Pub Date : 2023-07-15DOI: 10.5755/j01.itc.52.2.32029
Gang Chen, Chunmei Wen
A panel is an event-centric starting point for implementing a model-based interactive system. The design and construction of an interactive panel involve deciding what information to display, how to display it, and ways to implement the design intent to produce an interactive panel. Traditionally, the design of panels has been implicit in the deployed applications, rather than explicitly considered as digital artifacts. In addition, users must realize this implicit design manually by coding or configuring it on programming platforms, resulting in hampered and time-consuming control and analysis. Besides, current tools do not have a unified generation mechanism, which makes it difficult for cooperation. In this paper, we propose a unified framework Mod2Panel, which enables users to draw their interactive panel designs as models and can automatically generate interactive panels from these models. The models are described in a modeling language that involves structures, behaviors, layout, and parameters. Mod2Panel also provides a GUI-assisted editor for customization to fine-tune the generated panels and update their associated models. With the capabilities of Mod2Panel, users can unify prototyping, generation and deployment in this framework for purposes of operation and control. We evaluate its effectiveness and efficiency in applied case studies on complex control systems and system modeling, in which Mod2Panel successfully generates interactive panels to support control monitoring and system-level analysis. The operations in the generated panel systems demonstrate the effectiveness of Mod2Panel for real-world scenarios.
{"title":"Mod2Panel: A Design Framework for Model-Based Automated Generation of Interactive Panels","authors":"Gang Chen, Chunmei Wen","doi":"10.5755/j01.itc.52.2.32029","DOIUrl":"https://doi.org/10.5755/j01.itc.52.2.32029","url":null,"abstract":"A panel is an event-centric starting point for implementing a model-based interactive system. The design and construction of an interactive panel involve deciding what information to display, how to display it, and ways to implement the design intent to produce an interactive panel. Traditionally, the design of panels has been implicit in the deployed applications, rather than explicitly considered as digital artifacts. In addition, users must realize this implicit design manually by coding or configuring it on programming platforms, resulting in hampered and time-consuming control and analysis. Besides, current tools do not have a unified generation mechanism, which makes it difficult for cooperation. In this paper, we propose a unified framework Mod2Panel, which enables users to draw their interactive panel designs as models and can automatically generate interactive panels from these models. The models are described in a modeling language that involves structures, behaviors, layout, and parameters. Mod2Panel also provides a GUI-assisted editor for customization to fine-tune the generated panels and update their associated models. With the capabilities of Mod2Panel, users can unify prototyping, generation and deployment in this framework for purposes of operation and control. We evaluate its effectiveness and efficiency in applied case studies on complex control systems and system modeling, in which Mod2Panel successfully generates interactive panels to support control monitoring and system-level analysis. The operations in the generated panel systems demonstrate the effectiveness of Mod2Panel for real-world scenarios.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"53 1","pages":"471-486"},"PeriodicalIF":1.1,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80123951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-15DOI: 10.5755/j01.itc.52.2.32532
S. Karthikeyini, R. Sagayaraj, N. Rajkumar, Punitha Kumaresa Pillai
Data encryption before transmission is still a crucial step in lowering security concerns in cloud-based environments. Steganography and image encryption methods validate the security of confidential data while it is being transmitted over the Internet. The paper presents the Ant Colony Optimization with Encryption Curve cryptography-based steganography technique to enhance the security of medical image management (ACO-ECC-SMIM). The initial stage is to create the stego images for the used cover image, the ACO algorithm-based image steganography technique is used. The creation of the encryption process is a key focus of the suggested ACO-ECC-SMIM strategy. The encryption process is initially carried out using an ECC technique, or elliptic curve cryptography. To maximize PSNR, the ACO technique is employed to optimize the crucial production process in the ECC model. The host image is subjected to an integer wavelet transform, and the coefficients have been altered. To determine the ideal coefficients where to conceal the data, the ACO optimization technique is utilized. The decryption and sharing reconstruction procedures are then carried out on the receiver side to create the original images. In image 1, the ACO-ECC-SMIM model showed an improved PSNR of 59.37dB. Image 5 has an improved PSNR of 59.53dB thanks to the ACO-ECC-SMIM model. A large-scale experimental investigation was conducted to show the improved performance of the proposed PIOE-SMIM method, and the findings demonstrated the superiority of the ACO-ECC-SMIM model over other approaches.
{"title":"Security in Medical Image Management Using Ant Colony Optimization","authors":"S. Karthikeyini, R. Sagayaraj, N. Rajkumar, Punitha Kumaresa Pillai","doi":"10.5755/j01.itc.52.2.32532","DOIUrl":"https://doi.org/10.5755/j01.itc.52.2.32532","url":null,"abstract":"Data encryption before transmission is still a crucial step in lowering security concerns in cloud-based environments. Steganography and image encryption methods validate the security of confidential data while it is being transmitted over the Internet. The paper presents the Ant Colony Optimization with Encryption Curve cryptography-based steganography technique to enhance the security of medical image management (ACO-ECC-SMIM). The initial stage is to create the stego images for the used cover image, the ACO algorithm-based image steganography technique is used. The creation of the encryption process is a key focus of the suggested ACO-ECC-SMIM strategy. The encryption process is initially carried out using an ECC technique, or elliptic curve cryptography. To maximize PSNR, the ACO technique is employed to optimize the crucial production process in the ECC model. The host image is subjected to an integer wavelet transform, and the coefficients have been altered. To determine the ideal coefficients where to conceal the data, the ACO optimization technique is utilized. The decryption and sharing reconstruction procedures are then carried out on the receiver side to create the original images. In image 1, the ACO-ECC-SMIM model showed an improved PSNR of 59.37dB. Image 5 has an improved PSNR of 59.53dB thanks to the ACO-ECC-SMIM model. A large-scale experimental investigation was conducted to show the improved performance of the proposed PIOE-SMIM method, and the findings demonstrated the superiority of the ACO-ECC-SMIM model over other approaches.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"160 1","pages":"276-287"},"PeriodicalIF":1.1,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86367960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-15DOI: 10.5755/j01.itc.52.2.32796
B. Hemalatha, P. Bhuvaneswari, Mahesh Nataraj, G. Shanmugavadivel
Human gender and age identification play a prominent role in forensics, bio-archaeology, and anthropology. Dental images provide prominent indications used for the treatment or diagnosis of disease and forensic investigation. Numerous dental age identification techniques come with specific boundaries, namely minimum reliability and accuracy. Gender identification approaches are not widely researched, whereas the effectiveness and accuracy of classification are not practical and very minimal. Drawbacks in the existing system are considered in the formulation of the proposed approach. Deep learning approaches can effectively rectify issues of drawbacks in other classifiers. The accuracy and performance of a classifier are enhanced with the deep convolutional neural network. The fuzzy C-Means Clustering approach is used for segmentation, and Ant Lion Optimization is used for optimal feature score selection. The selected features are classified using a deep convolutional neural network (DCNN). The performance of the proposed technique is investigated with existing classifiers, and DCNN outperforms other classifiers. The proposed technique achieves 91.7% and 91% accuracy for the identification of gender and age, respectively.
{"title":"Human Dental Age and Gender Assessment from Dental Radiographs Using Deep Convolutional Neural Network","authors":"B. Hemalatha, P. Bhuvaneswari, Mahesh Nataraj, G. Shanmugavadivel","doi":"10.5755/j01.itc.52.2.32796","DOIUrl":"https://doi.org/10.5755/j01.itc.52.2.32796","url":null,"abstract":"Human gender and age identification play a prominent role in forensics, bio-archaeology, and anthropology. Dental images provide prominent indications used for the treatment or diagnosis of disease and forensic investigation. Numerous dental age identification techniques come with specific boundaries, namely minimum reliability and accuracy. Gender identification approaches are not widely researched, whereas the effectiveness and accuracy of classification are not practical and very minimal. Drawbacks in the existing system are considered in the formulation of the proposed approach. Deep learning approaches can effectively rectify issues of drawbacks in other classifiers. The accuracy and performance of a classifier are enhanced with the deep convolutional neural network. The fuzzy C-Means Clustering approach is used for segmentation, and Ant Lion Optimization is used for optimal feature score selection. The selected features are classified using a deep convolutional neural network (DCNN). The performance of the proposed technique is investigated with existing classifiers, and DCNN outperforms other classifiers. The proposed technique achieves 91.7% and 91% accuracy for the identification of gender and age, respectively.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"271 1","pages":"322-335"},"PeriodicalIF":1.1,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86730531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-15DOI: 10.5755/j01.itc.52.2.32690
Qimin Xu, Zhao Xin, Liao Longjie, L. Yameng, Li Na
An efficient and accurate LiDAR place recognition methodology is proposed for vehicle localization. First, the Iris-LOAM is proposed to overcome the disadvantages of low accuracy of loop-closure detection and low efficiency of map construction in the existing LOAM-series methods. The method integrates the LiDAR-Iris global descriptor and Normal Distribution Transform (NDT) registration method into the loop-closure detection module of LiDAR Odometry and Mapping (LOAM), thereby improving the accuracy and efficiency of map construction. For the shortcomings of low map loading and matching efficiency, the Random Sample Consensus method is used to remove the ground point cloud information. The Voxel Grid method is used to down sample the loaded map. Finally, the NDT method is adopted for point cloud map matching to obtain the position information. Show that the Iris-LOAM has higher efficiency than the SC-LeGO-LOAM. The average time of point cloud map matching is reduced by 39.5%. The place recognition can be executed to achieve accuracy vehicle localization.
{"title":"Efficient and Accurate Vehicle Localization Based on LiDAR Place Recognition","authors":"Qimin Xu, Zhao Xin, Liao Longjie, L. Yameng, Li Na","doi":"10.5755/j01.itc.52.2.32690","DOIUrl":"https://doi.org/10.5755/j01.itc.52.2.32690","url":null,"abstract":"An efficient and accurate LiDAR place recognition methodology is proposed for vehicle localization. First, the Iris-LOAM is proposed to overcome the disadvantages of low accuracy of loop-closure detection and low efficiency of map construction in the existing LOAM-series methods. The method integrates the LiDAR-Iris global descriptor and Normal Distribution Transform (NDT) registration method into the loop-closure detection module of LiDAR Odometry and Mapping (LOAM), thereby improving the accuracy and efficiency of map construction. For the shortcomings of low map loading and matching efficiency, the Random Sample Consensus method is used to remove the ground point cloud information. The Voxel Grid method is used to down sample the loaded map. Finally, the NDT method is adopted for point cloud map matching to obtain the position information. Show that the Iris-LOAM has higher efficiency than the SC-LeGO-LOAM. The average time of point cloud map matching is reduced by 39.5%. The place recognition can be executed to achieve accuracy vehicle localization.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"16 1","pages":"562-575"},"PeriodicalIF":1.1,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80166490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-15DOI: 10.5755/j01.itc.52.2.32841
Junyan Li
Lane detection problem has been considered as an important computer vision task in autonomous driving. While it has received massive research attention in the literature, the problem is not yet fully solved. In this paper, we present a comprehensive literature review for lane detection, especially those with deep learning models. The latest collection of lane detection datasets is presented. We further fill the research gap by proposing a novel lane detection dataset named MudLane, which focuses on the lane detection task on suburban roads.
{"title":"Lane Detection with Deep Learning: Methods and Datasets","authors":"Junyan Li","doi":"10.5755/j01.itc.52.2.32841","DOIUrl":"https://doi.org/10.5755/j01.itc.52.2.32841","url":null,"abstract":"Lane detection problem has been considered as an important computer vision task in autonomous driving. While it has received massive research attention in the literature, the problem is not yet fully solved. In this paper, we present a comprehensive literature review for lane detection, especially those with deep learning models. The latest collection of lane detection datasets is presented. We further fill the research gap by proposing a novel lane detection dataset named MudLane, which focuses on the lane detection task on suburban roads.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"21 1","pages":"297-308"},"PeriodicalIF":1.1,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81887211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-15DOI: 10.5755/j01.itc.52.2.32777
Suma Christal, Mary Sundararajan, G. Bharathi, Umasankar Loganathan, Surendar Vadivel
Smart healthcare systems in the cloud-based IoT framework for the prediction of heart disease improve the patient's health status and minimizes the death rate. The prediction of heart disease is a challenging one. Early prediction of heart disease may reduce the risk of patient illness and monitoring in real-time to avoid the risk. The view of existing algorithms is inaccurate in early prediction which took a lot of time for the prediction and inaccurate early prediction of heart disease. To overcome these issues, this paper proposed a sparse autoencoder with Galactic Swarm Optimization (SAE-GSO) algorithm. A sparse encoder predicts heart disease and enhances the accurate prediction, tuning the parameters of sparsity regularity in the sparse autoencoder, Galactic Swarm optimization algorithm is implemented. The proposed work enhances the prediction rate of heart diseases, minimizing the error rate, and maximizing the accuracy. The accuracy rate of the proposed work of SAE-GSO in the Cleveland Dataset produces got 92.23 %, GBT got 65.12 %, SAE got 87.34%, and NB got 83.16 %. The accuracy rate of the proposed work of SAE-GSO in the Framingham Dataset produced 92.59 %, GBT got 69.16 %, SAE got 86.25%, and NB got 82.37%.
{"title":"Improved Smart Healthcare System of Cloud-Based IoT Framework for the Prediction of Heart Disease","authors":"Suma Christal, Mary Sundararajan, G. Bharathi, Umasankar Loganathan, Surendar Vadivel","doi":"10.5755/j01.itc.52.2.32777","DOIUrl":"https://doi.org/10.5755/j01.itc.52.2.32777","url":null,"abstract":"Smart healthcare systems in the cloud-based IoT framework for the prediction of heart disease improve the patient's health status and minimizes the death rate. The prediction of heart disease is a challenging one. Early prediction of heart disease may reduce the risk of patient illness and monitoring in real-time to avoid the risk. The view of existing algorithms is inaccurate in early prediction which took a lot of time for the prediction and inaccurate early prediction of heart disease. To overcome these issues, this paper proposed a sparse autoencoder with Galactic Swarm Optimization (SAE-GSO) algorithm. A sparse encoder predicts heart disease and enhances the accurate prediction, tuning the parameters of sparsity regularity in the sparse autoencoder, Galactic Swarm optimization algorithm is implemented. The proposed work enhances the prediction rate of heart diseases, minimizing the error rate, and maximizing the accuracy. The accuracy rate of the proposed work of SAE-GSO in the Cleveland Dataset produces got 92.23 %, GBT got 65.12 %, SAE got 87.34%, and NB got 83.16 %. The accuracy rate of the proposed work of SAE-GSO in the Framingham Dataset produced 92.59 %, GBT got 69.16 %, SAE got 86.25%, and NB got 82.37%.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"42 1","pages":"529-540"},"PeriodicalIF":1.1,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87501500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-15DOI: 10.5755/j01.itc.52.2.31803
Shengbin Liang, Jiangyong Jin, Wencai Du, Shenming Qu
The number of tourist attractions reviews, travel notes and other texts has grown exponentially in the Internet age. Effectively mining users’ potential opinions and emotions on tourist attractions, and helping to provide users with better recommendation services, which is of great practical significance. This paper proposes a multi-channel neural network model called Pre-BiLSTM combined with a pre-training mechanism. The model uses a combination of coarse and fine- granularity strategies to extract the features of text information such as reviews and travel notes to improve the performance of text sentiment analysis. First, we construct three channels and use the improved BERT and skip-gram methods with negative sampling to vectorize the word-level and vocabulary-level text, respectively, so as to obtain more abundant textual information. Second, we use the pre-training mechanism of BERT to generate deep bidirectional language representation relationships. Third, the vectors of the three channels are input into the BiLSTM network in parallel to extract global and local features. Finally, the model fuses the text features of the three channels and classifies them using SoftMax classifier. Furthermore, numerical experiments are conducted to demonstrate that Pre-BiLSTM outperforms the baselines by 6.27%, 12.83% and 18.12% in average in terms of accuracy, precision and F1-score.
{"title":"A Multi-Channel Text Sentiment Analysis Model Integrating Pre-training Mechanism","authors":"Shengbin Liang, Jiangyong Jin, Wencai Du, Shenming Qu","doi":"10.5755/j01.itc.52.2.31803","DOIUrl":"https://doi.org/10.5755/j01.itc.52.2.31803","url":null,"abstract":"The number of tourist attractions reviews, travel notes and other texts has grown exponentially in the Internet age. Effectively mining users’ potential opinions and emotions on tourist attractions, and helping to provide users with better recommendation services, which is of great practical significance. This paper proposes a multi-channel neural network model called Pre-BiLSTM combined with a pre-training mechanism. The model uses a combination of coarse and fine- granularity strategies to extract the features of text information such as reviews and travel notes to improve the performance of text sentiment analysis. First, we construct three channels and use the improved BERT and skip-gram methods with negative sampling to vectorize the word-level and vocabulary-level text, respectively, so as to obtain more abundant textual information. Second, we use the pre-training mechanism of BERT to generate deep bidirectional language representation relationships. Third, the vectors of the three channels are input into the BiLSTM network in parallel to extract global and local features. Finally, the model fuses the text features of the three channels and classifies them using SoftMax classifier. Furthermore, numerical experiments are conducted to demonstrate that Pre-BiLSTM outperforms the baselines by 6.27%, 12.83% and 18.12% in average in terms of accuracy, precision and F1-score.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"16 1","pages":"263-275"},"PeriodicalIF":1.1,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74495408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-28DOI: 10.5755/j01.itc.52.1.32278
Xiayun Hu, Xiaobin Hu, Jingxian Li, Kun You
Video summarization based on generative adversarial networks (GANs) has been shown to easily produce more realistic results. However, most summary videos are composed of multiple key components. If the selection of some video frames changes during the training process, the information carried by these frames may not be reasonably reflected in the identification results. In this paper, we propose a video summarization method based on selecting keyframes over GANs. The novelty of the proposed method is the discriminator not only identifies the completeness of the video, but also takes into account the value judgment of the candidate keyframes, thus enabling the influence of keyframes on the result value. Given GANs are mainly designed to generate continuous real values, it is generally challenging to generate discrete symbol sequences during the summarization process directly. However, if the generated sample is based on discrete symbols, the slight guidance change of the discrimination network may be meaningless. To better use the advantages of GANs, the study also adopts the video summarization optimization method of GANs under a collaborative reinforcement learning strategy. Experimental results show the proposed method gets a significant summarization effect and character compared with the existing cutting-edge methods.
{"title":"Generative Adversarial Networks for Video Summarization Based on Key-frame Selection","authors":"Xiayun Hu, Xiaobin Hu, Jingxian Li, Kun You","doi":"10.5755/j01.itc.52.1.32278","DOIUrl":"https://doi.org/10.5755/j01.itc.52.1.32278","url":null,"abstract":"Video summarization based on generative adversarial networks (GANs) has been shown to easily produce more realistic results. However, most summary videos are composed of multiple key components. If the selection of some video frames changes during the training process, the information carried by these frames may not be reasonably reflected in the identification results. In this paper, we propose a video summarization method based on selecting keyframes over GANs. The novelty of the proposed method is the discriminator not only identifies the completeness of the video, but also takes into account the value judgment of the candidate keyframes, thus enabling the influence of keyframes on the result value. Given GANs are mainly designed to generate continuous real values, it is generally challenging to generate discrete symbol sequences during the summarization process directly. However, if the generated sample is based on discrete symbols, the slight guidance change of the discrimination network may be meaningless. To better use the advantages of GANs, the study also adopts the video summarization optimization method of GANs under a collaborative reinforcement learning strategy. Experimental results show the proposed method gets a significant summarization effect and character compared with the existing cutting-edge methods.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"5 1","pages":"185-198"},"PeriodicalIF":1.1,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76757976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-28DOI: 10.5755/j01.itc.52.1.32199
Nallarasu Krishnan, K. Raja, Sheela Divakaran
Interney of Things (IoT) enabled by Wireless Sensor Network (WSN) is the principal idea behind target tracking, environment survelance, and patients monitoring systems in which human attentions are very crucial for round the clock. Since the sensor nodes that constitute the IoT is power constrained, it is suffering energy related problems which further badly affect the lifetime of the core sensor network. A well-knows topology management and routing scheme called Clustering is widely used for WSNs in maximizing the network lifetime due to its intrinsic characteristics. Clustering solves the energy constrained issues of WSN by providing a local infrastructure like arrangement to manage the network and resources in suitable manner. Various clustering approaches have been proposed so far by scientific community to address energy issues of WSN. But these existing approaches fail to provide required clustering output to improve lifetime by balancing the energy consumption in efficient manner. In this work, we propose a Minimized Intra-cluster Transmission Distance Clustering Protocol (MITDCP) to improve lifetime of WSN by innovatively clustering and intelligently placing the Base Station (BS). Innovative clustering involves a FCM (Fuzzy C Means) with Cluster Balancing algorithm to create balanced clusters. Then the proposed work makes use of back off timer weighted with residual energy to select and rotate Cluster Head (CH). Simulations show that our proposed work has achieved significant improvement in lifetime of WSN beneath the IoT systems when compared with Improved Energy Efficiency Clustering Protocol (IEECP).
{"title":"Maximization of WSN Based IoT Systems Lifetime by Minimized Intra-cluster Transmission Distance Clustering Protocol","authors":"Nallarasu Krishnan, K. Raja, Sheela Divakaran","doi":"10.5755/j01.itc.52.1.32199","DOIUrl":"https://doi.org/10.5755/j01.itc.52.1.32199","url":null,"abstract":"Interney of Things (IoT) enabled by Wireless Sensor Network (WSN) is the principal idea behind target tracking, environment survelance, and patients monitoring systems in which human attentions are very crucial for round the clock. Since the sensor nodes that constitute the IoT is power constrained, it is suffering energy related problems which further badly affect the lifetime of the core sensor network. A well-knows topology management and routing scheme called Clustering is widely used for WSNs in maximizing the network lifetime due to its intrinsic characteristics. Clustering solves the energy constrained issues of WSN by providing a local infrastructure like arrangement to manage the network and resources in suitable manner. Various clustering approaches have been proposed so far by scientific community to address energy issues of WSN. But these existing approaches fail to provide required clustering output to improve lifetime by balancing the energy consumption in efficient manner. In this work, we propose a Minimized Intra-cluster Transmission Distance Clustering Protocol (MITDCP) to improve lifetime of WSN by innovatively clustering and intelligently placing the Base Station (BS). Innovative clustering involves a FCM (Fuzzy C Means) with Cluster Balancing algorithm to create balanced clusters. Then the proposed work makes use of back off timer weighted with residual energy to select and rotate Cluster Head (CH). Simulations show that our proposed work has achieved significant improvement in lifetime of WSN beneath the IoT systems when compared with Improved Energy Efficiency Clustering Protocol (IEECP).","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"21 1","pages":"140-154"},"PeriodicalIF":1.1,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86101583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-28DOI: 10.5755/j01.itc.52.1.32323
S. Sathyavathi, K. R. Baskaran
Age prediction is the task of extracting features from the human face image. Human aging factors can be expressed as multifactorial, gradual, time-dependent, physical, and biological damage. Attributes are extracted from a face image, and the aging factor depends on cells, tissues, and all living organisms. Human age prediction is distinct from chronological age prediction. Each human’s biological identity has unique characteristics. Age prediction depends on the maturity process of organs, other tissues, and cells. Many research works have been done on age classification using various techniques from human face images. It is a difficult task to the analysis of facial appearance. Issues in the existing algorithm are inefficient and require more computation time and storage space. To address these issues, this paper proposed a Deep convolutional neural network (DCNN) with a Cuckoo search algorithm (DCNN-CS). In this proposed work, DCNN-CS produces an effective age prediction from the human face image within a minimum execution time, handling a large dataset. The accuracy rate of the convolutional neural network (CNN) got 81.32, the Deep Neural Network (DNN) got 82.34, the Long short-term memory (LSTM) got 88.12, and the proposed work SLSTM-DNN got 91.45.
{"title":"An Intelligent Human Age Prediction from Face Image Framework Based on Deep Learning Algorithms","authors":"S. Sathyavathi, K. R. Baskaran","doi":"10.5755/j01.itc.52.1.32323","DOIUrl":"https://doi.org/10.5755/j01.itc.52.1.32323","url":null,"abstract":"Age prediction is the task of extracting features from the human face image. Human aging factors can be expressed as multifactorial, gradual, time-dependent, physical, and biological damage. Attributes are extracted from a face image, and the aging factor depends on cells, tissues, and all living organisms. Human age prediction is distinct from chronological age prediction. Each human’s biological identity has unique characteristics. Age prediction depends on the maturity process of organs, other tissues, and cells. Many research works have been done on age classification using various techniques from human face images. It is a difficult task to the analysis of facial appearance. Issues in the existing algorithm are inefficient and require more computation time and storage space. To address these issues, this paper proposed a Deep convolutional neural network (DCNN) with a Cuckoo search algorithm (DCNN-CS). In this proposed work, DCNN-CS produces an effective age prediction from the human face image within a minimum execution time, handling a large dataset. The accuracy rate of the convolutional neural network (CNN) got 81.32, the Deep Neural Network (DNN) got 82.34, the Long short-term memory (LSTM) got 88.12, and the proposed work SLSTM-DNN got 91.45.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"45 1","pages":"245-257"},"PeriodicalIF":1.1,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88026335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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