Mohammad Nazari; Hossein Rahmani; Dadfar Momeni; Motahare Nasiri
Abstract
Graph representation of data can better define relationships among data components and thus provide better and richer analysis. So far, movies have been represented in graphs many times using different features for clustering, genre prediction, and even for use in recommender systems. In constructing ...
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Graph representation of data can better define relationships among data components and thus provide better and richer analysis. So far, movies have been represented in graphs many times using different features for clustering, genre prediction, and even for use in recommender systems. In constructing movie graphs, little attention has been paid to their textual features such as subtitles, while they contain the entire content of the movie and there is a lot of hidden information in them. So, in this paper, we propose a method called MoGaL to construct movie graph using LDA on subtitles. In this method, each node is a movie and each edge represents the novel relationship discovered by MoGaL among two associated movies. First, we extracted the important topics of the movies using LDA on their subtitles. Then, we visualized the relationship between the movies in a graph, using the cosine similarity. Finally, we evaluated the proposed method with respect to measures genre homophily and genre entropy. MoGaL succeeded to outperforms the baseline method significantly in these measures. Accordingly, our empirical results indicate that movie subtitles could be considered a rich source of informative information for various movie analysis tasks.
M. Mohammadzadeh; H. Khosravi
Abstract
Today, video games have a special place among entertainment. In this article, we have developed an interactive video game for mobile devices. In this game, the user can control the game’s character by his face and hand gestures. Cascading classifiers along with Haar-like features and local binary ...
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Today, video games have a special place among entertainment. In this article, we have developed an interactive video game for mobile devices. In this game, the user can control the game’s character by his face and hand gestures. Cascading classifiers along with Haar-like features and local binary patterns are used for hand gesture recognition and face detection. The game’s character moves according to the current hand and face state received from the frontal camera. Various ideas are used to achieve the appropriate accuracy and speed. Unity 3D and OpenCV for Unity are employed to design and implement the video game. The programming language is C#. This game is written in C# and developed for both Windows and Android operating systems. Experiments show an accuracy of 86.4% in the detection of five gestures. It also has an acceptable frame rate and can run at 11 fps and 8 fps in Windows and Android respectively.
H.3.9. Problem Solving, Control Methods, and Search
Heydar Toossian Shandiz; Mohsen Erfan Hajipour; Amir Ali Bagheri
Abstract
The aim of this paper is to create an efficient controller that can precisely track the position of autonomous surface vessels by utilizing the dynamic inversion control technique. One of the key objectives of this controller is to mitigate or eliminate the effects of environmental disturbances like ...
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The aim of this paper is to create an efficient controller that can precisely track the position of autonomous surface vessels by utilizing the dynamic inversion control technique. One of the key objectives of this controller is to mitigate or eliminate the effects of environmental disturbances like wind, waves, and water flow. On the other hand, intelligent methods are used to remove disturbances and fixing modeling errors. These methods include the use of fuzzy methods to adjust the control parameters in the linear controller used in the dynamic inversion controller and the use of perceptron neural network along with the dynamic inversion controller. The effectiveness of the proposed methods is evaluated not only based on the step response but also on their ability to track a complex path. Finally, the proposed methods have been compared with one of the classic methods, namely the PID control. This evaluation provides insights into how the proposed methods fare in terms of both step response and trajectory tracking when compared to the traditional PID control approach.
H.3. Artificial Intelligence
Thomas Njoroge Kinyanjui; Kelvin Mugoye; Rachael Kibuku
Abstract
This paper presents a Multi-Head Self-Attention Fusion Network (MHSA-FN) for real-time crop disease classification, addressing key limitations in existing models, including suboptimal feature extraction, inefficient feature recalibration, and weak multi-scale fusion. Unlike prior works that rely solely ...
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This paper presents a Multi-Head Self-Attention Fusion Network (MHSA-FN) for real-time crop disease classification, addressing key limitations in existing models, including suboptimal feature extraction, inefficient feature recalibration, and weak multi-scale fusion. Unlike prior works that rely solely on CNNs or transformers, MHSA-FN integrates MobileNetV2, EfficientNetV2, and Vision Transformers (ViTs) with a structured multi-level attention framework for enhanced feature learning. A gated fusion mechanism and a Multiscale Fusion Module (MSFM) optimize local texture details and global spatial relationships. The model was trained on a combined dataset of PlantVillage and locally collected images, improving adaptability to real-world conditions. It achieved 98.66% training accuracy and 99.0% test accuracy across 76 disease classes, with 99.34% precision, 99.01% recall, and 99.04% F1 score. McNemar’s test (p = 0.125) and Bayesian superiority probability (0.851) validated its robustness. Confidence variance analysis (0.000010) outperformed existing models, demonstrating MHSA-FN as a scalable, high-performance AI solution for precision agriculture in resource-constrained environments.
Fatemeh Alinezhad; Kourosh Kiani; Razieh Rastgoo
Abstract
Gender recognition is an attractive research area in recent years. To make a user-friendly application for gender recognition, having an accurate, fast, and lightweight model applicable in a mobile device is necessary. Although successful results have been obtained using the Convolutional Neural Network ...
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Gender recognition is an attractive research area in recent years. To make a user-friendly application for gender recognition, having an accurate, fast, and lightweight model applicable in a mobile device is necessary. Although successful results have been obtained using the Convolutional Neural Network (CNN), this model needs high computational resources that are not appropriate for mobile and embedded applications. To overcome this challenge and considering the recent advances in Deep Learning, in this paper, we propose a deep learning-based model for gender recognition in mobile devices using the lightweight CNN models. In this way, a pretrained CNN model, entitled Multi-Task Convolutional Neural Network (MTCNN), is used for face detection. Furthermore, the MobileFaceNet model is modified and trained using the Margin Distillation cost function. To boost the model performance, the Dense Block and Depthwise separable convolutions are used in the model. Results on six datasets confirm that the proposed model outperforms the MobileFaceNet model on six datasets with the relative accuracy improvements of 0.02%, 1.39%, 2.18%, 1.34%, 7.51%, 7.93% on the LFW, CPLFW, CFP-FP, VGG2-FP, UTKFace, and own data, respectively. In addition, we collected a dataset, including a total of 100’000 face images from both male and female in different age categories. Images of the women are with and without headgear.
A. Torkaman; K. Badie; A. Salajegheh; M. H. Bokaei; Seyed F. Fatemi
Abstract
Recently, network representation has attracted many research works mostly concentrating on representing of nodes in a dense low-dimensional vector. There exist some network embedding methods focusing only on the node structure and some others considering the content information within the nodes. In this ...
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Recently, network representation has attracted many research works mostly concentrating on representing of nodes in a dense low-dimensional vector. There exist some network embedding methods focusing only on the node structure and some others considering the content information within the nodes. In this paper, we propose HDNR; a hybrid deep network representation model, which uses a triplet deep neural network architecture that considers both the node structure and content information for network representation. In addition, the author's writing style is also considered as a significant feature in the node content information. Inspired by the application of deep learning in natural language processing, our model utilizes a deep random walk method to exploit inter-node structures and two deep sequence prediction methods to extract nodes' content information. The embedding vectors generated in this manner were shown to have the ability of boosting each other for learning optimal node representation, detecting more informative features and ultimately a better community detection. The experimental results confirm the effectiveness of this model for network representation compared to other baseline methods.
S. Mavaddati; S. Mavaddati
Abstract
Development of an automatic system to classify the type of rice grains is an interesting research area in the scientific fields associated with modern agriculture. In recent years, different techniques are employed to identify the types of various agricultural products. Also, different color-based and ...
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Development of an automatic system to classify the type of rice grains is an interesting research area in the scientific fields associated with modern agriculture. In recent years, different techniques are employed to identify the types of various agricultural products. Also, different color-based and texture-based features are used to yield the desired results in the classification procedure. This paper proposes a classification algorithm to detect different rice types by extracting features from the bulk samples. The feature space in this algorithm includes the fractal-based features of the extracted coefficients from the wavelet packet transform analysis. This feature vector is combined with other texture-based features and used to learn a model related to each rice type using the Gaussian mixture model classifier. Also, a sparse structured principal component analysis algorithm is applied to reduce the dimension of the feature vector and lead to the precise classification rate with less computational time. The results of the proposed classifier are compared with the results obtained from the other presented classification procedures in this context. The simulation results, along with a meaningful statistical test, show that the proposed algorithm based on the combinational features is able to detect precisely the type of rice grains with more than 99% accuracy. Also, the proposed algorithm can detect the rice quality for different percentages of combination with other rice grains with 99.75% average accuracy.
Maryam Khazaei; Nosratali Ashrafi-Payaman
Abstract
Nowadays, whereas the use of social networks and computer networks is increasing, the amount of associated complex data with graph structure and their applications, such as classification, clustering, link prediction, and recommender systems, has risen significantly. Because of security problems and ...
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Nowadays, whereas the use of social networks and computer networks is increasing, the amount of associated complex data with graph structure and their applications, such as classification, clustering, link prediction, and recommender systems, has risen significantly. Because of security problems and societal concerns, anomaly detection is becoming a vital problem in most fields. Applications that use a heterogeneous graph, are confronted with many issues, such as different kinds of neighbors, different feature types, and differences in type and number of links. So, in this research, we employ the HetGNN model with some changes in loss functions and parameters for heterogeneous graph embedding to capture the whole graph features (structure and content) for anomaly detection, then pass it to a VAE to discover anomalous nodes based on reconstruction error. Our experiments on AMiner data set with many base-lines illustrate that our model outperforms state-of-the-arts methods in heterogeneous graphs while considering all types of attributes.
H.3.2.2. Computer vision
Mobina Talebian; Kourosh Kiani; Razieh Rastgoo
Abstract
Fingerprint verification has emerged as a cornerstone of personal identity authentication. This research introduces a deep learning-based framework for enhancing the accuracy of this critical process. By integrating a pre-trained Inception model with a custom-designed architecture, we propose a model ...
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Fingerprint verification has emerged as a cornerstone of personal identity authentication. This research introduces a deep learning-based framework for enhancing the accuracy of this critical process. By integrating a pre-trained Inception model with a custom-designed architecture, we propose a model that effectively extracts discriminative features from fingerprint images. To this end, the input fingerprint image is aligned to a base fingerprint through minutiae vector comparison. The aligned input fingerprint is then subtracted from the base fingerprint to generate a residual image. This residual image, along with the aligned input fingerprint and the base fingerprint, constitutes the three input channels for a pre-trained Inception model. Our main contribution lies in the alignment of fingerprint minutiae, followed by the construction of a color fingerprint representation. Moreover, we collected a dataset, including 200 fingerprint images corresponding to 20 persons, for fingerprint verification. The proposed method is evaluated on two distinct datasets, demonstrating its superiority over existing state-of-the-art techniques. With a verification accuracy of 99.40% on the public Hong Kong Dataset, our approach establishes a new benchmark in fingerprint verification. This research holds the potential for applications in various domains, including law enforcement, border control, and secure access systems.
I.5. Social and Behavioral Sciences
Havva Alizadeh Noughabi
Abstract
Social media platforms have transformed information consumption, offering personalized features that enhance engagement and streamline content discovery. Among these, the Twitter Lists functionality allows users to curate content by grouping accounts based on shared themes, fostering focused interactions ...
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Social media platforms have transformed information consumption, offering personalized features that enhance engagement and streamline content discovery. Among these, the Twitter Lists functionality allows users to curate content by grouping accounts based on shared themes, fostering focused interactions and diverse perspectives. Despite their widespread use, the relationship between user-generated content and List subscription behaviors remains insufficiently explored. This study examines the alignment between users' post topics and their subscribed Lists, along with the influence of activity levels on this alignment. The role of content diversity in shaping subscription patterns to Lists covering a range of topics is also investigated. Additionally, the extent to which the affective characteristics—sentiment and emotion—of user posts correspond with the emotional tone of subscribed List content is analyzed. Utilizing a comprehensive Twitter dataset, advanced techniques for topic modeling, sentiment analysis, and emotion extraction were applied, and profiles for both users and Lists were developed to facilitate the exploration of their interrelationship. These insights advance the understanding of user interactions with Lists, informing the development of personalized recommendation systems and improved content curation strategies, with broad implications for social media research and platform functionality.
F. Jafarinejad; R. Farzbood
Abstract
Image retrieval is a basic task in many content-based image systems. Achieving high precision, while maintaining computation time is very important in relevance feedback-based image retrieval systems. This paper establishes an analogy between this and the task of image classification. Therefore, in the ...
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Image retrieval is a basic task in many content-based image systems. Achieving high precision, while maintaining computation time is very important in relevance feedback-based image retrieval systems. This paper establishes an analogy between this and the task of image classification. Therefore, in the image retrieval problem, we will obtain an optimized decision surface that separates dataset images into two categories of relevant/irrelevant images corresponding to the query image. This problem will be viewed and solved as an optimization problem using particle optimization algorithm. Although the particle swarm optimization (PSO) algorithm is widely used in the field of image retrieval, no one use it for directly feature weighting. Information extracted from user feedbacks will guide particles in order to find the optimal weights of various features of images (Color-, shape- or texture-based features). Fusion of these very non-homogenous features need a feature weighting algorithm that will take place by the help of PSO algorithm. Accordingly, an innovative fitness function is proposed to evaluate each particle’s position. Experimental results on Wang dataset and Corel-10k indicate that average precision of the proposed method is higher than other semi-automatic and automatic approaches. Moreover, the proposed method suggest a reduction in the computational complexity in comparison to other PSO-based image retrieval methods.
R. Serajeh; A. Mousavinia; F. Safaei
Abstract
Classical SFM (Structure From Motion) algorithms are widely used to estimate the three-dimensional structure of a stationary scene with a moving camera. However, when there are moving objects in the scene, if the equation of the moving object is unknown, the approach fails. This paper first demonstrates ...
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Classical SFM (Structure From Motion) algorithms are widely used to estimate the three-dimensional structure of a stationary scene with a moving camera. However, when there are moving objects in the scene, if the equation of the moving object is unknown, the approach fails. This paper first demonstrates that when the frame rate is high enough and the object movement is continuous in time, meaning that acceleration is limited, a simple linear model can be effectively used to estimate the motion. This theory is first mathematically proven in a closed-form expression and then optimized by a nonlinear function applicable for our problem. The algorithm is evaluated both on synthesized and real data from Hopkins dataset.
H. Aghabarar; K. Kiani; P. Keshavarzi
Abstract
Nowadays, given the rapid progress in pattern recognition, new ideas such as theoretical mathematics can be exploited to improve the efficiency of these tasks. In this paper, the Discrete Wavelet Transform (DWT) is used as a mathematical framework to demonstrate handwritten digit recognition in spiking ...
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Nowadays, given the rapid progress in pattern recognition, new ideas such as theoretical mathematics can be exploited to improve the efficiency of these tasks. In this paper, the Discrete Wavelet Transform (DWT) is used as a mathematical framework to demonstrate handwritten digit recognition in spiking neural networks (SNNs). The motivation behind this method is that the wavelet transform can divide the spike information and noise into separate frequency subbands and also store the time information. The simulation results show that DWT is an effective and worthy choice and brings the network to an efficiency comparable to previous networks in the spiking field. Initially, DWT is applied to MNIST images in the network input. Subsequently, a type of time encoding called constant-current-Leaky Integrate and Fire (LIF) encoding is applied to the transformed data. Following this, the encoded images are input to the multilayer convolutional spiking network. In this architecture, various wavelets have been investigated, and the highest classification accuracy of 99.25% is achieved.
H.3.2.2. Computer vision
Zobeir Raisi; Valimohammad Nazarzehi; Rasoul Damani; Esmaeil Sarani
Abstract
This paper explores the performance of various object detection techniques for autonomous vehicle perception by analyzing classical machine learning and recent deep learning models. We evaluate three classical methods, including PCA, HOG, and HOG alongside different versions of the SVM classifier, and ...
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This paper explores the performance of various object detection techniques for autonomous vehicle perception by analyzing classical machine learning and recent deep learning models. We evaluate three classical methods, including PCA, HOG, and HOG alongside different versions of the SVM classifier, and five deep-learning models, including Faster-RCNN, SSD, YOLOv3, YOLOv5, and YOLOv9 models using the benchmark INRIA dataset. The experimental results show that although classical methods such as HOG + Gaussian SVM outperform other classical approaches, they are outperformed by deep learning techniques. Furthermore, Classical methods have limitations in detecting partially occluded, distant objects and complex clothing challenges, while recent deep-learning models are more efficient and provide better performance (YOLOv9) on these challenges.
H.3.8. Natural Language Processing
Milad Allahgholi; Hossein Rahmani; Parinaz Soltanzadeh
Abstract
Stance detection is the process of identifying and classifying an author's point of view or stance towards a specific target in a given text. Most of previous studies on stance detection neglect the contextual information hidden in the input data and as a result lead to less accurate results. In this ...
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Stance detection is the process of identifying and classifying an author's point of view or stance towards a specific target in a given text. Most of previous studies on stance detection neglect the contextual information hidden in the input data and as a result lead to less accurate results. In this paper, we propose a novel method called ConSPro, which uses decoder-only transformers to consider contextual input data in the process of stance detection. First, ConSPro applies zero-shot prompting of decoder only transformers to extract the context of target in the input data. Second, in addition to target and input text, ConSPro uses the extracted context as the third type of parameter for the ensemble method. We evaluate ConSPro on SemEval2016 and the empirical results indicate that ConSPro outperforms the non-contextual approaches methods, on average 9% with respect to f-measure. The findings of this study show the strong capabilities of zero-shot prompting for extracting the informative contextual information with significantly less effort comparing to previous methods on context extraction.
M. Rezaei; H. Nezamabadi-pour
Abstract
The present study aims to overcome some defects of the K-nearest neighbor (K-NN) rule. Two important data preprocessing methods to elevate the K-NN rule are prototype selection (PS) and prototype generation (PG) techniques. Often the advantage of these techniques is investigated separately. In this paper, ...
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The present study aims to overcome some defects of the K-nearest neighbor (K-NN) rule. Two important data preprocessing methods to elevate the K-NN rule are prototype selection (PS) and prototype generation (PG) techniques. Often the advantage of these techniques is investigated separately. In this paper, using the gravitational search algorithm (GSA), two hybrid schemes have been proposed in which PG and PS problems have been considered together. To evaluate the classification performance of these hybrid models, we have performed a comparative experimental study including a comparison between our proposals and some approaches previously studied in the literature using several benchmark datasets. The experimental results demonstrate that our hybrid approaches outperform most of the competitive methods.
M. R. Fallahzadeh; F. Farokhi; A. Harimi; R. Sabbaghi-Nadooshan
Abstract
Facial Expression Recognition (FER) is one of the basic ways of interacting with machines and has been getting more attention in recent years. In this paper, a novel FER system based on a deep convolutional neural network (DCNN) is presented. Motivated by the powerful ability of DCNN to learn features ...
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Facial Expression Recognition (FER) is one of the basic ways of interacting with machines and has been getting more attention in recent years. In this paper, a novel FER system based on a deep convolutional neural network (DCNN) is presented. Motivated by the powerful ability of DCNN to learn features and image classification, the goal of this research is to design a compatible and discriminative input for pre-trained AlexNet-DCNN. The proposed method consists of 4 steps: first, extracting three channels of the image including the original gray-level image, in addition to horizontal and vertical gradients of the image similar to the red, green, and blue color channels of an RGB image as the DCNN input. Second, data augmentation including scale, rotation, width shift, height shift, zoom, horizontal flip, and vertical flip of the images are prepared in addition to the original images for training the DCNN. Then, the AlexNet-DCNN model is applied to learn high-level features corresponding to different emotion classes. Finally, transfer learning is implemented on the proposed model and the presented model is fine-tuned on target datasets. The average recognition accuracy of 92.41% and 93.66% were achieved for JAFEE and CK+ datasets, respectively. Experimental results on two benchmark emotional datasets show promising performance of the proposed model that can improve the performance of current FER systems.
H.3. Artificial Intelligence
Mohammad Hossein Shayesteh; Behrooz Shahrokhzadeh; Behrooz Masoumi
Abstract
This paper provides a comprehensive review of the potential of game theory as a solution for sensor-based human activity recognition (HAR) challenges. Game theory is a mathematical framework that models interactions between multiple entities in various fields, including economics, political science, ...
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This paper provides a comprehensive review of the potential of game theory as a solution for sensor-based human activity recognition (HAR) challenges. Game theory is a mathematical framework that models interactions between multiple entities in various fields, including economics, political science, and computer science. In recent years, game theory has been increasingly applied to machine learning challenges, including HAR, as a potential solution to improve recognition performance and efficiency of recognition algorithms. The review covers the shared challenges between HAR and machine learning, compares previous work on traditional approaches to HAR, and discusses the potential advantages of using game theory. It discusses different game theory approaches, including non-cooperative and cooperative games, and provides insights into how they can improve the HAR systems. The authors propose new game theory-based approaches and evaluate their effectiveness compared to traditional approaches. Overall, this review paper contributes to expanding the scope of research in HAR by introducing game-theoretic concepts and solutions to the field and provides valuable insights for researchers interested in applying game-theoretic approaches to HAR.
H.5. Image Processing and Computer Vision
Jalaluddin Zarei; Mohammad Hossein Khosravi
Abstract
Agricultural experts try to detect leaf diseases in the shortest possible time. However, limitations such as lack of manpower, poor eyesight, lack of sufficient knowledge, and quarantine restrictions in the transfer of diseases to the laboratory can be acceptable reasons to use digital technology to ...
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Agricultural experts try to detect leaf diseases in the shortest possible time. However, limitations such as lack of manpower, poor eyesight, lack of sufficient knowledge, and quarantine restrictions in the transfer of diseases to the laboratory can be acceptable reasons to use digital technology to detect pests and diseases and finally dispose of them. One of the available solutions in this field is using convolutional neural networks. On the other hand, the performance of CNNs depends on the large amount of data. While there is no suitable dataset for the native trees of South Khorasan province, this motivates us to create a suitable dataset with a large amount of data. In this article, we introduce a new dataset in 9 classes of images of Healthy Barberry leaves, Barberry Rust disease, Barberry Pandemis ribeana Tortricidae pest, Healthy Jujube leaves, Jujube Ziziphus Tingid disease, Jujube Parenchyma-Eating Butterfly pest, Healthy Pomegranate leaves, Pomegranate Aphis punicae pest, and Pomegranate Leaf-Cutting Bees pest and also check the performance of several well-known convolutional neural networks using all gradient descent optimizer algorithms on this dataset. Our most important achievement is the creation of a dataset with a high data volume of pests and diseases in different classes. In addition, our experiments show that common CNN architectures, along with gradient descent optimizers, have an acceptable performance on the proposed dataset. We call the proposed dataset ”Birjand Native Plant Leaves (BNPL) Dataset”. It is available at the address https://kaggle.com/datasets/ec17162ca01825fb362419503cbc84c73d162bffe936952253ed522705228e06.
E. Kalhor; B. Bakhtiari
Abstract
Feature selection is the one of the most important steps in designing speech emotion recognition systems. Because there is uncertainty as to which speech feature is related to which emotion, many features must be taken into account and, for this purpose, identifying the most discriminative features is ...
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Feature selection is the one of the most important steps in designing speech emotion recognition systems. Because there is uncertainty as to which speech feature is related to which emotion, many features must be taken into account and, for this purpose, identifying the most discriminative features is necessary. In the interest of selecting appropriate emotion-related speech features, the current paper focuses on a multi-task approach. For this reason, the study considers each speaker as a task and proposes a multi-task objective function to select features. As a result, the proposed method chooses one set of speaker-independent features of which the selected features are discriminative in all emotion classes. Correspondingly, multi-class classifiers are utilized directly or binary classifications simply perform multi-class classifications. In addition, the present work employs two well-known datasets, the Berlin and Enterface. The experiments also applied the openSmile toolkit to extract more than 6500 features. After feature selection phase, the results illustrated that the proposed method selects the features which is common in the different runs. Also, the runtime of proposed method is the lowest in comparison to other methods. Finally, 7 classifiers are employed and the best achieved performance is 73.76% for the Berlin dataset and 72.17% for the Enterface dataset, in the faced of a new speaker .These experimental results then show that the proposed method is superior to existing state-of-the-art methods.
Z. Teimoori; M. Salehi; V. Ranjbar; Saeed R. Shehnepoor; Sh. Najari
Abstract
Nowadays, some e-advice websites and social media like e-commerce businesses, provide not only their goods but a new way that their customers can give their opinions about products. Meanwhile, there are some review spammers who try to promote or demote some specific products by writing fraud reviews. ...
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Nowadays, some e-advice websites and social media like e-commerce businesses, provide not only their goods but a new way that their customers can give their opinions about products. Meanwhile, there are some review spammers who try to promote or demote some specific products by writing fraud reviews. There have been several types of researches and studies toward detecting these review spammers, but most studies are based on individual review spammers and few of them studied group review spammers, nevertheless it should be mentioned that review spammers can increase their effects by cooperating and working together. More words, there have been many features introduced in order to detect review spammers and it is better to use the efficient ones. In this paper we propose a novel framework, named Network Based Group Review Spammers which tries to identify and classify group review spammers with the usage of the heterogeneous information network. In addition to eight basic features for detecting group review spammers, three efficient new features from previous studies were modified and added in order to improve detecting group review spammers. Then with the definition of Meta-path, features are ranked. Results showed that by using the importance of features and adding three new features in the suggested framework, group review spammers detection is improved on Amazon dataset.
D. Data
Zahra Ghorbani; Ali Ghorbanian
Abstract
Increasing the accuracy of time-series clustering while reducing execution time is a primary challenge in the field of time-series clustering. Researchers have recently applied approaches, such as the development of distance metrics and dimensionality reduction, to address this challenge. However, using ...
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Increasing the accuracy of time-series clustering while reducing execution time is a primary challenge in the field of time-series clustering. Researchers have recently applied approaches, such as the development of distance metrics and dimensionality reduction, to address this challenge. However, using segmentation and ensemble clustering to solve this issue is a key aspect that has received less attention in previous research. In this study, an algorithm based on the selection and combination of the best segments created from a time-series dataset was developed. In the first step, the dataset was divided into segments of equal lengths. In the second step, each segment is clustered using a hierarchical clustering algorithm. In the third step, a genetic algorithm selects different segments and combines them using combinatorial clustering. The resulting clustering of the selected segments was selected as the final dataset clustering. At this stage, an internal clustering criterion evaluates and sorts the produced solutions. The proposed algorithm was executed on 82 different datasets in 10 repetitions. The results of the algorithm indicated an increase in the clustering efficiency of 3.07%, reaching a value of 67.40. The obtained results were evaluated based on the length of the time series and the type of dataset. In addition, the results were assessed using statistical tests with the six algorithms existing in the literature.
N. Taghvaei; B. Masoumi; M. R. Keyvanpour
Abstract
In general, humans are very complex organisms, and therefore, research into their various dimensions and aspects, including personality, has become an attractive subject of research. With the advent of technology, the emergence of a new kind of communication in the context of social networks has also ...
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In general, humans are very complex organisms, and therefore, research into their various dimensions and aspects, including personality, has become an attractive subject of research. With the advent of technology, the emergence of a new kind of communication in the context of social networks has also given a new form of social communication to humans, and the recognition and categorization of people in this new space have become a hot topic of research that has been challenged by many researchers. In this paper, considering the Big Five personality characteristics of individuals, first, categorization of related work is proposed, and then a hybrid framework based on Fuzzy Neural Networks (FNN), along with, Deep Neural Networks (DNN) has been proposed that improves the accuracy of personality recognition by combining different FNN-classifiers with DNN-classifier in a proposed two-stage decision fusion scheme. Finally, a simulation of the proposed approach is carried out. The proposed approach is using the structural features of Social Networks Analysis (SNA), along with a linguistic analysis (LA) feature extracted from the description of the activities of individuals and comparison with the previous similar researches. The results, well-illustrated the performance improvement of the proposed framework up to 83.2 % of average accuracy on myPersonality dataset.
F. Salimian Najafabadi; M. T. Sadeghi
Abstract
An important sector that has a significant impact on the economies of countries is the agricultural sector. Researchers are trying to improve this sector by using the latest technologies. One of the problems facing farmers in the agricultural activities is plant diseases. If a plant problem is diagnosed ...
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An important sector that has a significant impact on the economies of countries is the agricultural sector. Researchers are trying to improve this sector by using the latest technologies. One of the problems facing farmers in the agricultural activities is plant diseases. If a plant problem is diagnosed soon, the farmer can treat the disease more effectively. This study introduces a new deep artificial neural network called AgriNet which is suitable for recognizing some types of agricultural diseases in a plant using images from the plant leaves. The proposed network makes use of the channel shuffling technique of ShuffleNet and the channel dependencies modeling technique of SENet. One of the factors influencing the effectiveness of the proposed network architecture is how to increase the flow of information in the channels after explicitly modelling interdependencies between channels. This is in fact, an important novelty of this research work. The dataset used in this study is PlantVillage, which contains 14 types of plants in 24 groups of healthy and diseased. Our experimental results show that the proposed method outperforms the other methods in this area. AgriNet leads to accuracy and loss of 98% and 7%, respectively on the experimental data. This method increases the recognition accuracy by about 2% and reduces the loss by 8% compared to the ShuffleNetV2 method.
H.5. Image Processing and Computer Vision
Khosro Rezaee
Abstract
Traditional Down syndrome identification often relies on professionals visually recognizing facial features, a method that can be subjective and inconsistent. This study introduces a hybrid deep learning (DL) model for automatically identifying Down syndrome in children's facial images, utilizing facial ...
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Traditional Down syndrome identification often relies on professionals visually recognizing facial features, a method that can be subjective and inconsistent. This study introduces a hybrid deep learning (DL) model for automatically identifying Down syndrome in children's facial images, utilizing facial analysis techniques to enhance diagnostic accuracy and enable real-time detection. The model employs the MobileNetV2 architecture to address dataset bias and diversity issues while ensuring efficient feature extraction. The framework also integrates the structure with optimized Bidirectional Long Short-Term Memory (BiLSTM) to enhance feature classification. Trained and validated on facial images from children with Down syndrome and healthy controls from the Kaggle dataset, the model achieved 97.60% accuracy and 97.50% recall. The approach also integrates cloud and edge processing for efficient real-time analysis, offering adaptability to new images and conditions.
