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.
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.
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.
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.
Document and Text Processing
A.R. Mazochi; S. Bourbour; M. R. Ghofrani; S. Momtazi
Abstract
Converting a postal address to a coordinate, geocoding, is a helpful tool in many applications. Developing a geocoder tool is a difficult task if this tool relates to a developing country that does not follow a standard addressing format. The lack of complete reference data and non-persistency of names ...
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Converting a postal address to a coordinate, geocoding, is a helpful tool in many applications. Developing a geocoder tool is a difficult task if this tool relates to a developing country that does not follow a standard addressing format. The lack of complete reference data and non-persistency of names are the main challenges besides the common natural language process challenges. In this paper, we propose a geocoder for Persian addresses. To the best of our knowledge, our system, TehranGeocode, is the first geocoder for this language. Considering the non-standard structure of Persian addresses, we need to split the address into small segments, find each segment in the reference dataset, and connect them to find the target of the address. We develop our system based on address parsing and dynamic programming for this aim. We specify the contribution of our work compared to similar studies. We discuss the main components of the program, its data, and its results and show that the proposed framework achieves promising results in the field by finding 83\% of addresses with less than 300 meters error.
H. Kamali Ardakani; Seyed A. Mousavinia; F. Safaei
Abstract
Stereo machine vision can be used as a Space Sampling technique and the cameras parameters and configuration can effectively change the number of Samples in each Volume of space called Space Sampling Density (SSD). Using the concept of Voxels, this paper presents a method to optimize the geometric configuration ...
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Stereo machine vision can be used as a Space Sampling technique and the cameras parameters and configuration can effectively change the number of Samples in each Volume of space called Space Sampling Density (SSD). Using the concept of Voxels, this paper presents a method to optimize the geometric configuration of the cameras to maximize the SSD which means minimizing the Voxel volume and reducing the uncertainty in localizing an object in 3D space. Each pixel’s field of view (FOV) is considered as a skew pyramid. The uncertainty region will be created from the intersection of two pyramids associated with any of the cameras. Then, the mathematical equation of the uncertainty region is developed based on the correspondence field as a criterion for the localization error, including depth error as well as X and Y axes error. This field is completely dependent on the internal and external parameters of the cameras. Given the mathematical equation of localization error, the camera’s configuration optimization is addressed in a stereo vision system. Finally, the validity of the proposed method is examined by simulation and empirical results. These results show that the localization error will be significantly decreased in the optimized camera configuration.
M. R. Okhovvat; M. T. Kheirabadi; A. Nodehi; M. Okhovvat
Abstract
Minimizing make-span and maximizing remaining energy are usually of chief importance in the applications of wireless sensor actor networks (WSANs). Current task assignment approaches are typically concerned with one of the timing or energy constraints. These approaches do not consider the types and various ...
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Minimizing make-span and maximizing remaining energy are usually of chief importance in the applications of wireless sensor actor networks (WSANs). Current task assignment approaches are typically concerned with one of the timing or energy constraints. These approaches do not consider the types and various features of tasks WSANs may need to perform and thus may not be applicable to some types of real applications such as search and rescue missions. To this end, an optimized and type aware task assignment approach called TATA is proposed that considers the energy consumption as well as the make-span. TATA is an optimized task assignment approach and aware of the distribution necessities of WSANs with hybrid architecture. TATA comprises of two protocols, namely a Make-span Calculation Protocol (MaSC) and an Energy Consumption Calculation Protocol (ECal). Through considering both time and energy, TATA makes a tradeoff between minimizing make-span and maximizing the residual energies of actors. A series of extensive simulation results on typical scenarios show shorter make-span and larger remaining energy in comparison to when stochastic task assignment (STA), opportunistic load balancing (OLB), and task assignment algorithm based on quasi-Newton interior point (TA-QNIP) approaches is applied.
F.4.18. Time series analysis
Ali Ghorbanian; Hamideh Razavi
Abstract
In time series clustering, features are typically extracted from the time series data and used for clustering instead of directly clustering the data. However, using the same set of features for all data sets may not be effective. To overcome this limitation, this study proposes a five-step algorithm ...
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In time series clustering, features are typically extracted from the time series data and used for clustering instead of directly clustering the data. However, using the same set of features for all data sets may not be effective. To overcome this limitation, this study proposes a five-step algorithm that extracts a complete set of features for each data set, including both direct and indirect features. The algorithm then selects essential features for clustering using a genetic algorithm and internal clustering criteria. The final clustering is performed using a hierarchical clustering algorithm and the selected features. Results from applying the algorithm to 81 data sets indicate an average Rand index of 72.16%, with 38 of the 78 extracted features, on average, being selected for clustering. Statistical tests comparing this algorithm to four others in the literature confirm its effectiveness.
A. H Safari-Bavil; S. Jabbehdari; M. Ghobaei-Arani
Abstract
Generally, the issue of quality assurance is a specific assurance in computer networks. The conventional computer networks with hierarchical structures that are used in organizations are formed using some nodes of Ethernet switches within a tree structure. Open Flow is one of the main fundamental protocols ...
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Generally, the issue of quality assurance is a specific assurance in computer networks. The conventional computer networks with hierarchical structures that are used in organizations are formed using some nodes of Ethernet switches within a tree structure. Open Flow is one of the main fundamental protocols of Software-defined networks (SDNs) and provides the direct access to and change in program of sending network equipment such as switches and routers, physically and virtually. Lack of an open interface in data sending program has led to advent of integrated and close equipment that are similar to CPU in current networks. This study proposes a solution to reduce traffic using a correct placement of virtual machines while their security is maintained. The proposed solution is based on the moth-flame optimization, which has been evaluated. The obtained results indicate the priority of the proposed method.
A.R. Tajary; H. Morshedlou
Abstract
With the advent of having many processor cores on a single chip in many-core processors, the demand for exploiting these on-chip resources to boost the performance of applications has been increased. Task mapping is the problem of mapping the application tasks on these processor cores to achieve lower ...
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With the advent of having many processor cores on a single chip in many-core processors, the demand for exploiting these on-chip resources to boost the performance of applications has been increased. Task mapping is the problem of mapping the application tasks on these processor cores to achieve lower latency and better performance. Many researches are focused on minimizing the path between the tasks that demand high bandwidth for communication. Although using these methods can result in lower latency, but at the same time, it is possible to create congestion in the network which lowers the network throughput. In this paper, a throughput-aware method is proposed that uses simulated annealing for task mapping. The method is checked on several real-world applications and simulations are conducted on a cycle-accurate network on chip simulator. The results illustrate that the proposed method can achieve higher throughput while maintaining the delay in the NoC.
H.3.2.10. Medicine and science
Fahimeh Hafezi; Maryam Khodabakhsh
Abstract
Coronavirus disease as a persistent epidemic of acute respiratory syndrome posed a challenge to global healthcare systems. Many people have been forced to stay in their homes due to unprecedented quarantine practices around the world. Since most people used social media during the Coronavirus epidemic, ...
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Coronavirus disease as a persistent epidemic of acute respiratory syndrome posed a challenge to global healthcare systems. Many people have been forced to stay in their homes due to unprecedented quarantine practices around the world. Since most people used social media during the Coronavirus epidemic, analyzing the user-generated social content can provide new insights and be a clue to track changes and their occurrence over time. An active area in this space is the prediction of new infected cases from Coronavirus-generated social content. Identifying the social content that relates to Coronavirus is a challenging task because a significant number of posts contain Coronavirus-related content but do not include hashtags or Corona-related words. Conversely, posts that have the hashtag or the word Corona but are not really related to the meaning of Coronavirus and are mostly promotional. In this paper, we propose a semantic approach based on word embedding techniques to model Corona and then introduce a new feature namely semantic similarity to measure the similarity of a given post to Corona in semantic space. Furthermore, we propose two other features namely fear emotion and hope feeling to identify the Coronavirus-related posts. These features are used as statistical indicators in a regression model to estimate the new infected cases. We evaluate our features on the Persian dataset of Instagram posts, which was collected in the first wave of Coronavirus, and demonstrate that the consideration of the proposed features will lead to improved performance of the Coronavirus incidence rate estimation.
K. Kiani; R. Hematpour; R. Rastgoo
Abstract
Image colorization is an interesting yet challenging task due to the descriptive nature of getting a natural-looking color image from any grayscale image. To tackle this challenge and also have a fully automatic procedure, we propose a Convolutional Neural Network (CNN)-based model to benefit from the ...
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Image colorization is an interesting yet challenging task due to the descriptive nature of getting a natural-looking color image from any grayscale image. To tackle this challenge and also have a fully automatic procedure, we propose a Convolutional Neural Network (CNN)-based model to benefit from the impressive ability of CNN in the image processing tasks. To this end, we propose a deep-based model for automatic grayscale image colorization. Harnessing from convolutional-based pre-trained models, we fuse three pre-trained models, VGG16, ResNet50, and Inception-v2, to improve the model performance. The average of three model outputs is used to obtain more rich features in the model. The fused features are fed to an encoder-decoder network to obtain a color image from a grayscale input image. We perform a step-by-step analysis of different pre-trained models and fusion methodologies to include a more accurate combination of these models in the proposed model. Results on LFW and ImageNet datasets confirm the effectiveness of our model compared to state-of-the-art alternatives in the field.
Z. Falahiazar; A.R. Bagheri; M. Reshadi
Abstract
Spatio-temporal (ST) clustering is a relatively new field in data mining with great popularity, especially in geographic information. Moving objects are a type of ST data where the available information on these objects includes their last position. The strategy of performing the clustering operation ...
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Spatio-temporal (ST) clustering is a relatively new field in data mining with great popularity, especially in geographic information. Moving objects are a type of ST data where the available information on these objects includes their last position. The strategy of performing the clustering operation on all-time sequences is used for clustering moving objects. The problem with density-based clustering, which uses this strategy, is that the density of clusters may change at any point in time because of the displacement of points. Hence, the input parameters of an algorithm like DBSCAN used to cluster moving objects will change and have to be determined again. The DBSCAN-based methods have been proposed so far, assuming that the value of input parameters is fixed over time and does not provide a solution for their automatic determination. Nonetheless, with the objects moving and the density of the clusters changing, these parameters have to be determined appropriately again at each time interval. The paper used a dynamic multi-objective genetic algorithm to determine the parameters of the DBSCAN algorithm dynamically and automatically to solve this problem. The proposed algorithm in each time interval uses the clustering information of the previous time interval to determine the parameters. Beijing traffic control data was used as a moving dataset to evaluate the proposed algorithm. The experiments show that using the proposed algorithm for dynamic determination of DBSCAN input parameters outperforms DBSCAN with fixed input parameters over time in terms of the Silhouette and Outlier indices.
Z. Shahpar; V. Khatibi; A. Khatibi Bardsiri
Abstract
Software effort estimation plays an important role in software project management, and analogy-based estimation (ABE) is the most common method used for this purpose. ABE estimates the effort required for a new software project based on its similarity to previous projects. A similarity between the projects ...
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Software effort estimation plays an important role in software project management, and analogy-based estimation (ABE) is the most common method used for this purpose. ABE estimates the effort required for a new software project based on its similarity to previous projects. A similarity between the projects is evaluated based on a set of project features, each of which has a particular effect on the degree of similarity between projects and the effort feature. The present study examines the hybrid PSO-SA approach for feature weighting in analogy-based software project effort estimation. The proposed approach was implemented and tested on two well-known datasets of software projects. The performance of the proposed model was compared with other optimization algorithms based on MMRE, MDMRE, and PRED(0.25) measures. The results showed that weighted ABE models provide more accurate and better effort estimates relative to unweighted ABE models and that the PSO-SA hybrid approach has led to better and more accurate results compared with the other weighting approaches in both datasets.
B.3. Communication/Networking and Information Technology
Newsha Nowrozian; Farzad Tashtarian; Yahya Forghani
Abstract
Wireless rechargeable sensor networks (WRSNs) are widely used in many fields. However, the limited battery capacity of sensor nodes (SNs) prevents its development. To extend the battery life of SNs, they can be charged by a mobile charger (MC) equipped with radio frequency-based wireless power transfer ...
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Wireless rechargeable sensor networks (WRSNs) are widely used in many fields. However, the limited battery capacity of sensor nodes (SNs) prevents its development. To extend the battery life of SNs, they can be charged by a mobile charger (MC) equipped with radio frequency-based wireless power transfer (WPT). The paper addressed the issue of optimizing route planning and charging based on an MC with directional charging in on-demand networks. A mixed integer linear programming model (MILP) is proposed to obtain the appropriate stopping points (SPs) and orientation charging angles to respond to input requests in the shortest possible time and with minimum energy consumption. First, to select the SPs and the orientation charging direction, we utilize a clustering and discretization technique while minimizing the number of SPs and maximizing the charging cover. Then, to decrease the charging time of the required SNs as well as the MC's energy consumption, we propose a heuristic search algorithm for adjusting the moving path for the directional mobile charger. Finally, experimental simulations are performed to evaluate the performance of the proposed directional charging scheduling algorithm, and the results reveal that the suggested approach outperforms existing studies in terms of MC energy consumption, charging delay, and distance traveled.
N. Elyasi; M. Hosseini Moghadam
Abstract
In this paper, we use the topological data analysis (TDA) mapper algorithm alongside a deep convolutional neural network in order to classify some medical images.Deep learning models and convolutional neural networks can capture the Euclidean relation of a data point with its neighbor data points like ...
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In this paper, we use the topological data analysis (TDA) mapper algorithm alongside a deep convolutional neural network in order to classify some medical images.Deep learning models and convolutional neural networks can capture the Euclidean relation of a data point with its neighbor data points like the pixels of an image and they are particularly good at modeling data structures that live in the Euclidean space and not effective at modeling data structures that live in the non-Euclidean spaces. Topological data analysis-based methods have the ability to not only extract the Euclidean, but also topological features of data.For the first time in this paper, we apply a neural network as one of the filter steps of the Kepler mapper algorithm to classify skin cancer images. The major advantage of this method is that Kepler Mapper visualizes the classification result by a simplicial complex, where neural network increases the accuracy of classification. Furthermore, we apply TDA mapper and persistent homology algorithms to analyze the layers of Xception network in different training epochs. Also, we use persistent diagrams to visualize the results of the analysis of layers of the Xception network and then compare them by Wasserstein distances.
A. Hasan-Zadeh; F. Asadi; N. Garbazkar
Abstract
For an economic review of food prices in May 2019 to determine the trend of rising or decreasing prices compared to previous periods, we considered the price of food items at that time. The types of items consumed during specific periods in urban areas and the whole country are selected for our statistical ...
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For an economic review of food prices in May 2019 to determine the trend of rising or decreasing prices compared to previous periods, we considered the price of food items at that time. The types of items consumed during specific periods in urban areas and the whole country are selected for our statistical analysis. Among the various methods of modelling and statistical prediction, and in a new approach, we modeled the data using data mining techniques consisting of decision tree methods, associative rules, and Bayesian law. Then, prediction, validation, and standardization of the accuracy of the validation are performed on them. Results of data validation in the urban and national area and the results of the standardization of the accuracy of validation in the urban and national area are presented with the desired accuracy.
H.6.3.3. Pattern analysis
Meysam Roostaee; Razieh Meidanshahi
Abstract
In this study, we sought to minimize the need for redundant blood tests in diagnosing common diseases by leveraging unsupervised data mining techniques on a large-scale dataset of over one million patients' blood test results. We excluded non-numeric and subjective data to ensure precision. To identify ...
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In this study, we sought to minimize the need for redundant blood tests in diagnosing common diseases by leveraging unsupervised data mining techniques on a large-scale dataset of over one million patients' blood test results. We excluded non-numeric and subjective data to ensure precision. To identify relationships between attributes, we applied a suite of unsupervised methods including preprocessing, clustering, and association rule mining. Our approach uncovered correlations that enable healthcare professionals to detect potential acute diseases early, improving patient outcomes and reducing costs. The reliability of our extracted patterns also suggest that this approach can lead to significant time and cost savings while reducing the workload for laboratory personnel. Our study highlights the importance of big data analytics and unsupervised learning techniques in increasing efficiency in healthcare centers.
F.2. Numerical Analysis
S. Sareminia
Abstract
In recent years, the occurrence of various pandemics (COVID-19, SARS, etc.) and their widespread impact on human life have led researchers to focus on their pathology and epidemiology components. One of the most significant inconveniences of these epidemics is the human mortality rate, which has highly ...
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In recent years, the occurrence of various pandemics (COVID-19, SARS, etc.) and their widespread impact on human life have led researchers to focus on their pathology and epidemiology components. One of the most significant inconveniences of these epidemics is the human mortality rate, which has highly social adverse effects. This study, in addition to major attributes affecting the COVID-19 mortality rate (Health factors, people-health status, and climate) considers the social and economic components of societies. These components have been extracted from the countries’ Human Development Index (HDI) and the effect of the level of social development on the mortality rate has been investigated using ensemble data mining methods. The results indicate that the level of community education has the highest effect on the disease mortality rate. In a way, the extent of its effect is much higher than environmental factors such as air temperature, regional health factors, and community welfare. This factor is probably due to the ability of knowledge-based societies to manage the crises, their attention to health advisories, lower involvement of rumors, and consequently lower incidence of mental health problems. This study shows the impact of education on reducing the severity of the crisis in communities and opens a new window in terms of cultural and social factors in the interpretation of medical data. Furthermore, according to the results and comparing different types of single and ensemble data mining methods, the application of the ensemble method in terms of classification accuracy and prediction error has the best result.
Oladosu Oladimeji; Olayanju Oladimeji
Abstract
Breast cancer is the second major cause of death and accounts for 16% of all cancer deaths worldwide. Most of the methods of detecting breast cancer are very expensive and difficult to interpret such as mammography. There are also limitations such as cumulative radiation exposure, over-diagnosis, false ...
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Breast cancer is the second major cause of death and accounts for 16% of all cancer deaths worldwide. Most of the methods of detecting breast cancer are very expensive and difficult to interpret such as mammography. There are also limitations such as cumulative radiation exposure, over-diagnosis, false positives and negatives in women with a dense breast which pose certain uncertainties in high-risk population. The objective of this study is Detecting Breast Cancer Through Blood Analysis Data Using Classification Algorithms. This will serve as a complement to these expensive methods. High ranking features were extracted from the dataset. The KNN, SVM and J48 algorithms were used as the training platform to classify 116 instances. Furthermore, 10-fold cross validation and holdout procedures were used coupled with changing of random seed. The result showed that KNN algorithm has the highest and best accuracy of 89.99% and 85.21% for cross validation and holdout procedure respectively. This is followed by the J48 with 84.65% and 75.65% for the two procedures respectively. SVM had 77.58% and 68.69% respectively. Although it was also discovered that Blood Glucose level is a major determinant in detecting breast cancer, it has to be combined with other attributes to make decision as a result of other health issues like diabetes. With the result obtained, women are advised to do regular check-ups including blood analysis in order to know which of the blood components need to be worked on to prevent breast cancer based on the model generated in this study.
H.3. Artificial Intelligence
Saheb Ghanbari Motlagh; Fateme Razi Astaraei; Mojtaba Hajihosseini; Saeed Madani
Abstract
This study explores the potential use of Machine Learning (ML) techniques to enhance three types of nano-based solar cells. Perovskites of methylammonium-free formamidinium (FA) and mixed cation-based cells exhibit a boosted efficiency when employing ML techniques. Moreover, ML methods are utilized to ...
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This study explores the potential use of Machine Learning (ML) techniques to enhance three types of nano-based solar cells. Perovskites of methylammonium-free formamidinium (FA) and mixed cation-based cells exhibit a boosted efficiency when employing ML techniques. Moreover, ML methods are utilized to identify optimal donor complexes, high blind temperature materials, and to advance the thermodynamic stability of perovskites. Another significant application of ML in dye-sensitized solar cells (DSSCs) is the detection of novel dyes, solvents, and molecules for improving the efficiency and performance of solar cells. Some of these materials have increased cell efficiency, short-circuit current, and light absorption by more than 20%. ML algorithms to fine-tune network and plasmonic field bandwidths improve the efficiency and light absorption of surface plasmonic resonance (SPR) solar cells. This study outlines the potential of ML techniques to optimize and improve the development of nano-based solar cells, leading to promising results for the field of solar energy generation and supporting the demand for sustainable and dependable energy.
