bluuebunny/crossref_metadata_2025 huggingface dataset:
Online Multi-object Tracking with YOLOv9 and DeepSORT Optimized by Optical Flow
Authors: Djarah Djalal , Benmakhlouf Abdeslam , Zidani Ghania , Khettache Laid
Published: December 2024

To ensure reliable environmental perception in the realm of autonomous driving, precise and robust multi-object tracking proves imperative. This study proposes an innovative approach to multi-object tracking by combining YOLOv9's sophisticated detection capabilities with an enhanced DeepSORT tracking algorithm, enriched through the integration of optical flow. In the proposed method, the YOLOv9 detector acutely identifies objects in input images, and these detected entities are subsequently transmitted to the optimized DeepSORT tracking algorithm. The principal contribution of this study lies in improving the Kalman filter measurement model within DeepSORT by incorporating robust local optical flow, thus adding a velocity dimension to the filter's update vector. This novel approach significantly improves tracking resilience in the face of occlusions, rapid movements, and appearance changes. Evaluations on MOT17 and KITTI show substantial improvement gains of 2.42%, 2.85%, and 1.84% for HOTA, MOTA, and IDF1, respectively, on MOT17, and 1.94% in MOTA and 2.09% in HOTA on KITTI. The proposed method particularly excels in managing scenarios involving dense traffic and light variations, which are recurrent problems in dynamic urban environments. This enhanced performance positions the proposed solution as an essential component of future perception architectures for autonomous vehicles, promising safer and more efficient navigation in the complex real world.
Flexural Behavior of High Volume Fly Ash-Self Compacting Concrete Beams to Normal Concrete Beams with Conventional Steel Reinforcement
Authors: Rohman Rosyid Kholilur , Wibowo Rendi Gusta , Afrianto Arif
Published: December 2024

This research determines the comparison between the flexural behavior of Normal Concrete (NC) beams and High-Volume Fly Ash Self Compacting Concrete (HVFA-SCC) beams. The research data was obtained from full-scale beam tests using four-point loading. Tests were carried out on 6 NC and 6 HVFA-SCC beam specimens with dimensions of 150 mm × 250 mm × 2000 mm. The test specimens varied with main reinforcement of 12 mm, 16 mm, and 19 mm diameter. The results of the studies show that the crack patterns of the NC and HVFA-SCC beams are almost identical to those of the flexural failure mode, while the HVFA-SCC beam has greater ductility than the NC beam. The nominal flexural strength (Mn) of HVFA-SCC beams can be calculated using the Mn formula in ACI 318-19.
Application of LightGBM Algorithm in Production Scheduling Optimization on Non-Identical Parallel Machines
Authors: Ait Ben Hamou Khalid , Jarir Zahi , Elfirdoussi Selwa
Published: December 2024

Production scheduling plays a decisive role in supply chain management, directly influencing the operational efficiency and competitiveness of companies. This study explores the effectiveness of the LightGBM algorithm for production scheduling on non-identical parallel machines, comparing it to algorithms such as logistic regression, KNN, decision tree, and XGBoost. LightGBM was chosen for its speed of execution and its ability to handle large amounts of data. The results show that LightGBM outperforms the other models in terms of RMSE, MAE, explained variance score, and R² score for regression tasks, as well as in classification accuracy for certain features. Its superiority is attributed to its ability to efficiently handle data complexity while reducing computational complexity through its leaf tree growth technique. This study highlights LightGBM's potential for improving the efficiency of supply chain management systems and the challenges associated with computational scalability for large datasets. The results suggest that LightGBM is a robust and effective solution to optimize production scheduling, paving the way for future research in this field.
An Efficient Novel Model for Multi-Story Building Construction Quantity Estimation using Coupled MATLAB-Revit Software
Authors: Alazawy Suha Falih Mahdi , Aldhamad Saja Hadi Raheem , Salih Bilal Muiassar M. , Al Zwainy Faiq Mohammed Sarhan
Published: December 2024

The nature of the construction business and building projects necessitates the capacity to manage extensive and intricate data and documentation. The processes for documenting, exchanging, and updating data constitute one of the principal administrative challenges being currently confronted by the construction project management. As a consequence of the continued reliance on paper-based processes evidenced in the Iraqi businesses and construction projects, a considerable volume of documentation is likely to accumulate, thereby increasing the complexity and time required for specific data to be retrieved. In this study, the Support Vector Machine (SVM) and Building Information Modeling (BIM) models were used to document projects by employing the MATLAB-Revit software. The findings demonstrate that the project timeline is also recorded because it is related to the suggested model, which is designed to produce an effective model that mimics reality. A comparative analysis of the data pertaining to the foundations, columns, walls (24 cm and 12 cm), floors, and slabs of four multi-story buildings, was conducted. This analysis was divided into three categories: estimated, SVM-BIM, and actual documentation. The findings indicated that the proposed model demonstrated a high degree of accuracy in predicting the material quantities required in building construction. These values were found to be in close proximity to, and aligned with, the actual documentation.
Performance of Steel Beams reinforced by CFRP Sheets with Fire-Retardant Coating
Authors: Kharnoob Majid M. , Al Zand Ahmed , Khalaf Doaa H. , Sabti Lana M.
Published: December 2024

The objective of this study was to determine the physical, mechanical, and chemical aspects of bonding and priming Fire-Retardant Coatings (FRCs) to steel surfaces before and after fire testing. Carbon Fiber Reinforced Polymers (CFRP) have been used to strengthen steel components. Certain types of polymer systems have demonstrated high fire retardancy without additives, while others require additives to achieve optimum fire resistance. A wide range of compounds are available to enhance the fire retardant properties of these materials, characterized by their chemical nature and behavior (such as halogenated, metal complex, silicon-based, and phosphorus additives) and mode of action (either condensed or gas-phase active systems).This article provides a comprehensive overview of fire retardant additives for CFRP used in various large-scale applications, including the aerospace, automotive, railway, electronics, and civil engineering industries, as well as their fire retardant mechanisms at the microscopic, macroscopic, and nanoscale levels. In addition to fire retardant properties, this study also discusses the effects of additives on other material parameters and coatings, such as glass transition temperature, mechanical performance, and FRP processability. The primary focus is on thermoset systems, with a brief mention of thermoplastics according to the matrix compounds relevant to the FRP market size. Test results show that the direct velocity ultrasonic value varied between 3.016 and 3.618 km/s, giving an estimated compressive strength of 15.974 MPa. The standard deviation was 0.533 km/s with a relative standard deviation of 16.407%.
Automatic Bug Triaging Process: An Enhanced Machine Learning Approach through Large Language Models
Authors: Chhabra Deepshikha , Chadha Raman
Published: December 2024

Bug resolution and maintenance are the most critical phases of the software development life cycle. The traditional bug triaging concept refers to the manual assignment of bugs to the appropriate developer after reading the bug details from the bug tracker and further resolving it. The advent of machine learning algorithms provides various solutions for automated bug triaging. Machine learning algorithms can be used to classify bugs and assign each to a developer. Reducing manual efforts optimizes bug-triaging by utilizing manpower in other software development processes. Furthermore, machine learning Large Language Models (LLMs) can be used to take advantage of their natural language processing features and capabilities. This study proposes a machine learning-based embed chain LLM approach for automatic bug triaging. This approach is used to automatically classify bug reports. Based on the results, the appropriate developer is recommended. In addition, the proposed approach is used to automatically predict the priority of bug reports. This paper also discusses the strengths and challenges of the proposed approach.
Improved Automatic Drowning Detection Approach with YOLOv8
Authors: Alharbi Nouf , Aljohani Layan , Alqasir Anhar , Alahmadi Taghreed , Alhasiri Rehab , Aldajan Dalia
Published: December 2024

Although swimming is a popular activity that promotes relaxation and stress relief, drowning remains a serious global problem. According to the World Health Organization (WHO), drowning is the third most common cause of death. This study delves into implementing deep learning techniques for precise drowning detection. From this point of view, a drowning detection system was designed using the YOLOv8 model, which is a powerful tool for object detection and classification tasks. Using a large dataset, the YOLOv8 model was trained to recognize drowning patterns and movements and increase the likelihood of successful rescue operations by reducing response times and improving water safety. The proposed system uses deep learning techniques and YOLOv8 technology with data augmentation techniques to enhance the model's robustness to variations in lighting, pose, and background conditions. The system performance was evaluated using the Swimming and Drowning Detection dataset achieving 90.1% accuracy compared to 88.5% with YOLOv5.
An Assessment of Strength Characteristics of Transparent Concrete in Pavement
Authors: Singh Manish Pratap , Sinha Sanjeev
Published: December 2024

This study explores the potential of transparent concrete for use in roadway applications, focusing on its ability to transmit light through the incorporation of Plastic Optical Fibres (POFs) within the concrete matrix and evaluating both its mechanical strength and its light transmission capabilities. This material can be applied to various road elements, such as surface illumination, signage, roadblocks, medians, curbs, and areas with changing geometry. The light transmission performance was evaluated using voltage, light sources, and Light-Dependent Resistors (LDRs). A comparative analysis was performed between samples with varying POF content and spacing and traditional concrete. The findings reveal that compressive and flexural strengths remain largely unaffected by the inclusion of POFs up to 1.5% by weight, beyond which a decrease in strength is observed. Additionally, the light transmission efficiency improves significantly with an increase in the POF volume ratios.
Reliable Vehicular Ad Hoc Networks for Intelligent Transportation Systems based on the Snake Optimization Algorithm
Authors: Al-Maliki Hanadi , AL-Asadi Hamid Ali Abed , Abduljabbar Zaid Ameen , Nyangaresi Vincent Omollo
Published: December 2024

Vehicular Ad Hoc Networks (VANETs) represent an environment in which mobility exceeds the normal values, topology changes rapidly, and safety constraints are too high. The fundamental problem with VANETs is making transmission, acceptance, and sending out of messages between vehicles as timely, reliable, and secure as possible. The current study aims to address these challenges by applying the Snake Optimization Algorithm (SOA), enhancing network protocol efficiency, performance, and robustness. In this work, a comprehensive examination of the effects of optimal SOA on VANET protocols is provided over networks with different node sizes of 100, 250, and 500. End-to-end delay, path delivery overhead, and average number of hops improved after the utilization of SOA in all the considered networl configurations.
An Experimental Study on Industrial Concete Pile Foundation in Soft Soil: Comparison of Monolithic and Pile with Welded Joints
Authors: Effendi Rustam , Pratiwi Ade Yuniati , Chairunnisa Nursiah , Alpindi Nor Muhammad , Nurwidayati Ratni , Krasna Wiku Adhiwicaksana
Published: December 2024

This study presents an experimental investigation into the industrial foundation piles. The research carried out employed both destructive and non-destructive testing methods to evaluate the concrete compressive strength and flexural strength capacity of the piles under monotonic loading. The Destructive Test (DT) involves a 28-day cylinder concrete compressive test, while the Non-Destructive Test (NDT) utilizes the Ultrasonic Pulse Velocity (UPV) and Rebound Hammer (RH) tests. The flexural strength test is conducted using a loading frame, and the results are compared to the GeoPIV-RG, which measures the displacement during the test. The experimental investigations provide insights into the behavior of pile joints when subjected to monotonic load in soft and loose soil. The results indicate a significant difference between the compressive strength obtained from the DT and NDT, with a ratio of 0.64-0.74. Furthermore, the failure occurred at the joints, rather than the welded area, with the ratio of the initial stiffness of the piles with joints to the monolithic pile being 0.15 for zig-zag welded and 0.30 for circular welded, and reaching an average value of 0.225. According to the GeoPIV-RG result, the displacement is similar to the flexural strength test result.
Enhancing Sentiment Analysis of Indonesian Tourism Video Content Commentary on TikTok: A FastText and Bi-LSTM Approach
Authors: Ariyus Dony , Manongga Danny , Sembiring Irwan
Published: December 2024

Sentiment analysis is a method used to measure public opinion or the emotions of a group of people with similar interests based on their reactions to an event through text, images, videos, or audio on social media. However, such online data presents several challenges that can hinder the sentiment analysis process. These challenges stem mainly from the freedom that users have to post their content. Additionally, irrelevant opinions, often referred to as fake opinions, can also arise. The Bi-LSTM approach processes input sequences bidirectionally, allowing the model to capture information from both previous and subsequent contexts. This method is well-suited for sentiment analysis tasks due to its ability to recognize language nuances and relationships between different parts of the text. This study integrates a Bi-LSTM model with FastText word embeddings to filter out irrelevant opinions considered spam. The dataset consists of 150,351 TikTok comments taken from 100 popular videos related to tourist attractions. The experimental results show that the proposed Bi-LSTM model outperforms other models such as LSTM, CNN, GRU, MD-LSTM, and Peephole LSTM, achieving a test accuracy of 89.18%. Furthermore, when slang word translation is performed to convert slang into formal words, the Bi-LSTM model shows further improvement, with test accuracy reaching 93.10%, again surpassing the baseline models. These results demonstrate the robustness of the proposed method in handling noisy and informal language, thus improving the accuracy of sentiment analysis in the context of social media. This study provides a foundation for future research to improve sentiment analysis by addressing domain-specific challenges such as data imbalance and noise in social media data.
Investigation of Concrete Paving Block Characteristics and Performance across Different Shapes and Thicknesses
Authors: Iduwin Tommy , Hadiwardoyo Sigit Pranowo , Sumabrata R. Jachrizal , Lumingkewas Riana Herlina , Rivai Andri Irfan
Published: December 2024

Concrete Paving Blocks (CPBs) are generally used in pavement structures. Quite often there are differences in the test result characteristics of cube and block-shaped samples. This research aims to analyze the characteristics of differences in height by considering 60 samples from five different factories. The sample dimensions are 200 mm length and 100 mm width with varying heights of 60 mm, 80 mm, and 100 mm. The tests include water absorption, compressive strength, flexural strength, tensile splitting strength, skid resistance, and abrasion resistance. The results of the mortar content show no significant difference between the five sources. According to the findings, the weight loss value depends on strength, and tensile splitting strength is influenced by density, with higher density producing higher tensile splitting strength. It was also found that the compressive strength test method is more realistic when using cube-shaped samples, while beam-shaped samples are more suitable for identifying bending characteristics. Finally, it was shown that the flexural strength value is influenced by density.
Mobility Prediction Algorithms for Handover Management in Heterogeneous LiFi and RF Networks: An Ensemble Approach
Authors: Sanusi Jaafaru , Adeshina Steve , Aibinu Abiodun Musa , Oshiga Omotayo , Prasad Rajesh , Dayyabu Abubakar
Published: December 2024

Light Fidelity (LiFi) is a communication technology that operates in the Visible Light (VL) region, using light as a medium to enable ultra-high-speed communication. The spectrum occupied by LiFi does not overlap with the Radio Frequency (RF) spectrum. Thus, they can be used in a hybrid manner to enhance the Quality of Service (QoS) for users. However, in a heterogeneous LiFi and RF network, users experience constant handovers due to the small coverage area of the LiFi and their frequent movement. This study proposes an intelligent handover scheme, where the network parameters of the users are used to train four machine learning models, namely an Artificial Neural Network (ANN), an Adaptive Neurofuzzy Inference System (ANFIS), a Support Vector Machine (SVM), and a Regression Tree (RT), to predict the mobility of the users, so that the central network can have a priori mobility information to ensure seamless connectivity. Furthermore, the performance of the standalone models was enhanced by integrating ensemble learning techniques such as the Simple Averaging Ensemble (SAE), Weighted Averaging Ensemble (WAE), and a Meta-Learning Ensemble (MLE). The results show that the ensemble algorithms improved prediction performance, with an average error decrease of 44.40%, 53.53%, and 61.03% for SAE, WAE, and MLE, respectively, which further demonstrated the effectiveness and robustness of using ensemble algorithms to predict user mobility.
Strength and Thermal Properties of Hollow Foamed Concrete Blocks considering Various Parameters
Authors: Sulaiman Abubaker Mohammed , Hilal Ameer A. , Al-Azzawi Zaid
Published: December 2024

Hot weather is one of the main problems the residential building construction field faces, especially in the southern hemisphere. Therefore, there is an increasing need to produce materials capable of reducing the high temperature impact. These materials should be characterized by thermal insulation properties without, though, their mechanical properties and load resistance being affected. In this research, cuboid and hexagonal hollow concrete blocks were produced from lightweight foam concrete with different hole shapes and a hole ratio of 30%. An analytical study was conducted for 8 models using the ANSYS v16 program, while the compression behavior and thermal performance of the selected models were studied. Ordinary Portland Cement (OPC), water, sand, and foaming agents were deployed in the production of foamed concrete. In addition to the use of admixtures, materials, such as Superplasticizer (SP), Class F Fly Ash (FA), and Silica Fume (SF), were also utilized. Moreover, the effect of the hole’s shape and the method of bonding were studied. The compressive strength of the concrete blocks, bond shear strength, thermal conductivity, and thermal resistance were tested. It was found that the cuboid shape of the hole block H7 was the most acceptable compared to the other shapes, with a compressive strength of 3.75 MPa, thermal conductivity of 0.149 W/m.k, and a bond shear strength of 0.157 MPa. At the same time, it was found that using bonding adhesive material gave the best results, with the cuboid blocks being compared to using mortar and mechanical bonding.
Α Combined Metaheuristic Optimization Technique for Optimal Site and Scaling of PVDG System in a Radial Distribution Network
Authors: Alturki Mansoor
Published: December 2024

Although integrating Renewable Energy Resources (RERs) into distribution systems offers benefits such as clean energy and free availability, it also introduces challenges, such as Inverse Power Flow (IPF) issues. This study proposes an efficient approach to address these issues by optimizing the placement and sizing of Photovoltaic Distributed Generation (PVDG) systems in Radial Distribution Networks (RDNs). The proposed strategy involves selecting the optimal PVDG location using the Loss Sensitivity Factor (LSF) and determining the optimal PVDG size with the Artificial Bee Colony (ABC) algorithm. This method aims to minimize active power losses and enhance the voltage profile in the investigated system. The performance of the ABC algorithm was evaluated against other optimization methods, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The effectiveness of the proposed strategy was tested and validated on τηε IEEE 15-Βus and IEEE 85-Βus RDNs. The results obtained show that the ABC algorithm outperformed the other methods in reducing power losses and improving voltage profiles.
Integrating Six Sigma Principles towards Multi-Objective Production Planning for Enhanced Quality in the Al Kharj Manufacturing Sector
Authors: Alqahtani Mohammed , Alam Teg
Published: December 2024

Effective production planning and creating high-quality items are two of the most critical factors in establishing long-term success in the manufacturing business. This study presents Goal Programming (GP), a Multi-Objective Optimization technique for decision-making issues, incorporating Six Sigma concepts to tackle the industrial sector's complex production planning problems. The aim is to provide a systematic framework for decision-making, ensuring a comprehensive approach to quality engineering in manufacturing processes. The current study entails considerable practical implications regarding the fridge industry in specific practical ways. Production planning was enhanced by evaluating market demand, manufacturing costs, and sales data using LINGO. Deploying Six Sigma to find the limits on demand, helped reduce production costs while making additional revenue from sales. Sensitivity analysis revealed that by the following year, the firm is expected to have decreased manufacturing costs by 13.13% to minimize expenses, while it is also anticipated to have reduced sales by 1.12% to maximize revenue. The GP technique demonstrated that the Al Kharj fridge industry could optimize sales revenue and costs. The suggestions provided based on these findings are actionable and have the potential to be effectively implemented in the industrial sector.
Capsule-based and TCN-based Approaches for Spoofing Detection in Voice Biometry
Authors: Borodin Kirill , Kudryavtsev Vasiliy , Mkrtchian Grach , Gorodnichev Mikhail
Published: December 2024

Nowadays, deep neural networks are in a phase of rapid development. Simultaneously, the field of biometric forgery is also advancing. Systems that can successfully pass face verification systems are emerging and continuously improving deepfake videos and voice messages are created. These developments can have a negative impact on a person’s reputation or cause serious security breaches. This paper proposes an approach for spoofing detection in voice biometrics using the ASVspoof2019 LA dataset The model is trained and validated on subsets representing one type of attack, and evaluated on a subset containing more advanced types of spoofing attacks, demonstrating the model’s ability to generalize to more complex attack scenarios. Two models, capsule-based and TCN-based, are proposed, noted as ResCapsGuard and Res2TCNGuard, respectively. ResCapsGuard achieved an Equal Error Rate (EER) value of 2.27, while Res2TCNGuard reached an EER value of 1.49. Notebooks with our models are available in repositories in github. Due to the fact that a random part is cut out of the audio, the results may vary.
The Impact of Change Orders on the Waste Materials of Large-Scale Projects
Authors: Waty Mega , Sulistio Hendrik , Prihatiningsih Aniek
Published: December 2024

Change orders (CO) are formal agreements that alter, add to, or modify the work specified in a contract document. These changes often necessitate adjustments to the project scope, potentially requiring contract modification. Generally, CO have been identified as a significant contributor to Wast Materials (WM) in road improvement projects, as outlined in waste management recommendations. The impact of waste management on project success is substantial, as materials constitute a critical component of construction, accounting for approximately 40-60% of the total project cost. This research aimed to determine the impact of CO on project waste management, with a specific focus on large-scale projects. To achieve this objective, a 4-stage decision-making process was adopted using the Delphi method to design and distribute questionnaires. The results identified 2 variables, comprising 21 indicators, that contribute to waste management in road construction projects. Additionally, 3 primary impacts of CO were recorded, affecting costs, quality, and implementation time. Further analysis using Exploratory Factor Analysis (EFA) and Smart PLS 4.0 revealed that CO had a significant impact on 2 variables: procurement and material handling, as well as implementation and material planning. These variables, consisting of 21 indicators, accounted for 69% of the observed effects, with a prediction accuracy rate of 67.7% regarding the impact of changes in construction work.
Evaluating Flexural Strength of Steel Fiber Reinforced Geopolymer Concrete using the ResNet Approach and Sensitivity Analysis
Authors: Minh Tran Nhat , Khoa Nguyen Tan , Thuy Nguyen Ninh , Tuan Le Anh
Published: December 2024

The present study evaluates the performance of fiber-reinforced geopolymer, especially its flexural strength, using a Deep Learning (DL) approach, Deep Residual Network (ResNet), and the experimental work is presented. A total of 245 mixtures were employed to generate the data for the ResNet training and validating procedures. In the proposed model, the Fly Ash (FA) content, sodium silicate solution/solid binder ratio, curing temperature, curing time, fiber volume fraction, fiber length (l) and diameter (d), as well as fiber tensile strength, were considered as input factors. In contrast, flexural strength was the output parameter. The effectiveness of ResNet was evaluated by three statistical factors, correlation coefficient (R2), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). ResNet validation revealed the effectiveness of predictive methods with 94.5%, 0.292 MPa, and 4.068% for R2, RMSE, and MAPE, respectively. The suggested models may be used as standard mixtures for geopolymer concrete reinforced with steel fibers.
Determinants of Consumer’s Adoption of Latest Version Smartphones: An Empirical Study of Saudi Consumers
Authors: Boutaleb Brahim , Toukabri Maher
Published: December 2024

This empirical study investigates the determinants of the adoption of the latest version of smartphones among Saudi Arabian consumers. Through a quantitative approach and data collected from 377 participants, the research explores factors, such as personal characteristics, social influences, product features, price considerations, social media impact, online purchasing behavior, and the role of advertisements. Descriptive and inferential analyses provide insights into the sample population's demographic profiles and occupational patterns. The ANOVA test evaluated the relationship between various factors and consumer adoption of the latest smartphones. The results indicated no significant difference in the adoption behavior based on the personal characteristics, influence from family and friends, or product features. However, data for other hypotheses were missing, impeding a comprehensive analysis. While the test provided useful understanding, further examination and interpretation are necessary to draw conclusive insights into the factors influencing smartphone adoption. This study contributes valuable knowledge into the complex dynamics of smartphone adoption in Saudi Arabia, offering targeted strategies to manufacturers, marketers, and policymakers, and aiming to enhance market penetration and consumer engagement.
A Comparative Analysis of Machine Learning Techniques for URL Phishing Detection
Authors: Albishri Adel Ataih , Dessouky Mohamed M.
Published: December 2024

The growing threat of URL phishing attacks raises the need for advanced detection systems to protect digital environments. This paper explores the effectiveness of various machine learning models in classifying URLs as phishing or benign, focusing on the random forest model. Using ensemble learning, the random forest demonstrated superior accuracy and reliability compared to traditional methods, achieving consistent performance with accuracy rates between 99.93% and 99.98%. The model's performance was evaluated daily over eight days, highlighting its robustness in handling real-world scenarios. This study utilized GridSearchCV to optimize model hyperparameters, enhancing model robustness and minimizing overfitting. Future research directions include advanced feature engineering, deep learning techniques, and multimodal data integration to further improve phishing detection systems.
Determination of the Ultimate Bearing Capacity of a Single Barrette Wall using FEA and Cubic Nonlinear Regression
Authors: Dang Truong Xuan , Nguyen Phuong Tuan , Vo Luan Nhat , Tran Hoa Van Vu , Nguyen Tuan Anh
Published: December 2024

This study analyzes the mechanical behavior of barrette walls under various load levels, a critical issue in the design and construction of structures subjected to large loads. The primary objective of the research is to determine the nonlinear relationship between load and settlement of barrette walls, as well as to assess the maximum load-bearing capacity of the walls under diverse loading conditions. The finite element analysis method was employed to simulate the detailed interaction between the barrette wall and the soil, combined with cubic and linear regression analysis techniques to establish the model of the relationship between load and settlement displacement. The research results reveal a nonlinear relationship between load and settlement of the wall, with an inflection point occurring at a load level of approximately 12,000 kN, where the change in settlement becomes more pronounced. The cubic regression equation achieved a coefficient of determination R² = 0.999, demonstrating the high accuracy of the model. The maximum load-bearing capacity of the barrette wall was determined to be 15,745.59 kN, providing a clear scientific basis for evaluating the load-bearing capacity of structures. The conclusions from this study affirm the importance of using finite element simulations in soil mechanics analysis and the design of structures subjected to large loads. The achieved results not only enhance understanding of the behavior of Barrette walls but also contribute to the development of new technical solutions and design methods, with the potential for wide application in the construction and geotechnical engineering sectors.
Evaluating the Impact of Oil Market Shocks on Sovereign Credit Default Swaps in Major Oil-Exporting Economies
Authors: Belkhir Nadia , Alhashim Mohammed , Naifar Nader
Published: December 2024

This study analyzes the impact of oil market fluctuations on Sovereign Credit Default Swaps (SCDS) in three key oil-exporting economies: Saudi Arabia, Russia, and the United Arab Emirates (UAE). The study investigates how various oil shocks, namely demand, supply, and market risk, affect sovereign credit risk and how these effects are transmitted within and across these economies. Time-domain and frequency-domain analyses were used to categorize oil market shocks and structural break analysis was incorporated to account for significant global events. The findings indicate that Saudi Arabia is a primary source of credit risk volatility, influencing Russia and the UAE, with the latter being significantly affected as a net recipient of such risks. Structural breaks, such as those associated with the COVID-19 pandemic, introduce shifts in impact patterns. This study underscores the significant role of demand shocks in shaping sovereign credit risk across the countries examined. These insights are essential for policymakers, investors, and financial analysts focused on sovereign credit risk management in oil-exporting economies, highlighting the importance of considering structural changes in economic conditions.
Assessment of Kalimantan Coal Ash Properties and Potential as Sustainable Construction Material
Authors: Prasetia Irfan , Wicakso Doni Rahmat , Ma’ruf Muhammad Afief , Krasna Wiku Adhiwicaksana
Published: December 2024

This study analyzes the physical and chemical characteristics of Fly Ash and Bottom Ash (FABA) from Kalimantan's coal-fired power plants to assess their effectiveness and potential as sustainable construction materials. The characterization was conducted through a series of tests following ASTM C311 standards, while the mechanical properties of mortars incorporating Fly Ash (FA) were evaluated following ASTM C618 standards. The results indicate that all FA samples meet the ASTM C618 Fineness requirements, with particle sizes below 45 μm, confirming their suitability as pozzolanic materials. In contrast, Bottom Ash (BA) samples do not meet the Fineness standard but still show potential for use in other construction applications. Chemical analysis utilizing X-ray Fluorescence (XRF) further supports the potential use of FABA from Kalimantan, revealing a high content of reactive oxides, SiO2, Al2O3, Fe2O3, which justify the classification as Class F for PLTU Asam-Asam (AA) and PLTU Bengkayang (BK), and the classification as Class C for PLTU Pulang Pisau (PP). X-Ray Diffraction (XRD) analysis also validates the high silica (SiO2) content and low levels of deleterious compounds. Additionally, the mechanical properties confirm the effectiveness of FA from PLTU AA with a Strength Activity Index (SAI) value exceeding 100%. The findings of this research provide strong evidence for the potential of Kalimantan FABA as a sustainable material while contributing to enhanced compressive strength and durability in concrete application.
Structural Analysis of Latticed Steel Transmission Towers Subjected to Nondeterministic Wind Loads
Authors: Souza Rechtman Mariana , Santos da Silva Jose Guilherme
Published: December 2024

Lattice steel towers are commonly used to support overhead power transmission lines. However, the dynamic behavior of these structures is often overlooked in current design practices. Given that many accidents involving these towers occur even at basic wind speeds lower than those specified in the project, it is likely that dynamic actions play a significant role in these failures. This study proposes a method to accurately simulate the interaction between transmission line cables and towers under non-deterministic wind loads to assess displacements and forces in the steel towers. The study examines a transmission line system consisting of towers, conductors, shield wires, and insulators, featuring a central suspension tower of 32.86 m in height, flanked by two end towers with 450 m spans. Finite element modeling was developed to account for the dynamic characteristics of the wind. Wind loads were modeled as a random process based on their statistical properties. The results revealed significant differences in displacement and force values when comparing the results provided by static and dynamic analyses. The structural design of a base leg member indicated potential failure at higher wind velocities, highlighting the importance of considering the wind dynamic effects in the design.
Utilizing Explainable AI and Biosensors for Clinical Diagnosis of Infectious Vector-Borne Diseases
Authors: Vaiyapuri Thavavel
Published: December 2024

Infectious Diseases (ID) are a significant global threat due to their epidemic nature and substantial impact on mortality rates. COVID-19 has proven this assertion by wreaking havoc on human wellness and healthcare resources. This has underscored the need for early ID diagnosis to restrict the spread and protect human lives. Recently, Artificial Intelligence (AI)-assisted biosensors have shown great potential to assist physicians in making decisions to minimize mortality rates. However, their adoption in clinical practice is still in its infancy, primarily due to the challenges faced by physicians to interpret decisions derived from these black-box systems. The objective of this study is to earn the trust of physicians to promote their acceptance and widespread adoption in healthcare. Against this backdrop, this research is a pioneering effort to investigate not only the diagnostic accuracy of several Machine Learning (ML) algorithms for ID but more specifically how to leverage the benefits of Shapley values to provide valuable insights regarding the contribution of clinical features for early ID diagnosis. This analysis examines four ML algorithms that stem from different theories, such as Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), Support Vector Classifier (SVC), and Multilayer Perceptron (MLP). The visual analysis results presented for local and global interpretation facilitate the observation of the marginal impact of each clinical feature on a patient-by-patient basis. Therefore, the results of this study are expected to aid practitioners in better evaluating the diagnostic decisions of the ML models developed and boost the use of AI-assisted biosensors for ID diagnoses.
Design of Deep Learning Techniques for PCBs Defect Detecting System based on YOLOv10
Authors: Ruengrote Sumarin , Kasetravetin Kittikun , Srisom Phanuphop , Sukchok Theeraphan , Kaewdook Don
Published: December 2024

As Printed Circuit Boards (PCBs) are critical components in electronic products, their quality inspection is crucial. This study focuses on quality inspection to detect PCB defects using deep learning techniques. Traditional widely used quality control methods are time-consuming, labor-intensive, and prone to human errors, making the manufacturing process inefficient. This study proposes a deep-learning approach using YOLOv10. Through the incorporation of architectural improvements such as CSPNet and PANet that improve feature extraction and fusion, as well as a dual assignments mechanism that increases localization accuracy, YOLOv10 offers significant improvements over earlier versions, such as YOLOv5 and YOLOv8, and Faster R-CNN models. These innovations allow YOLOv10 to deliver superior performance in terms of both speed and precision. The experiments used a custom dataset consisting of 1,260 PCB samples collected from the industry. The dataset was partitioned into 80% for model training and 20% for testing. The model was trained for 100 epochs with a batch size of 32 to evaluate its performance in identifying various PCB defects. YOLOv10, with its optimized architecture, fully utilized its capabilities while requiring less computational power than YOLOv5 and YOLOv8, especially in resource-constrained environments. Despite resource constraints, YOLOv10 achieved high accuracy, with a precision of at least 96% and a recall of 97%, surpassing earlier YOLO models and Faster R-CNN. It also achieved 99% mAP and more than 96% F1 score. These improvements in speed and accuracy make YOLOv10 a highly efficient solution for automated PCB inspection, reducing manual effort and offering fast and accurate classification adaptable to various applications.
The Effect of CoCoSo Method on the Ranks of Alternatives: A Case Study of Copper Electrical Wire Selection
Authors: Thinh Hoang Xuan , Duc Duong Van , Bao Nguyen Chi
Published: December 2024

When using MCDM (Multi-Criteria Decision-Making) methods to rank alternatives, decision makers’ opinions have a huge influence on the ranking results. The decision makers’ opinions can vary depending on the chosen MCDM method, data normalization method, and weighting method. For some MCDM methods, during the application process, users also need to choose the value of a certain coefficient (called the user coefficient). Obviously, the value of the user coefficient depends on users’ opinions, and of course, these opinions can affect the ranking of the alternatives. In this article, the effects of users’ opinions on the ranks of the alternatives when using the CoCoSo (Combined Compromise Solution) method are investigated. Users’ opinions (including the weighting criteria method and the user coefficient) are considered the input of the investigation process. Organizing the investigation of the effects of these two parameters on the ranks of alternatives was applied to the case of copper electrical wire selection. The results show that the users’ opinions have little effect on the ranks of alternatives. This result confirms CoCoSo's outstanding advantage.
Prediction of Myocardial Infarction Complications using Gradient Boosting
Authors: Khamis Gamal Saad Mohamed , Mohammed Zakariya M. S. , Alanazi Sultan Munadi , Mahmoud Ashraf F. A. , Abdalla Faroug A. , Bkheet Sana Abdelaziz
Published: December 2024

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, representing a significant public health challenge. Myocardial Infarction (MI), a severe manifestation of CVDs, contributes substantially to these fatalities. Machine learning holds great promise for predicting MI. This study explores the potential of Gradient Boosting (GB) techniques for this purpose, explicitly focusing on CatBoost, LightGBM, XGBoost, and XGBoost Random Forest. The study leverages GB's embedded feature selection, missing-value handling, and hyperparameter tuning capabilities. Performance was evaluated using multiple metrics: Area Under the Curve (AUC), classification accuracy, F1 score, precision, recall, and Matthews Correlation Coefficient (MCC). A probabilistic comparison matrix was used to assess the relative performance of the GB models. The results demonstrate the superiority of CatBoost, achieving a classification accuracy of 94.9%, an AUC of 0.992, a recall of 94.9%, and an MCC of 0.82. The probabilistic comparison further confirms CatBoost's superior performance. These findings contribute to MI prediction, highlighting the predictive potential of the CatBoost algorithm and ultimately aiding the fight against MI to achieve better patient outcomes.
Superpixel-based C-SVC for Brain Tissue Classification in MRI Scans
Authors: Tareef Afaf
Published: December 2024

Accurate identification of brain tissue is an ill-posed problem due to the inhomogeneous intensity and the extremely complicated and irregular border between endocrine tissues. This study introduces a superpixel-based approach to brain tissue classification in MRI scans. The proposed approach starts with image smoothing and feature highlighting, followed by image splitting based on the SLIC superpixel and merging strategy. Then, distinct superpixel-based appearance and boundary features are extracted and refined by minimizing redundancy and maximizing relevance technique before sending to the C-support vector classifier. Finally, a refinement step is adopted based on morphological characteristics and the distance regularized level set evolution model to modify the matter contour. The proposed approach was evaluated and compared with ten existing algorithms using the publicly accessible IBSR dataset. The experimental results show the better efficiency of the proposed approach in delimiting the contour of each matter than the other approaches in the literature.
A Manual Disinfection Enhancement Method utilizing a Pulsed-Xenon Ultraviolet Device in Accordance with the Effect on the Contamination Levels of Vancomycin-Resistant Enterococci (VRE) and Methicillin-Resistant Staphylococcus Aureus (MRSA)
Authors: Alhmoud Saeed Hussein , Alsaqer Khitam
Published: December 2024

In hospitals, Ultraviolet (UV) disinfection lowers the rates of nosocomial infections; surface decontamination systems using Pulsed Xenon Ultraviolet light (PPX-UV) may be useful in lowering the microbiological load. This study aims to evaluate and compare Methicillin-Resistant Staphylococcus aureus (MRSA) and Vancomycin Resistant Enterococci (VRE) using manual plus PPX-UV disinfection technology versus standard manual room cleaning. Samples of high-touch surfaces from 20 rooms were taken both before and after both group the manual cleaning alone and the manual plus PPX-UV. Post-cleaning results showed a notable reduction in colony counts for both VRE (99%) and MRSA (98%) when comparing manual cleaning to manual plus PPX-UV treatment. The manual method showed higher colony counts for both bacteria compared to the manual plus PPX-UV method, with statistically significant differences in incidence rate ratios observed (p < .05). The study findings demonstrate that while manual cleaning methods can reduce microbial load, the manual plus PPX-UV method is notably more effective in achieving lower colony counts post-cleaning. This study underscores the importance of employing effective disinfection strategies in healthcare environments.
Enhanced Diagnosis of Lung Cancer through an Ensemble Learning Model leveraging an Adaptive Optimization Algorithm
Authors: Ben Ammar Lassaad
Published: December 2024

Early and accurate diagnosis of lung cancer is crucial to improving patient outcomes and survival rates. Machine and deep learning models have emerged as promising tools to improve the accuracy and efficiency of disease diagnosis. However, achieving optimal diagnostic performance remains a challenging task in medical research. This study integrates ensemble learning techniques with an adaptive optimization algorithm to enhance the accuracy of lung cancer diagnosis. By combining the predictive potential of multiple base classifiers, the ensemble-learning model improves overall performance and mitigates the weaknesses of individual classifiers. Additionally, the adaptive optimization algorithm dynamically adjusts the model parameters to optimize the classification performance. The effectiveness of the approach was evaluated using a comprehensive dataset that includes lung cancer images. Rigorous evaluation and comparison with state-of-the-art models showed that the proposed method achieved superior diagnostic performance, reaching an overall accuracy of 99%.