IAES International Journal of Artificial Intelligence (IJ-AI)
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U-Net for wheel rim contour detection in robotic deburring
Get PDFAutomating robotic deburring in the automotive sector demands extreme precision in contour detection, particularly for complex components like wheel rims. This article presents the application of the U-Net architecture, a deep learning technique, for the precise segmentation of the outer contour of wheel rims. By integrating U-Net's capabilities with OpenCV, we have developed a robust system for wheel rim contour detection. This system is particularly well-suited for robotic deburring environments. Through training on a diverse dataset, the model demonstrates exceptional ability to identify wheel rim contours under various lighting and background conditions, ensuring sharp and accurate segmentation, crucial for automotive manufacturing processes. Our experiments indicate that our method surpasses conventional techniques in terms of precision and efficiency, representing a significant contribution to the incorporation of deep learning in industrial automation. Specifically, our method reduces segmentation errors and improves the efficiency of the deburring process, which is essential for maintaining quality and productivity in modern production lines
Boosting industrial internet of things intrusion detection: leveraging machine learning and feature selection techniques
Get PDFThe rapid integration of industrial internet of things (IIoT) technologies into Industry 4.0 has revolutionized industrial efficiency and automation, but it has also exposed critical vulnerabilities to cyber threats. This paper delves into a comprehensive evaluation of machine learning (ML) classifiers for detecting anomalies in IIoT environments. By strategically applying feature selection techniques, we demonstrate significant enhancements in both the accuracy and efficiency of these classifiers. Our findings reveal that feature selection not only boosts detection rates but also minimizes computational demands, making it a cornerstone for developing resilient intrusion detection systems (IDS) tailored for Industry 4.0. The insights garnered from this study pave the way for deploying more robust security frameworks, safeguarding the integrity and reliability of IIoT infrastructures in modern industrial settings
A novel approach to enhancing software quality assurance through early detection and prevention of software faults
Get PDFThe current manuscript presents a predictive mechanism towards analyzing software defects by developing a line-level fault prediction technique. Current methodologies rely on customized attributes and overlook the sophisticated structural and semantic characteristics inherent in programming languages. This oversight often led to suboptimal defect identification, as code defects are intricately scrambled with their contextual environment. Moreover, conventional software defect prediction (SDP) strategies, typically focusing on larger code units such as modules or classes, impede precise error localization. To address these challenges, this study proposes an automated scheme utilizing a recurrent neural network (RNN) with an attention layer to analyze line-level quantifiers within the code, such as the number of pairwise operations and single operand operators. The efficacy of this learning-driven scheme is validated through comprehensive experiments conducted on several C++ programs. The experimental results demonstrate a 95.8% recall, 83.12% precision, and 90.35% accuracy in identifying fault-prone lines within a testing dataset. These outcomes confirm the effectiveness of proposed SDP scheme in accurately identifying the defects and highlighting its inter-project capabilities, exhibiting the model's adaptability across different software projects
Balancing and metaheuristic techniques for improving machine learning models in brain stroke prediction
Get PDFA brain stroke, medically referred to as a stroke, represents a critical condition triggered by the disruption of blood flow to a region of the brain. Early detection of stroke is crucial to prevent fatal complications. In this study, we worked with an unbalanced dataset of 4981 entries on stroke, which we balanced using the K-means synthetic minority over-sampling technique (KMeansSMOTE) algorithm. We then employed five machine learning algorithms: decision tree, random forest, support vector machine, K-nearest neighbors, and gradient boosting. We compared the hyperparameter optimization of these algorithms using four metaheuristic techniques: gray wolf optimization, particle swarm optimization, genetic algorithm, and artificial bee colony. The models' effectiveness was evaluated using multiple metrics, such as accuracy, recall, precision, F1-score, and area under the receiver operating characteristic curve. Our findings indicate that the random forest optimized by the genetic algorithm achieved the best performance, with an accuracy of 97.39% and an F1-score of 97.35%. This study highlights the effectiveness of balancing and metaheuristics techniques in optimizing machine learning models for stroke forecasting
Leveraging artificial intelligence through long short-term memory approach for correcting faults in Chinese language sentences
Get PDFThis research focus on leveraging artificial intelligence (AI) to manage the challenges faced by non-native speakers in correcting faults and misconstructions in Chinese language sentences. Learners commonly struggle with mispronunciation, incorrect character usage, improper sentence structures, and grammatical mistakes. To tackle these issues, this study generally aims to improve and optimize AI for correcting faults in Chinese language for non-native speakers. This project employs long short-term memory (LSTM) approach based on Hanyu Shuiping Kaoshi (HSK) word ordering errors (WOE) dataset. The effectiveness of leveraging LSTM in detecting and correcting errors in Chinese language sentence have been demonstrated. LSTM shows the capability to be learn Chinese sentence structure, identify mistakes, and correct them. In summary, this research seeks to benefits the power of AI to provide innovative solutions for detecting, correcting faults and misconstructions in Chinese language sentences. This paper essentially useful for those who wish to learn how to correct their Chinese writing and enhance language proficiency among non-native speakers
Optimizing real-time data preprocessing in IoT-based fog computing using machine learning algorithms
Get PDFIn the era of the internet of things (IoT), managing the massive influx of data with minimal latency is crucial, particularly within fog computing environments that process data close to its origin. Traditional methods have been inadequate, struggling with the high variability and volume of IoT data, which often leads to processing inefficiencies and poor resource allocation. To address these challenges, this paper introduces a novel machine learning-driven approach named real-time data preprocessing in IoT-based fog computing using machine learning algorithms (IoT-FCML). This method dynamically adapts to the changing characteristics of data and system demands. The implementation of IoT-FCML has led to significant performance enhancements: it reduces latency by approximately 0.26%, increases throughput by up to 0.3%, improves resource efficiency by 0.20%, and decreases data privacy overhead by 0.64%. These improvements are achieved through the integration of smart algorithms that prioritize data privacy and efficient resource use, allowing the IoT-FCML method to surpass traditional preprocessing techniques. Collectively, the enhancements in processing speed, adaptability, and data security represent a substantial advancement in developing more responsive and efficient IoT-based fog computing infrastructures, marking a pivotal progression in the field
Enhancing touchless smart locker systems through advanced facial recognition technology: a convolutional neural network model approach
Get PDFAs the world recovers from COVID-19, demand for contactless systems is increasing, promising safety and convenience. Touchless technology, particularly public locker security systems that use facial recognition and hand detection, is advancing rapidly. The system minimizes physical contact, increasing user safety. It uses advanced models such as multi-task cascaded convolutional networks (MTCNN) and RetinaFace, FaceNet512, ArcFace, and visual geometry group (VGG)-Face for face detection and recognition, with a combination of RetinaFace, ArcFace, and L2 norm Euclidean or cosine as the most effective distance metric method, where the accuracy reaches 96 and 90%. 'Yourvault', an application demonstrating this efficient security feature, provides notifications for mask detection, facial authenticity and locker status, offering a solution to the problem of convenience and security of public spaces. Future research could investigate the impact of photo age on facial recognition accuracy, potentially making touchless systems more efficient. In general, the application of this technology is an important step towards a safer and more comfortable world after the pandemic. This model approach can be followed up with more optimal facial recognition
An optimal pheromone-based route discovery stage for 5G communication process in wireless sensor networks
Get PDFThe rapid advancement of 5G communication underscores the need for heightened efficiency within wireless sensor networks (WSNs), where challenges such as data loss, inefficiency, and jitter are exacerbated by complex operations. This paper presents the optimal pheromone-based route discovery stage (OpRDS) algorithm, inspired by the natural foraging behaviors of ants, as a novel solution designed to optimize routing processes in the dynamic and demanding 5G environments. The study conducts a comparative analysis of OpRDS against traditional routing protocols, including the ad hoc on-demand distance vector (AODV), destination-sequenced distance-vector (DSDV), dynamic source routing (DSR), and zone routing protocol (ZRP), focusing on key performance metrics such as packet delivery ratio (PDR), latency, throughput, routing overhead (RO), energy consumption (EC), network lifespan, route discovery speed, and scalability. Our results reveal that OpRDS significantly outperforms the conventional protocols, evidencing a 2% increase in PDR, a 5.5% decrease in latency, a 6.7% rise in throughput, an 8.3% reduction in RO, an 11.1% decrease in EC (resulting in an 11% extension of network lifespan), a 10% improvement in route discovery speed, and a 6.7% enhancement in scalability. These findings highlight the algorithm's superior efficiency and adaptability in addressing the robust demands of 5G networks
Estimating broiler heat stress using computer vision and machine learning
Get PDFTo optimize and enhance the efficiency of broiler chicken farming, it is essential to maintain the chicken’s welfare, as heat stress can decrease growth efficiency. The temperature-humidity index (THI) is a key indicator used to determine if chickens are experiencing heat stress. Precision livestock farming (PLF) based on computer vision is one method that can assist farmers in continuously and automatically monitoring the condition of their chickens. This research developed a computer vision-based PLF system to observe chickens with CP 707 strain in a commercial farm using the Mask region-based convolutional neural network (Mask R-CNN) method and object tracking algorithms to analyze features such as the cluster index, unrest index, and the distance traveled by broilers. The results indicated that all features tend to inversely correlate with the THI value, with the cluster index showing the most noticeable tendency. Additionally, it was found that external factors, such as the presence of farmers around the observation area, can affect the chickens' behavior, although the cluster index feature is relatively resilient to disturbances if the operator is not captured by the camera. It was concluded that there is a relationship between the features and the THI value; however, these features are not yet sufficient to distinguish the condition of chickens under high and low THI conditions in real-time
Intelligent assessment of harmonic distortion compliance in reverse osmosis systems
Get PDFThis study explores the critical challenge of harmonic distortion compliance in reverse osmosis (RO) desalination systems, with a focus on aligning with international standards, specifically IEC 61000, IEEE 519, and EN 50160. High-power equipment, particularly high-pressure pumps (HPP), introduces significant harmonic distortions, threatening power quality and operational reliability. To address this issue, we integrated advanced machine learning (ML) algorithms, namely decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) to assess harmonic compliance and predict total harmonic distortion (THD) under four operational scenarios. All data used for training and testing were obtained from real-time measurements taken at a large-scale desalination plant using a power quality analyzer (QUALISTAR CA 8336), which guarantees the practical relevance of the analysis. The models were trained on harmonic order and amplitude data and evaluated using accuracy, precision, recall, and F1-score metrics. Among the models, MLP demonstrated superior performance, achieving an accuracy of 99.11% and an F1-score of 98.9%, making it a robust tool for harmonic compliance assessment. SVM and RF also showed commendable results, while DT proved effective for basic analysis. This research underscores the potential of AI-driven approaches in mitigating harmonic-related challenges, optimizing power quality, and enhancing operational efficiency in RO plants. These findings offer a pathway toward more reliable and energy-efficient industrial operations