Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media networks. This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage. Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis. The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models. Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a marginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Naïve Bayes, Multinomial Naïve Bayes, Multinomial Naïve Bayes with Bagging, Logistic Regression and a similar enhancement is observed with AdaBoost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.
{"title":"Text Mining – A Comparative Review of Twitter Sentiments Analysis","authors":"Sandeep Kumar, Sushma Patil, Dewang Subil, Noureen Nasar, Sujatha Arun Kokatnoor, Balachandran Krishnan","doi":"10.2174/2666255816666230726140726","DOIUrl":"https://doi.org/10.2174/2666255816666230726140726","url":null,"abstract":"\u0000\u0000Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media networks.\u0000\u0000\u0000\u0000This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage.\u0000\u0000\u0000\u0000Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis.\u0000\u0000\u0000\u0000The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models.\u0000\u0000\u0000\u0000Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a marginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Naïve Bayes, Multinomial Naïve Bayes, Multinomial Naïve Bayes with Bagging, Logistic Regression and a similar enhancement is observed with AdaBoost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42350271","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 : 2023-07-14DOI: 10.2174/2666255816666230714111503
Mohamed Ezzat, H. Hefny, Ammar Mohmmed
Wi-Fi Direct technology enables users to share services in groups, and support Service discovery at the data link layer before creating a P2P Group, and it can be used as a collaborative application integrated into vehicles for multimedia transfer and group configuration between V2X. Compared to cellular networks, Wi-Fi Direct offers a high transmission data rate at a cheaper cost. However, there are numerous hurdles to using Wi-Fi Direct in vehicles, including the fact that Wi-Fi Direct communication has a relatively small coverage area, disconnection may occur multiple times, and the distance between vehicles changes often in a moving setting, which negatively affects the quality of service delivery. Previous studies disregarded the motion and direction of moving objects. The main contribution of this paper is to use Wi-Fi Direct among vehicles to reduce reliance on the 5G network, thereby addressing the previous challenges. In particular, the main contribution of this paper is to introduce a set of scenarios based on different speeds, directions, and distances between vehicles. The state of the packets is monitored in each scenario to compute the packets delay and loss. We present a new contribution to the services discovery by providing V2V IE with a set of services that reflect the user's interest, such as Web pages, SMS, Audio links, and Video links, using the Generic Advertisement Protocol GAS, and a comparison between the traditional P2P IE and the new V2V IE. Furthermore, the paper introduces a stable Wi-Fi Direct Fuzzy C-Means FCM clustering method based on important parameters impacting the group formation, such as the location, the destination, the direction, the speed of the vehicle, and the user’s Interests List. Based on the results of the FCM, there is still uncertainty in choosing the appropriate time to provide the services to the vehicles. We propose a Type-2 Fuzzy Logic Handover T2FLH system to solve the problem of handling uncertainty about dealing with the available services. Using the simulation on OMNeT++, the proposed scenarios with the fuzzy c-means FCM clustering method are compared to get the best clusters. Then the results were compared with the Type-2 Fuzzy T2FLH system to extract the best scenarios. We concluded from the results of previous experiments that Wi-Fi Direct can be used with vehicles at low speeds and high speeds. In the case of low speeds, it works efficiently depending on OMNET++ results. Therefore, Wi-Fi Direct can be used in vehicle stations and work sites that use limited-speed vehicles such as Clarks machines to alert safety and provide them with information about the devices around them. Bearing in mind that the speed of devices is limited in work areas. In the case of high speeds, the results are significantly improved using the proposed Type-2 fuzzy Logic Handover T2FLH system to model uncertainty and imprecision in a better way. Relying on T2FLH has led to a decrease in the rate of P
{"title":"Multimedia Transfer over Wi-Fi Direct based on Fuzzy \u0000Clustering for Vehicular Communications","authors":"Mohamed Ezzat, H. Hefny, Ammar Mohmmed","doi":"10.2174/2666255816666230714111503","DOIUrl":"https://doi.org/10.2174/2666255816666230714111503","url":null,"abstract":"\u0000\u0000Wi-Fi Direct technology enables users to share services in groups, and support Service discovery at the data link layer before creating a P2P Group, and it can be used as a collaborative application integrated into vehicles for multimedia transfer and group configuration between V2X. Compared to cellular networks, Wi-Fi Direct offers a high transmission data rate at a cheaper cost. However, there are numerous hurdles to using Wi-Fi Direct in vehicles, including the fact that Wi-Fi Direct communication has a relatively small coverage area, disconnection may occur multiple times, and the distance between vehicles changes often in a moving setting, which negatively affects the quality of service delivery. Previous studies disregarded the motion and direction of moving objects.\u0000\u0000\u0000\u0000The main contribution of this paper is to use Wi-Fi Direct among vehicles to reduce reliance on the 5G network, thereby addressing the previous challenges. In particular, the main contribution of this paper is to introduce a set of scenarios based on different speeds, directions, and distances between vehicles. The state of the packets is monitored in each scenario to compute the packets delay and loss. We present a new contribution to the services discovery by providing V2V IE with a set of services that reflect the user's interest, such as Web pages, SMS, Audio links, and Video links, using the Generic Advertisement Protocol GAS, and a comparison between the traditional P2P IE and the new V2V IE. Furthermore, the paper introduces a stable Wi-Fi Direct Fuzzy C-Means FCM clustering method based on important parameters impacting the group formation, such as the location, the destination, the direction, the speed of the vehicle, and the user’s Interests List.\u0000\u0000\u0000\u0000Based on the results of the FCM, there is still uncertainty in choosing the appropriate time to provide the services to the vehicles. We propose a Type-2 Fuzzy Logic Handover T2FLH system to solve the problem of handling uncertainty about dealing with the available services. Using the simulation on OMNeT++, the proposed scenarios with the fuzzy c-means FCM clustering method are compared to get the best clusters. Then the results were compared with the Type-2 Fuzzy T2FLH system to extract the best scenarios.\u0000\u0000\u0000\u0000We concluded from the results of previous experiments that Wi-Fi Direct can be used with vehicles at low speeds and high speeds. In the case of low speeds, it works efficiently depending on OMNET++ results. Therefore, Wi-Fi Direct can be used in vehicle stations and work sites that use limited-speed vehicles such as Clarks machines to alert safety and provide them with information about the devices around them. Bearing in mind that the speed of devices is limited in work areas. In the case of high speeds, the results are significantly improved using the proposed Type-2 fuzzy Logic Handover T2FLH system to model uncertainty and imprecision in a better way. Relying on T2FLH has led to a decrease in the rate of P","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45858478","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 : 2023-07-12DOI: 10.2174/2666255816666230712141148
Manju, Deepti Singh, A. Jatain
Chatbots use artificial intelligence (AI) and natural language processing (NLP) algorithms to construct a clever system. By copying human connections in the most helpful way possible, chatbots emulate individuals and serve as virtual assistants. They easily interface and respond to customers' requests. In the modern technical environment, these conversation agents or chatbots are considered the next-generation invention. Chatbot has become more popular in the business field right now as it can reduce customer service cost and handle multiple users at a time. There are many techniques used to involve such intelligent experts in daily business. A comprehensive analysis of the methods is needed to determine the viability of the different strategies. This paper tracks the progress of this invention and further clarifies the influence of chatbots on numerous businesses. Besides, a survey of the multiple chatbot methodologies suggested by various researchers is provided. Along with the survey, a chatbot e-commerce customer service is designed to provide an efficient and accurate answer for any query based on the dataset of frequently asked questions. This chatbot can reduce customer service costs and can handle multiple customers at the same time.
{"title":"Artificial Intelligence and Natural Language Processing Inspired Chabot Technologies","authors":"Manju, Deepti Singh, A. Jatain","doi":"10.2174/2666255816666230712141148","DOIUrl":"https://doi.org/10.2174/2666255816666230712141148","url":null,"abstract":"\u0000\u0000Chatbots use artificial intelligence (AI) and natural language processing (NLP) algorithms to construct a clever system. By copying human connections in the most helpful way possible, chatbots emulate individuals and serve as virtual assistants. They easily interface and respond to customers' requests. In the modern technical environment, these conversation agents or chatbots are considered the next-generation invention. Chatbot has become more popular in the business field right now as it can reduce customer service\u0000cost and handle multiple users at a time. There are many techniques used to involve such intelligent experts in daily business. A comprehensive analysis of the methods is needed to determine the viability of the different strategies. This paper tracks the progress of this invention and further clarifies the influence of chatbots on numerous businesses. Besides, a survey of the multiple chatbot methodologies suggested by various researchers is provided. Along with the survey, a chatbot e-commerce customer service is designed to provide an efficient and accurate answer for any query based on the dataset of frequently asked questions. This chatbot can reduce customer service costs and can handle multiple customers at the same time.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49446128","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 : 2023-07-01DOI: 10.2174/266625581606230606155431
{"title":"Patent Selections","authors":"","doi":"10.2174/266625581606230606155431","DOIUrl":"https://doi.org/10.2174/266625581606230606155431","url":null,"abstract":"","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135154682","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 : 2023-07-01DOI: 10.2174/2666255816666220920112251
Lingjing Kong, Ying Zhou, Huijing Wang
Background: Network security is getting more serious and has attracted much attention in recent years. Anomaly detection is an important technology to identify bad network flows and protect the network, which has been a hot topic in the network security field. However, in an anomaly detection system, the unknown network flows are always identified as some known flows in the existing solutions, which results in poorer identification performance. Objective: Aiming at detecting unknown flows and improving the detection performance, based on the KDD’99 dataset from a simulated real network environment, we analyzed the dataset and the main factors which affect the accuracy, and proposed a more robust and effective anomaly detection model (READM) to improve the accuracy of the detection. Methods: Based on unknown flows determination, the extra unknown type class is trained by neural network and identified by deep inspection method. Then, the identification result for unknown class will be updated to the detection system. Finally, the newly proposed robust and effective anomaly detection model (READM) is constructed and validated. Results: Through experiments comparison and analysis, the results indicate that READM achieves higher detection accuracy and less prediction time, which proves more efficient and shows better performance. Conclusion: Our study found that the existence of unknown flows always results in error detection and becomes the main factor influencing the detection performance. So, we propose a robust and effective anomaly detection model based on the construction and training of the extra unknown traffic class. Through the comparison of three experiments with different ways of thinking, it is proved that READM improves detection accuracy and reduces prediction time. Besides, after comparing with other solutions, it also shows better performance and has great application value in this field.
{"title":"A Robust and Effective Anomaly Detection Model for Identifying Unknown Network Traffic","authors":"Lingjing Kong, Ying Zhou, Huijing Wang","doi":"10.2174/2666255816666220920112251","DOIUrl":"https://doi.org/10.2174/2666255816666220920112251","url":null,"abstract":"Background: Network security is getting more serious and has attracted much attention in recent years. Anomaly detection is an important technology to identify bad network flows and protect the network, which has been a hot topic in the network security field. However, in an anomaly detection system, the unknown network flows are always identified as some known flows in the existing solutions, which results in poorer identification performance. Objective: Aiming at detecting unknown flows and improving the detection performance, based on the KDD’99 dataset from a simulated real network environment, we analyzed the dataset and the main factors which affect the accuracy, and proposed a more robust and effective anomaly detection model (READM) to improve the accuracy of the detection. Methods: Based on unknown flows determination, the extra unknown type class is trained by neural network and identified by deep inspection method. Then, the identification result for unknown class will be updated to the detection system. Finally, the newly proposed robust and effective anomaly detection model (READM) is constructed and validated. Results: Through experiments comparison and analysis, the results indicate that READM achieves higher detection accuracy and less prediction time, which proves more efficient and shows better performance. Conclusion: Our study found that the existence of unknown flows always results in error detection and becomes the main factor influencing the detection performance. So, we propose a robust and effective anomaly detection model based on the construction and training of the extra unknown traffic class. Through the comparison of three experiments with different ways of thinking, it is proved that READM improves detection accuracy and reduces prediction time. Besides, after comparing with other solutions, it also shows better performance and has great application value in this field.","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136261103","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 : 2023-07-01DOI: 10.2174/266625581605230530100113
{"title":"Patent Selections","authors":"","doi":"10.2174/266625581605230530100113","DOIUrl":"https://doi.org/10.2174/266625581605230530100113","url":null,"abstract":"","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135154894","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 : 2023-07-01DOI: 10.2174/266625581606230606152759
Vangipuram Radhakrishna
{"title":"Meet the Regional Editor","authors":"Vangipuram Radhakrishna","doi":"10.2174/266625581606230606152759","DOIUrl":"https://doi.org/10.2174/266625581606230606152759","url":null,"abstract":"","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135154678","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 : 2023-07-01DOI: 10.2174/2666255815666220426132637
JinYu Wang, ChangGong Zhang, HaiTao Yang
Aims: To solve the problem of difficult acquisition of airport runway destruction image data. Objectives: This paper introduces SinGAN, a single-sample generative adversarial network algorithm. Methods: To address the shortcomings of SinGAN in image realism and diversity generation, an improved algorithm based on the combination of Gaussian error linear unit GELU and efficient channel attention mechanism ECANet is proposed Results: Experiments show that its generated image results are subjectively better than SinGAN and its lightweight algorithm ConSinGAN, and the model can obtain an effective balance in both quality and diversity of image generation. Conclusion: The algorithm effect is also verified using three objective evaluation metrics, and the results show that the method in this paper effectively improves the generation effect compared with SinGAN, in which the SIFID metric is reduced by 46.67%.
{"title":"Improved SinGAN for Single-Sample Airport Runway Destruction Image Generation","authors":"JinYu Wang, ChangGong Zhang, HaiTao Yang","doi":"10.2174/2666255815666220426132637","DOIUrl":"https://doi.org/10.2174/2666255815666220426132637","url":null,"abstract":"Aims: To solve the problem of difficult acquisition of airport runway destruction image data. Objectives: This paper introduces SinGAN, a single-sample generative adversarial network algorithm. Methods: To address the shortcomings of SinGAN in image realism and diversity generation, an improved algorithm based on the combination of Gaussian error linear unit GELU and efficient channel attention mechanism ECANet is proposed Results: Experiments show that its generated image results are subjectively better than SinGAN and its lightweight algorithm ConSinGAN, and the model can obtain an effective balance in both quality and diversity of image generation. Conclusion: The algorithm effect is also verified using three objective evaluation metrics, and the results show that the method in this paper effectively improves the generation effect compared with SinGAN, in which the SIFID metric is reduced by 46.67%.","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136261100","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 : 2023-07-01DOI: 10.2174/266625581605230530092230
Evangelos Sapountzakis
{"title":"Meet the Regional Editor","authors":"Evangelos Sapountzakis","doi":"10.2174/266625581605230530092230","DOIUrl":"https://doi.org/10.2174/266625581605230530092230","url":null,"abstract":"","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135154893","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 : 2023-06-22DOI: 10.2174/2666255816666230622125353
Kavita Pandey, Abhimanyu Chandak
Plant diseases are one of the major contributors to economic loss in the agriculture industry worldwide. Detection of disease at early stages can help in the reduction of this loss. In recent times, a lot of emphasis has been done on disease detection due to the overall increase in production as well as the loss of grape number. With deep learning, having a promising future and having the advantages of automatic learning and feature extraction, the use of these techniques has now been widely spread. This paper reviewed the existing deep-learning techniques available for grape disease detection. Firstly, covering the various steps in a grape disease detection model ranging from the various sources of image acquisition, the different image augmentation techniques and the various models used, and the parameters required to evaluate. Secondly, the study summarizes the important findings of all literature available on the theme. The paper also tries to highlight the various challenges faced by the researchers and the common trend among them, so that future research on the topic can achieve higher performance.
{"title":"An Exploration Of Deep Learning Techniques For The Detection Of Grape Diseases","authors":"Kavita Pandey, Abhimanyu Chandak","doi":"10.2174/2666255816666230622125353","DOIUrl":"https://doi.org/10.2174/2666255816666230622125353","url":null,"abstract":"\u0000\u0000Plant diseases are one of the major contributors to economic loss in the agriculture industry worldwide. Detection of disease at early stages can help in the reduction of this loss. In recent times, a lot of emphasis has been done on disease detection due to the overall increase in production as well as the loss of grape number. With deep learning, having a promising future and having the advantages of automatic learning and feature extraction, the use of these techniques has now been widely spread. This paper reviewed the existing deep-learning techniques available for grape disease detection. Firstly, covering the various steps in a grape disease detection model ranging from the various sources of image acquisition, the different image augmentation techniques and the various models used, and the parameters required to evaluate. Secondly, the study summarizes the important findings of all literature available on the theme. The paper also tries to highlight the various challenges faced by the researchers and the common trend among them, so that future research on the topic can achieve higher performance.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48854871","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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