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Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods...
Contexts in source publication
Context 1
... true positive rate (TPR) represents defective apples correctly classified as defective apples, true negative rate (TNR) is the non-defective apples correctly classified as non-defective apples, false positive rate (FPR) is the defective apples incorrectly classified as non-defective apples and false negative rate (FNR) are the non-defective apples incorrectly classified as defective apples. The average results for K-fold cross-validation are presented in Table 2. Following the K-fold cross-validation for the 2-fold experiment, a total number of 275 images from the defective class and 275 images from the non-defective class are divided into two equal parts for training and testing. ...
Context 2
... part consists of 137 images and 138 images for the defective and non-defective classes, respectively. In Table 2, the classification performances are led by the SPM method for all tested folds. For 2-fold experiment, the SPM method achieved 93.27% classification accuracy. ...
Context 3
... accuracy of all methods are increased with the increment number of folds as presented in Table 2. To obtained the most suitable classifier for all the methods, 10-fold cross-validation is applied and the obtained performances are compared. ...
Context 4
... the same experiment, the time taken during training and testing for all folds are also measured as presented in Table 2. From these results, the training time for all methods became longer as the number of folds increased. ...
Context 5
... the same experiment, the time taken during training and testing for all folds are also measured as presented in Table 2. From these results, the training time for all methods became longer as the number of folds increased. ...
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Citations
... Despite the SSD model able to achieve the highest F1 Score among the model tested as presented in Table 3 and Table 4, the reasons that reduce the performance for all the model tested are due to the small sample dataset utilized in the experiment. The model of the CNN approach requires a large number of images for training in order to obtain a desired result [22][23][24][25][26][27]. While in this research, a small number of vehicle dataset is use as a proof-of-concept to classify the types of vehicles. ...
The usage of automobiles in cities and metropolitan areas has increased drastically throughout the years and there is a need to monitor the flow of road traffic to improve the traffic congestion and safety. One of the best ways to monitor the traffic is using an artificial intelligence and machine learning. An automatic vehicle tracking system based on artificial intelligence and machine learning can offers capability to analyse the real-time traffic video data for the purpose of traffic surveillance. The computer vision is one of the subsets in machine learning that can train the computer to understand the visual data and perform specific tasks such as object detection and classification. A Vision-based system can be proposed to detect road accidents, predict traffic congestion and further road traffic analytics. This can improve the safety in transportation where it can recognize types of vehicles on the road, detecting road accidents, predicting the traffic congestion and further road traffic analytics. In the context of road traffic monitoring, the parameters of the traffic such as the type and number of vehicles that passes through must be recorded in order to gain valuable insights and make prediction such as the occurrence of traffic congestion. However, this requires reliable informative and accurate data as input for analytics. Therefore, in this research the deep learning model for vehicle classification is investigated to detect, classify types of vehicles and further predictive analytics. The vehicle classification is proposed based on Single Shot Detector (SSD) architecture model. The proposed model is tested on five different classes of vehicles with a total of 1263 images. Experimental results show that SSD model able to achieve 0.721 of precision, 0.741 of recall and 0.731 of F1 Score. Finally, the result show that the SSD model is more accurate among all the models for all the performance measure with the difference of more than 0.052 of precision, 0.706 of recall and 0.05 of F1 Score.
... A 10-fold cross-validation was employed, whereby the training data set was divided into 10 equally-sized folds, and the model was created using 90% of the data in each fold, with the remaining data used to evaluate the model's accuracy. The process was repeated 10 times, with each fold being used for one of the 10 training steps (20,21). The area under the receiver operating characteristic (ROC) curve, also known as area under the curve (AUC), was used as the primary accuracy metric during the grid search (22). ...
Background
The aim of this study is to develop a predictive model utilizing deep learning and machine learning techniques that will inform clinical decision-making by predicting the 1-year postoperative recovery of patients with lumbar disk herniation.
Methods
The clinical data of 470 inpatients who underwent tubular microdiscectomy (TMD) between January 2018 and January 2021 were retrospectively analyzed as variables. The dataset was randomly divided into a training set (n = 329) and a test set (n = 141) using a 10-fold cross-validation technique. Various deep learning and machine learning algorithms including Random Forests, Extreme Gradient Boosting, Support Vector Machines, Extra Trees, K-Nearest Neighbors, Logistic Regression, Light Gradient Boosting Machine, and MLP (Artificial Neural Networks) were employed to develop predictive models for the recovery of patients with lumbar disk herniation 1 year after surgery. The cure rate score of lumbar JOA score 1 year after TMD was used as an outcome indicator. The primary evaluation metric was the area under the receiver operating characteristic curve (AUC), with additional measures including decision curve analysis (DCA), accuracy, sensitivity, specificity, and others.
Results
The heat map of the correlation matrix revealed low inter-feature correlation. The predictive model employing both machine learning and deep learning algorithms was constructed using 15 variables after feature engineering. Among the eight algorithms utilized, the MLP algorithm demonstrated the best performance.
Conclusion
Our study findings demonstrate that the MLP algorithm provides superior predictive performance for the recovery of patients with lumbar disk herniation 1 year after surgery.
... At any stage of the production process, the data can be collected using sensors. The collected data is sent, shared, and traded to increase customer satisfaction and output [1]. ...
Smart manufacturing optimizes processes and marketing transactions, leading to more efficient manufacturing processes. Computational modeling is used to execute the improvement, which requires trustworthy and meaningful information as input. This paper uses a vision sensor for capturing accurate data. Hybrid techniques for vision-based classification and feature extraction methods were used. The ORB (Oriented FAST and Rotated BRIEF) and BRISK (Binary Robust Invariant Scalable Keypoints) feature recognition and characterization processes were chosen based on evaluating several feature recognition and characterization processes when applied to acoustic information recorded by an Apple dataset. After the feature extraction, a Deep CNN (DCNN) method is used for the classification process to achieve efficient accuracy. The vision-based algorithms classify the apple as damaged or not damaged, allowing for automated examination, classification, and analysis. To test the classifiers, 600 apple photos were obtained. There are 300 non-faulty apples and 300 damaged apples in the photographs. The faulty group encompasses a wide range of defective kinds and severities. The effectiveness of twofold, threefold, fourfold, fivefold, and tenfold detectors is evaluated. The Softmax classifiers were used in the study, achieving 98.84% classification accuracy for a tenfold dataset.
... Here, pre-trained CNN models are used. Ismail, Idris [42] classified [29] achieved 92% success in the detection of defective fruits with the CNN + SVM model. Buyukarikan and Ulker [16] classified images in a dataset containing different lighting conditions and physiological disorders with ratio of 96.11%. ...
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... Pre-trained CNN models were used in these studies. Ismail, Idris [48], CNN ? SVM achieved 98.15% success. ...
Physiological disorders in apples are due to post-harvest conditions. For this reason, automatic identification of physiological disorders is important in obtaining agricultural information. Image processing is one of the techniques that can help achieve the features of physiological disorders. Physiological disorders during image acquisition can be affected by the changes in brightness values created by different lighting conditions. This changes the results of the classification. In recent years, the convolutional neural network (CNN) has been a successful approach in automatically obtaining deep features from raw images in image classification problems. The study aims to classify physiological disorders using machine learning (ML) methods according to extracted deep features of the images under different lighting conditions. The data sets were created by acquired images (1080 images) and augmentation images (4320 images). Deep features were extracted using five popular pre-trained CNN models in these data sets, and these features were classified using five ML methods. The highest average accuracy was obtained with the VGG19(fc6) + SVM method in the data set-1 and data set-2 and were 96.11 and 96.09%, respectively. With this study, physiological disorders can be determined early, and needed precautions can be taken before and after harvest, not too late.
... A general SFDI experiment under the condition of two frequencies (e.g., 0 and 0.2 mm −1 ), three phases (e.g., 0, and ), and one wavelength takes about five to twenty seconds or even longer, which cannot meet the requirements of high real-time applications. Many efforts have thus been made to accelerate the speed, such as SSOP, which reduces the number of phase-images from three to one, improving the efficiency by approximately three times [89,[104][105][106][107][108][109][110][111]. Development of hardware configurations, like the use of singlepixel camera, instead of industrial CCD camera, could further speed up the optical property measurement using the SFDI [54]. ...
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... Partial least squares regression (PLS) was applied to predict the quality parameters of color space CIELAB, SSC and TP. [13] proposed a vision base apple classification for smart manufacturing. The proposed system was using vision sensors for data acquisition and differentiate the defective and non-defective apples. ...
Library reflects the diversity, character and expectation of our young generation before going into the society and make contribution for the country development. Due to the rapid technology change, the automated library is now becoming a trend to provide fast services. Old handling technique and maintenance are no longer efficient to support a good service to the client. During peak hours, clients need to wait in a long queue for just returning a book. Old technique of sorting process before shelving may cause repetitive
stress injuries to the librarian which will lower down the work efficiency. The objective of this research is to develop an intelligent book returning and sorting system using IoT to enhance the efficiency of the library system. In this research, the book returning, and sorting system was designed to sort the book using servo motor with the aid of infrared sensor. The RFID technology was implemented for data input as part of the book returning system. The validation RFID data will be done by Laravel GUI system. IoT system was used for communication between book returning system and GUI system. The feedback message from the GUI will be further used for sorting purpose. The performance of sorting system was evaluated by servo frequency test, servo action time test, servo positional test, DC motor test, DC motor duty cycle test, servo motor weight carrying test, DC motor weight carrying test and internet connection test. From the testing, the user was estimated to wait for sorting process in total of 8 seconds for returning in total the second book if using the old system technique. However, the proposed system allow the user to return the book in one shot
without waiting for the sorting process. For future enhancement, pneumatic cylinder and industrial grade of RFID sensor can be used for getting a higher performance efficiency and accuracy. For software part, enhance the login system by adding more admin users so that different authority power level can be set for different staff level in library management system.
... Smart manufacturing is the advancement in manufacturing process through the integration of computer control and various high level adaptability technologies to optimize productivity [1]. A huge volume, variety and velocity of data in smart manufacturing or referred to as big data, offers an opportunity not only for managing large amount of information, but also to improved diagnostics and prognostics capabilities [2,3]. ...
... A huge volume, variety and velocity of data in smart manufacturing or referred to as big data, offers an opportunity not only for managing large amount of information, but also to improved diagnostics and prognostics capabilities [2,3]. The analytics in the manufacturing process can shift from a reactionary to a predictive practice [1,4,5] by improving the existing capabilities such as product defect detection and supporting new capabilities for future planning and prediction [1,4,5]. In delivering high quality predictive solution for future planning, the data quality is the most important big data factor [2]. ...
... A huge volume, variety and velocity of data in smart manufacturing or referred to as big data, offers an opportunity not only for managing large amount of information, but also to improved diagnostics and prognostics capabilities [2,3]. The analytics in the manufacturing process can shift from a reactionary to a predictive practice [1,4,5] by improving the existing capabilities such as product defect detection and supporting new capabilities for future planning and prediction [1,4,5]. In delivering high quality predictive solution for future planning, the data quality is the most important big data factor [2]. ...
Smart manufacturing optimizes productivity with the integration of computer control and various high level adaptability technologies including the big data evolution. The evolution of big data offers optimization through data analytics as a predictive solution in future planning decision making. However, this requires accurate and reliable informative data as input for analytics. Therefore, in this paper, the fusion features for apple classification is investigated to classify between defective and non-defective apple for automatic inspection, sorting and further predictive analytics. The fusion features with Decision Tree classifier called Curvelet Wavelet-Gray Level Co-occurrence Matrix (CW-GLCM) is designed based on symmetrical pattern. The CW-GLCM is tested on two apple datasets namely NDDA and NDDAW with a total of 1110 apple images. Each dataset consists of a binary class of apple which are defective and non-defective. The NDDAW consists more low-quality region images. Experimental results show that CW-GLCM successfully classify 98.15% of NDDA dataset and 89.11% of NDDAW dataset. A lower classification accuracy is observed in other five existing image recognition methods especially on NDDAW dataset. Finally, the results show that CW-GLCM is more accurate among all the methods with the difference of more than 10.54% of classification accuracy.
Horticulture has been extensively studied and practiced for thousands of years. Horticulture is concerned with producing, processing, and merchandising techniques for fruits, vegetables, flowers, spices, mushrooms, medicinal, ornamental, and aromatic plants. The horticulture sector faces significant barriers, such as an infestation of various diseases and pests on the plants, inappropriate grading of products, poor quality control strategies, improper management of irrigation, and declination of soil fertility. As a result, the cultivators face huge crop and revenue losses. In most cases, layman cultivators suffer from adequate knowledge and expertise to handle these issues. To ensure sustained productivity, sufficient knowledge, and judgment of human experts are required in time. However, such experts are very scarce compared to the demands of the growing cultivation worldwide. This leads to the degradation of both the quantity and quality of horticultural products. To mitigate the lack of sufficient human experts, automated computer-based decision support systems using artificial intelligence techniques have been deployed in various horticulture sectors for precise decision-making. Since 1985, applications of several such systems based on artificial intelligence techniques have been attempted for horticultural crop improvement. This chapter briefly describes the basic structures of six popular artificial intelligence techniques and some remarkable applications in horticultural crop improvement.
The article provides information about the information-measuring system of the layout of the complex for sorting apples, which allows you to determine the maximum diameter of an apple when it moves along a roller conveyor. The control algorithm is based on the use of automated thresholding using the method of moments, as well as the Advanced Morphology operation. Studies have shown that the maximum diameter measurement error does not exceed 2 mm, and the influence of radial distortion is reduced to 0.17 % as a result of calibration.
