bluuebunny/crossref_metadata_2025 huggingface dataset:
How Visual Merchandising Shapes Apparel Buying Behavior?
Authors: Enock.I, J. Srinivasan
Published: February 2025

This study investigates how visual merchandising impacts the purchase decision with respect to apparel using accidental sampling technique and the total of 120 respondents. In order to evaluate the relationships between visual merchandising elements and consumer buying behavior, the current research adopts Structural Equation Modelling (SEM) as its primary analysis method. The research concludes the strategic significance of window displays, in-store positioning, and the model styling in influencing shopper behavior, embedded with actionable recommendations to develop a better merchandising strategy.
A Blockchain Based Datasharing System for Health Care Applications
Authors: V Srinadh
Published: February 2025

Researchers recently were battling to produce an ever-more effective prediction model. Studies have found that when the artificial intelligence (AI) model is trained with a wealth of information, it performs better and generalizes better. Research institutions, testing labs, hospitals, and other organizations can share their information and work together to improve the training algorithm. All companies wanted to respect the confidentiality of their data, but they still want effective and precise teaching methods for a spectrum of uses. Regarding the ethical and regulatory concerns about medical data privacy, data sharing among numerous organizations is limited. In health care systems,we describe a unique solution that integrates locally taught AI from over blockchain for enhancing disease prediction by addressing the identified gap, while maintaining anonymity and allowing data exchange. Using the information to ensure privacy poses a number of issues. This study provides a Modified Needleman-Wunsch algorithm (MNWA) for data sharing. The Artificial Neural Network (ANN) model is used for trust-enabled smart contracts. Python software performs the implementation work. A detailed experimental survey was performed to test the importance of our proposed strategy for effective early prediction and diagnosis. The proposed model's extensive experiments demonstrate a proposed method may detect ailments and boost efficiency.
Precision Agriculture in the Digital Age: IoT and Machine Learning Synergy for Improved Agricultural Productivity
Authors: Ambuj Kumar Misra
Published: February 2025

This study explores the integration of Internet of Things (IoT) devices and machine learning (ML) to enhance agricultural productivity. By leveraging IoT sensors deployed across agricultural farms, real-time data on environmental and agronomic factors such as temperature, humidity, soil moisture, and crop health is collected. The dataset, spanning multiple farms over two years, also incorporates satellite imagery for comprehensive crop health analysis. Using machine learning algorithms, the data is analyzed to predict key variables, enabling informed decision-making. This approach aims to optimize resource management, improve crop yields, and address challenges like climate change. The study highlights the synergistic potential of IoT and ML in transforming modern agriculture into a more efficient and sustainable industry.
Assessing Security Risks in Information Systems through the Utilization of the Open Web Application Security Project (OWASP) Framework
Authors: Gusti Ayu Prathita Ananta, Benfano Soewito
Published: February 2025

The proliferation of digital technology has resulted in the growing utilization of applications to facilitate the execution of organizational business activities. This has a direct influence on the growing likelihood of cybercrime. To mitigate cybercrime, it is imperative to ascertain the present state of security of the IT infrastructure. Multiple paradigms exist for assessing the present security of IT infrastructure. Nevertheless, these frameworks still exhibit deficiencies, as none of them offer a comprehensive elucidation of the execution of security evaluation. Consequently, the author of this research paper presents a more comprehensive framework for evaluating the existing condition of IT infrastructure. The implementation of security evaluation consists of two distinct phases: penetration testing and risk measurement. The initial stage involves the utilization of the Web Security Testing Guide, a penetration testing framework developed by OWASP to enhance the security of web-based applications. The second phase employs the OWASP Risk Rating Methodology, a systematic approach for assessing the anticipated level of risk. The research yielded the discovery of 7 vulnerabilities, with 4 vulnerabilities classified as having a medium risk level and 3 vulnerabilities classified as having a low risk level. Security evaluation is conducted on vulnerabilities categorized as having a medium risk level due to their potential to cause losses and harm to IT infrastructure. The assessment is conducted by offering suggestions for enhancement. These recommendations are anticipated to serve as a guide for enhancing security on IT infrastructure in the future.
On the Deployment of IoT-Based Approach for an Efficient Water Management and Treatment
Authors: Maram Saleh Miralam
Published: February 2025

A key issue faced in the contemporary world is the management of a vital resource of life, water. The shortage of support for standardization in monitoring and management equipment is currently causing interoperability issues in information and communications technology systems for water control. Various water management procedures, including transportation, utilization, system identification, and service of equipment are affected by this issue. This study aims to demonstrate a comprehensive and integrated IoT-based technique for monitoring and environmental quality by developing innovative methods and new knowledge on IoT applications in stormwater and wastewater and the environmental and human challenges of water contaminants and their treatment. This study shows how IoT can transform water quality monitoring and treatment. A more resilient, sustainable, and equitable water management framework will be achieved by real-time water quality monitoring, operational efficiency optimization, contaminant management, and scaling solutions. This research could pave the way for smarter, more adaptive water systems worldwide, addressing current and future water issues and improving community and environmental well-being.
Enhancing Remote Work Performance through Effective Leadership
Authors: Amina Madzak
Published: February 2025

The COVID-19 pandemic has significantly reshaped work dynamics, pushing remote work to the forefront. This study explores the impact of transformational leadership on employee performance in remote working environments. Conducted among 231 respondents from various sectors in Bosnia and Herzegovina, the research investigates how leadership styles affect employee performance in a post-pandemic period. The findings highlight that transformational leadership significantly enhances employee performance across all dimensions. Remote working benefits, however, show negligible impact on performance, while remote working disadvantages and job autonomy have medium to large effects. These results underline the importance of effective leadership and autonomy in optimizing employee performance in remote work settings. These insights highlight the need for organizations to adapt their leadership strategies to the evolving digital work environment, ensuring that remote work remains a viable and productive option for the future. Research provides valuable guidance for businesses aiming to navigate the new normal successfully, emphasizing the crucial role of transformational leadership in the digital age.
A Novel Hybrid Optimization Tuned Deep-Long Short-Term Memory Model in COVID-19 Chest CT Image Segmentation
Authors: G. Jose Moses
Published: February 2025

Corona viral disease (COVID-19) has been declared a worldwide pandemic due to its fast spread. In order to prevent future infection and to protect lives, early detection of COVID-19 cases is crucial because failure to do so might result in death. The segmentation of patient COVID-19 chest computed tomography (CT) image data is proposed in this study using the tuned Deep Long Short-Term Memory (Deep-LSTM) classifier by Grey Wolf Grasshopper Optimisation (GWGHO). The proposed model segmentation accuracy is enhanced by applying the recommended approach to tune the Deep-LSTM weights optimally. The suggested method is created by combining the characteristics of grasshoppers and grey wolves in such a manner as to inherit both animals' benefits in solving optimisation challenges. By applying the GWGHO algorithm's tuning procedure, the convergence is also improved. The suggested GWGHO-Deep LSTM model's efficacy is confirmed by an investigation of the model's performance indices, including segmentation accuracy, recall, precision, and Area under curve (AUC). The suggested GWGHO-Deep LSTM model's segmentation accuracy was achieved at 97.5226%, demonstrating the effectiveness of the model in segmenting data.
Tp-DyHEQN: Trust Priority based Dynamic Homomorphic Elliptic Curve Encryption Algorithm Enabled Blockchain based Deep Learning Model for Secure Data Sharing
Authors: Jolly R. Nikhade, Shrikant V. Sonekar
Published: February 2025

In recent times, data transmission in Wireless Sensor Network environments has become more prevalent. Nevertheless, these networks encounter numerous challenges during data transmission, including decreased network longevity, lower security, and lower energy efficiency. Furthermore, while authentication techniques ensure data authenticity, current mechanisms have security flaws such as identity spoofing, lack of transparency and scalability issues. Therefore, to overcome these challanges, a blockchain-based trust model known as trust priority-based dynamic homomorphic elliptic curve encryption algorithm enabled quantum convolutional neural network (Tp-DyHEQN), which enables secure data sharing and energy-efficient routing. The proposed trust priority-based energy loss minimization algorithm (Tp-EMA) enables energy-efficient routing, which minimizes the energy consumption of nodes and enhances the lifetime of the network. Additionally, the Tp-DyHEQN model detects the malicious activities of nodes, thus maintaining data privacy and safeguarding the network from unauthorized node activities. Moreover, the dynamic homomorphic elliptic curve encryption approach allows for computations to be performed on encrypted data without needing to decrypt it first enhancing network scalability and data integrity. The validation results prove that, compared to conventional approaches the proposed algorithm exhibits superior attack detection performance with an accuracy of 96.18% and a maximum privacy ratio of 0.92 for 500 nodes.
Proposed Improving Protection of Cloud Computing Environments Based on Machine Learning Techniques
Authors: Osamah M. Abduljabbar
Published: February 2025

Cloud computing contains a huge amount of data, which makes it a common target for cyberattacks to access confidential data using various illegal methods. One of these attacks is the Structure Query Language injection attack SQLIA, It is categorised as one of the most prevalent threats to obtaining, modifying, or destroying data by the Open Web Application Security Project (OWASP). Therefore, it has become necessary to create a model to detect attacks on data on the cloud to protect it, in order to increase trust between individuals and institutions and not make this data available to people who are not authorized to access it. To solve these problems, this study presents a proposal to improve the protection of the cloud computing environment through two contributions.The first contribution is developing a machine learning model, known as a logistic regression framework, that serves as a mediator between the client and the server. Its goal is to ascertain the kind of requests that are received from the customer layer and whether or not they include hazardous or typical payloads. The second contribution is illustrating how dangerous it is for the cloud computing infrastructure and for consumers and organisations to rely on false forecasts regarding the confidentiality, integrity, and real-time availability of data.. The results obtained from applying the proposed model showed a very high accuracy of 99.82, and showed low rates of false negatives and positives. In addition, the time it takes to determine the type of request sent is 0.1514 seconds.
An XAI-Driven Support System to Enhance the Detection and Diagnosis of Liver Tumor for Interventional Radiologist
Authors: Thaya I M,
Published: February 2025

In healthcare, the use of opaque deep learning models often results in limited transparency, potential bias, and inaccuracies, leading to a lack of trust among healthcare providers and patients. To address these challenges, this work integrates Explainable Artificial Intelligence (XAI) methods to enhance the transparency and interpretability of AI models, particularly in liver tumor segmentation. By employing XAI techniques, such as GradCAM (Gradient-weighted Class Activation Mapping), the proposed approach provides visual explanations that highlight the most critical regions influencing the model's predictions. This study focuses on combining state-of-the-art deep learning models, achieving a high accuracy of 99%, to ensure precise and reliable segmentation of liver tumors. GradCAM further enhances this process by generating heatmaps that explain the AI's decision-making, fostering trust and reliability among medical professionals. Beyond segmentation, the framework extends to decision support systems that offer transparent insights into medical decision-making, predictive analytics for patient outcome forecasting, and natural language processing for analyzing medical data. This approach ultimately empowers interventional medical professionals with accurate, interpretable, and trustworthy AI solutions, transforming how liver tumors are analyzed and segmented
Government Funding and Its Influence on Product Innovation in Micro, Small, and Medium Enterprises (MSMEs): A Comprehensive Review
Authors: Devesh Chand
Published: February 2025

Introduction: Background: Micro, small, and medium enterprises form the critical part of driving economic growth, employment, and innovation in most countries around the world. However, they equally face important barriers, such as financial constraints, difficulties in accessing technology, and competitive pressures from larger firms. Government funding through grants, loans, tax incentives, and innovation hubs seeks to mitigate these barriers by enabling MSMEs to innovate, develop new products, and enter new markets.Methods: This review evaluates the role that government funding has played in the product innovation process of MSMEs. Various mechanisms of funding were assessed in regard to their efficacy in driving innovations, and some challenges facing MSMEs in accessing the programs. Through comparative case studies and cross-regional analyses, insight is developed concerning the effectiveness of government initiatives and policy frameworks in place.Results: Government funding has increased the R&D capacity among MSMEs, helped create new products, and enabled market expansion. For instance, initiatives such as Horizon 2020 of the European Union have contributed to improving MSME technological innovation. Yet, obstacles are set in the convoluted process of application and rigid eligibility criteria, and in differences among regional mechanisms that impede accessibility and effectiveness for this kind of initiative. This is risky because it creates overdependence on government funding that may be discontinued in the case of changes in policy. It has been responding to these kinds of challenges with the development of collaborative funding mechanisms and innovation ecosystems by forming partnerships between MSMEs and research institutions.Conclusions: The most crucial role played by government funding is in overcoming the financial and technical problems of MSMEs in the development of a new product. Some useful policy measures for maximizing impact would include simplification of application procedures, designing programs considering heterogeneous needs, and encouraging joint innovations. Long-term sustainability of government-funded innovations and removal of regional and sectoral imbalance are some aspects that future studies may consider. These findings support the argument for more targeted, efficient, and inclusive funding approaches to enhance innovation in MSMEs and promote economic growth.
Optimized Feature Selection techniques for Distributed Intrusion Detection System (DIDS) in IoT Environment
Authors: B.Karthikeyan,
Published: February 2025

Introduction: In Internet of Things (IoT), optimal data set management and feature learning are the important problems that affect the attack detection’s accuracy.
Objectives: To select the optimal features in the dataset, using advanced optimization techniques and classify the data as normal or anomaly,using ML based algorithms.
Methods: In this paper, we propose to design anoptimized feature selection technique for Distributed Intrusion Detection System (DIDS)in IoT environments. During the preprocessing phase, t-distributed Stochastic Neighbor Embedding (t-SNE) is applied for data exploration and visualizing the high-dimensional data and Principal Component Analysis (PCA) technique is applied for dimensionality reduction.Then, for selecting the optimal features from the preprocessed dataset, the Improved Gravitational Search Algorithm (IGSA) is applied. Finally, for classifying the data as normal or anomaly, the XGBoost classifier is applied.
Results: Experimental results show that the optimized XGBoost classifier attains highest accuracy and F1-score values, when compared to the other classifiers
Conclusion: The proposed DIDS thus protects the IoT networks from external attacks quickly and effectively
Big Data Analytics in Supply Chain Information Systems: Improving Operational Efficiency and Predictive Accuracy
Authors: Vasubabu Machavarapu
Published: February 2025

Big Data Analytics (BDA) has become a transformative force in supply chain management, enabling organizations to derive actionable insights from diverse and voluminous data. By integrating advanced analytical techniques, companies can enhance operational efficiency, bolster predictive accuracy, and improve overall decision-making. This paper provides a comprehensive review of BDA applications within supply chain information systems, covering fundamental concepts, tools, and techniques while examining key challenges such as data quality, security, and organizational change management. Through an exploration of real-world case studies and best practices, the paper emphasizes the crucial role BDA plays in driving innovation and resilience across global supply networks. It concludes with recommendations on effective implementation strategies, alongside insights into future directions—particularly the rise of autonomous decision-making and digital twin technologies. The findings underscore BDA’s potential to reshape supply chain processes, offering a competitive advantage to organizations willing to adopt and invest in data-driven capabilities.
Knowledge Management Systems in the Digital Age:Enhancing Collaboration and Decision-Making in Orginizations
Authors: Munikrishnaiah Sundararamaiah
Published: February 2025

Knowledge Management Systems (KMS) have evolved significantly in the digital age, emerging as essential tools for organizations seeking to harness collective expertise, foster collaboration, and drive effective decision-making. This review paper explores the core concepts of KMS, the technologies underpinning them, and their role in enhancing collaborative processes and strategic outcomes. By examining theoretical frameworks, real-time data trends, and case examples, we highlight best practices and emerging challenges in implementing and optimizing KMS in contemporary organizational settings.
Exploring the Impact of Data Locality in Distributed Databases: A Machine Learning-Driven Approach to Optimizing Data Placement Strategies
Authors: Rafea M. Ibrahim
Published: February 2025

Data locality significantly influences the performance of distributed databases, affecting query response times and resource utilization. This study investigates the role of data locality in enhancing the efficiency of distributed systems through a machine learning-driven approach to optimize data placement strategies. By analyzing access patterns, network latencies, and computational loads, we develop predictive models that inform dynamic data placement decisions. Utilizing reinforcement learning algorithms, the study adapts to fluctuating workloads, effectively minimizing data transfer times and maximizing throughput. Empirical results illustrate substantial improvements in query performance and resource management, highlighting the efficacy of intelligent data locality strategies. This study paves the way for future advancements in artificial intelligence-driven optimization for distributed database architectures.
Management of Social Media and Digital Marketing with Ai
Authors: Suraj Sharma
Published: February 2025

Introduction: Advancements in digital marketing have provided diverse implications of AI in different business models. Therefore, this empirical analysis focuses on analysing the incorporation of AI in the management of Social Media and Digital Marketing. In addition, through a quantitative analysis objective of the study were met, and reliable results were delivered.
Literature Review: For a better comprehension of the topic past literature was critically analysed and indigenous opinions were formulated. In addition, all the past literature was cited appropriately in the literature review portion.
Methodology: For analysing the implication of AI in managing digital marketing primary data was collected through a qualitative analysis all the factors were analysed. Moreover, a survey population of 65 was chosen and they were surveyed using a questionnaire with 13 questions.
Findings: A thorough examination of the linear regression table is provided in the empirical analysis results section. The study also included a descriptive analysis of the association in order to understand the nature and outliers of the data.
Discussion: A thorough explanation of the findings is provided in the study's discussion section. Furthermore, a thorough discussion of the findings was provided in the study.
Conclusion: Overall analysis is summarised with the findings of the study thus a brief discussion is provided in the conclusion.
Strategic Integration of Big Data Analytics and Business Intelligence for Enhancing Supply Chain Resilience in SMEs
Authors: Ibaa Mahdi Saleh
Published: February 2025

This study examines the impact of Big Data Analytics (BDA) capabilities on supply chain resilience and the role of Business Intelligence capabilities as a mediator, especially in small and medium-sized enterprises (SMEs). The cross-sectional method through survey responses was adopted for hypothesis testing. In all, 153 responses were obtained and analyzed using the approach based on partial least squares with SmartPLS3. The results indicated that capabilities in BDA significantly contributed to the anticipation, response, and recovery dimensions of supply chain resilience. Further, it was realized that BI capabilities mediated the process of change of information into actionable insights that enhance agility and reactivity. This mediation effect is especially beneficial for SMEs, as it amplifies the impact of BDA by effective data utilization. The findings further depict that BDA technology integration with strong BI capabilities enhances decision-making, risk management, and operational efficiency to drive supply chain resilience plus competitiveness..
Impact of CSR Activities in the Development of State Economy
Authors: Binay Kumar Panjiyar
Published: February 2025

Within the larger context of state-level economic development, this study explores the dynamic interaction between Corporate Social Responsibility (CSR) initiatives and their significant impact on State economy, which are further categorized into infrastructure, skill development, and generation of employment opportunities. The study thoroughly analyses the hypotheses using an extensive technique that includes correlation test, regression analysis, ANOVA, and chi-square tests. This creates a compelling story about the significant impact that CSR plays. The study highlights the potential of business participation as a catalyst for worthy change by finding a positive association between the size of CSR successes and the state's overall economic development. Furthermore, the study unveils the noteworthy contributions of corporate social responsibility (CSR) towards increased job prospects and improved skill enhancement, underscoring the complex advantages that surpass financial gains. Crucially, the study demonstrates a strong correlation between CSR efforts and infrastructure growth, illuminating particular domains in which these endeavors have a notable impact. Despite acknowledging certain inherent limits, the study provides insightful information for stakeholders, businesses, and policymakers looking to strategically connect corporate social responsibility (CSR) efforts with the state's holistic development objectives. This study adds to the body of knowledge in academia and offers practical recommendations for encouraging sustainable economic growth through ethical business practices.
A Novel Document Representation Method for Author Profiling using Auto-Encoders
Authors: Karunakar Kavuri,
Published: February 2025

Author profiling is used for identifying certain demographic characteristics including age, gender, religion, language, nationality, and others of an author for a certain text. Author profiling has applications in different areas such as marketing, security, education, and forensics. Most of the research works concentrated on predicting the age and gender of an author by analyzing the writings of authors. The researchers started research on author profiling by using different types of stylistic features. Later, they realized that same set of stylistic features are used by the different classes of age and gender. Then, the research community observed that the writing styles of different authors can best differentiate by using the content-based features such as words that are utilized in the writing of a text. Some set of successful research works are used feature selection methods to identify the best relevant words from the datasets, and some other set of works are used term weight measures to denote the term importance within a document to represent the documents as vectors. The performance of author profiling approaches mainly depends on the type of information is used for representing the documents as vectors. In this work, we developed a novel document representation method by using different types of information in document representation. In the proposed method, the document representation considers three varieties of information such as the compressed feature representation of document identified by the auto-encoder, the contextualized information of word embeddings, and the importance of a word within a document to represent the documents as vectors. In this work, we conducted experiment on two standard datasets such as reviews dataset and Twitter dataset those are provided in PAN 2014 competition and PAN 2016 competitions respectively. In these datasets, the dataset pertaining to gender consists of two classes of documents such as female and male, and the dataset pertaining to age consists of five classes of documents such as 18-24, 25-34, 35-49, 50-64, and 65-xx. The proposed document representation method shows best performance for age and gender prediction on two datasets when compared with several popular methods of author profiling.
A Study on Liquidity Management and Profitability of Selected Public Sector Banks in India
Authors: A. A. Attarwala
Published: February 2025

The study of major public sector banks in India from 2010-11 to 2023-24 revealed key trends in their financial health. It focused on five banks: State Bank of India (SBI), Bank of Baroda (BOB), Punjab National Bank (PNB), Bank of India (BOI), and IDBI. SBI and BOB generally showed positive profitability ratios, while BOI and IDBI faced challenges and losses. Liquidity ratios were stable for SBI and PNB, but fluctuated for others, with IDBI having a strong liquidity position. Debt-equity ratios decreased across all banks, indicating less reliance on debt. Regression analysis showed liquidity significantly impacted financial indicators like Net Profit and ROCE, though the effect varied. Dividend Payout Ratios differed, with SBI paying more dividends consistently, while BOI and IDBI paid less. ANOVA tests confirmed significant differences in financial ratios among the banks. The study underscores the need to consider multiple financial indicators to understand a bank's health and the dynamic nature of the banking sector, advising stakeholders to tailor strategies to each bank's unique situation.
Exploring the Additional Factors Driving Behavioural Intention and User Behaviour in AI-Powered E-Commerce among Young Consumers: A UTAUT Model Approach
Authors: S. Shyni Carmel Mary
Published: February 2025

Purpose: This study explores how the use of AI tools impacts the purchasing behavior of young Indian consumers aged 18–35 years on e-commerce websites. It seeks to analyze how performance expectancy, effort expectancy, social influence, and facilitating conditions affect behavioral intentions and use behavior. Besides, it probes the moderating effects of age, psychological factors, privacy concerns, and role of special events, offers, and coupons in converting product consideration into actual sales.
Design/methodology/Approach: The present investigation employs Structural Equation Modelling (SEM) to examine the data obtained from an online survey conducted among young Indian consumers. This study utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to investigate the connections between the specified dependent variables and their influence on consumer purchasing decisions.
Findings: Findings of the results demonstrate that the influence of AI-driven personalized recommendations, effort expectancy, social influence, facilitating conditions, and psychological factors on behavioural intentions and use behaviour of young consumers are quite significant. The privacy concern and user experience are very critical moderating factors. At the same time, seasonal deals and rewards can become decisive factors to convert the consideration set into actual purchases, especially for younger consumers.
Originality/value: This research enhances the existing body of literature by utilizing the UTAUT framework to investigate the influence of AI tools on consumer purchasing behavior within the Indian e-commerce sector. It specifically examines the interplay among seasonal promotions, psychological influences, and privacy apprehensions, providing insights into how these factors inform the buying choices of younger consumers.
Environmental Accounting and Its Influence on Sustainable Development of a Private Company in Trujillo, 2024
Authors: Orellana Meza
Published: February 2025

The present study aims to determine the influence of environmental accounting on the sustainable development of a private company in Trujillo, Peru, during 2024. The research is classified as applied and uses a quantitative approach with a causal correlational design. The sample consists of 50 employees selected through non- probabilistic intentional sampling. Validated questionnaires were used to measure the variables of environmental accounting and sustainable development, evaluating dimensions such as environmental management, green accounting, environmental budgeting, financial design, business competitiveness, and operational decisions. The results indicate a significant, positive, and moderate influence of environmental accounting on sustainable development, explaining 25.2% of its variability. Additionally, positive associations were found with the dimensions of financial design, business competitiveness, and operational decisions. Descriptively, environmental accounting predominantly shows a high level (54%), while sustainable development is mostly at a regular level (52%). The general conclusion of the study is that environmental accounting has a moderate, positive, and significant influence on the sustainable development of a private company in Trujillo.
Estimating Pain Intensity from Facial Expressions
Authors: Yogita Chavan
Published: February 2025

Facial expressions are intricate, change over time, and are difficult to decipher, hence automated emotion recognition using facial expressions has been a popular research area in computer vision, which focuses on image processing and pattern recognition. The research community has access to many facial expression databases, which are crucial tools for analyzing a variety of face expression detection methods, but still the existing models are affected by over-fitting issues. In this research work,  a framework using Hybrid optimization based deep CNN (HOA based deep CNN) has been developed that can recognize pain by facial expression and evaluate the pain's intensity. The proposed model uses UNBC-McMaster Shoulder Pain Expression Archive Database (UNBC) as the database. From the identified region of interest (ROI), feature extraction is done using the RESNET 101, facial activity descriptors and hybrid weighted facial activity descriptors. The collected features are processed using ensemble deep CNN classifiers. The optimization methods Adam, Stochastic Gradient Descent (SGD), Cat Swarm, and Grey Wolf (GWO) are used together to optimize the ensemble deep CNN. The hybrid optimization offers the best tuning, which substantially reduces the computing time and speeds up the convergence. The accuracy, sensitivity, and specificity achieved by the HOA-based deep CNN model based on the TP are 90.06%, 98.38%, and 99.35%, respectively. For the K-fold, the accuracy, sensitivity, and specificity achieved are 94.95%, 97.33%, and 99.04%, respectively.
AI and IoT in Supply Chains: Creating Intelligent and Autonomous Business Operations
Authors: G V Radhakrishnan
Published: February 2025

This research explores the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in supply chain management, emphasizing their role in enabling intelligent and autonomous business operations. It examines how these technologies enhance efficiency, decision-making, and adaptability in modern supply chains. A systematic review of existing literature, case studies, and industry reports is conducted to analyze the impact of AI and IoT in supply chain processes. The research highlights that AI-driven predictive analytics and IoT-enabled real-time monitoring significantly enhance supply chain visibility, efficiency, and responsiveness. Automation through AI-powered decision-making and IoT-based smart tracking systems reduces operational risks, minimizes costs, and optimizes resource allocation. However, challenges related to cybersecurity, data privacy, and integration complexities remain key concerns. This study contributes to both academic and industry discussions by offering insights into the evolving role of AI and IoT in supply chains. Practically, it provides guidance for businesses seeking to implement intelligent supply chain solutions. Socially, the findings emphasize the need for ethical AI deployment, data security, and workforce upskilling to navigate the shift toward autonomous operations. This research presents a unique synthesis of AI and IoT advancements in supply chain management, highlighting their synergistic impact on creating more adaptive, resilient, and intelligent operations. By addressing both opportunities and challenges, it serves as a valuable resource for researchers, practitioners, and policymakers in the field.
A Comprehensive Review of Modern EEG Signal Processing for Emotional and Stress Detection: From Data to Insight
Authors: Ashvini Bamanikar
Published: February 2025

Using the brain's thoughts to communicate with the outside world is known as brain-computer interface. Electroencephalography (EEG) has emerged as a powerful non-invasive method for understanding brain activity and developing applications in neurology, medicine, and human-computer interaction.
Robust signal processing techniques, which convert raw EEG data into meaningful insights, are crucial to the effectiveness of EEG-based systems. This paper aimed to discuss the various approaches that need to be adjusted for each stage of brain signal processing, with a number of approaches and experimental results reported and compared in previous surveys. The advantages and disadvantages of each method, such as signal capture, signal augmentation, feature extraction, and signal classification, are easily and clearly interpreted in this work.
Mathematical Modeling of Epidemics: A Study on the Spread of Infectious Diseases
Authors: Vasantha Gadipally
Published: February 2025

Infectious diseases continue to be a major threat to international health and hence the need for best strategies for prediction, control and prevention. Mathematical modeling of epidemics is one of the most effective approaches to analyzing the processes that occur within a population and making related decisions. Towards this end, this paper offers a study of the mathematical modelling of epidemics with emphasis on the SIR model and its variants. In this paper, we examine how differential equations can be used to describe the spread of diseases and its control by tools like vaccination and isolation as well as changes in contact rates. The paper also consists of a section on validation and calibration using real world data and given simulation results. We showed that mathematical models can accurately show the likelihood of the spread of the infection and how interventions may best be applied.
WHEN A SHAPE LOSES SHAPE. THE MEANING OF SHAPE AND ITS SYNONYMS IN ENGLISH AND POLISH
Authors: KARCZEWSKA Małgorzata
Published: February 2025

The paper analyses the meaning of the term shape and its (partial) synonyms form and figure in English, as well as their Polish equivalents kształt, forma and figura. The nouns are proven to be polysemous, with some of the meanings not related to the idea of shape. English – Polish equivalence is partial, as a sign of common human experience. In some cases, cross-equivalence can be observed, while, in some other cases, no equivalence is observed.
PECULIARITIES OF GERMAN GENDER-ORIENTED ADVERTISEMENT TEXTS
Authors: KHVALYBOHA Tetiana , FEDCHYSHYN Nadiya , YELAHINA Nataliia , KLISHCH Halyna
Published: February 2025

The article defines the peculiarities of German gender-oriented advertisement texts (ATs), summarizes and systematizes domestic and foreign approaches to the study of this problem, and reveals the lexical-stylistic features of German gender-oriented ATs, as well as the syntactic and compositional-structural characteristics of the latter, with an emphasis on language tools that enable gender specifics to be realized in various aspects.
Since an advertisement is mainly addressed either to a woman or to a man, or to both sexes, the advertisement text has certain compositional, stylistic features, as well as some thematic and lexical orientation. Thus, it is proved that AT can be both gender-neutral and gender-marked.
MINOAN AND THE MACHINES: COMPUTATIONAL APPROACHES TO THE DECIPHERMENT OF LINEAR A
Authors: MANOJ Pratham , PERONO CACCIAFOCO Francesco
Published: February 2025

In this paper, we review some of the significant literature surrounding the computational decipherment of Linear A, an undeciphered writing system from Bronze Age Crete (Aegean Sea), ‘hiding’ the so-called (unknown) Minoan language. Specifically, we summarize the methodology and results of two machine learning-based techniques that might be applicable to the study of Linear A, the first relying on modeling language decipherment as a minimum cost-flow problem and the second on comparing phonetic values across languages by using a generative framework. Additionally, we evaluate the effectiveness and limitations of these techniques and compare their efficacy and relevance to the decipherment of Linear A. We also review a consonantal cluster-based approach (a cryptanalytic ‘brute force attack’) and a feature-based similarity method, both of which run parallel to these machine learning techniques, and briefly discuss the advantages and shortcomings of the latter in comparison to the former. The aim of this paper is to provide our readers with a synthetic, but precise outline of new computational approaches for the decipherment of Linear A and to encourage further studies and research on this topic.
PROPUNERE DE METODOLOGIE PENTRU TRANSPUNEREA ÎN FORMAT TEXT STRUCTURAT (XML) A NORMELOR TEHNICE DE REDACTARE PENTRU DICȚIONARUL LIMBII ROMÂNE
Authors: TEODORESCU Claudius-Marian
Published: February 2025

This article aims to present in a plain, systematic, and pragmatic manner a modality for modernising the whole contents of the Technical Guidelines for the Romanian Language Dictionary, by structuring and ordering the text based on principles that nowadays govern the domain of technical documentation. For this, the methodology and the results that can be obtained are presented in detail.
DINCOLO DE CUVINTE. DIMENSIUNI CONCEPTUALE ALE IUBIRII ÎN TEXTELE BIBLICE EBRAICE
Authors: VLADIMIRESCU Mihai
Published: February 2025

The study examines the concept of hesed in the Hebrew biblical tradition, revealing its semantic depth beyond conventional translations and highlighting its relational and ethical character in the manifestation of divine and human love. The study addresses the diversity of scholarly interpretations, highlighting that “hesed” represents a combination of loyalty, love and kindness, reflecting not only divine attributes, but also principles governing human behavior in interpersonal relationships. The paper also explores the relationship between “hesed” and other biblical concepts, such as covenant and faithfulness, arguing that it plays an essential role in shaping the spiritual ethos of Judaism and in shaping relationships between the divine and the human. Thus, the study contributes to a nuanced understanding of how the Jewish religious tradition conceptualizes the fundamental virtues of love and devotion.
LEXICAL LACUNAS AND THEIR ELIMINATION IN THE TARGET TEXT
Authors: ZAHORODNIA Liudmyla , TSEPENIUK Tetiana , SHAYNER Hanna , KUSHKA Beata
Published: February 2025

The article is concerned with the phenomenon of lacunarity and methods of translating lexical lacunas from English into Ukrainian. The scientific research attempts to reveal the main reasons for the appearance of lexical lacunas in interlingual communication. The authors conclude that the main methods of discarding lacunas in the target texts are the compensation method and the method of filling the gap. Advantages and disadvantages of both methods are described in this paper.
DEVELOPING PRESENTATION SKILLS. PREREQUISITES OF GOOD PRESENTATIONS
Authors: FRÎNCULESCU Iulia Cristina
Published: February 2025

Presentation skills are essential for specialists and researchers of different specialties. The present paper focuses on the prerequisites for an effective speech, listing down some requirements and things to avoid in terms of conference presentations. Attention is equally given to crafting the presentation and to the actual speech given at a scientific event.
THE DISTRIBUTION OF IDIOMS IN A GENERAL ENGLISH CORPUS: A VIEW FROM THE PERSPECTIVE OF QUANTITATIVE CORPUS LINGUISTICS
Authors: KOVALIUK Yurii
Published: February 2025

The present paper deals with the quantitative and statistic aspects of idioms in a General English corpus, such as the Corpus of Contemporary American English (COCA), within the frameworks of a cognitive usage-based approach to idioms and Quantitative Corpus Linguistics. It is argued that the frequency of idioms may contribute to their cognitive entrenchment. For this purpose, the frequency of select idioms is considered in five genres of the COCA: spoken, fiction, academic, popular magazines, and news, in terms of quantitative corpus linguistics measures of attraction and reliance. It was found that, based on their occurrence rates, select English idioms may exhibit a higher level of entrenchment compared to the others. In addition, it was observed that the idioms that occur less frequently in the corpus tend to achieve higher reliance scores than their more frequent counterparts.
VATROSLAV JAGIĆ ON THE UKRAINIAN LANGUAGE AND FOLK POETRY AS A MANIFESTATION OF THE FOLK SPIRIT
Authors: KRAVCHUK Tetiana , KASHUBA Oleksandra , TURCHYN Andrii , NAVOLSKA Halyna , PANCHENKO Valeriy
Published: February 2025

The article examines the cultural and ideological principles of V. Jagić’s interpretation of the problems of the Ukrainian language, as well as how V. Jagić assessed the status and prospects of the Ukrainian language at the turn of the century. Jagić’s arguments (linguistic, palaeographic, historical), as well as his personal confidence in the antiquity and originality of the Ukrainian language convinced many Ukrainian and Russian philologists. However, Jagić’s not very optimistic views on the status of the Ukrainian language in the late 19th and early 20th centuries and the prospects for its further development fully corresponded to his pro-Russian sentiments and his long-held dream of cultural unity of Slavic peoples. Although Jagić did not devote certain works to Ukrainians and their language, always analysing it in “connection” or comparison with Russian and other Slavic languages (except for the memorandum on the Ruthenian language), these explorations, given the huge authority of their author, in the times of national demarcation and the greatest anti-Ukrainian reaction, were of considerable importance both in Ukrainian linguistics and in Slavic studies in general.
THE FACTUALITY OF FOOTBALL TELEVISION REPORTS FROM A TEXT-CENTRED PERSPECTIVE
Authors: STRUHANETS Pavlo
Published: February 2025

The article characterizes the factuality of football reporting from a text-centred perspective. It is noted that the facts in the text are explained by means of text structures built on the basis of focal and background factums. The system of factual text indicators which convey information about the progress of the game, the organizers and participants of the football match, the place of the event, the time, the status of the match, the stage of the tournament, etc. was analysed.
Vacina HPV: benefícios da vacinação de mulheres tratadas por lesão intraepitelial cervical
Authors: Neves Nilma Antas
Published: February 2025

Desde 2006, as vacinas HPV bivalente e quadrivalente foram introduzidas no Brasil, inicialmente para mulheres até 26 anos, mas agora aprovadas para ambos os sexos, de 9 a 45 anos. Estudos mostraram sua eficácia em mulheres maduras e investigaram os benefícios da vacinação em pacientes com lesões prévias, como lesão intraepitelial de alto grau tratada com procedimento eletrocirúrgico com alça. Um estudo de 2018 evidenciou uma redução de 81,2% nas recidivas entre mulheres vacinadas após 30 dias do procedimento eletrocirúrgico com alça. Outro estudo revelou uma diminuição nas recidivas para mulheres vacinadas após 3 meses do tratamento. Em 2020, pesquisa na Lancet com mais de 21 mil mulheres apontou uma diminuição de 59% no risco de novas lesões cervicais após a vacinação, independentemente do tipo de HPV. A hipótese sugere que a vacinação bloqueia a infecção de novas células adjacentes às lesões, prevenindo a aquisição de novos tipos de HPV. A vacinação é benéfica para mulheres de todas as idades com lesões cervicais, sendo recomendado iniciar o mais rápido possível após o diagnóstico, junto ao tratamento indicado. Esses resultados destacam a importância da
vacinação na prevenção de recidivas e no controle das lesões cervicais.