Maintaining the well-being of individuals is greatly influenced by a healthy lifestyle and balanced diet. The identification and Segmentation of food items can be improved by utilizing a mobile-based system in this era of rapid lifestyle changes and technology. This article introduces a novel system that, upon receiving input images, detects and Segmentation the food items within the images. The system utilizes deep learning techniques and models, employing the YOLO algorithm. By incorporating regression-based simple methods, the system achieves the capability to detect and categorize food items in a single pass through the network, aiming to enhance accuracy and speed in the detection process. YOLOv7 was employed for food detection and YOLOv5, YOLOv7, and YOLOv8 was utilized for image Segmentation. Based on the results, the accuracy, recall, and average precision values for YOLOv7 were 0.844, 0.924, and 0.932, respectively. Furthermore, the Instance Segmentation performance of YOLOv7 outperformed YOLOv5 and YOLOv8, with precision, recall, and mean average precision values of 0.959, 0.943, and 0.906, respectively. These findings underscore the high accuracy in detecting Iranian foods and the remarkable speed and precision in food image Segmentation attainable through advanced deep-learning algorithms. Consequently, this study establishes that accurate detection of Iranian foods can be accomplished through the utilization of sophisticated deep-learning techniques. This research focuses on promoting a healthy lifestyle through intelligent technology and novel deep learning algorithms in Iran.

Revolution is an extraordinary phenomenon in the development trend of societies. Complexity, multi-dimentionality and multi-layerdness in concept and diversity in its origin, process and Instance are among its features. Another feature is that there is no consensus and agreement over a specific and acceptable definition for all schools of thought and approaches.Reviewing these characteristics in this article, the author tries to achieve a comprehensive definition from educational point of view so that when they encounter definitions, students and researchers can have a primary and relatively clear image of the concept. Finally an image of revolution model will be presented from Marxist and Islamic viewpoint.

It is possible to commission of quasi-intentional felony, the subject of three clauses of article 291, by omission with this condition that the most prominent example of felonies is to be committed by the omission in C section (falseness) of this article. In clause (a) and (b) of this article, the behavior is not effective and can consist of action and omission. The most challenging part of this research is the possibility of commission of simple mistake felony by omission. The commission of simple mistake felony, the subject of clause (a) of Article 292 is not possible by omission, since in this assumption, or the perpetrator is not responsible for the lack of the condition of ability or, in the case of liability, the crime is intentional or quasi-intentional. In clause (b) of this article, if the minor is undertaking in accordance with Article 85 of the Non-Litigious Matters Act, and a felony is committed by commission, this is simple mistake felony. Finally, although the commission of simple mistake felony, the subject of clause (c) of Article 292, is rare by omission, but it cannot be falsified.

Background and Objectives: Cadastral boundary detection deals with locating the boundary of the ownership and use of land. Recently, there has been high demand for accelerating and improving the automatic detection of cadastral mapping. As this problem is in its starting point, there are few researches using deep learning algorithms. Methods: In this paper, we develop an algorithm with a Mask R-CNN core followed with geometric post-processing methods that improve the quality of the output. Many researches use classification or semantic Segmentation but our algorithm employs Instance Segmentation. Our algorithm includes two parts, each of which consists of a few phases. In the first part, we use Mask R-CNN with the backbone of a pre-trained ResNet-50 on the ImageNet dataset. In the second part, we apply three geometric post-processing methods to the output of the first part to get better overall output. Here, we also use computational geometry to introduce a new method for simplifying lines which we call pocket-based simplification algorithm.Results: We used 3 google map images with sizes 4963 × 2819, 3999 × 3999, and 5520 × 3776 pixels. And divide them to overlapping and non-overlapping 400×400 patches used for training the algorithm. Then we tested it on a google map image from Famenin region in Iran. To evaluate the performance of our algorithm, we use popular metrics Recall, Precision, and F-score. The highest Recall is 95%, which also maintains a high precision of 72%. This results in an F-score of 82%.Conclusion: The idea of semantic Segmentation to derive boundary of regions, is new. We used Mask R-CNN as the core of our algorithm, that is known as a very suitable tools for semantic Segmentation. Our algorithm performs geometric post-process improves the f-score by almost 10 percent. The scores for a region in Iran containing many small farms is very good.

This paper focuses on the problem of ensemble classification for text-independent speaker verification. Ensemble classification is an efficient method to improve the performance of the classification system. This method gains the advantage of a set of expert classifiers. A speaker verification system gets an input utterance and an identity claim, then verifies the claim in terms of a matching score. This score determines the resemblance of the input utterance and pre-enrolled target speakers. Since there is a variety of information in a speech signal, state-of-the-art speaker verification systems use a set of complementary classifiers to provide a reliable decision about the verification. Such a system receives some scores as input and takes a binary decision: accept or reject the claimed identity. Most of the recent studies on the classifier fusion for speaker verification used a weighted linear combination of the base classifiers. The corresponding weights are estimated using logistic regression. Additional researches have been performed on ensemble classification by adding different regularization terms to the logistic regression formulae. However, there are missing points in this type of ensemble classification, which are the correlation of the base classifiers and the superiority of some base classifiers for each test Instance. We address both problems, by an Instance based classifier ensemble selection and weight determination method. Our extensive studies on NIST 2004 speaker recognition evaluation (SRE) corpus in terms of EER, min DCF and min CLLR show the effectiveness of the proposed method.