Pub Date : 2021-09-21DOI: 10.21608/ijicis.2021.82008.1106
maha alhumayani, Mahmoud Monir, R. Ismail
Human Activity Recognition (HAR) is a domain that has shown great interest in the past years and tills now. The main cause for this is that it can be used in various applications. There exist several devices and sensors that can capture and record activities. In this paper, a survey about the machine learning and deep learning methodologies in HAR is provided with information about the data, filtering methods, feature extraction methods, classification, and different performance measurements. The main aim is to target the old and the recent papers published in HAR and to determine whether the machine learning or deep learning methods is better in performance. In addition to this, the survey will cover the types of actions or activities that are predicted. Then, a discussion about the main points obtained from the survey. Finally, the conclusions, limitations, and challenges in HAR are presented clearly. Human activity recognition (HAR) can be known with various types of definitions. HAR is preserved to be a field of studying and identifying the movements of the individuals or the action of the human based on sensor data . These movements can be different activities such as walking, talking, standing, and sitting. They are also called indoor activities.
{"title":"machine and deep learning approaches for human activity recognition","authors":"maha alhumayani, Mahmoud Monir, R. Ismail","doi":"10.21608/ijicis.2021.82008.1106","DOIUrl":"https://doi.org/10.21608/ijicis.2021.82008.1106","url":null,"abstract":"Human Activity Recognition (HAR) is a domain that has shown great interest in the past years and tills now. The main cause for this is that it can be used in various applications. There exist several devices and sensors that can capture and record activities. In this paper, a survey about the machine learning and deep learning methodologies in HAR is provided with information about the data, filtering methods, feature extraction methods, classification, and different performance measurements. The main aim is to target the old and the recent papers published in HAR and to determine whether the machine learning or deep learning methods is better in performance. In addition to this, the survey will cover the types of actions or activities that are predicted. Then, a discussion about the main points obtained from the survey. Finally, the conclusions, limitations, and challenges in HAR are presented clearly. Human activity recognition (HAR) can be known with various types of definitions. HAR is preserved to be a field of studying and identifying the movements of the individuals or the action of the human based on sensor data . These movements can be different activities such as walking, talking, standing, and sitting. They are also called indoor activities.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114195351","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 : 2021-09-16DOI: 10.21608/ijicis.2021.78853.1098
Soha Ahmed
Accurate extraction of land cover types from thematic maps using satellite images still constitutes a critical challenge. The selection of a suitable satellite image classification algorithm is considered a crucial prerequisite for successful classification results that are required for various applications. The optimal classification algorithm is considered a significant key for improving classification accuracy. The principal foci of this study were to compare, analyze the performance, and assess the effectiveness of four classification algorithms including ISODATA, K-means, pixel-based and segment-based classification techniques to attain accurate land cover extraction from remote sensing data. The classified images were validated with ground control points obtained from field visits in addition to the DigitalGlobe and Google Earth Pro. The overall accuracy of the ISODATA, K-means, pixel, and segment-based classifications were 81.82%, 77.27%, 92.42%, and 87.88%, respectively. The results revealed that the pixel-based classification presented a superior in terms of the overall accuracy and kappa coefficient.
{"title":"Comparison of Satellite Images Classification Techniques using Landsat-8 Data for Land Cover Extraction","authors":"Soha Ahmed","doi":"10.21608/ijicis.2021.78853.1098","DOIUrl":"https://doi.org/10.21608/ijicis.2021.78853.1098","url":null,"abstract":"Accurate extraction of land cover types from thematic maps using satellite images still constitutes a critical challenge. The selection of a suitable satellite image classification algorithm is considered a crucial prerequisite for successful classification results that are required for various applications. The optimal classification algorithm is considered a significant key for improving classification accuracy. The principal foci of this study were to compare, analyze the performance, and assess the effectiveness of four classification algorithms including ISODATA, K-means, pixel-based and segment-based classification techniques to attain accurate land cover extraction from remote sensing data. The classified images were validated with ground control points obtained from field visits in addition to the DigitalGlobe and Google Earth Pro. The overall accuracy of the ISODATA, K-means, pixel, and segment-based classifications were 81.82%, 77.27%, 92.42%, and 87.88%, respectively. The results revealed that the pixel-based classification presented a superior in terms of the overall accuracy and kappa coefficient.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125208085","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 : 2021-09-15DOI: 10.21608/ijicis.2021.79070.1099
Noureldin Laban, B. Abdellatif, Hala Moushier, Howida A. Shedeed, M. Tolba
Recent years have witnessed a great development in the use of deep learning in the applied fields in general, including the improvement of remote sensing. Satellite imagery classification has played a prominent role in various development processes. This paper presents a new improvement in automatic urban classification using One Dimension Convolutional Neural Network (1DCNN) architecture. The suggested approach has three enhancement processes. First, select training boxes for different classes and create many pixels with variable class signatures. This makes the training process dependent on the broadband of signature for the classes. Second, modified 1D convolution was used to re-encode pixel values to increase distinguish power. Third, adding a new median filter layer at the end of network architecture to remove pixels like noise to make the resulting map smoother. An image of Greater Cairo is used and the different urban classes are defined within it. The proposed method was compared to other methods based on pixels. The proposed method proved to be numerically and visually superior.
{"title":"ENHANCED PIXEL BASED URBAN AREA CLASSIFICATION OF SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORK","authors":"Noureldin Laban, B. Abdellatif, Hala Moushier, Howida A. Shedeed, M. Tolba","doi":"10.21608/ijicis.2021.79070.1099","DOIUrl":"https://doi.org/10.21608/ijicis.2021.79070.1099","url":null,"abstract":"Recent years have witnessed a great development in the use of deep learning in the applied fields in general, including the improvement of remote sensing. Satellite imagery classification has played a prominent role in various development processes. This paper presents a new improvement in automatic urban classification using One Dimension Convolutional Neural Network (1DCNN) architecture. The suggested approach has three enhancement processes. First, select training boxes for different classes and create many pixels with variable class signatures. This makes the training process dependent on the broadband of signature for the classes. Second, modified 1D convolution was used to re-encode pixel values to increase distinguish power. Third, adding a new median filter layer at the end of network architecture to remove pixels like noise to make the resulting map smoother. An image of Greater Cairo is used and the different urban classes are defined within it. The proposed method was compared to other methods based on pixels. The proposed method proved to be numerically and visually superior.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124827874","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 : 2021-09-04DOI: 10.21608/ijicis.2021.77345.1094
Eman M. Aboelela, Walaa K. Gad, R. Ismail
: Through the state-of-the-art digitalization, we can see a massive growth in user-generated content on the web that provides feedback from people on a variety of topics. However, manually managing large-scale user feedback would be a difficult task and a waste of time. Therefore, the concept of sentiment analysis is emerged. Sentiment analysis is a computerized study of individuals' feelings and opinions about an entity or product. It can be executed at three different levels: document level, sentence or phrase level, and feature level. This paper proposes a novel ontology-based approach for feature level sentiment analysis. The proposed approach extracts the product features using semantic similarity and Wordnet ontology and uses the SentiWordent dictionary to classify the users’ comments as positive and negative. Furthermore, it manages negative words to obtain more precise classification results. The proposed approach is assessed by using two benchmark amazon products’ datasets in terms of accuracy; recall, precision, and f-measure. The performance reaches to 92.4% accuracy, 97.2% precision, 92.8 % recall, and 94.4% f-measure.
{"title":"ONTOLOGY-BASED APPROACH FOR FEATURE LEVEL SENTIMENT ANALYSIS","authors":"Eman M. Aboelela, Walaa K. Gad, R. Ismail","doi":"10.21608/ijicis.2021.77345.1094","DOIUrl":"https://doi.org/10.21608/ijicis.2021.77345.1094","url":null,"abstract":": Through the state-of-the-art digitalization, we can see a massive growth in user-generated content on the web that provides feedback from people on a variety of topics. However, manually managing large-scale user feedback would be a difficult task and a waste of time. Therefore, the concept of sentiment analysis is emerged. Sentiment analysis is a computerized study of individuals' feelings and opinions about an entity or product. It can be executed at three different levels: document level, sentence or phrase level, and feature level. This paper proposes a novel ontology-based approach for feature level sentiment analysis. The proposed approach extracts the product features using semantic similarity and Wordnet ontology and uses the SentiWordent dictionary to classify the users’ comments as positive and negative. Furthermore, it manages negative words to obtain more precise classification results. The proposed approach is assessed by using two benchmark amazon products’ datasets in terms of accuracy; recall, precision, and f-measure. The performance reaches to 92.4% accuracy, 97.2% precision, 92.8 % recall, and 94.4% f-measure.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116031416","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 : 2021-07-31DOI: 10.21608/ijicis.2021.73726.1087
Mona A. Ahmed, A. Salem
Multimodal biometric systems roughly used to achieve extreme recognition accuracy. This paper reports a novel multimodal biometric system employing intelligent technique to authenticate human by fusion of dorsal hand, palm and finger veins pattern. We improved an image analysis technique to separate region of interest (ROI) from dorsal hand, palm and finger veins image. After separating ROI we construct a sequence of preprocessing steps to enhance dorsal hand, palm and finger veins images using Median filter, Wiener filter, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Homomorphic filter to improve vein image. Our intelligent technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation. The database selected was Bosphorus Hand Vein Database, CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database) and the Shandong University Machine Learning and Applications Homologous Multi-modal Traits (SDUMLA-HMT). The accomplished result for the fusion of three biometric traits was Correct Recognition Rate (CRR) is 99.21% with False Reject Rate (FRR) 0.04%.
{"title":"INTELLIGENT sYSTEM FOR HUMAN AUTHENTICATION USING FUSION OF DORSAL HAND, PALM AND FINGER VEINS","authors":"Mona A. Ahmed, A. Salem","doi":"10.21608/ijicis.2021.73726.1087","DOIUrl":"https://doi.org/10.21608/ijicis.2021.73726.1087","url":null,"abstract":"Multimodal biometric systems roughly used to achieve extreme recognition accuracy. This paper reports a novel multimodal biometric system employing intelligent technique to authenticate human by fusion of dorsal hand, palm and finger veins pattern. We improved an image analysis technique to separate region of interest (ROI) from dorsal hand, palm and finger veins image. After separating ROI we construct a sequence of preprocessing steps to enhance dorsal hand, palm and finger veins images using Median filter, Wiener filter, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Homomorphic filter to improve vein image. Our intelligent technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation. The database selected was Bosphorus Hand Vein Database, CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database) and the Shandong University Machine Learning and Applications Homologous Multi-modal Traits (SDUMLA-HMT). The accomplished result for the fusion of three biometric traits was Correct Recognition Rate (CRR) is 99.21% with False Reject Rate (FRR) 0.04%.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116094576","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 : 2021-07-31DOI: 10.21608/ijicis.2021.77309.1093
A. Raouf, Alshaimaa Abo-Alian, N. Badr
In a multi-tenant business environment, tenants share the same applications and databases to store their data. Due to the widespread use of a multi-tenant environment, the service providers face difficult challenges daily. These challenges are condensed in how to guarantee the quality of service provided to tenants, which are documented in a formal document known as a Service Level Agreement (SLA). In addition, SLA should consider the irregular patterns of workload of tenants which may affect the level of guarantee. In this research, an Enhanced Multi-Tenant Database Management System (EMT DBMS) is proposed. In addition, an Enhanced Multi-tenant Migration Algorithm called EMT-M is presented, which aims to migrate the violated tenants depending on both the number of SLA violations and the variance rate. Experimental results prove that the proposed EMT-M algorithm is ideal for migrating violated tenants, as it reduces the number of SLA violations compared to previous migration algorithms.
{"title":"Multi-Tenant RDBMS Migration in the Cloud Environment","authors":"A. Raouf, Alshaimaa Abo-Alian, N. Badr","doi":"10.21608/ijicis.2021.77309.1093","DOIUrl":"https://doi.org/10.21608/ijicis.2021.77309.1093","url":null,"abstract":"In a multi-tenant business environment, tenants share the same applications and databases to store their data. Due to the widespread use of a multi-tenant environment, the service providers face difficult challenges daily. These challenges are condensed in how to guarantee the quality of service provided to tenants, which are documented in a formal document known as a Service Level Agreement (SLA). In addition, SLA should consider the irregular patterns of workload of tenants which may affect the level of guarantee. In this research, an Enhanced Multi-Tenant Database Management System (EMT DBMS) is proposed. In addition, an Enhanced Multi-tenant Migration Algorithm called EMT-M is presented, which aims to migrate the violated tenants depending on both the number of SLA violations and the variance rate. Experimental results prove that the proposed EMT-M algorithm is ideal for migrating violated tenants, as it reduces the number of SLA violations compared to previous migration algorithms.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122148269","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 : 2021-07-19DOI: 10.21608/IJICIS.2021.75525.1090
Reem T. Haweel, Noha A. Seada, S. Ghoniemy, A. El-Baz
Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with impairments in social and lingual abilities. The current gold standard for diagnosing is the autism diagnostic observation schedule (ADOS) plus expert clinical judgement. The actual cause for autism is still unknown. Early ASD diagnosis is critical for conducting personalized treatment plans and can lead to significant development enhancements. Machine learning techniques, specially deep learning, have been widely incorporated in attempts to develop objective computer-aided technologies to diagnose autism with brain imaging modalities. Task-based functional magnetic resonance imaging (TfMRI) is a brain imaging modality that reveals functional activity of the brain in response to different experiments to study the effects of a brain disease or disorder. This study provides a comprehensive review on researches that deploy traditional machine learning and deep learning techniques in diagnosing ASD based on TfMRI. Classification results manifest that TfMRI holds early autism biomarkers and suggest future research to establish multi-modal studies that integrate TfMRI with structural, functional, clinical and gnomic data with higher number of participating subjects.
{"title":"A REVIEW ON AUTISM SPECTRUM DISORDER DIAGNOSIS USING TASK-BASED FUNCTIONAL MRI","authors":"Reem T. Haweel, Noha A. Seada, S. Ghoniemy, A. El-Baz","doi":"10.21608/IJICIS.2021.75525.1090","DOIUrl":"https://doi.org/10.21608/IJICIS.2021.75525.1090","url":null,"abstract":"Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with impairments in social and lingual abilities. The current gold standard for diagnosing is the autism diagnostic observation schedule (ADOS) plus expert clinical judgement. The actual cause for autism is still unknown. Early ASD diagnosis is critical for conducting personalized treatment plans and can lead to significant development enhancements. Machine learning techniques, specially deep learning, have been widely incorporated in attempts to develop objective computer-aided technologies to diagnose autism with brain imaging modalities. Task-based functional magnetic resonance imaging (TfMRI) is a brain imaging modality that reveals functional activity of the brain in response to different experiments to study the effects of a brain disease or disorder. This study provides a comprehensive review on researches that deploy traditional machine learning and deep learning techniques in diagnosing ASD based on TfMRI. Classification results manifest that TfMRI holds early autism biomarkers and suggest future research to establish multi-modal studies that integrate TfMRI with structural, functional, clinical and gnomic data with higher number of participating subjects.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115908260","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 : 2021-07-19DOI: 10.21608/IJICIS.2021.73581.1086
Hamzah A. Alsayadi, A. Abdelhamid, I. Hegazy, Zaki Taha
End-to-end deep learning approach has greatly enhanced the performance of speech recognition systems. With deep learning techniques, the overfitting stills the main problem with a little data. Data augmentation is a suitable solution for the overfitting problem, which is adopted to improve the quantity of training data and enhance robustness of the models. In this paper, we investigate data augmentation method for enhancing Arabic automatic speech recognition (ASR) based on end-to-end deep learning. Data augmentation is applied on original corpus for increasing training data by applying noise adaptation, pitch-shifting, and speed transformation. An CNN-LSTM and attention-based encoder-decoder method are included in building the acoustic model and decoding phase. This method is considered as state-of-art in end-to-end deep learning, and to the best of our knowledge, there is no prior research employed data augmentation for CNN-LSTM and attention-based model in Arabic ASR systems. In addition, the language model is built using RNN-LM and LSTM-LM methods. The Standard Arabic Single Speaker Corpus (SASSC) without diacritics is used as an original corpus. Experimental results show that applying data augmentation improved word error rate (WER) when compared with the same approach without data augmentation. The achieved average reduction in WER is 4.55%.
{"title":"Data Augmentation for Arabic Speech Recognition Based on End-to-End Deep Learning","authors":"Hamzah A. Alsayadi, A. Abdelhamid, I. Hegazy, Zaki Taha","doi":"10.21608/IJICIS.2021.73581.1086","DOIUrl":"https://doi.org/10.21608/IJICIS.2021.73581.1086","url":null,"abstract":"End-to-end deep learning approach has greatly enhanced the performance of speech recognition systems. With deep learning techniques, the overfitting stills the main problem with a little data. Data augmentation is a suitable solution for the overfitting problem, which is adopted to improve the quantity of training data and enhance robustness of the models. In this paper, we investigate data augmentation method for enhancing Arabic automatic speech recognition (ASR) based on end-to-end deep learning. Data augmentation is applied on original corpus for increasing training data by applying noise adaptation, pitch-shifting, and speed transformation. An CNN-LSTM and attention-based encoder-decoder method are included in building the acoustic model and decoding phase. This method is considered as state-of-art in end-to-end deep learning, and to the best of our knowledge, there is no prior research employed data augmentation for CNN-LSTM and attention-based model in Arabic ASR systems. In addition, the language model is built using RNN-LM and LSTM-LM methods. The Standard Arabic Single Speaker Corpus (SASSC) without diacritics is used as an original corpus. Experimental results show that applying data augmentation improved word error rate (WER) when compared with the same approach without data augmentation. The achieved average reduction in WER is 4.55%.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134387555","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 : 2021-07-19DOI: 10.21608/IJICIS.2021.77015.1092
Mariam Hassanein, S. Rady, Wedad Hussein, Tarek F. Gharib
Recently, researches focused on studying how the Big Five personality traits are manifested on social networks. These researches proved the presence of relationships between the Big Five Personality traits and various social networks features extracted from users’ generated content. In this paper, the relationships between the Big Five personality traits (Openness to experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) and attributes of personality characteristics identified as the Personal Values and Human Needs. These attributes or namely features, are extracted from users’ posts on social media. The relationship between the traits and proposed attributes is investigated through Pearson correlation coefficients. A dataset for 564 Twitter users is used in an experimental study, where findings proved the presence of relevant correlations between the traits and the proposed personality characteristic features. The Conscientiousness, Agreeableness, and Neuroticism traits showed strong relations existence with all of the Personal Values features, while the Openness to experience and Neuroticism traits showed strong correlations with Liberty and Self-expression Needs features consecutively. The proposed study verified the effectiveness of the proposed Personal Values and Human Needs features as indicators for the Big Five personality traits, proving their ability for personality characteristics classification.
{"title":"EXTRACTING RELATIONSHIPS BETWEEN BIG FIVE MODEL AND PERSONALITY CHARACTERISTICS IN SOCIAL NETWORKS","authors":"Mariam Hassanein, S. Rady, Wedad Hussein, Tarek F. Gharib","doi":"10.21608/IJICIS.2021.77015.1092","DOIUrl":"https://doi.org/10.21608/IJICIS.2021.77015.1092","url":null,"abstract":"Recently, researches focused on studying how the Big Five personality traits are manifested on social networks. These researches proved the presence of relationships between the Big Five Personality traits and various social networks features extracted from users’ generated content. In this paper, the relationships between the Big Five personality traits (Openness to experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) and attributes of personality characteristics identified as the Personal Values and Human Needs. These attributes or namely features, are extracted from users’ posts on social media. The relationship between the traits and proposed attributes is investigated through Pearson correlation coefficients. A dataset for 564 Twitter users is used in an experimental study, where findings proved the presence of relevant correlations between the traits and the proposed personality characteristic features. The Conscientiousness, Agreeableness, and Neuroticism traits showed strong relations existence with all of the Personal Values features, while the Openness to experience and Neuroticism traits showed strong correlations with Liberty and Self-expression Needs features consecutively. The proposed study verified the effectiveness of the proposed Personal Values and Human Needs features as indicators for the Big Five personality traits, proving their ability for personality characteristics classification.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123434181","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 : 2021-07-18DOI: 10.21608/IJICIS.2021.60216.1055
Noorhan Khaled, M. Aref, M. Marey
Dense video captioning involves detecting interesting events and generating textual descriptions for each event in an untrimmed video. Many machine intelligent applications such as video summarization, search and retrieval, automatic video subtitling for supporting blind disabled people, benefit from automated dense captions generator. Most recent works attempted to make use of an encoder-decoder neural network framework which employs a 3D-CNN as an encoder for representing a detected event frames, and an RNN as a decoder for caption generation. They follow an attention based mechanism to learn where to focus in the encoded video frames during caption generation. Although the attention-based approaches have achieved excellent results, they directly link visual features to textual captions and ignore the rich intermediate/high-level video concepts such as people, objects, scenes, and actions. In this paper, we firstly propose to obtain a better event representation that discriminates between events nearly ending at the same time by applying an attention based fusion. Where hidden states from a bi-directional LSTM sequence video encoder, which encodes past and future surrounding context information of a detected event are fused along with its visual (R3D) features. Secondly, we propose to explicitly extract bi-modal semantic concepts (nouns and verbs) from a detected event segment and equilibrate the contributions from the proposed event representation and the semantic concepts dynamically using a gating mechanism while captioning. Experimental results demonstrates that our proposed attention based fusion is better in representing an event for captioning. Also involving semantic concepts improves captioning performance.
{"title":"Bidirectional Temporal Context Fusion with Bi-Modal Semantic Features using a gating mechanism for Dense Video Captioning","authors":"Noorhan Khaled, M. Aref, M. Marey","doi":"10.21608/IJICIS.2021.60216.1055","DOIUrl":"https://doi.org/10.21608/IJICIS.2021.60216.1055","url":null,"abstract":"Dense video captioning involves detecting interesting events and generating textual descriptions for each event in an untrimmed video. Many machine intelligent applications such as video summarization, search and retrieval, automatic video subtitling for supporting blind disabled people, benefit from automated dense captions generator. Most recent works attempted to make use of an encoder-decoder neural network framework which employs a 3D-CNN as an encoder for representing a detected event frames, and an RNN as a decoder for caption generation. They follow an attention based mechanism to learn where to focus in the encoded video frames during caption generation. Although the attention-based approaches have achieved excellent results, they directly link visual features to textual captions and ignore the rich intermediate/high-level video concepts such as people, objects, scenes, and actions. In this paper, we firstly propose to obtain a better event representation that discriminates between events nearly ending at the same time by applying an attention based fusion. Where hidden states from a bi-directional LSTM sequence video encoder, which encodes past and future surrounding context information of a detected event are fused along with its visual (R3D) features. Secondly, we propose to explicitly extract bi-modal semantic concepts (nouns and verbs) from a detected event segment and equilibrate the contributions from the proposed event representation and the semantic concepts dynamically using a gating mechanism while captioning. Experimental results demonstrates that our proposed attention based fusion is better in representing an event for captioning. Also involving semantic concepts improves captioning performance.","PeriodicalId":244591,"journal":{"name":"International Journal of Intelligent Computing and Information Sciences","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115241037","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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