Individuals across different industries, including but not limited to agriculture, drones, pharmaceuticals and manufacturing, are increasingly using thermal cameras to achieve various safety and security goals. This widespread adoption is made possible by advancements in thermal imaging sensor technology. The current literature provides an in-depth exploration of thermography camera applications for detecting faults in sectors such as fire protection, manufacturing, aerospace, automotive, non-destructive testing and structural material industries. The current discussion builds on previous studies, emphasising the effectiveness of thermography cameras in distinguishing undetectable defects by the human eye. Various methods for defect

HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023

For several applications, it is very important to have an edge detection technique matching human visual contour perception and less sensitive to noise. The edge detection algorithm describes in this paper based on the results obtained by Maximum a posteriori (MAP) and Maximum Entropy (ME) deblurring algorithms. The technique makes a trade-off between sharpening and smoothing the noisy image. One of the advantages of the described algorithm is less sensitive to noise than that given by Marr and Geuen techniques that considered to be the best edge detection algorithms in terms of matching human visual contour perception.

Pavement crack and pothole identification are important tasks in transportation maintenance and road safety. This study offers a novel technique for automatic asphalt pavement crack and pothole detection which is based on image processing. Different types of cracks (transverse, longitudinal, alligator-type, and potholes) can be identified with such techniques. The goal of this research is to evaluate road surface damage by extracting cracks and potholes, categorizing them from images and videos, and comparing the manual and the automated methods. The proposed method was tested on 50 images. The results obtained from image processing showed that the proposed method can detect cracks and potholes and identify their severity levels wit

This study was conducted at the College of Education for Pure Sciences (Ibn Al-Haitham), University of Baghdad. The aim of this study was to isolate and diagnose fungi from fish feedstuff samples, and also detection of aflatoxin B1 and ochratoxin A in fish muscles and feedstuffs. Randomly, the samples were collected from some fish farms from Baghdad, Babil, Wasit, Anbar, and Salah al-Din provinces. This study included the collection of 35 feedstuff samples and 70 fish muscle samples, and each of the two fish samples fed on one sample of the feedstuff. The results showed the presence of several genera of different fungi including Aspergillus spp, Mucor spp., Penicillium spp., Yeast spp., Fusarium spp., Rhizopus spp., Scopiolariopsis spp., Ep

This study aims to analyze the spectral properties of plasma produced from rice husk(Rh) using the laser breakdown spectroscopy (LIBS) method. The plasma generation process used the fundamental harmonic (1064 nm) of a Q-switched Nd:YAG laser. Yttrium aluminum garnet (YAG) is a man-made crystalline material. The laser fired pulses with a duration of 10 ns and a repetition rate of 6 Hz. Thus, the energy outputs achieved were 50–200 mJ at the wavelength of 1064 (nm). The silica content in the rice hulls was verified using an XRF measurement, which revealed the presence of silica in the rice hulls in a high percentage. Precise beam focusing was achieved by focusing the laser on the target material. This target material is placed with

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection