N. Shayanfar; V. Derhami; M. Rezaeian
Abstract
In video prediction it is expected to predict next frame of video by providing a sequence of input frames. Whereas numerous studies exist that tackle frame prediction, suitable performance is not still achieved and therefore the application is an open problem. In this article multiscale processing is ...
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In video prediction it is expected to predict next frame of video by providing a sequence of input frames. Whereas numerous studies exist that tackle frame prediction, suitable performance is not still achieved and therefore the application is an open problem. In this article multiscale processing is studied for video prediction and a new network architecture for multiscale processing is presented. This architecture is in the broad family of autoencoders. It is comprised of an encoder and decoder. A pretrained VGG is used as an encoder that processes a pyramid of input frames at multiple scales simultaneously. The decoder is based on 3D convolutional neurons. The presented architecture is studied by using three different datasets with varying degree of difficulty. In addition, the proposed approach is compared to two conventional autoencoders. It is observed that by using the pretrained network and multiscale processing results in a performant approach.
F. Kaveh-Yazdy; S. Zarifzadeh
Abstract
Due to their structure and usage condition, water meters face degradation, breaking, freezing, and leakage problems. There are various studies intended to determine the appropriate time to replace degraded ones. Earlier studies have used several features, such as user meteorological parameters, usage ...
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Due to their structure and usage condition, water meters face degradation, breaking, freezing, and leakage problems. There are various studies intended to determine the appropriate time to replace degraded ones. Earlier studies have used several features, such as user meteorological parameters, usage conditions, water network pressure, and structure of meters to detect failed water meters. This article proposes a recommendation framework that uses registered water consumption values as input data and provides meter replacement recommendations. This framework takes time series of registered consumption values and preprocesses them in two rounds to extract effective features. Then, multiple un-/semi-supervised outlier detection methods are applied to the processed data and assigns outlier/normal labels to them. At the final stage, a hypergraph-based ensemble method receives the labels and combines them to discover the suitable label. Due to the unavailability of ground truth labeled data for meter replacement, we compare our method with respect to its FPR and two internal metrics: Dunn index and Davies-Bouldin Index. Results of our comparative experiments show that the proposed framework detects more compact clusters with smaller variance.
H.5. Image Processing and Computer Vision
Fatemeh Zare mehrjardi; Alimohammad Latif; Mohsen Sardari Zarchi
Abstract
Image is a powerful communication tool that is widely used in various applications, such as forensic medicine and court, where the validity of the image is crucial. However, with the development and availability of image editing tools, image manipulation can be easily performed for a specific purpose. ...
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Image is a powerful communication tool that is widely used in various applications, such as forensic medicine and court, where the validity of the image is crucial. However, with the development and availability of image editing tools, image manipulation can be easily performed for a specific purpose. Copy-move forgery is one of the simplest and most common methods of image manipulation. There are two traditional methods to detect this type of forgery: block-based and key point-based. In this study, we present a hybrid approach of block-based and key point-based methods using meta-heuristic algorithms to find the optimal configuration. For this purpose, we first search for pair blocks suspected of forgery using the genetic algorithm with the maximum number of matched key points as the fitness function. Then, we find the accurate forgery blocks using simulating annealing algorithm and producing neighboring solutions around suspicious blocks. We evaluate the proposed method on CoMoFod and COVERAGE datasets, and obtain the results of accuracy, precision, recall and IoU with values of 96.87, 92.15, 95.34 and 93.45 respectively. The evaluation results show the satisfactory performance of the proposed method.
V. Ghasemi; A. Ghanbari Sorkhi
Abstract
Deploying m-connected k-covering (MK) wireless sensor networks (WSNs) is crucial for reliable packet delivery and target coverage. This paper proposes implementing random MK WSNs based on expected m-connected k-covering (EMK) WSNs. We define EMK WSNs as random WSNs mathematically expected to be both ...
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Deploying m-connected k-covering (MK) wireless sensor networks (WSNs) is crucial for reliable packet delivery and target coverage. This paper proposes implementing random MK WSNs based on expected m-connected k-covering (EMK) WSNs. We define EMK WSNs as random WSNs mathematically expected to be both m-connected and k-covering. Deploying random EMK WSNs is conducted by deriving a relationship between m-connectivity and k-coverage, together with a lower bound for the required number of nodes. It is shown that EMK WSNs tend to be MK asymptotically. A polynomial worst-case and linear average-case complexity algorithm is presented to turn an EMK WSN into MK in non-asymptotic conditions. The m-connectivity is founded on the concept of support sets to strictly guarantee the existence of m disjoint paths between every node and the sink. The theoretical results are assessed via experiments, and several metaheuristic solutions have been benchmarked to reveal the appropriate size of the generated MK WSNs.
S. Bayatpour; Seyed M. H. Hasheminejad
Abstract
Most of the methods proposed for segmenting image objects are supervised methods which are costly due to their need for large amounts of labeled data. However, in this article, we have presented a method for segmenting objects based on a meta-heuristic optimization which does not need any training data. ...
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Most of the methods proposed for segmenting image objects are supervised methods which are costly due to their need for large amounts of labeled data. However, in this article, we have presented a method for segmenting objects based on a meta-heuristic optimization which does not need any training data. This procedure consists of two main stages of edge detection and texture analysis. In the edge detection stage, we have utilized invasive weed optimization (IWO) and local thresholding. Edge detection methods that are based on local histograms are efficient methods, but it is very difficult to determine the desired parameters manually. In addition, these parameters must be selected specifically for each image. In this paper, a method is presented for automatic determination of these parameters using an evolutionary algorithm. Evaluation of this method demonstrates its high performance on natural images.
F.2.7. Optimization
Mahsa Dehbozorgi; Pirooz Shamsinejadbabaki; Elmira Ashoormahani
Abstract
Clustering is one of the most effective techniques for reducing energy consumption in wireless sensor networks. But selecting optimum cluster heads (CH) as relay nodes has remained as a very challenging task in clustering. All current state of the art methods in this era only focus on the individual ...
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Clustering is one of the most effective techniques for reducing energy consumption in wireless sensor networks. But selecting optimum cluster heads (CH) as relay nodes has remained as a very challenging task in clustering. All current state of the art methods in this era only focus on the individual characteristics of nodes like energy level and distance to the Base Station (BS). But when a CH dies it is necessary to find another CH for cluster and usually its neighbor will be selected. Despite existing methods, in this paper we proposed a method that considers node neighborhood fitness as a selection factor in addition to other typical factors. A Particle Swarm Optimization algorithm has been designed to find best CHs based on intra-cluster distance, distance of CHs to the BS, residual energy and neighborhood fitness. The proposed method compared with LEACH and PSO-ECHS algorithms and experimental results have shown that our proposed method succeeded to postpone death of first node by 5.79%, death of 30% of nodes by 25.50% and death of 70% of nodes by 58.67% compared to PSO-ECHS algorithm
H.3.11. Vision and Scene Understanding
S. Bayatpour; M. Sharghi
Abstract
Digital images are being produced in a massive number every day. Acomponent that may exist in digital images is text. Textual information can beextracted and used in a variety of fields. Noise, blur, distortions, occlusion, fontvariation, alignments, and orientation, are among the main challenges for ...
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Digital images are being produced in a massive number every day. Acomponent that may exist in digital images is text. Textual information can beextracted and used in a variety of fields. Noise, blur, distortions, occlusion, fontvariation, alignments, and orientation, are among the main challenges for textdetection in natural images. Despite many advances in text detection algorithms,there is not yet a single algorithm that addresses all of the above problemssuccessfully. Furthermore, most of the proposed algorithms can only detecthorizontal texts and a very small fraction of them consider Farsi language. Inthis paper, a method is proposed for detecting multi-orientated texts in both Farsiand English languages. We have defined seven geometric features to distinguishtext components from the background and proposed a new contrast enhancementmethod for text detection algorithms. Our experimental results indicate that theproposed method achieves a high performance in text detection on natural images.
N. Majidi; K. Kiani; R. Rastgoo
Abstract
This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model ...
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This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks simultaneously. We use the residual layers in our model to make repetitive layers, increase the depth of the model, and make an end-to-end model. Furthermore, we employed a deep network in up-sampling step instead of bicubic interpolation method used in most of the previous works. Since the image resolution plays an important role to obtain rich information from the medical images and helps for accurate and faster diagnosis of the ailment, we use the medical images for resolution enhancement. Our model is capable of reconstructing a high-resolution image from low-resolution one in both medical and general images. Evaluation results on TSA and TZDE datasets, including MRI images, and Set5, Set14, B100, and Urban100 datasets, including general images, demonstrate that our model outperforms state-of-the-art alternatives in both areas of medical and general super-resolution enhancement from a single input image.
M. Nasiri; H. Rahmani
Abstract
Determining the personality dimensions of individuals is very important in psychological research. The most well-known example of personality dimensions is the Five-Factor Model (FFM). There are two approaches 1- Manual and 2- Automatic for determining the personality dimensions. In a manual approach, ...
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Determining the personality dimensions of individuals is very important in psychological research. The most well-known example of personality dimensions is the Five-Factor Model (FFM). There are two approaches 1- Manual and 2- Automatic for determining the personality dimensions. In a manual approach, Psychologists discover these dimensions through personality questionnaires. As an automatic way, varied personal input types (textual/image/video) of people are gathered and analyzed for this purpose. In this paper, we proposed a method called DENOVA (DEep learning based on the ANOVA), which predicts FFM using deep learning based on the Analysis of variance (ANOVA) of words. For this purpose, DENOVA first applies ANOVA to select the most informative terms. Then, DENOVA employs Word2Vec to extract document embeddings. Finally, DENOVA uses Support Vector Machine (SVM), Logistic Regression, XGBoost, and Multilayer perceptron (MLP) as classifiers to predict FFM. The experimental results show that DENOVA outperforms on average, 6.91%, the state-of-the-art methods in predicting FFM with respect to accuracy.
C.3. Software Engineering
Saba Beiranvand; Mohammad Ali Zare Chahooki
Abstract
Software Cost Estimation (SCE) is one of the most widely used and effective activities in project management. In machine learning methods, some features have adverse effects on accuracy. Thus, preprocessing methods based on reducing non-effective features can improve accuracy in these methods. In clustering ...
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Software Cost Estimation (SCE) is one of the most widely used and effective activities in project management. In machine learning methods, some features have adverse effects on accuracy. Thus, preprocessing methods based on reducing non-effective features can improve accuracy in these methods. In clustering techniques, samples are categorized into different clusters according to their semantic similarity. Accordingly, in the proposed study, to improve SCE accuracy, first samples are clustered based on original features. Then, a feature selection (FS) technique is separately done for each cluster. The proposed FS method is based on a combination of filter and wrapper FS methods. The proposed method uses both filter and wrapper advantages in selecting effective features of each cluster, with less computational complexity and more accuracy. Furthermore, as the assessment criteria have significant impacts on wrapper methods, a fused criterion has also been used. The proposed method was applied to Desharnais, COCOMO81, COCONASA93, Kemerer, and Albrecht datasets, and the obtained Mean Magnitude of Relative Error (MMRE) for these datasets were 0.2173, 0.6489, 0.3129, 0.4898 and 0.4245, respectively. These results were compared with previous studies and showed improvement in the error rate of SCE.
R. Mohammadian; M. Mahlouji; A. Shahidinejad
Abstract
Multi-view face detection in open environments is a challenging task, due to the wide variations in illumination, face appearances and occlusion. In this paper, a robust method for multi-view face detection in open environments, using a combination of Gabor features and neural networks, is presented. ...
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Multi-view face detection in open environments is a challenging task, due to the wide variations in illumination, face appearances and occlusion. In this paper, a robust method for multi-view face detection in open environments, using a combination of Gabor features and neural networks, is presented. Firstly, the effect of changing the Gabor filter parameters (orientation, frequency, standard deviation, aspect ratio and phase offset) for an image is analysed, secondly, the range of Gabor filter parameter values is determined and finally, the best values for these parameters are specified. A multilayer feedforward neural network with a back-propagation algorithm is used as a classifier. The input vector is obtained by convolving the input image and a Gabor filter, with both the angle and frequency values equal to π/2. The proposed algorithm is tested on 1,484 image samples with simple and complex backgrounds. The experimental results show that the proposed detector achieves great detection accuracy, by comparing it with several popular face-detection algorithms, such as OpenCV’s Viola-Jones detector.
A.H. Damia; M. Esnaashari; M.R. Parvizimosaed
Abstract
In the structural software test, test data generation is essential. The problem of generating test data is a search problem, and for solving the problem, search algorithms can be used. Genetic algorithm is one of the most widely used algorithms in this field. Adjusting genetic algorithm parameters helps ...
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In the structural software test, test data generation is essential. The problem of generating test data is a search problem, and for solving the problem, search algorithms can be used. Genetic algorithm is one of the most widely used algorithms in this field. Adjusting genetic algorithm parameters helps to increase the effectiveness of this algorithm. In this paper, the Adaptive Genetic Algorithm (AGA) is used to maintain the diversity of the population to test data generation based on path coverage criterion, which calculates the rate of recombination and mutation with the similarity between chromosomes and the amount of chromosome fitness during and around each algorithm. Experiments have shown that this method is faster for generating test data than other versions of the genetic algorithm used by others.
H. Gholamalinejad; H. Khosravi
Abstract
Optimizers are vital components of deep neural networks that perform weight updates. This paper introduces a new updating method for optimizers based on gradient descent, called whitened gradient descent (WGD). This method is easy to implement and can be used in every optimizer based on the gradient ...
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Optimizers are vital components of deep neural networks that perform weight updates. This paper introduces a new updating method for optimizers based on gradient descent, called whitened gradient descent (WGD). This method is easy to implement and can be used in every optimizer based on the gradient descent algorithm. It does not increase the training time of the network significantly. This method smooths the training curve and improves classification metrics. To evaluate the proposed algorithm, we performed 48 different tests on two datasets, Cifar100 and Animals-10, using three network structures, including densenet121, resnet18, and resnet50. The experiments show that using the WGD method in gradient descent based optimizers, improves the classification results significantly. For example, integrating WGD in RAdam optimizer increased the accuracy of DenseNet from 87.69% to 90.02% on the Animals-10 dataset.
A. Omondi; I.A. Lukandu; G.W. Wanyembi
Abstract
Redundant and irrelevant features in high dimensional data increase the complexity in underlying mathematical models. It is necessary to conduct pre-processing steps that search for the most relevant features in order to reduce the dimensionality of the data. This study made use of a meta-heuristic search ...
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Redundant and irrelevant features in high dimensional data increase the complexity in underlying mathematical models. It is necessary to conduct pre-processing steps that search for the most relevant features in order to reduce the dimensionality of the data. This study made use of a meta-heuristic search approach which uses lightweight random simulations to balance between the exploitation of relevant features and the exploration of features that have the potential to be relevant. In doing so, the study evaluated how effective the manipulation of the search component in feature selection is on achieving high accuracy with reduced dimensions. A control group experimental design was used to observe factual evidence. The context of the experiment was the high dimensional data experienced in performance tuning of complex database systems. The Wilcoxon signed-rank test at .05 level of significance was used to compare repeated classification accuracy measurements on the independent experiment and control group samples. Encouraging results with a p-value < 0.05 were recorded and provided evidence to reject the null hypothesis in favour of the alternative hypothesis which states that meta-heuristic search approaches are effective in achieving high accuracy with reduced dimensions depending on the outcome variable under investigation.
E. Pejhan; M. Ghasemzadeh
Abstract
This research is related to the development of technology in the field of automatic text to image generation. In this regard, two main goals are pursued; first, the generated image should look as real as possible; and second, the generated image should be a meaningful description of the input text. our ...
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This research is related to the development of technology in the field of automatic text to image generation. In this regard, two main goals are pursued; first, the generated image should look as real as possible; and second, the generated image should be a meaningful description of the input text. our proposed method is a Multi Sentences Hierarchical GAN (MSH-GAN) for text to image generation. In this research project, we have considered two main strategies: 1) produce a higher quality image in the first step, and 2) use two additional descriptions to improve the original image in the next steps. Our goal is to focus on using more information to generate images with higher resolution by using more than one sentence input text. We have proposed different models based on GANs and Memory Networks. We have also used more challenging dataset called ids-ade. This is the first time; this dataset has been used in this area. We have evaluated our models based on IS, FID and, R-precision evaluation metrics. Experimental results demonstrate that our best model performs favorably against the basic state-of-the-art approaches like StackGAN and AttGAN.
H.3. Artificial Intelligence
Saiful Bukhori; Muhammad Almas Bariiqy; Windi Eka Y. R; Januar Adi Putra
Abstract
Breast cancer is a disease of abnormal cell proliferation in the breast tissue organs. One method for diagnosing and screening breast cancer is mammography. However, the results of this mammography image have limitations because it has low contrast and high noise and contrast as non-coherence. This research ...
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Breast cancer is a disease of abnormal cell proliferation in the breast tissue organs. One method for diagnosing and screening breast cancer is mammography. However, the results of this mammography image have limitations because it has low contrast and high noise and contrast as non-coherence. This research segmented breast cancer images derived from Ultrasonography (USG) photo using a Convolutional Neural Network (CNN) using the U-Net architecture. Testing on the CNN model with the U-Net architecture results the highest Mean Intersection over Union (Mean IoU) value in the data scenario with a ratio of 70:30, 100 epochs, and a learning rate of 5x10-5, which is 77%, while the lowest Mean IoU in the data scenario with a ratio 90:10, 50 epochs, and a learning rate of 1x10-4 learning rate, which is 64.4%.
H. Sarabi Sarvarani; F. Abdali-Mohammadi
Abstract
Bone age assessment is a method that is constantly used for investigating growth abnormalities, endocrine gland treatment, and pediatric syndromes. Since the advent of digital imaging, for several decades the bone age assessment has been performed by visually examining the ossification of the left hand, ...
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Bone age assessment is a method that is constantly used for investigating growth abnormalities, endocrine gland treatment, and pediatric syndromes. Since the advent of digital imaging, for several decades the bone age assessment has been performed by visually examining the ossification of the left hand, usually using the G&P reference method. However, the subjective nature of hand-craft methods, the large number of ossification centers in the hand, and the huge changes in ossification stages lead to some difficulties in the evaluation of the bone age. Therefore, many efforts were made to develop image processing methods. These methods automatically extract the main features of the bone formation stages to effectively and more accurately assess the bone age. In this paper, a new fully automatic method is proposed to reduce the errors of subjective methods and improve the automatic methods of age estimation. This model was applied to 1400 radiographs of healthy children from 0 to 18 years of age and gathered from 4 continents. This method starts with the extraction of all regions of the hand, the five fingers and the wrist, and independently calculates the age of each region through examination of the joints and growth regions associated with these regions by CNN networks; It ends with the final age assessment through an ensemble of CNNs. The results indicated that the proposed method has an average assessment accuracy of 81% and has a better performance in comparison to the commercial system that is currently in use.
H. Khodadadi; V. Derhami
Abstract
A prominent weakness of dynamic programming methods is that they perform operations throughout the entire set of states in a Markov decision process in every updating phase. This paper proposes a novel chaos-based method to solve the problem. For this purpose, a chaotic system is first initialized, and ...
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A prominent weakness of dynamic programming methods is that they perform operations throughout the entire set of states in a Markov decision process in every updating phase. This paper proposes a novel chaos-based method to solve the problem. For this purpose, a chaotic system is first initialized, and the resultant numbers are mapped onto the environment states through initial processing. In each traverse of the policy iteration method, policy evaluation is performed only once, and only a few states are updated. These states are proposed by the chaos system. In this method, the policy evaluation and improvement cycle lasts until an optimal policy is formulated in the environment. The same procedure is performed in the value iteration method, and only the values of a few states proposed by the chaos are updated in each traverse, whereas the values of other states are left unchanged. Unlike the conventional methods, an optimal solution can be obtained in the proposed method by only updating a limited number of states which are properly distributed all over the environment by chaos. The test results indicate the improved speed and efficiency of chaotic dynamic programming methods in obtaining the optimal solution in different grid environments.
H.3. Artificial Intelligence
Akram Pasandideh; Mohsen Jahanshahi
Abstract
Link prediction (LP) has become a hot topic in the data mining, machine learning, and deep learning community. This study aims to implement bibliometric analysis to find the current status of the LP studies and investigate it from different perspectives. The present study provides a Scopus-based bibliometric ...
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Link prediction (LP) has become a hot topic in the data mining, machine learning, and deep learning community. This study aims to implement bibliometric analysis to find the current status of the LP studies and investigate it from different perspectives. The present study provides a Scopus-based bibliometric overview of the LP studies landscape since 1987 when LP studies were published for the first time. Various kinds of analysis, including document, subject, and country distribution are applied. Moreover, author productivity, citation analysis, and keyword analysis is used, and Bradford’s law is applied to discover the main journals in this field. Most documents were published by conferences in the field. The majority of LP documents have been published in the computer science and mathematics fields. So far, China has been at the forefront of publishing countries. In addition, the most active sources of LP publications are lecture notes in Computer Science, including subseries lecture notes in Artificial Intelligence (AI) and lecture notes in Bioinformatics, and IEEE Access. The keyword analysis demonstrates that while social networks had attracted attention in the early period, knowledge graphs have attracted more attention, recently. Since the LP problem has been approached recently using machine learning (ML), the current study may inform researchers to concentrate on ML techniques. This is the first bibliometric study of “link prediction” literature and provides a broad landscape of the field.
Z. Anari; A. Hatamlou; B. Anari; M. Masdari
Abstract
The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. ...
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The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since the membership functions of each web page are different from those of other web pages, so automatic finding the number and position of TMF is significant. In this paper, a different reinforcement-based optimization approach called LA-OMF was proposed to find both the number and positions of TMFs for fuzzy association rules. In the proposed algorithm, the centers and spreads of TMFs were considered as parameters of the search space, and a new representation using learning automata (LA) was proposed to optimize these parameters. The performance of the proposed approach was evaluated and the results were compared with the results of other algorithms on a real dataset. Experiments on datasets with different sizes confirmed that the proposed LA-OMF improved the efficiency of mining fuzzy association rules by extracting optimized membership functions.
Seyyed A. Hoseini; P. Kabiri
Abstract
When a camera moves in an unfamiliar environment, for many computer vision and robotic applications it is desirable to estimate camera position and orientation. Camera tracking is perhaps the most challenging part of Visual Simultaneous Localization and Mapping (Visual SLAM) and Augmented Reality problems. ...
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When a camera moves in an unfamiliar environment, for many computer vision and robotic applications it is desirable to estimate camera position and orientation. Camera tracking is perhaps the most challenging part of Visual Simultaneous Localization and Mapping (Visual SLAM) and Augmented Reality problems. This paper proposes a feature-based approach for tracking a hand-held camera that moves within an indoor place with a maximum depth of around 4-5 meters. In the first few frames the camera observes a chessboard as a marker to bootstrap the system and construct the initial map. Thereafter, upon arrival of each new frame, the algorithm pursues the camera tracking procedure. This procedure is carried-out in a framework, which operates using only the extracted visible natural feature points and the initial map. Constructed initial map is extended as the camera explores new areas. In addition, the proposed system employs a hierarchical method on basis of Lucas-Kanade registration technique to track FAST features. For each incoming frame, 6-DOF camera pose parameters are estimated using an Unscented Kalman Filter (UKF). The proposed algorithm is tested on real-world videos and performance of the UKF is compared against other camera tracking methods. Two evaluation criteria (i.e. Relative pose error and absolute trajectory error) are used to assess performance of the proposed algorithm. Accordingly, reported experimental results show accuracy and effectiveness and of the presented approach. Conducted experiments also indicate that the type of extracted feature points has not significant effect on precision of the proposed approach.
Y. Sharafi; M. Teshnelab; M. Ahmadieh Khanesar
Abstract
A new multi-objective evolutionary optimization algorithm is presented based on the competitive optimization algorithm (COOA) to solve multi-objective optimization problems (MOPs). Based on nature-inspired competition, the competitive optimization algorithm acts between animals such as birds, cats, bees, ...
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A new multi-objective evolutionary optimization algorithm is presented based on the competitive optimization algorithm (COOA) to solve multi-objective optimization problems (MOPs). Based on nature-inspired competition, the competitive optimization algorithm acts between animals such as birds, cats, bees, ants, etc. The present study entails main contributions as follows: First, a novel method is presented to prune the external archive and at the same time keep the diversity of the Pareto front (PF). Second, a hybrid approach of powerful mechanisms such as opposition-based learning and chaotic maps is used to maintain the diversity in the search space of the initial population. Third, a novel method is provided to transform a multi-objective optimization problem into a single-objective optimization problem. A comparison of the result of the simulation for the proposed algorithm was made with some well-known optimization algorithms. The comparisons show that the proposed approach can be a better candidate to solve MOPs.
F. Jafarinejad
Abstract
In recent years, new word embedding methods have clearly improved the accuracy of NLP tasks. A review of the progress of these methods shows that the complexity of these models and the number of their training parameters grows increasingly. Therefore, there is a need for methodological innovation for ...
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In recent years, new word embedding methods have clearly improved the accuracy of NLP tasks. A review of the progress of these methods shows that the complexity of these models and the number of their training parameters grows increasingly. Therefore, there is a need for methodological innovation for presenting new word embedding methodologies. Most current word embedding methods use a large corpus of unstructured data to train the semantic vectors of words. This paper addresses the basic idea of utilizing from structure of structured data to introduce embedding vectors. Therefore, the need for high processing power, large amount of processing memory, and long processing time will be met using structures and conceptual knowledge lies in them. For this purpose, a new embedding vector, Word2Node is proposed. It uses a well-known structured resource, the WordNet, as a training corpus and hypothesis that graphic structure of the WordNet includes valuable linguistic knowledge that can be considered and not ignored to provide cost-effective and small sized embedding vectors. The Node2Vec graph embedding method allows us to benefit from this powerful linguistic resource. Evaluation of this idea in two tasks of word similarity and text classification has shown that this method perform the same or better in comparison to the word embedding method embedded in it (Word2Vec). This result is achieved while the required training data is reduced by about 50,000,000%. These results provide a view of capacity of the structured data to improve the quality of existing embedding methods and the resulting vectors.
H.3. Artificial Intelligence
Ali Zahmatkesh Zakariaee; Hossein Sadr; Mohamad Reza Yamaghani
Abstract
Machine learning (ML) is a popular tool in healthcare while it can help to analyze large amounts of patient data, such as medical records, predict diseases, and identify early signs of cancer. Gastric cancer starts in the cells lining the stomach and is known as the 5th most common cancer worldwide. ...
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Machine learning (ML) is a popular tool in healthcare while it can help to analyze large amounts of patient data, such as medical records, predict diseases, and identify early signs of cancer. Gastric cancer starts in the cells lining the stomach and is known as the 5th most common cancer worldwide. Therefore, predicting the survival of patients, checking their health status, and detecting their risk of gastric cancer in the early stages can be very beneficial. Surprisingly, with the help of machine learning methods, this can be possible without the need for any invasive methods which can be useful for both patients and physicians in making informed decisions. Accordingly, a new hybrid machine learning-based method for detecting the risk of gastric cancer is proposed in this paper. The proposed model is compared with traditional methods and based on the empirical results, not only the proposed method outperform existing methods with an accuracy of 98% but also gastric cancer can be one of the most important consequences of H. pylori infection. Additionally, it can be concluded that lifestyle and dietary factors can heighten the risk of gastric cancer, especially among individuals who frequently consume fried foods and suffer from chronic atrophic gastritis and stomach ulcers. This risk is further exacerbated in individuals with limited fruit and vegetable intake and high salt consumption.
J. Tayyebi; E. Hosseinzadeh
Abstract
The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal ...
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The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is presented to cluster incomplete fuzzy data. The method substitutes missing attribute by a trapezoidal fuzzy number to be determined by using the corresponding attribute of q nearest-neighbor. Comparisons and analysis of the experimental results demonstrate the capability of the proposed method.
