bluuebunny/crossref_metadata_2025 huggingface dataset:
New congruences for $2$-color partitions and $t$-core partitions
Authors: Fathima S. N. , Pore Utpal , Sriraj M. A. , Reddy Polaepalli Siva Kota
Published: February 2025

Let $c_N(n)$ denote the number of $2$-color partition of $n$ subject to the restriction that one of the colors appears only in parts that are divisible by $N$. If $t$ is a positive integer, then a partition of a nonnegative integer $n$ is a $t$-core if none of the hook numbers of the associated Ferrers-Young diagram is a multiple of $t$. Let $a_t(n)$ denote the number of $t$-core partitions of $n$. In this paper, we obtain new congruences modulo $3$ for the $2$-color partition function $c_{11}(n)$, $t$-core partition functions $a_5(n)$ and $a_{11}(n)$.
Features of the implantation capacity of the endometrium after conservative myomectomy
Authors: Stepanets Yaroslav
Published: February 2025

Determination of the level of estrogen and progesterone receptor expression in the endometrial tissue during the implantation window in women with a history of conservative myomectomy is an urgent medical problem of our time.The objective: to determine the level of expression of estrogen and progesterone receptors in the endometrial tissue and to find out their relationship with the presence of PTSD in women of reproductive age with recurrent uterine leiomyoma. Materials and methods.Group I (main) – 108 patients with myectomy of submucosal uterine leiomyoma according to the developed method and PTSD. Group II (comparison) – 102 patients with myectomy of submucous leiomyoma of the uterus by the traditional method with PTSD. Group III (control) -– 30 patients with PTSD without a diagnosis of leiomyoma in the history, planning a pregnancy (with a successful pregnancy in the history, which ended in childbirth).To detect estrogen and progesterone receptors in the endometrium, an immunohistochemical method was used using the En Vision imaging system with diaminobenzidine.Results. It was found that the level of ER expression increases in leiomyoma recurrence after CME with concomitant diagnosis of PTSD. Along with an increase in the level of ER expression in endometrial cells, the expression of PR decreases. It was found that the amount of PR in the main group decreases by 38.3% in endometrial hyperplasia without atypia, by 46.3% in atypical endometrial hyperplasia and by 20.4% in endometrial polyps compared to the control group of women. The analysis of the results shows a relationship between the form of hyperplasia and the receptor phenotype of the endometrium in leiomyoma recurrence after CME and PTSD. It was found that the greatest changes are observed in the main group. The correlation between PTSD and estrogen and progesterone receptor expression in women with reproductive plans and recurrent matuca leiomyoma was shown. Conclusions. Increased expression of estrogen receptors and decreased expression of progesterone receptors in women with reproductive plans and recurrent uterine leiomyoma and PTSD leads to impaired endometrial implantation capacity and, as a result, to failure to become pregnant or to terminate it at a short term.Keywords: estrogen receptors, progesterone receptors, uterine leiomyoma, PTSD.
КОМУНІКАТИВНІ РЕФЛЕКСІЇ ПІД ЧАС ВІЙНИ: ОБРАЗ ВОРОГА В СУЧАСНИХ МЕДІА
Authors: ДОБРОДУМ О. В. , МАРТИНЮК Е. І. , НИКИТЧЕНКО О. Е.
Published: February 2025

У статті проаналізовано комунікативні рефлексії під час російсько-української війни, стверджується, що у сучасних медіа часто використовуються образи ворога, які можуть впливати на сприйняття подій і формування громадської думки та водночас є потужним інструментом маніпуляції громадською думкою та мобілізації підтримки. Зазначено, що під час війни комунікація часто супроводжується високим емоційним навантаженням – страх і ненависть, відчай і надія переплітаються в спілкуванні людей, створюючи складні емоційні патерни. Акцентується на тому, що перед викликом сучасної війни журналісти відіграють важливу роль у наданні інформації про майбутні конфлікти, і їхнє завдання – донести до аудиторії об’єктивні та точні дані про політичну обстановку, ескалацію між країнами та можливі загрози. Журналісти мають бути готові до ризику, але при цьому залишатися вірними своїм професійним принципам, їхня робота допомагає суспільству розуміти складні події та вплив війни на світ. Важливо підтримувати незалежність журналістики та поважати тих, хто ризикує своїм життям, щоб донести до нас правду про війну, адже журналістика – це інструмент, який може допомогти людям дізнатися правду про війну, вона може допомогти захистити права людини і зробити світ більш справедливим. Журналістика відіграє важливу роль у сучасному світі, будучи джерелом інформації, інструментом контролю влади та платформою для діалогу. В умовах сучасних війн роль журналістики зростає багаторазово, оскільки журналісти повинні бути готові до нових викликів і працювати, щоб суспільство отримувало достовірну та об’єктивну інформацію. Важливою роллю журналістики є запобігання конфліктам: журналістика перед війною може сприяти запобіганню конфліктів, якщо вона активно намагається знайти мирні рішення. Вивчення комунікативних стратегій під час війни допомагає зрозуміти, як формуються і змінюються суспільні настрої, що особливо важливо для політиків, військових і соціологів.
Nevus of ota with pigmentation of buccal mucosa and lips
Authors:   Department of Dermatology, University Hospital Regensburg, Franz-Josef-Strauss-Allee 11, 93053 Regensburg, Germany., Schreieder Laura
Published: February 2025

Nevus of ota or oculodermal melanocytosis, is a benign condition characterized by blue or gray-brown hyperpigmentation, primarily distributed along the divisions of the trigeminal nerve. The pigmentation typically affects both the skin and ocular structures.
A Study on Predictive Modelling of Student Academic Performance using Machine Learning Method
Authors: Shoukath TK
Published: February 2025

Predicting students’ academic performance is a crucial initiative in the field of education, as it allows educators and administrators to spot students who may require extra assistance, customize educational resources to meet individual requirements, and improve overall educational results. Conventional approaches to forecasting academic achievement, such as statistical analysis and expert evaluation, have certain drawbacks in terms of precision and scalability. The emergence of machine learning (ML) methods provides a possible alternative by utilizing extensive datasets and advanced algorithms to reveal patterns and generate more precise predictions. This investigation’s primary goal is to explore the predictive modelling of student academic performance by improving the accuracy of predictions through the utilization of machine learning techniques. The study was conducted utilizing the Python programming environment. The prediction of student academic performance was carried out utilising the Bidirectional long short-term memory (Bi-LSTM) based Weighted Cost Effective Random Forest algorithm. The study utilized the Deep Encoder CNN-Bi-LSTM for optimal feature extraction to foresee student academic performance. The extracted features were then classified using the Weighted Cost Effective Random Forest (WECRF) classifier, and the classification was evaluated in terms of accuracy, precision, specificity, sensitivity, and recall. The issues addressed include the class imbalance, computational complexity, cost, and huge dimensional issue, among others. The random forest method achieved Precision Score - 0.72, Recall Score - 0.68, F1 Score - 0.69, and Accuracy - 0.77 in this study. Moreover, the suggested technique facilitates the automated forecasting and enhancement of students' future academic performance. Keywords: Predictive Modelling; Students; Academic Performance; Machine Learning; Bi-LSTM; CNN-Bi-LSTM; Deep Learning.
Applying Citation Analysis and Machine Learning with Real-World Documents to Improve Judicial Decision-Making
Authors: Aastha Budhiraja
Published: February 2025

The integration of citation analysis and machine learning to enhance judicial decision-making processes. As legal professionals face increasing challenges such as information overload, cognitive biases, and the volume of complex cases, leveraging advanced technologies is vital for improving accuracy and efficiency in legal outcomes. It employs a mixed-methods approach, utilizing a comprehensive dataset of judicial documents preprocessed through natural language processing (NLP) techniques. Citation analysis identifies influential cases and citation patterns, while machine learning algorithms, including support vector machines and neural networks, model the relationships between case characteristics and outcomes. The findings reveal that the machine learning models achieved an overall accuracy of 96%, with robust performance metrics indicating high precision and recall. The results underscore the potential of combining citation analysis with machine learning to provide deeper insights into judicial patterns and enhance the predictability and consistency of legal judgments. Ethical considerations surrounding the use of these technologies are also discussed, emphasizing the need for balanced implementation that supports human judgment. Ultimately, highlights a transformative approach to legal analytics, aiming to improve judicial decision-making and ensure fairness and transparency in the legal system.
Development of a Multilayer Fuzzy Expert System for the Diagnosis of Chronic Kidney Disease
Authors: Arvind Sharma
Published: February 2025

In the initial stages of chronic kidney disease, it has some hidden characteristics that lead to a delay in its detection and diagnosis. The growth of kidney impairment or damage can be stopped or slowed down by making an early diagnosis. Hence, in this paper, to overcome this issue, a multilayer fuzzy expert system has been developed by using fuzzy logic, which assists in the diagnosis of chronic kidney disease at its early stages. The developed system has two layers in which the first layer is used to detect if a patient has clinical symptoms of having CKD. Similarly, the second layer of the system is used to evaluate the current stage of chronic kidney disease from which an individual is suffering. The input variables for layer 1 are age, diabetic Mellitus, smoking, hypertension and family history. Moreover, the laboratory tests are considered as the input variables for later 2 to confirm the stage of the disease. Hence, glomerular filtration rate, serum creatinine, albumin, blood urea nitrogen and pus cell in urine are the input variables for layer 2. This research work also evaluated the performance of the developed system on the basis of classification accuracy. As a result, this system provided 99.33% classification accuracy for the diagnosis of chronic kidney disease.
Thematic Literature Review of Factors Influencing Electric Vehicle Adoption in Hunan Province’s Public Sector, China
Authors: Zhao Binliang
Published: February 2025

This study examines factors influencing the adoption of electric vehicles (EVs) in Hunan's urban public transportation sector, focusing on policy incentives, economic challenges, and infrastructure governance. Barriers include insufficient charging infrastructure, limited vehicle performance, and low stakeholder awareness of EV technology. Economic challenges such as high upfront costs, elevated charging tariffs, and uncertain long-term financial benefits further hinder adoption. Governance issues, including fragmented institutional roles, poor coordination between national and local authorities, and weak regulatory frameworks, impede infrastructure development and maintenance. The study develops a study framework grounded in the Socio-Technical System (STS) and the extended Technology Adoption Model (TAM2). It emphasizes the need for integrated strategies to address these challenges and proposes directions for future study to fill existing gaps.
Cyberbullying Prevention in Online Games: Implementation of Malaysia Cyber Security Strategy (MCSS) through Awareness and Education
Authors: Muhammad Fakhru Rizuan Che Omar
Published: February 2025

Cyberbullying is a growing concern, particularly among adolescents and children, with the increased variety of online games available. It has been linked to various mental health problems, including social anxiety, depression, and suicidal thoughts. The purpose of this study is to explore the implementation of awareness and educational strategies within Malaysia's Cyber Security Strategy (MCSS) framework to prevent cyberbullying in online games. A semi-structured interview was conducted with four experts from various fields. The discussion revealed that the government should develop a prevention curriculum that addresses cyberbullying in online games through simulation, using viral short videos as learning materials. The government should strengthen and promote a self-regulation campaign to ensure adolescents' safety while playing online games. The commitment from peer support groups and partnerships with gaming influencers helps raise awareness about cyberbullying in online games. These findings contribute to understanding how educational and awareness strategies can be effectively implemented to combat cyberbullying in online games among Malaysian adolescents.
Sentiment Classification on Multivariate Feature Selection on Social Media dataset using Hybrid Machine Learning Techniques
Authors: Sudeep K. Hase
Published: February 2025

Sentiment classification is a crucial component of natural language processing that focuses on analyzing and classifying the emotional tone conveyed in text data. With the rapid proliferation of social media platforms, the ability to accurately discern public sentiment has become vital for applications spanning marketing, political forecasting, and public opinion analysis. This abstract delves into the implementation of hybrid machine learning techniques for sentiment classification, leveraging multivariate feature selection methods on diverse social media datasets. Traditional machine learning models, though effective, often struggle with the complexity and high dimensionality of social media data, which may include text, emojis, images, and metadata. A hybrid machine learning approach, combining the strengths of various models, addresses these challenges by optimizing both feature selection and classification accuracy. The proposed framework begins with robust data preprocessing, including text normalization and tokenization. Advanced feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF), word embeddings (Word2Vec, GloVe), and sentiment lexicons are utilized to capture the intricate semantic characteristics of the text. For multivariate feature selection, techniques such as Recursive Feature Elimination (RFE), Chi-square tests, and correlation-based feature selection (CFS) are employed to identify and retain the most informative features, thereby improving model efficiency. The classification stage integrates hybrid models, combining the predictive power of algorithms such as Support Vector Machines (SVM), Random Forests, and ensemble learning methods (e.g., gradient boosting). These models are tuned using cross-validation and grid search to enhance generalization performance. The hybrid approach demonstrates superior performance in terms of accuracy, precision, recall, and F1-score compared to standalone machine learning models. The combination of comprehensive feature selection and robust classification algorithms effectively mitigates overfitting and enhances scalability. Empirical results from experiments on real-world social media datasets indicate that the proposed method is adept at capturing nuanced sentiment variations and ensuring high classification accuracy, proving its effectiveness for dynamic and large-scale data analysis.
A Novel Multi-Stage Feature Selection and Classification Model for Accurate Liver Disease Detection
Authors: Gurmeet Kaur Saini
Published: February 2025

Liver disease is a pervasive and pressing global health challenge whose conditions vary from viral infections, fatty liver disease, to more severe conditions such as cirrhosis and hepatocellular carcinoma. In order to detect liver disease at earliest stages, there is an urgent need of developing an accurate and efficient diagnostic model. Considering this, a novel and highly accurate NeuroOpt model is proposed in this manuscript for detecting and classifying stages of liver disease in patients. The proposed model utilizes Neural Network architecture for classifying disease whose performance is enhanced by self-tuning process through GWOA2 (Grasshopper and Whale Optimization Algorithm) method. Moreover, an effective three-stage Feature Selection method is also used in the proposed work for meticulously choosing vital attributes from given dataset. The key innovation of this work is the development of a dynamic self-tuned NeuroOpt liver disease detection model along with three-stage Feature Selection that not only predicts the presence and absence of liver disease in humans but also classifies them into multiple stages using two datasets instead of only one. Through the utilization of two datasets, the proposed model became dynamic due to its high accuracy rate for both binary and multi-stage classifications respectively. The efficacy of proposed approach is examined and validated on Indian Liver Patient Dataset (ILPD) and Cirrhosis Prediction Dataset (CPD) using MATLAB software for binary and multi-stage disease classifications respectively. Experimental outcomes determine that proposed model is able to detect disease in patients with an accuracy of 97% (binary classification) on ILPD dataset, whereas, it attained an accuracy of 98.9% for multi-stage disease classification of CPD dataset to prove its effectiveness.
Credit Risk Identification and Prevention Strategies of Small and Medium-sized Banks Based on Big Data Technology
Authors: Yabin Zhang
Published: February 2025

The topic of this analysis is the application of big data technology to improve credit risk identification and prevention measures in SME banks. T¬he research, it highlights credit risk’s crucial role in banking and underscores the problems smaller banks encounter when dealing with this risk due to limited resources and less evolved tools. An extensive review is conducted, showing the progression of credit risk management and big data integration into financial risk management. It discusses the revolutionary aspect of big data in credit risk analysis as well as its practical applications in small and medium-sized banks. The secondary data from Kaggle datasets are used within a quantitative research approach. Regression analysis and hypothesis testing are some of the statistical tools used in EViews to uncover patterns and correlations related to credit risk. The study determines essential factors that affect credit risk, such as borrower credit score, loan amount, interest rate, and employment. It assesses the effectiveness of big data analytics in forecasting and mitigating this risk, focusing on accuracy and model resilience. This implies that the findings show that the borrowers have a moderate level of credit risk, and the traditional financial metrics have minimal influence on the Big Data risk score predictions, which require advanced analytical approaches. Big data technology can take more than traditional credit scoring, giving small and medium-sized banks a more advanced credit risk perspective. Limitations of the research include using a simulated dataset and the range of the analysed variables. Real-world data and variables should be used in future research studies.
An Unequal Radial Division-based Clustering to minimize Energy Hole for Larger Wireless Sensor Networks
Authors: Bhaskar Prince
Published: February 2025

Energy conservation in Wireless Sensor Networks (WSN) is a key research area because sensors are energy-constrained devices. Sensor energy saving in the large and harsh geographical environment is a major challenge in WSN. This paper presents novel strategies for enhancing energy efficiency in routing and data collection, employing clustering techniques and mobile sinks to address this challenge. The sensing area is considered a circular shape and divided into blocks. The blocks are formed by dividing the circular area into concentric circles and dividing sector lines to form unequal blocks. Based on the block node centroid, the block head is selected. The block and block head are similar to the cluster and cluster head respectively. Two mobile base stations (MBS) are used to collect block head data. Two MBS move with a predefined path with constant speed and halt at sojourn points to collect data from the blockheads. After collecting data, it transfers data to the gateway nodes or base station. In simulation results, the mobile node-based scenario outperforms the centralized base station and static block heads communication. The proposed work is also compared with existing works and this scheme shows a 21% improvement in network life and end-to-end delay.
Decentralized E-Voting and Governance System Using Blockchain
Authors: Thanga Revathi S
Published: February 2025

A democratic form of governance is the fairest and most effective system of authority in the human society. A key feature of any democratic authority is free and fair elections. However ensuring free and fair elections is an arduous task due to corrupt political practices, irresponsible management and human error. Using modern technologies like blockchain, can be an effective approach to reduce any intentional or unintentional attempt to sabotage the voting process. In this paper we lay out an overview of the methodology we propose for an efficient execution of the blockchain based voting system.
Efficacy Analysis of Boosting Techniques for Road-Surface Detection Using Very High Resolution Trispectral Satellite Images
Authors: M. K. Linga Murthy
Published: February 2025

Boosting techniques are machine-learning algorithms that combine the strengths of several weak learners to increase the accuracy of predictive models. They are invaluable for tasks involving objects, fraud, and analysis. Optimization algorithms iteratively construct models by concentrating on the errors made by earlier models, thereby increasing their overall accuracy and precision. Changing the weights of misclassified instances helps manage imbalanced data such as anomalous or fraudulent instances. Boosting techniques are resistant to noise, focusing on hard-to-classify cases and mitigating the impact of noise through weighting schemes to produce more dependable detection models. Boosting techniques can be used for many detection problems where the desired features are few compared to non-desired ones. They offer flexibility in model building, as they are not restricted to a particular type of base learner and can be used with decision trees, neural networks, and other algorithms. Updating the model and adding new data without retraining it through incremental learning is also possible. High-dimensional data can be handled effectively by boosting techniques, particularly for object detection in images and videos. In this study, the WHU dataset was used to detect road surfaces in an image. A smaller number of desired pixels than the others mimic an imbalanced binary classification problem. In this study, we tested adaptive boosting, gradient boosting, histogram gradient boosting, and light gradient boosting machines to analyze the efficacy of boosting techniques.
Ensemble Convolutional Layers and Modified Wild Horse Optimized Learning Framework for Kidney Tumor Classification from CT Images
Authors: R.Mary Victoria
Published: February 2025

Our society is affected by the most common disease Kidney Tumor (KT) in humans. The early diagnosis of KT may reduce the risk of death rates. Preventive measures can be taken to reduce the severe effects and overcome the tumor progression. Traditional methods consume time and tedious task. Deep Learning (DL) methods are emerging now to save time for diagnosis, to improve the accuracy of detection and reduce the physician’s manpower. In this research work, detection system is developed to detect KT in Computed Tomography (CT) images. Convolutional Neural Networks (CNNs) integrated with Modified Wild Horse Optimization (MWHO) is developed to test and train the network. The images from the Kaggle dataset are taken for the experiment. The dataset is divided into two as 80% is for training and 20% is for testing purpose. The accuracy values provided by CNN, CNN combined with particle swarm optimization (PSO) and CNN combined with genetic algorithm (GA) models are 86.6%, 86.8%, and 96%, respectively. The accuracy value provided by the proposed classification method is 100%. The proposed model achieved promising results when the number of classes able to be predicted (K) is equal to 5.
SVM-RBN Model with Attentive Feature Culling Method for Early Detection of Fruit Plant Diseases
Authors: Piyush Sharma
Published: February 2025

Accurate disease identification and early disease management strategies are required in India to achieve high production standards and good quality in fruits and vegetables. Image-based evaluations approaches have evolved nowadays as a result of technical developments. However, producing the wrong decision may have a negative impact on productivity. Thus, this study offered hybrid SVM-RBN model with attentive feature culling method for automatically recognizing diseases in apple fruits with high accuracy. As a result, the model generated more effective outcomes with 96% accuracy, 99% precision, 94% recall, and 93% F1 Score. Thus, by employing this technology, one may detect fruit plant illnesses at an early stage, thereby increasing fruit yield.
Enhancing Community Cooperation through Network Governance and Smart Specialisation to Mitigate Social Conflicts
Authors: Oleksandra Korchynska
Published: February 2025

Introduction: The relevance of the research topic is primarily in introducing the concepts of network management and smart specialisation to strengthen social ties, avoid social conflicts and increase the competitiveness of communities through interactive information exchange and active participation of stakeholders.
Objectives: The main aim of writing a scientific article is to substantiate the need to apply network management practices and smart specialisation within the framework of community cooperation to prevent social conflicts.
Methods: During the study, several general scientific methods of cognition were used, including induction, synergy, comparative analysis, deductive and analytical methods, and systematisation and generalisation.
Results: The study identified areas for effectively implementing network management principles in Ukraine, including developing a unified methodology for evaluating results, a long-term register of initiatives and partnership mechanisms, strengthening territorial communities, adapting the project approach, and increasing its maturity. In addition, priority areas for improving the current strategy of smart specialisation of Ukraine's regions and ways to optimise community cooperation have been identified, including introducing a data collection system, coordinating participants, integrating technologies, and developing communication strategies.
Conclusions: Thus, the developed measures to optimise community cooperation, based on network management and smart specialisation of regions, allow for meeting society's current needs and strengthening the community population's trust in state institutions, ensuring their effectiveness and sustainable development of the region of Ukraine.
Exploring the Role of Digital Communication Tools in Shaping Organisational Culture and Enhancing Internal Communication
Authors: Alla Poltoratska
Published: February 2025

Introduction: The issue of optimising the organisational culture of enterprises and improving the communication space within companies is becoming increasingly relevant. Currently, the effectiveness of personnel policy and implementing an employee motivation system play a significant role in management processes.
Objectives: The study aims to provide an extended analysis of the functionality of modern digital tools in the context of the development of communication and the organisational culture of companies.
Methods: In the process, a set of complementary research methods was used: analysis method, synthesis method, comparison method, induction and deduction method, historical and logical method and method of scientific abstraction, prognostic method, generalisation method.
Results: The priority directions of digital transformation of organisational and communication culture are identified. The innovative conceptual model of organisational culture and communication space has been based on strategic management and the defined goals of enterprise development. The importance of introducing the concept of continuous self-development of personnel and continuous improvement of communication skills, with the use of innovative digital tools, is proved, which helps to increase the level of trust, respect and mutual understanding in the team, stimulates employees to perform their duties responsibly.
Conclusions: Thus, the research establishes the apparent need to transform the organisational culture of modern companies in the direction of critical thinking, teamwork, adaptability, creativity and mobility, which will allow the most efficient integration of digital processes into the management environment with minimal risks. It has been proven that a high level of corporate culture ensures the prestige and competitiveness of an enterprise.
Management’s Influence on Digital Transformation and Enhancing Workforce Professional Growth
Authors: Olena Mykhailovska
Published: February 2025

Introduction: In modern society, digital literacy and competencies are gaining dominant importance, significantly impacting economic growth, professional activity, and the competitiveness of companies. The article discusses the role of management in the context of the digital transformation of the economy and highlights the obstacles hindering the implementation of HR Tech technologies.
Objectives: The study aims to identify marketing and management development specifics in the digital economy.
Methods: A combination of theoretical and empirical methods was used to solve the tasks set, including analysis, synthesis, theoretical modelling, and generalisation of materials presented in scientific literature and specialised databases on the researched problem.
Results: The advantages of automating learning processes are demonstrated, and the role of the personnel development manager in this context is examined. The role of the Training and Development (T&D) manager, responsible for continuous employee training and developing their professional skills and knowledge, is described. This, in turn, contributes to the organisation’s achievement of set goals and more effective utilisation of employee potential. The manager of the digital era plans training manages the budget, selects suitable courses and programmes, evaluates employee professional development, develops tools for automating training, builds a talent pool, and regulates processes in human capital resource management.
Conclusions: Thus, it is important to note that modern technologies play a vital role in effectively managing human resources, and their use is becoming an integral part of a successful business. In modern society, digital literacy and competencies are gaining dominant importance, significantly impacting economic growth, professional activity, and the competitiveness of companies.
Methodology for Conducting an Independent Information Security Audit of the Institution
Authors: Mykola Masesov
Published: February 2025

Introduction: Any computer without special protection exchanging data with other computers, especially when using the Internet, is bound to be infected with malware and, as a result, its performance will be reduced or impossible and/or taken under control.
Objectives: The purpose of this article is to define information security is to protect data information and sup-porting infrastructure from accidental or intentional interference which can cause the loss of information or its unauthorized modification and in case of force majeure - to minimize the damage caused by such actions.
Methods: General scientific methods were used in the course of the study, including the structural-logical method, analysis and synthesis.
Results: Noted the need for regular independent information security audit in the institution to assess the real state of cybersecurity in the institution, the ability to withstand constantly changing and adapting the external and internal threats to information security, as well as for timely recommendations for bringing and improving the protection of systems in line with certain requirements.
Conclusions: Thus, it is worth noting this article reveals the main stages of the methodology in the institution on the information security effectiveness, and some of the indicators, offered coefficients require additional study and research, which is a promising direction to further research.
Regulatory Challenges and Cybersecurity Approaches in Cloud-Based Accounting Systems
Authors: Iryna Shchyrba
Published: February 2025

Introduction: The article examines the development of cloud-based accounting systems and their regulatory support in 2020–2024. The influence of the latest cloud technologies, Microsoft Azure, Amazon Web Services, and Google Cloud, on accounting automation and financial management is analysed. Current regulatory requirements of the European Union, the United States and the countries of the East Asian region are considered with a focus on data security and risk management.
Objectives: The article aims to identify the impact of regulatory requirements and cybersecurity strategies on developing cloud accounting systems in the corporate sector.
Methods: The research methodology used two groups of methods: theoretical and empirical.
Results: The article analyses how developed countries are tightening their cybersecurity and financial regulation requirements, given the increase in transaction volumes and the number of cyber threats. The main development directions of the cloud accounting systems market for the next five years are identified. The article pays special attention to the role of government agencies in developing regulatory standards for cloud accounting systems. The article highlights the impact of international legislation on the structure of data protection systems, such as AWS and Google Cloud. The strategies of the Financial Conduct Authority in the UK and the Monetary Authority of Singapore, which implement technological requirements for cloud providers to ensure market security and stability, are considered. The article outlines the importance of compliance with international standards for cloud solutions.
Conclusions: Thus, the article highlights the challenges of growing cyber threats amid the growing use of cloud-based platforms for financial data processing. The main cybersecurity strategies based on data encryption and multi-factor authentication are outlined.
Predicting Future Rainfall with Various Machine Learning Models
Authors: Sunil Gupta
Published: February 2025

This research presents a machine learning technique that improves the prediction of the annual rainfall total. Predicting the amount and timing of precipitation in a given region is known as rainfall prediction. The global community is quite concerned about the accuracy of rain forecasts. People know this is the cause of floods and other natural disasters every year. Any number of industries might feel the effects of inclement weather, including farming, building, power generating, and tourism. Precipitation forecasting is one of the most challenging and uncertain undertakings due to the far-reaching effects it has on human society. The only way to reduce needless pain and financial losses is with timely and accurate predictions. Using historical meteorological data for a single day in major Australian cities, this paper describes a series of experiments that build models that can anticipate the possibility of rain tomorrow using cutting-edge machine learning techniques. This comparative study will look at inputs, methodologies, and pre-processing strategies in great detail. Using a variety of measures that measure the algorithms' capacity to understand weather data and predict the likelihood of precipitation, the results reveal how well these machine learning algorithms performed. Machine learning has proven to be extremely useful at predicting when it will rain, which is currently the most fundamental need, At the moment, it's quite tough to say with any certainty when it will rain. In our endeavors to forecast precipitation, we have employed a plethora of methods, such as Decision Tree Algorithm, Linear Regression, Support Vector Regression, Random Forest Regressor, and Random Forest Classifier. In agricultural contexts, the effective rainfall is a key factor in deciding the rate of crop growth. Forecasting rainfall using machine learning can improve water resource planning, agricultural production, and water usage prediction.
The Impact of AI-Driven Social Media Advertising on Consumer Purchasing Decision
Authors: Himani Devi
Published: February 2025

The rapid growth in Social media has changed a new form of advertising for businesses called social media advertising. With dynamic changes the digital market has experienced an expansion, reshaping business activities with advance technology. E-commerce has introduced a new purchasing and selling habit of consumers. Artificial intelligence integration changed online shopping by providing personalized customer experiences that have a profound impact. The AI really assists in stimulating profits by transforming inquiries into purchases using extensive information about potential buyers from e-business sites. A sample of responses was also collected from people in the marketing industry and from online platform customers.Lately, the conventional marketing strategy has been reformed because of Artificial Intelligence (AI) technology that gave marketers personalized instruments for constructing advertisements depending on shopping history and internet inquiries. Also AI enables programmatic advertising in real time, manipulating current browsing behaviour to optimize ad targeting on social media platforms.
Bridging the Virtual Divide: Psychological Safety and Organizational Support in China's Hybrid Internet Workplaces
Authors: Aobo Yang
Published: February 2025

This study explores the connections between psychological safety, perceived organizational support, organizational citizenship behavior, and employee performance within the hybrid work environment of China's Internet industry. As organizations rapidly transition to hybrid work models, understanding these relationships has become essential for maintaining organizational effectiveness. A quantitative approach was used, with structural equation modeling applied to data from 458 employees in Chinese Internet companies. The findings reveal strong positive correlations between psychological safety, perceived organizational support, and both organizational citizenship behavior and employee performance. Notably, organizational citizenship behavior partially mediates the relationships between psychological safety, perceived organizational support, and employee performance. The study extends Conservation of Resources theory to hybrid work contexts, offering new insights into the significance of psychological safety in such settings. The results suggest that promoting psychological safety and organizational support is essential in hybrid work environments, with psychological safety having a particularly strong impact on organizational citizenship behavior (β = 0.57, p<0.001). These findings provide useful guidance for managers aiming to enhance employee experience and performance in the evolving work landscape, highlighting the importance of fostering a supportive organizational atmosphere in hybrid models.
Reducing Redundancy in Software Testing: A K-Means Clustering Approach to Test Case Minimization
Authors: Sanjay Sharma
Published: February 2025

This study explores a clustering-based approach using k-means clustering to optimize test case minimization, a crucial aspect of enhancing software testing efficiency. Traditional methods often fail to cope with the complexity and scale of modern systems, leading to excessive redundancy and resource consumption. By employing k-means clustering, we effectively group similar test cases into clusters, enabling a reduction in the number of test cases while maintaining adequate test coverage. This approach offers several benefits, including reduced testing time, improved resource utilization, and enhanced fault detection capabilities. Our method ensures that critical areas of software functionality are thoroughly tested, even with fewer test cases, leading to cost savings and efficient resource allocation. The results demonstrate that this clustering-based test case minimization technique is both scalable and practical, making it suitable for large-scale software testing environments
The Role of Artificial Intelligence in Enhancing Decision-Making in Enterprise Information Systems
Authors: Prachi Juyal
Published: February 2025

Artificial Intelligence (AI) has rapidly evolved from a theoretical concept to a practical toolkit for businesses seeking to gain competitive advantage through data-driven insights. This paper examines the role of AI in enhancing decision-making within Enterprise Information Systems (EIS). By leveraging AI-driven tools, algorithms, and real-time data integration, organizations can optimize processes and improve business intelligence. This study draws on recent industry case studies and real-time datasets—such as streaming data from IoT devices, sales platforms, and third-party data services—to illustrate successful use cases. A conceptual framework is presented for implementing AI-driven decision-making, focusing on data governance, machine learning pipelines, and interpretability. The paper concludes with recommendations for enterprise architects, data strategists, and IT leaders, emphasizing the importance of building agile, AI-ready enterprise infrastructures that can adapt to evolving market demands.
Entrepreneurship, Market Orientation, and Marketing Performance in Culinary Business: Mediating Effects of Innovation and Competitive Advantage
Authors: Westim Ratang
Published: February 2025

This study examines the mediating effects of innovation and competitive advantage on entrepreneurial orientation, market orientation, and marketing performance in the culinary business sector of Jayapura and Papua. This study uses quantitative exploratory analysis; within the population of 20 business owners who registered with the Tax Service Office in Jayapura, a questionnaire was completed to obtain their perspectives on these variables and their mediating effects. After validity and reliability tests and regression analysis using a structural equation model, this study found significant effects between entrepreneurship and market orientation, product innovation, and competitive advantage on marketing performance, and between entrepreneurship and product innovation, as well as between market orientation and competitive advantage. Product innovation has been proven to mediate entrepreneurship and marketing performance, along with competitive advantage in market orientation and marketing performance. Although this study is limited to culinary business respondents in the region, the findings are crucial in encouraging the development of small businesses, especially those engaged in the culinary sector, despite the amount of production, product variants, and their offered quality. Considering their ability to improve the regional economy, governments should pay more attention to the movement and success of these businesses.
Integrating Natural Language Processing with AdaBoost, Random Forest, and Logistic Regression for an Advanced Ensemble-Based Network Intrusion Detection Model
Authors: Putta Srivani
Published: February 2025

Higher numbers and more complicated traffic data passing over your network requires that you have some advanced ways to properly identify security threats. In this paper, we design an ensemble intrusion detection system that unites different machine learning strategies such as due to Natural Language Processing (NLP), AdaBoost, Random Forest and Logistic Regression to identify several kinds of network intrusions. Problem: To deal with more and more stronger attacks, detect rate of the traditional Intrusion Detection Systems remains feckless. Based on the results of this analysis, the important features are extracted from network traffic logs by applying NLP and feed it to espresso classifier in the ensemble approach. Because AdaBoost increases the performance of weak learners, Random Forest provides robustness and Logistic Regression provides interpretability in final decision making. It is trained and tested on benchmark datasets like the NSL-KDD, and CICIDS2017. Overall, in the case results ensemble models were better performance than all individual classifiers regarding accuracy, precision and recall especially in rare case of attack types. Using NLP for feature extraction has enabled the detection of sophisticated attack signatures, showing that this model performs well for real-time security monitoring in high throughput networks like corporate or cloud environments.
A Study into the Detection of Malaria using Artificial Intelligence
Authors: Pil-Kee Min
Published: February 2025

Malaria is among the most frequent mosquito-borne infections and a major public health issue across the globe. Currently, the conventional method for diagnosing malaria is a microscopic study of a stained blood film. Artificial Neural Networks (ANN) might be used to diagnose sickness in red blood cells, according to researchers. For this aim, characteristics / variables are generated from data collected from digital holographic pictures of blood cells and fed into an artificial neural network (ANN), which identifies the cell is infected or not. The current research concentrates on the use of artificial intelligence to identify malaria.
The Emergence of Green Marketing to Achieve Sustainable Development
Authors: Deageon Kim
Published: February 2025

People live in an environment that is being impacted by their activity. Environmental activities are impacting human life at the same time. Human actions have led to environmental damage, degradation of forests, global climate change, etc. Companies have shown care for the conservation of the environment as part of their social obligation as society grows increasingly concerned about it. Concepts such as "Green Marketing" as well as "Environment Marketing" have given the enterprises of future new directions and are now vital to their survival. In today's corporate climate, phrases like "go green," "environmentally friendly," "protect the environment," "save energy," and many other have gained popularity. Maintaining the safety of our natural environment has become crucial and difficult in the present scenario of globalisation. Green marketing has established itself as a viable and innovative instrument for enterprises to reconcile their commercial operations with a commitment to preserving the environment. No business can deny the value of the natural environment, it is a truth. More than that, numerous businesses have offered clients distinct items that are recyclable, renewable, and reusable as a way to gain a competitive advantage over their rivals. This paper aims to comprehend how, particularly in the Indian context, green marketing methods have evolved into viable commercial tools. It looks at how commercial organisations are embracing green tactics to get an edge over rivals. The conclusion discusses the potential and challenges firms have when using green marketing and explains the state of the Indian market today.