bluuebunny/crossref_metadata_2025 huggingface dataset:
HEPATITIS B VACCINATION STATUS AMONG A TARGET GROUP BORN IN 1992-2000: A STUDY FROM BULGARIA
Authors:   Laboratory of Virology, Military Medical Academy, Sofia, Bulgaria., Gotseva Aneliya , Velcheva Desislava ,   MDL “Cibalab”, Sofia, Bulgaria., Naseva Emilia ,   Department of Health Economics, Faculty of Public Health, Medical University - Sofia, Bulgaria.
Published: May 2024

The purpose of this study is to determine the prevalence of stress among students of various health Hepatitis B virus (HBV) infection is a significant cause of morbidity and mortality and a leading cause of chronic liver disease. Vaccination has a key role in hepatitis B prevention. Compulsory immunization for all healthy newborns was introduced in Bulgaria in 1992 as part of the WHO global strategy. Purpose: The aim of this study was to determine the extent of post-vaccination seroprotection among persons born in 1992 – 2000 who received recombinant hepatitis B vaccine. Material/methods: A total of 923 serum samples of a target group of vaccinated individuals (412 males and 511 females) at a mean age of 23.0±2.7 years were tested over a two-year period (2018-2019). The quantitative analysis of hepatitis B surface antibodies (anti-HBs) levels was performed by CLIA using a LIAISON® anti-HBs II quantitative diagnostic kit (Dia Sorin, Italy). Results: All 923 individuals included in the study were hepatitis B surface antigen (HBsAg) negative. Protective anti-HBs titers ranging from 11 to >1000 mIU/mL were found in 45.3% of them. The sex distribution of the tested subjects varied significantly between years (p=0.023). The difference in protective anti-HBs levels between the two sexes was non-significant (p>0.05). There was a weak negative correlation between year of birth and anti-HBs titer (Pearson's r=0.351, p<0.001). Conclusions: The results of this large study conducted among subjects immunized against hepatitis B in childhood showed varying levels of post-vaccination seroprotection. On average, 23 years after universal immunization, 54.7% of the study cohort had no protective levels of anti-HBs (negative or equivocal result).
The Pyogenic Hepatic Abscess: To Drain or not to Drain, that is the Question. Clinical Presentation and Outcome Evaluation after Conservative and Invasive Treatment
Authors:   Department of Surgical, Oncological and Oral Sciences, University of Palermo, Policlinico P. Giaccone. Via Liborio Giuffré 5, 90127 Palermo, Italy., R Guercio , MP Proclamà ,   Department of Surgical, Oncological and Oral Sciences, University of Palermo, Policlinico P. Giaccone. Via Liborio Giuffré 5, 90127 Palermo, Italy., L Licari ,   Department of Surgical, Oncological and Oral Sciences, University of Palermo, Policlinico P. Giaccone. Via Liborio Giuffré 5, 90127 Palermo, Italy., M Marcianò ,   Department of Surgical, Oncological and Oral Sciences, University of Palermo, Policlinico P. Giaccone. Via Liborio Giuffré 5, 90127 Palermo, Italy., P Venturelli ,   Department of Surgical, Oncological and Oral Sciences, University of Palermo, Policlinico P. Giaccone. Via Liborio Giuffré 5, 90127 Palermo, Italy., G Salamone ,   Department of Surgical, Oncological and Oral Sciences, University of Palermo, Policlinico P. Giaccone. Via Liborio Giuffré 5, 90127 Palermo, Italy.
Published: May 2024

Background: Pyogenic hepatic abscess (PHA) is an uncommon infectious disease of the liver that should represents a life-threatening condition if not correctly diagnosed and managed. Nowadays there are not precise indications and clear evidences on the best therapeutic approach and on the superiority of a treatment. Methods: Patients with diagnosis of PHA admitted at the Policlinico “Paolo Giaccone” at Palermo University Hospital between January 2010 and December 2022 were identified in a prospective database. Among the demographic, epidemiological and clinical data collected we included the therapeutic choices – antibiotic treatment, image guided percutaneous drainage and surgical operation – and their effect in terms of healing time, treatment failure and time of hospitalization. Results: Patients with single abscesses treated with antibiotics have significant high healing time (p=0.0001) and in-hospital time (p=0.0001) and no differences in treatment failure than patients treated with invasive techniques. Patients that underwent surgical operation have significant higher healing time than percutaneous group (p=0.002) such as higher mean hospital stay (p=0.03 for abscesses <5 cm and p=0.02 for abscesses larger than 5 cm). The last, the percutaneous approach shows a failure rate significantly higher than the surgical procedure (p=0.009 for abscesses <5 cm and p=0.001 for abscesses larger than 5 cm). Conclusions: The study proposed showed the antibiotic therapy is the first line treatment and should first empirically and then directly modified on specimen culture results. Image guided percutaneous drainage should be considered when the antibiotic therapy alone is not sufficient or as bridge to surgery if surgical indications are proven.
Ruptured popliteal artery aneurysm: An initial presentation of Behçet’s disease
Authors:   Department of Vascular Surgery, Royal Free Hospital, London, UK., Theocharopoulos I , Crisp E ,   Department of Vascular Surgery, Royal Free Hospital, London, UK., Harris M ,   Department of Vascular Surgery, Royal Free Hospital, London, UK., Hamilton H ,   Department of Vascular Surgery, Royal Free Hospital, London, UK.
Published: May 2024

Behçet’s Disease (BD) is an uncommon systemic vasculitic disorder, that can affect multiple organ systems. Arterial and venous aneurysmal and occlusive manifestations can occur. We report a case of a 25-year-old male presenting with a 4-week history of left leg pain and swelling, with acute deterioration due to ruptured popliteal aneurysm, confirmed on imaging. He underwent emergency popliteal bypass with ipsilateral reversed great saphenous vein. Subsequent workup revealed pulmonary aneurysms and features suggestive of BD. He received aggressive immunosuppressive therapy to good effect. The management of the patient’s popliteal aneurysm, diagnostic procedure and subsequent management are discussed.
Social Media Data Mining and Mental Health Status Assessment Based on Sentiment Analysis
Authors: Lihao Wang
Published: May 2024

The usage of the word Power quality in recent times acquired intensified interest due to the complex industrial processes. The usage of intelligent tools to improve power quality is increasing day by day, as assumption of present day power system as a linear model is unsatisfactory. This paper deals with analysis of Differential Evolution (DE), Hybrid Differential Evolution (HDE) and Variable Scaling Hybrid Differential Evolution for harmonic reduction in the source current with optimal tuning of PI controller gain values. Shunt Active power Filter is one of the better solution to suppress the source current harmonics which are induced into power system because of nonlinear loads. Current controller called HBCC is considered for gating operation of switches in Voltage Source Inverter. The Intelligent tuned PQ theory is used for reference current generation. The then obtained compensating currents are injected at point of common coupling for current disturbance mitigation. Simulations of MATLAB/SIMULINK environment of the present work shows the efficacy.
Development and Optimization of Language Reading Comprehension Aids Based on Natural Language Processing
Authors: Chuqing Zhang
Published: May 2024

This study looks into the development and optimization of language reading comprehension aids based on Natural Language Processing (NLP) and assesses their usefulness in improving reading comprehension skills. Using a structured methodology that includes data collection, model training, interface design, and empirical evaluation, the study highlights the revolutionary potential of NLP technology in improving comprehension instruction. A random sample of 100 individuals from various educational backgrounds was randomized to experimental and control groups, with the former using NLP-based tools and the latter using traditional study methods. Following a four-week intervention session, participants were given standardized reading comprehension exams. The results show a considerable difference in comprehension scores between the experimental and control groups, with the former scoring significantly higher. Also, interaction with certain characteristics in the experimental group, such as text summary and tailored recommendations, was strongly connected with comprehension outcomes. These findings emphasize the relevance of tailored, adaptive help in text comprehension and the potential of NLP-based aids in boosting educational outcomes. While the study provides persuasive evidence for the efficacy of NLP technology in improving reading comprehension, additional research is needed to investigate the findings' generalizability across varied groups and educational settings.
Communication and Influence of Traditional Culture Based on Social Network Analysis
Authors: Jia Zhao
Published: May 2024

Social media significantly impacts traditional cultures, yet existing methods struggle to capture the delicate complexities of online cultural discussions. This lack of depth limits understanding of how traditions evolve and adapt in the digital age. To address this, CaCCGAN: Social Culture Analysis, a framework leveraging social network analysis (SNA) is proposed. Content-Aware Cycle-Consistent Generative Adversarial Network (CaCCGAN) explores communication and influence dynamics surrounding traditional culture on social media platforms. CaCCGAN gathers real-time data on traditional culture from Weibo, a prominent Chinese platform, focusing on relevant keywords. By employing Term Frequency-Inverse Document Frequency (TF-IDF) and a Binary Waterwheel Plant Optimization Algorithm (BWPOA), CaCCGAN identifies user-tradition relationships, influential users, and online communities. Furthermore, the CaCCGAN model analyzes communication styles and generates content reflecting these relationships. Analyzing this generated content offers valuable insights for stakeholders interested in cultural preservation, targeted marketing campaigns, and content creation strategies. The proposed CaCCGAN: Social Culture Analysis model attains 25.23%, 21.06% and 55.11% higher Precision value and 29.096%, 41.64% and 33.33% higher F1 Score value compared with the existing method such as PA-BiLSTM Neural Encoding for Cultural Adaptation and Emotion Analysis in Modern Media Communication (PA-BiLSTM),  MFCSNet: Modeling Musician-Follower Dynamics in a Complex Social Network to Measure Musical Influence (MFCSNet)   and Utilizing Social Media Data Analytics to Integrate Traditional Culture Through Digital Media Interaction and Dissemination (I-RankClus-WOA) respectively. This proposed CaCCGAN: Social Culture Analysis model presents a promising approach to understanding the evolving dynamics of communication and influence surrounding traditional culture online.
Time Series Analysis and Algorithm for Fluctuation Pattern Recognition and Forecasting of Economic Indicators
Authors: Pingfen Liu
Published: May 2024

This study examines the use of artificial neural network (ANN) algorithms in machine learning for recognizing fluctuation patterns and forecasting economic indicators in the field of political economy. In today's changing global economic scene, economic forecasting is critical for informing policy decisions, directing strategic planning, and managing risks. Traditional statistical approaches and time series analysis have long been used for this purpose, but the advent of ANN algorithms provides a new way to capture the intricate dynamics of economic systems. Using ANNs' intrinsic flexibility and nonlinear modelling capabilities, this study analyzes their ability to effectively forecast future swings in economic indices. This work intends to understand the strengths, limits, and practical consequences of ANN-based forecasting models by conducting a complete assessment of literature, methodological methods, and empirical studies. This study adds to the continuing discussion of economic forecasting approaches by diving into theoretical frameworks, methodological concerns, and empirical data. Finally, incorporating ANN algorithms into machine learning offers promising opportunities for improving the understanding of economic processes and supporting informed decision-making in the political economy.
Machine Vision-based Trajectory Tracking of Honey Bee Crawling Motion
Authors: Zhiye Yao
Published: May 2024

With the maturity of machine vision technology in artificial intelligence and the rise of the concept of "smart agriculture", machine vision-based methods are widely interested in studying biological behavior, but the technology for acquiring biological behavior information is still unsound. In this paper, we propose a machine vision-based bee crawling trajectory tracking method, which automatically records the bee crawling trajectory in the video information and obtains the description of bee crawling behavior. The method establishes the description of bee crawling motion through bee target detection, bee crawling motion tracking and bee crawling motion prediction as a method to study bee crawling behavior through artificial intelligence techniques. The method has the advantages of high timeliness and low labor cost, and reduces the problems of traditional research through manual observation records and bioassays methods with high influence of human subjective judgment factors and high application costs.
Design an Effective, Faster Region-Based Convolutional Neural Network with Optimization for the Human Tracking System
Authors: M. Srilatha
Published: May 2024

Nowadays, real-time Human Tracking System (HTS) is a crucial topic in computer vision and image processing with applications like robotic perception, scene understanding, video surveillance, image compression, medical image analysis, and augmented reality, among many others. In this paper, we design a Faster Region-based Convolutional Neural Network (FR-CNN) with Crow Search Optimization (FR-CNN-CSO) architecture to improve computational complexity and enhance the performance of HTS. The system is implemented in a Python environment with video input. Remove unnecessary data from the gathered datasets during preprocessing. Next, feature extraction is processed using Histograms of Oriented Gradients (HOG). Then update, the extracted features into a designed FR-CNN model for identifying and tracking a person using crow search fitness. The main goal of the developed approach is to attain accurate prediction results and improve the computational complexity by achieving less execution time. Finally, the experimental outcomes show the reliability of the designed system by other conventional techniques in terms of accuracy, precision, recall, F-measure, and execution time.
Estimating the Optimal State of Charge for Electric Car Batteries Using an Extended Kalman Filter
Authors: S. Deivasigamani
Published: May 2024

The efficient management of battery state of charge (SOC) is crucial for maximizing the performance, range, and longevity of electric vehicle (EV) batteries. This paper presents a novel approach for estimating the optimal state of charge of electric car batteries using an Extended Kalman Filter (EKF). The EKF is a recursive algorithm that combines measurements from various sensors with a dynamic battery model to estimate the current SOC and predict future SOC values with high accuracy. The paper provides a detailed explanation of the EKF algorithm and its application to battery SOC estimation, highlighting its ability to handle nonlinearities, uncertainties, and measurement noise inherent in battery systems. Furthermore, this research presents a simulation-based validation of the proposed EKF approach using real-world driving data from electric vehicles. The simulation results demonstrate the effectiveness of the EKF algorithm in accurately estimating the SOC of electric car batteries under various operating conditions, including different driving patterns, temperatures, and battery degradation scenarios.
Novel Optimization-Driven Feature Selection Approach for Enhancing Phishing Website Detection Accuracy
Authors: Mousa Saeed Muslim
Published: May 2024

Phishing attacks continue to pose significant threats to cybersecurity, prompting the need for robust detection mechanisms. This study introduces an optimization-driven feature selection approach aimed at enhancing the accuracy of phishing website detection. By systematically selecting and prioritizing relevant features, guided by advanced optimization techniques, the proposed approach outperforms traditional baseline methods across various evaluation metrics. Experimental results demonstrate notable improvements in detection accuracy and robustness compared to conventional techniques. The optimization-driven approach offers scalability, adaptability, and efficiency in navigating the feature space, making it suitable for diverse datasets and scenarios. This study contributes to the advancement of phishing detection systems by providing a systematic and effective approach for identifying relevant features and minimizing false alarms and false negatives. Future research should explore the integration of machine learning models and conduct real-world validation studies to further validate the effectiveness and generalizability of the proposed approach.
Artificial Intelligence and Machine Learning in Financial Services: Risk Management and Fraud Detection
Authors: Satwinder Singh
Published: May 2024

The field of financial risk management is undergoing a significant transformation due to the advancements in artificial intelligence (AI) and the underlying machine learning (ML) techniques that provide the foundation of AI. These developments hold the potential to revolutionize the way the user’s approach and address financial risk. The expansion of AI-driven solutions has opened up various opportunities for comprehending and managing risk. These opportunities encompass a wide range of activities, such as determining appropriate lending amounts for customers in banking, issuing warning signals to financial market traders regarding position risk, identifying instances of customer and insider fraud, enhancing compliance efforts, and mitigating model risk. The prime objective of this study is to investigate the application of AI and ML in the Financial Services industry, with a specific focus on Risk Management and Fraud Detection. This study proposes an intelligent and distributed approach for detecting Internet financial fraud using Big Data. The methodology entails the utilization of the graph embedding algorithm Node2Vec for the purpose of acquiring knowledge and representing the structural characteristics of the financial network graph in the form of compact vectors with reduced dimensions. This facilitates the intelligent and effective categorization and forecasting of data samples from a dataset of significant magnitude through the utilization of a deep neural network. Based on the study's findings, it was observed that the F1-Score test outcomes obtained from the Node2Vec algorithm range from 67.1% to 73.4%. These results surpass the outcomes achieved by the other two algorithms used for comparison. This finding demonstrates that Node2Vec has greater stability in terms of overall performance and yields superior categorization outcomes.
Injury Risk Prediction and Prevention Algorithm for Athletes Based on Data Mining
Authors: Xuelian Dong
Published: May 2024

The injury risk prediction and prevention algorithm for athletes employs data mining techniques to analyze various factors contributing to athlete injuries, such as training load, biomechanics, previous injuries, and environmental conditions. By collecting and analyzing vast amounts of data from wearable devices, training logs, and medical records, the algorithm identifies patterns and trends associated with injury occurrence. Using machine learning models, it predicts the likelihood of an athlete sustaining an injury based on these patterns. Additionally, the algorithm provides personalized injury prevention strategies tailored to each athlete's risk profile, including adjusting training regimens, modifying technique, and recommending recovery protocols.  Injury prevention is a critical aspect of athlete safety and performance optimization in sports. This paper proposed a novel PMin-Max injury risk prediction and prevention algorithm designed to provide comprehensive assessments of injury risks for athletes. The algorithm leverages data mining techniques to compute both best-case and worst-case probabilities of injury, enabling practitioners to understand the range of potential outcomes and associated uncertainties. By computing minimum and maximum probabilities of injury for individual athletes and generating risk scores, the algorithm facilitates tailored preventive measures based on the severity of predicted injury risks. Through simulation experiments and analysis of simulated results, we demonstrate the algorithm's effectiveness in identifying athletes with elevated injury risks and highlighting situations where injury risks are relatively low.
Realization of Chinese-English Bilingual Speech Dialogue System using Machine Translation Technology
Authors: Zhao Hongdan
Published: May 2024

The realization of a Chinese-English bilingual speech dialogue system through machine translation technology involves developing a sophisticated system capable of seamlessly translating spoken language between Chinese and English in real-time. This system employs cutting-edge machine learning algorithms, neural networks, and natural language processing techniques to accurately interpret and translate speech inputs from one language to another. By integrating advanced speech recognition and translation models, users can engage in fluid and natural conversations across language barriers, opening up new possibilities for cross-cultural communication and interaction. This paper introduces a Statistical Phase-based Bilingual Speech (SPBS) system designed to facilitate seamless language translation and dialogue between multiple languages, with a focus on Chinese and English. Leveraging advanced machine learning models and techniques, such as Recurrent Neural Networks (RNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) architecture, the SPBS system achieves high translation accuracy, computational efficiency, and fluency of translations. The system's multilingual model attains an impressive translation accuracy of 97% while processing 10 sentences per second, with positive feedback on the fluency of translations. Trained on a substantial dataset of 1 million bilingual sentence pairs, the SPBS model maintains a compact size of 500 MB. Furthermore, the paper presents the machine learning settings and training progress of the SPBS system, demonstrating its effectiveness in accurately classifying and translating speech inputs across languages. The system's multilingual model attains an impressive translation accuracy of 97% while processing 10 sentences per second, with positive feedback on the fluency of translations. Trained on a substantial dataset of 1 million bilingual sentence pairs, the SPBS model maintains a compact size of 500 MB.
Design and Implementation of Teaching Quality Assessment System for Universities Based on Data Mining Algorithms
Authors: Du Gang
Published: May 2024

A teaching quality assessment system for universities based on data mining algorithms utilizes advanced analytical techniques to evaluate and enhance the effectiveness of teaching practices. By leveraging data mining algorithms, such as clustering, classification, and association rule mining, this system can analyze various educational data sources, including student performance metrics, course evaluations, and instructor feedback. This paper introduces Generative Pre-trained Data Analytics (GPDA) as a novel approach for assessing teaching quality in educational settings. GPDA leverages advanced data analytics techniques to predict teaching quality metrics, classify teaching quality data, and forecast various aspects of teaching effectiveness. Through comprehensive analysis and evaluation, GPDA demonstrates its accuracy, reliability, and versatility in capturing the complexities of teaching quality across diverse educational contexts. Through comprehensive analysis and evaluation, GPDA achieves a Root Mean Squared Error (RMSE) ranging from 0.152 to 0.190 and R-squared (R²) values ranging from 0.698 to 0.805, showcasing its accuracy and reliability in predicting teaching quality metrics across diverse educational contexts. Furthermore, strong positive correlations between the synthetic data generated by GPDA and real teaching quality data, with correlation coefficient values ranging from 0.87 to 0.92, validate the fidelity of GPDA-generated synthetic data.
Course Design Based on Association Rule Mining in Blended Teaching of College English
Authors: Yuan Yuan
Published: May 2024

Course design based on association rule mining in blended teaching of college English offers a data-driven approach to curriculum development, enhancing the effectiveness of English language instruction. By analyzing patterns and relationships within student performance data, association rule mining algorithms identify correlations between different learning activities, topics, and assessment outcomes. This information enables instructors to design courses that strategically integrate online and offline learning components, ensuring a cohesive and engaging learning experience. Additionally, association rule mining facilitates the identification of potential dependencies between course elements, allowing for the optimization of teaching sequences and resource allocation. This paper investigates the use of the Extraction Apriori Rule Mining Blended (EARMB) technique to enhance teaching effectiveness in English instruction within blended learning environments. Through the analysis of association rules derived from instructional data, significant relationships between instructional components and student outcomes are identified, providing valuable insights for course design and instructional strategy development. A structured course design framework is presented, integrating various instructional activities and assessment methods to address diverse language skills and learning objectives. Additionally, a comparative analysis of different teaching approaches, including traditional classroom instruction, online learning platforms, and blended teaching, demonstrates the efficacy of blended teaching in promoting student engagement, improving learning outcomes, and enhancing overall student satisfaction.
Blockchain Targets Integrated IoT for Smart Healthcare Systems - A Bibliometric Analysis
Authors: Chen Hao
Published: May 2024

Blockchain technology and the Internet of Things (IoT) for smart healthcare systems has emerged as a promising area of research, as evidenced by a bibliometric analysis. This analysis explores the scholarly literature on the integration of blockchain and IoT in healthcare, identifying key trends, influential publications, and research directions. By leveraging blockchain's decentralized and immutable nature alongside the IoT's ability to collect and transmit real-time health data from various devices, smart healthcare systems aim to enhance data security, interoperability, and patient privacy. Bibliometric analysis provides valuable insights into the evolution of research in this field, highlighting emerging topics, interdisciplinary collaborations, and potential applications that can revolutionize healthcare delivery and management in the future. This paper introduces the Weighted Key Signature for Smart Healthcare Systems (WKSS), a framework designed to optimize healthcare operations within smart environments. With advanced technologies such as blockchain, Internet of Things (IoT), and integration technologies, WKSS aims to improve patient care, resource utilization, and overall healthcare system efficiency. The framework encompasses key components including digital signature generation, computational efficiency, and bibliometric analysis to provide insights into emerging trends and key contributors within smart healthcare systems. Through a comprehensive analysis, including simulations and bibliometric studies, this paper demonstrates the effectiveness of WKSS in reducing patient waiting times, enhancing resource utilization rates, and improving healthcare provider efficiency. Through simulations and bibliometric analysis, significant improvements were observed in key metrics. The average patient waiting time decreased from 45 to 34 minutes, resource utilization rates increased from 70% to 85%, and examination room occupancy rose from 60% to 75%. Additionally, physician and nurse utilization rates improved from 80% to 90% and from 75% to 85%, respectively.
Design And Realization of A Comprehensive Quality Assessment System for College Students Based on the Bp Neural Network
Authors: Du Hui
Published: May 2024

Educational evaluation plays a crucial role in ensuring the quality and effectiveness of teaching, learning, and institutional performance. Traditional evaluation methods often struggle to capture the complex and multifaceted nature of educational processes, leading to limitations in assessing quality comprehensively. In response, this paper introduces the Fuzzy Interface Memetic Algorithm for Quality Assessment (FIMA-QA), a novel framework that integrates fuzzy logic modeling with memetic algorithm optimization to provide a nuanced and adaptable approach to educational evaluation. FIMA-QA leverages fuzzy logic principles to represent input variables and assessment criteria in linguistic terms, allowing for the expression of qualitative relationships and uncertainties inherent in educational assessment. Through memetic algorithm optimization, FIMA-QA iteratively refines fuzzy rule bases to enhance assessment accuracy and reliability over successive generations. This combination of fuzzy logic modeling and evolutionary optimization enables FIMA-QA to effectively evaluate various dimensions of educational quality, including teaching effectiveness, student engagement, learning outcomes, and institutional performance. Empirical studies demonstrate the efficacy of FIMA-QA in capturing the qualitative nature of assessment criteria and providing accurate and comprehensive evaluations of educational processes. For instance, teaching effectiveness assessments ranged from 7.5 to 9.8, student engagement from 8.2 to 9.5, learning outcomes from 8.0 to 8.9, and institutional performance from 8.7 to 9.4, showcasing the numerical precision of FIMA-QA.
Dr First Aider: A vertical chatbot for First Aid
Authors: Vinothini Kasinathan
Published: May 2024

The lack of first aid knowledge can have several implications especially in an emergency situation. The delayed or inadequate response may increase the risk of complications and even result in loss of life. In effort to increase preparedness and first aid awareness among the public, a vertical chatbot called Dr. FirstAider is proposed. The objective of Dr. FirstAider is to provide knowledge on recognizing early sign and symptoms of medical conditions or injuries, so the public is able to respond effectively in emergency situations. The prototype of Dr. FirstAider is developed based on the Engati tool and evaluated via a questionnaire among 30 respondents. The feedback received on the usability of Dr. FirstAider are positive with some suggestions for future work such as dedicating specific sections for children or elderly.
Integrated Index Vector Method and Hybrid Ant Lion Chaotic Evolutionary Programming Optimization (HALCEP) Technique for Loss Minimization in Distribution System
Authors: Muhammad Danial Mohd Zuraimi
Published: May 2024

Distribution system is the final section of the electrical power system, apart from the transmission and generation system. The distribution system acts as the interface that connects the high-voltage network to the low-voltage consumer service point.  Consequently, the distribution system contributes the most of the overall losses in the electrical power system. Hence, evolutionary computation was introduced to accomplish a reduction of the previously said losses. The evolutionary computation involves optimization techniques to allocate distributed generation (DG) to the distribution system. Unfortunately, several optimization techniques are not capable of achieving accurate results and are trapped at local optima. This paper presents the Integrated Index Vector Method and Hybrid Ant Lion Chaotic Evolutionary Programming Optimization (HALCEP) technique for loss minimization in a distribution system. In this study, Evolutionary Programming (EP), Ant Lion Optimization (ALO) and HALCEP optimization engines are developed. The validation process was simulated on the IEEE 33-bus radial distribution system (RDS) model. Comparative studies were performed to gain a clear observation of the advantage of the developed HALCEP over the conventional EP and the ALO techniques, effectively showing its capability to outperform them.
An Assessment of Cybersecurity Awareness among Academic Employees at Quirino State University: Promoting Cyber Hygiene
Authors: Joselle D. Concepcion
Published: May 2024

With the ever-changing and increasing dependence on technology, employee behavior poses a significant cybersecurity risk, and the education sector is no exception. To secure sensitive information and ensure educational activities, enhancing cybersecurity awareness among academic employees is vital. This study aimed to assess cybersecurity awareness at Quirino State University. An online questionnaire survey was presented to employees to assess their comprehension of cybersecurity and adherence to existing rules and procedures. The survey results were examined to establish the level of cybersecurity awareness and opportunities for development. While the majority of academic personnel had a basic awareness of cybersecurity, there were still areas for development, such as the risk of device threats. The findings of this study hold significant implications for the education sector, emphasizing the need for a comprehensive approach to cybersecurity awareness. This approach includes regular training sessions, the establishment of effective policies, and the integration of technology-based solutions. By implementing such measures, the university can foster a proactive defense strategy and create a safer digital landscape for the entire institution, promoting a culture of cyber hygiene among its employees.
SMOTE-based Deep LSTM System with GridSearchCV Optimization for Intelligent Diabetes Diagnosis
Authors: Smita Panigrahy
Published: May 2024

Diabetes is a metabolic illness initiated by either inadequate insulin creation by the pancreas or the body's reduced responsiveness to insulin. It is characterised by consistently high levels of blood sugar and symptoms such as frequent urination, thirst, and increased appetite. Untreated diabetes can result in significant problems that impact crucial organs, presenting potentially fatal dangers. In order to address the need for accurate diagnosis, researchers have utilised artificial intelligence to develop the G-LSTM system. This system employs a novel method that combines SMOTE-based deep LSTM and GridSearchCV optimization to classify diabetes. This technique effectively tackles the issue of class imbalance in diabetes datasets, demonstrating an exceptional level of prediction accuracy. When tested on the PIMA dataset, G-LSTM demonstrated exceptional performance with an accuracy of 97.12%. Additionally, it produced high precision, recall, F1-score, AUC, and MCC values of 97.12%, 0.963, 0.954, 0.887, 0.989, and 0.882, respectively. The results highlight the higher performance of the G-LSTM method compared to other techniques, suggesting its use for clinical investigation of diabetes patients. This innovative intelligent diagnostic framework not only demonstrates the potential of artificial intelligence in healthcare, but also highlights its crucial role in enhancing the precision and effectiveness of diabetes diagnosis and treatment.
The Role of Renewable Energy in Enhancing Environmental Sustainability in Saudi Arabia: Analysis of Policies and Legislation Related to Renewable Energy and Evaluation of their Effectiveness
Authors: Anoud A. G. Almutairi
Published: May 2024

In this paper, we will study the role of renewable energy in promoting environmental sustainability in Saudi Arabia in light of existing policies and legislation. This study assesses the effectiveness of these current policies and legislation in encouraging the energy transition. A survey was conducted among around 200 experts in the field of energy and the environment to highlight the current state of initiatives taken to encourage the transition to clean energy and identify the strengths and weaknesses weaknesses of current policies and laws. By examining these policies and their impact, the study aims to provide insights to improve renewable energy frameworks and maximize large-scale contribution to the energy transition to mitigate environmental challenges in Saudi Arabia.
Analysis of Artificial Intelligence Based MPPT in PV Grid Connected System
Authors: Bandana Gautam
Published: May 2024

The There has been a rise in the demand for electrical power during the past 10 years. Installing a new power generator (PG) is a costly and time-consuming procedure. For this reason, solar power plants are considered a viable alternative for meeting the current energy demand. But crucial maintenance and output power balance are the key issues facing solar power facilities. In order to reduce output power balance and maintenance problems in solar plants, an appropriate approach is required. This study offers a novel method for tracking the maximum power for hybrid photovoltaic (PV) and wind energy systems (WES): single maximum power point tracking, or MPPT. In the proposed MPPT technique, a radial basis function network (RBFN) control mechanism is based on an artificial neural network (ANN).
AI-Enhanced Demand Response Strategies in Smart Grids: Toward Sustainable Energy Future
Authors: Hassan Hadi M.A Al-Fatlawi
Published: May 2024

To accomplish a sustainable energy future, this article researches the consolidation of computerized reasoning (AI) into request reaction components inside smart grids. While considering the joining of sustainable power sources and expanding energy utilization, current energy frameworks depend altogether on load the executives and continuous energy request reaction. Conventional methodologies battle with the intricacies of dynamic energy situations. Results from the examination feature the worth of AI and ML for enhancing energy use. Versatile learning for energy proficiency, exact interest determining, continuous observing, and sustainable power source mix are completely made conceivable by these innovations. An intensive vision for the Smart Lattice is introduced, underscoring financial matters, proficiency, wellbeing, ecological obligation, security, and reliability. We check out at the benefits and downsides of incorporating energy stockpiling gadgets and the job of circulated framework insight. The determination gives a careful future vision to AI-enhanced request reaction in smart grids by featuring research issues connected with self-learning frameworks, complete robotization, self-recuperating grids, fitting and-play advances, network safety, and labor force improvement.
Trust based Reputation Framework for Data Security in Cloud Environment
Authors: K.K.Baseer
Published: May 2024

Cloud computing relies on the Internet and is susceptible to numerous attacks, hence security is the most significant issue.It takes a long time to create trust values for trust authority and cloud servers.Research into trust relationships and trust-based security mechanisms in cloud environments is important because trust has been regarded as a security relationship that is more important than authorization.In a cloud computing environment, trust should exist between cloud users and service providers.As a result, a trust-based reputation framework with fuzzy rules for cloud data security is proposed.The trust values of data centers (DCs) and cloud users (CUs) are independently determined within this framework.The DC examines the CU's reputation and disregards it if it is poor.The user evaluates the serving DC's reputation during transmission to the user and disregards low reputations.The results achieved demonstrate that the trust of the proposed framework performs better.
Dataset for Pharmaceutical Salt Business to Improve the Welfare of Salt's Farmers
Authors: Irfat Hista Saputra
Published: May 2024

This dataset presents data on the percentage of problem priorities, solution priorities and appropriate strategy priorities for developing a pharmaceutical salt business using the Analytic Network Process (ANP). This data consists of data on problems, solutions and strategies. Problem data includes production problems, supporting problems, market problems and stakeholder problems. Solution data includes fundamental solutions, technical solutions, macro strategic solutions and roadmap solutions. Meanwhile, strategy data includes a strategy for preparing a grand design for a community salt agribusiness pattern, a strategy for BUMN and salt processing companies to collaborate with salt farmer cooperatives, a strategy to increase human resource development, and a strategy for fulfilling permits to support the production of packaged salt. Numerical data was obtained from Forum Group Discussion with five experts, five practitioners and five regulators using a priority scale. Cluster priority analysis using Super Decision software version 2.10.
Optimizing Industrial Reliability: A Comparative Study of Hot and Cold Standby Configurations in Three-Unit Parallel Systems
Authors: Prawar
Published: May 2024

This comparative research study explores the efficacy of two parallel systems, Model A employing a hot standby configuration and Model B utilizing a cold standby setup, each comprising three units operating based on demand. The investigation focuses on evaluating the reliability and cost-effectiveness of these systems, employing metrics such as Mean Time to System Failure, availability at full and reduced capacity, repairer busy periods, downtime, and profit analysis using Semi-Markov and regenerative point techniques. Results reveal that Model B (cold standby) outperforms Model A (hot standby) across multiple parameters, including Mean Time to System Failure, availability, repairer busy periods, downtime, and profitability. Specifically, Model B demonstrates higher values for Mean Time to System Failure, Availability at full and reduced capacity, Busy period for repairmen, and Profit in Rs. Consequently, it is inferred that Model B proves to be more reliable and efficient than Model A. These findings underscore the potential advantages of employing cold standby configurations in similar machinery setups, offering insights to enhance system performance and minimize downtime, thus suggesting avenues for optimizing cold standby parallel systems based on the study's outcomes.
An Inventory System for Ilocos Sur Polytechnic State College
Authors: Franz Jan Mico L. Canillas
Published: May 2024

This research presents an inventory management system designed for the Ilocos Sur Polytechnic State College (ISPSC) to enhance the efficiency of its property and supply operations. The system integrates technological advancements, including web-based frameworks and web 3.0 technologies such as blockchain, to streamline inventory processes. Utilizing an Agile framework and Design Thinking methodology, the research identifies user requirements, designs, iterates, and tests the system for usability. Through interviews, user narratives, and SUS evaluations involving 137 participants from ISPSC Main Campus, the system demonstrated excellent usability, with an overall SUS score of 82.9. The results underscore the system's effectiveness in addressing inventory management needs, providing a valuable resource for academic institutions aiming to optimize their inventory processes and enhance operational efficiency.
Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network
Authors: Javaid Ahmad Malik
Published: May 2024

Smart Car's era ushers in new challenges, some of which are in the field of information security such as wise cyber-attack prevention. Traditional IDS systems would lose everything in such a dynamic environment with their centralized architectures, and this approach could create single points of failure and privacy issues.  Along with interconnectedness, cyber security becomes an inevitable problem as smart vehicles are incorporated into a daily life. Traditional security mechanisms usually lack scalability and privacy, which brings about the need to develop alter-nate or innovative methods. This research demonstrates a mixed security system that combines both federated learning and blockchain technologies to improve intrusion detection in smart vehicular networks. We evaluated the effectiveness of this framework using four machine learn-ing models as respectively; Support Vector Machine (SVM), Decision Tree, Neural Network, and Random Forest. Empirical results show that SVM had the highest accuracy of both 93.88% in training and 91.84% in validation, which is higher than Decision Tree, Neural Network, and Random Forest models. These findings evidently demonstrate that the federated learning and blockchain are a strong solution for the plausible security of smart vehicle networks; with SVM being employed mostly in complex security scenarios.
DOI: https://doi.org/10.52783/pst.353
Az intézményválasztás döntési háttere és páciensmotivációja a magánfinanszírozású nagyízületi protetikai műtétek esetében
Authors: Lengyel Lívia , Lám Judit
Published: May 2024

Bevezetés A térdprotézis-műtéti páciensek klinikai eredményekkel kapcsolatos elvárásait megismerendő, valamint egy e célra fejlesztett nemzetközi mérőeszköz hazai statisztikai validálása céljából indítottunk kutatást 2023-ban. A kutatás egy részében azt is vizsgáltuk, milyen tényezők játszanak szerepet a magánintézményt választó páciensek döntésében.
Módszerek A kutatás nulladik fázisában a kérdőívet pilot tesztnek vettettük alá mind csípő-, mind térdprotézis-műtéti páciensek körében. A válaszadókat az online térben, közösségi médiában rekrutáltuk tematikus betegcsoportokban, 2023 júliusa és szeptembere között.
Eredmények A minta nagysága 393 fő, melynek 23,15%-a választott a csípő- vagy térdprotézis-műtétre magánkórházat. Jelen tanulmányban ennek a 91, magánkórházat választó páciensnek a motivációját, intézményválasztásának hátterét mutatjuk be. A műtéti helyszín kiválasztásánál az ortopéd sebész személye a leginkább befolyásoló tényező, a magánkórházat választók 87%-a először az operatőrről dönt.
Következtetések Az ortopéd orvos szerepe mellett nem elhanyagolható, milyen információhoz jut hozzá a beteg, legyen az akár állami -, akár magánműtét. A hiteles, a beteg egészségértéséhez illeszkedő széleskörű tájékoztatás mellett a megalapozott döntéshez szükség lenne transzparenciára, mindkét szektor minőségi indikátorainak bemutatására.