Software defects detection is one of the most important challenges of software development and it is the most prohibitive process in software development. The early detection of fault-prone modules helps software project managers to allocate the limited cost, time, and effort of developers for testing the defect-prone modules more intensively. In this paper, according to the importance of software defects detection, a method based on fuzzy sets and evolutionary algorithms is proposed. Due to the imbalanced nature of software defect detection datasets, benefits of fuzzy clustering algorithms were used to data sampling and more attention to the minority class. This method is a combined algorithm which, firstly has used fuzzy c-mean clustering as weighted bootstrap sampling. Weight of data (their membership’ s degrees) increases for minority class. In the next step, the subtractive clustering algorithm is applied to produce the classifier which was trained by produced data in the previous step. The binary genetic algorithm was utilized to select appropriate features. The results and also comparisons with eight popular methods in software defect detection literature, show an acceptable performance of the proposed method. The experiments were performed on ten real-world datasets with a wide range of data sizes and imbalance rates. Also T-test is used as the statistical significance test for pair wise comparison of our proposed method against the others. The final results of T-test are shown in tables for three performance measures (G-mean, AUC and Balanced) over various datasets. (As the obtained results apparently show our proposed method has the ability to improve three aforementioned performance criteria simultaneously). Some methods just have improved the G-mean measure while the AUC and Balance criteria have lower values than the others. Securing a high level of three performance measures simultaneously illustrates the ability of our proposed algorithm for handling the imbalance problem of software defects detection datasets.

In this paper, two kinds of defects in Golden Delicious apples are recognized: bruise and russet. Russet is divided to two classes: russet in stem-end and russet out of stem-end. Apples are graded into three classes I, II and rejected, according to European standard. To grade the apples, it is necessary to classify apple images into six classes: stem, calyx, bruise, russet in stem-end, russet out of stem-end and healthy. In this method, after pixel-based classification based on RGB color features by a perception neural network, correction in classification and stem detection is made. Hue and saturation features are used to correct the image regions classified to bruise. The correction of regions classified to calyx, russet in stem-end and russet out of stem-end is made based on the distance from the gravity center of the stem to the gravity center of each region. This paper presents a new method for defect classification and sub classification of russet to two classes, russet in stem-end and russet out of stem-end. Experimental results of the proposed algorithm show that the correct grading rate of 120 apple images is 81.66%. The grading errors result from misdetection of stem and errors in defect detection.

Ensuring the production of non-defect high-quality tires is an essential part of the tire industry. X-ray inspection is one of the best methods to detect tire defects. In this paper, a new approach has been presented for detecting tire defects in X-ray images based on an entropy filter, the extraction of texture properties of patches by Local Binary Pattern, and, finally, the classification of defects using the Support Vector Machine method. In the proposed method, an entropy filter was first applied to the input. The parts of the image with different patterns were then selected as candidate regions and these regions were classified by the patch classifier. All the defects were detected and classified and, finally, the efficiency of the algorithm was evaluated. By applying this algorithm to the dataset the best performance was obtained by the LBP descriptor and the linear SVM classifier with 98\% defect location accuracy and 97\% defect detection accuracy were achieved. In order to analyze the performance, used the deep model as a classifier, thus demonstrating that the deep model has a high capability for learning complex patterns. This proposed method is sensitive to local texture and could well describe texture information, which is appropriate for most kinds of tire defects.

This paper presents a new methodology for the automated inspection of pipes. Standard inspection systems are based on closed-circuit television cameras which are mounted on remotely controlled robots and connected to remote video recording devices. The main problems of such camera-based inspection systems are: 1) the lack of visibility in the interior of the pipes and 2) the poor quality of the obtained images because of difficult lighting conditions. The focus of this research is the automated detection and location of defects in the internal surface of pipes.The proposed optical system is an assembly of a CCD camera and a laser diode to create a ring-shaped pattern. The camera obtains images of the light projections on the pipe wall. A novel method for extracting and analyzing intensity variations in the obtained images is described. The image data analysis is based on image processing algorithms. Finally, an image of the pipe wall is generated by extracting the intensity information existing in the pipe pictures. defects and anomalies can be detected using this extracted image.

Given the significant role of the railway sector in transportation, railway managers and operators place great importance on traffic and maintenance costs. While existing track wayside monitoring systems can detect geometric defects in train wheels, like flats, they do not provide a severity assessment. To address this limitation, the WAY4SafeRail project aims to enhance rail safety by assessing the condition of train wheels. As an initial step in employing Artificial Intelligence Techniques, this paper presents a portion of the research conducted within the WAY4SafeRail project, specifically focusing on numerical simulations of wheel defects, in particular wheel flats. The proposed methodology has demonstrated its reliability and cost-effectiveness in identifying wheel defects.

Introduction: We aimed to compare the effectiveness of wideband absorbance in detecting ossicular chain discontinuity with intraoperative findings. Materials and Methods: In this study, 58 ears from 38 patients with chronic otitis media (COM) were included. Twenty-six ears with perforation and intact ossicular chain were determined as Group 1, 12 ears with perforation and ossicular chain defects were determined as Group 2, and 20 ears with normal hearing and intact tympanic membrane were determined as Group 3. The comparison of the groups was made considering the static (non-pressure) absorbance analysis performed using wideband tympanometry. Results: When perforation sites were evaluated in Group 1 and Group 2,there were 12 anterior perforations, 7 posterior perforations, and 19 subtotal perforations. Air conduction thresholds in Group 2 were significantly (P<0. 05) higher than in Group 1, as expected in pure tone audiometry. When wideband absorbance (WBA) measurements were evaluated in all 3 groups, no significant difference (P>0. 05) was found between the frequencies 226 to 1000 Hz. WBA measurements at 8 frequencies between 1888-2311 Hz in Group 1 were significantly lower than Group 3 (P<0. 05). WBA measurements at 4 frequencies between 3462-3886 Hz frequencies in Group 2 were significantly lower than Group 1 (P<0. 05). Conclusions: Our findings concluded that a significant decrease in absorbance values in the narrow frequency range may be valuable in predicting ossicular chain defects.