bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Implementation of a High Gain Hexagon Loop Antenna for 5G and WLAN Application
Authors: Al-Zahrani Ali Y. , Najim Mohd
Published: August 2024

In this work, the design and implementation of a high-gain Hexagon Loop Antenna (HLA) for 5G and WLAN application is presented. Initially, a hexagon patch was designed to resonate at 3.5 GHz. A loop-based radiator was tailored to obtain miniaturization without affecting the overall performance of the proposed antenna. A CPW feed was utilized to maintain the wide bandwidth with improved return loss. FR-4 material was used as a dielectric to retain the low cost of the antenna. The return loss was kept well below -10 dB, from 3.2 GHz to 6.4 GHz. The gain of the proposed Microstrip Hexagon Loop Antenna (MHLA) is 5.3 dBi at 3.5 GHz and 8.2 dBi at 5.8 GHz, respectively. To improve the gain and directivity of the proposed antenna, a square-shaped AMC unit cell was considered, and finally, a [4×4] ground plane AMC was integrated with the antenna. The radiation pattern of the antenna is stable and low cross-polarization is maintained for the desired band. A simulation of the proposed antenna was carried out in the ANSYS HFSS tool and the experimental results were measured inside an anechoic chamber. The experimental and simulated results of the proposed antenna are in good agreement. Its simple and low-profile structure, lower cost, and high gain ensure that the proposed high gain antenna is well suited for sub-6 GHz band and wireless application.
Improving Image Inpainting through Contextual Attention in Deep Learning
Authors: Charef Ayoub , Ouqour Ahmed
Published: August 2024

Image processing is vital in modern technology, offering a diverse range of techniques for manipulating digital images to extract valuable information or enhance visual quality. Among these techniques, image inpainting stands out, involving the reconstruction or restoration of missing or damaged regions within images. This study explores advances in image inpainting and presents a novel approach that integrates coarse-to-fine inpainting and attention-based inpainting techniques. The proposed method leverages deep learning methods to enhance the quality and efficiency of image inpainting, achieving robust and high-quality results that balance structural integrity and contextual coherence. A comprehensive evaluation and comparison with existing methods showed that the proposed approach had superior performance in maintaining structural integrity and contextual coherence within images.
Solving the Multi-objective Travelling Salesman Problem by an Amalgam of Fruit Fly Optimization and Ant Colony Optimization
Authors: Deshpande Archana A. , Raut Seema , Vaidya Nalini V.
Published: August 2024

In this article, the multi-objective Travelling Salesman Problem (TSP), which includes the optimization of two competing and incompatible goals, is taken into account. There is not a single ideal strategy that enhances all the objective functions at once. Usually, one of the goals is considered a constraint or both goals are combined into one objective function. This work provides an extremely efficient Ant Colony Optimization (ACO)-based multi-objective Fruit Fly Optimization Algorithm (FFOA). Using FFOA, which was normalized and initialized to the pheromone quantity for ACO, the present study first establishes a local solution. To evaluate the optimization results a combined method of FFOA and ACO is carried out.
Intellectual Property Design with PUF-based Hardware Security
Authors: Podugu Devi Pradeep , Kamala Kumari A. , Sabbavarapu Srinivas
Published: August 2024

With the advent of networked systems in almost all current applications, security poses a great threat to the design industry. The participation of several people in different design abstract stages in the hierarchical design industry makes the design vulnerable to security threats. To address these security issues, this study used PUFs to create signatures on Intellectual Property (IP) to protect against malicious attacks. The proposed method exhibits significant resilience to ML-based attacks.
Test Case Generation Approach for Android Applications using Reinforcement Learning
Authors: Usman Asmau , Boukar Moussa Mahamat , Suleiman Muhammed Aliyu , Salihu Ibrahim Anka
Published: August 2024

Mobile applications can recognize their computational setting and adjust and respond to actions in the context. This is known as context-aware computing. Testing context-aware applications is difficult due to their dynamic nature, as the context is constantly changing. Most mobile testing tools and approaches focus only on GUI events, adding to the deficient coverage of applications throughout testing. Generating test cases for various context events in Android applications can be achieved using reinforcement learning algorithms. This study proposes an approach for generating Android application test cases based on Expected State-Action-Reward-State-Action (E-SARSA), considering GUI and context events for effective testing. The proposed method was experimentally evaluated on eight Android applications, showing 48-96% line of code coverage across them, which was higher than Q-testing and SARSA.
The Effect of Hybrid Fibers on Some Properties of Structural Lightweight Self-Compacting Concrete by using LECA as Partial Replacement of Coarse Aggregate
Authors: Ali Salah Mahdi , Awad Hadeel K.
Published: August 2024

Self-Compacting Concrete (SCC) is a concrete with high workability. It fills the molds and passes between the narrow openings of reinforcing steel bars without the need for any mechanical pressure or compaction and without the need of a vibrator. Structural Lightweight Self Compacting Concrete (SLWSCC) is an innovative concrete developed in recent years. This concrete type combines the characteristics of lightweight concrete and SCC. This study focused on preparing the appropriate mixture to obtain SLWSCC by using Lightweight Expanded Clay Aggregate (LECA) as a volumetric partial replacement of coarse aggregate by 20, 40, 60, and 80%, reinforced by volumetric ratios of single and hybrid Micro steel Fiber (MF) and Hooked steel Fiber (HF( of 1.5 MF, 0.75 HF+0.75 MF, 1 HF+0.5 MF, and 1.5 HF (%) to evaluate the fresh properties through slump flow, T500mm, V-funnel, L-box, and segregation tests. The results showed that all mixtures fell within the limits of EFNARC/2005. It was found that single and hybrid fiber addition reduces slump flow, L-box, and segregation, while the T500mm and V-funnel values increased. The hard properties of SLWSCC reinforced by fibers, such as compressive strength, flexural strength, splitting tensile strength, oven dry density, and water absorption were studied. The addition of fibers raises compressive, splitting tensile, and flexural strength, with the maximum augmentation of 21.4, 43.4, and 53.8%, respectively, occurring when adding 1 HF + 0.5 MF. The highest value of oven dry density was acquired when adding 1.5 MF and the highest water absorption rate was acquired after the addition of 1.5 HF.
Enhancing the Capacity of Large LEO Satellites with Internetworked Small Piggybacks for Low Latency Payload Data Transmission
Authors: Ramalakshmi V, , Madhavareddy Venkata Narayana , Immadi Govardhani , Srinivasan V. V.
Published: August 2024

In most cases, the utilization of the costly payload onboard Low Earth Orbit (LEO) satellites is restricted by the limited throughput of the payload data downlink to the ground station during the visibility window. The usefulness of these data in critical applications reduces due to the large latency of the process. Different techniques involving efficient modulation schemes, increased power within the allowed level and frequency band, and capacity enhancement using close-by satellites have been studied and implemented with their relative merits and limitations in an attempt to reduce the latency of the data to the user. The present study proposes a constellation of low-cost data relay satellites placed in the same orbital plane along with the main satellite, increasing directly the effective visibility window. As a result, the utility of the main satellite is also increased by the same factor. A detailed analysis of the constellation and configuration of the relay satellites is presented in this paper.
Analysis of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing with Dynamic Optimal Power Allocation
Authors: Srinivasulu Owk , Kumar P. Rajesh
Published: August 2024

Multiple Input Multiple Output (MIMO) is a technology that combines multiple antennas and Orthogonal Frequency Division Multiplexing (OFDM) modulation to increase the data rate and spectral efficiency of wireless communication systems. Equalization problems are one of the key issues with MIMO-OFDM systems, which are caused by multiple antennas and subcarriers. Optimal Power Allocation (OPA) in MIMO-OFDM is a critical component for achieving high spectral efficiency and improving the overall performance of the system. OPA refers to the process of allocating power to the different subcarriers and antennas in MIMO-OFDM systems in an optimal way. This study proposes the Dynamic OPA (DOPA) algorithm for MIMO-OFDM systems to dynamically adjust power allocation based on the changing channel conditions and reduce pilot contamination issues. This approach allocates more power to subcarriers and antennas with better channel conditions and less power to subcarriers and antennas with worse channel conditions. DOPA in MIMO-OFDM systems is necessary to maximize data rates, minimize interference, improve system capacity, and reduce power consumption. Simulation results showed that the proposed MIMO-OFDM-DOPA had reduced bit error rates, mean square error, and transmitter power and increased energy efficiency and capacity compared to existing state-of-the-art methods.
Incorporation of High Volume Ground Granulated Slag From Blast Furnaces in Pavement Quality Concrete
Authors: Patel Vikram J. , Juremalani Jayesh , Kumavat Hemraj R.
Published: August 2024

Supplementary cementitious materials (SCMs) are commonly introduced into the concrete mix to increase their properties, addressing the current need for durable and robust pavements. With every ton of Ordinary Portland Cement (OPC) produced through processes such as fossil fuel combustion and limestone fermentation, carbon dioxide is released into the atmosphere. Conversely, Ground Granulated Blast-furnace Slag (GGBS), being abundantly available, presents a viable more environmentally friendly alternative to cement for various concrete applications. This work studies the influence of GGBS, in combination with chemical admixtures, on M40 grade binary blended concrete mixtures. The aim of the study was to improve the strength characteristics at various stages of concrete curing. The results indicated that the GGBS-incorporated concretes (replacing 60%, 65%, and 70% of the cement) exhibited an increase in compressive strength after prolonged curing. The average density of fresh concrete mixes containing GGBS did not exhibited a noticeable increase. A marginal disparity in air content was observed in the replacement mix. Minimal length changes were observed in the drying shrinkage test after a curing duration of 360 days compared to conventional concrete mixtures.
Evaluating the Effectiveness of Requirement Patterns for Data Intensive Application Development
Authors: Raymond Renita , Savarimuthu Margret Anouncia
Published: August 2024

In the rapidly evolving landscape of data-intensive applications, the precision and clarity of software requirements play a pivotal role in these applications’ development process. This research focuses on the validation of a specifically designed Transformation Requirement Pattern (TFReqPat) for data-intensive applications, such as banking, e-commerce, and healthcare. The main objective is to ascertain the completeness, correctness, and unambiguity of requirements captured using this novel pattern. Traditional approaches to requirement documentation often suffer from inconsistencies leading to the exploration of structured patterns that promise enhanced clarity and reuse. This article focuses on acceptable verification and validation procedures deployed as concrete methods to validate the adequacy of the captured requirements. Through the generation of stringent acceptance criteria, it is ensured that the documented requirements were adherent to developmental standards with fewer ambiguities. As per the proposed validation process, the correctness and completeness of the specified requirements were determined through the acceptance criteria that check for the presence of process, domain dependency, behavior, and storage details in the specifications. Accordingly, the obtained evaluation score was 93.1%, 88.5%, and 75.2% for correctness and 94.8%, 92.9%, and 76.4%, respectively, for completeness. These findings significantly underscore the suitability of the proposed pattern for data-intensive applications, marking it as a more efficient and effective methodology compared to ad-hoc reuse approaches. This article not only contributes a validated requirement pattern to the field but also highlights the importance of structured requirement documentation in enhancing software development outcomes for data-intensive domains.
Compressive Strength Analysis of Renewable Mortar after Portland Cement Replacement with Waste Ash
Authors: Syarif Muhammad , Nanda Abdul Rakhim , Nurnawaty . , Imran Hamzah Al , Karim Nenny , Yusri Andi
Published: August 2024

There are many environmental problems caused by factory waste. Sugar factory waste, in the form of bagasse ash, and PLTU factory waste, in the form of fly ash, are currently in the spotlight of science studies. This study used waste bagasse and fly ash to substitute Portland cement as the main ingredients for mortar. Baggash and fly ash waste were collected, processed, and then used to replace cement by up to 40% to determine to what extent they could be used in brick masonry work, wall plastering, and masonry paste. Experimental tests were carried out on mortar cube samples measuring 5×5×5 cm, comparing four types of samples consisting of Portland cement, bagasse ash, and fly ash. The compressive strength results were obtained after 28 days. Normal Mortar (MN=11.75 MPa) had higher compressive strength than the substitute mortar types MA (3.23 MPa), MB (3.09 MPa), and MC (2.98) MPa. According to SNI 6882-2014, MA, MB, and MC mortars can be used as O-type mortar (2.4 MPa). Therefore, they can be applied to wall plastering or walls not bearing loads.
Deep Learning Techniques for Lung Cancer Recognition
Authors: Vemula Suseela Triveni , Sreevani Maddukuri , Rajarajeswari Perepi , Bhargavi Kumbham , Tavares Joao Manuel R. S. , Alankritha Sampath
Published: August 2024

Globally, lung cancer is the primary cause of cancer-related mortality. Higher chance of survival depends on the early diagnosis of lung nodules. Manual lung cancer screenings depends on the human factor. The variability in size, texture, and shape of lung nodules may pose a challenge for developing accurate automatic detection systems. This article proposes an ensemble approach to tackle the challenge of lung nodule detection. The goal was to improve prediction accuracy by exploring the performance of multiple transfer learning models instead of relying solely on deep learning models. An extensive dataset of CT scans was gathered to train the built deep learning models. This research paper is focused on the Convolutional Neural Networks' (CNNs') ability to automatically learn and adapt to discernible features in the lung images which is particularly beneficial for accurate classification, aiding in identifying true and false labels, and ultimately enhancing lung cancer diagnostic accuracy. This paper provides a comparative analysis of the performance of CNN, VGG-16, and VGG-19. Notably, the built transfer learning model VGG-16 achieved a remarkable accuracy of 95%, surpassing the baseline method.
Synthesis of an Orbit Tracking Controller for a 2DOF Helicopter based on Sequential Manifolds with Stabilization Time in the Presence of Disturbances
Authors: Chiem Nguyen Xuan , Thang Le Tran
Published: August 2024

This study presents the design of a controller for a two-degree-of-freedom (2-DOF) helicopter based on sequential invariant manifolds with exponential convergence. The system is decomposed into two subsystems for pitch and yaw angles, and exponentially stable manifolds are constructed for each subsystem. The control law is found based on sequential manifolds and the Analytical Design of Aggregated Regulators (ADAR) method. The controller is designed to increase the system's stability against disturbances while ensuring stability over a finite period of time. The response time of the system can be evaluated in advance through the parameters of the designed manifold. The robustness of the control law for external disturbances was proven using the Lyapunov function in the design process. Finally, the effectiveness of the proposed controller based on the synergetic control theory is demonstrated by numerical simulation results and a comparison with the backstepping controller.
Detection of Unsafe Behavior in conveying Vehicle Parts using Computer Vision
Authors: Vazquez-Monjaras Carlos Eduardo , Cárdenas-Robledo Leonor Adriana , Reta Carolina
Published: August 2024

Deep Learning (DL) has experienced notable growth in various applications, which highlights its use in vision systems for object detection. The present work proposes a proof of concept for detecting unsafe acts in a vehicle assembly plant. The employment of Convolutional Neural Networks (CNNs) for either object or event detection was studied, and a vision system specifically trained for real-time detection of unsafe acts carried out by personnel while conveying car body parts was implemented. The intention of this research is to prevent workplace accidents and promote safety in the production environment by creating a personalized dataset composed of images that capture some incorrect ways of loading the car body doors, labeled as unsafe acts. For this purpose, a YOLOv8 DL model was trained to recognize unsafe behaviors, and after the test execution, the system efficiently identified safe and unsafe acts. Therefore, the proposal is feasible to be deployed to improve surveillance in daily operations, deliver automated reports for decision-making, and establish countermeasure actions.
Real-Time Monitoring for a Building-Integrated Photovoltaic System based on the Internet of Things and a Web Application
Authors: Ftirich Atef , Bouaziz Bechir , Bacha Faouzi
Published: August 2024

Building-Integrated Photovoltaic (BIPV) systems have become the most attractive clean solution for generating sustainable energy in building structures. Thus, the challenge of improving their efficiency is of extreme importance. The deployment of remote monitoring systems based on the Internet of Things (IoT) presents an opportunity to reduce the overall costs associated with BIPV systems. However, the performance of these monitoring systems varies depending on different parameters and environmental conditions. This paper presents a low-cost IoT-based prototype for monitoring of a solar photovoltaic panel connected to a battery. The current, voltage, and temperature values of the panel and battery, along with environmental parameters such as humidity, temperature, and solar irradiance were measured using low-cost relevant sensors. The data were sent and stored in PostgreSQL database servers through the Raspberry Pi4 Wi-Fi microcontroller board. Real-time visualization of data was facilitated through Web monitoring interfaces. The intelligent monitoring system was implemented in a facility located in Gabes, Tunisia.
A New Model of Fault-Tolerant Predictive Current Control of Multilevel Cascaded H-Bridge Inverters for Induction Motors
Authors: Chung Mai Van , Ha Vo Thanh
Published: August 2024

This study proposes a fault-tolerant method for controlling multilevel inverters using predictive control strategies to tackle semiconductor valve open circuit problems, making a substantial step towards ensuring smooth functionality and sustained performance. The proactive error detection mechanism, based on analyzing differences between the output voltage and the H-bridge control signals, offers a sophisticated approach to fault management. With an advanced SVM voltage modulation algorithm, the system efficiently handles potential faults by optimizing switching combinations to achieve standard voltage vectors. This method ensures maximum output voltage and maintains balanced operation across three phases, resulting in an optimal operational state. The viability and effectiveness of the proposed solution are conclusively established through a comprehensive analysis and rigorous testing. MATLAB simulations confirmed the integrity of the proposed method, demonstrating its ability to accurately address current, torque, and speed requirements. The findings highlight the competence of multilevel inverters in practice, presenting them as user-friendly, secure, and capable of meeting diverse quality standards.
An Advanced Deep Learning Approach for Precision Diagnosis of Cotton Leaf Diseases: A Multifaceted Agricultural Technology Solution
Authors: Nagarjun Ashwathnarayan , Manju Nagarajappa , Darem Abdulbasit A. , Siddesha Shivarudraswamy , Yahya Abdulsamad E. , Alhashmi Asma A.
Published: August 2024

During the past few decades, cotton leaf diseases have become a significant challenge for farmers, leading to substantial losses in harvests, productivity, and financial resources. Traditional observation methods are often time-consuming, costly, and prone to inaccuracies, exacerbating the plight of farmers in detecting and identifying diseases in their early stages. The consequences of late detection are dire, and both crops and farmers are under the brunt of prolonged infections. This study proposes a method to improve the detection of cotton leaf diseases by applying advanced deep transfer learning techniques. Using models such as ResNet101, Inception v2, and DenseNet121, and fine-tuning parameters utilizing the Nesterov accelerated gradient, the proposed system offers a powerful tool for farmers to swiftly and accurately diagnose leaf diseases. This system allows users to simply upload an image of a cotton leaf. After sophisticated image processing techniques, a Convolutional Neural Network (CNN) is deployed to detect the presence of cotton leaf diseases with high precision and efficiency. The experimental results demonstrated the effectiveness of transfer learning approaches, with the CNN achieving an impressive accuracy of 99%, while ResNet101, Inception v2, and DenseNet121 achieved 75.36%, 97.32%, and 97.16%, respectively. These findings underscore the potential of deep learning techniques to revolutionize disease detection in agricultural contexts, offering farmers a powerful tool to mitigate the impact of diseases on their crops.
A Case Study of Surface Roughness Improvement for C40 Carbon Steel and 201 Stainless Steel using Ultrasonic Assisted Vibration in Cutting Speed Direction
Authors: Nguyen Thanh Trung , Tuan Truong Cong , Vu Toan Thang
Published: August 2024

The surface roughness of mechanical parts plays an important role in evaluating the machining performance. However, achieving fine surface finishes on small-diameter shafts through traditional lathes poses challenges due to low cutting speed and workpiece stiffness. To address this issue, in the present work, we applied ultrasonic-assisted vibration aligned with the cutting speed direction to enhance the turning process of small shafts made of C40 Carbon steel or 201 stainless steel. The workpieces were machined by Ultrasonic Assisted Turning (UAT) at three different cutting speeds, ranging from 15 to 36 m/min, while maintaining a constant feed rate and depth of cut. To facilitate comparison with conventional turning (CT), the cutting parameters remained consistent, and both methods were performed for the same duration. UAT necessitates the use of a specialized turning inserts’ fixture known as a horn to transmit ultrasonic vibrations from the generator to the tooltip. This study also presents the design methodology and the performance evaluation of the horn. Surface roughness was assessed using the arithmetical mean height, Ra. In UAT, the roughness Ra exhibited the most significant reduction for C40 Carbon steel, reaching a decrease of 308% at a cutting speed of 15 m/min, whereas for 201 stainless steel, Ra did not vary by more than 23% across different cutting speeds.
Computer Architectures Empowered by Sierpinski Interconnection Networks utilizing an Optimization Assistant
Authors: Iqbal Muhammad Waseem , Alshammry Nizal
Published: August 2024

The current article discusses Sierpinski networks, which are fractal networks with certain applications in computer science, physics, and chemistry. These networks are typically used in complicated frameworks, fractals, and recursive assemblages. The results derived in this study are in mathematical and graphical format for particular classes of these networks of two distinct sorts with two invariants, K-Banhatti Sombor (KBSO) and Dharwad, along with their reduced forms. These results can facilitate the formation, scalability, and introduction of novel interconnection network topologies, chemical compounds, and VLSI processor circuits. The mathematical expressions employed in this research offer modeling insights and design guidelines to computer engineers. The derived simulation results demonstrate the optimal ranges for a certain network. The optimization assistant tool deployed in this work provides a single maximized value representing the maximum optimized network. These ranges can be put into service to dynamically establish a network according to the requirements of this paper.
Development of a Risk Breakdown Structure in Mega Projects based on Different Case Studies
Authors: Othman Shamal Ali , Jaff Dalshad Kakasor Ismael , Oztas Ahmet
Published: August 2024

Rapid urbanization, globalization, and population growth have led to an increase in megaprojects in recent decades. Consequently, as construction projects move forward, a wider range of risks arise. By looking into and managing risk factors in advance of their occurrence, it is vital to reduce their negative effects. In construction projects, risk management is seen as a critical procedure that helps meet project objectives in relation to schedule, budget, quality, safety, and sustainability concerns. This study aims to examine and gain a deeper understanding of project-related risks in mega projects. This study also develops a risk breakdown structure in mega projects based on a literature review. This will help project participants manage these risks in their projects properly. Another objective of this study is to examine the methodologies used in data collection for determining and categorizing risks in mega projects. Finally, it is concluded that the risk factors in mega projects can be divided into two categories: internal and external risks whereas the main risk factors in mega projects are categorized as execution, construction, technical, economic and financial, environmental, social, political, and other.
Detecting and Mitigating Data Poisoning Attacks in Machine Learning: A Weighted Average Approach
Authors: Maramreddy Yogi Reddy , Muppavaram Kireet
Published: August 2024

Adversarial attacks, in particular data poisoning, can affect the behavior of machine learning models by inserting deliberately designed data into the training set. This study proposes an approach for identifying data poisoning attacks on machine learning models, the Weighted Average Analysis (VWA) algorithm. This algorithm evaluates the weighted averages of the input features to detect any irregularities that could be signs of poisoning efforts. The method finds deviations that can indicate manipulation by adding all the weighted averages and comparing them with the predicted value. Furthermore, it differentiates between binary and multiclass classification instances, accordingly modifying its analysis. The experimental results showed that the VWA algorithm can accurately detect and mitigate data poisoning attacks and improve the robustness and security of machine learning systems against adversarial threats.
A Comparison of the Surface Pressure Distribution of Circular Cables and Helical Fillet Cables under Wind Attack: A Wind Tunnel Test Study
Authors: Nguyen Duy Thao , Vo Duy Hung , Do Viet Hai
Published: August 2024

This study examines the aerodynamic performance and surface pressure distribution features of circular and helical fillet stay cables when subjected to wind using wind tunnel testing. The research seeks to clarify the aerodynamic performance disparities between conventional circular stay cables and helical fillet cables, providing valuable insights into their appropriateness for cable-supported structures exposed to wind-induced vibrations. The study initially investigates the aerodynamic efficiency of circular and helical fillet cables. Afterward, the wind tunnel captures the distribution of surface pressure on both cable surfaces. The findings suggest that circular stay cables may undergo cable dry galloping, whereas helical fillet cables demonstrate stability when subjected to wind forces. Furthermore, there are noticeable differences in the surface pressure distribution patterns between circular stay cables and helical fillet cables. Circular stay cables provide a symmetric distribution of pressure, with uniform pressure magnitudes along their surfaces, forming a symmetric pattern. On the other hand, helical fillet cables exhibit modified airflow patterns, leading to asymmetric pressure on the cable surface. Furthermore, the dry galloping observed in circular cables is attributed to the presence of low-frequency components. In contrast, helical fillet cables exhibit a more regulated incidence of low-frequency vortices, making them less prone to wind-induced vibrations.
TQU-SLAM Benchmark Feature-based Dataset for Building Monocular VO
Authors: Le Van-Hung , Do Huu-Son , Phan Van-Nam , Te Trung-Hieu
Published: August 2024

This paper introduces the TQU-SLAM benchmark dataset, which includes 160,631 RGB-D frame pairs with the goal to be used in Dell Learning (DL) training of Visual SLAM and Visual Odometry (VO) construction models. It was collected from the corridors of three interconnected buildings with a length of about 230 m. The ground-truth data of the TQU-SLAM benchmark dataset, including the 6-DOF camera pose, 3D point cloud data, intrinsic parameters, and the transformation matrix between the camera coordinate system and the real world, were prepared manually. The TQU-SLAM benchmark dataset was tested based on the PySLAM framework with traditional features, such as SHI_TOMASI, SIFT, SURF, ORB, ORB2, AKAZE, KAZE, and BRISK and features extracted from DL LIKE VGG. Experiments were also conducted on DPVO for VO estimation. The camera pose estimation results were evaluated and presented in detail, while the challenges of the TQU-SLAM benchmark dataset were analyzed.
A Surface Roughness Prediction Model for SKT4 Steel Milling
Authors: Sukkam Chaiyakron , Chaijit Seksan
Published: August 2024

Predicting surface roughness is critical in manufacturing processes like grinding, particularly for materials such as SKT4 steel, where a precise surface finish is imperative. Precise roughness prediction facilitates the optimization of process parameters to achieve the desired surface quality, consequently diminishing the need for supplementary operations such as grinding or polishing. This, in turn, decreases costs and lead times. This study aimed to develop a surface roughness prediction model tailored for milling SKT4 steel by designing experiments to analyze the influence of cutting parameters on surface roughness, collecting and analyzing data related to machining parameters in process modeling, and developing and validating the model. The analysis of variance (ANOVA) results highlights the significant influence of the interaction between rotational speed and cutting depth on skin roughness. The linear regression model demonstrates clear variability in the data (R2 of approximately 99.74%) and exhibits effective predictive capabilities (pred. R2 of approximately 83.56%). The maximum impact on skin roughness was observed at a rotational speed of 1500 RPM, a feed rate of 300 mm per minute, and a cutting depth of 0.2 mm. Increasing rotational speed leads to smoother skin, whereas higher feed rates result in decreased smoothness. However, skin roughness shows minimal fluctuation with changes in feed rate and cutting depth. The model accurately predicts the average skin roughness values.
Developing a Program for Practice Management and Productivity Improvement of Infrastructure Projects by using Business Information Modeling
Authors: Khamees Saja Abd Alrazaq , Mohammed Sawsan Rasheed
Published: August 2024

The employment of Business Information Modeling (BIM) may assist the resident engineering department in reaching its long-term strategic goals. The suggested computer software was created and tested using the first package of new projects for roads, bridges, and tunnels aimed at solving the traffic congestion crisis taking place in the capital city of Iraq, Baghdad. In the context of the current study, questionnaires and personal interviews with specialists in the field of road, tunnel, and overpass construction were employed. The aim of implementing BIM is to assess the introduced program’s efficiency, verify its performance, and identify any errors, defects, or challenges that users may encounter during its application. The computer program underwent successful testing at several Resident Engineer's Offices (REOs) within the initial package of new projects aimed at addressing traffic congestion issues in Baghdad through independent verification checks. Initially, the program was utilized with the assistance of a user guide to facilitate its operation and provide a detailed description of each program feature which can provide effective decision-making and enhance REOs' computer skills.
Advancing Email Spam Classification using Machine Learning and Deep Learning Techniques
Authors: Alsuwit Meaad Hamad , Haq Mohd Anul , Aleisa Mohammed A.
Published: August 2024

Email communication has become integral to various industries, but the pervasive issue of spam emails poses significant challenges for service providers. This research proposes a study leveraging Machine Learning (ML) and Deep Learning (DL) techniques to effectively classify spam emails. Methods such as Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and Artificial Neural Networks (ANNs) are employed to construct robust models for accurate spam detection. By amalgamating these techniques, the aim is to enhance efficiency and precision in spam detection, aiding email and IoT service providers in mitigating the detrimental effects of spam. Evaluation of the proposed models revealed promising outcomes. LR, RF, and NB achieved an impressive accuracy of 97% and an F1-Score of 97.5%, showcasing their efficacy in accurately identifying spam emails. The ANN model demonstrated slightly superior performance, with 98% accuracy and 97.5% F1-score, suggesting potential improvements in accuracy and robustness in spam filtering systems. These findings underscore the viability of both traditional ML algorithms and DL approaches in addressing the challenges of email spam classification, paving the way for more effective spam detection mechanisms in electronic communication platforms.
Leveraging Machine Learning for Android Malware Analysis: Insights from Static and Dynamic Techniques
Authors: Haq Mohd Anul , Khuthaylah Majed
Published: August 2024

In this study, the domain of Android malware detection was explored with a specific focus on leveraging the potential of Machine Learning (ML). At the time of this study, Android had firmly established its dominance in the mobile landscape and IoT devices, necessitating a concerted effort to fortify its security against emerging malware threats. Static analysis methods were scrutinized as vital sources of feature extraction for ML, while dynamic analysis methods were employed to analyze the behavior of applications in real or simulated environments. Additionally, a hybrid method, combining both static and dynamic analyses, was investigated. The study evaluated four ML models: XGBoost, Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT), revealing compelling insights into their performance metrics. Notably, RF achieved the highest accuracy of 0.99, closely followed by SVM with an accuracy of 0.96. These results underscore the potential effectiveness of ML techniques in bolstering Android malware detection and mitigating security risks. As the research progressed, it underscored the latent power of integrating ML into the framework of Android malware analysis. With an eye towards the future, the overarching goal was to empower enhanced security measures and foster a resilient mobile ecosystem through the insights gleaned from this investigation.
Advancing IoT Security: Integrative Machine Learning Models for Enhanced Intrusion Detection in Wireless Sensor Networks
Authors: Mopuru Bhargavi , Pachipala Yellamma
Published: August 2024

This paper introduces a breakthrough approach to enhancing intrusion detection capabilities within Wireless Sensor Networks (WSNs) by implementing the Enhanced Wireless Intrusion Detection System (EW-IDS). Leveraging a sophisticated blend of Machine Learning (ML) algorithms, including Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), the proposed model effectively streamlines feature selection, resulting in a robust detection framework. Extensive evaluations demonstrate that EW-IDS not only achieves a high accuracy rate of 96%, but also consistently surpasses traditional models in precision, recall, and F1 Score metrics. These achievements underscore the model’s superior ability to differentiate between benign and malicious network activities. The implementation of EW-IDS marks a significant advance in securing the Internet of Things (IoT) environments against a diverse range of cyber threats, enhancing both the security protocols and operational efficiency of WSNs. This study provides a novel intrusion detection solution and offers valuable insights into the application of ML in complex security settings.
Evaluating the Ultimate Performance of Pylon-Head Joints with Numerical Analysis
Authors: Salah Wael A. , Darweesh Moustafa S.
Published: August 2024

This study presents a comprehensive Finite Element (FE) model of the multiple-cable-to-pylon head joint within a specific cable roof structure. The analysis focuses on the upper part of the pylon substructure, particularly the pylon head joint, to examine its localized behavior under a set of internal forces derived from a simplified FE model. The steel tubular components of the pylon substructure were precisely simulated using thin shell elements. The designers of this particular roof structure proposed two solutions for reinforcing the pylon-head joint, while an additional novel strengthening technique was introduced, aimed at enhancing the joint's performance. These three strengthening methods, along with the original design joint, were modeled numerically, and the joint's effectiveness was assessed. The findings of the analysis indicate that the newly proposed strengthening technique exhibits greater potential for stiffening the considered pylon-head joint compared to the other introduced solutions. The study concludes with significant insights relevant to practical applications.
Fuzzy Logic Energy Management System-based Nonlinear Sliding Mode Controller for the Stabilization of DC Microgrids
Authors: Boubaker Sahbi , Jouili Khalil
Published: August 2024

Access to energy is critical for improving living conditions in remote and isolated areas. The integration of Renewable Energy Sources (RESs) and energy storage technologies becomes critical for sustainable energy supply, particularly in distant locations without access to the main grid. The isolated operation of RESs may face numerous problems in operation and reliability, hence, investing Direct Current Microgrids (DCMGs) can be adopted as an effective solution allowing Renewable Energy (RE) integration and contributing to efficient system operation. However, several issues related to monitoring, control, and diagnosis may be encountered under such conditions. The control of a PV-based RE system and a battery/ supercapacitor-based energy storage system in a DCMG is examined in this research. For this aim, a hierarchical control method is proposed. The proposed approach is based on a Sliding Mode Controller (SMC) and the Lyapunov stability theory. To manage load and energy generation, an energy management system based on fuzzy logic was designed. Global asymptotic stability has been demonstrated using Lyapunov stability analysis. The overall system behavior, including the proposed DCMG and controllers, was simulated. The results indicated that the system performs well under varying output and loads.
Flatness-based Motion Planning and Model Predictive Control of Industrial Cranes
Authors: Khanh Hoa Bui Thi , Thi Mai Hoang , Hue Luu Thi , Nguyen Tung Lam , Nguyen Danh Huy
Published: August 2024

This study develops a new controller for an industrial crane system in a three-dimensional space. First, the dynamic model of the industrial crane system with two subsystems, the tower crane and the overhead crane is presented. A bidirectional mapping is established between the system's input and output, allowing for efficient trajectory generation. Additionally, the design process explicitly considers the system's kinematic constraints, ensuring safe and feasible motions. This designed trajectory serves as an input for Model Predictive Control (MPC). The MPC is designed with the dual objectives of trajectory tracking and payload anti-swing. Finally, simulations are conducted and the results are compared with those of other control strategies under different cases to demonstrate the effectiveness of the proposed method.
Optimizing the Location and Capacity of DGs and SOPs in Distribution Networks using an Improved Artificial Bee Colony Algorithm
Authors: Linh Nguyen Tung , Long Pham Vu
Published: August 2024

This study proposes an improved method of the Artificial Bee Colony (ABC) algorithm for the distribution network in scenarios where distributed generation sources and Soft Open Points (SOPs) are connected to optimize power control. Improvement is achieved by integrating the ABC algorithm with the Grenade Explosion Method and Cauchy to accelerate the ABC algorithm's speed. The objective function is considered to reduce power losses over a day. The proposed method was tested on the IEEE-33 bus test system under various scenarios: Case 1 with 3 DGs installed, Case 2 with 3 DGs and 1 SOP simultaneously installed in the distribution network, and Case 3 having the same configuration as Case 2 but operating for 24 hours. In addition to reducing power losses, the voltage at the nodes in the distribution grid was also improved, maintained above 0.95 pu and close to 1 pu. Case 3 showed that integrating a Wind Turbine (WT), two Photovoltaic (PV) generators, and one SOP during operation resulted in the lowest energy losses, smaller than a system with only one WT and two PVs, and significantly lower than the baseline system without any DGs and SOPs. Therefore, employing SOPs in a distribution network with integrated DGs can offer significant benefits in reducing energy losses.
The Impact of E-Marketing on the Preference to Purchase Hybrid Cars by Increasing Awareness: An Empirical Study of Hybrid Car Users in the Northern Border Region, Saudi Arabia
Authors: Arous Jamal Ali , Louail Bilal , El Rabbat Samar Ahmed , Hima Nadya Ali , Barakat Shaima Ahmad
Published: August 2024

This research examines the impact of e-marketing on consumers' preference to purchase hybrid cars, through the conduction of a field study on the hybrid car users in the Northern Border Region in Saudi Arabia. The research aims to comprehend the relationship between e-marketing campaigns and increasing consumer awareness of the benefits of hybrid cars, and how their decisions are affected by this awareness when purchasing cars. The research aims to shed light on the role of e-marketing in encouraging the use of hybrid cars and identifying the factors influencing consumers' decisions in this context. The research aims to identify factors influencing users' responses to such information, including advertising campaigns, environmental attitudes, future orientations, and governmental incentives. A survey was conducted to a total of 385 hybrid car users and data analysis relied on structural equation modeling deploying the Amos software. The results indicate the existence of a relationship and impact between e-marketing campaigns and user awareness, leading to an increased preference for hybrid cars.
Dynamic Adaptation in Deep Learning for Enhanced Hand Gesture Recognition
Authors: Hashi Abdirahman Osman , Hashim Siti Zaiton Mohd , Asamah Azurah Bte
Published: August 2024

The field of Human-Computer Interaction (HCI) is progressing quickly with the incorporation of gesture recognition, which requires advanced systems capable of comprehending intricate human movements. This study introduces a new Dynamic Adaptation Convolutional Neural Network (DACNN) that can adjust to different human hand shapes, orientations, and sizes. This allows for more accurate identification of hand gestures over a wide range of variations. The proposed model includes a thorough process of collecting and preparing data from the Sign Language MNIST dataset. This is followed by a strong data augmentation procedure that provides a wide variety of realistic variations. The architecture utilizes sophisticated convolutional layers to leverage the capabilities of deep learning to extract and synthesize essential gesture features. A rigorous training procedure, supplemented with a ReduceLROnPlateau callback, was used to assure the model's generalization and efficiency. The experimental findings provide remarkable results, showing a substantial accuracy of 99% in categorizing a wide range of hand movements. This study makes a significant contribution to the field of hand gesture recognition by introducing morphological operations, thus enriching input data quality and expanding the model's applicability in diverse HCI environments.
A Secure and Reliable Framework for Explainable Artificial Intelligence (XAI) in Smart City Applications
Authors: Algarni Mohammad , Mishra Shailendra
Published: August 2024

Living in a smart city has many advantages, such as improved waste and water management, access to quality healthcare facilities, effective and safe transportation systems, and personal protection. Explainable AI (XAI) is called a system that is capable of providing explanations for its judgments or predictions. This term describes a model, its expected impacts, and any potential biases that may be present. XAI tools and frameworks can aid in comprehending and trusting the output and outcomes generated by machine-learning algorithms. This study used XAI methods to classify cities based on smart city metrics. The logistic regression method with LIME achieved perfect accuracy, precision, recall, and F1-score, predicting correctly all cases.