Mir Mohsen Pedram | Kharazmi University (original) (raw)
Papers by Mir Mohsen Pedram
The Journal Of Psychological Science
A. Application of structural equation model in explaining the role of cognitive flexibility, emot... more A. Application of structural equation model in explaining the role of cognitive flexibility, emotional self-regulation and actively openminded thinking on changing people's attitudes.
Cellular nonlinear network (CNN) provides an infrastructure for Cellular Automata to have not onl... more Cellular nonlinear network (CNN) provides an infrastructure for Cellular Automata to have not only an initial state but an input which has a local memory in each cell with much more complexity. This property has many applications which we have investigated it in proposing a robust cryptology scheme. This scheme consists of a cryptography and steganography sub-module in which a 3D CNN is designed to produce a chaotic map as the kernel of the system to preserve confidentiality and data integrity in cryptology. Our contributions are three-fold including (1) a feature descriptor is applied to the cover image to form the secret key while conventional methods use a predefined key, (2) a 3D CNN is used to make a chaotic map for making cipher from the visual message, and (3) the proposed CNN is also used to make a dynamic k-LSB steganography. Conducted experiments on 25 standard images prove the effectiveness of the proposed cryptology scheme in terms of security, visual, and complexity ana...
The Neuroscience Journal of Shefaye Khatam
Artificial intelligence researchers are trying to implement human intelligence on the machine. Th... more Artificial intelligence researchers are trying to implement human intelligence on the machine. This s tudy aimed to develop an appropriate predictive computer model to evaluate the effectiveness of mindfulness-based cognitive therapy on irritability. Materials and Methods: The design of the present s tudy is quasi-experimental with a pre-tes t and pos t-tes t method. 135 individuals who referred to Khane Mehr counseling center in Mashhad and participated in an 8-session mindfulness-based cognitive therapy (MBCT) course were included in this s tudy. Totally, 11 MBCT courses were held and 10 to 14 people participated in each course. Participants completed the irritability ques tionnaire (Pourafrouz & et al.) at two s tages (before treatment and after treatment). In order to examine the differences from pretes t to pos t-tes t in this research, the variance analysis of repeated measures was used. Results: There was a significant difference between pre-tes t and pos t-tes t irritability scores. The effect of mindfulness was 83%. To develop the prediction model, three Bayesian, regression, and neural network models were compared. The Bayesian model, with 93% accuracy tes t data, was considered the mos t appropriate model. Moreover, the Bayesian models with input and output clus tering (85.7%), the Bayesian with classification (71.49%), and the sequential neural network (64.29%) were identified as suitable models to predict the effectiveness of 8-session mindfulness courses on reducing irritability. The Bayesian model with output clus tering, one-output regression, and the Convulsions Neural Network did not have sufficient predictive accuracy for the effectiveness of mindfulness. Conclusion: Using cognitive modeling, we can predict the efficacy of mindfulness-based cognitive therapy on irritability.y
Abstract-Gene expression profiling plays an important role in a broad range of areas in biology. ... more Abstract-Gene expression profiling plays an important role in a broad range of areas in biology. Microarray data often contains multiple missing expression values, which can significantly affect subsequent analysis In this paper, a new method based on fuzzy clustering and genes semantic similarity is proposed to estimate missing values in microarray data. In the proposed method, microarray data are clustered based on genes semantic similarity and their expression values and missing values are imputed with values generated from cluster centers Genes similarity in clustering process determine with their semantic similarity obtained from gene ontology as well as their expression values. The experimental results indicate that the proposed method outperforms other methods in terms of Root Mean Square error. Keywords-microarray, missing value estimation, fuzzy clustering, semantic similarity M I.
Journal of AI and Data Mining, 2021
With the rapid development of textual information on the web, sentiment analysis is changing to a... more With the rapid development of textual information on the web, sentiment analysis is changing to an essential analytic tool rather than an academic endeavor and numerous studies have been carried out in recent years to address this issue. By the emergence of deep learning, deep neural networks have attracted a lot of attention and become mainstream in this field. Despite the remarkable success of deep learning models for sentiment analysis of text, they are in the early steps of development and their potential is yet to be fully explored. Convolutional neural network is one of the deep learning methods that has been surpassed for sentiment analysis but is confronted with some limitations. Firstly, convolutional neural network requires a large number of training data. Secondly, it assumes that all words in a sentence have an equal contribution to the polarity of a sentence. To fill these lacunas, a convolutional neural network equipped with the attention mechanism is proposed in this ...
his paper deals with a new algorithm for solving the problems of reinforcement learning in contin... more his paper deals with a new algorithm for solving the problems of reinforcement learning in continuous spaces. For continuous states and actions, we use a new method based on selforganizing neural network, DIGNET. Two self-organizing neural network, DIGNETs, are used in this method, which having simple structure, can give an appropriate approximate of state/action space. The network is able to adapt inconsistent nature of reinforcement learning environments fine since the system parameters in DIGNET are self-adjusted in an autonomous way during the learning procedure. Automatic and competitive production and elimination of attraction wells, considering parameters of attraction well, threshold, age and depth, lead to flexibility of proposed algorithm to solve continuous problems and finally, the attraction wells of output DIGNET (action) will concentrate on the areas with high reward, providing an appropriate illustration for a continuous state space. At the end, we also present the r...
ArXiv, 2021
Sentiment analysis is known as one of the most crucial tasks in the field of natural language pro... more Sentiment analysis is known as one of the most crucial tasks in the field of natural language processing and Convolutional Neural Network (CNN) is one of those prominent models that is commonly used for this aim. Although convolutional neural networks have obtained remarkable results in recent years, they are still confronted with some limitations. Firstly, they consider that all words in a sentence have equal contributions in the sentence meaning representation and are not able to extract informative words. Secondly, they require a large number of training data to obtain considerable results while they have many parameters that must be accurately adjusted. To this end, a convolutional neural network integrated with a hierarchical attention layer is proposed which is able to extract informative words and assign them higher weight. Moreover, the effect of transfer learning that transfers knowledge learned in the source domain to the target domain with the aim of improving the perform...
Global journal of computer science and technology, 2010
Gene expression profiling plays an important role in a broad range of areas in biology. Microarra... more Gene expression profiling plays an important role in a broad range of areas in biology. Microarray data often contains multiple missing expression values, which can significantly affect subsequent analysis In this paper, a new method based on fuzzy clustering and genes semantic similarity is proposed to estimate missing values in microarray data. In the proposed method, microarray data are clustered based on genes semantic similarity and their expression values and missing values are imputed with values generated from cluster centers Genes similarity in clustering process determine with their semantic similarity obtained from gene ontology as well as their expression values. The experimental results indicate that the proposed method outperforms other methods in terms of Root Mean Square error. Keywords-microarray, missing value estimation, fuzzy clustering, semantic similarity
Sequences are one of the most important types of data. Recently, mining and analysis of sequence ... more Sequences are one of the most important types of data. Recently, mining and analysis of sequence data has been studied in several fields. Sequence database mining and change mining is an example of data mining to study temporal data. Specific changes might be important to decision maker in different time periods to schedule future activities. Working with long sequences requires useful method. This paper presents a study on similarity measure and ranking sequence data. We employed sequence distance function based on structural features to measure the similarity, and a multi-criteria decision making techniques to rank them.
Article history: Received 11 July 2014 Received in revised form 25 August 2014 Accepted 28 Septem... more Article history: Received 11 July 2014 Received in revised form 25 August 2014 Accepted 28 September 2014 Available online 20 October 2014
Plasmonics, 2021
Alzheimer’s disease (AD) is a disease in cognitive regions in the human brain. Also the detection... more Alzheimer’s disease (AD) is a disease in cognitive regions in the human brain. Also the detection of AD in the early stage is too important. In this regard, a surface plasmon resonance (SPR) biosensor which comprises prism-silver/gold-SiO2 is proposed to detect of AD with high sensitivity. The properties of the AD sensor are numerically investigated with finite-difference time domain (FDTD) with different structural parameters. For this purpose, the effect of the incident beam waist, structural parameters, prism materials, and the temperature at 632 nm are then inspected to improve the structural parameters of the AD biosensor including the sensitivity and figure of merit (FOM). Our numerical calculations indicate that the proposed AD biosensor is able to operate with ultra-high sensitivity with maximum FOM of 31 and sensitivity of 1200 nm/refractive index unit (RIU) for small change of Δn\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \...
Computational and Mathematical Methods in Medicine, 2021
The automatic diagnosis of Alzheimer's disease plays an important role in human health, espec... more The automatic diagnosis of Alzheimer's disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer's disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer's symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer's severity. The relationship between Alzheimer's patients' functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Ex...
Software quality depends on several factors such as on time delivery; within budget and fulfillin... more Software quality depends on several factors such as on time delivery; within budget and fulfilling user's needs. Complexity is one of the most important factors that may affect the quality. Therefore, measuring and controlling the complexity result in improving the quality. So far, most of the researches have tried to identify and measure the complexity in design and code phase. However, when we have the code or design for software, it is too late to control complexity. In this article, with emphasis on Requirement Engineering process, we analyze the causes of software complexity, particularly in the first phase of software development, and propose a requirement based metric. This metric enables a software engineer to measure the complexity before actual design and implementation and choose strategies that are appropriate to the software complexity degree, thus saving on cost and human resource wastage and, more importantly, leading to lower maintenance costs.
Sequence mining, a branch of data mining, is recently an important research area, which recognize... more Sequence mining, a branch of data mining, is recently an important research area, which recognizes subsequences repeated in a temporal database. Fuzzy sequence mining can express the problem as quality form that leads to more desirable results. Sequence mining algorithms focus on the items with support higher than a specified threshold. Considering items with similar mental concepts lead to general and more compact sequences in database which might be indistinguishable in situations where the support of individual items are less than threshold. This paper proposes an algorithm to find sequences with more general concepts by considering mental similarity between items by the use of fuzzy ontology.
Sequence mining is one of very important fields in data mining studies in recent decade. In fact,... more Sequence mining is one of very important fields in data mining studies in recent decade. In fact, sequence mining recognizes subsequences repeated in a temporal database. All proposed sequence mining algorithms focus only on the items with support higher than specified threshold. Considering items with similar mental concepts can lead to some general and more compact sequences in database which might not be distinguished before when the support of individual items were less than threshold. In this paper an algorithm is proposed to find sequences containing more general concepts by considering mental similarity between the items. In this work, fuzzy ontology is used to describe the similar mental concepts.
Sentiment analysis is considered as one of the most essential tasks in the field of natural langu... more Sentiment analysis is considered as one of the most essential tasks in the field of natural language processing and cognitive science. In order to enhance the performance of sentiment analysis techniques, it is necessary to not only classify the sentences based on their sentimental labels but also to extract the informative words that contribute to the classification decision. In this regard, deep neural networks based on the attention mechanism have achieved considerable progress in recent years. However, there is still a limited number of studies on attention mechanisms for text classification and especially sentiment analysis. To fill this lacuna, a Convolution Neural Network (CNN) integrated with attention layer is presented in this paper that is able to extract informative words and assign them higher weights based on the context. In the attention layer, the proposed model employs a context vector and tries to measure the importance of a word as the similarity between the conte...
Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alz... more Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer's disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k-nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer's disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural netw...
Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality of ... more Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much ...
Topological Data Analysis (TDA) is a new emerging and fast growing field of data science providin... more Topological Data Analysis (TDA) is a new emerging and fast growing field of data science providing a set of tools from algebra, topology, and geometry to extract features from data based on its topological and geometrical features. This paper combines available methods from topological data analysis including persistent homology, persistent entropy, and persistent diagrams to build a strong topological feature extractor model from the topological properties of an image. By feeding features extracted by the topological model to machine learning models, we perform the classification task on DeepSat (SAT-4) dataset.
The Journal Of Psychological Science
A. Application of structural equation model in explaining the role of cognitive flexibility, emot... more A. Application of structural equation model in explaining the role of cognitive flexibility, emotional self-regulation and actively openminded thinking on changing people's attitudes.
Cellular nonlinear network (CNN) provides an infrastructure for Cellular Automata to have not onl... more Cellular nonlinear network (CNN) provides an infrastructure for Cellular Automata to have not only an initial state but an input which has a local memory in each cell with much more complexity. This property has many applications which we have investigated it in proposing a robust cryptology scheme. This scheme consists of a cryptography and steganography sub-module in which a 3D CNN is designed to produce a chaotic map as the kernel of the system to preserve confidentiality and data integrity in cryptology. Our contributions are three-fold including (1) a feature descriptor is applied to the cover image to form the secret key while conventional methods use a predefined key, (2) a 3D CNN is used to make a chaotic map for making cipher from the visual message, and (3) the proposed CNN is also used to make a dynamic k-LSB steganography. Conducted experiments on 25 standard images prove the effectiveness of the proposed cryptology scheme in terms of security, visual, and complexity ana...
The Neuroscience Journal of Shefaye Khatam
Artificial intelligence researchers are trying to implement human intelligence on the machine. Th... more Artificial intelligence researchers are trying to implement human intelligence on the machine. This s tudy aimed to develop an appropriate predictive computer model to evaluate the effectiveness of mindfulness-based cognitive therapy on irritability. Materials and Methods: The design of the present s tudy is quasi-experimental with a pre-tes t and pos t-tes t method. 135 individuals who referred to Khane Mehr counseling center in Mashhad and participated in an 8-session mindfulness-based cognitive therapy (MBCT) course were included in this s tudy. Totally, 11 MBCT courses were held and 10 to 14 people participated in each course. Participants completed the irritability ques tionnaire (Pourafrouz & et al.) at two s tages (before treatment and after treatment). In order to examine the differences from pretes t to pos t-tes t in this research, the variance analysis of repeated measures was used. Results: There was a significant difference between pre-tes t and pos t-tes t irritability scores. The effect of mindfulness was 83%. To develop the prediction model, three Bayesian, regression, and neural network models were compared. The Bayesian model, with 93% accuracy tes t data, was considered the mos t appropriate model. Moreover, the Bayesian models with input and output clus tering (85.7%), the Bayesian with classification (71.49%), and the sequential neural network (64.29%) were identified as suitable models to predict the effectiveness of 8-session mindfulness courses on reducing irritability. The Bayesian model with output clus tering, one-output regression, and the Convulsions Neural Network did not have sufficient predictive accuracy for the effectiveness of mindfulness. Conclusion: Using cognitive modeling, we can predict the efficacy of mindfulness-based cognitive therapy on irritability.y
Abstract-Gene expression profiling plays an important role in a broad range of areas in biology. ... more Abstract-Gene expression profiling plays an important role in a broad range of areas in biology. Microarray data often contains multiple missing expression values, which can significantly affect subsequent analysis In this paper, a new method based on fuzzy clustering and genes semantic similarity is proposed to estimate missing values in microarray data. In the proposed method, microarray data are clustered based on genes semantic similarity and their expression values and missing values are imputed with values generated from cluster centers Genes similarity in clustering process determine with their semantic similarity obtained from gene ontology as well as their expression values. The experimental results indicate that the proposed method outperforms other methods in terms of Root Mean Square error. Keywords-microarray, missing value estimation, fuzzy clustering, semantic similarity M I.
Journal of AI and Data Mining, 2021
With the rapid development of textual information on the web, sentiment analysis is changing to a... more With the rapid development of textual information on the web, sentiment analysis is changing to an essential analytic tool rather than an academic endeavor and numerous studies have been carried out in recent years to address this issue. By the emergence of deep learning, deep neural networks have attracted a lot of attention and become mainstream in this field. Despite the remarkable success of deep learning models for sentiment analysis of text, they are in the early steps of development and their potential is yet to be fully explored. Convolutional neural network is one of the deep learning methods that has been surpassed for sentiment analysis but is confronted with some limitations. Firstly, convolutional neural network requires a large number of training data. Secondly, it assumes that all words in a sentence have an equal contribution to the polarity of a sentence. To fill these lacunas, a convolutional neural network equipped with the attention mechanism is proposed in this ...
his paper deals with a new algorithm for solving the problems of reinforcement learning in contin... more his paper deals with a new algorithm for solving the problems of reinforcement learning in continuous spaces. For continuous states and actions, we use a new method based on selforganizing neural network, DIGNET. Two self-organizing neural network, DIGNETs, are used in this method, which having simple structure, can give an appropriate approximate of state/action space. The network is able to adapt inconsistent nature of reinforcement learning environments fine since the system parameters in DIGNET are self-adjusted in an autonomous way during the learning procedure. Automatic and competitive production and elimination of attraction wells, considering parameters of attraction well, threshold, age and depth, lead to flexibility of proposed algorithm to solve continuous problems and finally, the attraction wells of output DIGNET (action) will concentrate on the areas with high reward, providing an appropriate illustration for a continuous state space. At the end, we also present the r...
ArXiv, 2021
Sentiment analysis is known as one of the most crucial tasks in the field of natural language pro... more Sentiment analysis is known as one of the most crucial tasks in the field of natural language processing and Convolutional Neural Network (CNN) is one of those prominent models that is commonly used for this aim. Although convolutional neural networks have obtained remarkable results in recent years, they are still confronted with some limitations. Firstly, they consider that all words in a sentence have equal contributions in the sentence meaning representation and are not able to extract informative words. Secondly, they require a large number of training data to obtain considerable results while they have many parameters that must be accurately adjusted. To this end, a convolutional neural network integrated with a hierarchical attention layer is proposed which is able to extract informative words and assign them higher weight. Moreover, the effect of transfer learning that transfers knowledge learned in the source domain to the target domain with the aim of improving the perform...
Global journal of computer science and technology, 2010
Gene expression profiling plays an important role in a broad range of areas in biology. Microarra... more Gene expression profiling plays an important role in a broad range of areas in biology. Microarray data often contains multiple missing expression values, which can significantly affect subsequent analysis In this paper, a new method based on fuzzy clustering and genes semantic similarity is proposed to estimate missing values in microarray data. In the proposed method, microarray data are clustered based on genes semantic similarity and their expression values and missing values are imputed with values generated from cluster centers Genes similarity in clustering process determine with their semantic similarity obtained from gene ontology as well as their expression values. The experimental results indicate that the proposed method outperforms other methods in terms of Root Mean Square error. Keywords-microarray, missing value estimation, fuzzy clustering, semantic similarity
Sequences are one of the most important types of data. Recently, mining and analysis of sequence ... more Sequences are one of the most important types of data. Recently, mining and analysis of sequence data has been studied in several fields. Sequence database mining and change mining is an example of data mining to study temporal data. Specific changes might be important to decision maker in different time periods to schedule future activities. Working with long sequences requires useful method. This paper presents a study on similarity measure and ranking sequence data. We employed sequence distance function based on structural features to measure the similarity, and a multi-criteria decision making techniques to rank them.
Article history: Received 11 July 2014 Received in revised form 25 August 2014 Accepted 28 Septem... more Article history: Received 11 July 2014 Received in revised form 25 August 2014 Accepted 28 September 2014 Available online 20 October 2014
Plasmonics, 2021
Alzheimer’s disease (AD) is a disease in cognitive regions in the human brain. Also the detection... more Alzheimer’s disease (AD) is a disease in cognitive regions in the human brain. Also the detection of AD in the early stage is too important. In this regard, a surface plasmon resonance (SPR) biosensor which comprises prism-silver/gold-SiO2 is proposed to detect of AD with high sensitivity. The properties of the AD sensor are numerically investigated with finite-difference time domain (FDTD) with different structural parameters. For this purpose, the effect of the incident beam waist, structural parameters, prism materials, and the temperature at 632 nm are then inspected to improve the structural parameters of the AD biosensor including the sensitivity and figure of merit (FOM). Our numerical calculations indicate that the proposed AD biosensor is able to operate with ultra-high sensitivity with maximum FOM of 31 and sensitivity of 1200 nm/refractive index unit (RIU) for small change of Δn\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \...
Computational and Mathematical Methods in Medicine, 2021
The automatic diagnosis of Alzheimer's disease plays an important role in human health, espec... more The automatic diagnosis of Alzheimer's disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer's disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer's symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer's severity. The relationship between Alzheimer's patients' functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Ex...
Software quality depends on several factors such as on time delivery; within budget and fulfillin... more Software quality depends on several factors such as on time delivery; within budget and fulfilling user's needs. Complexity is one of the most important factors that may affect the quality. Therefore, measuring and controlling the complexity result in improving the quality. So far, most of the researches have tried to identify and measure the complexity in design and code phase. However, when we have the code or design for software, it is too late to control complexity. In this article, with emphasis on Requirement Engineering process, we analyze the causes of software complexity, particularly in the first phase of software development, and propose a requirement based metric. This metric enables a software engineer to measure the complexity before actual design and implementation and choose strategies that are appropriate to the software complexity degree, thus saving on cost and human resource wastage and, more importantly, leading to lower maintenance costs.
Sequence mining, a branch of data mining, is recently an important research area, which recognize... more Sequence mining, a branch of data mining, is recently an important research area, which recognizes subsequences repeated in a temporal database. Fuzzy sequence mining can express the problem as quality form that leads to more desirable results. Sequence mining algorithms focus on the items with support higher than a specified threshold. Considering items with similar mental concepts lead to general and more compact sequences in database which might be indistinguishable in situations where the support of individual items are less than threshold. This paper proposes an algorithm to find sequences with more general concepts by considering mental similarity between items by the use of fuzzy ontology.
Sequence mining is one of very important fields in data mining studies in recent decade. In fact,... more Sequence mining is one of very important fields in data mining studies in recent decade. In fact, sequence mining recognizes subsequences repeated in a temporal database. All proposed sequence mining algorithms focus only on the items with support higher than specified threshold. Considering items with similar mental concepts can lead to some general and more compact sequences in database which might not be distinguished before when the support of individual items were less than threshold. In this paper an algorithm is proposed to find sequences containing more general concepts by considering mental similarity between the items. In this work, fuzzy ontology is used to describe the similar mental concepts.
Sentiment analysis is considered as one of the most essential tasks in the field of natural langu... more Sentiment analysis is considered as one of the most essential tasks in the field of natural language processing and cognitive science. In order to enhance the performance of sentiment analysis techniques, it is necessary to not only classify the sentences based on their sentimental labels but also to extract the informative words that contribute to the classification decision. In this regard, deep neural networks based on the attention mechanism have achieved considerable progress in recent years. However, there is still a limited number of studies on attention mechanisms for text classification and especially sentiment analysis. To fill this lacuna, a Convolution Neural Network (CNN) integrated with attention layer is presented in this paper that is able to extract informative words and assign them higher weights based on the context. In the attention layer, the proposed model employs a context vector and tries to measure the importance of a word as the similarity between the conte...
Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alz... more Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer's disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k-nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer's disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural netw...
Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality of ... more Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much ...
Topological Data Analysis (TDA) is a new emerging and fast growing field of data science providin... more Topological Data Analysis (TDA) is a new emerging and fast growing field of data science providing a set of tools from algebra, topology, and geometry to extract features from data based on its topological and geometrical features. This paper combines available methods from topological data analysis including persistent homology, persistent entropy, and persistent diagrams to build a strong topological feature extractor model from the topological properties of an image. By feeding features extracted by the topological model to machine learning models, we perform the classification task on DeepSat (SAT-4) dataset.