Naaman Omar Yaseen

The increasing number of vehicles in all over the world day by day, have made traffic control becomes a serious and major problem. Sometimes, it becomes difficult to detect the plate license number of a vehicle that violates traffic rules. In this paper, an efficient model is proposed to detect and locate number license plate of a vehicle that is given in color images. The model is constructed by using different features instead of single feature to improve its performance based on multi descriptors (texture features). The work use multi- boosting model (AdaBoost) based on HOG descriptors, which precisely localize the ROI of the given vehicle images. A new North Iraq Vehicle Images (NI-VI) dataset, which is used to test the proposed model, is introduced. The license number plates of North Iraq uses Arabic font. All images of this dataset, are taken under various circumstances and different weather conditions to simulate all realistic examples about vehicles. The developed model produced 89.66% accuracy score. ... Read More

An automatic number plate detection (ANPD) and automatic number plate recognition (ANPR) systems are robust technologies that are used for detecting and recognizing the number plates of vehicles. In this paper, a new dataset, which is called North Iraq-Vehicle Images (NI-VI) of three provinces (Duhok, Erbil, and Sulaimani) for vehicle images, is presented. There are 1500 images in this dataset. They were gathered from real-time by using handled cameras to form a realistic dataset of the vehicle images. The main contribution of this work is the creation of a new dataset for license plate of vehicles in north Iraq with Arabic fonts in different and difficult conditions. The dataset includes three categories of images: rotated, scaled and translated images. The resolutions of images are 4288 x 2848 and 5184 x 3456. Moreover, some images created for bad weather conditions, such as snowy, dusty and low lighting. Some dirty plate images also considered in the dataset. The purpose of introducing this dataset is to provide and produce a realistic dataset for ANPD and as well as for ANPR systems. ... Read More

Forest fires are a serious environmental concern that causes economic and ecological harm as well as puts human lives in danger. Controlling such a condition necessitates quick identification. One option is to employ artificial intelligence (AI) techniques based on some measurements, such as those supplied by meteorological stations. Meteorological measurements namely temperature, relative humidity, rain, and wind are known to impact forest fires, and numerous fire indices, such as the Forest Fire Weather Index (FWI), rely on this information. In this paper, a deep learning approach namely the long short-term memory (LSTM) based regression method is used for efficient prediction of the forest fires. The LSTM approach is a recurrent neural network (RNN) that has become popular recently in the field of machine learning. A dataset that contains 12 features and 536 instances is used in the experimental works. The dataset is available in the UCI machine repository. The hold-out cross-validation method is used in the experiments and various metrics are used to evaluate the accuracy of the proposed model achievements. The results show that the proposed method produces reasonable predictions and outperforms traditional machine learning approaches. ... Read More

K-means clustering is known to be the most traditional approach in machine learning. It's been put to a lot of different uses. However, it has difficulty with initialization and performs poorly for non-linear clusters. Several approaches have been offered in the literature to circumvent these restrictions. Kernel K-means (KK-M) is a type of K-means that falls under this group. In this paper, a two-stepped approach is developed to increase the clustering performance of the K-means algorithm. A transformation procedure is applied in the first step where the low-dimensional input space is transferred to a high-dimensional feature space. To this end, the hidden layer of a Radial basis function (RBF) network is used. The typical K-means method is used in the second part of our approach. We offer experimental results comparing the KK-M on simulated data sets to assess the correctness of the suggested approach. The results of the experiments show the efficiency of the proposed method. The clustering accuracy attained is higher than that of the KK-M algorithm. We also applied the proposed clustering algorithm on image segmentation application. A series of segmentation results were given accordingly. ... Read More

In this study, a method that recognizes the sound of hail is proposed for a system designed to minimize the damage caused by hail to vehicles. The designed system uses signal processing and machine learning. The sounds received by a microphone in the vehicle were converted into frequency space and the kernel density estimation of the frequency values occurring in a certain time interval (approximately 2 seconds) was obtained. This is based on the prediction that the histogram of the frequency of hail falling on the car can have a defining characteristic. In this context, it has been designed to create a two-class machine learning problem, including full sound samples and ambient sound samples. A solution to the machine learning problem was sought with the Support Vector Machines (SVM) algorithm. The SVM algorithm was chosen due to its simplicity and fast working dynamics. While learning is offline in the SVM algorithm, testing is done online. Related software was implemented using MATLAB. In experimental studies, we collected a dataset where almost 500 hail sound segments were used and similarly 400 ambient sound segments were collected. A hold out cross validation approach with various split ratio values are used. It has been seen that the proposed method predicts hail sounds with 92.22% accuracy when the hold out cross validation ration is 90% and 10%. ... Read More