bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing Sliding Mode Controller in a DC Microgrid with Variant Constant Power Loads
Authors: Al-Modaffer Ameen M. , Chlaihawi ِAmer A. , Shabeeb Dhulfiqar M.
Published: August 2024

The optimization of a suitable controlling method is a priority in running any DC/DC boost converter effectively. However, a problem may arise as the occurring oscillations in the microgrid caused by the incremental negative resistance of the Constant Power Poad (CPL) variation may lead to system instability. In order to tackle this intrinsic problem, three proposed Sliding Mode Control (SMC) methods were simulated and examined against multiple variations of CPL in MatLab/Simulink. Integral Sliding Mode Control (ISMC) and Two-variable Sliding Mode Control (TSMC) methods showed a better system performance than the Low Pass Filter SMC (LPFSMC) in terms of stability of output voltage in both steady state and transient conditions. The output voltages of ISMC and TSMC had a margin of error of approximately 1 V in the steady-state response and a minor overshoot of less than 1% in the transient response. The steady-state output voltage when using LPFSMC showed approximately 3 V of error and the transient state had a noticeable overshoot near 3%. However, all three controlling methods had a similar efficiency of around 98%. The outstanding robustness of ISMC exhibited the highest voltage stability with the lowest chattering in both steady state and transient responses through the compensation of adequate current to satisfy the CPL requirement.
A Study of Single Stone Column Bearing Capacity from a Full-Scale Plate Load Test in Long Son Project
Authors: Dang Hong Lam
Published: August 2024

The ultimate bearing capacity of stone columns is very important in the design of soil improvement. The bulging failure mechanism is the most common failure mechanism reported. However, it depends on the surrounding soil at the site, necessitating a site-specific study of single-stone column bearing capacity. The current paper presents the full-scale plate load test of the single stone column in the Long Son Petrochemical Project, Vietnam in order to verify the bearing capacity of single stone column. A single stone column of 800 mm diameter was installed at the site by vibroflot. An 800 m circle full-scale plate test was carried out on site. The stone properties followed the Vietnamese standard TCVN 7572. The settlement result of the plate load test verifies the single stone column bearing capacity of 882.5 kPa.
The Performance of Stable Zones Protocol for Heterogeneous Wireless Sensor Networks
Authors: Khedhiri Kamel , Djabbour Djammal , Cherif Adnen
Published: August 2024

Wireless sensor networks are characterized by significant constraints, with the primary performance parameter being their lifetime. In the context of a wireless sensor network, the distance from the base station emerges as a critical factor that influences the energy consumption of the nodes, thus affecting the overall network lifetime. To address this issue, this study introduces the Stable Zones Protocol for Heterogeneous wireless sensor networks (SZP-H). This protocol strategically divides the network into distinct zones, each differing from the other in terms of its distance from the base station and the initial energy available. This protocol outperformed traditional protocols, effectively mitigating the challenges associated with node energy consumption and improving the overall performance of the wireless sensor network. The simulation results show that SZP-H achieves the highest possible stable period and lifetime and the highest throughput level compared to the FBECS, E-CAFL, and LEACH-FC protocols. Specifically, SZP-H achieves a remarkable extension of the network's lifetime by a ratio of 303%, and 275% compared to FBECS, E-CAFL, and LEACH-FC.
Dynamic Stability Enhancement of Wind Power Generation with Static VAR Compensator using Multiobjective Optimization Algorithms
Authors: Touti Ezzeddine Salah , Fterich Mohamed , Ali Aamir
Published: August 2024

Renewable energy, particularly wind energy, is expected to contribute significantly to the overall power generation. Induction machines are extensively used as generators in wind power generation because of their multiple benefits, such as robustness, reliability, and low cost and maintenance. However, due to the reactive power demand from the system to which they are connected, this type of generator brings new problems related to power quality, generally consisting of voltage regulation and reactive power compensation. These problems may cause voltage drops and dynamic instability. This study presents a metaheuristic method to attain a microgrid system with an optimal distribution based on its different constraints. The numerical model of an induction generator constructed in MATLAB/Simulink was used, and the simulation results obtained demonstrate the efficacy of the proposed metaheuristic technique.
Shearing Properties of Epoxy and Epoxy Bitumen as Bonding Material of Asphalt Overlay on Ultra-High Performance Concrete Slab
Authors: Dinh Trung Quang , Tran Thi Kim Dang , Ngo Ngoc Quy
Published: August 2024

This article discusses the results of direct shear and fatigue shear tests on epoxy resin and epoxy bitumen bonding materials. Shearing properties, including shear strength, shear stiffness, shear energy, and post-failure energy, are analyzed using results from direct shear tests at 30°C and 60°C. The fatigue tests used a direct shearing test with a pulse load of 1 Hz frequency at 60°C to analyze the fatigue life and plateau value based on the ratio of dissipated energy change versus load cycles curve. At 30°C, the shearing properties of the tested epoxy resin were approximately 60-70% higher than those of the tested epoxy bitumen. The epoxy resin possesses an outstanding advantage against the epoxy bitumen at high temperatures when applying the shear energy approach. At 60°C, the shear energy of the epoxy resin was 30.5% higher than that of the epoxy bitumen, while its shear strength and shear stiffness were 18.5% and 79% lower than those of the epoxy asphalt, respectively. The shear fatigue life of the epoxy resin after the energy method was more than ten times that of the epoxy bitumen, and its plateau value was only 10% of the epoxy bitumen. Regression analysis was also performed using fatigue shear test data to provide a fatigue shear equation in the form of an exponential function.
Multi-Objective Load-balancing Strategy for Fog-driven Patient-Centric Smart Healthcare System in a Smart City
Authors: Goel Gaurav , Chaturvedi Amit Kr
Published: August 2024

The spatially concentrated architecture of the cloud environment causes excessive latency and network congestion in traditional smart healthcare systems designed for smart cities. Fog computing underpins IoT-enabled smart city solutions for latency sensitivity by putting computing power closer to the network boundary. However, resource management issues degrade service quality and accelerate energy depletion in real-time smart healthcare systems, as the fog node workload has increased exponentially. This paper offers a fog-driven patient-centric smart healthcare system for an e-healthcare environment to maintain Quality of Service (QoS) during severe traffic load on a fog platform. The multi-objective EQLS (Energy-efficient QoS-aware Load balancing Strategy), is proposed to stabilize workload among processing nodes to increase real-time sensitivity of critical tasks within optimal response time and energy usage. Using the iFogSim simulator to present the significance of research work, the proposed technique is compared to existing load-balancing policies (Round Robin (RR) and Fog Node Placement Algorithm (FNPA)) regarding energy usage, response time, and cost. The simulation results reveal that EQLS saves 8.7% and 14.9% more energy and 6.2% and 13.4% greater response time over FNPA and RR, respectively. The results signify that the proposed approach can efficiently support real-time applications of smart cities.
A Study of the Development Strategy of the Wind Power Sector in Vietnam
Authors: Giap Luong Ngoc , Khanh Nguyen Binh , Trung Bui Tien , An Truong Nguyen Tuong , Vinh Tran The , Tu Le Tat
Published: August 2024

Nowadays, the wind power market is growing rapidly, while the cost of wind power equipment is decreasing, so Vietnam is currently building many wind power projects to ensure green energy development in its power system. However, some barriers in price mechanisms, and economic and technical conditions have also caused disadvantages in the process of developing wind power projects. This paper studies the SWOT-TOWS analysis to evaluate in greater detail the strengths, weaknesses, opportunities, and threat factors linked to it. Then the former compares the internal and external factors influencing the wind power industry on the way it exploits potential strategies. In general, Vietnam has satisfactory wind power development potential, and the wind power sector has been supported by a number of strong but unstable policies in the past. The rapid development of wind power capacity in recent times has also led to great challenges for investors and managers in actual operating conditions. Currently, investment costs for wind power plants are still quite high, Feed in Tariff (FIT) prices are not stable, transmission grid capacity is limited, and environmental treatment issues during construction and operation have not been fully considered.
Enhanced Intrusion Detection in IoT with a Novel PRBF Kernel and Cloud Integration
Authors: Mopuru Bhargavi , Pachipala Yellamma
Published: August 2024

The proliferation of Internet of Things (IoT) devices in various sectors has increased the need for robust security solutions capable of addressing complex network vulnerabilities and sophisticated cyber threats. This study introduces a novel architecture that integrates cloud computing with advanced machine learning techniques to provide efficient and scalable security in IoT systems. A unique Polynomial Radial Basis Function (PRBF) kernel is proposed to enhance the classification accuracy of Support Vector Machine (SVM) beyond traditional Gaussian and polynomial kernels. This study compares the proposed PRBF-SVM with Logistic Regression, SVM, and XGBoost models, optimized through rigorous hyperparameter tuning, to demonstrate significant improvements in detection rates. Furthermore, the integration of cloud services facilitates the offloading of computationally intensive tasks, ensuring scalability and real-time response capabilities. The results highlight the superior performance of the proposed model in accuracy, efficiency, and computation time, making a compelling case for its application in safeguarding IoT environments against evolving threats.
Enhancing the Quality of Ambulance Crew Work by detecting Ambulance Equipment using Computer Vision and Deep Learning
Authors: Hussain Jonab , Al-Masoody Nada , Alsuraihi Asmaa , Almogbel Fay , Alayed Asmaa
Published: August 2024

Ambulance crews play an important role in responding quickly to emergencies and rescuing patients by providing appropriate treatment. Typically, fully equipped emergency vehicles are used to transport ambulance personnel to emergency locations. The ambulance crew cleans, sterilizes, and prepares equipment after each patient transfer with great care. Additionally, they check more than 70 pieces of equipment twice a day using a checklist, which is a tedious, time-consuming, and error-prone task. This study uses computer vision and deep learning techniques to replace the manual checklist process for medical equipment to assist the crew and make the equipment availability check faster and easier. To accomplish this, a dataset containing 2099 images of medical equipment in ambulances was collected and annotated with 3000 labeled instances. An experimental study compared the performance of YOLOv9-c, YOLOv8n, and YOLOv7-tiny. YOLOv8n demonstrated the best performance with a mAP50 of 99.2% and a speed of 3.3 ms total time per image. Therefore, YOLOv8 was selected for the proposed system due to its high accuracy and detection speed, which make it suitable for mobile applications. The presence of an application integrated with computer vision and deep learning technologies in paramedic devices can assist in reviewing the equipment checklist, reducing human errors, speeding up the review process, and alleviating the burden on paramedics in their work.
Optimal Airport Selection for Iraq's Infrastructure Development: A TOPSIS Analysis
Authors: Naser Farah K. , Khaleel Tareq A.
Published: August 2024

This study investigates the application of the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method to enhance Iraq's airport infrastructure. The research aims to analyze the performance of major airports based on data collected from the Central Statistical Organization (CSO) of Iraq. A model is proposed to prioritize the airport development projects based on essential criteria, such as aircraft movement (landing and takeoff) and passenger movement (arrivals and departures). The airports are ranked according to the priority they have in infrastructure development, which is linked to their economic growth. In terms of priority, the first is Baghdad International Airport, Basra Airport follows, and Najaf Airport ranks third. Erbil and Sulaymaniyah airports rank lower due to infrastructure limitations and administrative challenges. The research underscores the importance of ongoing investments in airport infrastructure to accommodate the increasing number of passengers and bolster economic expansion.
Emulation Structures and Control of Wind-Tidal Turbine Hybrid Systems for Saudi Arabia Off-shore Development
Authors: Alanazi Abdulaziz , Touti Ezzeddine , Nichita Cristian , Mohamed Ashglaf
Published: August 2024

This paper presents the principles of developing an electromechanical emulator based on an original hybridization concept of a wind and tidal power system. Wind and tidal horizontal axis turbines showcase functional similarities and electromechanical coupling possibility. Tidal concepts are very close to those of wind power. Tidal turbine technology should thus reach maturity more quickly because it is possible for it to rely on a certain number of reliable and proven techniques developed for wind power. The proposed hybrid wind – tidal turbine system is electromechanically coupled on the axis of rotation of a single and common electric generator. An experimental simulation of the hybrid wind-tidal turbine system was carried out, using a developed architecture of an emulator system. The results are both numerical simulations carried out in the MATLAB/Simulink environment and tests obtained employing real-time emulators.
Exploring Advance Approaches for Drowning Detection: A Review
Authors: Alharbi Nouf
Published: August 2024

This research mainly explores the existing drowning detection methodologies, focusing primarily on the roles carried out by Machine Learning (ML) and Deep Learning (DL) algorithms. It directly emphasizes the dominance of ML in the analysis of raw sensor data along with the contribution of DL to computer vision, which also reveals the present gap between advanced vision along detection models. The holistic approaches are mainly advocated, potentially integrating wearable devices, vision-based systems, as well as sensors while also balancing their performance, regional applicability, and cost-effectiveness. The challenges aligned to enabling real-time detection and reduced latency are important for the time-sensitive realm of incidents related to drowning. Future directions necessarily include the exploration of advanced forms of vision models and segmentation techniques for innovative detection algorithms. Integration of wearable devices and sensors with the inclusion of vision-based systems is important for the required adaptability. The upcoming proposal aims to integrate robotics into rescue operations bringing revolution to response times. The study also covers the requirement for a compact combination of ML and DL algorithms and a generalized solution for the equilibrium maintenance between cost-effectiveness, sophistication, and regional applicability.
A Children's Psychological and Mental Health Detection Model by Drawing Analysis based on Computer Vision and Deep Learning
Authors: Alshahrani Amal , Almatrafi Manar Mohammed , Mustafa Jenan Ibrahim , Albaqami Layan Saad , Aljabri Raneem  Abdulrahman
Published: August 2024

Nowadays, children face different changes and challenges from an early age, which can have long-lasting impacts on them. Many children struggle to express or explain their feelings and thoughts properly. Due to that fact, psychological and mental health specialists found a way to detect mental issues by observing and analyzing different signs in children’s drawings. Yet, this process remains complex and time-consuming. This study proposes a solution by employing artificial intelligence to analyze children’s drawings and provide diagnosis rates with high accuracy. While prior research has focused on detecting psychological and mental issues through questionnaires, only one study has explored analyzing emotions in children's drawings by detecting positive and negative feelings. A notable gap is the limited diagnosis of specific mental issues, along with the promising accuracy of the detection results. In this study, different versions of YOLO were trained on a dataset of 500 drawings, split into 80% for training, 10% for validation, and 10% for testing. Each drawing was annotated with one or more emotional labels: happy, sad, anxiety, anger, and aggression. YOLOv8-cls, YOLOv9, and ResNet50 were used for object detection and classification, achieving accuracies of 94%, 95.1%, and 70.3%, respectively. YOLOv9 and ResNet50 results were obtained at high epoch numbers with large model sizes of 5.26 MB and 94.3 MB. YOLOv8-cls achieved the most satisfying result, reaching a high accuracy of 94% after 10 epochs with a compact model size of 2.83 MB, effectively meeting the study's goals.
Strengthening Reinforced Concrete Beams with Vertical Perforations by using Steel-Plate Tubing
Authors: Shneet Masharaq A. , Izzet Amer F.
Published: August 2024

This study aimed to experimentally compare the bending behavior of reinforced concrete beams in the midspan zone of the maximum bending region with and without vertically perforated openings. Five rectangular cross-section beams were produced for this purpose: one solid specimen, one with an opening without reinforcing steel, and the remaining three had openings reinforced with steel tubes of thickness 1, 1.5, and 2 mm. The square opening width was 40 mm. All specimens were tested under a symmetric four-point loading until failure. The test results showed that all specimens failed in bending. The presence of an opening negatively affects the ultimate load-bearing capacity and bending. However, the bending behavior of the reinforced concrete beams exhibited superior performance as the thickness of the steel tube increased. Additionally, the maximum load-bearing capacity increased with increasing tube thickness.
A New Computational Envelope Solution for Helical Gear Disc Tool Profiling
Authors: Hoang Long , Nguyen Thanh-Tuan
Published: August 2024

This paper presents a new computational envelope solution for profiling the helical gear disc tool. It uses the normal projection of the disc tool axis onto the helical gear surface to generate the characteristic curve and then automatically computes the geometric data of the characteristic curve to create the disc tool in 3D solid form. As a popular profile, the ISO heliacal gear was a typical proper example to verify and clarify the proposed solution. The solution can quickly create the helical gear disc tool in 3D solid form with high accuracy. The 3D comparison average error of the helical gear disc tool surfaces generated by the proposed solution and the Boolean method is 0.004 mm, and the RMS error is 0.009 mm.
Federated Learning for Privacy-Preserving Air Quality Forecasting using IoT Sensors
Authors: Alwabli Abdullah
Published: August 2024

Air quality forecasting is crucial for public health and urban planning. However, traditional machine learning models face challenges with centralized data collection, raising privacy and security concerns. Federated learning (FL) offers a promising solution by enabling model training across decentralized data sources while preserving data privacy. This study presents an FL framework for predicting the Air Quality Index (AQI) using data from many Internet of Things (IoT) sensors deployed in urban areas. The proposed FL framework facilitates model training using diverse sensor data while maintaining data privacy at each source. Local computational resources at the sensor level are used for initial data processing and model training, with only model updates shared centrally, reducing data transmission requirements. The FL model achieved comparable accuracy to centralized approaches while enhancing data privacy. This work represents a significant advancement for smart city initiatives and environmental monitoring, offering a scalable, real-time, and privacy-aware framework for air quality monitoring systems that leverage IoT technology.
The Impact of Supply Chain Delays on Inventory Levels and Sale Demand Fulfillment: Analyzing the Effects of Lead Times and In-Transit Quantities
Authors: Ladva Vipul , Shukla Madhu , Vaghela Chetansinh
Published: August 2024

Efficient inventory management is essential for maintaining a balance between supply and demand in various industries. This research study aims to quantitatively examine the impact of supply chain delays, with a specific emphasis on lead times and in-transit amounts, inventory levels, and the ability to meet sales demands. Mathematical modeling and statistical analysis are utilized to create prediction models that assess the impact of variations in lead time and quantities in transit on inventory stability and fulfillment rates. The study used regression analysis to ascertain the relationships between the indicated parameters and inventory outcomes. Also, machine learning algorithms like Random Forest and Linear Regression are applied to predict possible disruptions and optimize inventory levels. The methodology followed focuses on the Tri-Model Fusion Stacking approach, which combines various models to improve the predicted accuracy and offer a more comprehensive analysis. The main goal of this research is to provide practical insights that help organizations optimize their inventory management techniques, resulting in cost reduction and enhanced service levels. The findings aim to simplify the modification of inventory management techniques in light of up-to-date supply chain information, providing a notable improvement in the resources available to supply chain experts.
A Dual-Step Approach for Implementing Smart AVS in Cars
Authors: Poornima Bachu , Kumari P. Lalitha Surya
Published: August 2024

The Smart Autonomous Vehicular System (AVS) is designed to combine technologies such as sensors, cameras, radars, and machine learning algorithms in cars. The implementation of Smart AVS in smart cars has the potential to revolutionize the automotive industry and transform the way we think about transportation. In this paper, the implementation of Smart AVS in smart cars includes two steps. Firstly, the architecture is designed using Microsoft Threat Modelling tool. Secondly, with the use of Engineering Software, smart cars are constructed and simulated to verify and validate algorithms related to autonomous driving, path planning, and other intelligent functionalities. Simulating these algorithms in a controlled virtual environment helps to identify and address issues before implementation on physical vehicles. The main advantages of using the proposed model are early detection of vulnerabilities, realistic simulation of sensor inputs, communication protocol testing, cloud integration validation, user interface, and consumer experience, and validation of compliance with security standards.
A Performance Study of Different Approaches of Digital Image Compression Techniques
Authors: Mansouri Wahida Ali , Hamda Othman Salwa , Asklany Somia , Elmorsi Doaa Mohamed
Published: August 2024

Today, managing a large amount of information becomes increasingly crucial. Efficient storage and retrieval of digital data are essential for their effective utilization. This study investigates the efficacy of Spatial Domain Image Compression Techniques, which directly manipulate the original image to reduce its size by leveraging pixel spatial relationships. These techniques segment the image into blocks and process each block independently. Evaluation entails measuring perceptual quality through metrics, such as PSNR, WPSNR, NMSE, and SSIM applied to the compressed image. Experimental results provide a comparative analysis of the performance of these techniques.
Phase Field Modeling of Crack Propagation in Concrete Composite with Imperfect Interface
Authors: Le Gia-Khuyen , Nguyen Hoang-Quan , Nguyen Tien-Dung
Published: August 2024

In this study, a phase-field model with imperfect interface is developed to simulate the crack behavior of concrete at the mesoscale level. Concrete is treated as a biphasic material, comprising aggregates, a cementitious matrix, and interfaces between them, which are characterized using a level set function. Both cracks and interfaces are represented in a smeared sense by scalar fields ranging from 0 to 1. On the other hand, the displacement jump at the interface is described by an auxiliary field over the entire domain. This model effectively captures the complex crack patterns in concrete, including debonding cracks and bulk cracks. Furthermore, the results show that a strong interface can significantly enhance the mechanical performance of the material.
Influence of Magnetized Mixing Water on Different Levels of Concrete Strength using Different Curing Processes
Authors: Hussein Dhuha M. , Abbas Zena K.
Published: August 2024

This study investigated the impact of using Magnetic Water (MW) in concrete mixes on the mechanical properties of three normal concrete strength grades (15 MPa, 27.5 MPa, and 40 MPa) cured with three different methods (normal curing, water spraying, and coating). Compressive, flexural, and splitting strengths were tested. Results revealed that for the 15 MPa concrete, water spraying reduced compressive strength by 15.76% at 28 days compared to normal curing while coating curing increased it by 15.63%. Similar trends were observed for the 27.5 MPa (13.98% decrease for spraying, 13.60% increase for coating) and 40 MPa (10.81% decrease for spraying, 10.60% increase for coating) concrete grades. Flexural and splitting strength tests followed a similar pattern. For all concrete grades, water spraying led to reduced strength, while coating curing improved it. Overall, coating curing yielded the most favorable results across all strength grades, with the 15 MPa concrete showing the most significant improvements. These findings highlight the potential benefits of utilizing magnetic water in combination with coating curing to enhance the mechanical properties of concrete.
Explainable AI-based Framework for Efficient Detection of Spam from Text using an Enhanced Ensemble Technique
Authors: Alzahrani Ahmed
Published: August 2024

Today, identifying and preventing spam has become a challenge, particularly with the abundance of text-based content in emails, social media platforms, and websites. Although traditional spam filters are somewhat effective, they often struggle to keep up with new spam methods. The introduction of Machine Learning (ML) and Deep Learning (DL) models has greatly improved the capabilities of spam detection systems. However, the black-box nature of these models poses challenges to user trust due to their lack of transparency. To address this issue, Explainable AI (XAI) has emerged, aiming to make AI decisions more understandable to humans. This study combines XAI with ensemble learning, utilizing multiple learning algorithms to improve performance, and proposes a robust and interpretable system to detect spam effectively. Four classifiers were used for training and testing: Support Vector Machine (SVM), Logistic Regression (LR), Gradient Boost (GB), and Decision Tree (DT). To reduce overfitting, two independent spam email datasets were blended and balanced. The stacking ensemble technique, based on Random Forest (RF), was the best-performing model compared to individual classifiers, having 98% recall, 96% precision, and 97% F1-score. By leveraging XAI's interpretability, the model elucidates the reasoning behind its classifications, leading to the comprehension of hidden patterns associated with spam detection.
Miscibility Analysis of Ethanol-Diesel Blends with Additives: A Comprehensive Investigation
Authors: Patel Amit M. , Shah Ronakkumar R. , Patel Vijay K. , Sonawane Chandrakant
Published: August 2024

Diesel-Ethanol (DE) blends have gained attention as an alternative fuel due to their potential to reduce emissions and improve the performance of diesel engines. However, a significant challenge when dealing with DE blends is that of phase separation. Achieving optimal miscibility between ethanol and diesel often requires the addition of additives. This research article thoroughly examines the miscibility, blend stability, and phase separation of DE mixtures with various additives. To prepare blends for the miscibility analysis, DE blends with 10%, 15%, and 20% ethanol were mixed with selected additives (n-pentanol, n-butanol, ethyl acetate, and diethyl ether) at a 2% concentration in each blend. Prior to the miscibility analysis, all blends were mixed using a magnetic stirrer and a probe ultrasonicator. The results indicated that DE10 with 2% n-propanol, DE10 with 2% n-butanol, DE15 with 2% n-butanol, and DE10 with 2% diethyl ether exhibited good miscibility without phase separation or sedimentation after four weeks. However, it was noted that all the other blends with higher ethanol content were eventually phase-separated after four weeks.
Effects of utilizing Crumb Rubber as Aggregate in Asphalt Mixtures
Authors: Oleiwi Safa I. , Albayati Amjad K.
Published: August 2024

Experts have given much attention on the use of waste in asphalt paving because of its significance from a sustainability perspective. This paper evaluated the performance properties of asphalt concrete mixes modified with Crumb Rubber (CR) as a partial replacement for two grade sizes of fine aggregate (2.36, and 0.3 mm) at six replacement rates: 0%, 2%, 4%, 6%, 8%, and 10% by weight. Asphalt concrete mixes were prepared at their Optimum Asphalt Content (OAC) and then tested for their engineering properties. Marshall properties, fatigue, rutting, ideal CT index test, Scanning Electron Microscopy (SEM), and Energy-Dispersive X-ray (EDX) spectroscopy were deployed to examine the crystalline structure and elemental composition of the CR-modified and unmodified asphalt concrete mixtures. The results showed a difference in Marshall's characteristics. The CT index revealed that the optimum cracking tolerance was achieved with a 2% CR substitution. Wheel track test results indicated that a 4% CR addition improved the rutting resistance of the asphalt mixture. SEM and EDX analyses exhibited significant changes in microstructure and elemental composition with the addition of CR. The main findings reveal that the use of 2% CR as a partial replacement of fine aggregate contributes to the production of more durable asphalt concrete mixtures with better serviceability. However, these results are based on laboratory experiments and require field verification to ensure practical applicability and long-term performance.
Comparison of Τwo Modules in Sedimentation Process using Mathematical Techniques
Authors: Pidurkar Smita R. , Raut Seema , Bhorkar Mangesh
Published: August 2024

Raw water must be purified before being distributed to any village, town, or city. To purify raw water, various significant processes must be performed in a water treatment plant, with sedimentation being one of the most important. Solid particles in the form of dirt and other contaminants can be found in raw water, especially during the rainy season, which can be removed through sedimentation. Plate settlers or tube settlers are commonly utilized in sedimentation units to treat water quickly, reducing the detention time to 15-20 minutes. This study modified conventional tubes in terms of manufacturing and repositioning to increase turbidity removal in raw water. The experimental work was carried out for one year to calculate the balanced turbidity of the raw water using conventional and modified square-shaped tube settlers. Mathematical analysis was deployed to compare the output from one year's data from both experimental studies. Regression, R2, relative standard deviation, and ANOVA were employed to analyze the experimental models, and the observed and calculated findings were compared. The results show that the modified tube settlers removed more turbidity from the raw water than the conventional ones.
The Projection-Based Data Transformation Approach for Privacy Preservation in Data Mining
Authors: Raj Diana Judith Irudaya , Radhakrishnan Vijay Sai , Reddy Manyam Rajasekhar , Selvan Natarajan Senthil , Elangovan Balasubramanian , Ganesan Manikandan
Published: August 2024

Data mining is vital in analyzing large volumes of data to extract functional patterns and knowledge hidden within the data. Data mining has practical applications in various scientific areas, such as social networks, healthcare, and finance. It is important to note that data mining also raises ethical concerns and privacy considerations. Organizations must handle data responsibly, ensuring compliance with legal and ethical guidelines. Privacy-Preserving Data Mining (PPDM) refers to conducting data mining tasks while protecting the privacy of sensitive data. PPDM techniques aim to strike a balance between privacy protection and data utility. By employing PPDM techniques, organizations can perform safe and private data analysis, protecting sensitive information while deriving valuable insights from the data. The current paper uses geometric transformation-based projection techniques such as perspective projection, isometric projection, cabinet projection, and cavalier projection to protect data privacy and improve data utility. The suggested technique's performance was assessed with the K-means clustering technique. The UCI repository's Bank Marketing dataset was used to verify the error rate of the proposed projection techniques.
PSI-SAW and PSI-MARCOS Hybrid MCDM Methods
Authors: Dua Tran Van
Published: August 2024

This paper presents a study on the hybridization of Multi-Criteria Decision-Making (MCDM) methods: Preference Selection Index (PSI), Simple Additive Weighting (SAW), and Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS). The hybridization was conducted between the PSI and the other two methods, resulting in new methods, namely PSI-SAW and PSI-MARCOS. For each specific problem, applying these two hybrid methods to rank alternatives among the available options produces three different sets of rankings: one created by PSI, one by the hybrid PSI-SAW, and one by the hybrid PSI-MARCOS. The accuracy of the proposed models was tested in three different cases. The test results show that both proposed models exhibit high accuracy. This study provides users with highly accurate and useful methods for MCDM.
Optimizing TEM Image Segmentation: Advancements in DRU-Net Architecture with Dense Residual Connections and Attention Mechanisms
Authors: Naik M. Nagaraju , Dimmita Nagajyothi , Chintamaneni Vijayalakshmi , Rao P. Srinivasa , Rajeswaran Nagalingam , Jaffar Amar Y. , Aldosari Fahd M. , Eid Wesam N. , Alharbi Ayman A.
Published: August 2024

This study introduces an innovative enhancement to the U-Net architecture, termed Modified DRU-Net, aiming to improve the segmentation of cell images in Transmission Electron Microscopy (TEM). Traditional U-Net models, while effective, often struggle to capture fine-grained details and preserve contextual information critical for accurate biomedical image segmentation. To overcome these challenges, Modified DRU-Net integrates dense residual connections and attention mechanisms into the U-Net framework. Dense connections enhance gradient flow and feature reuse, while residual connections mitigate the vanishing gradient problem, facilitating better model training. Attention blocks in the up-sampling path selectively focus on relevant features, boosting segmentation accuracy. Additionally, a combined loss function, merging focal loss and dice loss, addresses class imbalance and improves segmentation performance. Experimental results demonstrate that Modified DRU-Net significantly enhances performance metrics, underscoring its effectiveness in achieving detailed and accurate cell image segmentation in TEM images.
Real-Time Rain Prediction in Agriculture using AI and IoT: A Bi-Directional LSTM Approach
Authors: Peeriga Radhika , Rinku Dhruva R. , Bhaskar J. Uday , Nagalingam Rajeswaran , Aldosari Fahd M. , Albarakati Hussain M. , Alharbi Ayman A. , Jaffar Amar Y.
Published: August 2024

Accurate rain forecasting is crucial for optimizing agricultural practices and improving crop yields. This study presents a real-time rain forecasting model using a Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm for an on-device AI platform. The model uses historical weather data to predict rainfall, enabling farmers to make data-driven decisions in irrigation, pest control, and field operations. This model enables farmers to optimize water use, conserve energy, and improve overall resource management. Real-time capabilities allow immediate adjustments to agricultural activities, mitigating risks associated with unexpected weather changes. The Bi-LSTM model achieved a mean accuracy of 92%, significantly outperforming the traditional LSTM (85%) and ARIMA (80%) models. This high accuracy is attributed to the model's bidirectional processing capability, which captures comprehensive temporal patterns in the weather data. Implementing this model can enhance decision-making processes for farmers, resulting in increased productivity and profitability in the agricultural sector.
Enhancing Arabic Fake News Detection: Evaluating Data Balancing Techniques Across Multiple Machine Learning Models
Authors: Aljohani Eman
Published: August 2024

The spread of fake news has become a serious concern in the era of rapid information dissemination through social networks, especially when it comes to Arabic-language content, where automated detection systems are not as advanced as those for English-language content. This study evaluates the effectiveness of various data balancing techniques, such as class weights, random under-sampling, SMOTE, and SMOTEENN, across multiple machine learning models, namely XGBoost, Random Forest, CNN, BIGRU, BILSTM, CNN-LSTM, and CNN-BIGRU, to address the critical challenge of dataset imbalance in Arabic fake news detection. Accuracy, AUC, precision, recall, and F1-score were used to evaluate the performance of these models on balanced and imbalanced datasets. The results show that SMOTEENN greatly improves model performance, especially the F1-score, precision, and recall. In addition to advancing the larger objective of preserving information credibility on social networks, this study emphasizes the need for advanced data balancing strategies to improve Arabic fake news detection systems.
A Study on the Influence of FDM Parameters on the Tensile Behavior of Samples made of ASA
Authors: Zisopol Dragos Gabriel , Minescu Mihail , Iacob Dragos Valentin
Published: August 2024

This paper presents the results of the study on the influence of FDM printing parameters on the tensile behavior of samples made of ASA. To perform the study, 27 tensile samples were made of ASA on Anycubic 4 Max 2.0 3D printer using as the height of the single-pass layer Lh = 0.10/0.15/0.20 mm and as filling percentage Id = 50/75/100%. The results obtained from tensile testing of the samples on the universal testing machine demonstrate the way in which the FDM parameters influence the tensile strength, the percentage elongation at break, and the elastic modulus. The parameter that significantly influences the tensile characteristics of ASA samples made using FDM is the filling percentage (Id).
Application of Unmanned Aerial Vehicle and Ground Control Point for Mapping and Road Geometric Review
Authors: Sompie Tampanatu P. F. , Makangiras Ralgie E. , Sumajouw Josef A. J. , Hombokau Chris
Published: August 2024

Technology implementation, particularly the use of Unmanned Aerial Vehicles (UAVs) and photogrammetry, is being employed in road works for regional and road planning. The current study deploys aerial photographs and data processing along with software, like Agisoft Metashape, PCI Geomatica, Global Mapper, and Autocad Civil 3D as an efficient and effective way to generate digital maps and perform geometric road reviews. The accuracy test of CE90 performed for horizontal accuracy was 0.003 m and the LE90 carried out for vertical accuracy was 0.006 m. This accuracy level is valuable for road planning, ensuring that the data utilized for decision-making are reliable and precise. The study focused on Wori Street spanning from Pandu to Kima Atas Street Manado, covering the section from Sta. 0+000 to Sta. 5+225, which is a collector road with a designated speed of 50 km/h. Among the 16 bends analyzed, 11 met highways’ standards for the collector road class, certifying compliance with safety guidelines. Furthermore, the existing road slope conforms to standard requirements, remaining below 8%. This adherence to safety criteria is vital for the design and operation of safe roads.
Assessing Institutional Performance using Machine Learning on Arabic Facebook Comments
Authors: Anwer Zainab Alwan , Abdalrada Ahmad Shaker
Published: August 2024

Social networks have become increasingly influential in shaping political and governmental decisions in Middle Eastern countries and worldwide. Facebook is considered one of the most popular social media platforms in Iraq. Exploiting such a platform to assess the performance of institutions remains underutilized. This study proposes a model to help institutions, such as the Iraqi Ministry of Justice, evaluate their performance based on sentiment analysis on Facebook. Different machine learning algorithms were used, such as Support Vector Machine (SVM), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Naive Bayes (NB), and Random Forest (RF). Extensive experimental analysis was performed using a large dataset extracted from Facebook pages belonging to the Iraqi Ministry of Justice. The results showed that SVM achieved the highest accuracy of 97.774% after retaining certain stop words, which proved to have a significant impact on the accuracy of the algorithms, ensuring the correct classification of comments while preserving the sentence's meaning.
An Efficient Optimization System for Early Breast Cancer Diagnosis based on Internet of Medical Things and Deep Learning
Authors: Naz Amna , Khan Hamayun , Ud Din Irfan , Ali Arshad , Husain Mohammad
Published: August 2024

Improving patient outcomes and treatment efficacy requires effective early detection of breast cancer. Recently, medical diagnostics has been transformed by merging the Internet of Things (IoT) technology with AI and ML methods. Better and faster diagnoses have been made possible by this revolutionary synergy, which allows the study of both real-time and historical data. Unfortunately, many people still die from breast cancer because modern diagnostics are not good enough to catch the disease in its early stages, even though medical science has come a long way. To overcome this obstacle, this study introduces a new medical diagnostic system that utilizes IoT to accurately distinguish between patients with and without tumors. The proposed model achieved 95% classification accuracy in differentiating between non-tumor and tumor instances by utilizing a Convolutional Neural Network (CNN) with hyperparameter adjustment. This approach can improve the accuracy and efficiency of breast cancer diagnosis by integrating medical devices with AI applications and healthcare infrastructure. In the long run, this study could help reduce breast cancer deaths by increasing early detection rates. This study can revolutionize healthcare delivery and improve patient outcomes on a global scale through continued innovation and collaboration with medical IoT technology.
Developing an Automatic 3D Solid Reconstruction System from only Two 2D Views
Authors: Hoang Long , Nguyen Thanh Tuan , Tran Hoang Anh , Nguyen Duc Huy
Published: August 2024

Three-dimensional (3D) solid models of mechanical machine parts are widely used in modern mechanical engineering. One expected approach in creating 3D models is automatically reconstructing them from 2D engineering drawings. This work expands the previous automatic reconstruction methods by adding principles, algorithms, and coding to reconstruct oblique planes on the part. The proposed method uses only two views as the input to reconstruct the 3D solid part, including oblique planes. The proposed method has been implemented by a program written in the ARX 2018 language running on the AutoCAD 2021 platform to reconstruct multiple 3D parts from their two views. Experimental test results on many samples confirmed that the proposed method is reliable, absolutely accurate, and achieves a high reconstruction speed. The output 3D model has also been tested and confirmed for compatibility with CAD/CAM software such as Solid Works, Inventor, and PTC Creo.
NavigateNow: Innovative Indoor Mapping for Seamless Navigation
Authors: Anushree K  , Prathik Raj  , Sathwik  , Ambresh  , Poornima K
Published: August 2024

A University Campus can span vast areas and may consist of multiple campuses. Annually, numerous students enroll in the university, leading to the construction of new buildings, the introduction of new courses, and the establishment of various facilities such as departments, cafeterias, libraries, etc. This expansion can pose challenges for newcomers in navigating the campus efficiently and on time. Similarly, new faculty members, staff, and visitors encounter difficulties in finding their way around. In recent times, a significant number of students, faculty, and staff have turned to using Android smartphones for personal use. A web-based Global Positioning System (GPS) application, integrated with videos and images, would greatly assist in locating specific places and finding the most direct routes from one's current location to the desired destination. This solution would alleviate the frustration and confusion experienced by individuals within the campus. The application has been developed using HTML, CSS, and the Django framework, with Python as the programming language and MySQL as the database for data storage
A Review on Implementation of Shift Register using FSM
Authors: Sanjan Kumar  , Ashik E K  , Harsha  , Shaikh Mohammed Afzan  , Dr. Sathisha
Published: August 2024

This study presents a way of decomposing Finite State Machines (FSMs) using shift registers as the memory for the FSM network components. Every part of the network is implemented using a different shift register. The FSM network's testability is enhanced by this method. The state splitting technique is explained in order to reduce the amount of shift registers that are used. In this implementation, every FSM state is defined to match a particular shift register configuration. Input signals and clock pulses control state transitions, guaranteeing precise data shifting from one stage to the next. The shifting, output, and initialization procedures are all included in the FSM architecture, which offers a comprehensible and upgradable structure for shift register operation