Pub Date : 2023-09-11DOI: 10.1080/00051144.2023.2254975
V. Priya, V. Praveena, L. R. Sujithra
Transfer learning approaches in natural language processing have been explored and evolved as a potential solution for solving many problems in recent days. The current research on aspect-based summarization shows unsatisfactory accuracy and low-quality generated summaries. Additionally, the potential advantages of combining language models with parallel processing have not been explored in the existing literature. This paper aims to address the problem of aspect-based extractive text summarization using a transfer learning approach and an optimization method based on map reduce. The proposed approach utilizes transfer learning with language models to extract significant aspects from the text. Subsequently, an optimization process using map reduce is employed. This optimization framework includes an in-node mapper and reducer algorithm to generate summaries for important aspects identified by the language model. This enhances the quality of the summary, leading to improved accuracy, particularly when applied to electrical power system documents. By leveraging the strengths of natural language models and parallel data processing techniques, this model presents an opportunity to achieve better text summary generation. The performance metric used is accuracy, measured with the ROUGE tool, incorporating precision, recall and f-measure. The proposed model demonstrates a 6% improvement in scores compared to state-of-the-art techniques.
{"title":"A parallel optimization and transfer learning approach for summarization in electrical power systems","authors":"V. Priya, V. Praveena, L. R. Sujithra","doi":"10.1080/00051144.2023.2254975","DOIUrl":"https://doi.org/10.1080/00051144.2023.2254975","url":null,"abstract":"Transfer learning approaches in natural language processing have been explored and evolved as a potential solution for solving many problems in recent days. The current research on aspect-based summarization shows unsatisfactory accuracy and low-quality generated summaries. Additionally, the potential advantages of combining language models with parallel processing have not been explored in the existing literature. This paper aims to address the problem of aspect-based extractive text summarization using a transfer learning approach and an optimization method based on map reduce. The proposed approach utilizes transfer learning with language models to extract significant aspects from the text. Subsequently, an optimization process using map reduce is employed. This optimization framework includes an in-node mapper and reducer algorithm to generate summaries for important aspects identified by the language model. This enhances the quality of the summary, leading to improved accuracy, particularly when applied to electrical power system documents. By leveraging the strengths of natural language models and parallel data processing techniques, this model presents an opportunity to achieve better text summary generation. The performance metric used is accuracy, measured with the ROUGE tool, incorporating precision, recall and f-measure. The proposed model demonstrates a 6% improvement in scores compared to state-of-the-art techniques.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980619","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 : 2023-09-10DOI: 10.1080/00051144.2023.2253064
A. Abdul Hayum, J. Jaya, B. Paulchamy, R. Sivakumar
Breast cancer is most dangerous cancer among women. Image processing techniques are used for Breast cancer detection. A Block-based cross diagonal texture matrix (BCDTM) method is used first to extract Haralick’s features from each mammography ROI. Likewise, wrapper method is utilized to choose the crucial features from the condensed feature vector. There are lot of factors that affects the quality of the images such as salt or pepper noise. As a result, this is less precise and more prone to mistakes because of human. In order to address the problems, input breast image is first pre-processed via median filtering to reduce noise. ROI segmentation is done using weighted K means clustering. Feature extraction, texture and form descriptors based on Centroid Distance Functions (CDF) and BCDTM are used. Kernel Principal Component Analysis (KPCA) is used as dimensionality reduction on the extracted features. Improved Cuckoo Search Optimization (ICSO) is used to compute relevant feature selection. Modified Recurrent Neural Network (MRNN) is utilized to classify breast cancer into benign and malignant. Results show that the suggested model achieved highest accuracy, precision and recall values compared with other state-of-the-art approaches.
{"title":"A modified recurrent neural network (MRNN) model for and breast cancer classification system","authors":"A. Abdul Hayum, J. Jaya, B. Paulchamy, R. Sivakumar","doi":"10.1080/00051144.2023.2253064","DOIUrl":"https://doi.org/10.1080/00051144.2023.2253064","url":null,"abstract":"Breast cancer is most dangerous cancer among women. Image processing techniques are used for Breast cancer detection. A Block-based cross diagonal texture matrix (BCDTM) method is used first to extract Haralick’s features from each mammography ROI. Likewise, wrapper method is utilized to choose the crucial features from the condensed feature vector. There are lot of factors that affects the quality of the images such as salt or pepper noise. As a result, this is less precise and more prone to mistakes because of human. In order to address the problems, input breast image is first pre-processed via median filtering to reduce noise. ROI segmentation is done using weighted K means clustering. Feature extraction, texture and form descriptors based on Centroid Distance Functions (CDF) and BCDTM are used. Kernel Principal Component Analysis (KPCA) is used as dimensionality reduction on the extracted features. Improved Cuckoo Search Optimization (ICSO) is used to compute relevant feature selection. Modified Recurrent Neural Network (MRNN) is utilized to classify breast cancer into benign and malignant. Results show that the suggested model achieved highest accuracy, precision and recall values compared with other state-of-the-art approaches.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136072348","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 : 2023-09-04DOI: 10.1080/00051144.2023.2241775
N. Priya, P. B. Pankajavalli
In wireless sensor networks (WSNs), communication between the wireless nodes requires minimum response delay, minimum congestion and communication reliability. A wide variety of sensors produces a mixture of heterogeneous traffics with different reliability requirements. The article focuses on high traffic congestion which affects communication and produces latency. In the existing approaches, the congestion was controlled and the optimization was done during the time of node deployment. In the proposed method, high traffic congestion was controlled by a hop-by-hop approach which was applied in the statically deployed sensor nodes, the optimization was performed at the time of communication. To provide a uninterrupted communication to the WSNs the proposed approach analyses the occupancy ratio of the buffer and evaluates the downstream node congestion level. Here, the Harmony Search Algorithm is considered for design the optimal sensor network with Support Vector Machine (SVM). The experimental result shows the effectiveness and feasibility of the HSA-SVM environment. Also, it significantly enhances communication in diverse traffic conditions, specifically in heavy traffic areas with limited data.
{"title":"High traffic communication congestion control for wireless sensor networks based on harmony search optimization","authors":"N. Priya, P. B. Pankajavalli","doi":"10.1080/00051144.2023.2241775","DOIUrl":"https://doi.org/10.1080/00051144.2023.2241775","url":null,"abstract":"In wireless sensor networks (WSNs), communication between the wireless nodes requires minimum response delay, minimum congestion and communication reliability. A wide variety of sensors produces a mixture of heterogeneous traffics with different reliability requirements. The article focuses on high traffic congestion which affects communication and produces latency. In the existing approaches, the congestion was controlled and the optimization was done during the time of node deployment. In the proposed method, high traffic congestion was controlled by a hop-by-hop approach which was applied in the statically deployed sensor nodes, the optimization was performed at the time of communication. To provide a uninterrupted communication to the WSNs the proposed approach analyses the occupancy ratio of the buffer and evaluates the downstream node congestion level. Here, the Harmony Search Algorithm is considered for design the optimal sensor network with Support Vector Machine (SVM). The experimental result shows the effectiveness and feasibility of the HSA-SVM environment. Also, it significantly enhances communication in diverse traffic conditions, specifically in heavy traffic areas with limited data.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48802493","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 : 2023-09-04DOI: 10.1080/00051144.2023.2253065
K. S. A. S. Murugan, M. Marsaline Beno, R. Sankar, Mahendran Ganesan
In micro-grids, energy management is described as an information and control system that assures that both the generating and distribution systems deliver electricity at the lowest operating costs. Renewable energy sources (RESs), including electric vehicles (EVs), can be successfully used and carbon emissions reduced by establishing a DC multi-microgrid system (MMGS), which includes renewable energy sources (RESs) and the distribution network. A Multi-Microgrid based Energy Management (MM-GEM) system is suggested to increase the economics of MMGS and minimize the distribution network's network loss. MMG is a network of dispersed generators, energy storage, and adjustable loads in a distribution system that is linked. Furthermore, its operation is deconstructed to reduce communication and control costs with the decentralized structure. “Aside from enhancing system resilience, the MMGEMS substantially impacts energy efficiency, power quality, and dependability". Typical MMGEMS functionality and architecture are shown in detail. This is followed by examining current and developing technologies for monitoring and interacting with data among the MMG clusters. In addition, a wide range of MMG energy planning and control systems for interactive energy trading, multi-energy management, and resilient operations are fully examined and researched. The economic effect of the EVs’ energy transfer over time and place is examined.
{"title":"An optimal approach to DC multi-microgrid energy management in electric vehicles (EV)","authors":"K. S. A. S. Murugan, M. Marsaline Beno, R. Sankar, Mahendran Ganesan","doi":"10.1080/00051144.2023.2253065","DOIUrl":"https://doi.org/10.1080/00051144.2023.2253065","url":null,"abstract":"In micro-grids, energy management is described as an information and control system that assures that both the generating and distribution systems deliver electricity at the lowest operating costs. Renewable energy sources (RESs), including electric vehicles (EVs), can be successfully used and carbon emissions reduced by establishing a DC multi-microgrid system (MMGS), which includes renewable energy sources (RESs) and the distribution network. A Multi-Microgrid based Energy Management (MM-GEM) system is suggested to increase the economics of MMGS and minimize the distribution network's network loss. MMG is a network of dispersed generators, energy storage, and adjustable loads in a distribution system that is linked. Furthermore, its operation is deconstructed to reduce communication and control costs with the decentralized structure. “Aside from enhancing system resilience, the MMGEMS substantially impacts energy efficiency, power quality, and dependability\". Typical MMGEMS functionality and architecture are shown in detail. This is followed by examining current and developing technologies for monitoring and interacting with data among the MMG clusters. In addition, a wide range of MMG energy planning and control systems for interactive energy trading, multi-energy management, and resilient operations are fully examined and researched. The economic effect of the EVs’ energy transfer over time and place is examined.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48996116","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 : 2023-08-31DOI: 10.1080/00051144.2023.2250639
K. Ragini, K. Gunaseelan, R. Dhanusuya
ABSTRACT Broadcasting in wireless channels causes security vulnerabilities since both the intended receiver and the eavesdropper may receive the information. Physical layer security (PLS) ensures the confidentiality of information transmitted wireless medium, even in the presence of eavesdroppers, without relying on cryptographic techniques implemented at higher layers. A PLS method for cooperative relay based Orthogonal Frequency Division Multiplexing (OFDM) with optimal relay selection and power optimization is proposed. In order to increase the overall system’s secrecy rate, a hybrid relaying and water filling based optimal power allocation is performed for multi-relay assisted OFDM-based wireless networks. By changing the eavesdroppers’ distances, the performance efficiency of the proposed system is verified. The analysis is carried out for both Full Duplex (FD) and Half Duplex (HD) systems and their performances are compared with existing equal power allocation technique. The proposed method combines relay selection and novel power optimization process to improve secrecy rate than the existing power allocation methods for both HD and FD systems.
{"title":"Physical layer security based on full duplex and half-duplex multi relay assisted OFDM system","authors":"K. Ragini, K. Gunaseelan, R. Dhanusuya","doi":"10.1080/00051144.2023.2250639","DOIUrl":"https://doi.org/10.1080/00051144.2023.2250639","url":null,"abstract":"ABSTRACT Broadcasting in wireless channels causes security vulnerabilities since both the intended receiver and the eavesdropper may receive the information. Physical layer security (PLS) ensures the confidentiality of information transmitted wireless medium, even in the presence of eavesdroppers, without relying on cryptographic techniques implemented at higher layers. A PLS method for cooperative relay based Orthogonal Frequency Division Multiplexing (OFDM) with optimal relay selection and power optimization is proposed. In order to increase the overall system’s secrecy rate, a hybrid relaying and water filling based optimal power allocation is performed for multi-relay assisted OFDM-based wireless networks. By changing the eavesdroppers’ distances, the performance efficiency of the proposed system is verified. The analysis is carried out for both Full Duplex (FD) and Half Duplex (HD) systems and their performances are compared with existing equal power allocation technique. The proposed method combines relay selection and novel power optimization process to improve secrecy rate than the existing power allocation methods for both HD and FD systems.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47083949","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 : 2023-08-30DOI: 10.1080/00051144.2023.2251231
V. Selvakumar, C. Akila
Diabetic retinopathy (DR) is a retinal disorder that may lead to blindness in people all over the world. The major cause of DR is diabetes for a longer period and early detection is the only solution to prevent the vision. This paper focuses on the classes of Normal eye (No DR), Mild NPDR (Non-Proliferative Diabetic Retinopathy), Moderate NPDR, Severe NPDR, and PDR. On retinal fundus images, an effective method for identifying diabetic retinopathy (DR) is proposed by combining the U-Net architecture with the K-nearest neighbours (KNN) algorithm. The U-Net architecture is used for segmenting exudates in retinal pictures, and the KNN algorithm is used for final classification. The combination of U-Net and KNN enables accurate feature extraction and efficient classification, effectively overcoming the computational challenges common to deep learning models. The experiments are carried out utilizing a publicly available dataset of retinal fundus images from Kaggle to assess the effectiveness of our suggested strategy. The proposed architecture provides precise output when compared to other models GoogleNet, ResNet18, and VGG16. The proposed model provides a training accuracy of 82.96% and detection of PDR with high accuracy in the short period which prevents loss of vision in early stage.
{"title":"Efficient diabetic retinopathy diagnosis through U-Net – KNN integration in retinal fundus images","authors":"V. Selvakumar, C. Akila","doi":"10.1080/00051144.2023.2251231","DOIUrl":"https://doi.org/10.1080/00051144.2023.2251231","url":null,"abstract":"Diabetic retinopathy (DR) is a retinal disorder that may lead to blindness in people all over the world. The major cause of DR is diabetes for a longer period and early detection is the only solution to prevent the vision. This paper focuses on the classes of Normal eye (No DR), Mild NPDR (Non-Proliferative Diabetic Retinopathy), Moderate NPDR, Severe NPDR, and PDR. On retinal fundus images, an effective method for identifying diabetic retinopathy (DR) is proposed by combining the U-Net architecture with the K-nearest neighbours (KNN) algorithm. The U-Net architecture is used for segmenting exudates in retinal pictures, and the KNN algorithm is used for final classification. The combination of U-Net and KNN enables accurate feature extraction and efficient classification, effectively overcoming the computational challenges common to deep learning models. The experiments are carried out utilizing a publicly available dataset of retinal fundus images from Kaggle to assess the effectiveness of our suggested strategy. The proposed architecture provides precise output when compared to other models GoogleNet, ResNet18, and VGG16. The proposed model provides a training accuracy of 82.96% and detection of PDR with high accuracy in the short period which prevents loss of vision in early stage.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42016145","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 : 2023-08-29DOI: 10.1080/00051144.2023.2251229
K. Harini, S. Uma Maheswari
Gesture recognition has gained a lot of popularity as it allows humans to communicate with real or virtual systems through gestures, offering new and natural interaction modalities. Recent technologies, such as augmented reality (AR) and the Internet of Things (IoT), have witnessed enormous growth in computer applications that focus on human–computer interaction (HCI). However, a few of these tactics make use of a combination of methods, such as image segmentation, pre-processing, and classification. The hessian-based multiscale filtering and YCbCr colour space are used to separate the gesture region to be recognized. A modified marker-controlled watershed method is employed to segment the gesture contour along with the eight-connector graph to increase recognition precision. The proposed hand gesture recognition methodology uses Self Organizing Map (SOM) with Deep Convolutional Neural Network (DCNN) provides better results with fast convergence speed. Experiments were carried out on a dataset of 30 static and 6 dynamic gestures and also evaluated on a publicly available IIITA-ROBITA ISL Gesture Database to show the effectiveness. The results show that the suggested method can recognize gesture classes with 95.63% accuracy rate without significantly affecting the recognition time. The proposed algorithm was then implemented to control household appliances.
{"title":"A novel static and dynamic hand gesture recognition using self organizing map with deep convolutional neural network","authors":"K. Harini, S. Uma Maheswari","doi":"10.1080/00051144.2023.2251229","DOIUrl":"https://doi.org/10.1080/00051144.2023.2251229","url":null,"abstract":"Gesture recognition has gained a lot of popularity as it allows humans to communicate with real or virtual systems through gestures, offering new and natural interaction modalities. Recent technologies, such as augmented reality (AR) and the Internet of Things (IoT), have witnessed enormous growth in computer applications that focus on human–computer interaction (HCI). However, a few of these tactics make use of a combination of methods, such as image segmentation, pre-processing, and classification. The hessian-based multiscale filtering and YCbCr colour space are used to separate the gesture region to be recognized. A modified marker-controlled watershed method is employed to segment the gesture contour along with the eight-connector graph to increase recognition precision. The proposed hand gesture recognition methodology uses Self Organizing Map (SOM) with Deep Convolutional Neural Network (DCNN) provides better results with fast convergence speed. Experiments were carried out on a dataset of 30 static and 6 dynamic gestures and also evaluated on a publicly available IIITA-ROBITA ISL Gesture Database to show the effectiveness. The results show that the suggested method can recognize gesture classes with 95.63% accuracy rate without significantly affecting the recognition time. The proposed algorithm was then implemented to control household appliances.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42364967","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 : 2023-08-29DOI: 10.1080/00051144.2023.2251230
M. Navin Kumar, S. Sophia, B. Nivedetha
Optimizing testing power is a paramount concern in modern digital circuit design, particularly as the intricacy of circuits continues to rise. This issue becomes even more pronounced with the scaling down of feature sizes due to advancements in process technology. The increase in testing power dissipation, beyond the regular operational state, raises red flags for both designers and test engineers. This paper introduces a novel cluster-based approach, shedding light on the efficient reordering of test vectors to mitigate the heightened switching activity. This technique aims to reduce the power consumption during testing, while still ensuring efficient error detection, maintaining the test period, and preserving the original order of the scan chain. Importantly, this reordering method does not introduce any additional area or test time overhead, minimizing the risks associated with these factors. The potential impact of this approach is demonstrated through concrete examples drawn from ISCAS’89 benchmark circuits. The results showcase a notable 11% reduction in switching activity, all while preserving fault coverage levels.
{"title":"Cluster-based test vector re-ordering for reduced power dissipation in digital circuits","authors":"M. Navin Kumar, S. Sophia, B. Nivedetha","doi":"10.1080/00051144.2023.2251230","DOIUrl":"https://doi.org/10.1080/00051144.2023.2251230","url":null,"abstract":"Optimizing testing power is a paramount concern in modern digital circuit design, particularly as the intricacy of circuits continues to rise. This issue becomes even more pronounced with the scaling down of feature sizes due to advancements in process technology. The increase in testing power dissipation, beyond the regular operational state, raises red flags for both designers and test engineers. This paper introduces a novel cluster-based approach, shedding light on the efficient reordering of test vectors to mitigate the heightened switching activity. This technique aims to reduce the power consumption during testing, while still ensuring efficient error detection, maintaining the test period, and preserving the original order of the scan chain. Importantly, this reordering method does not introduce any additional area or test time overhead, minimizing the risks associated with these factors. The potential impact of this approach is demonstrated through concrete examples drawn from ISCAS’89 benchmark circuits. The results showcase a notable 11% reduction in switching activity, all while preserving fault coverage levels.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46627316","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 : 2023-08-26DOI: 10.1080/00051144.2023.2250640
S. Manoj, C. Valliyammai
Every year India faces many cyclones and erratic monsoon seasons are common in recent times. Cyclones destroy the infrastructure and lead to loss of life and damage property in coastal areas. The agriculture sector is also affected by random and unexpected rainfall. In recent years, India gets rainfall during the harvest season which leads to financial loss. Also, the number of drought events is on the rise in the Indian subcontinent as the rainwater is not managed properly. Farmers need to know whether the monsoon rainfall pattern has been shifted or not and need to shift their agricultural activity accordingly to handle the impacts of climate change. From the rainfall and accumulated cyclone energy (ACE) data analysis, it is found that monsoon seasons in India are not shifted, but, rainfall is intense during the initial months of each monsoon season. ACE values are predicted using techniques such as ARIMA, LSTM, Prophet, and stacked ensemble with multi-layer perceptron. Based on the experimental results, the proposed stacked ensemble model achieves 91% accuracy.
{"title":"Analysis of shift in Indian monsoon and prediction of accumulated cyclone energy in Indian subcontinent using deep learning","authors":"S. Manoj, C. Valliyammai","doi":"10.1080/00051144.2023.2250640","DOIUrl":"https://doi.org/10.1080/00051144.2023.2250640","url":null,"abstract":"Every year India faces many cyclones and erratic monsoon seasons are common in recent times. Cyclones destroy the infrastructure and lead to loss of life and damage property in coastal areas. The agriculture sector is also affected by random and unexpected rainfall. In recent years, India gets rainfall during the harvest season which leads to financial loss. Also, the number of drought events is on the rise in the Indian subcontinent as the rainwater is not managed properly. Farmers need to know whether the monsoon rainfall pattern has been shifted or not and need to shift their agricultural activity accordingly to handle the impacts of climate change. From the rainfall and accumulated cyclone energy (ACE) data analysis, it is found that monsoon seasons in India are not shifted, but, rainfall is intense during the initial months of each monsoon season. ACE values are predicted using techniques such as ARIMA, LSTM, Prophet, and stacked ensemble with multi-layer perceptron. Based on the experimental results, the proposed stacked ensemble model achieves 91% accuracy.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44061624","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 : 2023-08-22DOI: 10.1080/00051144.2023.2247910
Krishna Kumar L., L. S.
Artificial Intelligence (AI) and the constant paradigm shift in road traffic have led to a need for significant improvement in road safety to minimize traffic accidents. LiFi helps minimize accidents by transmitting data between multiple vehicles (i.e. Vehicle-to-Vehicle (V2V)) and between vehicles and infrastructure (i.e. Vehicle-to-Infrastructure (V2I)) without interference. LiFi uses light to transmit data between devices or vehicles, which ensures efficient data transmission speed and is therefore considered a safe technology. A method called Deep Jacobian Regression and Tate Bryant Euler Recommendation (DJR-TBER) is proposed in this paper based on V2V and V2I autonomous vehicle communication. The proposed method DJR-TBER consists of an input layer, four hidden layers and finally an output layer. Sensors are first used to obtain the information. A linear regression-based speed evaluation model is developed and followed by a Jacobi matrix-based distance evaluation model in the hidden layer. The third hidden layer by developing a distance evaluation model. The use of Laplacian function ensures secure V2I communication for the autonomous vehicle. Finally, a Tate-Bryant-Euler angle-based model for emergency handling is proposed in the hidden layer to optimally consider the aspect of braking in emergency situations and thus increase driving safety.
{"title":"Jacobian linear regression and Tate Bryant Euler angle enabled autonomous vehicle LiFi communication sustained IOT","authors":"Krishna Kumar L., L. S.","doi":"10.1080/00051144.2023.2247910","DOIUrl":"https://doi.org/10.1080/00051144.2023.2247910","url":null,"abstract":"Artificial Intelligence (AI) and the constant paradigm shift in road traffic have led to a need for significant improvement in road safety to minimize traffic accidents. LiFi helps minimize accidents by transmitting data between multiple vehicles (i.e. Vehicle-to-Vehicle (V2V)) and between vehicles and infrastructure (i.e. Vehicle-to-Infrastructure (V2I)) without interference. LiFi uses light to transmit data between devices or vehicles, which ensures efficient data transmission speed and is therefore considered a safe technology. A method called Deep Jacobian Regression and Tate Bryant Euler Recommendation (DJR-TBER) is proposed in this paper based on V2V and V2I autonomous vehicle communication. The proposed method DJR-TBER consists of an input layer, four hidden layers and finally an output layer. Sensors are first used to obtain the information. A linear regression-based speed evaluation model is developed and followed by a Jacobi matrix-based distance evaluation model in the hidden layer. The third hidden layer by developing a distance evaluation model. The use of Laplacian function ensures secure V2I communication for the autonomous vehicle. Finally, a Tate-Bryant-Euler angle-based model for emergency handling is proposed in the hidden layer to optimally consider the aspect of braking in emergency situations and thus increase driving safety.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45653217","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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