bluuebunny/crossref_metadata_2025 huggingface dataset:
A CONV-EGBDNN Model for the Classification and Detection of Mango Diseases on Diseased Mango Images utilizing Transfer Learning
Authors: Kalaivani Ramalingam , Saravanan Arunachalam
Published: June 2024

Mango fruits are highly valued for their taste, flavor, and nutritional value, making them a popular choice among consumers. However, mango fruits are susceptible to various diseases that can significantly affect their yield and quality. Therefore, accurate and timely detection of these diseases is crucial for effective disease management and minimizing losses in mango production. Computer-aided diagnosis techniques have emerged as a promising tool for disease detection and classification in mango fruits. This study adopts an image classification approach to identify various diseases in mangos and distinguish them from healthy specimens. The pre-processing phase involves a Wiener filter for noise removal, followed by Otsu's threshold-based segmentation as a crucial operation. Subsequently, features are extracted by implementing the ResNet50 model. The proposed model was experimentally verified and validated, demonstrating optimal results with an accuracy of 98.25%. This high accuracy rate highlights the effectiveness of the XG-Boost classifier in accurately categorizing mango images into different disease categories. The experimental results strongly support the potential practical application of the model in the agricultural industry for disease detection in mango crops.
hFedLAP: A Hybrid Federated Learning to Enhance Peer-to-Peer
Authors: Elshair Ismail , Khanzada Tariq J. S.
Published: June 2024

The concept of Federated Learning (FL) is a branch of Machine Learning (ML) that enables localized training of models without transferring data from local devices to a central server. FL can be categorized into two main topologies: Aggregation Server Topology (AST) and Peer-to-Peer (P2P). While FL offers advantages in terms of data privacy and decentralization, it also exhibits certain limitations in efficiency and bottleneck. However, the P2P topology does not require a server and allows only for a small number of devices. To overcome these limitations, this study proposes a hybrid FL Aggregation of P2P (hFedLAP) that mitigates some of the limitations of AST by combining it with P2P. This fusion model helps to remove the bottleneck and combines the advantages of both topologies. In the proposed hFedLAP model, clients are organized into 49 groups, each consisting of 51 clients, including one in each group serving as a client and an admin node in a P2P setup. In these groups, communication is restricted to admin nodes, supporting a maximum of 2,495 devices. Platform accuracy is maintained by implementing measures to prevent new devices with inadequate accuracy levels from joining until they attain the minimum required accuracy. The experimental results of hFedLAP were compared with AST and P2P using the MNIST dataset, showing that hFedLAP outperformed AST and P2P, achieving remarkable accuracy and scalability, with accuracy levels reaching 98.81%.
Theoretical and Experimental Analysis of Group Piles of Jet and Concrete Columns using the Double Grouting Technique Subjected to Axial Loading on Sandy Soil
Authors: Al-Khadaar Rana M. , Ahmed Mahmood D.
Published: June 2024

This research deals with modifying improvement techniques by using a new technology depending on the properties of the subsoil and the surrounding soil. Jet grouting is one of these techniques utilized instead of normal deep foundations, such as piles, peers, and raft foundations, because it increases the bearing capacity, reduces the settlement, and decreases permeability. In this research, the effect of double-pile and (2*2) jet column piles was studied in the laboratory by employing a jet grouting machine for sand soil, and the results were compared with those of similarly distributed concrete piles. Moreover, the two groups were theoretically analyzed with the 3D ABAQUS finite element software. It was found that with the jet pile, the applied load is greater and the settlement is smaller than that with the concrete pile. The ultimate pile ratio obtained through laboratory tests in the (2*1) jet pile and the concrete pile groups was 71.2% and 75%, respectively. The settlement ranged from 0.00135 to 0.00148 m with the jet pile and ranged from 0.034 to 0.035 m with the concrete pile.
Big Data and Cloud Computing Opportunities and Application Areas
Authors: Abueid Aws I.
Published: June 2024

The exponential growth of digital data has created new challenges and opportunities to process, store, and analyze large datasets. Cloud computing has transformed the way businesses manage their IT infrastructure by providing a scalable and cost-effective platform for data storage and processing. Because of this, businesses now have access to new opportunities to exploit big data and thus acquire insights into the behavior of their customers, increase the efficiency of their operations, and drive innovation. Furthermore, big data analysis enables the exploration of links between a set of independent data, revealing many aspects that enable the prediction of correct decisions that can aid the achievement of desired goals. With this in mind, the purpose of this article is to investigate the ways in which big data analysis can be put to use in fields such as healthcare, presenting specific instances of effective use along the way. The purpose of this study is to provide a comprehensive overview of the opportunities and application areas offered by big data and cloud computing, as well as to highlight the need for businesses and organizations to adopt these technologies to maintain their competitive edge in the digital age. Furthermore, this paper emphasizes the importance of big data analysis in facilitating decision-making and goal achievement, and it encourages businesses and organizations to adopt these technologies to stay competitive in an increasingly data-driven world. It also discusses the ethical, legal, and security issues that arise when dealing with large amounts of data, as well as ways to address these challenges.
Development of a Collaborative Intelligent Individual Education Program System using a Prototyping Approach
Authors: Yusop Nor Shahida Mohamad , Rosli Marshima Mohd , Farid Nur Farahin , Nazri Nur Aqila Syafika Mohd , Jamil Nursuriati , Ramli Muhammad Izzad
Published: June 2024

The current paper describes the development of an online Collaborative Intelligent Individual Education Platform (CIIP) that is specifically designed for children with ASD based on experts' assessments and progress reports. The online platform facilitates the progress of children with special needs as it is established on their individual needs and can be accessible anywhere. The CIIP system was developed following a prototyping model approach that comprised initial requirements, design, prototyping, customer evaluation, review and refinement, development, testing, and maintenance. Two cycles of prototyping evaluation were conducted to confirm the final requirements. The results of the prototype evaluation by the stakeholders indicated that 29 changes were required before progressing to the final development of CIIP. System testing was carried out with expert testers to ensure the CIIP functions and the satisfaction of the expected requirements. The results showed that 22% of the test cases failed due to difficulties with complicated interconnections in several modules. Despite these challenges, CIIP was able to meet the requirement specifications and perform as expected.
Unlocking Success: Exploring the Impact of Human Resource Competence on Job Performance in Pakistan's Engineering Sector
Authors: Gogia Eimad Hafeez , Shao Zhen , Akhter Ali Raza
Published: June 2024

Based on innovation diffusion theory, this paper develops a research model to investigate the influence mechanism of Human Resources (HR) competency on employees' job performance through the mediation effect of HR analytics and the positive moderation effect of Τechnology Αdoption (ΤΑ). A survey was carried out in civil engineering firms in Pakistan, adapting measures with good reliability and validity from different sources, and collecting data through social media platforms and questionnaires from HR professionals. The responses of 297 respondents were collected and structural equation modeling was applied. The results show that there is a positive and significant relationship between HR competency and employee job performance, a significant partial mediation of HR analytics in the relationship between HR competency and employee job performance, and a significant positive moderation of ΤΑ in the mediation of HR analytics in the relationship between HR competency and employee job performance. This study provides an essential contribution to the diffusion of innovation theory, as ΤΑ is slow compared to other countries, and practical guidelines for the fast adoption of technology and HR analytics in the HR departments of civil engineering organizations to enhance talented employees' performance.
Influence of Deflection Deformations on the Sustainability of the Landfill Cover: Analysis and Recommendations
Authors: Jamei Mehrez , Mabrouk Abdelkader , Alassaf Yahya
Published: June 2024

The design of cover landfill requires an optimum thickness of the compacted fine soil layer with small permeability. In general, the objective is to reduce the thickness of the landfill cover. However, for a thin layer, and under natural evaporation, denser crack network growths occur during the desiccation by drying. Cracks change the geometrical properties during the drying and wetting cycles and significantly compromise the role of the cover layer, by inducing an infiltration water flow and gas migration. An important differential flexure deformation occurs. The landfill cover, where stiffness and tensile and shear strengths were reduced is being progressively damaged. Thus, this paper aims 1) to quantify the flexural deformation and 2) to provide a methodology and a guideline for studying the integrity of a cover landfill. So, a mechanical model is proposed and implemented in Code Bright software. The methodology starts from the calibration and the validation of the mechanical model based on 1) four-point flexural beam tests and 2) on existing published results. A physical prototype was employed to demonstrate the flexure deformation, and the crack development. Moreover, short natural fibers were mixed and embedded in the soil to make the soil reinforcement and delay crack propagation. In addition to the experimental investigation, mathematical constitutive equations were proposed, in which the contribution of short fibers in terms of increase of tensile strength was introduced. Finally, a simple case study was considered to demonstrate the role of the fiber-soil composite on flexural deformation and tensile stress distribution across the cover layer. An analysis of the numerical results was conducted to support the use of short fibers as reinforcement, which is an environmentally friendly and sustainable solution.
Determination of Optimum Test Parameter Level Ranges for Machining Processes
Authors: Alabayed Mohamed Almokhtar K. , Alghatous Safa Aisa Sasi , Ozarpa Cevat , Korkmaz Seyma , Cetin M. Huseyin , Basher Ibrahim Salem A.
Published: June 2024

In this study, the ideal experimental design planning for the machining process was investigated. Two experimental designs were created by differentiating the parameter levels considered in the drilling process of stainless-steel. Close and far-level designs were obtained by creating 20% and 40% differences between the parameter levels. In the experimental system prepared according to the Taguchi method, surface roughness and cutting forces were measured as the output parameters. The results were analyzed statistically by optimization, analysis of variance and correlation analysis, and visually by chip morphology examination. According to the findings, it was determined that a 20% difference between the parameter levels was more appropriate in terms of experimental system stability, statistical data significance, and chip morphology.
Driving Innovation: Prosumer Incentives in Peer-to-Peer Energy Trading
Authors: Mahmoud Marwan , Ben Slama Sami
Published: June 2024

Peer-to-peer energy trading is an innovative idea that overcomes several technological and industrial hurdles. It allows industries and consumers, including knowledgeable prosumers, to trade excess energy with distributed generation sources, such as solar, wind, and electric vehicles, thus promoting a significant reduction in overall energy consumption. Real-Time Pricing (RTP) is increasingly essential in integrating smart home device Demand Response (DR) strategies. RTP improves energy management and enables customers to respond intelligently to price fluctuations. In this vein, this paper proves how DR and peer-to-peer (P2P) energy trading could affect energy prices by allowing producers (consumers) and smart home users to interact directly rather than through the traditional grid. The two-pronged planning approach substantially contributes to the reduction of electricity costs. DR enables P2P energy trading, while deep learning algorithms adapt smart home devices to RTP dynamics. Simulation results show that using P2P energy trading and DR in smart homes can significantly eliminate costs. This hybrid approach increases the energy efficiency of Smart Grid (SG) 2.0 technology and makes it more adaptable and cost-effective.
Investigation of an Antireflective Coating System for Solar Cells based on Thin Film Multilayers
Authors: Khmissi Hammadi , Azeza Bilel , Bouzidi Mohamed , Al-Rashidi Zainab
Published: June 2024

The optical loss due to reflection is a significant barrier to the quantum efficiency of solar cells. In this work, an antireflective coating based on multilayers of metal oxides (TiO2, SiO2, ZnO) was prepared with the spin coating method. The coatings' antireflective, hydrophobic, and photocatalytic properties were examined. Based on the requirements met by the refractive index, a methodical selection of material and thickness for each layer was made in order to achieve near-zero reflection. The performance of different coating systems was examined by evaluating the percentage transmittance in the visible light range (400 nm - 800 nm). The optical properties of the obtained samples were studied with regard to transmittance and reflectance. The surface wettability of antireflective coating films was assessed by measuring the Water Contact Angle (WCA). The photocatalytic characteristics were evaluated by analyzing of the degradation of 0.02 mM Methylene Blue (MB) solutions after sunlight exposure for varying durations at midday.
Drivers of Renewable Energy Use in Saudi Arabia: Evidence from Wavelet Local Multiple Correlation Approach
Authors: Aloui Chaker , Ben Hamida Hela , Hathroubi Salem
Published: June 2024

This study examines the impact of various factors, including oil rents, government effectiveness, economic complexity, and economic growth, on the use of renewable energy in Saudi Arabia. Employing a novel time-localized wavelet multiple regression correlation framework, the unique approach followed reveals significant and positive interconnections between these factors and promotes renewable energy utilization in the long run. However, the aforementioned factors’ short-term correlations are substantially lower and insignificant for some time intervals. Importantly, the analysis performed shows that oil rents and government effectiveness play a dominant role among the other factors. These findings have crucial policy implications, highlighting the need for effective governance and the potential for diversifying energy sources in Saudi Arabia.
Anomaly Detection in IIoT Transactions using Machine Learning: A Lightweight Blockchain-based Approach
Authors: Okfie Mayar Ibrahim Hasan , Mishra Shailendra
Published: June 2024

The integration of secure message authentication systems within the Industrial Internet of Things (IIoT) is paramount for safeguarding sensitive transactions. This paper introduces a Lightweight Blockchain-based Message Authentication System, utilizing k-means clustering and isolation forest machine learning techniques. With a focus on the Bitcoin Transaction Network (BTN) as a reference, this study aims to identify anomalies in IIoT transactions and achieve a high level of accuracy. The feature selection coupled with isolation forest achieved a remarkable accuracy of 92.90%. However, the trade-off between precision and recall highlights the ongoing challenge of minimizing false positives while capturing a broad spectrum of potential threats. The system successfully detected 429,713 anomalies, paving the way for deeper exploration into the characteristics of IIoT security threats. The study concludes with a discussion on the limitations and future directions, emphasizing the need for continuous refinement and adaptation to the dynamic landscape of IIoT transactions. The findings contribute to advancing the understanding of securing IIoT environments and provide a foundation for future research in enhancing anomaly detection mechanisms.
Metaheuristic Optimization of Maximum Power Point Tracking in PV Array under Partial Shading
Authors: Taha Mohammed Qasim , Mohammed Mohammed Kareem , El Haiba Bamba
Published: June 2024

Optimal energy harvesting is dependent on the efficient extraction of energy from photovoltaic (PV) arrays. Maximum Power Point Tracking (MPPT) algorithms are crucial in achieving the maximum power harvest from the PV systems. Therefore, in response to a fluctuating power generation rate due to shading of the PV, the MPPT algorithms must dynamically adapt to the PV array's Maximum Power Point (MPP). In this article, three metaheuristic optimization MPPT techniques, utilized in DC converters connected to the array of 4 PV panels, are compared. The Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), which are used to optimize MPPT in the converter, are compared. This research evaluates the efficiency of each optimization method in converging to MPP under 2 s after partial shading of the PV with respect to velocity and accuracy. All algorithms exhibit fast MPPT optimization. However, among the evaluated algorithms, the PSO was distinguished for its higher stability and efficiency.
Improved and Efficient Object Detection Algorithm based on YOLOv5
Authors: Alsuwaylimi Amjad A. , Alanazi Rakan , Alanazi Sultan Munadi , Alenezi Sami Mohammed , Saidani Taoufik , Ghodhbani Refka
Published: June 2024

Object detection is a fundamental and impactful area of exploration in computer vision and video processing, with wide-ranging applications across diverse domains. The advent of the You Only Look Once (YOLO) paradigm has revolutionized real-time object identification, particularly with the introduction of the YOLOv5 architecture. Specifically designed for efficient object detection, YOLOv5 has enhanced flexibility and computational efficiency. This study systematically investigates the application of YOLOv5 in object identification, offering a comprehensive analysis of its implementation. The current study critically evaluates the architectural improvements and additional functionalities of YOLOv5 compared to its previous versions, aiming to highlight its unique advantages. Additionally, it comprehensively evaluates the training process, transfer learning techniques, and other factors, advocating the integration of these features to significantly enhance YOLOv5's detection capabilities. According to the results of this study, YOLOv5 is deemed an indispensable technique in computer vision, playing a key role in achieving accurate object recognition. The experimental data showed that YOLOv5-tiny performed better than anticipated, with a mean Average Precision (mAP) of 60.9% when evaluated using an Intersection Over Union (IoU) criterion of 0.5. Compared to other approaches, the proposed framework is distinguished by significant improvements in the mean average accuracy, computational flexibility, and dependability. As a result, YOLOv5 is suitable for a wide range of real-world applications, since it is both sophisticated and resilient in addressing present issues in the fields of computer vision and video processing.
Steel Collar Strengthening of a Slab-Column Connection under Eccentric Load
Authors: Ali Hanaa Abdulbaset , Al-Sherrawi Mohannad H.
Published: June 2024

The current study focuses on the punching shear resistance of reinforced concrete flat slabs with steel collars, examining it both experimentally and numerically. Six square flat slab specimens were casted and tested under static load, axial load, and eccentric load. The effects of the steel collars and eccentricity on the load-displacement behavior, ultimate load capacity, cracking load, failure mode, stiffness, failure angle, and ductility, were investigated. The results demonstrated that using steel collars in slab-column connection greatly increases the shear capacity of the slab under eccentric loads and moments. The strengthened slabs' ultimate capacity increased by 34% and 61%, respectively, compared to that of the slabs without collars. ABAQUS simulation results were in good accordance with the experiments. The findings underline the efficiency of the steel collars in increasing the efficiency of slab-column connections with punching shear, which is a cost-effective strengthening technique. This research provides knowledge about slab-column connections and offers relevant indications for the design and strengthening of the construction.
A Study on the Prediction of Apartment Prices using the GBRT model: A Case Study in Vinh City, Vietnam
Authors: Tran Ha-Lan , Thi Thuy-Linh Tran , Tran Thanh-Vu , Thi Doan-Huong Doan , Nguyen Trong-Ha
Published: June 2024

This study aims to propose an efficient Machine Learning (ML) model, namely Gradient Boosting Regression Trees (GBRT), to predict apartment prices considering the fluctuation of construction material prices and the annual inflation index. For developing the ML model, 480 apartments in Vinh City (Vietnam) were considered. The input parameters employed while training the ML model were the area of the apartments, the number of bedrooms/restrooms, the apartment class, nearby health or education services, investment potential, and parking, whereas the apartment price was the output of the model. The results show that the GBRT model predicts the apartment price accurately with a high value of 0.997 and a small RMSE of 0.26. Additionally, the obtained a20-index is very high, almost 1.0. Finally, a practical graphical user interface was developed to facilitate the prediction of the apartment price in terms of usability.
Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care
Authors: Aparna Shivampeta , Gottumukkala Himabindu , Shivampet Nitya , Muppavaram Kireet , Ramayanam Chaitanya C. V.
Published: June 2024

Many individuals face issues with their teeth, requiring the expertise of dentists to provide necessary care. Despite the advancements in dental techniques, there is a persistent shortage of dentists, prompting the development of tools to help the latter efficiently perform patient treatment. The current research focuses on refining the precision of the vital dental treatment known as dental fillings. The approach involves utilizing the Mask Region-based Convolutional Neural Network (MaskRCNN) with different variants of ResNET, such as ResNET50, ResNET101 C4, Dilated C5, and Feature Pyramid Network (FPN), to analyze diverse dental radiographs. By training on a broad range of tooth images, this methodology creates a pixel-based masking system, improving dentists' ability to precisely identify filling levels. Consequently, this innovation contributes significantly to expediting and refining the accuracy of dental treatments, ultimately benefiting individuals with tooth problems. Additionally, as a future prospect, this model can enable robots to perform dental operations as it provides pixel-level information necessary for the treatment.
Adaptive Method for Feature Selection in the Machine Learning Context
Authors: El Touati Yamen , Slimane Jihane Ben , Saidani Taoufik
Published: June 2024

Feature selection is a fundamental aspect of machine learning that is crucial for improving the accuracy and efficiency of models. It carefully analyzes the abundance of data to identify the most significant characteristics, hence improving the accuracy of predictions and minimizing the likelihood of model overfitting. This technique not only optimizes model training by reducing computational requirements, but also enhances the model's interpretability, resulting in more transparent and reliable predictions. The deliberate omission of unnecessary variables is a process of improving the model and also constitutes a crucial measure toward achieving more flexible and comprehensible results in machine learning. An analysis to assess the effectiveness of feature selection on regression models was conducted. The impact was measured using Mean Squared Error (MSE) metrics. A variety of regression algorithms were evaluated, and then feature selection techniques, including statistical and algorithmic methods, such as SelectKBest, PCA, and RFE with Linear Regression and Random Forest, were applied. After selecting the features, linear models demonstrated improvements in mean squared error (MSE), highlighting the value of removing unnecessary data. This study emphasizes the subtle impact of feature selection on model performance, calling for a tailored strategy to maximize prediction accuracy.
A High-Gain Dual-Band Slotted Microstrip Patch Antenna For 5G Cellular Mobile Phones
Authors: Nahas Mouaaz
Published: June 2024

Microstrip patch antennas have been widely used in contemporary mobile communication technology, including 5G. Previous studies in the area have shown that such antennas can be optimized to operate in different bands of 5G. This study proposes a microstrip patch antenna designed to operate at 26 and 28 GHz and aimed at improving the gain and other radiation characteristics by adding a combination of different slot shapes to a single rectangular patch that is very common and popular in 5G antennas. The results show that the gain is noticeably increased by inserting two hammer slots and a rectangular slot in the middle between them. The dimensions of the slots are optimized using the CST Studio Suite simulator. A comparative analysis was performed to demonstrate the superiority of the proposed over previous designs in terms of gain value and other radiation parameters. The results suggest that such a very simple and low-profile antenna can be a good candidate for 5G mobile applications.
Advanced Android Malware Detection through Deep Learning Optimization
Authors: Alhussen Ahmed
Published: June 2024

Android stands out as one of the most prevalent mobile operating systems globally, due to its widespread adoption and open-source nature. However, its susceptibility to malware attacks, facilitated by the ability to install third-party applications without centralized control, poses significant security challenges. Despite efforts to integrate security measures, the proliferation of malicious activities and vulnerabilities emphasizes the need for advanced detection techniques. This study implemented and optimized Long Short-Term Memory (LSTM) and Neural Network (NN) models for malware detection on the Android platform. Leveraging meticulous hyperparameter tuning and robust data preprocessing techniques, this study aimed to increase the efficacy of LSTM and NN models in identifying and mitigating various forms of malware. The results demonstrate remarkable performance, with the LSTM model achieving an accuracy of 99.24%, precision of 99.07%, recall of 98.79%, and F1-score of 98.93%, and the NN model attaining an accuracy of 99.18%, precision of 99.02%, recall of 98.84%, and F1-score of 98.93%. By addressing these challenges and achieving such high levels of accuracy and effectiveness, this study contributes significantly to the ongoing endeavor to fortify defenses against cyber threats, thus fostering a safer digital environment for users worldwide.
Study of the Tensile Strength and Shore Hardness Behavior of PE100 SDR11 Electrofusion Welded and Artificially aged Pipes
Authors: Zisopol Dragos Gabriel , Minescu Mihail , Iacob Dragos Valentin , Voicu Nicoleta
Published: June 2024

This paper presents the tensile strength and Shore D hardness behavior of electrofusion-welded and artificially aged polyethylene (PE) pipes of the PE100 SDR11 classification with a nominal diameter of 125 mm and a wall thickness of 11.40 mm. For the study, 12 samples were taken from the body of a PE100 SDR11 pipe (9 of which were obtained from the fusion-welded joint). Subsequently, the 12 samples were divided into 3 groups of 4 pieces (1 unwelded sample and 3 welded samples). Following the Arrhenius method, the samples of the 2 groups (group II and group III) were artificially aged, those belonging to group II were aged 10 for years and those belonging to group III were aged for 20 years. Subsequently, all 12 samples were tested for tensile strength and Shore D hardness. The 10-year aging of the welded samples increased the tensile strength by 12.31% and the 20-year artificial aging increased the tensile strength by 18.44%. For the unwelded samples, artificial aging for 10 years increased the tensile strength by 11.12%, whereas aging for 20 years increased the tensile strength by 12.63%. Artificial aging of the PE100 SDR 11 pipes does not have a significant influence on the Shore D hardness, which was found within the high range of hardnesses. The results show that the PE100 SDR pipes welded by electrofusion can be used for 20 years with safety.
Customer Churn Prediction for Telecommunication Companies using Machine Learning and Ensemble Methods
Authors: Alotaibi Muteb Zarraq , Haq Mohd Anul
Published: June 2024

This study investigates customer churn, which is a challenge in the telecommunications sector. Using a dataset of telecom customer churn, multiple classifiers were employed, including Random Forest, LGBM, XGBoost, Logistic Regression, Decision Trees, and a custom ANN model. A rigorous evaluation was conducted deploying cross-validation techniques to capture nuanced customer behavior. The models were optimized by hyperparameter tuning, improving their customer churn prediction results. An ensemble averaging method was also adopted, achieving an accuracy of 0.79 and a recall of 0.72 in the test data, which was slightly lower than that of the LGBM, XGBoost, and Logistic Regression. These findings contribute to the development of more reliable churn prediction models to ameliorate the customer retention rates and the operational performance of the service providers.
Cascaded and Separate Channel Estimation based on CNN for RIS-MIMO Systems
Authors: Hussein Wala'a , Noordin Nor K. , Audah Kamil , Rasid Mod Fadlee B. A. , Ismail Alyani Binti , Flah Aymen
Published: June 2024

With the dramatic increase in mobile users and wireless devices accessing the network, the performance of 5G wireless communication systems is severely challenged. Reconfigurable Intelligent Surface (RIS) has received much attention as one of the promising technologies for 6G due to its ease of deployment, low power consumption, and low price. This study aims to improve accuracy, reliability, and the capacity to estimate channel characteristics between transmitter and receiver. However, this is practically challenging for the following reasons. Due to the lack of active components for baseband signal processing, low-cost passive RIS elements can only reflect incident signals but without the capability to transmit/receive pilot signals for channel estimation as active transceivers in conventional wireless communication systems. This study presents different channel estimation methods for RIS-MIMO systems that use deep learning techniques.
An Effective Method for the Detection of Wall Brick Defects using Machine Vision
Authors: Tran Ngoc-Tien , Le Ngoc-Duy , Le Van-Nghia
Published: June 2024

The production lines for wall bricks have achieved a high level of automation. Most brick production lines in developing countries have automated the steps up to placing the bricks in the kiln. However, the manual loading and unloading of bricks after firing still remains. This manual process reduces labor productivity and increases the cost of the final product. To address this issue, this study aims to utilize machine vision algorithms to detect cracks in bricks, thereby facilitating the automation of the brick loading and unloading process. A comprehensive image processing method is developed, which combines square detection and moment algorithms to analyze image properties. This integrated approach enables the accurate detection of cracks and the determination of their respective areas, ensuring precise and reliable results. By detecting defects in the bricks, we can replace faulty ones and employ robots to automatically handle rows of bricks. The study's results demonstrate the proposed method's ability to accurately identify brick defects. These findings are significant as they contribute to the automation of brick loading and unloading, which can be implemented in large-scale brick factories, leading to a safer and more efficient working environment.
Review of Ti3C2Tx MXene Nanofluids: Synthesis, Characterization, and Applications
Authors: Samylingam Ilancheliyan , Kadirgama Kumaran , Samylingam Lingenthiran , Aslfattahi Navid , Ramasamy Devarajan , Sazali Norazlianie , Harun Wan Sharuzi Wan , Kok Chee Kuang
Published: June 2024

MXene-based nanofluids are important because of their thermal and rheological properties, influencing scientific and industrial applications. MXenes, made of titanium carbides and nitrides, are investigated for nanofluid enhancement. This review covers MXene nanofluid creation, characterization, and application. To produce nanoscale MXene particles, two-dimensional materials are dissolved and dispersed in a base fluid. The stability and efficacy of MXene nanofluids depend on production methods, such as chemical exfoliation, electrochemical etching, and mechanical delamination. Improved heat transfer coefficients and thermal conductivity from MXene nanofluids help resolve heat transfer, energy efficiency, and thermal control problems. This extensive review also addresses long-term safety and the necessity for standardized characterization methodologies, helping researchers optimize MXene-based nanofluids in many technological fields
Investigating the Feasibility of Integrating Vegetation into Solar Chimney Power Plants in the Tamanrasset Region
Authors: Ali Sellami , Djaouida Benlahcene
Published: June 2024

This work investigates integrating vegetation into solar chimney power plants (SCPPs) using numerical simulations of an SCPP prototype in Spain. A 2D axisymmetric computational fluid dynamics model with radiation heat transfer was employed to evaluate the impact of vegetation beneath the solar collector roof on system performance. Different SCPP configurations were analyzed: a standard design, one with a secondary collector roof, and another with secondary and tertiary collector roofs. Results indicate the secondary and tertiary roof configuration exhibited the highest annual electricity generation capacity of 34-80 kW. While introducing vegetation under the collector appears feasible, it is likely to reduce the overall energy output. In summary, simulations suggest that vegetation influences SCPP operation, decreasing power production, while incorporating multiple collector roofs enhances the generation capacity.
An Improved Form of Hazen-Williams Equation for Pressurized Flow
Authors: Darweesh Moustafa S. , Salah Wael A. , Awwad Tarek M. , Ragab Ehab M. , Ahmed Anwar A.
Published: June 2024

This study performed a sensitivity analysis to correlate the frictional head loss calculated by the Darcy-Weisbach (D-W) and the Hazen-Williams (H-W) formulas. For a broad variety of fluid temperatures, velocities, and pipeline diameters, this study considered an extensive discussion and analysis to determine friction loss within pressurized pipelines using Microsoft Excel. Regression analysis and statistical tools were applied to improve the relationship between the two equations. A more accurate expression was developed to calculate the friction loss in terms of the H-W equation. The estimated values were compared with previous experimental and numerical studies, and a good agreement was observed. The proposed model was evaluated using WaterGEMS software in an application example of a water supply system against the D-W and H-W equations. Good agreements were recorded between predicted values and previous studies, with an error of less than 1%. These findings can be used to improve the hydraulic design of engineering applications.
Establishing a Budget for Optimal Response Strategies for Risks Categorized into Distinct Groups by using a Mathematical Model and Genetic Algorithm
Authors: Aljorany Hiba Omer , Mahjoob Ahmed Mohammed Raoof
Published: June 2024

Construction projects may be subjected to various risks which must be identified, evaluated, and a suitable response to each risk must be determined. The risk response stage is a crucial and significant phase in risk management that requires particular attention. This paper proposes an effective mathematical model for determining the most suitable strategy and action in dealing with both primary and secondary risk events in different risk categories that may arise in a construction project. It also provides a method for estimating or forecasting the anticipated budget for a risk response plan. Another contribution of this study is the development of an innovative approach that combines binary programming with the genetic algorithm. The efficacy of the proposed methodology was examined by its implementation in a real geothermal project. The results demonstrated that the proposed framework serves as a useful tool to tackle the challenges related to the selection and optimization of risk response strategies, as well as setting an appropriate budget for the risk response plan. The suggested model can help decision-makers to assess the variety of viable risk response actions and strategies and arrive at a more well-informed decision.
Behavior of Modified Reactive Powder Concrete Containing Sustainable Materials and Reinforced with Micro-Steel Fibers
Authors: Saeed Rusul Hussein , Fawzi Nada Mahdi
Published: June 2024

Reusing and recycling construction debris offers intriguing opportunities for resource conservation and waste disposal site economies. This study investigates the feasibility of using 10 mm crushed brick as coarse aggregate in Modified Reactive Powder Concrete. Natural sand was substituted with crushed brick aggregate by 25, 50, and 100%. Up to 7 and 28 days of age, the tensile strength, absorption, and void content of the mixtures were compared with those of a mixture without coarse aggregate. According to the test results, it is feasible to produce Modified Reactive Powder Concrete (MRPC) with coarse aggregate or shattered bricks. Compared to the reference mixture, the tensile strength of MRPC decreased as the replacement ratio of broken bricks increased. At 7 and 28 days of testing, the tensile strength increased by 10.2 and 12.06 with 25% crushed bricks compared to normal reactive powder concrete. Tensile strength decreased by 7.2% and 6.27% at 7 days and by 9.89% and 8.87% at 28 days when replacing fine sand with crushed brick aggregate at rates of 50% and 100%, respectively. Compared to the reference mixture, the absorption and void content of MRPC with 25, 50, and 100% crushed brick increased by 13.6, 61.2, and 116% and 15.9, 62.1, and 136%, respectively.
Optimal Surface Grinding Regression Model Determination with the SRP Method
Authors: Thinh Hoang Xuan , Dua Tran Van
Published: June 2024

The construction of the regression models used to control machining processes is the objective of many experimental studies. Therefore, the effectiveness of the machining process control largely depends on the regression model’s accuracy. This study was conducted to determine the optimal regression model of surface grinding. Accordingly, eight different surface grinding regression models were constructed, including one model without data transformation and seven models that utilized various data transformations. The seven data transformations employed entailed square root transformation, logarithmic transformation, inverse transformation, exponential transformation, asinh transformation, Box-Cox transformation, and Johnson transformation. The process of determining the optimal model was carried out considering five parameters: R2, R2(adj), R2(pred) (predicted R2), MAE (Mean Absolute Error), and MSE (Mean Squared Error). SRP (Simple Ranking Process) was the optimization method followed to identify the best regression model. The Box-Cox transformation was recognized as the most accurate surface grinding regression model.
A Genetic Programming-Assisted Analytical Formula for Predicting the Permeability of Pervious Concrete
Authors: Le Ba-Anh , Vu Thai Son , Nguyen Hoang-Quan , Vu Viet Hung
Published: June 2024

This study proposes a new approach to construct predictive formulas for the permeability of Pervious Concrete (PC), which depends on PC mixture and porosity. To achieve this, a dataset of 195 samples collected from different sources was used. In the dataset the permeability is dependent on porosity, aggregate-to-cement ratio (AC), maximum nominal sizes (MS) of coarse aggregate, and water-to-cement or binder ratios (WC). From the dataset and through applying simple regression techniques, several analytical functions based on the Kozeny-Carman model were constructed and evaluated for their effectiveness in implementing independent datasets and similar analytical functions. Furthermore, for the first time, the Genetic Programming-based Symbolic Regression method was adopted to construct hybrid models combined with the Kozeny-Carman analytical model. The equation of the hybrid model ensures both basic physical conditions and efficiency while being simple enough for engineering-level applications.
Advancements in Modi Script Character Recognition: A Comprehensive Survey
Authors: Shilpa K. Aher  , Dr. G. U. Kharat  , Prof. N. B. Bankhele
Published: June 2024

The ancient Modi script is more complex to decipher than other scripts. However, the "Review on Modi Script Character Recognition System" provides a thorough overview of methods for doing so. Optical Character Recognition (OCR), Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Chain Code, Zerike moment feature extraction, and deep learning are only a few of the methods explored in this paper's literature review section. The methods for recognizing characters written in the Modi script are evaluated for their efficacy and suitability in this context. This report provides a broad overview of recent developments to help researchers and practitioners create effective recognition systems for the Modi script.
Dynamic Quill Quest using ML
Authors: Ankit Sharma  , Prabhakar Kumar Singh  , Bahushruth M Pujar  , Prof Manjula BS
Published: June 2024

Recommender systems are essential for screening online material according to user preferences in the quickly changing digital world of today. These systems are successful, but they have many shortcomings, especially when it comes to scalability and sparse data. In order to solve the cold start problem, the Singular Value Decomposition (SVD) algorithm is used in this research to practically develop a book recommendation system. We give a thorough rundown of the system's architecture, preprocessing procedures, dataset preparation, and SVD algorithm integration. This study tries to provide insights into the real-world problems and solutions for building efficient recommendation systems by outlining our methodology and going over the experimental findings. Our approach and results provide guidance for future research aimed at improving the reliability and effectiveness of recommender systems
Implementation and Evaluation of an E-Commerce Platform Using the MERN Stack
Authors: Tushar Pokhriyal  , Harshwardhan Chuni  , Vaibhav Singh Rawat  , Varun Chauhan  , Dr. Bipin Pandey
Published: June 2024

This paper explores the development of an e-commerce platform utilizing the MERN stack, a popular set of technologies comprising MongoDB, Express.js, React.js, and Node.js. The project's codebase provides a comprehensive example of a modern, full-stack JavaScript application. The study focuses on assessing the effectiveness, scalability, and user experience of using the MERN stack in building dynamic web applications.
Stakeholders’ Satisfaction on Institutional Assessment: A Proposal for Unified Feedback Management System with Text Analytics and Sentiment Analysis
Authors: Rea Mie A. Omas-as  , Riah E. Encarnacion
Published: June 2024

In higher educational institutions, the collection and analysis of stakeholder feedback, comprising students, parents, faculty, and staff, plays a crucial role in assessing the effectiveness of various aspects of the institution's operations. The process of manually evaluating and consolidating this information is both time-consuming and prone to errors. Thus, delays in data evaluation and consolidation lead to delays on generating reports. To address this challenge and to align with the ISO 9001 requirements, the study aims to enhance the customer satisfaction by focusing on feedback collection of services, academic programs, and automation of client satisfaction survey. The descriptive research design involves a combination of qualitative and quantitative methods to gather comprehensive insights and address the project's objectives effectively. Data analysis techniques such as frequency distribution, weighted mean score, and standard deviation help identify patterns and trends in the gathered data. Employing mix-methodology research approach ensures a comprehensive understanding of stakeholders' feedback needs and preferences for the proposed system that is responsive to stakeholders’ input and effectively addresses the challenges of the current feedbacking process. The findings of this study underscore the importance of transitioning to a more efficient and automated feedback management system to enhance stakeholder satisfaction, improve institutional performance, and support data-driven decision-making. The proposed web-based Stakeholder Feedback management system holds the potential to address the identified challenges and contribute to the continuous improvement of the institution's feedback management practices
Solar Power Seed Sowing Machine
Authors: Suraj Temkar  , Siddharth Chile  , Aditya Yadav  , Rohit Ahire  , Tejas Khairmode  , Shreya Chavan
Published: June 2024

This study presents the design and development of a solar-powered seed sowing machine aimed at enhancing efficiency and sustainability in agriculture. The machine utilizes solar energy to power its operation, reducing the reliance on fossil fuels and minimizing carbon emissions. It is equipped with features for automatic seed sowing at optimal depths and spacing, improving crop yield and reducing manual labor. The machine's design is cost-effective and suitable for use in remote areas with limited access to electricity. Overall, this innovation offers a sustainable solution for modernizing agriculture practices