Security systems must be continuously developed in order to cope with new challenges. One example of such challenges is the proliferation of sexual harassment against women in public places, such as public toilets and public transportation. Although separately designated toilets or waiting and seating areas in public transports are provided, enforcing these restrictions need constant manual surveillance. In this paper we propose an automatic gender classification system based on an individual’s facial characteristics. We evaluate the performance of QLRBP and MLLPQ as feature extractors combined with SVM or kNN as classifiers. Our experiments show that MLLPQ gives superior performance compared to QLRBP for either classifier. Furthermore, MLLPQ is less computationally demanding compared to QLRBP. The best result we achieved in our experiments was the combination of MLLPQ and kNN classifier, yielding an accuracy rate of 92.11%.
{"title":"The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition","authors":"Septian Abednego, Iwan Setyawan, Gunawan Dewantoro","doi":"10.5614/itbj.ict.res.appl.2021.15.3.4","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2021.15.3.4","url":null,"abstract":"Security systems must be continuously developed in order to cope with new challenges. One example of such challenges is the proliferation of sexual harassment against women in public places, such as public toilets and public transportation. Although separately designated toilets or waiting and seating areas in public transports are provided, enforcing these restrictions need constant manual surveillance. In this paper we propose an automatic gender classification system based on an individual’s facial characteristics. We evaluate the performance of QLRBP and MLLPQ as feature extractors combined with SVM or kNN as classifiers. Our experiments show that MLLPQ gives superior performance compared to QLRBP for either classifier. Furthermore, MLLPQ is less computationally demanding compared to QLRBP. The best result we achieved in our experiments was the combination of MLLPQ and kNN classifier, yielding an accuracy rate of 92.11%.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44762581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-19DOI: 10.5614/itbj.ict.res.appl.2021.15.2.6
L. Varghese, Vanitha Kandasamy
Deep learning techniques have been used effectively for rubber crop yield prediction. A hybrid of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) is the best technique for crop yield prediction because it can effectively handle uncertainty of features. Hence, in this paper, a hybrid CNN-RNN method is proposed to forecast Hevea yields based on environmental data in Kerala state, India. The proposed hybrid CNN-RNN method reduces the internal covariate shift of CNN by batch normalization and solves the gradient vanishing or exploding problem of RNN using LSTM with a cell activation mechanism. The proposed method has three essential characteristics: (i) it captures the time dependency of environmental factors and improves the inherent computational time; (ii) it is capable of generalizing the yield prediction under uncertain conditions without loss of prediction accuracy; (iii) combined with the back propagation and feed forward method it can reveal the extent to which samples of weather conditions and soil data conditions are suitable to provide a clear boundary between rubber yield variations.
{"title":"Convolution and Recurrent Hybrid Neural Network for Hevea Yield Prediction","authors":"L. Varghese, Vanitha Kandasamy","doi":"10.5614/itbj.ict.res.appl.2021.15.2.6","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2021.15.2.6","url":null,"abstract":"Deep learning techniques have been used effectively for rubber crop yield prediction. A hybrid of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) is the best technique for crop yield prediction because it can effectively handle uncertainty of features. Hence, in this paper, a hybrid CNN-RNN method is proposed to forecast Hevea yields based on environmental data in Kerala state, India. The proposed hybrid CNN-RNN method reduces the internal covariate shift of CNN by batch normalization and solves the gradient vanishing or exploding problem of RNN using LSTM with a cell activation mechanism. The proposed method has three essential characteristics: (i) it captures the time dependency of environmental factors and improves the inherent computational time; (ii) it is capable of generalizing the yield prediction under uncertain conditions without loss of prediction accuracy; (iii) combined with the back propagation and feed forward method it can reveal the extent to which samples of weather conditions and soil data conditions are suitable to provide a clear boundary between rubber yield variations.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43238844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-07DOI: 10.5614/itbj.ict.res.appl.2021.15.2.4
Vuttichai Vichianchai, Sumonta Kasemvilas
This paper proposes a new term frequency with a Gaussian technique (TF-G) to classify the risk of suicide from Thai clinical notes and to perform sentiment analysis based on Thai customer reviews and English tweets of travelers that use US airline services. This research compared TF-G with term weighting techniques based on Thai text classification methods from previous researches, including the bag-of-words (BoW), term frequency (TF), term frequency-inverse document frequency (TF-IDF), and term frequency-inverse corpus document frequency (TF-ICF) techniques. Suicide risk classification and sentiment analysis were performed with the decision tree (DT), naïve Bayes (NB), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) techniques. The experimental results showed that TF-G is appropriate for feature extraction to classify the risk of suicide and to analyze the sentiments of customer reviews and tweets of travelers. The TF-G technique was more accurate than BoW, TF, TF-IDF and TF-ICF for term weighting in Thai suicide risk classification, for term weighting in sentiment analysis of Thai customer reviews for Burger King, Pizza Hut, and Sizzler restaurants, and for the sentiment analysis of English tweets of travelers using US airline services.
{"title":"A New Term Frequency with Gaussian Technique for Text Classification and Sentiment Analysis","authors":"Vuttichai Vichianchai, Sumonta Kasemvilas","doi":"10.5614/itbj.ict.res.appl.2021.15.2.4","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2021.15.2.4","url":null,"abstract":"This paper proposes a new term frequency with a Gaussian technique (TF-G) to classify the risk of suicide from Thai clinical notes and to perform sentiment analysis based on Thai customer reviews and English tweets of travelers that use US airline services. This research compared TF-G with term weighting techniques based on Thai text classification methods from previous researches, including the bag-of-words (BoW), term frequency (TF), term frequency-inverse document frequency (TF-IDF), and term frequency-inverse corpus document frequency (TF-ICF) techniques. Suicide risk classification and sentiment analysis were performed with the decision tree (DT), naïve Bayes (NB), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) techniques. The experimental results showed that TF-G is appropriate for feature extraction to classify the risk of suicide and to analyze the sentiments of customer reviews and tweets of travelers. The TF-G technique was more accurate than BoW, TF, TF-IDF and TF-ICF for term weighting in Thai suicide risk classification, for term weighting in sentiment analysis of Thai customer reviews for Burger King, Pizza Hut, and Sizzler restaurants, and for the sentiment analysis of English tweets of travelers using US airline services.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42481263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-07DOI: 10.5614/itbj.ict.res.appl.2021.15.2.2
Z. Taha, Hafsa Jassim, Anas A. Ahmed, Ikhlas M. Farhan
This study investigated structure strategies and exploratory scenarios for a half mode substrate integrated waveguide (HMSIW) antenna. The proposed antenna consists of three Hilbert cells, which are simulated by using CST programming. The antenna was manufactured with the realities of minor imperfections and high incorporation. The proposed structure offers a suitable substrate integrated waveguide (SIW) with about a decrease in size by half. In addition, Hilbert cells were added to realize the triple-band characteristics with good impedance matching, radiation patterns, and radiation performance. The antenna was fabricated on h = 1 mm thick dielectric substrate with dielectric constant (𝜀𝑟 = 4.3). The Hilbert cells were drilled on the top plane of the antenna substrate and fed using a microstrip transmission line. The proposed antenna is small, with a slot side length of approximately half of the guided wavelength. The three developed Hilbert cell HMSIW antenna resonates at 3.25, 5.94 and 6.5 GHz with a bandwidth of 2.97, 2.25 and 2.29% within a return loss of ‑38.77, ‑35.82 -23.35 dB, respectively. The results showed enhancements in antenna gain of 3.56, 4.97 and 6.43 dBi, with a radiation efficiency of -1.253, -0.493 and -0.586 dB, respectively.
{"title":"Design and Implementation of Triple Band Half Mode Substrate Integrated Waveguide (HMSIW) Antenna with Compact Size","authors":"Z. Taha, Hafsa Jassim, Anas A. Ahmed, Ikhlas M. Farhan","doi":"10.5614/itbj.ict.res.appl.2021.15.2.2","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2021.15.2.2","url":null,"abstract":"This study investigated structure strategies and exploratory scenarios for a half mode substrate integrated waveguide (HMSIW) antenna. The proposed antenna consists of three Hilbert cells, which are simulated by using CST programming. The antenna was manufactured with the realities of minor imperfections and high incorporation. The proposed structure offers a suitable substrate integrated waveguide (SIW) with about a decrease in size by half. In addition, Hilbert cells were added to realize the triple-band characteristics with good impedance matching, radiation patterns, and radiation performance. The antenna was fabricated on h = 1 mm thick dielectric substrate with dielectric constant (𝜀𝑟 = 4.3). The Hilbert cells were drilled on the top plane of the antenna substrate and fed using a microstrip transmission line. The proposed antenna is small, with a slot side length of approximately half of the guided wavelength. The three developed Hilbert cell HMSIW antenna resonates at 3.25, 5.94 and 6.5 GHz with a bandwidth of 2.97, 2.25 and 2.29% within a return loss of ‑38.77, ‑35.82 -23.35 dB, respectively. The results showed enhancements in antenna gain of 3.56, 4.97 and 6.43 dBi, with a radiation efficiency of -1.253, -0.493 and -0.586 dB, respectively.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46619912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-07DOI: 10.5614/itbj.ict.res.appl.2021.15.2.1
M. W. A. Kesiman, G. Pradnyana
This paper proposes an initial scheme towards the development of an automatic word indexation system for Balinese lontar (palm leaf manuscript) collections. The word indexation system scheme consists of a sub module for patch image extraction of text areas in lontars and a sub module for word image transliteration. This is the first word indexation system for lontar collections to be proposed. To detect parts of a lontar image that contain text, a Gabor filter is used to provide initial information about the presence of text texture in the image. An adaptive sliding patch algorithm for the extraction of patch images in lontars is also proposed. The word image transliteration sub module was built using the long short-term memory (LSTM) model. The results showed that the image patch extraction of text areas process succeeded in optimally detecting text areas in lontars and extracting the patch image in a suitable position. The proposed scheme successfully extracted between 20% to 40% of the keywords in lontars and thus can at least provide an initial description for prospective lontar readers of the content contained in a lontar collection or to find in which lontar collection certain keywords can be found.
{"title":"A Scheme Towards Automatic Word Indexation System for Balinese Palm Leaf Manuscripts","authors":"M. W. A. Kesiman, G. Pradnyana","doi":"10.5614/itbj.ict.res.appl.2021.15.2.1","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2021.15.2.1","url":null,"abstract":"This paper proposes an initial scheme towards the development of an automatic word indexation system for Balinese lontar (palm leaf manuscript) collections. The word indexation system scheme consists of a sub module for patch image extraction of text areas in lontars and a sub module for word image transliteration. This is the first word indexation system for lontar collections to be proposed. To detect parts of a lontar image that contain text, a Gabor filter is used to provide initial information about the presence of text texture in the image. An adaptive sliding patch algorithm for the extraction of patch images in lontars is also proposed. The word image transliteration sub module was built using the long short-term memory (LSTM) model. The results showed that the image patch extraction of text areas process succeeded in optimally detecting text areas in lontars and extracting the patch image in a suitable position. The proposed scheme successfully extracted between 20% to 40% of the keywords in lontars and thus can at least provide an initial description for prospective lontar readers of the content contained in a lontar collection or to find in which lontar collection certain keywords can be found.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46991516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-07DOI: 10.5614/itbj.ict.res.appl.2021.15.2.5
Razan Khalid Alhatimi, O. Almousa, Firas AlBalas
Power consumption and network lifetime are vital issues in wireless sensor network (WSN) design. This motivated us to find innovative mechanisms that help in reducing energy consumption and prolonging the lifetime of such networks. In this paper, we propose a hexagonal model for WSNs to reduce power consumption when sending data from sensor nodes to cluster heads or the sink. Four models are proposed for cluster head positioning and the results were compared with well-known models such as Power Efficient Gathering In Sensor Information Systems (PEGASIS) and Low-Energy Adaptive Clustering Hierarchy (LEACH). The results showed that the proposed models reduced WSN power consumption and network lifetime.
{"title":"Reducing Power Consumption in Hexagonal Wireless Sensor Networks Using Efficient Routing Protocols","authors":"Razan Khalid Alhatimi, O. Almousa, Firas AlBalas","doi":"10.5614/itbj.ict.res.appl.2021.15.2.5","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2021.15.2.5","url":null,"abstract":"Power consumption and network lifetime are vital issues in wireless sensor network (WSN) design. This motivated us to find innovative mechanisms that help in reducing energy consumption and prolonging the lifetime of such networks. In this paper, we propose a hexagonal model for WSNs to reduce power consumption when sending data from sensor nodes to cluster heads or the sink. Four models are proposed for cluster head positioning and the results were compared with well-known models such as Power Efficient Gathering In Sensor Information Systems (PEGASIS) and Low-Energy Adaptive Clustering Hierarchy (LEACH). The results showed that the proposed models reduced WSN power consumption and network lifetime.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":"47 2","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41288889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-07DOI: 10.5614/itbj.ict.res.appl.2021.15.2.3
Shanmugham Balasundaram, R. Balasundaram, Ganesan Rasuthevar, Christeena Joseph, A. Vimala, N. Rajendiran, Baskaran Kaliyamurthy
Heterogeneous regions present in tissue with respect to cancer cells are of various types. This study aimed to analyze and classify the morphological features of the nucleus and cytoplasm regions of tumor cells. This tissue morphology study was established through invasive ductal breast cancer histopathology images accessed from the Databiox public dataset. Automatic detection and classification was carried out by means of the computer analytical tool of deep learning algorithm. Residual blocks with short skip were employed with hidden layers of preserved spatial information. A ResNet-based convolutional neural network was adapted to perform end-to-end segmentation of breast cancer nuclei. Nuclei regions were identified through color and tubular structure morphological features. Based on the segmented and extracted images, classification of benign and malignant breast cancer cells was done to identify tumors. The results indicated that the proposed method could successfully segment and classify breast tumors with an average Dice score of 90.68%, sensitivity = 98.64, specificity = 98.68, and accuracy = 98.82.
{"title":"Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network","authors":"Shanmugham Balasundaram, R. Balasundaram, Ganesan Rasuthevar, Christeena Joseph, A. Vimala, N. Rajendiran, Baskaran Kaliyamurthy","doi":"10.5614/itbj.ict.res.appl.2021.15.2.3","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2021.15.2.3","url":null,"abstract":"Heterogeneous regions present in tissue with respect to cancer cells are of various types. This study aimed to analyze and classify the morphological features of the nucleus and cytoplasm regions of tumor cells. This tissue morphology study was established through invasive ductal breast cancer histopathology images accessed from the Databiox public dataset. Automatic detection and classification was carried out by means of the computer analytical tool of deep learning algorithm. Residual blocks with short skip were employed with hidden layers of preserved spatial information. A ResNet-based convolutional neural network was adapted to perform end-to-end segmentation of breast cancer nuclei. Nuclei regions were identified through color and tubular structure morphological features. Based on the segmented and extracted images, classification of benign and malignant breast cancer cells was done to identify tumors. The results indicated that the proposed method could successfully segment and classify breast tumors with an average Dice score of 90.68%, sensitivity = 98.64, specificity = 98.68, and accuracy = 98.82.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41482097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-06DOI: 10.5614/ITBJ.ICT.RES.APPL.2021.15.1.6
M. W. A. Kesiman, I. M. D. Maysanjaya, I. Pradnyana, I. M. G. Sunarya, P. Suputra
The aim of this research was to reveal and explore the characteristics of Balinese dance maestros by analyzing silhouette sequence patterns of Balinese dance movements. A method and complete scheme for the extraction and construction of silhouette features of Balinese dance movements are proposed to enable performing quantitative analysis of Balinese dance movement patterns. Two different feature extraction methods, namely the Histogram of Gradient (HoG) feature and the Scale Invariant Features Transform (SIFT) descriptor, were used to build the final feature, called the Bag of Visual Movement (BoVM) feature. This research also makes a technical contribution with the proposal of quantifying measures to analyze the movement patterns of Balinese dances and to create the profile and characteristics of dance maestros/creators. Eight Balinese dances from three different Balinese dance maestros were analyzed in this work. Based on the experimental results, the proposed method was able to visually detect and extract patterns from silhouette sequences of Balinese dance movements. Quantitatively, the pattern measures for profiling of Balinese dances and maestros revealed a number of significant characteristics of different dances and different maestros.
{"title":"Revealing the Characteristics of Balinese Dance Maestros by Analyzing Silhouette Sequence Patterns Using Bag of Visual Movement with HoG and SIFT Features","authors":"M. W. A. Kesiman, I. M. D. Maysanjaya, I. Pradnyana, I. M. G. Sunarya, P. Suputra","doi":"10.5614/ITBJ.ICT.RES.APPL.2021.15.1.6","DOIUrl":"https://doi.org/10.5614/ITBJ.ICT.RES.APPL.2021.15.1.6","url":null,"abstract":"The aim of this research was to reveal and explore the characteristics of Balinese dance maestros by analyzing silhouette sequence patterns of Balinese dance movements. A method and complete scheme for the extraction and construction of silhouette features of Balinese dance movements are proposed to enable performing quantitative analysis of Balinese dance movement patterns. Two different feature extraction methods, namely the Histogram of Gradient (HoG) feature and the Scale Invariant Features Transform (SIFT) descriptor, were used to build the final feature, called the Bag of Visual Movement (BoVM) feature. This research also makes a technical contribution with the proposal of quantifying measures to analyze the movement patterns of Balinese dances and to create the profile and characteristics of dance maestros/creators. Eight Balinese dances from three different Balinese dance maestros were analyzed in this work. Based on the experimental results, the proposed method was able to visually detect and extract patterns from silhouette sequences of Balinese dance movements. Quantitatively, the pattern measures for profiling of Balinese dances and maestros revealed a number of significant characteristics of different dances and different maestros.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46650046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-05DOI: 10.5614/ITBJ.ICT.RES.APPL.2021.15.1.5
Shirin Salarian, Amir Shahab Shahabi
The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy.
脑机接口是实现和设计智能医疗软件的主要工具之一。脑信号数据分析(EEG)是智能医疗诊断系统的主要任务之一。脑电图信号有许多组成部分，其中最重要的脑活动是P300组成部分。检测这一成分可以帮助检测异常和可视化身体器官的运动。本研究提出了一种以检测P300分量为目标的脑电信号处理新方法。通过中值滤波归一化降噪、递归神经网络特征提取、Twin Support Vector Machine分类等步骤，从BCI Competition IV EEG数据集中提取主要特征。然后，采用一系列评价标准对所提方法进行验证，并与同类方法进行比较。结果表明，该方法具有较高的精度。
{"title":"Extraction of the Major Features of Brain Signals using Intelligent Networks","authors":"Shirin Salarian, Amir Shahab Shahabi","doi":"10.5614/ITBJ.ICT.RES.APPL.2021.15.1.5","DOIUrl":"https://doi.org/10.5614/ITBJ.ICT.RES.APPL.2021.15.1.5","url":null,"abstract":"The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48076132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-05DOI: 10.5614/ITBJ.ICT.RES.APPL.2021.15.1.4
Ammar A. Bathich, S. I. Suliman, Hj. Mohd Asri Hj. Mansor, Sinan Ghassan Abid Ali, Raed M. T. Abdulla
Universal mobile networks require enhanced capability and appropriate quality of service (QoS) and experience (QoE). To achieve this, Long Term Evolution (LTE) system operators have intensively deployed femtocells (HeNBs) along with macrocells (eNBs) to offer user equipment (UE) with optimal capacity coverage and best quality of service. To achieve the requirement of QoS in the handover stage among macrocells and femtocells we need a seamless cell selection mechanism. Cell selection requirements are considered a difficult task in femtocell-based networks and effective cell selection procedures are essential to reduce the ping-pong phenomenon and to minimize needless handovers. In this study, we propose a seamless cell selection scheme for macrocell-femtocell LTE systems, based on the Q-learning environment. A novel cell selection mechanism is proposed for high-density femtocell network topologies to evaluate the target base station in the handover stage. We used the LTE-Sim simulator to implement and evaluate the cell selection procedures. The simulation results were encouraging: a decrease in the control signaling rate and packet loss ratio were observed and at the same time the system throughput was increased.
{"title":"Cell Selection Mechanism Based on Q-learning Environment in Femtocell LTE-A Networks","authors":"Ammar A. Bathich, S. I. Suliman, Hj. Mohd Asri Hj. Mansor, Sinan Ghassan Abid Ali, Raed M. T. Abdulla","doi":"10.5614/ITBJ.ICT.RES.APPL.2021.15.1.4","DOIUrl":"https://doi.org/10.5614/ITBJ.ICT.RES.APPL.2021.15.1.4","url":null,"abstract":"Universal mobile networks require enhanced capability and appropriate quality of service (QoS) and experience (QoE). To achieve this, Long Term Evolution (LTE) system operators have intensively deployed femtocells (HeNBs) along with macrocells (eNBs) to offer user equipment (UE) with optimal capacity coverage and best quality of service. To achieve the requirement of QoS in the handover stage among macrocells and femtocells we need a seamless cell selection mechanism. Cell selection requirements are considered a difficult task in femtocell-based networks and effective cell selection procedures are essential to reduce the ping-pong phenomenon and to minimize needless handovers. In this study, we propose a seamless cell selection scheme for macrocell-femtocell LTE systems, based on the Q-learning environment. A novel cell selection mechanism is proposed for high-density femtocell network topologies to evaluate the target base station in the handover stage. We used the LTE-Sim simulator to implement and evaluate the cell selection procedures. The simulation results were encouraging: a decrease in the control signaling rate and packet loss ratio were observed and at the same time the system throughput was increased.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47797163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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