bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting Unusual Activity Using CNN and Time Series for Enhanced Cyber Protection
Authors: M. Gopila
Published: January 2025

A great deal of systems for intrusion prevention and detection in use presently do not constantly capitalize on time series modeling's strength. System administrators will be able to better organize resource allocation and system readiness to fend off fraudulent activity by having access to time series models. In an attempt to plug the knowledge gap, we're investigating the potential of integrating a quantitatively derived time series modeling to the incumbent cyber safety system in a way that is simple to implement. This investigation conforms the GARMA (1, 1; 1, ±) hypothesis to data sets concerning cybercrime. Two separate approaches for the forecast are applied. Point assumptions have also been conducted to estimate the collision rate theoretically hours in progress, and the success rate of the models and estimating techniques is evaluated. Leveraging all of the earlier collected instances, our system executes ARIMA time series forecasting to forecast the projected number of strikes on a particular date in the future. Only a small portion of the data appropriate to an individual attack approach can be utilized through our equipment. To enhance accuracy, prediction models are frequently tweaked as new data is accumulated over time. When figuring out attacks of any type, our system exceeds naive forecasting methods by 14.1%, and when forecasting attacks of a particular decent, it crashes the competition by up to 21.2%. Furthermore, our technique manufactures a model that with greater certainty forecasts the behavior of future cyberattack concentrations.
Attracting Foreign Direct Investment in Vietnam Towards Sustainability
Authors: Nguyen Tien Minh
Published: January 2025

A gloomy economic picture, a decline in trade transactions, the potential destruction of global value chains, industries struggling due to the Covid-19 pandemic, lockdowns, and curfews in various parts of the world—this is what much of the media described about 2020. The Covid-19 pandemic, which began at the end of 2019 with unpredictable developments to this day, has been a shock to every aspect of the global economy. However, more importantly, the pandemic has contributed to accelerating the shift of international capital flows and reshaping global supply chains and value chains. With the vision of a developing country, Vietnam needs to seize the opportunity, strengthen its supporting industries, and create a healthy investment environment to maximize the opportunities brought by international capital flows, contributing to the rapid recovery of the economy in the post-Covid-19 period.
UAV Based Data Access Communication Control in Distributed Hash Table Mechanism
Authors: Shaik Mohammad Rafi
Published: January 2025

The combination of Internet of Things ( IoT ) and aeronautical integration, made possible by satellite and 6G technology for communication, has led to the emergence of the World Wide Web of unmanned aerial vehicles, or UAVs, often referred to as the Internet of Drones (IoD). Using cloud-based Connectivity of Predators (IoD) is a necessary choice to reduce the heavy workload that mobile UAVs and enable the storing and exchange of massive quantities of actual time UAV data. Protecting highly sensitive UAV data in a transparent, curious, open, and decentralized environment, while operating UAVs with limited resources, is a significant challenge. In our previous research project, SPNCE'21, we introduced a system called PATLDAC that devised a method for managing and regulating access to unmanned aerial vehicle (UAV) data stored in the cloud. This scheme offers robust policy privacy protection, restricted access duration, and comprehensive user traceability. Nevertheless, there are several disadvantages associated with it, such as rigid and centralized storage and retrieval of data in the cloud, as well as unreliable information in an untrustworthy cloud environment for accessing data and tracking users. To accomplish this goal, we propose the implementation of a method known as a blockchain-based privacy-aware control of data access (BPADAC) to securely share UAV data in an internet-based Internet of Drones (IoD) setting. Expanding on the detailed, trackable, and privacy-protecting features of our prior research, we improve it by integrating blockchain and Decentralized Hash Table (DHT) technology. This enables the decentralized and dependable access and storage the UAV data, combined with a secure and limited access method. The objective is to guarantee the delivery of cloud-based Unmanned Aerial Vehicle (UAV) data sharing services. Furthermore, we provide a strong and unquestionable user tracking mechanism to protect against the abuse of user keys and denial by traitors. In the end, we conduct a thorough security study and develop a working model of the system using the smart contracts of the Ethereum blockchain. The purpose of this is to assess the system's efficacy and showcase the feasibility of BPADAC.
Creating and Verifying a Deep Learning-Powered Automatic Algorithm for Chest Radiographs to Identify Active Pulmonary Tuberculosis
Authors: Bollu Siva Keshava Rao
Published: January 2025

Chest radiographs (CRs) that demonstrate active pulmonary tuberculosis are required to screen and diagnose conditions related to tuberculosis. An automated system has the potential to improve diagnostic performance while also accelerating the process of TB screening, which is a source of optimism. To build a deep learning-based automatic detection (DLAD) approach, thirteen radiologists who are board-certified went through 54,211 normal CRs and 6,778 CRs with active pulmonary tuberculosis. To ensure that DLAD was effective, six external, multinational datasets were used. To compare the efficacy of DLAD with that of physicians, fifteen doctors including thoracic radiotherapists, board-certified radiotherapists, and non-radiology physicians took part in an observer performance exam. To measure the effectiveness of lesion-wise localization along with image-wise classification, respectively, the area over the ROC (receiver operating characteristic) curves and the area under a separate free-response ROC curve were used. Determined were the DLAD's sensitivity and specificities using two cutoffs: high sensitivity [98%] and high specificity [98%] established by internal validation. It was shown that DLAD achieved a classification performance of 0.977-1.000 and a localisation performance of 0.973-1.000. In contrast to the high-specificity cutoff, which generated sensitivity and specificities ranging from 84.1% to 99.0% and 99.1% to 100%, correspondingly, the high-sensitivity cutoff produced 94.3% to 100% sensitivities and 91.1% to 100% specificities. Both localization (0.993 vs. 0.664-0.925) and classifying (0.993 vs. 0.746-0.971) were areas where DLAD outperformed all physician groups. When it came to reliably identifying active pulmonary TB upon CR, the DLAD fared better than clinicians, particularly thoracic radiologists.
Optimizing IoT Security with Blockchain: Overcoming Computational Challenges
Authors: Hitesh Gehani
Published: January 2025

The Internet of Things (IoT) has made its way into business and home applications, allowing for automation, monitoring, control, and analysis.     Blockchain-based solutions use encryption, hashing, permanence, credibility, transparency, trustworthiness, and other security measures to protect IoT networks from numerous attacks. However, integrating blockchains involves complex computational tasks such as creating hashes, verifying hashes, mining blocks, etc, which adds extra computational burden on the system. This increased computational burden often reduces the quality of service for the system, making it less suitable for real-time and high-performance applications. To address this issue, this text introduces an AI algorithm for generating side chains. These side chains offer high security performance like regular blockchains but are generally less computationally complex. As a result, they can be used for secure real-time IoT applications.
Principal Intersection Graph of Commutative Rings
Authors: Lamine Ngom
Published: January 2025

Let R be a commutative ring. The principal intersection graph of a commutative ring R, noted G_c (R), consist of all proper ideals of R as vertices. Two distinct vertices I and J are adjacent if I∩J≠0 and either I or J is a principal (cyclic) ideal. In this paper, we investigate some properties from graph theory of G_c (R) and its algebraic properties where R is a ring.
Class of Modules for Which Strongly Hopfian Modules are Noetherian
Authors: Mankagna Albert Diompy
Published: January 2025

Let R be an arbitrary ring and M a left R-module. In this paper we introduce the modules M such that every strongly Hopfian module in σ[M] is Noetherian. These modules will be called SF-modules. We characterize such modules and study their properties. Relationships between SF-modules and other classes of modules are given.
Comparative Analysis of Climate-Smart Agriculture for Marathwada Region Using Machine Learning Algorithms
Authors: Ramdas Gore
Published: January 2025

The economy of the Indian region known as Marathwada is mostly dependent on agriculture. But as smart agricultural technology advances, it becomes more and more necessary to use machine learning algorithms to enhance decision-making and maximize resource allocation. This study conducts a comprehensive comparative analysis of various machine learning classification algorithms to identify the most suitable approach for addressing agricultural challenges in the Marathwada region, and improve the crop production. The dataset includes historical yield statistics, crop type, soil, weather, and other agricultural characteristics. Six popular classification algorithms are used for Climate-Smart Agriculture such as Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree with 91%, 96%, 93%, 89%, and 98 % accuracy respectively Results indicate that Random Forest (96%) and Decision Tree (98%) consistently outperform other algorithms across various metrics. Decision trees are quite good at predicting agricultural production because of its ensemble structure and ability to handle complex interactions within the data. The study highlights how important feature selection and preprocessing are to improving the efficiency of machine learning algorithms. This comparison study offers insightful information about which machine learning classification algorithms are most suited for maximizing smart agricultural techniques in the Marathwada area.
Final Assignment Title Recommendation System Using Collaborative Filtering Method for Undergraduate Students of Informatics Engineering
Authors: Anang Pramono
Published: January 2025

The final project title selection process is a crucial stage for undergraduate students of Informatics Engineering. The difficulty in determining a title that is relevant to their interests and abilities is often an obstacle. This research aims to develop a final project title recommendation system that can assist students in finding suitable research topics. This system utilizes a collaborative filtering approach to analyze historical data on course grades and student preferences. The results show that the item-based collaborative filtering method produces more accurate and relevant recommendations compared to the user-based collaborative filtering method. The RMSE value of the item-based collaborative filtering method (0.4985) is lower than the RMSE value of the user-based collaborative filtering method (0.9759). While the accuracy value of the item-based collaborative filtering method (0.5903) is higher than the user-based collaborative filtering method (0.0153).This system has the potential to optimize the final project title selection process, accelerate study time, and improve the quality of student research.
Exploring the Impact of AI-based Knowledge Management Tools in Higher Education Institutions
Authors: Haftom Gebregziabher
Published: January 2025

This study looks at how well AI-based Knowledge Management (KM) tools work in Higher Education Institutions (HEIs). There is a big gap in knowing how these tools affect education. As education technologies change, it is important to understand how these tools help improve learning. We used a survey to gather views from four main groups: administrators, management, students, and teachers. Each group shared how they use and find KM tools helpful. We collected data through structured surveys and used different statistical tests like descriptive statistics, Mann-Whitney U tests, Kruskal-Wallis H tests, and Chi-Square tests to analyze the results. The study showed that AI-based KM tools help HEIs adapt better to global changes and improve remote learning and student motivation. However, the results were mixed on how well these tools improve KM processes and meet industry standards. This shows there is still room for improvement. The findings are important. They suggest that while AI-based KM tools can greatly improve education, their use needs to be carefully planned to meet specific educational needs. This research helps us understand AI in education better and guides HEIs in using KM technologies effectively.
A Study of Bipolar Fuzzy Prime Ideals of a Lattice
Authors: Venkata Kalyani U
Published: January 2025

This study explores the investigation of Bipolar Fuzzy Prime Ideals (BFPI) in lattices. We provide a detailed exploration of their properties, characterizations, and associated homomorphisms.
Introduction: Fuzzy set theory, introduced by Zadeh L.A., is grounded in the concept of membership functions where each element in a set is assigned a membership degree ranging between 0 and 1. Although this model effectively combines supporting and opposing evidence for element membership, it lacks explicit representation of the uncertainty or dual nature of these evidence. To address this limitation, Gau and Buehrer introduced the concept of vague sets, characterized by two functions: one for membership and another for non-membership, where their sum does not exceed one.
Further contributions to fuzzy set theory came from Atanassov's intuitionistic fuzzy sets and Bustince and Burillo's work showing their mathematical equivalence to vague sets. The dual-function approach of vague sets has been applied extensively in decision-making, control systems, and fault diagnosis. Lattice theory has also benefited from these advancements, with Ajmal and Thomas pioneering fuzzy sublattice theory, and later works exploring intuitionistic fuzzy lattices and vague lattices. Bipolar fuzzy sets (BFS), introduced by Lee K.M., extended fuzzy sets by incorporating dual notions of positive and negative membership values within a range of [-1, 1]. This extension enables interpretations of bipolar information, making BFS a valuable tool in decision-making and information processing.
Objectives: Introduction of Bipolar fuzzy prime ideals of a Lattice, study of their characterizations and associated homomorphisms.
Enhancing Graph-based Machine Learning through Lyndon Partial Words
Authors: R. Krishna Kumari
Published: January 2025

Objectives: This study integrates the combinatorial properties of Lyndon partial words with Graph-Based Machine Learning (GBML) to develop an innovative approach for sequence analysis. The research is particularly aimed at addressing challenges in fields like bioinformatics and natural language processing (NLP), where incomplete or fragmented data often hinder effective analysis. By leveraging the minimality and primitiveness inherent to Lyndon partial words, this study seeks to provide a robust framework for modeling and analyzing such data.
Methods: Graphs were constructed from Lyndon partial words, where nodes represent unique partial words or their conjugates, and edges signify relationships such as lexicographical proximity or shared substrings. These graphs were subjected to advanced GBML techniques, including community detection algorithms to uncover clusters of related patterns, and similarity analysis to measure structural and semantic relationships. Data preprocessing ensured the accurate representation of partial words while maintaining their combinatorial integrity.
Findings: The integration of Lyndon partial words into GBML demonstrates significant potential in pattern recognition and structural analysis, particularly for datasets characterized by fragmentation or incompleteness. The constructed graphs effectively capture underlying relationships and patterns, aiding in the discovery of meaningful insights in sequence data. This novel framework enables improved modeling of real-world scenarios, such as identifying recurring motifs in biological sequences or understanding linguistic variations in incomplete text datasets.
Novelty: By combining the theoretical elegance of Lyndon partial words with the computational power of GBML, this study introduces a novel methodology for tackling incomplete data in sequence analysis. The approach highlights the adaptability of combinatorial constructs for solving practical problems, offering new avenues for research in data-intensive domains like bioinformatics and NLP. The framework also underscores the importance of interdisciplinary solutions in advancing machine learning applications for complex and fragmented datasets.
Some Fixed Point Results for (α,ψ,φ)- Geraghty Contraction Mappings in Bipolar Metric Space
Authors: Diksha
Published: January 2025

In this paper, we explore a generalization of (α,ψ) - Geraghty contractions and investigate the existence and uniqueness of fixed points for mappings satisfying this condition. The study extends, improves, and generalizes some earlier results in the literature on this topic. We consider the interplay of three parameters: α,ψ  and φ, which play a crucial role in defining the contraction properties. Our goal is to establish fixed point theorems that encompass various scenarios within bipolar metric spaces.
Effective Information and Extraction an Improved Semantic Pattern Based Approach in LDA Model using Bigdata
Authors: K. Sundravadivelu
Published: January 2025

Big text data content is produced by more than ninety percent of online sources. The data occurs primarily in two forms, structured and unstructured, depending on the representation schemes underlying the devices or platforms that generate them. The focus of this research is on the unstructured text data, as it provides for unforeseen opportunities in decision-making tasks and wider scope for analytics. The proposed work attempts to mitigate the complexity by combining semantic analysis with content modelling. To achieve the data model, Latent Drichlet Allocation topic modelling technique is improved using Frequent Sequential Pattern mining algorithm. Hierarchical topic clusters in documents are viable to be extracted with the help of LDA when word associations are modelled using frequent and sequential patterns. The semantic patterns obtained from such processes are used for knowledge discovery. This knowledge can be used for other similar mining tasks such as classification, clustering or information extraction. This study has four primary contributions such as, to uncover patterns in unstructured text content, semantic information extraction, pattern improvements, topic modelling, and cluster assignments. The proposed model is compared with that of the three other existing alternatives in this regard, SLDA (Sentiment LDA), HME-LDA (Hierarchical clustering Max-Ent) and FP-LDA (Frequent Pattern). The experimental results prove that the proposed model outperformed existing models with a considerable margin.
Building Trust and Enhancing Knowledge Sharing: Exploring Human Resource Knowledge Management in Hubei’s Art Design Industry
Authors: Qiu Yu
Published: January 2025

This study explores the relationship between Human Resource Knowledge Management (HRKM), organisational trust, and employee knowledge sharing within the art design associations of Hubei, China. As the art design sector becomes increasingly globalised and competitive, effective management of knowledge resources is crucial for driving innovation and improving organisational performance. The aim of this research is to provide an in-depth understanding of how HRKM practices shape employee knowledge sharing behaviours, with a focus on the mediating role of organisational trust. Using Structural Equation Modelling (SEM) and data gathered from employees across various art design associations in Hubei, the study offers empirical evidence on the critical relationships among these variables. The findings reveal that successful HRKM practices are key to fostering organisational trust, which, in turn, enhances knowledge sharing. Trust promotes open communication, collaboration, and the exchange of ideas, leading to increased creativity and productivity. Key HRKM factors identified include recruitment strategies, employee training and development, and leadership practices. These elements together create a supportive environment for knowledge sharing, which ultimately boosts organisational effectiveness and competitive advantage. The study also highlights the importance of adapting HRKM policies to the specific cultural and contextual factors of the Chinese art design sector. By recognising the role of organisational trust as a mediator, organisations can cultivate a collaborative atmosphere that encourages knowledge sharing, improving both employee engagement and organisational performance. This research offers valuable insights for managers aiming to optimise HRKM practices, with a focus on trust-building strategies that enhance knowledge sharing and drive innovation in a competitive market.
Innovative Quantitative and Analytical Approaches to Art Therapy Utilizing Technology to Address Emotional Burnout
Authors: Alla Kolodyazhna
Published: January 2025

This article explores the intersection of quantitative analysis, art therapy, and technology as innovative approaches to addressing emotional burnout. As mental health challenges become increasingly prevalent in today's fast-paced society, traditional therapeutic methods may not always suffice. Integrating quantitative models and algorithms into art therapy can enhance therapeutic experience and deliver measurable outcomes for emotional well-being. This paper discusses analytical frameworks that can be applied to examine and optimize art therapy sessions, including data visualization techniques and statistical analysis of participant feedback. Key variables utilized in our analysis include emotional distress scores (EDS), creative expression indices (CEI), and engagement levels (EL), which are quantified through surveys and observational data. Additionally, we explore the role of technology, such as virtual reality and interactive software, in facilitating creative expression and engagement. Through case studies and empirical evidence, we demonstrate how these innovative approaches can lead to more effective interventions for emotional burnout, ultimately fostering resilience and promoting mental health in diverse populations.
Applications of Machine Learning in Computer Vision: A Review
Authors: R.Sugunthakunthalambigai
Published: January 2025

Machine Learning (ML) has become an important aspect of computer vision due to the increased efficiency, scalability and high accuracy in image recognition, object detection and classification. This work examines the performance of four chosen ML techniques CNNs, SVMs, RFs, and KNNs to a variety of visual tasks in healthcare, manufacturing, agriculture and environment surveillance. To assess the performance of these algorithms, accuracy, precision, recall, and the F1-score were conducted on the foundation of a strong data set of 50,000 annotated images. As it is shown the highest accuracy of the algorithm is 97.8%, which belongs to CNN while the lowest accuracy is 88.9% which is associated with KNN, while SVM and RF showed the accuracy of 92.4% and 90.6%% correspondingly. Another outstanding feature was higher accuracy of CNN; 98.2%, and quality, or recall; 96.9 %; thus CNN can be used in medical imaging, robotic quality control, etc. The comparative analysis with the related work showed noticeable enhancement in the efficiency and reliability; hence, the practicality of using ML in the real-life applications was also verified. However, there were challenges highlighted for future work including the computational requirement for such analyses, as well as ethical issues. This work also underscores the possibilities of ML for change in computer vision and opens new avenues in critical fields.
Quasi- Laplacian energy of some novel classes of graphs
Authors: Manash Protim Borah
Published: January 2025

We formulate the relationship of quasi-Laplacian energy of some novel classes of graphs with their corresponding original graphs. The novel graphs in our discussion are the -graph, -graph, -graph, total graph, and their join and corona operations graphs. The whole formulation is based on the relationship between quasi-Laplacian energy and the vertex degrees of the novel graph. It is also noted that quasi-Laplacian energy is closely related with the first Zagreb index, number of vertices and edges of the graph. The exact formulas of quasi-Laplacian energy of novel graphs are obtained in terms of the corresponding quasi-Laplacian energies, the first Zagreb indices, and the number of vertices and edges of the original graphs.
Advanced Deep Learning Approaches for Predicting Breast Cancer Genes Using Spectrographic Data
Authors: Shalini M
Published: January 2025

Breast cancer is the most frequently diagnosed cancer among women in urban regions of India, such as Mumbai, Delhi, Bengaluru, Bhopal, Kolkata, Chennai, and Ahmedabad, accounting for 25% to 32% of all female cancer cases. To address this growing concern, our study leverages advanced deep learning techniques to enhance the automated prediction of breast cancer-associated genes using spectrograms. Gene data, collected from reliable sources like the National Center for Biotechnology Information (NCBI), are transformed numerically using frequency-of-occurrence mapping and the VOSS representation method, which employs binary sequences and long-range fractional correlation analysis. These numerical representations are converted into spectrograms through Short-Time Fourier Transform (STFT), enabling their analysis using deep learning models, including 1D Convolutional Neural Networks (1DCNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Graph Neural Networks (GNN). Among these, GNN achieved the highest accuracy of 97.97%, followed by RNN with 93.88%, and CNN and 1D-CNN with 91.02% and 89.38%, respectively. These results highlight the potential of GNN as a precise and reliable tool for breast cancer gene prediction, offering significant promise for clinical applications.
Adaptive Machine Learning Approaches for Analyzing SME Growth and Financial Challenges in Andhra Pradesh
Authors: K Ravi Kiran Yasaswil
Published: January 2025

Role of SMEs in Andhra Pradesh: Small and Medium Enterprises (SMEs) have been key enablers for the development of Andhra Pradesh over the years, driving innovation, generating employment and leading to industrialization. This study explores the relationship between access to financing and SME growth through data science methods and computational approaches. By analyzing a dataset of 200 SMEs, we utilize advanced statistical and machine learning algorithms to uncover insights into financial health, borrowing behaviors, and credit access challenges. A few analytical methods (i.e., descriptive statistics, correlation analysis, and clustering) demonstrate heterogeneous characteristics of funding preference and performance metrics. The results reveal that SMEs display a preference for grants, as opposed to bank loans or self-funding, whereby exorbitant interest rates charged by banks and complicated cross-border regulations have deterred access to traditional funding avenues. Furthermore when doing predictive modeling the factors considered include external market factors such as trade volumes and currency fluctuations as well as internal factors relating to efficiency of operations on SME performance. Computational techniques discussed in the study could aid in creating targeted financial plans for SMEs, such as loan finding algorithms and decision support systems to enhance accessibility and sustainability. The advice in this regard would be to an institutional investor or asset owner to introduce digitalization platforms to support financing, to use AI-powered tools to improve levels of financial literacy, and to automatism regulatory compliance requirements. Policymakers and financial institutions, therefore, can build a strong ecosystem for SME growth, ensuring economic resilience and innovation in the region by adopting data-driven strategies.
Optimization of END -OF -LIFE strategies- Air conditioners
Authors: Jonnagiri Sowmya
Published: January 2025

Circular economy is a concept developed to seek sustainable development and resource usage optimization for sustaining the green environment.it is a deviation from the practice of manufacture, consume and dispose, reverse logistics is an activity that moves goods from customers back to sellers or manufacturers after their end-of life for repair and disposal.One common misconception about the circular economy is that it is an expensive and difficult endeavor. Its goal is to regenerate natural systems and ensure materials are reused rather than discarded, encompassing reverse logistics. Product recovery includes actions such as restoration, repair, refurbishment, manufacturing again, and recycling, all of which need an effective reverse logistics system.This network yields economic advantages by decreasing raw material acquisition, enhancing inventory management, and lowering the disposal of waste. A mixed-integer linear programming model for a multi-stage reverse logistics system is suggested to improve product recovery. optimization of refurbishment of air conditioners is considered in the paper.
Unveiling Beauty's Resilience: Golden Ratio's Influence on Facial Harmony and Post-Injury Markings
Authors: Palaniappan Sambandam
Published: January 2025

This research proposes a system that incorporates the golden ratio and Yolo algorithm for face analysis for plastic surgery. The system employs MATLAB to recognize faces and work out the geometric loci associated with the golden ratio. This data can help surgeons decide on the parts of the face that require augmentation. The system also uses a custom Yolo algorithm-based model to identify burns and scars on the facial area. This model recognizes features in pictures of burns and scars to tell the pattern of scarring present and its severity. The system comes with a unique technology for the preparation of a PDF report, aimed at surgeons, containing facial characteristics, golden proportions and a certain amount of scars. The experimental results reveal that the system successfully detected 95% of the facial features and calculated the golden ratio while 90% of the scars and burns were identified. This system provides an accurate and customized solution for plastic surgery.
Exploring Linguistic and Emotional Models for Audio Sentiment Analysis Using NLP
Authors: Sapna Sharma
Published: January 2025

Sentiment analysis is widely used to identify emotions and attitudes in text. With the growing popularity of audio-based social platforms and the significant rise in spoken data, sentiment analysis in the auditory domain has become increasingly important. This paper explores sentiment analysis in audio data using Natural Language Processing (NLP) techniques. We propose a novel method for extracting linguistic features and developing emotional models tailored to audio-based sentiment. In our experiments, we compare deep learning models with traditional NLP techniques, using a unique dataset to validate our findings. Sentiment analysis, also known as opinion mining, is a key subfield of NLP, focusing on extracting subjective information from textual data. The surge of user-generated content on online platforms like social media, blogs, and product reviews has amplified the importance of sentiment analysis for understanding public opinion and consumer behavior. This paper provides an overview of various approaches used in sentiment analysis, including machine learning, lexicon-based methods, and deep learning, highlighting their strengths and limitations. We discuss the trade-offs between accuracy, computational efficiency, and interpretability for each approach, while addressing challenges like sarcasm detection, context dependency, and domain-specific language. Additionally, we examine recent advancements in the field, such as the use of cross-sectional models and the integration of multiple data sources to provide a more comprehensive view of sentiment. Our results demonstrate the efficiency and high performance of the proposed models in capturing sentiment from audio data. The study also explores the ethical considerations, practical applications, and broader relevance of audio-based sentiment analysis across media and other domains. Finally, we conclude by discussing future directions, emphasizing the need for more robust models capable of handling diverse and complex data, along with ethical considerations for real-world applications.
Using W Transform for Solving Volterra Integro-Differential Equations
Authors: Amal M. Wadi
Published: January 2025

There are numerous uses for the Volterra integro-differential equation in the fields of mechanics, geometric probability, population dynamics, theory of rejuvenation, facts on particle size and the damping of string vibration, and transmission of heat issues. Finding the approximate or exact solutions to these equations is of interest to many mathematicians and scientists. Our aim of this paper is to explore and figure out the solution of the Volterra integro-differential equation with a convolution kernel. We now introduce the W transform for determining the solution of linear Volterra integro-differential equation of the second kind and their system. The ability of the W Transform to solve these equations is shown by real-world applications. Based on what we discovered, the W Transform is an effective method for locating precise answers to the linear Volterra integro-differential equation of the second kind and their system.
Automating Quality Assurance: The Role of Error Detection and Logging Algorithms in Multi-Environment Applications
Authors: Hema Sai Mukkamala
Published: January 2025

The study's primary focus is the quality of cross-platform application development. As the demand for apps grows, ensuring that they perform properly across all platforms and operating systems is more critical than ever. This study focuses on five essential quality criteria: scalability, performance, security, maintainability, and usability. It emphasizes developers' distinct concerns, such as device fragmentation, performance differences, and testing challenges. The article discusses recommended practices for improving application quality, such as CI/CD methodologies, dependable testing techniques, and sound design concepts. The essay forecasts future improvements impacting cross-platform programming and presents real-world examples to demonstrate effective implementation.
Some more Extensions on Product Cordial Graphs
Authors: Varsha Rathi
Published: January 2025

A mapping function , causes an labeling of edge  as specified  by   . This type of labeling is called product cordial labeling if it satisfies certain circumstances regarding the differences in the number of vertices and edges labeled with 0 and 1. In this investigation, we prove that the copies of concentric n-sunlet graphs, star of concentric n-sunlet graph and copies of shell graph satisfies these conditions, eventually they are product cordial graphs.
SMSDI: Secure Multiservice Spatial Data Infrastructure using Public Key Cryptography
Authors: Nikhil Khandare
Published: January 2025

With the aim of making spatial data readily available to people, enterprises, and governments for successful regional, state, and national decision-making, countries are focused on building spatial data infrastructure that can provide reliable spatiotemporal data. Nonetheless, numerous issues need to be dealt with before, during, and after the creation of spatial data infrastructure.Accuracy, dealing with big data, analysis of large spatial data, time to fetch data, privacy protection are different SDI issues. Huge work has been undertaken into the study and precision of spatial data, but relatively little attention is paid to the security of spatial data infrastructure. So this paper proposes an architecture for secure SDI based on elliptic curve cryptography to address this problem.Security is implemented at different levels of SDI, incorporating different features to improve SDI security. Such features include a modern authentication algorithm, secure calls to service, safe data transfer, and secure data storage in the database. The Secure-SDI proposed is safe against all well-known attacks, and proof of concept is also provided in security analysis
Smart SVC Placement for Loss Reduction in Power Systems: Tackling Generator Outages with IGEPO Optimization
Authors: Muhammad Nurhazeeq Nor Helmy
Published: January 2025

Increasing load demand and generator outages in power transmission networks can cause significant power losses and voltage instability. Addressing these challenges requires an optimal installation strategy for Static Var Compensators (SVC), which involves determining the best locations and sizes for the SVCs to ensure efficient, reliable, and cost-effective operation. Traditional optimization techniques often struggle with issues like local optima and inadequate exploration. This paper presents a novel approach, the Integrated Grasshopper Evolutionary Programming Optimization (IGEPO), which combines the Grasshopper Optimization Algorithm with Evolutionary Programming. The IGEPO method is applied to optimize the placement and sizing of SVCs, with the goal of minimizing losses, particularly during generator outages. Comparative studies on the IEEE 30-Bus RTS demonstrate that IGEPO outperforms both standalone Evolutionary Programming and the Grasshopper Optimization Algorithm in power system planning under normal conditions and during contingencies due to generator outages. Results are presented for the pre-SVC installation under both normal conditions and during generator outages to observe the impact of the outages and the subsequent SVC installation. The proposed algorithm has potential for broader applications and could be explored further in future studies.
Analysis of Non-Linear Characterization for Optical Properties of Thiourea Tartarate
Authors: S Raju
Published: January 2025

In recent years, Analysis of  nonlinear optical  characterization of Crystals play an important role in the opto-electronics and frequency conversion device applications. The preset work aim to synthesis and grow Thiourea tartrate single crystals by slow evaporation solution growth technique for higher Non Linear Optical efficiency. Thiourea Tartrate single crystals have been grown by the slow evaporation method at room temperature using aqueous solution. The prepared crystal structural characterization, Thermal stability, Impedance properties and NLO studies were carried out with the help of powder X-ray diffraction analysis, thermogravimetric and Kurtz-Perry methods, respectively. The crystal was found to be in the mono crystal structure. The thermal studies of Thiourea tartrate reveals that at crystal is stable up to 208 ºC and shows absence of water molecules below 100 ºC. The NLO study measured with the help of Kurtz and Perry powder SHG technique and the results confirms that Thiourea tartrate has 1.6 times greater NLO efficiency than the KDP. Impedance analysis of the Thiourea Tartrate Sample has two semi circles which might be due to the contributions from grain and grain boundary. As grown Thiourea tartrate crystals can be used up to 2080C only for any applications and SHG results confirms the suitability of the crystals for NLO applications.
Impatient Customer Queue with Single Working Vacation
Authors: A.Saila kumari
Published: January 2025

This paper analyzes an infinite buffer M/M/1 queue with single working vacation in which customers arrive according to a Poisson process. As soon as the system becomes empty, the server takes working vacation. The service rate during regular busy period, working vacation period and vacation times are assumed to be exponentially distributed. During working vacation customers may renege due to impatience and the impatient timer follows exponential distribution. The steady-state probabilities and performance measures are obtained by using probability generating function. Various numerical results are presented to show the effect of the some parameters on the system performance measures.
A Sustainable Framework for Implementing a Real Estate and TDR Exchange in Berlin: Leveraging Environmental Data and Regulatory Compliance
Authors: Shubhada Subhash Patil
Published: January 2025

Berlin’s rapid urbanization poses significant challenges, including housing shortages, urban sprawl, and the preservation of green spaces and cultural heritage. Current urban planning practices rely heavily on rigid zoning regulations and environmental protection laws, which, while effective in certain areas, lack the flexibility to address the city’s growing and diverse needs. This research introduces a Transferable Development Rights (TDR) Exchange as an innovative solution to sustainably balance urban growth with ecological and cultural preservation. By leveraging Berlin’s Environmental Atlas datasets—Inventory of Green and Open Spaces, Actual Use of Built-up Areas, and Vegetation Cover 2021—the proposed model creates a structured marketplace for TDR generation, transfer, and utilization.
The study evaluates the feasibility of implementing the TDR Exchange within Berlin’s existing regulatory framework, including the BauGB, BauNVO, and Berliner Bauordnung. Key findings highlight the potential for the TDR Exchange to incentivize brownfield redevelopment, promote affordable housing, and protect ecological assets. Recommendations include policy adjustments to integrate TDR mechanisms into current urban planning strategies and pilot projects in high-priority zones. This research provides actionable insights for policymakers and urban planners to adopt market-driven tools for sustainable development in Berlin.
Talk to Your Data: A Seq2seq Model for Transforming Natural Language Queries into SQL Statements
Authors: Ayesha Mafrah A M
Published: January 2025

Accessing and interacting with relational databases using Structured Query Language (SQL) can be challenging for users without technical expertise, posing a barrier to effective data management and analysis. This challenge highlights the need for solutions that bridge the gap between natural language and SQL, enabling more intuitive data queries. We propose a novel supervised deep learning model based on the BART-large-CNN architecture for transforming natural language queries into SQL statements to address this issue. This seq2seq model facilitates smoother and more efficient database interactions by allowing users to query data using everyday language. Our methodology includes training the model on a comprehensive dataset of paired natural language queries and corresponding SQL statements. We implement a custom training and evaluation pipeline for performance evaluation using the Hugging Face Transformers library and ROUGE metrics. The experimental results demonstrate that our model accurately translates various natural language queries into SQL statements, offering a robust solution for enhancing database accessibility and user experience.
Internet of Things for Social Education: Ensuring Accountability and Trust in Online Learning Platforms
Authors: R. Jayanthi
Published: January 2025

Social education transforms thanks to the Internet of Things (IoT) which allows smooth network connections and utilizes data analysis to create individual learning environments. IoT integration into education platforms elevates important responsibility and trust considerations that require immediate resolution. The present work elucidates the ways IoT-enabled educational frameworks strengthen openness combined with security functions and ethical standards yet addresses privacy issues alongside cybersecurity threats and digital disparity barriers. This paper studies important technological solutions that incorporate blockchain features for authentication proof verification processes and AI systems to evaluate academic conduct and real-time algorithmic methods for custom learning systems modeling. The research analyzes both regulatory standards and ethical norms essential for data protection in responsible usage frameworks. IoT-empowered learning systems have demonstrated effective methods through real-world examples that develop trust systems for all educational stakeholders including educators and learners and government representatives. The future development of online education will benefit from IoT because of its implementation of strong security protocols alongside both ethical AI protocols and clear data governance frameworks.
Pendaftaran Kompetisi Antar Sekolah pada SMA Don Bosco 1 Berbasis Web
Authors: Stevan Iskandar  , Ziveria Mira
Published: January 2025

SMA Don Bosco 1 Kelapa Gading adalah sekolah yang masih menggunakan sistem pendaftaran lomba konvensional dalam mengolah data peserta, sehingga dibutuhkan waktu lama untuk memprosesnya. Penelitian ini bertujuan untuk membangun sistem pendaftaran berbasis web yang mencakup pengolahan data lomba dan pendaftaran lomba dengan metode pengembangan sistem prototype yang mencakup analisis sistem menggunakan cross-functional diagram, desain sistem menggunakan Unified Modeling Language (UML), pengembangan sistem dan pemrograman menggunakan bahasa pemrograman Hypertext Preprocessor (PHP) dan database My Structure Query Language (MySQL) dan pengujian sistem menggunakan kotak hitam dengan pengujian alpha. Hasil dari penelitian ini adalah sistem yang dapat digunakan untuk mengelola data panitia, data lomba dan data pendaftaran lomba.
Hal- és halászati termékek fogyasztásának meghatározó tényezői
Authors: Mihály Tamás , Soós Mihály , Mihály-Karnai Laura
Published: January 2025

Napjainkban megfigyelhető, hogy a halfogyasztási szokások és a fogyasztást meghatározó tényezők folyamatosan változnak a globalizációnak köszönhetően. Az elmúlt években a felgyorsult világnak és a járványok világszintű terjedésének hatására a fogyasztói hangsúly az ár helyett a minőségre és a kényelemre helyeződik, hiszen egyre inkább előtérbe kerül az egészséges ételek fogyasztása, melyben a halak kiemelt szerepet kapnak. A cikk elsődleges célja, hogy feltérképezze a hal- és halászati termékek fogyasztásának sajátosságait, valamint azonosítsa azon tényezőket, melyek befolyásolják a fogyasztók hal- és halászati termékek fogyasztásának preferenciáit és döntéseit. A PRISMA keretrendszer alkalmazásával 2000 és 2023 között megjelent cikkeket választottunk ki a Google Scholar, a Web of Science és a Scopus segítségével, azonban a témához kapcsolódó korábbi kutatásokat is tanulmányoztuk. Az eredmények azt mutatják, hogy a fogyasztói döntéseket az egészségre, valamint az érzékszervre ható tényezők, a termelés jellemzői/sajátosságai és a könnyű elkészíthetőség, vagyis a kényelem befolyásolja leginkább.