مجید ایرانپور مبارکه

This study aims to investigate the potential of chaotic optimization algorithms in improving performance compared to other optimization methods, focusing on determining the appropriate shape parameter of radial basis functions for solving partial differential equations. In this research, a two-stage process is employed where the Kansa method, based on meshless local techniques, is combined with the FCW method. In the first stage, the FCW algorithm is utilized to obtain the optimal shape parameter for radial basis functions, followed by the Kansa method in the second stage to estimate the Root Mean Square (RMS) error for approximate solutions. Numerical results indicate that approximately 95% of the results obtained from two partial differential equations using PSO and FCW algorithms are similar. These results demonstrate the effectiveness and efficiency of this approach in estimating appropriate shape parameters for solving differential equations.Originality/Value: This study confirms the importance of chaos-based optimization algorithms in solving partial differential equations, which can contribute to future research in this field.

Protein function prediction is one of the main challenges in bioinformatics, which has many applications. In recent years, many researches in this field have been used machine learning methods. In these methods, First, different features should be extracted from the protein sequence and classification should be done based on the extracted features. The feature extraction methods are based on the physical and chemical properties of the protein sequence. Therefore, extracting suitable features from protein sequence increases and improves the performance of machine learning methods. In this paper, usage of a new set of features based on Position-Specific Scoring Matrix (PSSM), Pseudo-Position Specific Scoring Matrix (PsePSSM), K-gram, Amino Acid Composition (AAC) and the new Term Frequency and Category Relevancy Factor (TFCRF) method, which has not been used in this application so far, is proposed to extract suitable features. In the PSSM method for protein BLAST searches, a scoring matrix is used, in which amino acid substitution scores are given separately for each position in a multi-sequence protein alignment. The PsePSSM feature is described by considering different ranking correlation factors along a protein sequenc to preserve information about the amino acid sequence. The normalized occurrence frequency of a certain number of amino acids in the protein is calculated by the ACC method. An K-gram is a set of K successive items in a protein that  include amino acid. In the TFCRF weighting method, in addition to paying attention to how these are distributed in different sequences, how these are distributed in different classes is also paid attention to.The features extracted using this method give machine learning models a good discriminating power between data in classes. In the next step, classification is done using the extracted features using the rotation forest method. This classifier is a successful ensemble method for a wide range of data mining applications. In this method, the feature space is changed through Principal Component Analysis (PCA), which increases the power of this classifier. The proposed method has been compared to different classifiers. The results show that the efficiency of the proposed method is much better than other state-of–the-art methods in this application.

The use of modern technologies for detecting and measuring pest population density can be an important step in facilitating the implementation of integrated pest management programs and achieving more precise and effective control. In this study, the deep learning technique and convolutional neural network with AlexNet architecture were used for the automatic detection and counting of the tomato leaf miner, Tuta absoluta (Myrick) (Lepidoptera: Gelechiidae), which is one of the key pests of tomato plants in Iran. To collect images of adult T. absoluta insects, 15 delta traps were installed in two hectares of tomato farms at the Campus of Agricultural and Natural Resources, Razi University. The Sony DSC-WX100 camera with an effective sensor resolution of 18 megapixels was used to capture the images. The performance of the convolutional neural network with the AlexNet architecture was evaluated using the parameters of average accuracy, accuracy, and recall. For counting performance, the linear regression curve and coefficient of determination were used. The average accuracy (98.0), accuracy (100), and recall (100) parameters indicate the high performance of the convolutional neural network in detecting the tomato leaf miner, and the coefficient of determination (0.98) indicates the network's high accuracy in counting this pest. Overall, the results demonstrate that the neural network can provide a practical solution for the accurate detection and counting of this pest on tomato plants using captured images

Detection of browser attacks is considered a serious challenge in today’s web applications. Man in the Browser (MitB) attack is an important type of these attacks that can lead to changes in web page contents, interference in network traffic, session hijacking, and user information theft by using Trojans. In this paper, an efficient tool for real-time detection of MitB attacks through dynamic analysis of web pages based on the description of attack patterns is presented. The advantage of the proposed tool is that it is not limited to identifying one or more specific attacks and the identification method code is not embedded in the tool, but the patterns of different attacks are specified separately. In order to evaluate the presented tool, two vulnerable web services provided by OWASP, which have a wide range of known vulnerabilities, were used along with the BeEF penetration test framework, and a set of MitB attacks were practically implemented and evaluated by the tool. The same tests were performed using three other similar tools and compared with the developed tool. In addition to the superiority of the presented tool in terms of the independence of attack descriptions from the tool itself, the results show that the accuracy and readability of its diagnosis are better than similar tools.

Handwritten word recognition (HWR) is very important in document analysis and retrieval. In this paper, an off-line handwritten recognition system for Persian manuscript is introduced. For feature extraction, SIFT descriptors extracted densely from the block of word image and enriched by appending the normalized x and y coordinates and the scale they were extracted at. Linear discriminate analysis (LDA) is used for feature reduction. All words in the dictionary were hierarchically clustered by ISOCLUSE algorithm. In order to recognize the word images, multiple-class and two-class SVM classifiers methods were used. The experimental results showed a better performance in terms of speed and precision of two-class SVM method on the Iranshahr data set. The accuracy of proposed system by select 5 top cluster is shown 93.37% by 76.65% reduction of lexicon.

Persian specific Attribute for Handwritten Image Word spotting and recognition by Label-EmbeddingOCR methods cannot handle degraded printed documents and handwritten documents, Word spotting is an alternative way to search for query words. Nowadays, efficient Attribute-based Content Based Image Retrieval (CBIR) are developed for Image documents. In this paper new attributes, based on Persian writing Style are introduced for Persian word spotting. We compared and analyze our methods against the other state of the art methods. In addition, the proposed method can handle both Image and string queries, also it can be employed as a word recognition system. Experiments on two standard datasets, Farsa and Iranshahr, shows reasonable results.