the identification of exact match neighbors connecting individuals or goods based on their mutual evaluations in the precedent, online collaborative product suggestions seek to assist users in discovering their preferred items. However, the little data makes neighbor selection more difficult if the number of objects and users increases quickly. This study suggests a hybrid model-based approach for product recommendations that uses better Apriori algorithms to partition transformed user space. It makes use of the principal component analysis data reduction technique to classify products densely, potentially reducing the computational complexity of intelligent product suggestions. The experiment results indicate that, in addition to providing good accuracy performance, the suggested strategy might generate more personalized and reliable product suggestions than the present methods.
Characterization and Construction of Best Approximation Using Classical Tchebychev Alternation Theorem and Remez Algorithm: A Revisit
Authors: Raghava M
Published: January 2025

In this work, we study the Classical Tchebychev Alternation Theorem in Approximation Theory and an important application of this theorem. Using this characterization of the nearest polynomial given in the alternation theorem, many algorithms have been derived to construct the nearest polynomial in practical applications from the given data. One of the most important and often-used algorithms in this respect is the Remez Algorithm. Modern data-driven AI essentially needs deeper insights into the characterization of algorithms so that it behaves robustly. In this work, we give a detailed proof of the Tchebychev Alternation Theorem for the space C[a,b]. Using a generalized version of the theorem, we derive the Remez Algorithm giving iterative steps toward the construction of the nearest polynomial and prove the convergence theorem for the iterative process specified in this algorithm. Additionally, we explore how the Tchebychev Alternation Theorem and its associated algorithms, particularly the Remez Algorithm, can be leveraged in image inpainting. By treating missing pixel values as regions requiring approximation within a specified domain, the nearest polynomial formulation provides a mathematically sound framework for reconstructing high-fidelity images. This method ensures robustness against non-stationary artifacts and enables the preservation of curvilinear structures, edges, and other critical features in digital image restoration tasks. This insight highlights the significance of classical approximation theory in solving modern computational challenges such as image inpainting.
Time-Dependent Analysis of a Single-Server Queueing System with Disasters and Unreliable Repairs: A Probabilistic Approach to System Resilience
Authors: B. Janani
Published: January 2025

A single server Markovian queueing paradigm is studied. The server is prone to disaster when delivering service or during idle periods. When the server encountered problems, all waiting customers will be flushed away and the repair procedure began immediately. Arrivals are allowed to join the queue during the repair time.  The likelihood of restoring the server is p(p<1) after an exponential repair time. All waiting users leave the system if the server cannot be fixed. The time-dependent likelihood for a single server queue with disaster and an unreliable repairer is explicitly estimated for the first time. To demonstrate the significance of parameter values, performance measures and numerical figures were also supplied.
On Locally Ιnvo-Regular Ring
Authors: Yasmine Mohammed Ιsmael
Published: January 2025

An associative ring with identity D claimed to be locally Invo- regular ( L.Ι.Reg.Rings) if , for any element ӻ in D , either ӻ  or 1- ӻ is Invo-regular in D , that is  ӻ = ӻ v ӻ  or  1- ӻ = (1- ӻ) v (1- ӻ) for some involution element v in D , these rings due to Danchev [5] . In this article several instances, and properties of L.Ι.Reg.Rings are introduced and invest gules the behavior of these properties , as well as certain relation between L.Ι.Reg.Rings and other rings are discussed.
Leveraging Nonlinear Variational Inequalities with Hierarchical Graph Convolutional Networks for Adaptive Resource Management in Cloud Environments
Authors: Kiran R. Gavhale
Published: January 2025

The complexity and dynamism of cloud environments require new approaches to efficient resource provisioning and load balancing. Centralized approaches are often plagued with scalability problems, latency, and inefficient resource utilization under dynamic workloads. To overcome these shortcomings, we propose an advanced decentralized framework that utilizes deep learning and nonlinear variational inequalities for pre-emptive load balancing and cost-efficient resource provisioning. We develop our solution that is comprised of three complementary methods, which are Hierarchical Graph Convolutional Neural Networks for Load Balancing (HGCNN-LB), Reinforced Nonlinear Variational Learning (RNVL), and Adaptive Federated Variational Neural Optimization (AFVNO). By using a hierarchical graph representation of cloud nodes and tasks, HGCNN-LB can realize localized and global optimization and thus save around ~30% latency and ~25% costs due to resource over-provisioning. RNVL is able to predict workload dynamics and optimize resource provisioning based on reinforcement learning, nonlinear variational inequalities, to guarantee 90% provisioning accuracy with an average 98% SLA compliance. AFVNO is federated learning where nodes solve the problem of optimizing resources in a decentralized way with global consistency to ensure a reduction of data transfer by ~40% and the efficiency of using resources reaches ~85%. This holistic framework addresses the inherent nonlinearity and complexity of cloud systems by providing significant improvements in latency reductions, cost efficiency, and efficient use of resources. Our work stands out by combining deep learning, variational optimization, and decentralized architectures to establish an entirely new benchmark for adaptability and robustness within cloud environments, which thus paves the way towards scalable, cost-effective, and resilient cloud systems.
Advancing Intra-Vehicular Communication Security Using Seq2Seq Transformers and Q-Learning for Cyber-Attack Detection
Authors: Ankit Mundra
Published: January 2025

The increasing reliance on connected systems and autonomous features in modern vehicles has brought significant advancements in functionality and safety but has also introduced critical cybersecurity challenges, particularly in the Controller Area Network (CAN) bus. This work addresses these challenges by developing a robust framework for real-time intrusion detection using Seq2Seq Transformer architectures and Q-learning. The objective is to enhance detection accuracy and response speed for various cyber-attacks, including Denial of Service (DoS) and message spoofing, ensuring the secure operation of vehicular communication networks. The methodology integrates advanced feature engineering techniques, such as message frequency and temporal dynamics, to capture complex sequential patterns in CAN bus data. The Seq2Seq Transformer model identifies intricate attack patterns, while Q-learning optimizes decision-making for dynamic intrusion scenarios. Using the Car Hacking Dataset, the proposed framework is rigorously evaluated on key metrics, including precision, recall, F1 score, and detection time. Results demonstrate high detection accuracy with an F1 score exceeding 95%, reduced false alarms, and faster response times compared to traditional methods. By combining adaptive learning capabilities and powerful sequence modeling, this work establishes a scalable and effective solution to automotive cybersecurity challenges, paving the way for secure and intelligent connected vehicle ecosystems.
A Comprehensive Study on Nonlinear Variational Inequalities in Convex Optimization
Authors: Satpal Panika
Published: January 2025

This study investigates nonlinear variational inequalities (NVIs) has a core model in convex optimization (CO) and evaluates their theoretical and numerical relevance. The paper works through, NVIs in terms of formulation and properties of their solutions. Steady-state and time-dependent cases are addressed through projection methods and augmented Lagrangian methods to show their performance in solving optimization problems. The work presented here shows the usefulness of NVIs through practical examples in traffic equilibrium, in economic modeling, and in engineering optimization. Numerical experiments with the developed algorithms and detailed graphical results reveal the factors affecting convergence, the efficiency of NVIs and their applicability to solve various optimization problems.
Analysis of Artificial Intelligence Based Electronics Industry Digital era and its Applications
Authors: Sharmila Sengupta
Published: January 2025

The numerous attempts have been made throughout industrial history to minimize human error, control process complexity, cut waste, get rid of inefficiencies, and enhance the skills needed for modern manufacturing. Concurrently, the industrial sector has relentlessly fought against the obstacles pertaining to both quality and customer pricing. Pursuing these objectives has resulted in the implementation of significant improvements including digitization and more individualized client experiences. Furthermore, the manufacturing sector has survived the economic downturn.AI-powered computers have been deployed in many different contexts in recent years, and numerous research electronics products have taken note of this development. Experts in combining electronic design with computer AI technologies are among these researchers in large numbers. As a result, it makes it possible for numerous academics to investigate the increasingly complex development path of computer AI technology in more detail. It suggests the challenges associated with applying computer AI to electronic device design and delves more into the features and importance of this technology.
Sustainable and Resilient Seaweed derived Biofuel Production: A Smart Systems Approach
Authors: Ritu Arora
Published: January 2025

This study presents a smart production framework designed to enhance productivity, reduce waste, and lower energy consumption. By utilizing advanced automated production systems, the framework effectively reduces carbon monoxide emissions and air pollution. Moreover, it facilitates the sustainable production of biodiesel, a key alternative to conventional fossil fuels. Among various biodiesel feedstocks, seaweeds (macroalgae) stand out as a cost-effective and abundant non-edible source. The proposed model optimizes biodiesel production by maximizing energy efficiency while minimizing carbon emissions to produce high- purity biodiesel. A multi-objective mathematical model, optimized using a Genetic Algorithm, is employed to address goals of the biodiesel supply chain that are both environmentally and economically conscious. The smart production system incorporates a variable production rate and a two-stage automated inspection process to detect and remove impurities. Additionally, impure biodiesel is reprocessed after each production cycle to further enhance output quality. Numerical examples validate the model, showing that initial facility setup accounts for 45 percent of total supply chain costs, while environmental impacts represent 57 percent of the overall environmental footprint. A sensitivity analysis further evaluates the robustness of the proposed framework. A sensitivity analysis further assesses the model’s viability, offering valuable insights for stakeholders involved in biofuel production, trade, and regulatory compliance.
Exploring the Effect of Wall Characteristics on MHD Peristaltic Movement with Temperature Variation and Variable Viscosity of Jeffrey Fluid in a Flexible and Inclined Channel
Authors: Saif Razzaq Mohsin Al-Waily
Published: January 2025

Peristaltic motion of a Jeffrey fluid of constant viscosity and varying temperature through a medium in an inclined flexible tube with a porous channel. In this paper, Jeffrey fluid transmission was analyzed as well as the radially variable MHD was studied. The problem is formulated under long wavelengths and low Reynolds number approximations. Parametric analysis was presented to study the effect of (Darcy number; channel inclination angle; capacitance ratio; elasticity of channel walls). The flow lines show that the mundaye  moves at the same speed as the wave and increases it. The study also showed that increasing the number of mundaye reduces the temperature.
VACCIDOC: An Infant Immunization Tracker for Private Hospitals by Integrating OTP- based Verification
Authors: Renuka Rajendra Kajale
Published: January 2025

Vaccidoc aims to address inefficiencies in infant immunization management by providing a secure, integrated, and user-friendly digital platform tailored for private hospitals. The system leverages OTP-based verification for secure access, scheduling algorithms for optimized appointment booking, and analytics to track vaccination trends and outcomes. It incorporates a responsive frontend for patients, healthcare providers, and administrators; a robust backend for core functionalities such as user management and record-keeping; and a secure centralized database for vaccination data. Additionally, Vaccidoc integrates APIs for interoperability with national health systems and features an analytics engine for actionable public health insights. The proposed system reduces manual effort, ensures timely vaccinations, and improves vaccination coverage, offering significant benefits for patients, healthcare providers, and public health authorities.
New Operators using Β-Open Sets in a Quadripartitioned Neutrosophic Topological Spaces
Authors: Mohanarao Navuluri
Published: January 2025

In this paper, we introduce some new operators called Quadri partitioned neuromorphic β frontier, quadri- partitioned neutrosophic β border and Quadri partitioned neuromorphic β exterior with the help of quadripar- titioned neutrosophic β-open sets in quadripartitioned neutrosophic topological space. Also, we discuss the important properties of them and the relations between them.
Firefly Algorithm-Based Feature Selection for Accurate Prediction of Major Depressive Disorder from EEG Signals
Authors: K Padmaleela
Published: January 2025

Around the world, billions of people suffer from Major Depressive Disorder, a complex mental health issue. Most psychological assessments are subjective, and symptoms can fluctuate widely, making the diagnosis of Major Depressive Disorder challenging. Improved diagnostic accuracy can be achieved through innovative approaches like machine learning and optimization. In this study, we present the Firefly Algorithm, a nature-inspired metaheuristic, to enhance feature selection and classification for MDD diagnosis. Firefly-adapted artificial intelligence identifies the most relevant elements that lead to a correct diagnosis, adapting to the complexity of MDD datasets. Using real-world MDD datasets, we evaluate the FA's effectiveness compared to conventional machine learning methods. The results demonstrate that the Firefly Algorithm is a promising approach for improved MDD diagnosis, as it reduces computational complexity and boosts diagnostic accuracy. Specifically, the FA-optimized SVM model (96.24%) achieved the highest level of diagnostic accuracy, followed by KNN, ANN, QDA, and Naïve Bayes. These findings highlight how the FA can select the most suitable characteristics to yield more accurate and efficient models, potentially enhancing MDD diagnosis
Enhanced Image Segmentation in Breast Cancer Classification Leveraging Deep learning
Authors: Talari Swapna
Published: January 2025

In the recent years, the field of breast cancer research is using deep learning techniques to mitigate the problems of false positive and false negative cases caused by the breast cancer diagnosis done by the radiologist. Therefore, in this research we propose deep CNN (convolution neural networks) for classifying the mammogram image as cancerous or non-cancerous.  However, it is observed that, merely using deep learning techniques also has some limitations such as, uncertainty in breast mass classification on the dense breast mammograms. Thus, in this research the images are preprocessed and segmented prior classification. In this research images are preprocessed using Rolling Ball algorithm for background removal and then compared CLAHE and Unsharp masking for improving image contrast and visibility of the images. The proposed work segmented MIAS preprocessed images using k-means algorithm. These enhanced images are provided to Deep CNN (Convolution Neural Networks) and its features extracted for classifying images as benign, malignant or normal. To improve the CNN's structure and lessen overfitting, dropout and zero-padding are employed. The proposed work is tested on mammography images from the databases of the Mammographic Image Analysis Society (mini-MIAS), Breast Cancer Digital Repository (BCDR). The proposed system has, Accuracy:0.89%, Precision: 0.91%, Recall: 0.90%, F1-score 0.88% and for the MIAS dataset, respectively.
More on Contra Β-Open Mappings in a Quadripartitioned Neutrosophic Topological Spaces
Authors: Mohanarao Navuluri
Published: January 2025

In this article, we introduce the concept of a quadripartitioned neutrosophic contra β-continuous, quadriparti- tioned neutrosophic contra β-open and a quadripartitioned neutrosophic contra β-closed mappings in a quadri- partitioned neutrosophic topological spaces and studied some of their related properties. Further the work is extended to a quadripartitioned neutrosophic contra β-homeomorphism and a quadripartitioned neutrosophic contra β-Completely homeomorphism in a quadripartitioned neutrosophic topological spaces and establishes some of their related properties.
Optimized Vehicle Recovery: Leveraging AI and CCTV for Effective Tracking
Authors: Priya R. L
Published: January 2025

Vehicle theft is a rising crime across the globe. GPS trackers are not a standard fit in all vehicles and the lack of internet connectivity makes tracking down stolen vehicles challenging for officials. GPS signals can be affected by different environmental factors such as terrain, or dense foliage, which can cause inaccuracies in location tracking. In order to address these problems, the suggested solution makes use of the CCTV system to follow the route of a stolen car and speed up the search effort. The suggested concept presents a successful method for detecting and monitoring stolen cars through CCTV videos, utilizing advanced computer vision and deep learning methods. Reviewing the CCTV footage from various sources can expedite the reaction and improve the retrieval while upholding data privacy. The accuracy of predicting car color and car model was above 90% in the results. The car routes are forecasted with RMSE score of 0.000171. The suggested model proposes a tracking solution that utilizes the current CCTV infrastructure without requiring any extra hardware to be installed in the vehicles.
Structures of Nano-Topologies Via Graphs in the Human Double Circulation System
Authors: K. Rekha
Published: January 2025

Introduction: The key theme of present research work is to propose an initial left (resp. right) neighbourhood system for an ordering on nano-topological spaces. Based on these neighbourhoods, we explain how the connection between nano-topological spaces and digraph theory may be used in the study of human double circulation system.
Objectives: The“most current findings and medical application methods utilize graph theory to demonstrate certain complicated structures with advanced application trends. The basis of this work is the creation of a“nano-topological structures for the supremacy position of vertices of plain”digraphs. We use digraph for derive the nano-topologies in many scientific and medical models to ensure functionality.
 Methods: To construct a directed graph for the human double circulation system. In addition we derive the nano-topologies by using initial left (resp. right) neighbourhoods for certain probable sub-graphs and also verified the comparison between these two types of neighbourhoods
Results: In this paper we have derived certain nano-topological properties by creating initial left (resp. right) neighbourhoods and also we have identified the union and intersection of initial neighbourhoods might help us to ensure the cause of lung and heart failure.
Conclusions: The findings of the digraph application approaches that we acquired are really helpful to ensure the functionality of double circulation system in the human body.
A Genetic Algorithm Approach to Segment Household Objects from an Image
Authors: Smita Gour
Published: January 2025

In the field of computer image, Segmentation of colour image is being considered as one of the challenging problems. In order to effectively carry out tasks assigned by human operators, robots operating in domestic environments should be able to detect and distinguish the things utilized in the home. Segmentation enables robots to understand the context of the environment by identifying and categorizing objects.  An Image Segmentation using Genetic algorithm is presented in this work. It is a natural evolutionary approach for optimization problems. In most of the colour image segmentation techniques the clustering is used at beginning to segregate colour images and then Genetic Algorithm (GA) is applied just as an optimization tool. Here K-means Clustering technique along with genetic algorithm has been used to find optimal thresholds between the multiple objects and the complex background. For optimization a Genetic algorithm is applied on the clustering output in which segmentation is improved through different steps of GA. The experimental results shown that the combination of K-means and GA gave promising results with an accuracy of 90% on home object dataset.  Also, a comparison is made between the suggested algorithm and the most widely used watershed segmentation algorithm and it is proved that our algorithm has given equivalent results on both standard and real time datasets.
A Heuristic Method to Improve the Robustness of Flight Schedules: Estimating Optimal Slack and Re-Timing Flights
Authors: Khusnul Novianingsih
Published: January 2025

An investigation was conducted concerning the problem of flight delays to propose a solution to enhance the robustness of airline flight schedules. The robustness of flight schedules depended on the ability to reduce the effects of flight delays on daily operations. The approach to achieve this objective was optimizing flight schedules by allocating the optimal amount of slack to scheduled connections. Identifying appropriate slack between two successive flights involved modeling primary delay distributions at departure airports, considering both time intervals and delay lengths. A threshold approach was subsequently devised to determine the optimal slack based on this distribution. The ideal allocation of slack to schedules was achieved by modifying flight departure times, a technique known as re-timing. Recognizing that the optimal allocation of slack could make schedules infeasible, an optimization model was established for adjusting flight departure times. The proposed model produced new schedules that optimized all slack. The practical application of this model to the schedules of Indonesian airlines demonstrated the method's efficiency in improving the robustness of flight schedules.
Thermal Analytical Characterization of Gymnema sylvestre Using TGA-DTA Technique
Authors: Nirmala Kakde
Published: January 2025

Gymnema sylvestre, a well-known antidiabetic plant, has been extensively studied for its therapeutic potential across various traditional medicine systems. It is widely used to treat a range of ailments in humans. In modern herbal medicine, drug standardization is critical to ensure the quality and purity of herbal drugs. The TGA-DTA (Thermogravimetric Analysis - Differential Thermal Analysis) technique is commonly employed to study the physical and chemical changes of various herbal substances. In this study, the thermal and chemical characteristics of powdered leaves, stems, and roots of Gymnema sylvestre were analysed using TGA-DTA methods. TGA (Thermogravimetric Analysis) and DTA (Differential Thermal Analysis) are sophisticated thermal analysis techniques commonly used to examine the thermal properties, stability, and composition of a wide range of materials, including medicinal plants, pharmaceuticals, polymers, and other substances. The TGA and DTA curves were recorded over the temperature range of 35°C to 700°C to examine the decomposition patterns of Gymnema sylvestre plant material. In the DTA curve, an endothermic peak at 101°C was observed in all samples, corresponding to the evaporation of water from the surface and pores of the powder. A second weight loss between 350°C and 400°C is attributed to the degradation of cellulose and hemicellulose present in the plant. Further analysis suggests the possibility of lignin degradation occurring between 450°C and 700°C as the temperature is extended. The TGA analysis of the powdered leaves, stems, and roots of G. sylvestre provides valuable insights into the stability and composition of this medicinal plant. These results offer supporting data that can be utilized for the identification and standardization of Gymnema sylvestre in herbal drug analysis.
A Study of the Population Ecology of Asian Elephants in using the Logistic Growth Model
Authors: Ahamed Amani M
Published: January 2025

India is home to almost 60 percent of the world's Asian elephant population. However, with fewer than 52,000 Asian elephants left, they are listed as endangered species on the IUCN (International Union for Conservation of Nature) Red List. As keystone species, elephants are vital in maintaining ecosystem balance and require careful management. Estimating the population of these endangered animals in India is challenging due to limited resources. The optimal approach to model the population of Asian elephants in India using available data is the logistic model. This model will provide the population count of Asian elephants in India and help establish the boundaries caused by the assumptions of the logistic model. Significantly, it will also help predict whether the population of Asian elephants in India has stabilized or is likely to do so, shedding light on the efforts taken by the Indian authorities in the past decades.
A Comprehensive Review of Brain Tumor Detection Using Machine Learning and Deep Learning: Challenges, Gaps and Future Directions
Authors: Nitin B. Pawar
Published: January 2025

Patient survival is impacted by the growing prevalence of brain tumor, which makes prompt and precise detection extremely difficult. Machine Learning (ML) and Deep Learning (DL) have become essential for enhancing the identification and categorization of brain tumor due to developments in medical imaging and computational intelligence. This article offers a comprehensive overview of the approaches taken by several researchers to identify brain tumors using ML and DL models. The study points out the shortcomings of the existing approaches, including the need for improved segmentation algorithms, the small dataset size, and the absence of real-time data. In order to fill these gaps, author suggests a hybrid neural network model that reliably localizes tumor and classifies them as benign or malignant by combining Convolutional Neural Networks (CNNs) with sophisticated pre-processing and feature extraction techniques.
Global Perspectives on Determinants Influencing the Adoption of AI-Driven Sustainable Fintech Solutions
Authors: Shamli Sharma
Published: January 2025

Using a mixed methods approach that combines qualitative and quantitative research, this study investigates new developments in artificial intelligence (AI) applications in sustainable Fintech and empirically validates a conceptual research model. It examines important factors impacting the adoption of AI technologies in sustainable Fintech from a cross-national standpoint, highlighting the crucial aspects of advantages, costs, and sustainability characteristics that influence user adoption intentions. To provide a thorough evaluation of scholarly literature on AI, Fintech, and sustainability practices, a bibliometric keyword analysis is carried out using Web of Science and VOSviewer. Sustainability components like transparency and perceived accountability are added to this framework, which has its roots in the Value-Based Adoption Model (VAM) and the Technology Acceptance Model (TAM). The results highlight AI's dual function of promoting sustainable growth and increasing Fintech efficiency. To further the integration of AI in sustainable Fintech management, the study emphasizes the necessity of multidisciplinary cooperation between specialists in sustainability, finance, and AI. This research adds significantly to the body of knowledge in academia and offers useful information to industry professionals and policymakers by linking the domains of artificial intelligence and sustainable Fintech.
Human-Centric Design for Wearable Technology: Bridging Aesthetic and Functional Aspects in Smart Devices
Authors: Rasika Vishal Wattamwar
Published: January 2025

The paper proposes a human-centric approach to wearable technology, emphasizing the integration of both aesthetic and functional elements to enhance user experience. The methodology involves analyzing user feedback and behavior to identify key needs and design challenges, which will inform the iterative development of wearable devices. Prototyping tools will be used to create interactive models that facilitate user testing and refinement prior to final deployment. The proposed system architecture includes advanced sensor modules, AI algorithms for personalized recommendations, and ergonomic design principles to ensure comfort and usability. A continuous improvement loop, driven by user feedback, will guide the refinement of both hardware and software components, ensuring the device meets evolving user expectations. Additionally, the study will explore the balance between aesthetics and functionality, using case studies of successful wearables to illustrate best practices. This approach aims to create devices that are not only technically proficient but also deeply aligned with user preferences and needs, ultimately promoting greater adoption and sustained engagement.
A Novel Hybrid Model Based on Hierarchical and Density Clustering Approaches
Authors: Swati Gupta
Published: January 2025

Prediction is a developing domain that requires further advancement. In practical situations, one frequently encounters unsupervised datasets requiring analysis. Clustering techniques can efficiently resolve this difficulty. The current study designs the hybrid hierarchical density model to enhance clustering efficiency through a combination of hierarchical clustering and DBSCAN. Hierarchical clustering's initial partitioning quality, which lays a strong foundation for DBSCAN's refinement, is a key influencing factor for these enhancements in this model. DBSCAN's ability to adjust to local density variations further enhances clustering quality. This model is compared with conventional clustering models such as K-Mean Clustering, Grid-Based Clustering, Agglomerative Clustering, Gaussian Mixture Model, Constraint-Based Clustering, and Spectral Clustering using five different datasets named CIFAR-10, MNIST Handwritten Digits, Cats and Dogs, Plant Seedlings, and Oxford Pets Datasets. The goal of this study is to create more accurate and meaningful clusters by continuously improving parameters and using performance metrics that show average results, as the silhouette score value is 0.7, the Davies-Bouldin index is 0.43, the Rand index value is 0.89, the adjusted mutual information is 0.67, and Fowlkes-Mallow’s index is 0.57.
Enhancing ECG-Based Cardiac Anomaly Detection Using Deep Learning for Improved Diagnostic Efficiency and Clinical Precision
Authors: Monali Gulhane
Published: January 2025

Cardiovascular problems cause a lot of illness and death around the world. To improve early diagnosis and treatment accuracy, testing methods need to be improved. Focus of this work is on using deep learning methods to make electrocardiogram (ECG)-based heart abnormality identification more accurate and time-effective. In this study variety of deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks with Gated Recurrent Units (LSTM-GRU), Generative Adversarial Networks (GANs), and Visual Geometry Group networks (VGG), to create models that could detect subtle patterns in ECG signals that could point to possible heart problems. An extensive collection of thousands of labelled ECG records, ranging from normal to those with a variety of heart problems, was used to train these models. The suggested approach model architecture's performance was improved by fine-tuning many hyperparameters and confirmed using a different set of tests. According to our results, the LSTM-GRU model was more accurate and useful in clinical settings than other models. Accuracy of 97.5% was achieved by this model in catching time relationships and subtleties in ECG data. Furthermore, the VGG and CNN models did a good job of extracting features from raw ECG readings, and the GAN model was especially good at adding to the training dataset, which made the model more stable overall. The LSTM-GRU model was the best at finding heart abnormalities based on ECGs. This suggests that combining deep learning with complicated sequence modelling can greatly improve diagnostic accuracy and patient results. This method could be used in real life in automatic ECG analysis systems, giving doctors a useful way to check on heart health quickly and accurately.
Nonlinear Adaptive Federated Learning with Privacy Preservation for Edge-Cloud Systems
Authors: D. Rajalakshmi
Published: January 2025

The rapid-fire proliferation of edge- cloud systems has steered in unknown openings for cooperative intelligence while introducing critical challenges in data sequestration, diversity, and non-linear rigidity. This paper presents a new frame for Nonlinear Adaptive Federated Learning with sequestration Preservation acclimatized to edge- pall surroundings. The proposed approach integrates three vital methods Noise Addition for sequestration during preprocessing to ensure discrimination sequestration without compromising data utility; Privacy- Preserving Feature Selection for relating significant features while securing sensitive information; and Federated Generative Adversarial Networks (FedGANs) for robust, adaptive bracket that captures complex, on-linear patterns across miscellaneous datasets. The system leverages decentralized literacy to address. data distributions and employs inimical mechanisms to enhance model conception and delicacy. Experimental evaluations demonstrate significant advancements in sequestration, computational effectiveness, and model performance compared to traditional federated learning methods. This exploration underscores the eventuality of integrating advanced sequestration- conserving and adaptive ways to enable secure and scalable intelligence in edge- pall ecosystems.
A Legal Perspective on the Activities of Social Workers in Northern Nigeria
Authors: Adegbite Aderonke
Published: January 2025

This study examines the activities of social workers in northern Nigeria, a region deeply affected by insurgencies, armed conflicts, and other forms of internal disturbances. Social workers play a crucial role in safeguarding the lives and dignity of victims of armed conflict, providing assistance, promoting the application of international humanitarian law, and acting as neutral intermediaries between conflicting parties. While these are general functions of social workers globally, this research focuses on the specific roles they play in northern Nigeria. The study employs a doctrinal research methodology, which is particularly adequate and sufficient for this analysis due to its systematic and comprehensive focus on existing legal and academic resources. By relying on primary and secondary sources such as laws, textbooks, internet materials, periodicals, journals, and articles the doctrinal approach allows for a thorough and qualitative examination of documented data and perspectives on the contributions and challenges faced by social workers. This method is sufficient and was chosen because it provides the tools necessary to synthesize and critically analyze existing knowledge, highlighting patterns, identifying gaps, and drawing insights that may not require field-based or empirical investigation. Through the doctrinal method, the study identifies the extensive contributions of social workers in mitigating the impacts of internal strife and armed conflicts in northern Nigeria. It also illuminates the challenges they encounter and the strategies they use to overcome these obstacles. By relying on robust academic and professional literature, this method ensures a comprehensive understanding of the subject, making it an appropriate choice for the scope and objectives of the research.
Pengembangan Gim Edukasi Jenis Tipe Data dalam Pemrograman untuk Pelajar Tingkat SMA dan SMK
Authors: William Timothy Justin , Dirgantara Harya Bima
Published: January 2025

Tujuan di belakang penelitian ini adalah untuk mengembangkan sebuah gim edukasi untuk siswa tingkat SMA dan SMK untuk mengajarkan mereka sebuah konsep dalam pemrograman, yaitu tipe data. Peneliti menggunakan mesin gim Unity dan sebuah kerangka pengembangan gim bernama Game Development Life Cycle (GDLC) yang terdiri dari 6 tahap: Inisiasi, Pra-Produksi, Produksi, Uji Coba, Beta, dan Rilis. Pengembangan gim hanya menggunakan aset-aset yang gratis dan/atau dibuat oleh peneliti. Keluaran dari penelitian ini adalah sebuah gim bernama “Mari Berdata” yang dapat dimainkan secara gratis pada platform Desktop seperti PC dan laptop. Responden dari tahap Beta juga menyatakan bahwa mereka mempelajari sesuatu yang baru mengenai tipe data setelah memainkan “Mari Berdata”.
A magyarországi húsfeldolgozás, -tartósítás szakágazat koncentrációjának változása 2015–2021 között
Authors: Rácz Zsolt , Szőllősi László
Published: January 2025

A sertéságazat a 2015–2021-es időszakban több válsággal is szembenézett, ilyen volt a Covid-19-pandémia, az afrikai sertéspestis járvány és a takarmányárak világpiaci árának jelentős drágulása 2021-ben. Az ipari szektorban ismert a méretgazdaságosság meghatározó szerepe. A koncentrációval mérhető, hogy egy-egy gazdasági ágazatban mennyire összpontosul nagyüzemek tulajdonában a termelés. A tanulmány célja, hogy a húsfeldolgozás, -tartósítás (TEÁOR 10.11) szakágazat szereplőinek méretét elemezve, meghatározza és összehasonlítsa a magyar sertéshús-feldolgozás koncentráltságát 2015-ben és 2021-ben. E területre fókuszálva javaslatot kívánunk tenni a magyar sertéshús-feldolgozás versenyképességének növelésére. Szekunder adatgyűjtés keretében az EMIS adatbázisából nyert adatokat első lépésként térképek segítségével dolgoztuk fel, és vizsgáltuk a tevékenység területi koncentrációját. A szakágazati koncentrációt tovább elemeztük négyvállalatos koncentrációs ráta (CR4), Herfindahl–Hirschman-index (HHI), Gini-index és Lorenz-görbe segítségével.
A kapott eredmények alapján a szakágazat koncentrációja magas. Ugyanakkor a szakirodalmi adatok azt mutatják, hogy elmarad a fejlett nyugati országok (USA, Dánia) koncentrációjától. 2015-höz képest a mérlegfőösszeget vizsgálva 2021-ben a HHI, a CR4 és a Gini-index is növekedett. Ugyanezen időszakban az árbevétel alapján számított CR4 és Gini-index nőtt, a HHI pedig csökkent. A koncentráció növekedése azonban nem volt elegendő, hogy a magyar húsfeldolgozás, -tartósítás szakágazat koncentrációja utolérje a legversenyképesebb nyugati országok értékét. A magyar sertéshús-feldolgozás versenyképességének növekedését elősegítheti a koncentráció további fokozása és így a méretgazdaságosság növekedése. Éppen ezért javasolt a piaci szereplők közötti integrációs együttműködések létrehozása és kiszélesítése.
Influencing Factors In-Hospital School Education: Exploring the Context From the Teacher’s Perspective
Authors: Jiliberto Francisca , Zárate Alva Nair
Published: January 2025

Hospital teachers face unique challenges and require specific skills to cope with the demands of teaching within a hospital setting effectively. Additionally, the quality of education in hospital schools (HS) may be affected by factors such as resource availability and coordination with other professionals. This initial study examined the factors influencing education in HSs for hospitalised children with physical health conditions. Teachers from the eight HSs in Catalonia (N = 16) responded to an online questionnaire developed based on a scoping literature review that identified factors reported to influence education in HSs. Employing a mixed-method convergent parallel design, quantitative data from closed-ended questions and qualitative data from comments were analysed in parallel. Results showed that hospital teachers face challenges such as adapting to diverse educational stages and subjects, selecting teaching methods tailored to students’ individual needs, and coordinating with various professionals on a case-by-case basis. They often lack sufficient budgets, consistent access to resources, and opportunities for professional training. Participants’ views on the skills required by hospital teachers align closely with literature findings, as does their perception of the emotional impact of working in a HS. Understanding the unique context of HSs is crucial for stakeholders and policymakers to ensure inclusive and equitable quality education for all.
Implementación de tecnologías limpias como estrategia para mejorar la sostenibilidad ambiental en plantas industriales
Authors: Parrales Aldo , Argüello Luis , Cruz Bárbara , Díaz Juan
Published: January 2025

Este ensayo examina el impacto de la implementación de tecnologías limpias en plantas industriales para mejorar la sostenibilidad ambiental. Se analizan casos de estudio de empresas que han adoptado tecnologías limpias, como energías renovables, gestión eficiente de residuos y reducción de emisiones contaminantes.
Los resultados muestran que la implementación de tecnologías limpias ha demostrado reducir las emisiones de gases de efecto invernadero y disminuir el consumo de recursos naturales en las plantas industriales estudiadas. Además, se observa una optimización de los procesos de producción y una reducción de costos operativos.
Estos hallazgos respaldan la importancia de adoptar tecnologías limpias en el sector industrial para promover una producción más sostenible. La implementación exitosa de tecnologías limpias no solo beneficia al medio ambiente, sino que también puede generar ahorros económicos y mejorar la imagen corporativa de las empresas.
Rezension von: Würfel, Maria, Starke Frauen
Authors: Kliegel Marieluise , Würfel Maria
Published: January 2025

Maria Würfel: Starke Frauen. Oberschwäbische Äbtissinnen zwischen Reformation und Säkularisation (Oberschwaben – Ansichten und Aussichten 13). Stuttgart: Verlag Regionalkultur 2020; 160 S., 67 farbige Abb., 20,00 EUR
Research Overview of Treating Gestational Hypertension Disease with the Methods of Strengthening the Spleen for Eliminating Dampness and Calming the Liver to Stop Wind
Authors: Guo Jingheng , Zhu Lihong
Published: January 2025

Hypertensive disorders during pregnancy are common complications in the second and third trimesters, characterized by clinical manifestations of hypertension, proteinuria, and edema, and are one of the leading causes of maternal mortality. This article summarizes the clinical research on the treatment of hypertensive disorders during pregnancy with traditional Chinese medicine (TCM) in recent years, focusing on syndrome differentiation and treatment, as well as some external therapies, often in combination with Western medicine. The review primarily covers the research progress from the perspective of TCM on the etiology and pathogenesis, treatment, and medication of hypertensive disorders during pregnancy. It emphasizes that deficiency of the spleen is the fundamental cause of the disease mechanism, yin deficiency of the liver and kidney is the internal basis for the onset, and hyperactivity of liver yang is the key link in the onset. The treatment commonly employs methods that strengthen the spleen, promote diuresis, nourish yin, and calm the liver and extinguish wind. This provides innovative, safe, and effective treatment approaches for clinical practice.
Research Progress on Diagnosis and Treatment of Diarrhea Predominant Irritable Bowel Syndrome in Traditional Chinese Medicine
Authors: Luo Jintao , Liu Kaijian , Qiao Huixia , Liu Xinyu
Published: January 2025

The pathogenesis of diarrhea predominant irritable bowel syndrome has not yet been fully clarified in western medicine, and symptomatic treatments such as spasmolysis and antidiarrheal are generally used in clinic, which lacks systematicness and long-term effectiveness, and the clinical efficacy of some patients is not ideal. Traditional Chinese medicine has significant advantages in improving clinical symptoms, improving body immunity and improving patients' quality of life through syndrome differentiation and treatment. This article mainly summarizes the etiology and pathogenesis of IBS-D, and the treatment methods and prescriptions, in order to provide some ideas and references for the clinical diagnosis and treatment of IBS-D in TCM.
Effects of Nano-cerium Oxide on the Levels of DHT, TGF-β, and IGF-1 in the Local Skin of AGA Model Mice and Efficacy Observation
Authors: Dong Aoxuan , Zhang Guohui
Published: January 2025

Objective: Explore the improvement effect of nano-cerium oxide on mice with androgenetic alopecia. Methods: First, the backs of mice were depilated and an androgenetic alopecia model was established by treating them with 5mg/kg testosterone propionate. C57BL/6 mice were randomly divided into 5 groups: the blank group, the model group, the roller microneedle group, the nano-cerium oxide group, and the minoxidil group. Corresponding treatment substances were introduced into the depilated areas respectively. The ratio of terminal hairs to vellus hairs (T/V) in mice was observed through HE histological examination. The contents of dihydrotestosterone (DHT), transforming growth factor-β (TGF-β), and insulin-like growth factor-1(IGF-1) in skin tissues were determined by enzyme-linked immunosorbent assay (ELISA). Results: Compared with the model group, the drug - treated groups could increase the T/V ratio in mice, elevate the protein expression level of IGF-1, and decrease the protein expression level of TGF-β in mice as well as the content of DHT in mouse skin. Conclusions: Nano-cerium oxide has a certain ability to alleviate androgenetic alopecia. It can increase the ratio of terminal hair to vellus hair, providing some theoretical guidance for the development of drugs for the prevention and treatment of androgenetic alopecia.