bluuebunny/crossref_metadata_2025 huggingface dataset:
Developing Multi-Channel Pulse Signal Analysis for Enhanced Heart Rate Detection Using Facial Video Systems
Authors: Jyoti Vitthal Chhatrband
Published: February 2025

By leveraging advanced signal processing techniques and illumination correction, the study enhances the accuracy and robustness of heart rate detection systems under varying lighting conditions. This paper introduces the development of the Unified Pulse Detection from Complex Environments (UPDCE) Model, a deep learning framework designed for the non-invasive detection of heart rate from facial video data. Utilizing the UBFC-RPPG dataset, which includes video recordings under various illumination conditions, the model employs convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze multiple specific regions of interest namely, the forehead and chin. These areas are critical for capturing pulse signals influenced by subtle changes in skin coloration due to blood flow. The model processes video frames, extracted at a three-second interval, through stages of enhancement and normalization to improve data quality for subsequent analysis. Features are then extracted and temporally analyzed to detect and calculate heart rate accurately. Special emphasis is placed on overcoming challenges associated with diverse lighting and motion conditions. The system architecture ensures robust processing by incorporating techniques to optimize real-time operation and reduce computational load. The effectiveness of the UPDCE Model is validated through rigorous training and testing, demonstrating significant potential for real-world application in continuous health monitoring systems. This research contributes to advancements in remote photoplethysmography by highlighting methodological innovations and deployment strategies that enhance the accuracy and reliability of heart rate detection using facial video analysis.
Developing Next-Generation CapsNet Models for Enhanced White Blood Cell Classification in Medical Diagnostics
Authors: P. S. Gaikwad
Published: February 2025

Correct classification of white blood cells (WBCs) is very crucial for diagnosis and monitoring of haematological disorders. Although conventional convolutional neural networks (CNNs) perform, they can struggle with translational invariance and the precise spatial structures required for more complicated medical image categorisation. We aim to address these issues by developing next-generation Capsule Network (CapsNet) models that will provide more accurate and dependability in WBC categorisation. CapsNet models offer significant potential as a development over conventional CNNs because of their special designs that retain the internal spatial linkages of WBC components without compromising the purity of the found features. This work fast separates and sorts WBCs from microscopic images using a modified version of the CapsNet architecture along with dynamic routing and reconstruction regularisation. Thousands of annotated blood smear images from several public medical databases were included in the collection to exhibit a broad spectrum of disorders. We first improved and standardised the pictures using pre-processing methods. We then classified the WBCs using the CapsNet model in their proper groups. With a clear increase in precision and recall, the results of classification accuracy revealed that the CapsNet models outperformed conventional CNN models. The models may be extensively used in real-life medical diagnostic environments as they also operated well with shifting pictures and noise. This work demonstrates how CapsNet models might alter WBC classification, therefore improving diagnosis accuracy and resulting in improved patient outcomes.
Brand on Brain: Influence of Color, Age, Gender and Socioeconomic Status on Brand Recognition- A Psychophysical Experiment
Authors: Vaniza Dolwani
Published: February 2025

A psychophysical experiment was conducted to assess colors and brand recognition and to understand the relationship between participants' economic status, age, and gender on Brand Recognition. To accomplish the aims of the present work, Participants were presented with a series of colors and a selection of brand products, and they were asked to associate each color with the brand product they find most fitting. Moreover, the study investigated the role of demographic factors—age, gender, and socioeconomic status—as perceptual lenses through which individuals interpret and recognize brands. Do these factors influence the relationship between color and brand perception? To address these questions, an empirical experiment is conducted. This multidimensional exploration of color and branding, in conjunction with demographic influences, contributes nuanced insights into consumer behavior. It not only deepens our understanding of how branding shapes the consumer's mind but also provides actionable strategies for businesses seeking to tailor their branding approaches to diverse audiences. The results of this study illuminate the dynamic interplay of color, age, gender, and socioeconomic status in the realm of brand perception, offering valuable implications for marketers and brand strategists in an ever-evolving market landscape.  In today's competitive market, branding strategies are pivotal in shaping consumers' perceptions and choices.
Learning Organization and Organizational Agility in the Context of Organizational Performance: The Moderating Role of E-HRM Systems
Authors: Deden Kurniawan
Published: February 2025

Introduction: In the era of modern management, organizations are required to continuously innovate and improve performance through adaptive and learning-oriented management approaches. This study examines the impact of modern management approaches, specifically through innovation capability and E-HRM systems, on organizational performance.
Objectives: The primary objective of this study is to investigate the extent to which organizational agility and learning organization influence organizational performance, with innovation capability as a mediator and E-HRM systems as a moderator.
Methods: This research was conducted in December 2023 by collecting 408 questionnaires from entrepreneurs and management personnel in randomly selected SMEs in Jakarta, Indonesia. Data analysis was performed using SPSS and structural equation modeling (SEM) through SmartPLS 4 to evaluate the relationships between the variables.
Results: The findings reveal that organizational agility and learning organization have significant and positive impacts on organizational performance. Additionally, innovation capability significantly mediates the relationship between organizational agility, learning organization, and organizational performance. However, E-HRM systems show only a small moderating effect on the relationship between innovation capability and organizational performance.
Conclusions: The application of modern management approaches involving organizational agility and learning organization is crucial in enhancing organizational performance, particularly through strengthening innovation capability. Although the moderating effect of E-HRM systems is limited, these systems still play a role in reinforcing the relationship between innovation and organizational performance in the digital era.
The Interconnection of HR Strategies and Financial Performance in Modern Organizations
Authors: Farheen Ahmad
Published: February 2025

In modern organizations, the alignment between human resource (HR) strategies and financial performance has become increasingly significant. This paper examines the interconnection between HR practices and financial outcomes, emphasizing the role of strategic HR management in driving organizational success. The study explores key HR strategies, including talent acquisition, employee engagement, training and development, performance management, and compensation structures, and assesses their impact on financial metrics such as profitability, revenue growth, and return on investment.
The research highlights that organizations implementing proactive HR policies experience improved workforce productivity, reduced turnover costs, and enhanced innovation, all of which contribute to sustainable financial performance. Furthermore, it discusses the influence of employee satisfaction and well-being on operational efficiency and customer satisfaction, ultimately leading to higher financial gains. By analyzing existing literature and case studies, this review identifies best practices for integrating HR and financial strategies to achieve long-term business objectives.
Additionally, the study explores the evolving role of technology, such as artificial intelligence and data analytics, in optimizing HR processes and measuring their financial impact. It also considers the challenges organizations face in aligning HR strategies with financial goals, including budget constraints, changing workforce dynamics, and regulatory compliance.
The findings suggest that a well-structured HR framework, aligned with organizational goals, fosters a high-performance work culture, enhancing both employee retention and financial stability. The paper concludes by emphasizing the need for a data-driven, strategic approach to HR management to maximize financial performance in an increasingly competitive business environment. This paper serves as a valuable resource for business leaders, HR professionals, and policymakers seeking to enhance organizational efficiency through effective HR-financial integration.
AI-Driven Skin Cancer Detection: A Deep Learning Perspective
Authors: Yashwant S. Ingle
Published: February 2025

The rising incidence of skin cancer necessitates the development of accurate and efficient diagnostic methods. This study introduces a novel approach for detecting skin cancer using an encoding-decoding technique with convolutional neural networks (CNNs) to enhance feature extraction from skin lesion images. Advanced CNN architectures, including DenseNet201, VGG16, and Xception, are utilized to classify skin lesions into seven categories. A thorough evaluation on a large dataset confirms the effectiveness of the proposed method in accurately identifying various types of skin cancer. Furthermore, a comparative study of multiple CNN models provides key insights into their relative strengths and limitations for diagnostic purposes. This research contributes to improving computer-aided skin cancer detection, paving the way for more reliable and accessible screening solutions.
Deep Learning Hybrid Approach for Accurate SMS Spam Identification
Authors: Adel Al-Zebari
Published: February 2025

Short messaging service (SMS) is a popular application for mobile devices. People often use SMS when they are not suitable for voice calls. Nowadays, SMS is used for commercial purposes. These SMS can sometimes be useful. But sometimes, unwanted SMS, which is called spam SMS, can disturb mobile phone users. Thus, spam SMS detection becomes an important application for mobile phone service providers. Up to now, machine-learning approaches have been used for spam SMS detection. These approaches used various supervised learning methods for detection purposes. In this paper, three deep-learning approaches are used for SMS spam detection. These approaches are Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid CNN with LSTM, respectively. The mentioned all deep learning approaches are trained in an end-to-end fashion. As these approaches use numeric input data, the text input data is initially converted to numeric data. To do that, a pre-trained word embedding network is used to convert each SMS text data to an array of word vectors. An SMS spam dataset, which is downloaded from the UCI machine learning repository, is considered in experimental works for the performance evaluation of the mentioned deep learning approaches. Four performance evaluation metrics namely accuracy, precision, recall, and F-score are used in the performance evaluation. The experimental works show that the hybrid approach produces better detection results than the CNN and LSTM approaches. Besides, the result of the hybrid method is compared with some of the published results. Comparisons show that the hybrid method outperforms the other compared methods.
Detection of Credit Card Fraud Utilizing Transaction History Using Machine Learning
Authors: Akshita Sunerah
Published: February 2025

Over the past several years, fraudulent credit card transactions have surged, posing significant challenges to financial institutions and consumers alike. In 2021, an annual study revealed that over 50% of Americans experienced credit or debit card fraud, with approximately 127 million individuals falling victim at least once. Traditional fraud detection techniques are often inadequate due to their reliance on manual processes and rule-based systems, which are both time-consuming and prone to errors. This paper explores the application of machine learning (ML) algorithms to enhance the accuracy and efficiency of credit card fraud detection. Six supervised ML algorithms—Naïve Bayes, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Logistic Regression, and XGBoost—are evaluated using transaction history data to classify fraudulent and non-fraudulent activities. The study employs a comprehensive methodology involving data preprocessing, feature engineering, and model training, followed by performance evaluation based on accuracy, precision, recall, and F1-score. Results indicate that the SVM model outperforms other algorithms, achieving the highest accuracy in fraud classification. The findings underscore the potential of ML in automating and improving fraud detection systems, thereby mitigating financial losses and enhancing trust in financial services.
Emerging Trends of Artificial Intelligence and Human, Personal, or Customer Values using R Studio and Vos Viewer
Authors: Krithika P
Published: February 2025

The objective of the research is to visually examine the emerging research trends pertaining to the connection of artificial intelligence and human, personal, or customer value on a global scale. The methodology of the research employs bibliometric approaches from the Scopus database (2018-2022), a total of 2094 documents. The analysis of the study utilizes the VOS Viewer software and R studio software. Based on the findings of the study, it has been observed that the University of California and Li Y are the top institution and author that exhibit the highest level of activity in the domain of artificial intelligence, which mainly infused with customer value. Secondly, the domains of artificial intelligence have witnessed significant exploration in the subject fields of computer science (CS). Lastly, the research found main research themes such as artificial intelligence, big data, and internet of things, which will be useful for future researchers.
Impact of Oral Ice Cubes for Prevention of OM among Cancer Patients Receiving Chemotherapy: A Machine Learning Approach
Authors: Rawia Ahmed Mohammed Ebrahim
Published: February 2025

Oral Mucositis (OM) is a painful adversarial consequence of chemotherapy that affects the lining of the mouth and throat. The study goal is to investigate the efficiency of oral ice cubes on OM amongst cancer patients who receive chemotherapy with Machine Learning (ML) based predictive process. Quasi-investigational research was conducted for evaluating the efficacy of oral ice cubes on avoidance/minimization of OM among cancer patients receiving chemotherapy in V.S hospital in Chennai, India. Samples were selected by convenience sampling technique under quasi- investigational two groups namely pre-test and post-test research design. Among the total 60 samples, the first set of 30 samples in experimental group and next set of 30 samples in control group were selected. Before infusing chemotherapy, ice cubes were applied to the oral mucosa for 5 mins and 20 min post chemotherapy session. Observational checklist was assessed before and 7th day post intravenous chemotherapy to assess the clinical manifestations of OM. Naïve Bayes, support vector machine, and XGBoost are used to generate several predictive models. All the ‘t’ values were statistically substancial, demonstrating that there is a substantial difference among the cancer patients undergoing chemotherapy. It depicts the oral ice cube application on the minimizing clinical manifestation of OM was effective among cancer patients post chemotherapy status. It is also reported that there were considerable variations found in the mean total scores and those who received oral ice cubes had minimal clinical manifestation of OM. It can be concluded that oral ice cube application reduced the level of clinical manifestation of OM induced by chemotherapy. Standing orders can be given to all nurses to apply ice cubes to all patients before 5 minutes who receive chemotherapy so that we can minimize or prevent OM in all cancer patients. The same can be included in health policy.
Data Governance Evaluation of the Data Management Office at King Saud University based on the National Data Management Office standards
Authors: Yasmine Aljarallah, Mutasim Alfadhel
Published: February 2025

This study aimed to investigate the implementation of data governance at the Data Management Office of King Saud University by examining the adoption of the strategic plan, implementation mechanisms, and compliance with data governance standards and controls established by the National Data Management Office (NDMO), the national regulatory and reference authority for data management and governance.Using a descriptive-analytical approach, a questionnaire was designed based on three dimensions: the strategic dimension, the executive dimension, and the challenges dimension. The study sample included all 15 employees of the Data Management Office at King Saud University. The data were analyzed using IBM SPSS Modeler statistical software.The results of the study at the level of the main dimensions showed that the strategic dimension achieved the highest arithmetic mean (4.51) with a very high degree of compliance, followed by the executive dimension (3.69) with a high degree of compliance, and finally, the challenges dimension obtained a medium degree with an average of (3.05).The study revealed that the lack of data unification and its inconsistency across university departments, as well as the scarcity of financial resources that limit the improvement of the technological infrastructure at the Data Management Office, represent the most important challenges facing the implementation mechanisms of data governance at the Data Management Office at King Saud University.The researcher recommends the necessity of creating a clear framework for cooperation to define roles, responsibilities, and joint operations between the university colleges and the Data Management Office regarding data circulation, and strengthening cooperation with external parties with expertise in the field of data governance to improve implementation processes.
A Comprehensive Approach to Bank Cheque Preprocessing: Utilizing Split Attribute Character Analysis Combined with Multi-Branch Network Forest Classifier Techniques for Enhanced Data Accuracy and Analysis
Authors: Hitesh Chaitanyaswami
Published: February 2025

Bank cheques are primarily used for conducting financial transactions, resulting in their substantial daily processing volumes worldwide. The automation of the whole process of recognising and verifying cheques has the potential to significantly reduce both the time and expenditures associated with cheque execution. The field of automatic bank cheque processing system is now gaining prominence in the realm of computer vision, image processing, pattern recognition, machine learning, and deep learning. The study places particular emphasis on the sequential processes involved in the automated bank Cheque processing system, including picture capture, pre-processing, and extraction and identification. This article provides an overview of the sequential processes included in the automated data extraction system. This research aims to propose strategies for the automated processing of bank cheque images via the use of Split Attribute character analysis and Multi branch network forest classifier. The study indicates that the recommended technique demonstrates satisfactory performance by achieving high levels of accuracy, precision, recall, and F score
Neutrosophic Interval Moving Average and Linear Regression Approach to Stock Market Volatility Prediction
Authors: S. Bhuvaneswari
Published: February 2025

This study examines stock market closing prices using neutrosophic interval moving averages and neutrosophic interval linear regression techniques to address financial data uncertainty. The 5-day neutrosophic interval moving average achieved the lowest Mean Squared Error (MSE) of 43.43676, outperforming the 3-day (66.3552) and 10-day (89.1942) averages. In contrast, neutrosophic interval linear regression yielded a higher MSE of 126.7759, highlighting the superior accuracy of the 5-day moving average. This method successfully forecasted October values (25, 28, 29, 30), showcasing its reliability in trend analysis and prediction. The findings demonstrate the value of neutrosophic methods for enhanced stock market forecasting.
The Role of Digital Manufacturing in Achieving Operational Excellence/an Exploratory Study of the Opinions of Al-Kindi Factory Managers
Authors: Naba Ali Najmaldeen
Published: February 2025

The current study aimed to crystallize the cognitive and field premises of the study according to a philosophical perspective that simulates the contents of contemporary trends, within the framework of what is known as digital manufacturing as a contemporary technology that can contribute to achieving operational excellence, which represents a goal that contemporary companies seek, and on the basis of that, local companies now need to Adopting technical and administrative approaches that go beyond traditional frameworks in employing contemporary manufacturing techniques and providing a qualified human resource in a way that enables it to benefit from its resources efficiently and effectively.
We adopted the questionnaire form as a tool for collecting data and information, which was distributed to the individuals surveyed at the Al-Kindi factory, by (220) and (205) valid questionnaires were obtained. The data and information were analyzed using statistical programs (AMOS V.25), and the results showed support for its hypotheses and the positivity of its goals. The field verification was consistent with the theoretical objectives, and there was a correlation and influence relationship between the investigated variables, their techniques, and their dimensions. The study reached a set of conclusions, according to which we presented a number of proposals and a mechanism for implementing them.
Factors Impacting on Corporate Financial Performance: Evidence from Engineering Corporations
Authors: Mohammad Tayseer Alshaboul
Published: February 2025

This research aimed to investigate the influence of the board of directors on the financial performance of Jordanian engineering businesses listed on the Amman Stock Exchange (ASE) by assessing several variables, including board size, board independence, and CEO duality. Financial success is assessed by return on assets and Tobin's Q. Between 2013 and 2023, 143 engineering businesses were analysed. The research revealed that the indexed organisations at ASE from 2013 to 2023 had substantial financial success aligned with Jordan's enhanced comprehension and implementation of board of directors' characteristics. This research showed that board size and independence exerted an impact on CEO dualism about financial success. The report advises frequent evaluation of the codes and mandates that firms assess corporate governance principles via laws and regulations to promote adherence to these standards. Additionally, the expertise, commitment, and independence of board members are continuously evaluated. Stock exchanges need to provide seminars and workshops for business managers and decision-makers to improve comprehension of good corporate governance, particularly its significance.
Enhanced IEEE 802.16 Network Performance Utilizing Directional Antennas and Resource Allocation
Authors: Aydin M. Saeed,
Published: February 2025

This paper investigates the performance enhancement of WiMAX (IEEE 802.16) networks through the integration of directional antennas and innovative resource allocation algorithms. Two main methods are presented: a load-balancing technique to guarantee fair traffic distribution among relay stations (RS) and a multi-level search approach for the best communication path selection. When compared to baseline designs, simulations using OPNET software show notable performance gains, such as a 34% increase in throughput and a 26% decrease in delay. These results underscore the potential of the proposed approach in optimizing multi-hop wireless networks, with practical applications in different network scenarios such as WiMAX-based Networks for 5G and IoT.
Yield Analysis in Semiconductor Manufacturing: Techniques, Case Studies, and Future Direction
Authors: Namrata P Khope
Published: February 2025

Introduction: This paper aims to provide a comprehensive overview of yield analysis techniques, applications, and future directions in semiconductor manufacturing, highlighting their significance in improving productivity, quality, and competitiveness.
Objective: The scope of this paper includes Techniques Line yield, Die yield, Pareto yield, Functional yield, Parametric yield, Wafer Sort, Fishbone Diagrams, Scatter Plot, Regression Analysis, and Fault Tree Analysis.
Methods: The paper mainly focused on the examination of yield analysis in semiconductor manufacturing, including defect reduction, process optimization, and product quality improvement.
Result: The comprehensive analysis gives an in-depth analysis of yield analysis techniques strengths, weaknesses, opportunities, and threats (SWOT analysis) and their applications. Conclusion: Future Research Directions are opportunities for improving yield analysis techniques and their applications.
Enhancing Financial Management Behaviour through Knowledge, Attitudes, and Locus of Control: Insights for the Digital Age
Authors: Jayashree Kowtal
Published: February 2025

This study intends to find out how financial management behaviour impacts Higher Education Institution’s (HEI’s) students in India. In this study, the research how financial management is influenced by financial knowledge (FK), financial attitudes (FA), and locus of control (LOC). The primary data for this study was collected using questions that were distributed directly and indirectly, such as through social media. The sampling of the initial study consisted of one hundred and two respondents, who were chosen at random using the sampling technique through the google form-filling method. The results of this study demonstrate that financial behaviour is highly influenced by Financial Knowledge (FK), Financial Attitude (FA) and Locus of Control (LOC). But while managing their finances, everyone must decide whether they want to spend money or save money.  In general, one of the key criteria in establishing an individual's economic well-being is their ability to manage their finances. The data was analysed using the statistical tool of Anova, Correlation, Normality, Reliability Test, Normality Test to test the relationship between the variables.
Blockchain In Healthcare- A New Paradigm for Hospital Collaboration
Authors: Kimsy Gulhane
Published: February 2025

An integration of blockchain in the healthcare industry, has become a game changer that alters the means of managing, sharing and securing medical data. A new paradigm for hospital collaboration based on blockchain is considered in this research, which takes into account blockchain’s ability to simplify operations, secure data, and better care patients. The decentralized nature of blockchain makes it an ideal mechanism to assist in the management of electronic health records (EHRs) by securing sensitive patient information in a decentralized manner and enable authorized healthcare providers to access the information in real time. In hospitals smart contracts allow us to automate processes, reduce the administrative overhead and improve the transparency in collaborative care paradigms. Additionally, the paper studies the beneficial impact of blockchain on enabling more effective communication between hospitals, minimizing errors, and facilitating smooth sharing of the patient’s medical history across healthcare providers. In this paper, by qualitative and quantitative analysis, the benefits and encounters of blockchain acceptance in healthcare systems are evaluated and finally, a complete frame of blockchain implementation in healthcare systems is suggested. The results indicate that, even though blockchain has potential to greatly enhance hospital collaboration, its actual deployment is stymied by technical, regulatory, and organizational barriers. Ultimately, blockchain can transform healthcare delivery by offering a basis for improved, more secure, and more collaborative hospital networks.
Artificial Intelligence and Human-Centered in Linguistic Analysis: Self-Regulated Learning and Development
Authors: N. Vijayalakshmi
Published: February 2025

Recently, learning approaches have utilized Artificial Intelligence (AI) and the Internet of Things (IoT) to create an efficient learning environment. The application of IoT and AI to improve learning systems is thoroughly examined in this paper. Additionally discussed are various IoT and AI-based approaches and strategies related to e-learning, M-learning, methods employed, and particular applications. A new era of linguistic and literary analysis is ushered in by the convergence of technology and the humanities. The creative ways that artificial intelligence provides for comprehending and interpreting texts make it relevant. As it enables the discovery of meaning, style, and other aspects of language usage in texts, linguistic analysis of texts is a crucial component of philological investigation. In this human-centric paradigm, it also becomes imperative to investigate how AI aligns with human values. In particular, giving in-service teachers examples of how to apply the suggested framework improved their understanding of generative AI concepts and how to incorporate them into their instruction. To handle learning techniques more effectively, the results of this evaluation will guide the creation of strategies that combine IoT and AI.
NeoCoD: A New Standard in IoT-Based Predictive Analytics for Neonatal Health Monitoring
Authors: Prashant Jani
Published: February 2025

Introduction: The importance of neonatal health monitoring lies in early detection, and thus, addressing NICU (Neonatal Incentive Care Unit) patients with timely medical treatment. Current predictive models typically use only either medical imaging or sensor data, limiting their efficiency for real time detection of multiple neonatal conditions. To overcome this limitation, we introduce NeoCoD (Neonatal Cause of Disease Predictor), an IoT-based predictive analytics system that combines physiological markers based on sensor data with medical imaging data to better assess the health of newborns.
Objectives: The main purpose of this study was to design a hybrid deep learning model using ResNet50 (Residual Neural Network) for medical images feature extraction and Long Short-Term Memory (LSTM) for the temporal analysis of IoT based sensor data. The dataset contains 7132 samples of 270 neonates collected from Sparsh Medical Hospital, Ahmedabad, Dr. Babasaheb Ambedkar Hospital, Mumbai, and publicly available neonatal health data. We colligate neonatal diseases (e.g., Neonatal Respiratory Distress Syndrome (NRDS), Neonatal Sepsis, Jaundice, Hypothermia, Hypoglycemia, Neonatal Pneumonia, Neonatal Apnea, Perinatal Asphyxia, Neonatal Meningitis, Neonatal Encephalopathy, Hyperbilirubinemia, Low Birth Weight Complications) for our study.
Methods: The NeoCoD model pipeline consists of the following multi stages: data preprocessing, feature engineering, model training and performance evaluation. Feature scaling: the feature scaling was performed and rescaled the features to preprocess the information. We trained the model with categorical cross-entropy loss, Adam optimizer and regularization methods like dropout (0.3) layer and batch normalization. For comparing the performance of NeoCoD with ne trivial solutions, Random Forest and SVM models, accuracy, precision, recall, and F1-score are used.
Results: Experimental results show that NeoCoD considerably outperforms traditional models with accuracy of 92.5% against Random Forest (87.3%) and SVM (84.5%). This model is capable of rapid multimodal processing and prediction on incoming data, providing real-time predictions with 2 seconds response time which makes it particularly suitable for NICU applications.
Conclusions: These results establish NeoCoD as a high-accuracy, scalable neonatal health monitoring system for infant dieses prediction at earlier stage. Going forward, work will center on increasing generalisability across different hospital contexts, increasing computational efficiency, and improving interpretability for clinical decision-making.
Comprehensive Analysis of‎ Network Performance Usіng Vario‎‎‎‎us Metrics and Algorithms
Authors: Ahmed Abdulljabbar Hassoon Almelli
Published: February 2025

Using a variety of measures and sophisticated algorithms, this study offers a thorough examination of network performance. This research on network performance presents a variety of measures and a number of sophisticated algorithms in their implementation. In ascertaining the dependability, latency, and performance of the network in the course of this work, the following were required to be measured: the CPU speed, bandwidth, I/O speed, traceroute response time, and ping response time. Sophisticated techniques of load balancing and server selection were used in locating high-performing IPs; these include Pincer Search and BENSER. Various means of indicators are highlighted within the article as important in understanding network behavior, specifically for complex routing configurations involving a wide range of traffic loads. Performance assessment issues, relating particularly to rapid development in networking technologies such as 5G and the increasing spread of IoT devices, have been discussed. Real-time performance improvement in networks is sought to be achieved by applying the logic of combined AI with machine learning techniques.
Quantifying Live-Streaming in Book Retail Sector: An Empirical Approach
Authors: Aurellia Destiera Lenka
Published: February 2025

This study explores the growing phenomenon of live-streaming in the book retail sector and its implications for consumer behavior and business performance. Using an empirical approach, specifically structural equation model, we analyze live-streaming as an innovative marketing strategy that combines real-time engagement with personalized content delivery. Drawing on data from online book retailers, we investigate key factors influencing the effectiveness of live-streaming, including viewer engagement, trust-building, and purchase intentions. Our findings reveal that live-streaming significantly enhances consumer trust and satisfaction, leading to increased sales and customer loyalty. Furthermore, we highlight the moderating role of product type and audience demographics in shaping live-streaming outcomes. The study provides valuable insights for book retailers seeking to leverage live-streaming as a competitive advantage in a dynamic e-commerce landscape. By bridging academic theory with industry practice, this research contributes to a deeper understanding of digital transformation in retail and offers actionable recommendations for practitioners.
Increasing Online Awareness of Indonesian Satellite @TelkomsatSolution: A Quantitative Study
Authors: Aziziah Salsabila Zahra
Published: February 2025

Established to enhance telecommunications capabilities in Indonesia, Telkomsat operates a series  of satellites that support various services, including data, voice, and video transmission. The company aims to improve connectivity, especially in remote and underserved areas of the country, by utilizing satellite technology to bridge the digital divide. In the digital age, social media marketing has emerged as a key tactic for raising brand exposure. This study uses the Instagram account @telkomsatsolution as a case study to examine how social media marketing affects brand awareness. The study employed an online descriptive survey, and the questionnaire was the tool utilized to gather data. Context, cooperation, communication, and connection are the four main facets that are highlighted, along with how each affects brand recognition levels. A survey questionnaire was used to gather information from active social media users who follow or are viewers of the account. The findings indicate that brand recognition is greatly influenced by social media marketing, with each component having a supportive function. This study demonstrates the significant impact of social media marketing on brand awareness, as evidenced by the case of the Instagram account @telkomsatsolution. By focusing on the dimensions of context, collaboration, communication, and connection, the analysis highlights how these elements collectively enhance brand visibility and engagement. The findings underscore the effectiveness of targeted social media strategies in building awareness, particularly for a brand like Telkomsat that aims to connect underserved and remote areas through advanced telecommunications services.
Implementation Model of Agribusiness System in Strengthening Shallot Farmer Groups in Bali Province
Authors: I Gusti Agung Nyoman Dananjaya
Published: February 2025

One of the leading agricultural commodities in Bali Province is shallots. The shallot commodity is very promising and has economic value for farmers. The development of shallot production in Bali Province has fluctuated due to the non-optimal implementation of the shallot agribusiness system in each farmer group. The purpose of this research is to analyse the factors that influence the agribusiness system and the strengthening of farmer groups and to design an agribusiness system model in strengthening shallot farmer groups. We conducted this research purposively in Bali Province. The total population in this study amounted to 892 people, and the sampling technique in this study used a proportional random sampling technique so that the total sample was 90 people from 36 shallot farmer groups in Bali Province.
The findings showed that the farming subsystem, the processing subsystem, the marketing subsystem, the production facilities procurement subsystem, and the supporting institutions subsystem are the ones that affect the agribusiness system. On the other hand, the agribusiness system is what affects the strengthening of shallot farmer groups. The establishment of an agribusiness system implementation model aims to strengthen shallot farmer groups in Bali Province through a professional extension approach. The role of the Bali Provincial Government is expected to help facilitate technology and market shallot farmer group products so that market prices are more stable and young farmers are willing to produce and process shallots into a packaged product.
The Impact of International Investment Agreements on Human Rights: A Critical Analysis of Balancing Economic Interests and Human Rights Obligations
Authors: Venkateswaran P.K
Published: February 2025

International Investment Agreements (IIAs) have emerged as pivotal instruments in shaping global economic relations by fostering cross-border investments. These agreements, often aimed at promoting economic growth and stability, come with provisions that can significantly influence the human rights obligations of states. This study critically examines the interplay between IIAs and human rights, focusing on how economic interests are balanced against fundamental human rights obligations in both developed and developing countries.
The research investigates the legal and policy frameworks of IIAs, highlighting key elements such as investor-state dispute settlement (ISDS) mechanisms, expropriation clauses, and regulatory measures. These provisions, while essential for protecting investor interests, may inadvertently constrain states' ability to fulfill their human rights commitments, especially in areas like labor rights, environmental protection, and access to essential services. The study explores specific instances where IIAs have led to challenges in ensuring equitable human rights outcomes, such as disputes involving indigenous communities, public health policies, and social welfare programs. Study combines qualitative analyses of case law and treaty provisions with quantitative assessments of their socio-economic impacts. Case studies from Asia, Africa, and Latin America are presented to illustrate the practical implications of IIAs on human rights protection. The findings reveal a significant gap in the alignment of IIAs with international human rights standards, emphasizing the need for a more integrated approach that balances economic development with the protection of fundamental rights. The study concludes by proposing reforms to the existing IIA framework, advocating for the inclusion of explicit human rights provisions, enhanced transparency, and stronger accountability measures. It underscores the importance of fostering a harmonious relationship between economic governance and human rights, ensuring that international investment does not come at the expense of vulnerable populations or undermine state sovereignty in protecting public welfare.
An Energy Efficient Virtual Machines Placement in Cloud Datacenters using Adaptive Greedy Dingo Optimization Algorithm
Authors: Saravanan Madderi Sivalingam
Published: February 2025

More dependable service storage and a lower cost model for different forms of data storage are the primary objectives of the cloud data storage process, without taking infinite scalability into account. There are requirements that cloud users must fulfill because of stored procedures. However, since it impacts the data's quality and integrity, keeping a lot of data is crucial. Utilizing a hybrid heuristic technique, we suggest a cloud storage model that is dependable and effective to surpass these difficulties. Managing deployment and typical limits in cloud environments, as well as optimizing data storage, are the primary objectives of this system. Priorities are set for generic device capacity restrictions and data allocation rules. To overcome these constraints, we optimize the cloud data storage components using the Adaptive Greedy Dingo Optimization Algorithm (AGDOA). Ineffective virtual machine deployment (VMP) and the sharing of shared physical systems by several users are the outcomes of this, which raises interworking costs, wastes resources, consumes excessive power and creates security risks. A novel system called Secure and Multipurpose Virtual Machine Deployment (SM-VMP) is provided with efficient virtual machine migration capabilities to tackle the aforementioned difficulties. In addition to minimizing reciprocal communication delays and ensuring energy-efficient resource deployment between virtual machines, the suggested system places a strong emphasis on the timely and secure execution of user applications. Whale evolutionary optimization and non-dominated sorting-based genetic algorithms serve as inspiration for the proposed ISOA (Improved Seagull Optimization Algorithm), which is used to achieve VMP. The results are examined and contrasted with those obtained from previous approaches after the performance has been confirmed. Thus, for accurately storing cloud data, the suggested paradigm yields optimal outcomes.
Implementing Deep Learning Models for Accurate Brain Abnormality Detection and Positioning Using MRI and CT
Authors: Amita Jajoo
Published: February 2025

Medical imaging is mostly dependent on brain imbalance diagnosis as it enables physicians to locate and treat brain diseases early on. Conventional methods of diagnosis rely much on the views of professionals, who may take a long time and result in errors. Deep learning models have great potential to assist automate the categorisation of medical pictures, improve diagnosis accuracy, and lower the manual labour required in diagnosis process. However, modern models must be tuned to increase their accuracy as they often suffer with how they consume computer capability. Using MRI and CT pictures, this study uses deep learning models, especially ResNet50 and Xception, to sort and pinpoint brain problems. A number of classification models were made and tested to see how well they did in terms of accuracy, precision, and memory. These models included normal, lightweight, and fine-tuned versions of ResNet50 and Xception. The outcomes indicate that fine-tuned Xception did better than other models, with better localisation and classification accuracy. Also, combining MRI and CT scans was looked into as a way to improve model performance, which led to more consistent classification. A comparison of models shows that deep learning is good at automatically finding brain problems, which could lead to big steps forward in medical diagnosis. The study recognize optimised deep learning models make brain abnormality recognition much more accurate and reliable, cutting the need for human analysis. More study will be done in the future to improve model designs and add Explainability methods so that they can be used in clinical settings.
Assessing the Role of AI and Human Service Quality in Enhancing Student Satisfaction and Loyalty in the Educational Sector
Authors: C. Meera
Published: February 2025

The integration of Artificial Intelligence (AI) and human-driven services in the educational sector is transforming service delivery, significantly impacting student satisfaction and loyalty. This study explores the balance between AI-enabled and human service quality in enhancing student experiences within Arts and Science Colleges in Coimbatore City. A structured questionnaire was administered to 200 students aged 18–25, capturing insights on AI Service Quality (AISQ), Human Service Quality (HSQ), Student Satisfaction (SS), and Student Loyalty (SL). Using Structural Equation Modelling (SEM), the findings reveal that AISQ and HSQ positively influence SS and SL, with HSQ demonstrating a stronger impact. SS emerges as a critical mediator, explaining 65% of the variance in SL, underscoring its pivotal role in fostering loyalty. AISQ contributes to operational efficiency and personalization, while HSQ strengthens emotional connections through empathy and responsiveness. The results highlight that educational institutions must strategically integrate AI technologies with human-centric approaches to achieve optimal satisfaction and loyalty outcomes. These findings offer valuable implications for leveraging AI and human resources as complementary assets to enhance service ecosystems in higher education.