Text summarization is an important Natural Language Processing problem. Manual text summarization is a laborious and time-consuming task. Owing to the advancements in the field of Natural Language Processing, this task can be effectively moved from manual to automated text summarization. This paper proposes a model named Term Frequency-Inverse Document Frequency (TF-IDF) Summarization Tool which implements a text analytics approach called TF-IDF to generate a meaningful summary. TF-IDF is used to identify the topic or context of the text statistically. As data today is mostly unstructured in nature, this paper aims to explore a combination of NLP techniques such as Speech Recognition and Optical Character Recognition to summarize multimedia data as well. The TF-IDF Summarization Tool is seen to produce summaries with Jaccard's Similarity value of 67% and Rogue-1 of 64.9%, Rogue-2 of 48.2%, and Rogue-L of 56.4% based on a self-developed dataset.
{"title":"Summarization tool for multimedia data","authors":"Swarna Kadagadkai, Malini Patil, Ashwini Nagathan, Abhinand Harish, Anoop MV","doi":"10.1016/j.gltp.2022.04.001","DOIUrl":"10.1016/j.gltp.2022.04.001","url":null,"abstract":"<div><p>Text summarization is an important Natural Language Processing problem. Manual text summarization is a laborious and time-consuming task. Owing to the advancements in the field of Natural Language Processing, this task can be effectively moved from manual to automated text summarization. This paper proposes a model named Term Frequency-Inverse Document Frequency (TF-IDF) Summarization Tool which implements a text analytics approach called TF-IDF to generate a meaningful summary. TF-IDF is used to identify the topic or context of the text statistically. As data today is mostly unstructured in nature, this paper aims to explore a combination of NLP techniques such as Speech Recognition and Optical Character Recognition to summarize multimedia data as well. The TF-IDF Summarization Tool is seen to produce summaries with Jaccard's Similarity value of 67% and Rogue-1 of 64.9%, Rogue-2 of 48.2%, and Rogue-L of 56.4% based on a self-developed dataset.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"3 1","pages":"Pages 2-7"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666285X22000371/pdfft?md5=698ed5319affd6ce36a31758ea1ef0fb&pid=1-s2.0-S2666285X22000371-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86260976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.gltp.2022.04.009
Sunil S Harakannanavar , Shaik Roshan Sameer , Vikash Kumar , Sunil Kumar Behera , Adithya V Amberkar , Veena I. Puranikmath
The proposed approach uses ResNet-18 for feature extraction and with the help of temporal interest proposals generated for the video sequences, generates a video summary. The ResNet-18 is a convolutional neural network with eighteen layers. The existing methods don't address the problem of the summary being temporally consistent. The proposed work aims to create a temporally consistent summary. The classification and regression module are implemented to get fixed length inputs of the combined features. After this, the non-maximum suppression algorithm is applied to reduce the redundancy and remove the video segments having poor quality and low confidence-scores. Video summaries are generated using the kernel temporal segmentation (KTS) algorithm which converts a given video segment into video shots. The two standard datasets TVSum and SumMe are used to evaluate the proposed model. It is seen that the F-score obtained on TVSum and SumMe dataset is 56.13 and 45.06 respectively.
{"title":"Robust video summarization algorithm using supervised machine learning","authors":"Sunil S Harakannanavar , Shaik Roshan Sameer , Vikash Kumar , Sunil Kumar Behera , Adithya V Amberkar , Veena I. Puranikmath","doi":"10.1016/j.gltp.2022.04.009","DOIUrl":"10.1016/j.gltp.2022.04.009","url":null,"abstract":"<div><p>The proposed approach uses ResNet-18 for feature extraction and with the help of temporal interest proposals generated for the video sequences, generates a video summary. The ResNet-18 is a convolutional neural network with eighteen layers. The existing methods don't address the problem of the summary being temporally consistent. The proposed work aims to create a temporally consistent summary. The classification and regression module are implemented to get fixed length inputs of the combined features. After this, the non-maximum suppression algorithm is applied to reduce the redundancy and remove the video segments having poor quality and low confidence-scores. Video summaries are generated using the kernel temporal segmentation (KTS) algorithm which converts a given video segment into video shots. The two standard datasets TVSum and SumMe are used to evaluate the proposed model. It is seen that the F-score obtained on TVSum and SumMe dataset is 56.13 and 45.06 respectively.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"3 1","pages":"Pages 131-135"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666285X22000450/pdfft?md5=eed96dd5bed239cbd125280be4cf8fa1&pid=1-s2.0-S2666285X22000450-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84180448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.gltp.2022.03.019
Jalajakshi V, Myna A N
This paper is mainly discussed on importance and contribution of statistics to Data science and how it emerges as the most important factor to solve realistic problems which contains huge amount of data processing. There are various methods in statistics which help Analysis in data science which will be explained in detail. This work also emphasizes on importance of Data Science in this present technology. Statistics is proved to be an important discipline in regulating the work analyzed in the field of Data Science. This work compare various statistical approaches with This outlines the numerous potential data analysis approach processes which helps in examining the influence of quantitative statistical measures on data collection and optimization, data interpretation, data processing and modelling, testing and presenting and Various challenges faced in the process of data science using statistics is given in brief. Here there is a numerous way to enhance the data science techniques with the help of statistics methodologies.
{"title":"Importance of statistics to data science","authors":"Jalajakshi V, Myna A N","doi":"10.1016/j.gltp.2022.03.019","DOIUrl":"10.1016/j.gltp.2022.03.019","url":null,"abstract":"<div><p>This paper is mainly discussed on importance and contribution of statistics to Data science and how it emerges as the most important factor to solve realistic problems which contains huge amount of data processing. There are various methods in statistics which help Analysis in data science which will be explained in detail. This work also emphasizes on importance of Data Science in this present technology. Statistics is proved to be an important discipline in regulating the work analyzed in the field of Data Science. This work compare various statistical approaches with This outlines the numerous potential data analysis approach processes which helps in examining the influence of quantitative statistical measures on data collection and optimization, data interpretation, data processing and modelling, testing and presenting and Various challenges faced in the process of data science using statistics is given in brief. Here there is a numerous way to enhance the data science techniques with the help of statistics methodologies.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"3 1","pages":"Pages 326-331"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666285X22000255/pdfft?md5=d42da720f53ade5a6c5a7d114139ad91&pid=1-s2.0-S2666285X22000255-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84836696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.gltp.2022.04.025
Tanmay Srinath, Gururaja H.S.
Machine learning is fast becoming one of the central solutions to various real-world problems. Thanks to powerful hardware and large datasets, training a machine learning model has become easier and more rewarding. However, an inherent problem in various machine learning models is a lack of understanding of what goes on ’under the hood’. A lack of explainability and interpretability leads to lower levels of trust in the model's predictions, which means it can't be used in sensitive applications like diagnosing medical ailments and detecting terrorism. This has led to various advances in making machine learning explainable. In this paper various black-box models are used to classify credit card defaulters. These models are compared using different performance metrics, and explanations of these models are provided using a model-agnostic explainer. Finally, the best model-explainer combo is proposed with potential areas of future exploration.
{"title":"Explainable machine learning in identifying credit card defaulters","authors":"Tanmay Srinath, Gururaja H.S.","doi":"10.1016/j.gltp.2022.04.025","DOIUrl":"10.1016/j.gltp.2022.04.025","url":null,"abstract":"<div><p>Machine learning is fast becoming one of the central solutions to various real-world problems. Thanks to powerful hardware and large datasets, training a machine learning model has become easier and more rewarding. However, an inherent problem in various machine learning models is a lack of understanding of what goes on ’under the hood’. A lack of explainability and interpretability leads to lower levels of trust in the model's predictions, which means it can't be used in sensitive applications like diagnosing medical ailments and detecting terrorism. This has led to various advances in making machine learning explainable. In this paper various black-box models are used to classify credit card defaulters. These models are compared using different performance metrics, and explanations of these models are provided using a model-agnostic explainer. Finally, the best model-explainer combo is proposed with potential areas of future exploration.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"3 1","pages":"Pages 119-126"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666285X22000619/pdfft?md5=2b335814a3948b3b3fc036102af6708e&pid=1-s2.0-S2666285X22000619-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88544090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.gltp.2022.03.010
Manjunatha Badiger , Varuna Kumara , Sachin C N Shetty , Sudhir Poojary
Agricultural production is something on which the economy significantly relies. Leaf diseases in agriculture are the key issue for every nation, as the food demand is expanding at a rapid speed due to a rise in population. Skin disorders are usually seen in animals and humans, it is a particular sort of illness caused by germs or infection. Early and accurate identification and diagnosis of leaf and skin diseases are vital to keeping them from spreading. Image processing techniques can be used for disease detection which involves mathematical equations and mathematical transformations. For humans eyes image is a mixture of RGB colour, because of these colours we can extract some of the features from the image, but modern computer stores image in a mathematical format which means computer sees the image as numbers, hence after evaluating the image as a number arrays or matrix we will perform various transforms on them, these transforms will extract specific details from the picture, before transforming the image must go under various operation like feature adjustment which is also carried out mathematically. The project is implemented using K-Means Clustering and Support Vector Machine Algorithm in MATLAB through which we can detect and distinguish different types of leaf and skin diseases.
{"title":"Leaf and skin disease detection using image processing","authors":"Manjunatha Badiger , Varuna Kumara , Sachin C N Shetty , Sudhir Poojary","doi":"10.1016/j.gltp.2022.03.010","DOIUrl":"10.1016/j.gltp.2022.03.010","url":null,"abstract":"<div><p>Agricultural production is something on which the economy significantly relies. Leaf diseases in agriculture are the key issue for every nation, as the food demand is expanding at a rapid speed due to a rise in population. Skin disorders are usually seen in animals and humans, it is a particular sort of illness caused by germs or infection. Early and accurate identification and diagnosis of leaf and skin diseases are vital to keeping them from spreading. Image processing techniques can be used for disease detection which involves mathematical equations and mathematical transformations. For humans eyes image is a mixture of RGB colour, because of these colours we can extract some of the features from the image, but modern computer stores image in a mathematical format which means computer sees the image as numbers, hence after evaluating the image as a number arrays or matrix we will perform various transforms on them, these transforms will extract specific details from the picture, before transforming the image must go under various operation like feature adjustment which is also carried out mathematically. The project is implemented using K-Means Clustering and Support Vector Machine Algorithm in MATLAB through which we can detect and distinguish different types of leaf and skin diseases.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"3 1","pages":"Pages 272-278"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666285X22000152/pdfft?md5=a5f11391235f2c1beb16f881e5b303e7&pid=1-s2.0-S2666285X22000152-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75490944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.gltp.2022.03.018
Putty Srividya, Lavadya Nirmala Devi
Generally, wireless sensor networks (WSN) are being utilized in a wide range of fields like queue tracking, military applications, environmental applications, and so on. This approach is an attempt to focus on the detection of attack with the utilization of machine learning and optimization strategies. Primarily, the system model is initiated and the nodes are deployed randomly based on the size of the network. The cluster formation will be carried out with the use of energy competent Particle swarm optimization depending on the passive clustering mechanism (ECPSO-PCM) strategy. Using spatial correlation, groups correlation group will be formed. The probability of transmission is then estimated by taking into account the spatial correlation, quality of link among CH and cluster member nodes, and the node's residual energy of the network. The management of the trust is employed by the selection of cluster heads. If node consists of the criteria for trust coverage, then this node is chosen as the cluster head. If this condition is not satisfied, then it is chosen as a cluster member. The optimal range of cluster paths for effective transmission of data is carried using the Computation of optimal cluster path using Bio-inspired Hierarchical order chicken swarm optimization (BIHO-CSO) at which the distance and residual energy are major constraints. Once the optimum and trusted path is chosen, the classification and detection of attack are carried out using a Recursive Binary partitioning decision tree classifier (RBP-DT). The performance analysis is made and the attained outcomes are compared with traditional approaches to validate the supremacy of the presented scheme
{"title":"An optimal cluster & trusted path for routing formation and classification of intrusion using the machine learning classification approach in WSN","authors":"Putty Srividya, Lavadya Nirmala Devi","doi":"10.1016/j.gltp.2022.03.018","DOIUrl":"10.1016/j.gltp.2022.03.018","url":null,"abstract":"<div><p>Generally, wireless sensor networks (WSN) are being utilized in a wide range of fields like queue tracking, military applications, environmental applications, and so on. This approach is an attempt to focus on the detection of attack with the utilization of machine learning and optimization strategies. Primarily, the system model is initiated and the nodes are deployed randomly based on the size of the network. The cluster formation will be carried out with the use of energy competent Particle swarm optimization depending on the passive clustering mechanism (ECPSO-PCM) strategy. Using spatial correlation, groups correlation group will be formed. The probability of transmission is then estimated by taking into account the spatial correlation, quality of link among CH and cluster member nodes, and the node's residual energy of the network. The management of the trust is employed by the selection of cluster heads. If node consists of the criteria for trust coverage, then this node is chosen as the cluster head. If this condition is not satisfied, then it is chosen as a cluster member. The optimal range of cluster paths for effective transmission of data is carried using the Computation of optimal cluster path using Bio-inspired Hierarchical order chicken swarm optimization (BIHO-CSO) at which the distance and residual energy are major constraints. Once the optimum and trusted path is chosen, the classification and detection of attack are carried out using a Recursive Binary partitioning decision tree classifier (RBP-DT). The performance analysis is made and the attained outcomes are compared with traditional approaches to validate the supremacy of the presented scheme</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"3 1","pages":"Pages 317-325"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666285X22000243/pdfft?md5=a83eec440d2f0ba644692c18e7d6a82f&pid=1-s2.0-S2666285X22000243-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80376724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.gltp.2022.03.016
Sunil S. Harakannanavar , Jayashri M. Rudagi , Veena I Puranikmath , Ayesha Siddiqua , R Pramodhini
Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recommended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farmers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to 256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally, the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM), Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples.
{"title":"Plant leaf disease detection using computer vision and machine learning algorithms","authors":"Sunil S. Harakannanavar , Jayashri M. Rudagi , Veena I Puranikmath , Ayesha Siddiqua , R Pramodhini","doi":"10.1016/j.gltp.2022.03.016","DOIUrl":"10.1016/j.gltp.2022.03.016","url":null,"abstract":"<div><p>Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recommended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farmers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to 256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally, the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM), Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"3 1","pages":"Pages 305-310"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666285X22000218/pdfft?md5=9515932a974e4364b9c7d7e52fb3c6fb&pid=1-s2.0-S2666285X22000218-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88712912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.gltp.2022.04.024
T.K. Shashank , N. Hitesh , H.S. Gururaja
An object detection technique for robotic perception plays a vital role for robots to perform the task that it is functioned to do. In this paper, an efficient and accurate method for object detection for robots is proposed. The paper suggests implementing Few-shot object detection network for robotic vision using the Attention network and attention RPN module. The Multi-relation detector is used to compare two frames and eliminate negative objects from the frame which further enforces the suggested model. Using Contrastive training strategy, the robot is trained to exploit the resemblance between the few-shot support frame and query frame to detect the positive objects and eliminate the negative objects. This method is proposed to help robots perceive the object of interest to perform pick, place, and various other actions. This paper utilizes the COCO dataset to train the network which contains close to 1000 different categories. This method would help accelerate industry 4.0 and has potential in a wide range of applications.
{"title":"Application of few-shot object detection in robotic perception","authors":"T.K. Shashank , N. Hitesh , H.S. Gururaja","doi":"10.1016/j.gltp.2022.04.024","DOIUrl":"10.1016/j.gltp.2022.04.024","url":null,"abstract":"<div><p>An object detection technique for robotic perception plays a vital role for robots to perform the task that it is functioned to do. In this paper, an efficient and accurate method for object detection for robots is proposed. The paper suggests implementing Few-shot object detection network for robotic vision using the Attention network and attention RPN module. The Multi-relation detector is used to compare two frames and eliminate negative objects from the frame which further enforces the suggested model. Using Contrastive training strategy, the robot is trained to exploit the resemblance between the few-shot support frame and query frame to detect the positive objects and eliminate the negative objects. This method is proposed to help robots perceive the object of interest to perform pick, place, and various other actions. This paper utilizes the COCO dataset to train the network which contains close to 1000 different categories. This method would help accelerate industry 4.0 and has potential in a wide range of applications.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"3 1","pages":"Pages 114-118"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666285X22000607/pdfft?md5=e4cb3f10ea88646e0dbf160cf4fb2940&pid=1-s2.0-S2666285X22000607-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86804179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.gltp.2022.03.021
S. Anitha , Mary Metilda
In Recent, Twitter is the well-known public Network acquires a huge number of tweets. Sentiment analysis in twitter data are tremendously valuable in social media observing as it allows getting an overview of extensive global opinion in certain issue. This data are utilized for industrial, government, social and economic approaches by analyzing the tweets as per the requirement of the user. Processing and storing these data are more complicated to analyze. Hadoop is a distributed environment which process with Big and Huge variety of dataset which supports processing components that collectively called Hadoop Ecosystem. In this paper, regular tweets are analyzed by sentiment analysis technique in Hadoop Eco system. Dataset are taken from Kaggle data repository. This research has done by Apache Pig in Demonetization and Covid 19 twitter dataset.
{"title":"Apache Hadoop based effective sentiment analysis on demonetization and covid-19 tweets","authors":"S. Anitha , Mary Metilda","doi":"10.1016/j.gltp.2022.03.021","DOIUrl":"10.1016/j.gltp.2022.03.021","url":null,"abstract":"<div><p>In Recent, Twitter is the well-known public Network acquires a huge number of tweets. Sentiment analysis in twitter data are tremendously valuable in social media observing as it allows getting an overview of extensive global opinion in certain issue. This data are utilized for industrial, government, social and economic approaches by analyzing the tweets as per the requirement of the user. Processing and storing these data are more complicated to analyze. Hadoop is a distributed environment which process with Big and Huge variety of dataset which supports processing components that collectively called Hadoop Ecosystem. In this paper, regular tweets are analyzed by sentiment analysis technique in Hadoop Eco system. Dataset are taken from Kaggle data repository. This research has done by Apache Pig in Demonetization and Covid 19 twitter dataset.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"3 1","pages":"Pages 338-342"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666285X22000279/pdfft?md5=87efe66995c2f6c3010e66d29b19c7f7&pid=1-s2.0-S2666285X22000279-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85160181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.gltp.2022.03.020
CH. Naga Sai Kalyan, Chintalapudi V Suresh
In this paper, a seagull optimization algorithm (SOA) based 3-Degree-of-freedom (DOF) proportional-integral-derivative (3DOFPID) controller is suggested for load frequency control of multi-area interconnected power system (MAIPS). The considered MAIPS comprises of two areas with Thermal-Hydro-Nuclear generation units in each area. Analysis has been carried out by subjugating area-1 of MAIPS with a step load disturbance (SLD) of 10%. The sovereignty of presented SOA tuned 3DOFPID in regulating the stability of MAIPS is revealed upon comparing with the performances of 2DOFPID and conventional PID controllers. MIPS is analyzed dynamically without and with considering the nonlinear realistic constraint of communication time delays (CTDs) to demonstrate its impact on load frequency control performance. Simulation results disclosed that, MAIPS dynamical behavior is slightly more deviated up on considering CTDs and is justified.
{"title":"Higher Order Degree of Freedom Controller for Load Frequency Control of Multi Area Interconnected Power System with Time Delays","authors":"CH. Naga Sai Kalyan, Chintalapudi V Suresh","doi":"10.1016/j.gltp.2022.03.020","DOIUrl":"10.1016/j.gltp.2022.03.020","url":null,"abstract":"<div><p>In this paper, a seagull optimization algorithm (SOA) based 3-Degree-of-freedom (DOF) proportional-integral-derivative (3DOFPID) controller is suggested for load frequency control of multi-area interconnected power system (MAIPS). The considered MAIPS comprises of two areas with Thermal-Hydro-Nuclear generation units in each area. Analysis has been carried out by subjugating area-1 of MAIPS with a step load disturbance (SLD) of 10%. The sovereignty of presented SOA tuned 3DOFPID in regulating the stability of MAIPS is revealed upon comparing with the performances of 2DOFPID and conventional PID controllers. MIPS is analyzed dynamically without and with considering the nonlinear realistic constraint of communication time delays (CTDs) to demonstrate its impact on load frequency control performance. Simulation results disclosed that, MAIPS dynamical behavior is slightly more deviated up on considering CTDs and is justified.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"3 1","pages":"Pages 332-337"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666285X22000267/pdfft?md5=362ca1545daa5d693f195a6c16d6d1a0&pid=1-s2.0-S2666285X22000267-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75391520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}