محمد حسن عبدالأمير الميالي

Cross-Language Plagiarism Detection (CLPD)is used to automatically identify and extract plagiarism among documents in different languages.The main challenge of cross-languageplagiarism detection is the difference of text languages, where the original source can be analysed and translated, and plagiarism can be detected automatically by comparing suspected text with the original text. This paper proposes an Arabic-English cross-language plagiarism detection method,to automatically detect the semantic relatedness between the words of two suspect targeted files.The proposed method consists of four phases. The first phase is a pre-processing phase,the second involves key phrase extraction and translation, the third phase used plagiarism detection techniques and the fourth phase is the classification process, which using Linear Logistic Regression (LLR). The evaluation process is created using precision and recall measurements of a dataset consisting of Wikipedia articles. The experimental resultsachieved96% precision, 85% recall and 90.16% F-measure. The results show that the LLRalgorithm can be used effectively to detect Arabic-English cross-language plagiarism.

Active Appearance Model (AAM) is one of the most popular techniques that extract features by precise modelling of human faces under various physical and environmental circumstances. In such active appearance model, fitting the model with original image is a challenging task. State of the art shows some of the optimization methods that are applicable to resolve the optimization problem. This encourages us to review the recent improvements of AAM fitting based on optimization methods. This study provides an up-to-date review of researches as regards to AAM optimization; studies are reviewed and discussed in four different sections. This review evidently explores the limitations of the previous works to facilitate better understanding different kind of approaches. The aim of this study is to help as a guide for further studies.

Recent rapid growth in the demand for technology and image investigation in many applications, such as image retrieval systems and Visual Object Categorization (VOC), effective management of these applications has become crucial. Computer vision and its various applications are a primary focus of research. Content-based image retrieval is considered an extremely challenging issue and has remained an open research area. Obviously, the main challenge associated with this kind of research is the gap between the low-level features and the richness of the semantic concept of the human mind. This problem is called the semantic gap. Several methods have been proposed to increase the performance of the system and reduce the semantic gap. These proposed techniques make use of either global or local features or a combination of both global and local features on one side and the visual content and keyword-based retrieval on the other side. However, the aim of this study is to provide a constructive critique of the algorithms used in extracting the low-level features, either globally or locally or as a combination of both. In addition, it identifies the factors that can affect the low-level features that lead to the semantic gap. As well as, proposed a new framework to improve the Gabor filter and the edge histogram limitations. Finally, recommendations are made for the choice of the descriptors used to describe the low-level features, both locally and globally, depending on the area of limitations or drawbacks of the previous state-of-the-art research.

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

Face recognition has recently received significant attention, especially during the past few years. Many face recognition techniques were developed such as PSO-SVM and LDA-SVM However, inefficient features in the face recognition may lead to inadequate in the recognition results. Hence, a new face recognition system based on Genetic Algorithm and FFBNN technique is proposed. Our proposed face recognition system initially performs the feature extraction and these optimal features are promoted to the recognition process. In the feature extraction, the optimal features are extracted from the face image database by Genetic Algorithm (GA) with FFBNN and the computed optimal features are given to the FFBNN technique to carry out the training and testing process. The optimal features from the feature database are fed to the FFBNN for accomplishing the training process. The well trained FFBNN with the optimal features provide the recognition result. The optimal features in FFBNN by GA efficiently perform the face recognition process. The human face dataset called YALE is utilized to analyze the performance of our proposed GA-FFNN technique and also this GA-FFBNN is compared with standard SVM and PSO-SVM techniques.

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

A face recognition technique is one of the common computer applications that automatically identifies or authenticates a person from a digital image or a video frame in a video source. This face recognition procedure is done by matching the selected face features from the image and the face database. Several face recognition techniques recognize the faces by extracting features from an image of the subject's face. The main challenges in face recognition techniques are to extract distinctive features that are not affected by external variations. Active Appearance Model (AAM) is one of the popular solutions that able to extract face features by precise modelling of human faces under various physical and environmental circumstances. In active appearance model approach, fitting the model with target image is a challenging task. The previous works shows that optimization approach is appropriate to solve this problem. Nevertheless, another problem is applying the optimization itself. In this paper, a face recognition technique based on AAM is proposed. In the proposed technique, the fitting problem of AAM is resolved by presenting ABC algorithm. The human face dataset called CASIA is utilized to analyse the performance of the proposed.

One of the most recently developed face recognition technique has utilized PSO-SVM, this method lacks in the initial phase of the PSO technique. That is in PSO; initially the populations are generated in random manner. Due to this random process, the population results may also be in random. Thus, it is not certain that this method will produce precise result. Hence to avoid this drawback, a modified face recognition method is proposed in this paper. Here, a new face recognition method based on Opposition based PSO with SVM (OPSO-SVM) is introduced. To accomplish the face recognition with our proposed OPSO-SVM, initially feature extraction process is carried out on the image database. In the feature extraction process, the efficient features are extracted and then given to the SVM training and testing process. In OPSO, the populations are generated in two ways: one is random population as same as the normal PSO technique and the other is opposition population, which is based on the random population values. The optimized parameters in SVM by OPSO efficiently perform the face recognition process. Two human face databases FERET and YALE are utilized to analyze the performance of our proposed OPSO-SVM technique and also this OPSO-SVM is compared with PSO-SVM and standard SVM techniques.

No one can deny the fact that face recognition systems, as one of the most adaptable systems in biometric facial recognition, has interestingly been one of the fields that draw researchers’ attention to enhance the efficiency and efficacy of biometric applications. These researchers have mainly focused on the existed limitations such as extracting unique and differentiable features that are not disturbed by pose, as well as illuminations and other environmental and physical variations. As such, using an Active Appearance Model (AAM) is known to be a solution to extract features by precise modeling of human faces under various physical and environmental circumstances. This research paper tries to draw attention to using AAM based 1 to N face recognition system which the performance of such system can be seen in the experimental results.