میترا میرزارضایی

The process of architectural thinking, traditions, methods, and the vast body of architectural knowledge, similar to other artistic fields, currently stands at a critical juncture in its history. The emergence of Artificial Intelligence (AI) in architecture over the past seventy years signifies a paradigm shift, resulting in significant transformations within the field. Despite addressing factors that have hindered the acceleration of such remarkable change, the present study evaluates the progression and interaction of architectural developments with AI. One approach to investigating AI's impact on the world of architecture involves exploring the cultural, technical, and historical realms. The primary objective of this study was to demonstrate the adaptation of new AI-related findings within architecture while considering their historical developments from 1920 to 2023. The study employed a qualitative method, conducting a historical research based on a comparative analysis of architectural and AI achievements in ten-year periods. The findings suggest that the relationship between architecture and technology has grown substantially in parallel with the evolution of AI. The latest advancements in AI have not only influenced the design process but have also significantly affected the broader field of practice and thought. This adaptation can be categorized into four periods: architecture aided by standardization, architecture utilizing computers, architecture with the assistance of parameters, and architecture through the application of AI.

This study presents an approach for the automatic spatial arrangements in creating more economical buildings. More than 60 years of studies in the field of production of automatic spatial layouts have proved that architectural layouts are strongly affecting the course of such benefits; nevertheless, they mainly examined the subject mathematically. The purpose of this research was to provide a new category in the production of architectural layouts while it investigated the related applications. In addition, it compared the existing approaches and methods. Finally, the study introduced a model for automatic generation of spatial arrangements.

From out of 105 reliable national and international databases, 34 studies on the production of spatial arrangements were selected and analyzed using the content analysis method.

The results indicated that the production of automatic spatial layouts could be organized in six approaches from the perspective of problem representation approaches. Additionally, the benefits and applications of each approach examined based on qualitative criteria.

At the same time that a general model was provided by the study, the automatic spatial architectural layout design was also established in three different methods of part to whole and whole to part relationships along with the principle of expertise and its applications.

Domain adaptation has become an important issue today. A high percentage of data processing domain adaptation is done with a significant percentage of studies related to deep learning. Traditional methods often ignore the distance between the intra-class in source domain and target domain. As a result, models can be sensitive to outliers and noisy data, additionally increasing the negative transfer in the model. This method applied GAN to extract appropriate features and then used Fuzzy c-means to cluster train datasets in the target domain. Finally, based on the WMMD metric and CNN, the model estimates the final label data. Five real datasets are selected to generate eight transfer tasks. The results show that the superiority of the proposed model lies in transferring more knowledge from the source domain to the target domain. In this approach, firstly based on GAN extracting features from source domains and the target domain (without labels), then label estimation by Fuzzy c-means clustering, finding the center of Fuzzy c-means on target domain data, new data points with labels in target domain as a new input to feature extraction module and regenerate features by GAN based on new pseudo labels. Afterward, we apply WMMD metrics based on CNN to ultimately assign labels for the target domain. Consequently, classification tasks have been done. Empirical results on various benchmark datasets showcase the exceptional performance of the proposed method compared to state-of-the-art DA approaches, validating the proposed Deep-Learning Unsupervised Domain Adaptation approach efficacy. Overall, the approach shows potential for advancing domain adaptation research by offering an efficient and resilient approach for addressing domain shifts in real-world applications. Experimental results on visual object recognition and a digit dataset reveal that the proposed algorithm is robust, flexible, and significantly superior regarding accuracy compared to the baseline DA approaches. Based on the three and combined digit datasets, 1.7% and 2.4% accuracy improvement are achieved, respectively, compared to the best baseline DA approach results. In this research, we addressed the challenging issues of outlier and negative transfer in the context of domain adaptation. Despite significant progress in domain adaptation techniques, outliers and negative transfer instances continue to hinder models' generalization performance across different domains. Based on DNNs and the WMMD metric, our proposed method was designed to mitigate these issues and effectively enhance knowledge transfer between domains.

Recommender systems are systems that, over time, learn what product(s) or item(s) each person or customer is (are) likely to like and recommend it (them) to him/her. These systems often operate based on similar behaviors from other (possibly similar) people. Finding similar people is generally a highly time-consuming process due to the large number of users and inaccurate due to the lack of information. For this reason, some methods have resorted to increasing speed. On the other hand, some other methods have added additional information so that they can increase the accuracy of finding similar or neighboring users. Some others have resorted to hybrid methods. Recently, by the use of basic clustering methods, which is based on finding the most similar neighbors with the help of users’ clustering, as well as by using basic content analysis methods and sometimes adding ontology to these methods, researchers have been able to take the advantage of these methods in order to solve some of the above challenges acceptably. In the proposed hybrid recommender system, we have used a two-stage system in which, in the first stage, two models of predictions are made, then in the second stage, by a combining component, the results of the first two parts are combined and the obtained results are given to us as the final results of the system. In the first part, a system based on imputation of missing values fills in the blanks in the scoring matrix. For this end, among the methods of the missing data imputation, we designed a method that was compatible with filling the data set in very sparse conditions, and then generalized it to our own method. In this regard, we have proposed a method based on the grey distance clustering. In the second part, which itself is a hybrid ontology-based recommender system, we first extract the information of each item with the help of a web crawler, then based on a basic article, we produce our own limited ontology, and after that we apply our proposed method. Then, with the help of a proposed method, we improve the ontology structure, thus increasing the accuracy of measuring semantic similarity between the items and users in later stages, and significantly improving the effectiveness of the created recommendations. It should be noted that this ontology is not comprehensive. Finally, we measure the similarity of item-items, user-users, and user-items using an innovative basic ontology similarity measurement method. By the use of this similarity matrix, we cluster users and items, and then store similar users and items as a new feature in the user/item profile for each user/item. This will help us speed up the process of looking for similar users and similar items in the future. In fact, based on this feature, we have increased the speed of the whole work. Since we have set our goal to build a system that makes a balance between the two criteria of accuracy and speed, we use these two criteria to evaluate the proposed system using a real data set. The results of comparing our proposed method with some up-to-date similar methods presented in this field (using the same data set) implies that our method is slower than fast methods, although it is more accurate than them. These results also suggest that the proposed method is faster than accurate methods and its quality is more competitive or even better than them.

Nowadays, we are witnessing financial markets becoming more competitive, and banks are facing many challenges to attract more deposits from depositors and increase their fee income. Meanwhile, many banks use performance-based incentive plans to encourage their employees to achieve their short-term goals. In the meantime, fairness in the payment of bonuses is one of the important challenges of banks, because not paying attention to this issue can become a factor that destroys the motivation among employees and prevents the bank from achieving its short-term and mid-term goals. This article is trying to tackle the problem of optimizing the coefficients of branch performance evaluation indicators based on their business environment in one of the state banks of Iran. In this article, a two-objective genetic algorithm is proposed to solve the problem. This article is comprised of four main sections. The first section is dedicated to the problem definition which is what is our meaning of optimizing the importance coefficients of branches based on the business environment. The second section is about our proposed solution for the defined problem. In the third section, we are comparing the performance of the proposed two-objective genetic algorithm on the defined problem with the performance of four well-known multi-objective algorithms including NSGAII, SPEAII, PESAII, and MOEA/D. And finally, the set of ZDT problems which is a standard set of multi-objective problems is taken into account for evaluating the general performance of the proposed algorithm comparing four well-known multi-objective algorithms. Our proposed solution for solving the problem of optimizing branch performance coefficients includes two main steps. First, identifying the business environment of the branches and second, optimizing the coefficients with the proposed two-objective genetic algorithm. In the first step, the k-means clustering algorithm is applied to cluster branches with similar business environments. In the second step, to optimize the coefficients, it is necessary to specify the fitness functions. The defined problem is a two-objective problem, the first objective is to minimize the deviation of the real performance of the branches from the expected performance of them, and the second objective is to minimize the deviation of the coefficients from the coefficients determined by the experts. To solve this two-objective problem, a two-objective genetic algorithm is proposed. In this article, two approaches are adopted to compare the proposed solution performance. In the first stage, the results of applying the proposed two-objective genetic algorithm have been compared with the results of applying four well-known multi-objective genetic algorithms on the problem of optimizing the coefficients. The results of this comparison show that the proposed algorithm has outperformed the other compared methods based on the S indicator and run time, and it is also ranked second after the NSGAII algorithm in terms of the HV indicator. Finally, for evaluating the performance of the proposed algorithm with other well-known methods, the set of ZDT problems including ZDT1, ZDT2, ZDT3, ZDT4, and ZDT6 has also been taken into consideration. At this stage, the performance of the proposed algorithm has been compared with the four mentioned algorithms based on four key indicators, including GD, S, H, and run time. The results show, the proposed algorithm has outperformed significantly in terms of run time in all five ZDT problems. In terms of GD indicator, the performance of our proposed algorithm is located in the first or second rank among all considered algorithms. In addition, in terms of S and H indicators in many cases, the proposed algorithm outperformed the other well-known algorithms.

Breast cancer is currently the most frequently diagnosed cancer and leading cause of cancer death in women worldwide. Researchers have been researching the best treatment for breast cancer. Their focus has been on preventing recurrence after the initial treatment of patients. Choosing the appropriate model for survival time analysis is the main challenge in the survival analysis of these patients. In this applied research, using graph-based semi-supervised learning method a new survival analysis model is proposed for analyzing the survival of breast cancer patients. Also, a dataset of 3833 patients with breast cancer who were followed up for 5 years was used. The clinical and pharmacogenomics information, including the results of Tamoxifen use and effect on the treatment of invasive cancer, was also available and used. To validate the proposed model, by introducing the evaluation parameters and simulation of the models in MATLAB software, the model performance is compared with previous models of survival analysis. The results demonstrate that by applying the proposed model in predicting invasive disease-free survival time and when a combination of the clinical and the pharmacogenomics features were used the estimation accuracy was 14% higher than when only the clinical features were used. Moreover, the estimated accuracy was 15% higher than when only the pharmacogenomics features were used. The proposed survival analysis model has a high capability for identifying survival risk and high accuracy in predicting patients' survival time.

Using artificial intelligence tools in pharmacogenomics is one of the latest bioinformatics research fields. One of the most important drugs that determining its initial therapeutic dose is difficult is the anticoagulant warfarin. Warfarin is an oral anticoagulant that, due to its narrow therapeutic window and complex interrelationships of individual factors, the selection of its optimal dose is challenging.

Among the relatively successful methods of kernel-based estimation, comparison and identification of suitable kernels have not been researched. In the present research, while carefully examining this approach, different features of selection algorithms were analyzed based on expert opinions, and an appropriate subset of efficient predictor variables was identified for dose estimation.

In the current study, a dataset collected by the International Warfarin Consortium was used. The results showed that the support vector machine with a suitable kernel and a subset of the proposed features can successfully predict the ideal dose of warfarin for a significant percentage of patients with an error of approximately 0.7 mg per week.

The estimation was conducted using the least squares version of the support vector regression based on a suitable kernel and feature selection strategy. In this method, a better approach for predicting the optimal therapeutic dose of warfarin was presented, which can significantly reduce the wrong dose error and its consequences.

Sentiment analysis is a branch of computer science and natural language processing that seeks to familiarize machines with human emotions and make them recognizable. Both sentiment analysis and sarcasm which is a sub-field of the former, seek to correctly identify the hidden positive and negative emotions of the text. The use of sarcasm on social media, where criticism can be exercised within the context of humor, is quite common. Detection of sarcasm has a special effect on correctly recognizing the polarization of an opinion, and thus not only it can help the machine to understand the text better, but also makes it possible for the respective author to get his message across more clearly. For this purpose, 8000 Persian tweets that have emotional labels and examined for the presence or absence of sarcasm have been used. The innovation of this research is in extracting keywords from sarcastic sentences. In this research, a separate classifier has been trained to identify irony of the text. The output of this classifier is provided as an added feature to the text recognition classifier. In addition to other keywords extracted from the text, emoticons and hashtags have also been used as features. Naive Bayes, support vector machines, and neural networks were used as baseline classifiers, and finally the combination of classifiers was used to identify the feeling of the text. The results of this study show that identifying the irony in the text and using it to identify emotions increases the accuracy of the results.

K-nearest neighbors (KNN) based recommender systems (KRS) are among the most successful recent available recommender systems. These methods involve in predicting the rating of an item based on the mean of ratings given to similar items, with the similarity defined by considering the mean rating given to each item as its feature. This paper presents a KRS developed by combining the following approaches: (a) Using the mean and variance of item ratings as item features to find similar items in an item-wise KRS (IKRS); (b) Using the mean and variance of user ratings as user features to find similar users with a user-wise KRS (UKRS); (c) Using the weighted mean to integrate the ratings of neighboring users/items; (d) Using ensemble learning. Three proposed methods EVMBR, EWVMBR and EWVMBR-G are presented in this paper. All three methods are user-based, in which VM distance is used as a measure of the difference between users / items, to find neighboring users / items, and then the weighted average is weighted, respectively. Also, weights based on the Gaussian combined covariance model are used to predict unknown user ratings. Our empirical evaluations show that the proposed method EVMBR, EWVMBR and EWVMBR-G, which utilizes ensemble learning, are the most accurate among the methods evaluated. Depending on the dataset, the proposed method EWVMBR-G managed to achieve 20 to 30 percent lower mean absolute error than the original MBR. In terms of runtime, the proposed methods are comparable to the MBR and much faster than the slope-one method and the cosine- or Pearson-based KNN recommenders.

Recommender systems that predict user ratings for a set of items are known as subset of information filtration systems. They help users find their favorite items from thousands of available items. One of the most important and challenging problems that recommendation systems suffer from is the problem of dispersion. This means that due to the scatter of data in the system, they are not able to find popular items with the desired reliability and accuracy. This is especially true when there are a large number of items and users in the system and the filled ratings are low. Another challenging problem that these systems suffer from is their scalability. One of the major problems with these systems is the cold start. This problem occurs due to the small number of items rated by the user, i.e. the scatter of users. This problem is divided into two categories: new user and new item. The main focus of this article is on the problem of the new user type. This problem occurs when a new user has just logged in and has not rated any item yet, or when the user has already logged in but has been less active in rating. The goal is to address these three challenges. In this study, an ontology-based hybrid recommender system is introduced in which ontology is used in the content-based filtering section, while the ontology structure is improved by the collaborative filtering section. In this paper, a new hybrid approach based on combining demographic similarity and cosine similarity between users is presented in order to solve the cold start problem of the new user type. Also, a new approach based on combining ontological similarity and cosine similarity between items is proposed to solve the cold start problem of the new item type. The main idea of the proposed method is to extend users’/items’ profiles based on different mechanisms to create higher-performance profiles for users/items. The proposed method is evaluated in a real data set, and experiments show that the proposed method performs better than the advanced recommender system methods, especially in the case of cold start.

The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommender systems. Recently, many researchers have proved that using content models along with these systems can improve the efficacy of hybrid recommender systems. In this paper, we propose to use a new hybrid recommender system where we use a WordNet to improve its performance. This WordNet is also automatically generated and improved during its generation. Our ontology creates a knowledge base of concepts and their relations. This WordNet is used in the content collaborator section in our hybrid recommender system. We improve our ontological structure via a content filtering technique. Our method also benefits from a clustering task in its collaborative section. Indeed, we use a passive clustering task to improve the time complexity of our hybrid recommender system. Although this is a hybrid method, it consists of two separate sections. These two sections work together during learning. Our hybrid recommender system incorporates a basic memory-based approach and a basic model-based approach in such a way that it is as accurate as a memory-based approach and as scalable as a model-based approach. Our hybrid recommender system is assessed by a well-known data set. The empirical results indicate that our hybrid recommender system is superior to the state of the art methods. Also, our hybrid recommender system is more accurate and scalable compared to the recommender systems, which are simply memory-based (KNN) or basic model-based. The empirical results also confirm that our hybrid recommender system is superior to the state of the art methods in terms of the consumed time. While this method is more accurate than model-based methods, it is also faster than memory-based methods. However, this method is not much weaker in terms of accuracy than memory-based methods, and not much weaker in terms of speed than model-based methods.

Today, online social media with numerous users from ordinary citizens to top government officials, organizations, artists and celebrities, etc. is one of the most important platforms for sharing information and communication. These media provide users with quick and easy access to information so that the content of shared posts has the potential to reach millions of users in a matter of seconds. Twitter is one of the most popular and practical/used online social networks for spreading information, which, while being reliable, can also, be a source for spreading unrealistic and deceptive rumors as a result can have irreversible effects on individuals and society. Recently, several studies have been conducted in the field of rumor detection and verify using models based on deep learning and machine learning methods. Previous research into rumor detection has focused more on linguistic, user, and structural features. Concerning structural features, they examined the retweet propagation graph. However, in this study, unlike the previous studies, new structural features of the reply tree and user graph in extracting rumored conversations were extracted and analyzed from different aspects. In this study, the effectiveness of new structural features related to reply tree and user graph in detecting rumored conversations in Twitter events were evaluated from different aspects. First, the structural features of the reply tree and user graph were extracted at different time intervals, and important features in these intervals were identified using the Sequential Forward Selection approach. To evaluate the usefulness of valuable new structural features, these features have been compared with consideration of linguistic and user-specific features. Experiments have shown that combining new structural features with linguistic and user-specific features increases the accuracy of the rumor detection classification. Therefore, a rumor classification algorithm based on new structural, linguistic, and user-specific features in rumor conversation detection was proposed. This algorithm performs better than the basic methods and detects rumored conversations with greater accuracy. In addition, due to the importance of the source tweet user in conversations, this user was examined and analyzed from different aspects. The results showed that most rumored conversations were started by a small number of users. Rumors can be prevented by early identification of these users on Twitter events.

Classification is one of the most important tasks in data mining and machine learning; and the decision tree, as one of the most widely used classification algorithms, has the advantage of simplicity and the ability to interpret results more easily. But when dealing with huge amounts of data, the obtained decision tree would grow in size and complexity, and therefore require excessive running time. Almost all of the tree-construction algorithms need to store all or part of the training data set; but those algorithms which do not face memory shortages because of selecting a subset of data, can save the extra time for data selection. In order to select the best feature to create a branch in the tree, a lot of calculations are required. In this paper we presents an incremental scalable approach based on fast partitioning and pruning; The proposed algorithm builds the decision tree via using the entire training data set but it doesn't require to store the whole data in the main memory. The pre-pruning method has also been used to reduce the complexity of the tree. The experimental results on the UCI data set show that the proposed algorithm, in addition to preserving the competitive accuracy and construction time, could conquer the mentioned disadvantages of former methods.

Many of the real-world issues have multiple conflicting objectives that the optimization between contradictory objectives is very difficult. In recent years, the Multi-objective Evolutionary Algorithms (MOEAs) have shown great performance to optimize such problems. So, the development of MOEAs will always lead to the advancement of science. The Non-dominated Sorting Genetic Algorithm II (NSGAII) is considered as one of the most used evolutionary algorithms, and many MOEAs have emerged to resolve NSGAII problems, such as the Sequential Multi-Objective Algorithm (SEQ-MOGA). SEQ-MOGA presents a new survival selection that arranges individuals systematically, and the chromosomes can cover the entire Pareto Front region. In this study, the Archive Sequential Multi-Objective Algorithm (ASMOGA) is proposed to develop and improve SEQ-MOGA. ASMOGA uses the archive technique to save the history of the search procedure, so that the maintenance of the diversity in the decision space is satisfied adequately. To demonstrate the performance of ASMOGA, it is used and compared with several state-of-the-art MOEAs for optimizing benchmark functions and designing the I-Beam problem. The optimization results are evaluated by Performance Metrics such as hypervolume, Generational Distance, Spacing, and the t-test (a statistical test); based on the results, the superiority of the proposed algorithm is identified clearly.

In this paper, a novel method based on the graph is proposed to classify the sequence of variable length as feature extraction. The proposed method overcomes the problems of the traditional graph with variable length of data, without fixing length of sequences, by determining the most frequent instructions and insertion the rest of instructions on the set of “other”, save speed and memory. According to features and the similarities of them, a score is given to each sample and that is used for classification. To improve the results, the method is not used alone, but in the two approaches, this method is combined with other existing Technique to get better results. In the first approach, which can be considered as a feature extraction, extracted features from scoring techniques (Hidden Markov Model, simple substitution distance and similarity graph) on op-code sequences, hexadecimal sequences and system calls are combined at classifier input. The second approach consists of two steps, in the first step; the scores which obtained from each of the scoring Technique are given to the three support vector machine. The outcomes are combined according to the weight of each Technique and the final decision is taken based on the majority vote. Among the components of the support vector machine, when given a higher weight in the similarity graph method (the proposed method), the result is better, Because the similarity graph method is more accurate than the other two methods. Then, in the second section, considering the strengths and benefits of each classifier, classifier outputs are combined and the majority voting is used. Three methods have been tested for group combinations, including Ensemble Averaging, Bagging, and Boosting. Ensemble Averaging consisting of the combination of four classifiers of random forests, a support vector machine (as obtained in the previous section), K nearest neighbors and naive Bayes, and the final decision is taken based on the majority vote; therefore, it is used as the proposed method. The proposed approach could detect metamorphic malware from Vxheaven set and also determines categories of malware with accuracy of 97%, while the SSD and HMM methods under the same conditions could detect malware with an accuracy of 84% and 80% respectively.

Permeability is a property of the reservoir rock, which deals with the flow of fluid from the reservoir and is an important factor in oil and gas production. This parameter is measured via coring and core laboratory analysis, which is an expensive and time-consuming process and also is not a feasible approach for every oil and gas field. Nowadays, the permeability can also be calculated using the data of petrophysical logs by means of statistical and intelligent techniques. The present study uses four wells drilled in Kangan and Dalan formations within South Pars gas field to predict permeability using fuzzy logic. Out of totally eight features extracted from each well, four more effective features were selected using correlation-based feature selection tools. Then, regression, multi-layer perceptron, RBF neural network, Local Linear Model Trees (LOLIMOT), type-1 and type -2 fuzzy systems were utilized for permeability prediction. The results indicated that due to the uncertainty in the petrophysical and permeability parameters, type-2 Fuzzy systems cover better the uncertainties. The aforementioned method predicts the best number of rules using the GSA-GA (Gravitational Search Algorithm - Genetic algorithm) combined algorithms. Fuzzy membership functions were also improved using the K-means clustering algorithms. These improvements led to increased accuracy of the predicted permeability with a coefficient of 0.9768, and a decrease in the root mean square error to 0.1602.