Accurate testing of photovoltaic (PV)-based systems under varying environmental conditions is often limited by weather dependency, safety constraints, and poor repeatability of real PV modules. To address these limitations, this paper proposes a real-time photovoltaic panel emulator based on a data-driven approach that integrates experimentally measured characteristics with interpolation techniques. The proposed method combines look-up table (LUT) implementation with linear and cubic spline interpolation to reconstruct continuous I–V characteristics across a wide operating range, eliminating the need for iterative numerical solving and enabling fast real-time response. The emulator is implemented using MATLAB/Simulink and a dSPACE acquisition board, allowing dynamic adjustment of irradiance and temperature during operation. Experimental validation was conducted for irradiance levels between 400 W/m2 and 1,000 W/m2 and temperatures ranging from 10 °C to 60 °C. The results demonstrate stable and consistent reproduction of the I–V characteristics, with an estimated maximum deviation below approximately 5 % across the tested operating range and real-time response compatible with a sampling period of 0.1 s. Compared to conventional model-based and metaheuristic approaches, the proposed method offers reduced computational complexity, deterministic behavior, and improved suitability for real-time applications. These features make the emulator a flexible and reliable platform for testing PV power converters, maximum power point tracking (MPPT) algorithms, and control strategies under controlled laboratory conditions.
Women can suffer from alopecia due to multiple criteria, including thyroid disorders, hormonal imbalances, anemia, medications, genetics, vaccines, and other health conditions. Therefore, this study applies multi-criteria decision-making (MCDM) techniques to assess the various blood deficiencies and thyroid status disorders on alopecia severity and to support treatment prioritization. The 80 women diagnosed with various types of alopecia, using 14 blood tests, encompassing anemia, iron, as well as thyroid disorder types, had been analyzed. We employed the Fuzzy-Weighted Zero-Inconsistency (FWZIC) method with the aid of 8 dermatology and hematology experts to evaluate diverse types of blood deficiencies and thyroid status, which impact and cause alopecia in women. The second MCDM method we applied is the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to prioritize patients according to their severity, thereby enhancing the treatment prioritization and time management. The results of the proposed framework revealed that Serum ferritin, Thyroid Status, and FT3 were the most impactful tests, equal to (0.0907, 0.0821, 0.077), respectively, while Transferrin and MCHC had the least impact, equal to (0.0522, 0.046), respectively. Additionally, our results surfaced that the high ranks went to women with a significant thyroid dysfunction, specifically secondary hyperthyroidism and hypothyroidism, as well as abnormalities in FT3 and ferritin levels, which emphasizes the influence of Serum Ferritin, Thyroid Status, and FT3 on the severity of alopecia disease in women. Therefore, this study aimed to analyze the factors that affect women’s susceptibility to diverse types of alopecia, highlighting the role of anemia, iron deficiency, and thyroid disorders. The research methodology relied on MCDM methods to analyze these relationships. The results confirmed the importance of relying on specific laboratory tests to monitor disease progression, enabling rapid and accurate medical intervention for women suffering from alopecia.
The attribute reduction in rough set theory has considerable value in areas such as pattern classification, knowledge extraction, and data mining. Hybrid information systems usually contain discrete classification attributes and continuous numerical attributes. For such data, the neighborhood rough set model is typically used to handle hybrid attributes, but using a fixed neighborhood radius may affect the classification performance of the data. Meanwhile, as an effective tool for uncertainty reasoning, fuzzy evidence theory has advantages in handling fuzzy information and has been successfully applied in scenarios such as medical diagnosis and intelligent decision-making. This study proposes a neighborhood and fuzzy rough set model that combines data features to handle hybrid data, and designs two attribute reduction algorithms based on neighborhood and fuzzy theories to obtain reduced sets that retain the original data information. Specifically, based on the features of hybrid data, fuzzy similarity relations and neighborhood similarity relations are constructed for each attribute. This algorithm effectively improves the classification performance of the data by adopting differentiated similarity radii. On this basis, a new information entropy function is defined based on neighborhood and fuzzy theories to determine the importance of attributes. Based on the above indicators, the design idea of the attribute reduction algorithm is proposed. In the algorithm implementation process, information entropy is introduced to pre-sort attributes to optimize the search efficiency of attribute subsets and prioritize features with high discrimination. Experimental comparative analysis shows that the proposed IEDC and ARFC algorithms outperform existing methods in classification performance.
Physical Human-robot Interaction (pHRI) involves physical collaboration between humans and robots to perform tasks safely and efficiently in shared environments. Traditionally, pHRI behavior has been analyzed using analytical or simulation-based methods, which may suffer from human error and incomplete coverage of system behavior. Formal methods, such as Interactive Theorem Proving (ITP), offer a mathematically rigorous alternative by constructing logical models of system dynamics and verifying their properties through deduction and proof. This paper proposes an ITP-based approach for the formal analysis of pHRI dynamics using the HOL Light theorem prover. We formalize the one-dimensional admittance control equations, derive their Laplace-domain representations, and conduct a formal stability analysis of the corresponding open and closed-loop controllers. To complement the formal verification, the verified open-loop admittance controller is implemented and simulated in MATLAB, where the step-response and root-locus analyses confirm the formally proven stability guarantees. The results demonstrate that the proposed framework provides both mathematical rigor and practical consistency for ensuring safe and reliable human-robot interaction.
The widespread use of machine learning algorithms in dataset modeling requires a thorough understanding of the various tools likely to improve the modeling quality. Any machine learning algorithm has two types of parameters: model parameters and hyperparameters. Parameters are adjusted in the learning process, while hyperparameters are defined a priori. Hyperparameter tuning is an essential element in the modeling process due to its effect on the quality of results. This study focuses on enhancing the modeling accuracy of mosquito species distribution in Morocco by optimizing the hyperparameters of the employed algorithms. Three tuning methods were selected for this purpose: Genetic Algorithms, Bayesian Optimization, and Particle Swarm Optimization. The experimental results confirmed the effect of hyperparameter tuning on the modeling quality, with accuracy improvements ranging from 0.02 to 0.067. In addition, Genetic Algorithms and Bayesian Optimization proved more effective than Particle Swarm Optimization. The hyperparameter tuning process has optimized the modeling quality, which can only enhance the explanation of mosquito distribution.
The Modbus TCP/IP protocol, widely adopted in industrial communications, lacks essential security features, making it vulnerable to cyberattacks such as TCP Connection Exhaustion. This paper proposes a machine learning-based detection framework using the Random Forest (RF) algorithm to identify malicious traffic in Operational Technology (OT) networks. A simulated testbed was created using virtual machines to emulate Modbus server-client communication under normal and attack conditions. Our model achieved F1-score of 99.83 %, precision of 99.9 %, and recall of 99.7 %, clearly demonstrating its accuracy and robustness. These results validate the proposed approach as a lightweight, real-time, and effective intrusion detection system suitable for resource-constrained industrial environments.
Deep Vein Thrombosis (DVT) is a serious health issue in which a thrombus (blood clot) forms in one of the veins (typically in the legs). This condition can lead to various severe complications, such as post-thrombotic syndrome, pulmonary embolism, chronic vein insufficiency, and vein gangrene. It is challenging to diagnose DVT in advance since it can also occur without causing any symptoms. Hence, this study uses explainable machine learning techniques to predict DVT. Explainable Artificial Intelligence (XAI) techniques make the algorithms more robust due to their interpretability and transparency. Five different explainers have been utilized in this research, and according to them, ankle swelling, leg swelling, immobilization, and edema are the most essential parameters to identify DVT in patients. The explainable models can be used to predict DVT in advance so that appropriate care and medical attention can be provided to prevent the severe complications induced by this deadly condition.
In the realm of industrial automation, innovative AI-driven solutions are revolutionizing object detection and counting processes. This study presents a modified YOLOv10 model enhanced with the Ghost mechanism, including GhostConv and C3Ghost modules, designed to optimize computational efficiency while achieving superior detection accuracy. The proposed model excels in real-time applications, delivering a precision of 0.972, recall of 0.967, and mean Average Precision (mAP) scores of mAP@50=0.991 and mAP@50-95=0.799, all while reducing the parameter count to 6.5 M. These advancements address the challenges of fastener detection, particularly in cluttered environments and under diverse lighting conditions, paving the way for streamlined operations in manufacturing assembly lines. By leveraging specialized datasets tailored to factory-specific conditions and incorporating advanced algorithmic improvements, the model demonstrates its capacity to enhance inventory management and quality control processes. This study underscores the importance of lightweight yet robust AI models in modern manufacturing, setting a benchmark for scalable and efficient automation systems that cater to diverse industrial needs.
The need for efficient logistics management and supply chain optimization has grown with the rapid expansion of global trade and e-commerce. Traditional logistics methods often face inefficiencies, high costs, and slow response times, making it necessary to explore innovative solutions. This study integrates artificial intelligence (AI) and Internet of Things (IoT) technologies to optimize logistics processes in transportation and inventory management. By using the random forest algorithm, this research focuses on improving transportation route planning, reducing inventory costs, and enhancing order processing efficiency. Real-time sensor data from IoT devices such as GPS and RFID tags provided valuable inputs, ensuring accurate and up-to-date information for decision-making. The results demonstrate a 28 % reduction in transportation time, a 16.7 % decrease in fuel consumption, and a 20.2 % reduction in inventory holding costs. Additionally, customer satisfaction increased by 14.1 % points. These findings indicate that the integration of AI and IoT offers significant improvements in logistics management, presenting a promising avenue for cost reduction and enhanced customer service.
Accurate classification of child delivery mode is crucial for improving maternal and neonatal health. In developing countries like Ethiopia, clinical assessments alone often result in misguided medical interventions. Even though machine learning in healthcare has brought promises, the various algorithms along with different real-world datasets perform differently. Hence, the objective of this study was to develop a machine learning model for predicting child delivery mode based on real data. The study followed experimental and exploratory research design utilizing 1,072 antenatal records from Arba Minch General Hospital and Birbir Health Center, Ethiopia. 16 attributes were considered including the outcome, mode of delivery. Predictors included sociodemographic and clinical variables such as age, weight, blood pressure, previous CS, and fetal presentation. Five machine learning algorithms including Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and CatBoost were trained and evaluated using hold-out validation. Additionally, a recent deep learning model, TabPFN, and Long Short-Term Memory were examined to expand the exploration. The results showed that RF (93.1 %) achieved the highest accuracy. TabPFN scored the second best accuracy score (92.5 %), demonstrating its potential on smaller tabular data. LSTM performed better than SVM and LR highlighting the consideration of the inherent temporal characteristics of the data.
Cross-project defect prediction is an important method of identifying defects in a project. We extract knowledge from the source project and apply it to predict labels for the target project in cross-project defect prediction. However, insignificant and irrelevant features can have an impact on model performance. By selecting only significant and relevant features, hybrid feature selection can help achieve high prediction accuracy. Our goal is to investigate the effect of significant feature selection along with CNN classifier on cross-project defect prediction for multi-class datasets using a hybrid approach. We took advantage of the strengths of Random Forest and Recursive Feature Elimination Cross Validation, which can constructively select a few significant features. Our controlled experiment has a 1 Factor 2 Treatments design. Exploratory Data Analysis demonstrates that all versions of the PROMISE repository are multi-class and contain duplicated rows of data, a distribution gap between values, and imbalance classes. After removing duplicated rows, narrowing the data distribution gap, and balancing classes, we chose a significant feature set using a hybrid approach that included Random Forest and Recursive Feature Elimination Cross Validation. To predict Cross project defects, we used Convolutional Neural Network as a classifier, with SoftMax as the final layer. In terms of Area Under Curve, our experimental setup resulted in an average 78 % prediction accuracy measure across all 14 versions. Our experimental results showed that CNN classifier using features selected through Hybrid Feature Selection has a significant impact on defect prediction accuracy for different datasets in the Cross Project.
A rapid rise in machine learning-based applications has made it one of the most popular areas in the field of artificial intelligence (AI). The most commonly used libraries to implement the algorithms used in these applications are Scikit learn and Weka. It is challenging to test these machines learning based applications due to the Oracle Problem. The problem is when the expected outcome is not known and hence the testing of such applications cannot be performed via traditional testing techniques. One of the solution to the Oracle problem is the use of Metamorphic testing to test the machine learning applications. The code of machine learning algorithms is often ignored, when testing of ML-based applications is done. However, the usage of the machine learning algorithms within the libraries requires formal testing to improve reliability. This work evaluates the Metamorphic relations for machine learning algorithms by finding their kill rate while testing 5 machine learning (ANN, ID3, KNN, Naive Bayes, SVM) classifiers from the Scikit Learn library. This work also calculates the statement coverage, while testing the metamorphic relations. The relationship between the effectiveness of fault detection and code coverage is identified as well.
The binary classification of three-dimensional (3-D) objects for phase-only digital holographic information is performed using the various deep learning network models such as ResNet50, ResNet101, ResNet152, ResNet18, ResNet34, EfficientNetB0, DenseNet121, DenseNet169, Neural Architectural Search (NAS) Network, and InceptionV3. The four 3-D objects considered to perform binary classification are ‘triangle-square’, ‘circle-square’, ‘square-triangle’, and ‘triangle-circle’. The 3-D object ‘triangle-square’ has been considered for Class 1 and the remaining three 3-D objects have been considered for Class 2. The digital holograms of 3-D objects have been formed using the phase-shifting digital holographic (PSDH) technique and numerically reconstructed to obtain phase images. The phase image dataset consisting of 2,880 images was trained using all the various deep learning network models to obtain the results. The results such as loss/accuracy, loss/positive predictive value (PPV), and loss/sensitivity curves on the training/validation sets, error matrix, and performance metrics namely accuracy, PPV, sensitivity, F1-score, Matthews correlation coefficient (MCC), cohen_kappa_score (CKS), balanced_accuracy_score (BAS), jaccard_score (JS), log_loss (LL), hinge_loss (HL), and brier_score_loss (BSL) are shown for the binary classification task. Finally, the results such as receiver operating characteristic (ROC), and PPV-sensitivity curve are also shown to justify the performance of the work. The results obtained from the deep residual network models i.e. ResNet50, ResNet101, ResNet152, ResNet18, and ResNet34 were compared with other deep learning network models such as EfficientNetB0, DenseNet121, DenseNet169, NAS Network, and InceptionV3.
This study aims to develop an automatic system for detecting toxic comments in online environments, particularly on social networking platforms. The focus is on efficiently identifying and categorizing toxicity in user-generated comments to address the growing issue of harmful online content. A novel hybrid model, Text-BGRU-CNN, combining Bidirectional Gated Recurrent Unit (BGRU) and Text Convolutional Neural Network (Text-CNN), is introduced for multilabel toxicity detection. This model uses pre-trained word embeddings for word vector generation and a range of filters to extract local features and long-term dependencies in text. It incorporates a fully connected layer, a normalization layer, and an output layer for multilabel category prediction. The proposed hybrid model demonstrates superior classification accuracy in experimental trials. It was tested on a dataset divided into training and testing sets, enhanced by significant pre-processing. Structural modifications, including increasing dense units and filters, were evaluated. The final model, combining GRUs with a single CNN layer, achieved an accuracy of 0.9944 in classifying toxic comments. The study evidences the efficacy of a hybrid GRU and single-layer CNN model in online toxic comment classification. Results suggest that simpler model architectures, supplemented by extensive pre-processing, yield high accuracy and efficient training. The findings underscore the importance of further research to understand biases in trained classifiers and suggest exploring alternative methods for representing sequences and independent training of sparsely labeled classes in future work.
Asphalt roads play a vital role in land transportation systems, significantly contributing to the growth and development of economies and societies. However, over time, the quality of these roads deteriorates due to aging and the cumulative effects of wear and tear, leading to various pavement and road issues such as potholes, cracks, and damaged sidewalks. This paper aims to develop a deep learning model, specifically leveraging the YOLOv8 object detection framework, to detect and classify road infrastructure problems using images captured from unmanned aerial vehicles (UAVs). The model processes a series of road images from the Roboflow dataset and was trained on Google Colab, utilizing advanced machine learning techniques to analyze the images and accurately identify road damage. Subsequently, the model was evaluated using metrics such as accuracy, recall, precision, and F1-score. The results demonstrated that the model is both efficient and reliable. The model achieved high performance, with an F1-score of 94 %, precision of 93 %, and recall of 95 %, which indicates its effectiveness in identifying various road defects. By detecting and locating issues such as potholes, cracks, and sidewalk damage, this model offers a promising solution for maintaining road infrastructure, supporting smart transportation systems, enhancing road safety, and helping reduce hazards and accidents.
This paper proposes a computerized text analysis framework to examine the evolution of China’s industrial internet policies concerning SMEs. Analyzing a corpus of 310 policy documents concerning SMEs in industrial internet domain, we first investigated their external structure. Thematic sequence evolution analysis subsequently identified five key topics. Building on a pivotal policy issued in 2017, the evolution of China’s industrial internet policies concerning SMEs was categorized into two stages. The paper further explored the characteristics of the intergovernmental cooperation network, and examined the evolutionary paths of policy themes using a Sankey diagram. The findings indicate that the five topics in stage 2 are essentially the continuation and further development of the policy themes in stage 1, demonstrating strong policy coherence in industrial internet domain. Theoretically, this study addresses the IAD’s challenges in exploring the evolution of action situations and the diverse processes of structural changes over time, while overcoming the ACF’s limitations in interpreting the interactions of common belief systems and interests.
Deep learning techniques have been widely used in various fields. However, they face significant security challenges due to the existence of adversarial examples. Traditional black-box adversarial attack methods mainly rely on swarm intelligence optimization algorithms to identify optimal perturbation pixels, which requires intensive computational resources. In some typical applications such as medical image recognition, public datasets are often used to train deep learning models. It is worth noting that such dataset inherently contains some basis features for deep learning models to learn discriminative representations. And these features can serve as critical cues for constructing adversarial samples. Inspired by this observation, a novel adversarial attack method was proposed. First, some sensitive locations are identified within the dataset without querying the target model. Moreover, the adversarial attack samples are constructed based on these locations. Different from white-box and black-box attack, dataset characteristics are utilized to construct adversarial attack samples. The proposed method investigates naturally occurring vulnerabilities in the data, offering new insights for enhancing data augmentation techniques and attack strategies, while also providing a promising direction for improving model robustness. Experimental results demonstrate that this method can achieve attack effectiveness comparable to the Particle Swarm Optimization (PSO) algorithm.
Mobile robot navigation remains a critical challenge in robotics, with applications spanning autonomous vehicles, search and rescue, and other dynamic environments. In recent years, reinforcement learning (RL) has become a powerful approach for solving complex tasks such as robotic manipulation, gameplay, and autonomous driving. By enabling robots to learn optimal navigation strategies through interaction with their environment, RL offers a promising pathway to autonomous mobility. This review presents a comprehensive overview of recent advancements in RL as applied to mobile robot navigation. We begin by outlining core RL concepts, agents, environments, rewards, and value functions, explaining their roles in navigation. Key RL techniques, including Q-learning, deep reinforcement learning, Markov Decision Processes (MDPs), and policy gradient methods, are examined to highlight their transformative impact on navigation performance. The review also explores a range of practical applications and identifies current challenges and open research directions. Critical issues such as safety, sample efficiency, and scalability to real-world scenarios are discussed in depth to ensure robust deployment of RL-based systems. Lastly, this review synthesizes the state-of-the-art in reinforcement learning for mobile robot navigation, offering readers both a foundational understanding and valuable insights into emerging trends and future opportunities in this rapidly evolving field.
Traffic sign recognition plays a critical role in the development of autonomous driving systems and intelligent transportation networks. However, detecting small traffic signs in real-world scenarios, particularly those captured from vehicle-mounted cameras, remains a significant challenge due to their diminutive size, low resolution, and environmental noise. To address these challenges, we propose an innovative multi-strategy enhancement framework for YOLOv7, designed specifically to improve small target detection. The framework integrates several novel techniques: the SE attention mechanism is incorporated into the ELAN module of the backbone network to enhance feature discriminability; DySample replaces traditional upsampling methods in the head network to refine feature reconstruction; NWD loss is introduced as a superior alternative to CIoU loss, improving the localization accuracy of small objects; and PConv convolution is applied to reduce model parameters without sacrificing performance. Experimental results on the CCTSDB-2021 dataset demonstrate the effectiveness of these enhancements, with mAP@0.5 and mAP@0.5:0.95 improving by 6.6 and 15.2 %, respectively, compared to the original YOLOv7 model. The proposed algorithm outperforms YOLOv7 by 10.9 % in mAP@0.5 and by 11.89 % in mAP@0.5:0.95 on the TT100K dataset. Moreover, the optimized model achieves real-time inference at 83 FPS on the CCTSDB-2021 dataset, while reducing the number of parameters by 1.5 million, making it highly efficient for practical deployment in autonomous vehicles. These improvements not only enhance detection accuracy but also meet the real-time processing requirements of intelligent transportation systems.
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that leads to a decline in memory and cognitive functions. An early and accurate diagnosis is critical for effective management and treatment also tend to lack sufficient accuracy. Accurate and early discrimination of Alzheimer’s disease (AD) remains a critical challenge in medical imaging and computational neuroscience. Traditional diagnostic approaches, such as clinical assessments and neuroimaging, are often subjective and labor-intensive work. Recently, convolutional neural networks (CNNs) and handcrafted feature extraction techniques have shown promising results for automated AD classification. In order to improve AD detection accuracy, this study suggests a novel and efficient hybrid feature extraction method to address the accurate detection of Alzheimer’s disease (AD). This method combines deep feature representations taken from customized CNN with the Generalized Hadamard Difference (GHD) operator, a mathematical technique that is intended to capture subtle structural variations. This integrated approach leverages the complementary strengths of handcrafted and learned features to better characterize the complex patterns associated with AD progression. The publicly accessible OASIS-1 MRI dataset was employed to rigorously evaluate the proposed method, consisting of high-resolution T1-weighted brain images from cognitively normal and impaired subjects. Classification was performed using a Support Vector Machine (SVM) classifier, yielding an impressive overall accuracy of 99.50 %, surpassing many existing state-of-the-art methods. The results highlight that incorporating GHD with deep features improved the method’s ability to detect early and subtle manifestations of AD.
