With the wide application of industrial Internet of Things, the increasing amount of data and the complexity of data types, higher requirements are put forward for the performance of data acquisition gateway. In order to reduce the data acquisition time of the gateway and improve the data retrieval coverage of the gateway, a novel design method of composite data acquisition gateway based on 5G network is proposed. Based on the analysis of related technologies, the functional requirements of the composite data acquisition gateway are summarized, and the overall design of the gateway is completed. On this basis, the gateway hardware environment is constructed by designing the main control module, 5G module and FPGA program, and then the software program is designed by designing the data acquisition driver, 5G module driver, embedded software and protocol conversion process. The experimental results show that the data retrieval coverage of the gateway designed by this method is always above 92%, which is 6% higher than that of method 1. This shows that the method significantly improves the coverage of data search, speeds up the efficiency of data collection, and improves the performance of the data collection gateway, which proves the effectiveness and feasibility of the method and is conducive to promoting the intelligent development of the data collection gateway technology.
{"title":"Design of compound data acquisition gateway based on 5G network","authors":"Jufen Hu, G. Lorenzini","doi":"10.3233/web-220071","DOIUrl":"https://doi.org/10.3233/web-220071","url":null,"abstract":"With the wide application of industrial Internet of Things, the increasing amount of data and the complexity of data types, higher requirements are put forward for the performance of data acquisition gateway. In order to reduce the data acquisition time of the gateway and improve the data retrieval coverage of the gateway, a novel design method of composite data acquisition gateway based on 5G network is proposed. Based on the analysis of related technologies, the functional requirements of the composite data acquisition gateway are summarized, and the overall design of the gateway is completed. On this basis, the gateway hardware environment is constructed by designing the main control module, 5G module and FPGA program, and then the software program is designed by designing the data acquisition driver, 5G module driver, embedded software and protocol conversion process. The experimental results show that the data retrieval coverage of the gateway designed by this method is always above 92%, which is 6% higher than that of method 1. This shows that the method significantly improves the coverage of data search, speeds up the efficiency of data collection, and improves the performance of the data collection gateway, which proves the effectiveness and feasibility of the method and is conducive to promoting the intelligent development of the data collection gateway technology.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90297699","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}
A MANET consists of a self-configured group of transportable mobile nodes that lacks a central infrastructure to manage network traffic. To facilitate communication, govern route discovery, and manage resources, all moving nodes in multi-hop wireless networks (MANETs) work together. These networks struggle with dependability, energy consumption, and collision avoidance. The goal of this research project is to establish a new, dependable MANET routing model, where the selection of predictor nodes comes first. For selecting predictor nodes based on factors like distance, security (risk), Receiver Signal Strength Indicator (RSSI), Packet Delivery Ratio (PDR), and energy, the adaptive weighted clustering algorithm (AWCA) is used in this case. Using the Interfused Slime and Battle Royale Optimization with Arithmetic Crossover (IS&BRO–AC) model, the node with the lower weight is selected as the Cluster Head (CH). Additionally, mobility prediction is carried out, in which the node mobility is forecast using Improved Long Short Term Memory (LSTM) while taking distance and Receiver Signal Strength Indicator (RSSI) into account. Based on the forecast, trustworthy data transfer is implemented, ensuring more accurate and dependable MANET routing. The examination of RSSI, PDR, and other metrics is completed at the end.
MANET由一组自配置的可移动节点组成，这些节点缺乏管理网络流量的中心基础设施。为了方便通信、控制路由发现和管理资源，多跳无线网络(manet)中的所有移动节点都协同工作。这些网络在可靠性、能耗和避免碰撞等问题上挣扎。本研究的目标是建立一个新的、可靠的MANET路由模型，其中预测节点的选择是第一位的。基于距离、安全(风险)、RSSI (Receiver Signal Strength Indicator)、PDR (Packet Delivery Ratio)、能量等因素选择预测节点，采用自适应加权聚类算法(AWCA)。采用融合Slime和Battle Royale算法交叉优化(IS&BRO-AC)模型，选取权值较低的节点作为簇头(CH)。此外，还进行了移动性预测，在考虑距离和接收信号强度指标(RSSI)的情况下，利用改进长短期记忆(LSTM)预测节点的移动性。基于预测，实现了可信的数据传输，保证了更准确、可靠的MANET路由。最后完成RSSI、PDR和其他指标的检查。
{"title":"Reliable routing in MANET with mobility prediction via long short-term memory","authors":"Manjula A. Biradar, Sujata Mallapure","doi":"10.3233/web-220110","DOIUrl":"https://doi.org/10.3233/web-220110","url":null,"abstract":"A MANET consists of a self-configured group of transportable mobile nodes that lacks a central infrastructure to manage network traffic. To facilitate communication, govern route discovery, and manage resources, all moving nodes in multi-hop wireless networks (MANETs) work together. These networks struggle with dependability, energy consumption, and collision avoidance. The goal of this research project is to establish a new, dependable MANET routing model, where the selection of predictor nodes comes first. For selecting predictor nodes based on factors like distance, security (risk), Receiver Signal Strength Indicator (RSSI), Packet Delivery Ratio (PDR), and energy, the adaptive weighted clustering algorithm (AWCA) is used in this case. Using the Interfused Slime and Battle Royale Optimization with Arithmetic Crossover (IS&BRO–AC) model, the node with the lower weight is selected as the Cluster Head (CH). Additionally, mobility prediction is carried out, in which the node mobility is forecast using Improved Long Short Term Memory (LSTM) while taking distance and Receiver Signal Strength Indicator (RSSI) into account. Based on the forecast, trustworthy data transfer is implemented, ensuring more accurate and dependable MANET routing. The examination of RSSI, PDR, and other metrics is completed at the end.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81102721","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}
Cloud computing provides the on-demand service of the user with the use of distributed physical machines, in which security has become a challenging factor while performing various tasks. Several methods were developed for the cloud computing workflow scheduling based on optimal resource allocation; still, the security consideration and efficient allocation of the workflow are challenging. Hence, this research introduces a hybrid optimization algorithm based on multi-objective workflow scheduling in the cloud computing environment. The Regressive Whale Water Tasmanian Devil Optimization (RWWTDO) is proposed for the optimal workflow scheduling based on the multi-objective fitness function with nine various factors, like Predicted energy, Quality of service (QoS), Resource utilization, Actual task running time, Bandwidth utilization, Memory capacity, Make span equivalent of the total cost, Task priority, and Trust. Besides, secure data transmission is employed using the triple data encryption standard (3DES) to acquire enhanced security for workflow scheduling. The method’s performance is evaluated using the resource utilization, predicted energy, task scheduling cost, and task scheduling time and acquired the values of 1.00000, 0.16587, 0.00041, and 0.00314, respectively.
{"title":"Multi-objective secure aware workflow scheduling algorithm in cloud computing based on hybrid optimization algorithm","authors":"G. Narendrababu Reddy, S. Phani Kumar","doi":"10.3233/web-220094","DOIUrl":"https://doi.org/10.3233/web-220094","url":null,"abstract":"Cloud computing provides the on-demand service of the user with the use of distributed physical machines, in which security has become a challenging factor while performing various tasks. Several methods were developed for the cloud computing workflow scheduling based on optimal resource allocation; still, the security consideration and efficient allocation of the workflow are challenging. Hence, this research introduces a hybrid optimization algorithm based on multi-objective workflow scheduling in the cloud computing environment. The Regressive Whale Water Tasmanian Devil Optimization (RWWTDO) is proposed for the optimal workflow scheduling based on the multi-objective fitness function with nine various factors, like Predicted energy, Quality of service (QoS), Resource utilization, Actual task running time, Bandwidth utilization, Memory capacity, Make span equivalent of the total cost, Task priority, and Trust. Besides, secure data transmission is employed using the triple data encryption standard (3DES) to acquire enhanced security for workflow scheduling. The method’s performance is evaluated using the resource utilization, predicted energy, task scheduling cost, and task scheduling time and acquired the values of 1.00000, 0.16587, 0.00041, and 0.00314, respectively.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77009879","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}
One of the leading causes of death for people worldwide is liver cancer. Manually identifying the cancer tissue in the current situation is a challenging and time-consuming task. Assessing the tumor load, planning therapies, making predictions, and tracking the clinical response can all be done using the segmentation of liver lesions in Computed Tomography (CT) scans. In this paper we propose a new technique for liver cancer classification with CT image. This method consists of four stages like pre-processing, segmentation, feature extraction and classification. In the initial stage the input image will be pre processed for the quality enhancement. This preprocessed output will be subjected to the segmentation phase; here improved deep fuzzy clustering technique will be applied for image segmentation. Subsequently, the segmented image will be the input of the feature extraction phase, where the extracted features are named as Improved Gabor Transitional Pattern, Grey-Level Co-occurrence Matrix (GLCM), Statistical features and Convolutional Neural Network (CNN) based feature. Finally the extracted features are subjected to the classification stage, here the two types of classifiers used for classification that is Bi-GRU and Deep Maxout. In this phase we will apply the Crossover mutated COOT optimization (CMCO) for tuning the weights, So that we will improve the quality of the image. This proposed technique, present the best accuracy of disease identification. The CMCO gained the accuracy of 95.58%, which is preferable than AO = 92.16%, COA = 89.38%, TSA = 88.05%, AOA = 92.05% and COOT = 91.95%, respectively.
{"title":"Liver cancer classification via deep hybrid model from CT image with improved texture feature set and fuzzy clustering based segmentation","authors":"Vinnakota Sai Durga Tejaswi, V. Rachapudi","doi":"10.3233/web-230042","DOIUrl":"https://doi.org/10.3233/web-230042","url":null,"abstract":"One of the leading causes of death for people worldwide is liver cancer. Manually identifying the cancer tissue in the current situation is a challenging and time-consuming task. Assessing the tumor load, planning therapies, making predictions, and tracking the clinical response can all be done using the segmentation of liver lesions in Computed Tomography (CT) scans. In this paper we propose a new technique for liver cancer classification with CT image. This method consists of four stages like pre-processing, segmentation, feature extraction and classification. In the initial stage the input image will be pre processed for the quality enhancement. This preprocessed output will be subjected to the segmentation phase; here improved deep fuzzy clustering technique will be applied for image segmentation. Subsequently, the segmented image will be the input of the feature extraction phase, where the extracted features are named as Improved Gabor Transitional Pattern, Grey-Level Co-occurrence Matrix (GLCM), Statistical features and Convolutional Neural Network (CNN) based feature. Finally the extracted features are subjected to the classification stage, here the two types of classifiers used for classification that is Bi-GRU and Deep Maxout. In this phase we will apply the Crossover mutated COOT optimization (CMCO) for tuning the weights, So that we will improve the quality of the image. This proposed technique, present the best accuracy of disease identification. The CMCO gained the accuracy of 95.58%, which is preferable than AO = 92.16%, COA = 89.38%, TSA = 88.05%, AOA = 92.05% and COOT = 91.95%, respectively.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86676846","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}
Bikramaditya Panda, M. Mishra, B. P. Mishra, A. K. Tiwari
Crop and weed identification remains a challenge for unmanned weed control. Due to the small range between the chopping tine and the important crop location, weed identification against the annual crops must be extremely exact. This study endeavor included a literature evaluation, which included the most important 50 research publications in IEEE, Science Direct, and Springer journals. From 2012 until 2022, all of these papers are gathered. In fact, the diagnosis steps include: preprocessing, feature extraction, and crop/weed classification. This research analyzes the 50 research articles in several aspects, such as the dataset used for evaluations, different strategies used for pre-processing, feature extraction, and classification to get a clear picture of them. Furthermore, each work’s high performance in accuracy, sensitivity, and precision is demonstrated. Furthermore, the present hurdles in crop and weed identification are described, which serve as a benchmark for upcoming researchers.
{"title":"An extensive review on crop/weed classification models","authors":"Bikramaditya Panda, M. Mishra, B. P. Mishra, A. K. Tiwari","doi":"10.3233/web-220115","DOIUrl":"https://doi.org/10.3233/web-220115","url":null,"abstract":"Crop and weed identification remains a challenge for unmanned weed control. Due to the small range between the chopping tine and the important crop location, weed identification against the annual crops must be extremely exact. This study endeavor included a literature evaluation, which included the most important 50 research publications in IEEE, Science Direct, and Springer journals. From 2012 until 2022, all of these papers are gathered. In fact, the diagnosis steps include: preprocessing, feature extraction, and crop/weed classification. This research analyzes the 50 research articles in several aspects, such as the dataset used for evaluations, different strategies used for pre-processing, feature extraction, and classification to get a clear picture of them. Furthermore, each work’s high performance in accuracy, sensitivity, and precision is demonstrated. Furthermore, the present hurdles in crop and weed identification are described, which serve as a benchmark for upcoming researchers.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90096999","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}
Diagnosing plant disease is a major role to reduce adequate losses in yield production, which further leads to economic losses. The various disease control measures are accessible without a proper diagnosis of the disease which results in a waste of expenses and time. The diagnosis of disease using images leads to unsatisfactory results in the prevalent methods due to the image clarity. It is mainly caused by the worst performance of the existing pre-trained image classifiers. This issue can be controlled by the SMoGW-deep convolutional neural network (deep CNN) classifier for the accurate and precise classification of plant leaf disease. The developed method transforms the poor-quality captured images into high quality by the preprocessing technique. The preprocessed input images contain pixels on their dimension and also the value of the threshold is analyzed by the Otsu method by which the particular disease-affected region is extracted based on the image pixels. The region of interest is separated from the other parts of the input leaf image using the K-means segmentation technique. The stored features in the feature vector are fed forward to the deep CNN classifier for training and are optimized by the SMoGW optimization approach. The experiments are done and achieved an accuracy of 94.5% sensitivity of 94.525%, specificity of 94.6%, precision of 95% with 90% of training data and under K-fold training with 95% of accuracy, 95% of sensitivity, 94.1% of specificity, and 92.1% of precession is achieved for the SMoGW-optimized classifier approach that is superior to the prevalent techniques for disease classification and detection. The potential, as well as the capability of the proposed method, is experimentally demonstrated for plant leaf disease classification and identification.
{"title":"SMoGW-based deep CNN: Plant disease detection and classification using SMoGW-deep CNN classifier","authors":"A. Pahurkar, Ravindra M. Deshmukh","doi":"10.3233/web-230015","DOIUrl":"https://doi.org/10.3233/web-230015","url":null,"abstract":"Diagnosing plant disease is a major role to reduce adequate losses in yield production, which further leads to economic losses. The various disease control measures are accessible without a proper diagnosis of the disease which results in a waste of expenses and time. The diagnosis of disease using images leads to unsatisfactory results in the prevalent methods due to the image clarity. It is mainly caused by the worst performance of the existing pre-trained image classifiers. This issue can be controlled by the SMoGW-deep convolutional neural network (deep CNN) classifier for the accurate and precise classification of plant leaf disease. The developed method transforms the poor-quality captured images into high quality by the preprocessing technique. The preprocessed input images contain pixels on their dimension and also the value of the threshold is analyzed by the Otsu method by which the particular disease-affected region is extracted based on the image pixels. The region of interest is separated from the other parts of the input leaf image using the K-means segmentation technique. The stored features in the feature vector are fed forward to the deep CNN classifier for training and are optimized by the SMoGW optimization approach. The experiments are done and achieved an accuracy of 94.5% sensitivity of 94.525%, specificity of 94.6%, precision of 95% with 90% of training data and under K-fold training with 95% of accuracy, 95% of sensitivity, 94.1% of specificity, and 92.1% of precession is achieved for the SMoGW-optimized classifier approach that is superior to the prevalent techniques for disease classification and detection. The potential, as well as the capability of the proposed method, is experimentally demonstrated for plant leaf disease classification and identification.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78905564","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}
Li He, Jiashu Zhao, Yulong Gu, Mitchell Elbaz, Zhuoye Ding
User feedback data (e.g., clicks, dwell time in the product detail page) have been incorporated in the training process of many ranking models for better performance. Such approaches are widely used in many ranking applications, including search and recommendation. Recently, the inherent biases in user feedback data have been studied, which indicates how the users’ behaviors can be affected by factors other than relevancy. By identifying and removing these biases, the ranking models can be further improved. Researchers have developed a variety of debiasing methods on different bias factors. Most of them only focus on one type of bias and pay little attention to different types of bias from a unified perspective. In this paper, we conduct a comprehensive study of bias focusing on the application of ranking problems in recommender systems which is highly important for the research of web intelligence. Then, we share our experiences derived from designing and optimizing unbiased models to improve feeds recommendation. To uncover the effects of biases and achieve better ranking performance, we propose several unbiased models and compare with state-of-the-art models. We conduct extensive offline experiments on real datasets and validate the effectiveness of our method by performing online A/B testing in a real-world recommender system.
{"title":"A bias study and an unbiased deep neural network for recommender systems","authors":"Li He, Jiashu Zhao, Yulong Gu, Mitchell Elbaz, Zhuoye Ding","doi":"10.3233/web-230036","DOIUrl":"https://doi.org/10.3233/web-230036","url":null,"abstract":"User feedback data (e.g., clicks, dwell time in the product detail page) have been incorporated in the training process of many ranking models for better performance. Such approaches are widely used in many ranking applications, including search and recommendation. Recently, the inherent biases in user feedback data have been studied, which indicates how the users’ behaviors can be affected by factors other than relevancy. By identifying and removing these biases, the ranking models can be further improved. Researchers have developed a variety of debiasing methods on different bias factors. Most of them only focus on one type of bias and pay little attention to different types of bias from a unified perspective. In this paper, we conduct a comprehensive study of bias focusing on the application of ranking problems in recommender systems which is highly important for the research of web intelligence. Then, we share our experiences derived from designing and optimizing unbiased models to improve feeds recommendation. To uncover the effects of biases and achieve better ranking performance, we propose several unbiased models and compare with state-of-the-art models. We conduct extensive offline experiments on real datasets and validate the effectiveness of our method by performing online A/B testing in a real-world recommender system.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90613493","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}
Due to the enormous content of text available online through emails, social media, and news articles, it has become complicated to summarize the textual information from multiple documents. Text summarization automatically creates a comprehensive description of the document that retains its informative contents through the keywords, where Multi-Document Summarization (MDS) is a productive tool for data accumulation that creates a concise and informative summary from the documents. In order to extract the relevant information from the documents, Graph neural networks (GNNs) is the neural structure that detains the interrelation of the graph by progressing the messages between the graphical nodes. In the current years, the advanced version of GNNs, such as graph attention network (GAN), graph recurrent network, and graph convolutional network (GCN) provides a remarkable performance in text summarization with the advantage of deep learning techniques. Hence, in this survey, graph approaches for text summarization has been analyzed and discussed, where the recent text summarization model based on Deep learning techniques are highlighted. Further, the article provides the taxonomy to abstract the design pattern of Neural Networks and conducts a comprehensive of the existing text summarization model. Finally, the review article enlists the future direction of the researcher, which would motivate the enthusiastic and novel contributions in text summarizations.
{"title":"Over comparative study of text summarization techniques based on graph neural networks","authors":"Samina Mulla, N. Shaikh","doi":"10.3233/web-230014","DOIUrl":"https://doi.org/10.3233/web-230014","url":null,"abstract":"Due to the enormous content of text available online through emails, social media, and news articles, it has become complicated to summarize the textual information from multiple documents. Text summarization automatically creates a comprehensive description of the document that retains its informative contents through the keywords, where Multi-Document Summarization (MDS) is a productive tool for data accumulation that creates a concise and informative summary from the documents. In order to extract the relevant information from the documents, Graph neural networks (GNNs) is the neural structure that detains the interrelation of the graph by progressing the messages between the graphical nodes. In the current years, the advanced version of GNNs, such as graph attention network (GAN), graph recurrent network, and graph convolutional network (GCN) provides a remarkable performance in text summarization with the advantage of deep learning techniques. Hence, in this survey, graph approaches for text summarization has been analyzed and discussed, where the recent text summarization model based on Deep learning techniques are highlighted. Further, the article provides the taxonomy to abstract the design pattern of Neural Networks and conducts a comprehensive of the existing text summarization model. Finally, the review article enlists the future direction of the researcher, which would motivate the enthusiastic and novel contributions in text summarizations.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90897084","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}
Ageing and its related health conditions bring many challenges not only to individuals but also to society. Various MRI techniques are defined for the early detection of age-related diseases. Researchers continue the prediction with the involvement of different strategies. In that manner, this research intends to propose a new brain age prediction model under the processing of certain steps like preprocessing, feature extraction, feature selection, and prediction. The initial step is preprocessing, where improved median filtering is proposed to reduce the noise in the image. After this, feature extraction takes place, where shape-based features, statistical features, and texture features are extracted. Particularly, Improved LGTrP features are extracted. However, the curse of dimensionality becomes a serious issue in this aspect that shrinks the efficiency of the prediction level. According to the “curse of dimensionality,” the number of samples required to estimate any function accurately increases exponentially as the number of input variables increases. Hence, a feature selection model with improvement has been introduced in this paper termed an improved Chi-square. Finally, for prediction purposes, a Hybrid classifier is introduced by combining the models like Bi-GRU and DBN, respectively. In order to enhance the effectiveness of the hybrid method, Upgraded Blue Monkey Optimization with Improvised Evaluation (UBMOIE) is introduced as the training system by tuning the optimal weights in both classifiers. Finally, the performance of the suggested UBMIOE-based brain age prediction method was assessed over the other schemes to various metrics.
{"title":"Hybrid deep model for brain age prediction in MRI with improved chi-square based selected features","authors":"Vishnupriya G.S, S. Rajakumari","doi":"10.3233/web-230060","DOIUrl":"https://doi.org/10.3233/web-230060","url":null,"abstract":"Ageing and its related health conditions bring many challenges not only to individuals but also to society. Various MRI techniques are defined for the early detection of age-related diseases. Researchers continue the prediction with the involvement of different strategies. In that manner, this research intends to propose a new brain age prediction model under the processing of certain steps like preprocessing, feature extraction, feature selection, and prediction. The initial step is preprocessing, where improved median filtering is proposed to reduce the noise in the image. After this, feature extraction takes place, where shape-based features, statistical features, and texture features are extracted. Particularly, Improved LGTrP features are extracted. However, the curse of dimensionality becomes a serious issue in this aspect that shrinks the efficiency of the prediction level. According to the “curse of dimensionality,” the number of samples required to estimate any function accurately increases exponentially as the number of input variables increases. Hence, a feature selection model with improvement has been introduced in this paper termed an improved Chi-square. Finally, for prediction purposes, a Hybrid classifier is introduced by combining the models like Bi-GRU and DBN, respectively. In order to enhance the effectiveness of the hybrid method, Upgraded Blue Monkey Optimization with Improvised Evaluation (UBMOIE) is introduced as the training system by tuning the optimal weights in both classifiers. Finally, the performance of the suggested UBMIOE-based brain age prediction method was assessed over the other schemes to various metrics.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77393732","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}
IoT technologies & UAVs are frequently utilized in ecological monitoring areas. Unmanned Aerial Vehicles (UAVs) & IoT in farming technology can evaluate crop disease & pest incidence from the ground’s micro & macro aspects, correspondingly. UAVs could capture images of farms using a spectral camera system, and these images are been used to examine the presence of agricultural pests and diseases. In this research work, a novel IoT- assisted UAV- based pest detection with Arithmetic Crossover based Black Widow Optimization-Convolutional Neural Network (ACBWO-CNN) model is developed in the field of agriculture. Cloud computing mechanism is used for monitoring and discovering the pest during crop production by using UAVs. The need for this method is to provide data centers, so there is a necessary amount of memory storage in addition to the processing of several images. Initially, the collected input image by the UAV is assumed on handling the via-IoT-cloud server, from which the pest identification takes place. The pest detection unit will be designed with three major phases: (a) background &foreground Segmentation, (b) Feature Extraction & (c) Classification. In the foreground and background Segmentation phase, the K-means clustering will be utilized for segmenting the pest images. From the segmented images, it extracts the features including Local Binary Pattern (LBP) &improved Local Vector Pattern (LVP) features. With these features, the optimized CNN classifier in the classification phase will be trained for the identification of pests in crops. Since the final detection outcome is from the Convolutional Neural Network (CNN); its weights are fine-tuned through the ACBWO approach. Thus, the output from optimized CNN will portray the type of pest identified in the field. This method’s performance is compared to other existing methods concerning a few measures.
{"title":"Internet of Things assisted Unmanned Aerial Vehicle for Pest Detection with Optimized Deep Learning Model","authors":"Vijayalakshmi G, Radhika Y","doi":"10.3233/web-230062","DOIUrl":"https://doi.org/10.3233/web-230062","url":null,"abstract":"IoT technologies & UAVs are frequently utilized in ecological monitoring areas. Unmanned Aerial Vehicles (UAVs) & IoT in farming technology can evaluate crop disease & pest incidence from the ground’s micro & macro aspects, correspondingly. UAVs could capture images of farms using a spectral camera system, and these images are been used to examine the presence of agricultural pests and diseases. In this research work, a novel IoT- assisted UAV- based pest detection with Arithmetic Crossover based Black Widow Optimization-Convolutional Neural Network (ACBWO-CNN) model is developed in the field of agriculture. Cloud computing mechanism is used for monitoring and discovering the pest during crop production by using UAVs. The need for this method is to provide data centers, so there is a necessary amount of memory storage in addition to the processing of several images. Initially, the collected input image by the UAV is assumed on handling the via-IoT-cloud server, from which the pest identification takes place. The pest detection unit will be designed with three major phases: (a) background &foreground Segmentation, (b) Feature Extraction & (c) Classification. In the foreground and background Segmentation phase, the K-means clustering will be utilized for segmenting the pest images. From the segmented images, it extracts the features including Local Binary Pattern (LBP) &improved Local Vector Pattern (LVP) features. With these features, the optimized CNN classifier in the classification phase will be trained for the identification of pests in crops. Since the final detection outcome is from the Convolutional Neural Network (CNN); its weights are fine-tuned through the ACBWO approach. Thus, the output from optimized CNN will portray the type of pest identified in the field. This method’s performance is compared to other existing methods concerning a few measures.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91130818","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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