Pub Date : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768500
J. Palanimeera, K. Ponmozhi
Human activity identification is the automated interpretation of the movements happen in a video done by a human. Iterative Due to its wide applications in fields such as autonomous driving, biomedical imaging, and machine intelligence vision, among others, recognizing human activity in an image remains a tough and crucial research subj ect in the field of computer vision. Deep learning techniques have recently advanced, and models for image identification and classification, object detection, and speech recognition have been successfully implemented. Only a few examples include different aspects of human structure and movement, diffraction, a busy background, and so on. Moving cameras, changing lighting conditions and changing perspectives are all things to think about. Yoga is an excellent kind of physical activity. It's critical to maintain proper yoga posture. This research provides a unique technique for yoga asana detection based on feature extraction and representation Using a deep CNN model that has already been trained, followed by yoga asana recognition using a hybrid Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifier. With the constrained training datasets, it was discovered that previously learned CNN-based representations on large-scale annotated datasets may be applied to yoga asana recognition tasks. In real-time datasets, the suggested approach is tested on seven yoga asana (Pranamasana, Dhanurasan, Dandasana, Gomukhasan, Garudasana, Padmavrikshasana and Padmasan). The results show that the proposed scheme outperforms the state of the art methods.
{"title":"Transfer Learning with Deep Representations is Used to Recognition Yoga Postures","authors":"J. Palanimeera, K. Ponmozhi","doi":"10.1109/ICEEICT53079.2022.9768500","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768500","url":null,"abstract":"Human activity identification is the automated interpretation of the movements happen in a video done by a human. Iterative Due to its wide applications in fields such as autonomous driving, biomedical imaging, and machine intelligence vision, among others, recognizing human activity in an image remains a tough and crucial research subj ect in the field of computer vision. Deep learning techniques have recently advanced, and models for image identification and classification, object detection, and speech recognition have been successfully implemented. Only a few examples include different aspects of human structure and movement, diffraction, a busy background, and so on. Moving cameras, changing lighting conditions and changing perspectives are all things to think about. Yoga is an excellent kind of physical activity. It's critical to maintain proper yoga posture. This research provides a unique technique for yoga asana detection based on feature extraction and representation Using a deep CNN model that has already been trained, followed by yoga asana recognition using a hybrid Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifier. With the constrained training datasets, it was discovered that previously learned CNN-based representations on large-scale annotated datasets may be applied to yoga asana recognition tasks. In real-time datasets, the suggested approach is tested on seven yoga asana (Pranamasana, Dhanurasan, Dandasana, Gomukhasan, Garudasana, Padmavrikshasana and Padmasan). The results show that the proposed scheme outperforms the state of the art methods.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126721973","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768479
Ze Zhang
The damage of the optical fiber composite overhead ground wire (OPGW) seriously affects the safety and stability of the power system. In order to study the impact of lightning current on the OPGW damage, COMSOL Multiphysics is used to simulate the local temperature of the lightning strike. The results show that the A component of lightning current has little effect on OPGW damage, which is mainly caused by the subsequent C component. The C component of lightning current is more destructive to OPGW, and the amount of charge transferred is the main cause of damage. The simulation results provide references for future research on OPGW's lightning resistance and mechanical performance.
{"title":"Research on the Damage Characteristics of OPGW by Lightning Current Component Based on COMSOL","authors":"Ze Zhang","doi":"10.1109/ICEEICT53079.2022.9768479","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768479","url":null,"abstract":"The damage of the optical fiber composite overhead ground wire (OPGW) seriously affects the safety and stability of the power system. In order to study the impact of lightning current on the OPGW damage, COMSOL Multiphysics is used to simulate the local temperature of the lightning strike. The results show that the A component of lightning current has little effect on OPGW damage, which is mainly caused by the subsequent C component. The C component of lightning current is more destructive to OPGW, and the amount of charge transferred is the main cause of damage. The simulation results provide references for future research on OPGW's lightning resistance and mechanical performance.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122674090","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768495
T. Subash Ponraj, Rajeev K Sukumaran, S. Vignesh, M. Saravanan, T. Manikandan, M. Radhakrishnan
Underwater Wireless Sensor Networks is a popular sub domain of WSN. The Number of Ocean monitoring applications is increasing day-by-day. For effective operations of such underwater applications service provisioning plays a major role. Network Function Virtualization (NFV) has been considered as an effective technology to make flexible service provisioning in terrestrial network. Since UWSN also demands such flexible service provisioning capability, NFV can be adapted for UWSN as well. By providing network operations through NFV in UWSN operational consumptions and capital expenses will be reduced drastically. So, this research focuses on modeling end-end performance bounds on NFV based UWSN using Stochastic Network Calculus (SN C). Monitoring applications in UWSN expects on demand service provisioning capability from the underlying network. In order to model such on demand service provisioning features and capability of NFV, we have taken into account both the non-bursty and bursty type of traffic of UWSN. In UWSN for modeling estimation of current resource availability of Virtual Network Function nodes with multi-level traffic and their complicated NFV chain, using leftover service property and convolution associativity property of SNC has been used. The proposed mathematical model of NFV service provisioning in UWSN has been evaluated for its correctness using a simulation model. The results of the simulation model and the proposed analytical model have a very negligible difference. So, the proposed model can be adapted in real time of effective NFV based service provisioning in UWSN.
水下无线传感器网络是无线传感器网络的一个热门子领域。海洋监测应用的数量日益增加。对于此类水下应用的有效操作，服务提供起着重要作用。网络功能虚拟化(Network Function Virtualization, NFV)被认为是实现地面网络业务灵活提供的一种有效技术。由于UWSN也需要灵活的业务发放能力，因此NFV也可以适用于UWSN。通过NFV在UWSN中提供网络运营，将大大降低运营消耗和资本支出。因此，本研究的重点是利用随机网络微积分(SN C)对基于NFV的UWSN的端到端性能边界进行建模。UWSN中的监控应用需要底层网络提供随需应变的服务供应能力。为了模拟这种随需应变的服务提供特性和NFV的能力，我们考虑了UWSN的非突发和突发类型的流量。在UWSN中，利用SNC的剩余服务性质和卷积结合性对具有多级流量的虚拟网络功能节点及其复杂NFV链的当前资源可用性进行建模估计。利用仿真模型对所提出的UWSN中NFV业务提供数学模型的正确性进行了评价。仿真模型的结果与所提出的解析模型的结果相差很小。因此，该模型可以适应UWSN中基于NFV的业务提供的实时性。
{"title":"Stochastic Network Calculus for Network Function Virtualization in Underwater Wireless Sensor Networks","authors":"T. Subash Ponraj, Rajeev K Sukumaran, S. Vignesh, M. Saravanan, T. Manikandan, M. Radhakrishnan","doi":"10.1109/ICEEICT53079.2022.9768495","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768495","url":null,"abstract":"Underwater Wireless Sensor Networks is a popular sub domain of WSN. The Number of Ocean monitoring applications is increasing day-by-day. For effective operations of such underwater applications service provisioning plays a major role. Network Function Virtualization (NFV) has been considered as an effective technology to make flexible service provisioning in terrestrial network. Since UWSN also demands such flexible service provisioning capability, NFV can be adapted for UWSN as well. By providing network operations through NFV in UWSN operational consumptions and capital expenses will be reduced drastically. So, this research focuses on modeling end-end performance bounds on NFV based UWSN using Stochastic Network Calculus (SN C). Monitoring applications in UWSN expects on demand service provisioning capability from the underlying network. In order to model such on demand service provisioning features and capability of NFV, we have taken into account both the non-bursty and bursty type of traffic of UWSN. In UWSN for modeling estimation of current resource availability of Virtual Network Function nodes with multi-level traffic and their complicated NFV chain, using leftover service property and convolution associativity property of SNC has been used. The proposed mathematical model of NFV service provisioning in UWSN has been evaluated for its correctness using a simulation model. The results of the simulation model and the proposed analytical model have a very negligible difference. So, the proposed model can be adapted in real time of effective NFV based service provisioning in UWSN.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123058519","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768632
Molagavalli Jhansi, S. Bachu, N. U. Kumar, M. A. Kumar
Video object detection plays the major role in variety applications including security, remote sensing and hyperspectral. Deep learning-based algorithms have made significant advances in video object recognition in recent years. The conventional machine learning applications are resulted in poor accuracy. In this article, a unified deep learning based convolutional neural network (DLCNN) is developed for composite multi object recognition in videos. To enhance composite object recognition, DLCNN analyses a composite item as a collection of background and adds part information into feature information. Correct component information may help forecast the shape and size of a feature data, which helps solve challenges caused by different forms and sizes of various objects. Finally, the DLCNN draws a bounding box to detected object by using the background features. Further, the simulation results shows that the performance of proposed method is improved as compared to the state of art approaches.
{"title":"IODTDLCNN: Implementation of Object Detection and Tracking by using Deep Learning based Convolutional Neural Network","authors":"Molagavalli Jhansi, S. Bachu, N. U. Kumar, M. A. Kumar","doi":"10.1109/ICEEICT53079.2022.9768632","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768632","url":null,"abstract":"Video object detection plays the major role in variety applications including security, remote sensing and hyperspectral. Deep learning-based algorithms have made significant advances in video object recognition in recent years. The conventional machine learning applications are resulted in poor accuracy. In this article, a unified deep learning based convolutional neural network (DLCNN) is developed for composite multi object recognition in videos. To enhance composite object recognition, DLCNN analyses a composite item as a collection of background and adds part information into feature information. Correct component information may help forecast the shape and size of a feature data, which helps solve challenges caused by different forms and sizes of various objects. Finally, the DLCNN draws a bounding box to detected object by using the background features. Further, the simulation results shows that the performance of proposed method is improved as compared to the state of art approaches.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122232098","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768470
G. R. Reddy, A. Srinivas, S. Girija, R. Devi
Whenever the acquired images having flaws such as poor visual appearance to the eyes, noise, or low quality which degraded the quality of the image. In order to increase the visual appearance, image enhancement should be used. The primary goal of image enhancement is to suppress blemishes in an image while retaining useful information. Many researchers suggested different kinds of enhancement processes that produced positive results. The conventional Histogram Equalization (HE) is a popular technique for refining the quality of a taken image. However, it is possible that unwanted contrast enhancement may occur. As a result, we have an unnatural presence in a processed image, as well as visual objects. To address this, we developed a novel hybrid algorithm called Optimized Gamma Correction with Weighted Distribution (OGCWD), which combines the Differential Evolution algorithm and Adaptive Gamma Correction with Weighted Distribution. The proposed method is an automated transformation process that aids in improving the brightness of a lowered image. The proposed OGCWD algorithm outperforms state-of-the-art image enhancement techniques in terms of structural Similarity Index (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR).
{"title":"Enhancement of Images Using Optimized Gamma Correction with Weighted Distribution Via Differential Evolution Algorithm","authors":"G. R. Reddy, A. Srinivas, S. Girija, R. Devi","doi":"10.1109/ICEEICT53079.2022.9768470","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768470","url":null,"abstract":"Whenever the acquired images having flaws such as poor visual appearance to the eyes, noise, or low quality which degraded the quality of the image. In order to increase the visual appearance, image enhancement should be used. The primary goal of image enhancement is to suppress blemishes in an image while retaining useful information. Many researchers suggested different kinds of enhancement processes that produced positive results. The conventional Histogram Equalization (HE) is a popular technique for refining the quality of a taken image. However, it is possible that unwanted contrast enhancement may occur. As a result, we have an unnatural presence in a processed image, as well as visual objects. To address this, we developed a novel hybrid algorithm called Optimized Gamma Correction with Weighted Distribution (OGCWD), which combines the Differential Evolution algorithm and Adaptive Gamma Correction with Weighted Distribution. The proposed method is an automated transformation process that aids in improving the brightness of a lowered image. The proposed OGCWD algorithm outperforms state-of-the-art image enhancement techniques in terms of structural Similarity Index (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR).","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"208 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131618181","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768516
Rosario Gilmary, Akila Venketesan, M. Praveen, Hari R Prasath, Govindasamy Vaiyapuri
Twitter is an interactive microblogging platform where registered users share their thoughts using tweets. Currently, Twitter has reached almost 396.5 million users. The proportion of Twitter bots has grown with their popularity. It is estimated that about 52 million Twitter accounts are bots. Bot identification is significant to prevent false information, malware and protect the reliability of online discussions. Most techniques focus on Twitter's topological structure, neglecting the account heterogeneity. Further, they use supervised learning, which demands large training sets. In this paper, the user behaviors are modeled as DNA sequences. Information gain-based entropy is computed on fragments of DNA sequences throughterm frequency-inverse document frequency to determine DNA patterns that contribute to bots.
{"title":"Detection of Twitter Bots using DNA-based Entropy Technique","authors":"Rosario Gilmary, Akila Venketesan, M. Praveen, Hari R Prasath, Govindasamy Vaiyapuri","doi":"10.1109/ICEEICT53079.2022.9768516","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768516","url":null,"abstract":"Twitter is an interactive microblogging platform where registered users share their thoughts using tweets. Currently, Twitter has reached almost 396.5 million users. The proportion of Twitter bots has grown with their popularity. It is estimated that about 52 million Twitter accounts are bots. Bot identification is significant to prevent false information, malware and protect the reliability of online discussions. Most techniques focus on Twitter's topological structure, neglecting the account heterogeneity. Further, they use supervised learning, which demands large training sets. In this paper, the user behaviors are modeled as DNA sequences. Information gain-based entropy is computed on fragments of DNA sequences throughterm frequency-inverse document frequency to determine DNA patterns that contribute to bots.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131669874","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768410
P. Rajamani, K. A. Aravind, P. Nirgude
Composite insulators employed in HVAC or HVDC transmission lines experience common defects, viz. rodent / bird pecking damage, damage due to flashover and formation of fungi and algae growth on the shed. Disturbance due to radio frequency interference from transmission lines is a major design consideration for transmission line. Though each and every equipment of transmission line is commissioned in system after stringent testing, radio frequency interference from transmission line to nearby electrical equipment is inevitable in many occasions. This paper aims in presenting the impact of commonly occurring in-service defects in composite insulator on radio frequency interference spectra. RFI is recorded by emulating those common defects in actual sample with normal operating voltage. A 245 kV, 160 kN silicone composite long rod insulator was chosen for this purpose and experiments were performed with normal operating voltage of 245 kV transmission line.
{"title":"Impact of Common Defects in Silicone Composite Long Rod Insulators on Radio Frequency Interference Spectra","authors":"P. Rajamani, K. A. Aravind, P. Nirgude","doi":"10.1109/ICEEICT53079.2022.9768410","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768410","url":null,"abstract":"Composite insulators employed in HVAC or HVDC transmission lines experience common defects, viz. rodent / bird pecking damage, damage due to flashover and formation of fungi and algae growth on the shed. Disturbance due to radio frequency interference from transmission lines is a major design consideration for transmission line. Though each and every equipment of transmission line is commissioned in system after stringent testing, radio frequency interference from transmission line to nearby electrical equipment is inevitable in many occasions. This paper aims in presenting the impact of commonly occurring in-service defects in composite insulator on radio frequency interference spectra. RFI is recorded by emulating those common defects in actual sample with normal operating voltage. A 245 kV, 160 kN silicone composite long rod insulator was chosen for this purpose and experiments were performed with normal operating voltage of 245 kV transmission line.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131140753","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768564
Zhi Wei, Shuo Wang
Proximity sensor is a kind of sensor that detects the proximity of objects. When a ferromagnetic object passes through its sensing range at a certain speed, the magnetic field distribution of the sensor will be affected, and an output voltage signal will be generated according to the Faraday Electromagnetic Induction Principle. There are great differences in the shape and size of proximity sensors in different application fields. The general design approach is to fine tune the parameters according to the actual application scenarios according to commonsense. In this paper, the field-circuit combination method is used to systematically study the proximity sensor. Through the combination of the two methods, the sensor structure scheme is studied, and the design method relying solely on experience is improved; The quantitative calculation is carried out from the perspective of magnetic field, which solves the problem that it is difficult to theoretically calculate the magnetic circuit parameters when the yoke shape is irregular. According to the design model, the prototype and simple experimental devices are made, and the theoretical analysis is verified by experiments.
{"title":"Research on Passive Proximity Sensor based on Field-Circuit Combination Method","authors":"Zhi Wei, Shuo Wang","doi":"10.1109/ICEEICT53079.2022.9768564","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768564","url":null,"abstract":"Proximity sensor is a kind of sensor that detects the proximity of objects. When a ferromagnetic object passes through its sensing range at a certain speed, the magnetic field distribution of the sensor will be affected, and an output voltage signal will be generated according to the Faraday Electromagnetic Induction Principle. There are great differences in the shape and size of proximity sensors in different application fields. The general design approach is to fine tune the parameters according to the actual application scenarios according to commonsense. In this paper, the field-circuit combination method is used to systematically study the proximity sensor. Through the combination of the two methods, the sensor structure scheme is studied, and the design method relying solely on experience is improved; The quantitative calculation is carried out from the perspective of magnetic field, which solves the problem that it is difficult to theoretically calculate the magnetic circuit parameters when the yoke shape is irregular. According to the design model, the prototype and simple experimental devices are made, and the theoretical analysis is verified by experiments.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131096927","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768429
B. S. Reddy, I. Adum Babu, S. Bachu
In telemedicine, the authenticity and integrity of medical images must be safeguarded. Copyright protection is provided through robust medical image watermarking (MIW) methods, and the original pictures may be retrieved at the receiver's end. But existing algorithms have limits in terms of balancing the tradeoff between robustness, imperceptibility, and embedded capacity. Aside from that, most MIW algorithms aren't built for color images. This article proposes a novel MIW technique based on the redundant discrete wavelet transform (RDWT) with singular value decomposition (SVD) to increase their performance in preserving medical color picture information. First and foremost, the RDWT -SVD is a reliable solution as compared to the conventional DWT. Second, modifying the wavelet domain coefficient ensures that integer values in the spatial domain change and that the watermarking process is reversible. Finally, the embedding approach makes full advantage of the original image's features and watermarking. The simulation results showed that the proposed method decreases the amount of original picture change and improves imperceptibility as compared to the conventional approaches.
{"title":"Implementation of Medical Image Watermarking using RDWT and SVD for Secure Medical Data Transmission in Healthcare Systems","authors":"B. S. Reddy, I. Adum Babu, S. Bachu","doi":"10.1109/ICEEICT53079.2022.9768429","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768429","url":null,"abstract":"In telemedicine, the authenticity and integrity of medical images must be safeguarded. Copyright protection is provided through robust medical image watermarking (MIW) methods, and the original pictures may be retrieved at the receiver's end. But existing algorithms have limits in terms of balancing the tradeoff between robustness, imperceptibility, and embedded capacity. Aside from that, most MIW algorithms aren't built for color images. This article proposes a novel MIW technique based on the redundant discrete wavelet transform (RDWT) with singular value decomposition (SVD) to increase their performance in preserving medical color picture information. First and foremost, the RDWT -SVD is a reliable solution as compared to the conventional DWT. Second, modifying the wavelet domain coefficient ensures that integer values in the spatial domain change and that the watermarking process is reversible. Finally, the embedding approach makes full advantage of the original image's features and watermarking. The simulation results showed that the proposed method decreases the amount of original picture change and improves imperceptibility as compared to the conventional approaches.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133643605","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 : 2022-02-16DOI: 10.1109/ICEEICT53079.2022.9768573
A. Mary
In this modern era, many challenges are being faced by people like traffic congestion, limitation of parking lots, pollution of environment, etc. Ride sharing is one of the techniques for overcoming such challenges. However, its efficacy in lowering personal automobiles is dependent on its competence while preserving commercial feasibility from the viewpoint of both the vehicle operator and the passenger. Numerous techniques of cluster evaluation have been created which explore for sections of high-ranking intensity in the data, each such area being taken to imply a distinct group. In this research we propose a ride sharing methodology for predicting the Ride sharing fare using the Principal Component Analysis PCA.
{"title":"An Optimized technique for a Sapid Motor pooling Tariff Forecasting System","authors":"A. Mary","doi":"10.1109/ICEEICT53079.2022.9768573","DOIUrl":"https://doi.org/10.1109/ICEEICT53079.2022.9768573","url":null,"abstract":"In this modern era, many challenges are being faced by people like traffic congestion, limitation of parking lots, pollution of environment, etc. Ride sharing is one of the techniques for overcoming such challenges. However, its efficacy in lowering personal automobiles is dependent on its competence while preserving commercial feasibility from the viewpoint of both the vehicle operator and the passenger. Numerous techniques of cluster evaluation have been created which explore for sections of high-ranking intensity in the data, each such area being taken to imply a distinct group. In this research we propose a ride sharing methodology for predicting the Ride sharing fare using the Principal Component Analysis PCA.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114230346","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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