This paper systematically explores the applications of image processing techniques in machined surface analysis, a critical area in industries like manufacturing, aerospace, automotive, and healthcare. It examines the integration of image processing in traditional Computer Numerical Control (CNC) machining and micromachining, focusing on its role in tool wear analysis, workpiece detection, automatic CNC programming, and defect inspection. With AI and machine learning advancements, these technologies enhance defect detection, surface texture analysis, predictive maintenance, and quality optimization. The paper also discusses future advancements in high resolutions, 3D imaging, augmented reality, and Industry 4.0, highlighting their impact on productivity, precision, and challenges such as data privacy. In conclusion, image processing remains vital to improving manufacturing efficiency and quality control.

Fish is important for human health due to its high nutritional value. However, it is prone to spoilage due to its structural characteristics. Traditional freshness assessment methods, such as visual inspection, are subjective and prone to inconsistency. This study proposes a novel, cost-effective hybrid methodology for automated three-level fish freshness classification (Day 1, Day 2, Day 3) by integrating single-wavelength laser reflectance data with deep learning-based image features. A comprehensive dataset was created by collecting visual and laser data from 130 mackerel specimens over three consecutive days under controlled conditions. Image features were extracted using four pre-trained CNN architectures and fused with laser features to form a unified representation. The combined features were classified using SVM, MLP, and RF algorithms. The experimental results demonstrated that the proposed multimodal approach significantly outperformed single-modality methods, achieving average classification accuracy of 88.44%. This work presents an original contribution by demonstrating, for the first time, the effectiveness of combining low-cost laser sensing and deep visual features for freshness prediction, with potential for real-time mobile deployment.

Medical professionals need methods that provide reliable information in diagnosing and monitoring neurological diseases. Among such methods, studies based on medical image analysis are essential among the active research topics in this field. Tumor segmentation is a popular area, especially with magnetic resonance imaging (MRI). Early diagnosis of tumours plays an essential role in the treatment process. This situation also increases the survival rate of the patients. Manually segmenting a tumour from MR images is a difficult and time-consuming task within the anatomical knowledge of medical professionals. This has necessitated the need for automatic segmentation methods. Convolutional neural networks (CNN), one of the deep learning methods that provide the most advanced results in the field of tumour segmentation, play an important role. This study, tumor segmentation was performed from brain and heart MR images using CNN-based U-Net and ResNet50 deep network architectures. In the segmentation process, their performance was tested using Dice, Sensitivity, PPV and Jaccard metrics. High performance levels were sequentially achieved using the U-Net network architecture on brain images, with success rates of approximately 98.47%, 98.1%, 98.85%, and 96.07%

The healthy operation of solar energy panels used for electricity production from solar energy is important in terms of the efficiency of the energy produced. In this study, the types of damage that affect energy production from solar energy panels, the effects of these damages on energy production and the methods of detecting the damages are discussed in detail. Damages in solar energy panels occur due to environmental, mechanical, thermal and electrical factors. These damages directly affect energy production negatively and reduce efficiency.The types of damages were examined under four main headings in the study. These are mechanical damages (micro cracks, delamination), thermal damages (hot spot formation, overheating), electrical damages (short circuit, disconnections) and environmental damages (dusting, effect of UV rays). Early detection of damages is important to prevent energy losses and extend the life of the panels. When the damage detection methods were examined, they were grouped under three main headings: Traditional methods, advanced technology-based methods and other methods.The findings obtained in the study were compiled and the difficulties encountered in the damage detection processes, as well as the inadequacies of traditional methods and the advantages and disadvantages of advanced technological methods based on artificial intelligence were revealed, and solution suggestions for damage detection processes were presented. This study provides an important guide for the development and application of damage detection methods.

Detection of epileptic seizures is important for early diagnosis and treatment. It is known that the behavioral patterns of the brain in electroencephalogram (EEG) signals have huge and complex fluctuations. Diagnosing epilepsy by analyzing signals are costly process. Various methods are used to classify epileptic seizures. However, the inadequacy of these approaches in classifying signals makes it difficult to diagnose epilepsy. Complex network science produces effective solutions for analyzing interrelated structures. Using methods based on complex network analysis, it is possible to EEG signals analyze the relationship between signals and perform a classification process. In this study proposes a novel approach for classifying epileptic seizures by utilizing complex network science. In addition, unlike the studies in the literature, classification processes were carried out with lower dimensional signals by using 1-s EEG signals instead of 23.6-s full-size EEG signals. Using the topological properties of the EEG signal converted into a complex network, the classification process has been performed with the Jaccard Index method. The success of the classification process with the Jaccard Index was evaluated using Accuracy, F1 Score, Recall, and K-Fold metrics. In the results obtained, the signals of individuals with epileptic seizures were separated with an accuracy rate of 98.15%.