bluuebunny/crossref_metadata_2025_split huggingface dataset:
Allied Health Professionals’ Perceptions of Artificial Intelligence in the Clinical Setting: Cross-Sectional Survey
Authors: Hoffman Jane , Hattingh Laetitia , Shinners Lucy , Angus Rebecca L , Richards Brent , Hughes Ian , Wenke Rachel 
Published: September 2024

Background
Artificial intelligence (AI) has the potential to address growing logistical and economic pressures on the health care system by reducing risk, increasing productivity, and improving patient safety; however, implementing digital health technologies can be disruptive. Workforce perception is a powerful indicator of technology use and acceptance, however, there is little research available on the perceptions of allied health professionals (AHPs) toward AI in health care.

Objective
This study aimed to explore AHP perceptions of AI and the opportunities and challenges for its use in health care delivery.

Methods
A cross-sectional survey was conducted at a health service in, Queensland, Australia, using the Shinners Artificial Intelligence Perception tool.

Results
A total of 231 (22.1%) participants from 11 AHPs responded to the survey. Participants were mostly younger than 40 years (157/231, 67.9%), female (189/231, 81.8%), working in a clinical role (196/231, 84.8%) with a median of 10 years’ experience in their profession. Most participants had not used AI (185/231, 80.1%), had little to no knowledge about AI (201/231, 87%), and reported workforce knowledge and skill as the greatest challenges to incorporating AI in health care (178/231, 77.1%). Age (P=.01), profession (P=.009), and AI knowledge (P=.02) were strong predictors of the perceived professional impact of AI. AHPs generally felt unprepared for the implementation of AI in health care, with concerns about a lack of workforce knowledge on AI and losing valued tasks to AI. Prior use of AI (P=.02) and years of experience as a health care professional (P=.02) were significant predictors of perceived preparedness for AI. Most participants had not received education on AI (190/231, 82.3%) and desired training (170/231, 73.6%) and believed AI would improve health care. Ideas and opportunities suggested for the use of AI within the allied health setting were predominantly nonclinical, administrative, and to support patient assessment tasks, with a view to improving efficiencies and increasing clinical time for direct patient care.

Conclusions
Education and experience with AI are needed in health care to support its implementation across allied health, the second largest workforce in health. Industry and academic partnerships with clinicians should not be limited to AHPs with high AI literacy as clinicians across all knowledge levels can identify many opportunities for AI in health care.
Interpretability Using Reconstruction of Capsule Networks
Authors: Vranay Dominik , Ruzmetov Mykhailo , Sinčák Peter 
Published: September 2024

Abstract
This paper evaluates the effectiveness of different decoder architectures in enhancing the reconstruction quality of Capsule Neural Networks (CapsNets), which impacts model interpretability. We compared linear, convolutional, and residual decoders to assess their performance in improving CapsNet reconstructions. Our experiments revealed that the Conditional Variational Autoencoder Capsule Network (CVAECapOSR) achieved the best reconstruction quality on the CIFAR-10 dataset, while the residual decoder outperformed others on the Brain Tumor MRI dataset. These findings highlight how improved decoder architectures can generate reconstructions of better quality, which can enhance changes by deforming output capsules, thereby making the feature extraction and classification processes within CapsNets more transparent and interpretable. Additionally, we evaluated the computational efficiency and scalability of each decoder, providing insights into their practical deployment in real-world applications such as medical diagnostics and autonomous driving.
AI-Powered Obstacle Detection for Safer Human-Machine Collaboration
Authors: Krupáš Maros , Kot Mykyta , Kajáti Erik , Zolotová Iveta 
Published: September 2024

Abstract
This article deals with ensuring and increasing the safety of mobile robotic systems in human-machine collaboration. The goal of the research was to design and implement an artificial intelligence application that recognizes obstacles, including humans, and increases safety. The resulting mobile Android application uses a MiDaS model to generate a depth map of the environment from the drone’s camera to approximate the distance from all obstacles to avoid the drone’s collision. Besides, this work introduced us to DJI Mobile SDK and neural network optimizations for their use on smartphones.
Multi-Level Machine Learning Model to Improve the Effectiveness of Predicting Customers Churn Banks
Authors: Ngo Van-Binh , Vu Van-Hieu 
Published: September 2024

Abstract
This study presents a novel multi-level Stacking model designed to enhance the accuracy of customer churn prediction in the banking sector, a critical aspect for improving customer retention. Our approach integrates four distinct machine-learning algorithms – K-Nearest Neighbor (KNN), XGBoost, Random Forest (RF), and Support Vector Machine (SVM) – at the first level (Level 0). These algorithms generate initial predictions, which are then combined and fed into higher-level models (Level 1) comprising Logistic Regression, Recurrent Neural Network (RNN), and Deep Neural Network (DNN).
We evaluated the model through three scenarios: Scenario 1 uses Logistic Regression at Level 1, Scenario 2 employs a Deep Convolutional Neural Network (DNN), and Scenario 3 utilizes a Deep Recurrent Neural Network (RNN). Our experiments on multiple datasets demonstrate significant improvements over traditional methods. In particular, Scenario 1 achieved an accuracy of 91.08%, a ROC-AUC of 98%, and an AUC-PR of 98.15%. Comparisons with existing research further underscore the enhanced performance of our proposed model.
ANFIS-AMAL: Android Malware Threat Assessment Using Ensemble of ANFIS and GWO
Authors: Nwasra Nedal , Daoud Mohammad , Qaisar Zahid Hussain 
Published: September 2024

Abstract
The Android malware has various features and capabilities. Various malware has distinctive characteristics. Ransomware threatens financial loss and system lockdown. This paper proposes a threat-assessing approach using the Grey Wolf Optimizer (GWO) to train and tune the Adaptive Neuro-Fuzzy Inference System (ANFIS) to categorize Android malware accurately. GWO improves efficiency and efficacy in ANFIS training and learning for Android malware feature selection and classification. Our approach categorizes Android malware as a high, moderate, or low hazard. The proposed approach qualitatively assesses risk based on critical features and threats. Our threat-assessing mechanism’s scale categorizes Android malware. The proposed approach resolves the issue of overlapping features in different types of malware. Comparative results with other classifiers show that the ensemble of GWO is effective in the training and learning process of ANFIS and thus achieves 95% F-score, 94% specificity, and 94% accuracy. The ensemble makes fast learning possible and improves classification accuracy.
A Systematic Review of Rapidly Exploring Random Tree RRT Algorithm for Single and Multiple Robots
Authors: Muhsen Dena Kadhim , Raheem Firas Abdulrazzaq , Sadiq Ahmed T. 
Published: September 2024

Abstract
Recent advances in path-planning algorithms have transformed robotics. The Rapidly exploring Random Tree (RRT) algorithm underpins autonomous robot navigation. This paper systematically examines the uses and development of RRT algorithms in single and multiple robots to demonstrate their importance in modern robotics studies. To do this, we have reviewed 70 works on RRT algorithms in single and multiple robot path planning from 2015 to 2023. RRT algorithm evolution, including crucial turning points and innovative techniques, have been examined. A detailed comparison of the RRT Algorithm versions reveals their merits, limitations, and development potential. The review’s identification of developing regions and future research initiatives will enable roboticists to use RRT algorithms. This thorough review is essential to the robotics community, inspiring new ideas, helping problem-solving, and expediting single- and multi-robot system development. This highlights the necessity of RRT algorithms for autonomous and collaborative robotics advancement.
Exploring the Efficacy of GenAI in Grading SQL Query Tasks: A Case Study
Authors: Hamtini Thair , Assaf Abdelbaset J. 
Published: September 2024

Abstract
Numerous techniques, including problem-solving, seeking clarification, and creating questions, have been employed to utilize generative Artificial Intelligence (AI) in education. This study investigates the possibility of using Generate AI (GenAI) to grade Structured Query Language (SQL) queries automatically. Three models were used which are ChatGPT, Gemini, and Copilot. The study uses an experimental approach to assess how well the models perform in evaluating student responses by comparing the models’ accuracy with those of human experts. The results showed that despite some inconsistencies, GenAI holds great promise for streamlining. Thus, further research is required in light of inconsistent GenAI performance. If these issues were resolved, GenAI can be utilized in education. However, human oversight and ethical issues must always come first.
Sentiment Analysis of Product Reviews Using Transformer Enhanced 1D-CNN and BiLSTM
Authors: Rana Muhammad Rizwan Rashid , Nawaz Asif , Ali Tariq , Alattas Ahmed Saleh , AbdElminaam Diaa Salama 
Published: September 2024

Abstract
The rapid growth of Internet-enabled applications, such as social media platforms, e-commerce sites, and blogs, has led to a surge in user-generated content. This vast amount of data has made sentiment analysis increasingly valuable. Modern Aspect-Based Sentiment Analysis (ABSA) offers a more detailed approach by identifying sentiment trends related to specific aspects within the text. However, the challenge lies in analyzing reviews that are often short, unstructured, and filled with slang and emotive language, making it difficult to gauge customer opinions accurately. To address these issues, we proposed an effective hybrid approach “RoBERTa-1D-CNN-BiLSTM” for ABSA. Initially, the pre-trained Robustly Optimized BERT approach (RoBERTa) and One Dimensional Convolutional Neural Network (1D-CNN) models are used to extract features at the aspect level from the context of the review, following which classification is performed using Bidirectional Long Short-Term Memory (BiLSTM). The approach is evaluated on three cross-domain standards datasets, yielding an accuracy of 92.33%. The results of the experiments show that it surpasses the current leading methods in sentiment analysis and product recommendation.
Managing Cybersecurity: Digital Footprint Threats
Authors: Ketipov Rumen , Schnalle Roman , Doukovska Lyubka , Dehez Dustin 
Published: September 2024

Abstract
Managing cybersecurity and protecting data assets remain top priorities for businesses. Despite this, numerous data breaches persist due to malicious human actions, resulting in significant financial setbacks. However, many cybersecurity strategies overlook invisible or indirect threats within their scope, such as digital footprints. This paper examines the relationship between personality traits and user behavior concerning cybersecurity. The study suggests that human personality can be predicted using innovative techniques based on the digital hints individuals leave on the internet. Consequently, this information can be exploited for malicious actions against entities. As proposed, an effective strategy for improving behaviors and cultivating a security-oriented culture involves continually identifying relevant sources of cyber risks and implementing continuous awareness initiatives.
AHT-QCN: Adaptive Hunt Tuner Algorithm Optimized Q-learning Based Deep Convolutional Neural Network for the Penetration Testing
Authors: Railkar Dipali , Joshi Shubhalaxmi 
Published: September 2024

Abstract
Penetration Testing (PT), which mimics actual cyber attacks, has become an essential procedure for assessing the security posture of network infrastructures in recent years. Automated PT reduces human labor, increases scalability, and allows for more frequent evaluations. Real-world exploitation still challenges RL-based penetration testing because the agent’s many possible actions make it hard for the algorithm to converge. To resolve these shortcomings, a deep learning- model named Adaptive Hunt Tuner algorithm optimized Q-learning based deep Convolutional neural Network (AHT-QCN) is developed for efficient PT. Specifically, the Q-learning employed in this model improves its efficiency by enabling optimal policy learning for decision-making. In addition, the Adaptive Hunt Tuner (AHT) algorithm enhances the model’s performance by tuning its parameters with reduced computational time. The experimental outcomes demonstrate that the developed model attains 95.25% accuracy, 97.66% precision, and 93.81% F1 score.
Game Theory in the Realm of Pop Culture: A Novel Approach to Social Media Strategy
Authors: Talajić Mirko , Vrankić Ilko , Matić Lucia 
Published: September 2024

Abstract
This paper explores the intersection of game theory and pop culture as a potent strategy for amplifying social media engagement. A methodological analysis demonstrates how game theory principles can be effectively applied to social media campaigns by leveraging the universal appeal of pop culture. The study introduces a simple yet illustrative model that outlines the strategic use of pop culture elements to attract new followers and maximise net profit, considering variables such as potential follower base, cost of pop culture integration, and the preferences of different audience segments towards pop culture-infused products. Findings underscore the untapped potential of conscious game theory application in social media strategy design, offering a fresh perspective on achieving optimal engagement through a balance of appeal to pop culture enthusiasts and those favouring traditional content. This contribution is novel in its approach. It provides a comprehensive review of game theory’s utility in pop culture and presents a detailed framework for its application in social media campaigns. By bridging theoretical concepts with practical application, this research opens new avenues for strategic engagement in the digital age, making a significant addition to both academic research and practical marketing strategies.
A New Application with Conditional Risk Matrix and Decision Rules: An Assessment of the Sustainability and Performance Electric Power Company
Authors: Couto Ayrton Benedito Gaia do , Rangel Luís Alberto Duncan 
Published: September 2024

Abstract
The 2030 Agenda for Sustainable Development of the United Nations General Assembly focuses on economic development, poverty cessation, environmental sustainability, and good governance at all levels, including peace and safety. For electric power distribution concessionary companies in Brazil, it is necessary to evaluate their sustainability and performance according to the parameters laid by the National Electric Power Agency (ANEEL). For that purpose, sixty-two indicators were assembled that were directly and indirectly associated with sustainability for 38 companies in the period–2013-2021. This study proposes a new application with a conditional risk matrix and decision rules aimed at the integrated assessment of sustainability and performance of electric power companies using the conditional probability of indicators in the ascent or descent of the companies’ classification. A set of “if..., then...” decision rules were inferred by the algorithm VC-DomLEM using concepts from the Rough Sets Theory and the Dominance principle, Dominance-based Rough Set Approach (DRSA).
CC-De-YOLO: A Multiscale Object Detection Method for Wafer Surface Defect
Authors: Ma Jianhong , Zhang Tao , Ma Xiaoyan , Tian Hui 
Published: September 2024

Abstract
Surface defect detection on wafers is crucial for quality control in semiconductor manufacturing. However, the complexity of defect spatial features, including mixed defect types, large scale differences, and overlapping, results in low detection accuracy. In this paper, we propose a CC-De-YOLO model, which is based on the YOLOv7 backbone network. Firstly, the coordinate attention is inserted into the main feature extraction network. Coordinate attention decomposes channel attention into two one-dimensional feature coding processes, which are aggregated along both horizontal and vertical spatial directions to enhance the network’s sensitivity to orientation and position. Then, the nearest neighbor interpolation in the upsampling part is replaced by the CAR-EVC module, which predicts the upsampling kernel from the previous feature map and integrates semantic information into the feature map. Two residual structures are used to capture long-range semantic dependencies and improve feature representation capability. Finally, an efficient decoupled detection head is used to separate classification and regression tasks for better defect classification. To evaluate our model’s performance, we established a wafer surface defect dataset containing six typical defect categories. The experimental results show that the CCDe-YOLO model achieves 91.0% mAP@0.5 and 46.2% mAP@0.5:0.95, with precision of 89.5% and recall of 83.2%. Compared with the original YOLOv7 model and other object detection models, CC-De-YOLO performs better. Therefore, our proposed method meets the accuracy requirements for wafer surface defect detection and has broad application prospects. The dataset containing surface defect data on wafers is currently publicly available on GitHub (https://github.com/ztao3243/Wafer-Datas.git).
On feature extraction using distances from reference points
Authors: Piernik Maciej , Morzy Tadeusz , Susmaga Robert , Szczęch Izabela 
Published: September 2024

Abstract
Feature extraction is the key to a successfully trained classifier. Although many automatic methods exist for traditional data, other data types (e.g., sequences, graphs) usually require dedicated approaches. In this paper, we study a universal feature extraction method based on distance from reference points. First, we formalize this process and provide an instantiation based on network centrality. To reliably select the best reference points, we introduce the notion of θ-neighborhood which allows us to navigate the topography of fully connected graphs. Our experiments show that the proposed peak selection method is significantly better than a traditional top-k approach for centrality-based reference points and that the quality of the reference points is much less important than their quantity. Finally, we provide an alternative, neural network interpretation of reference points, which paves a path to optimization-based selection methods, together with a new type of neuron, called the Euclidean neuron, and the necessary modifications to backpropagation.
Prioritizing Vulnerability Factors of Global Food Supply Chains by Fermatean Fuzzy Analytical Hierarchy Process
Authors: Yalcin Selin , Ayyildiz Ertugrul 
Published: September 2024

Abstract
In response to heightened competition arising from globalization, companies are crafting strategies to sustain their operations. However, these strategies also introduce risks that require meticulous management. The onset of the COVID-19 pandemic has exacerbated disruptions in supply chains, including the vulnerable food supply chain (FCS), strained further by escalating food prices and resource depletion in recent times. Within this context, the vulnerability of global FSCs has escalated significantly due to government-imposed lockdowns during the pandemic. This study aims to comprehensively investigate the multifaceted disruptions in global FSCs caused by the COVID-19 pandemic. By delving deep into the complexities of these disruptions, it seeks to uncover the key factors contributing to the vulnerability of supply chains. Employing a blend of literature review and expert opinions, the study identifies and prioritizes factors using the Fermatean Fuzzy Analytical Hierarchy Process (FF-AHP). A two-level criteria framework consisting of three main criteria and eleven sub-criteria has been developed, taking into account expert recommendations and previous studies. According to the results obtained, it has been revealed that the Managerial factors within the main criteria are the most significant factors in the fragility of the FSC. Among these factors, it has been observed that Technology, Corporation, and Inventory Management are the leading criteria causing to the vulnerability of the FSC. This is the first study to investigate the vulnerabilities of FSC using fuzzy logic. The research underscores the imperative of comprehensive risk management strategies that encompass all stakeholders within the supply chain, particularly during unanticipated crises like pandemics.
Occurrence of Ochratoxin in Complete Feed Mixtures for Monogastric Animals
Authors: Harčárová Michaela , Naď Pavel , Šamudovská Alena Hreško , Bujňák Lukáš 
Published: September 2024

Abstract
Ochratoxin A is a foreign substance of natural origin. It can be found in a wide range of commodities, including animal feed. Ochratoxin A is a dangerous contaminant, which can have a negative effect on the health and production of animals. In this study, the incidence of ochratoxin A in a complete feed for broilers (n = 25) and pigs (n = 6) was determined. Ochratoxin A was detected in one sample of pigs feed (16.67 %) and its concentration was 1.221 µg.kg−1. This mycotoxin was not detected in the broiler feed samples. These results indicate that the feed samples collected were safe and do not pose a risk of acute mycotoxicosis in animals.
Occurrence of Cryptosporidium Oocysts in Faeces, Milk and Water Sources in Sedentary Fulani Herds in Selected Local Government Areas of Kaduna State, Nigeria
Authors: Esonu Daniel O. , Kabir Junaidu , Jatau Isa D. , Lawan Mohammed K. , Yusuf Mohammed S. , Aliyu Mohammed B. , Yusuf Fatima L. 
Published: September 2024

Abstract
Cryptosporidiosis is a neglected tropical zoonotic disease, commonly associated with a diarrhoea, caused by a protozoan parasite of the genus Cryptosporidium. The aim of this study was to determine the occurrence of Cryptosporidium oocysts in faeces, milk, and water sources, and its associated risk factors in sedentary Fulani herds. Seven hundred and fifty faecal samples (240, 180, 240 and 90 from cattle, sheep, goats and dogs, respectively), and 120 samples each, of milk and water were collected for this study. Faecal samples were concentrated by formol ether concentration technique, then stained using Modified Ziehl Neelsen staining method, to detect Cryptosporidium oocysts. Cryptosporidium oocysts in milk and water samples were also concentrated and stained with same stain. The prevalence of Cryptosporidium oocysts in (cattle, dog, sheep and goat faeces were 18.3 %, 15.6 %, 13.9 % and 4.2 %, respectively.), Significantly higher prevalence was observed in calves ≤ 1 year (P = 0.002), female cattle (P = 0.007) and animals with poor body condition (P < 0.001) than in the other groups. Goats with poor body condition scores had significantly higher prevalence (P = 0.008) than other groups while dogs ≤ 6 months of age also had significantly higher prevalence (P = 0.03) than older dogs. The prevalence of Cryptosporidium oocysts in bulk cow milk was 11.7 % while a prevalence of 13.3 % was observed in the water bodies sampled. Hence the findings of this study are of great public health significance, therefore, inhabitants of these LGAs should be informed and educated on the need for improvement of sanitary measures during the course of milking these animals, and the need for adequate pasteurization, of milk before consumption.