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EfficientNet-B0 Model for Face Mask Detection Based on Social Information Retrieval 基于社会信息检索的面罩检测高效网络- b0模型
Int. J. Inf. Syst. Model. Des. Pub Date : 2022-10-20 DOI: 10.4018/ijismd.313444
Moolchand Sharma, Harsh Gunwant, Pranay Saggar, Luv Gupta, Deepak Gupta
{"title":"EfficientNet-B0 Model for Face Mask Detection Based on Social Information Retrieval","authors":"Moolchand Sharma, Harsh Gunwant, Pranay Saggar, Luv Gupta, Deepak Gupta","doi":"10.4018/ijismd.313444","DOIUrl":"https://doi.org/10.4018/ijismd.313444","url":null,"abstract":"The world was introduced to the term coronavirus at the end of 2019, following which everyone was thrown into stress and anxiety. The pandemic has been a complete disaster, wreaking devastation and resulting in a significant loss of human life throughout the world. The governments of various countries have issued guidelines and protocols to be followed for stopping the surge in cases (i.e., wearing masks). Amidst all this chaos, the only weapon is technology. So, the detection of face masks is important. The authors utilized a dataset that included images of individuals in society wearing and not wearing masks. They gathered the information required to train a model by using deep networks like EfficientNetB0, MobileNetV2, ResNet50, and InceptionV3. With EfficientNet-B0, they have been able to achieve an accuracy of 99.70% on a two-class classification issue. These methods make face mask detection easier and help in knowledge discovery. These technological breakthroughs may aid in information retrieval as well as help society and guarantee that such a healthcare disaster does not occur again.","PeriodicalId":289800,"journal":{"name":"Int. J. Inf. Syst. Model. Des.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132608550","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}
引用次数: 1
InBiodiv-O: An Ontology for Biodiversity Knowledge Management InBiodiv-O:生物多样性知识管理本体
Int. J. Inf. Syst. Model. Des. Pub Date : 2022-10-20 DOI: 10.4018/ijismd.315021
Archana Patel, Sarika Jain, N. Debnath, Vishal Lama
{"title":"InBiodiv-O: An Ontology for Biodiversity Knowledge Management","authors":"Archana Patel, Sarika Jain, N. Debnath, Vishal Lama","doi":"10.4018/ijismd.315021","DOIUrl":"https://doi.org/10.4018/ijismd.315021","url":null,"abstract":"To present the biodiversity information, a semantic model is required that connects all kinds of data about living creatures and their habitats. The model must be able to encode human knowledge for machines to be understood. Ontology offers the richest machine-interpretable semantics that are being extensively used in the biodiversity domain. Various ontologies are developed for the biodiversity domain; however, these ontologies are not capable to define the Indian biodiversity information though India is one of the megadiverse countries. To semantically analyze the Indian biodiversity information, it is crucial to build an ontology that describes all the terms of this domain. Since the curation of the ontology depends on the domain where these are used, there is no ideal methodology defined yet. The aim of this article is to develop an ontology that semantically encodes all the terms of Indian biodiversity information in all its dimensions based on the proposed methodology. The evaluation of the proposed ontology depicts that ontology is well built in the specified domain.","PeriodicalId":289800,"journal":{"name":"Int. J. Inf. Syst. Model. Des.","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114198829","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}
引用次数: 0
An Enhanced Image Segmentation Approach for Detection of Diseases in Fruit 一种用于水果病害检测的增强图像分割方法
Int. J. Inf. Syst. Model. Des. Pub Date : 2022-10-20 DOI: 10.4018/ijismd.315281
B. Mishra, P. Tripathy, Saroja Kumar Rout, Chinmaya Ranjan Pattanaik
{"title":"An Enhanced Image Segmentation Approach for Detection of Diseases in Fruit","authors":"B. Mishra, P. Tripathy, Saroja Kumar Rout, Chinmaya Ranjan Pattanaik","doi":"10.4018/ijismd.315281","DOIUrl":"https://doi.org/10.4018/ijismd.315281","url":null,"abstract":"The progress in the realm of image segmentation has helped farmers to use nominal inputs for higher production within limited time. Preliminary identification of diseases on fruits is limited to naked eyes since the majority of these symptoms can only be identified by microscopic visuals. Image segmentation plays a vital part in distinguishing their infected parts from the disinfected ones. In this paper, clustering is used as an approach in image segmentation to cautiously discover the affected parts of the fruits by segmenting the affected areas from the non-affected parts. Four clustering techniques—IS-KM, IS-FEKM, IS-MKM, and IS-FECA—were employed for this purpose. The quality of segmentation was evaluated using few performance measures like SC, RMSE, MSE, MAE, NAE, and PSNR. The result obtained using IS-FECA is more reasonable compared to the other methods. Roughly each value of performance parameters confers better results for IS-FECA-based image segmentation method, which means proper separation of diseased parts in fruits from their un-affected ones is attainable.","PeriodicalId":289800,"journal":{"name":"Int. J. Inf. Syst. Model. Des.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122637763","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}
引用次数: 0
Deep Learning Model for Dynamic Hand Gesture Recognition for Natural Human-Machine Interface on End Devices 终端设备自然人机界面动态手势识别的深度学习模型
Int. J. Inf. Syst. Model. Des. Pub Date : 2022-06-22 DOI: 10.4018/ijismd.306636
Tsui-Ping Chang, Hung-Ming Chen, Shih-Ying Chen, Wei-Cheng Lin
{"title":"Deep Learning Model for Dynamic Hand Gesture Recognition for Natural Human-Machine Interface on End Devices","authors":"Tsui-Ping Chang, Hung-Ming Chen, Shih-Ying Chen, Wei-Cheng Lin","doi":"10.4018/ijismd.306636","DOIUrl":"https://doi.org/10.4018/ijismd.306636","url":null,"abstract":"As end devices have become ubiquitous in daily life, the use of natural human-machine interfaces has become an important topic. Many researchers have proposed the frameworks to improve the performance of dynamic hand gesture recognition. Some CNN models are widely used to increase the accuracy of dynamic hand gesture recognition. However, most CNN models are not suitable for end devices. This is because image frames are captured continuously and result in lower hand gesture recognition accuracy. In addition, the trained models need to be efficiently deployed on end devices. To solve the problems, the study proposes a dynamic hand gesture recognition framework on end devices. The authors provide a method (i.e., ModelOps) to deploy the trained model on end devices, by building an edge computing architecture using Kubernetes. The research provides developers with a real-time gesture recognition component. The experimental results show that the framework is suitable on end devices.","PeriodicalId":289800,"journal":{"name":"Int. J. Inf. Syst. Model. Des.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134091067","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}
引用次数: 0
Automated Knowledge Extraction of Liver Cysts From CT Images Using Modified Whale Optimization and Fuzzy C Means Clustering Algorithm 基于改进鲸鱼优化和模糊C均值聚类算法的CT图像肝囊肿知识自动提取
Int. J. Inf. Syst. Model. Des. Pub Date : 2022-06-22 DOI: 10.4018/ijismd.306644
R. Kaur, B. Khehra
{"title":"Automated Knowledge Extraction of Liver Cysts From CT Images Using Modified Whale Optimization and Fuzzy C Means Clustering Algorithm","authors":"R. Kaur, B. Khehra","doi":"10.4018/ijismd.306644","DOIUrl":"https://doi.org/10.4018/ijismd.306644","url":null,"abstract":"In this study, the integrated modified whale optimization and modified fuzzy c-means clustering algorithm using morphological operations are developed and implemented for appropriate knowledge extraction of a cyst from computer tomography (CT) images of the liver to facilitate modern intelligent healthcare systems. The proposed approach plays an efficient role in diagnosing the liver cyst. To evaluate the efficiency, the outcomes of the proposed approach have been compared with the minimum cross entropy based modified whale optimization algorithm (MCE and MWOA), teaching-learning optimization algorithm based upon minimum cross entropy (MCE and TLBO), particle swarm intelligence algorithm (PSO), genetic algorithm (GA), differential evolution (DE) algorithm, and k-means clustering algorithm. For this, various parameters such as uniformity (U), mean structured similarity index (MSSIM), structured similarity index (SSIM), random index (RI), and peak signal-to-noise ratio (PSNR) have been considered. The experimental results show that the proposed approach is more efficient and accurate than others.","PeriodicalId":289800,"journal":{"name":"Int. J. Inf. Syst. Model. Des.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130574072","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}
引用次数: 0
Knowledge-Infused Text Classification for the Biomedical Domain 生物医学领域的知识注入文本分类
Int. J. Inf. Syst. Model. Des. Pub Date : 2022-06-22 DOI: 10.4018/ijismd.306635
Sonika Malik, Sarika Jain
{"title":"Knowledge-Infused Text Classification for the Biomedical Domain","authors":"Sonika Malik, Sarika Jain","doi":"10.4018/ijismd.306635","DOIUrl":"https://doi.org/10.4018/ijismd.306635","url":null,"abstract":"Extracting knowledge from unstructured text and then classifying it is gaining importance after the data explosion on the web. The traditional text classification approaches are becoming ubiquitous, but the hybrid of semantic knowledge representation with statistical techniques can be more promising. The developed method attempts to fabricate neural networks to expedite and improve the simulation of ontology-based classification. This paper weighs upon the accurate results between the ontology-based text classification and traditional classification based on the artificial neural network (ANN) using distinguished parameters such as accuracy, precision, etc. The experimental analysis shows that the proposed findings are substantially better than the conventional text classification, taking the course of action into account. The authors also ran tests to compare the results of the proposed research model with one of the latest researches, resulting in a cut above accuracy and F1 score of the proposed model for various experiments performed at the different number of hidden layers and neurons.","PeriodicalId":289800,"journal":{"name":"Int. J. Inf. Syst. Model. Des.","volume":"54 18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124867210","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}
引用次数: 0
Hurricane Damage Detection From Satellite Imagery Using Convolutional Neural Networks 利用卷积神经网络从卫星图像中检测飓风损害
Int. J. Inf. Syst. Model. Des. Pub Date : 2022-06-22 DOI: 10.4018/ijismd.306637
Swapandeep Kaur, Sheifali Gupta, Swati Singh, Isha Gupta
{"title":"Hurricane Damage Detection From Satellite Imagery Using Convolutional Neural Networks","authors":"Swapandeep Kaur, Sheifali Gupta, Swati Singh, Isha Gupta","doi":"10.4018/ijismd.306637","DOIUrl":"https://doi.org/10.4018/ijismd.306637","url":null,"abstract":"Hurricanes are one of the most disastrous natural phenomena occurring on Earth that cause loss of human lives and immense damage to property as well. For assessment of this damage, windshield survey is commonly used, which is an error-prone and time-consuming method. For solving this problem, computer vision comes into the picture. In this paper, a convolutional neural network-based architecture has been proposed to classify the post-hurricane satellite imagery into damaged and undamaged building classes accurately. The model consists of five convolutional and five pooling layers followed by a flattening layer and two dense layers. For this, a dataset of Hurricane Harvey has been considered having 23000 satellite images each of size 128 X 128 pixels. With the proposed model, the author has achieved an accuracy of 92.91%, F1-score of 93%, sensitivity of 93.34%, specificity of 92.47%, and precision of 92.65% at a learning rate of 0.0001 and 30 epochs. Also, low false positive rate of 7.53% and false negative rate of 6.66% were obtained.","PeriodicalId":289800,"journal":{"name":"Int. J. Inf. Syst. Model. Des.","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126232691","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}
引用次数: 0
Research on Collaborative Machine English Translation Using the HIC Technology 基于HIC技术的协同机器英语翻译研究
Int. J. Inf. Syst. Model. Des. Pub Date : 2022-05-01 DOI: 10.4018/ijismd.300776
Jingjing Lv
{"title":"Research on Collaborative Machine English Translation Using the HIC Technology","authors":"Jingjing Lv","doi":"10.4018/ijismd.300776","DOIUrl":"https://doi.org/10.4018/ijismd.300776","url":null,"abstract":"Due to the rapid development of smart city, Hybrid Information Centric-Networking (HICN) emerges as a promising technology to enable the power of smart city. One of the most important application is the smart English translation, which becomes more and more popular with the process of Internationalization. In this work, we focus on studying the intelligent English translation is smart city using the HICN technology. Particularly, a method using collaborative machine learning and quality estimation technique is proposed, which sets a fixed threshold to filter pseudo-parallel data during unsupervised neural machine translation training. The quality estimation is used to evaluate and screen the pseudo-parallel data with high performance generated during reverse translation training. The results indicate that the proposed method outperforms the state-of-the-art methods.","PeriodicalId":289800,"journal":{"name":"Int. J. Inf. Syst. Model. Des.","volume":"111 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122631129","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}
引用次数: 0
AI for Health-Related Data Modeling: DCN Application Analysis 健康相关数据建模的AI: DCN应用分析
Int. J. Inf. Syst. Model. Des. Pub Date : 2022-05-01 DOI: 10.4018/ijismd.300780
N. Cheng
{"title":"AI for Health-Related Data Modeling: DCN Application Analysis","authors":"N. Cheng","doi":"10.4018/ijismd.300780","DOIUrl":"https://doi.org/10.4018/ijismd.300780","url":null,"abstract":"Data modeling of health-related data from Data Center (DC) has positive effects for health monitoring, disease prevention, and healthcare research. However, health-related data has the characteristics of huge, high-dimensional, and non-normalized, which are not beneficial to direct analysis, so data needs to be preprocessed before data modeling. This paper focuses on the features of health-related data, and outlier detection during data preprocessing is studied. Meanwhile, we propose an improved algorithm for health-related data based outlier detection. The experimental results reveal that the proposed outlier detection algorithm has a smaller running time, and more outliers are detected compared to three baselines. In addition, local importance based random forest feature selection algorithm is proposed to measure the importance of each feature. The experimental results indicate that the proposed algorithm can select optimal feature subset to apply health-related data.","PeriodicalId":289800,"journal":{"name":"Int. J. Inf. Syst. Model. Des.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134267777","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}
引用次数: 0
A Diffraction Service Composition Approach Based on S-ABCPC: An Improved ABC Algorithm 基于S-ABCPC的衍射服务组合方法:一种改进的ABC算法
Int. J. Inf. Syst. Model. Des. Pub Date : 2022-05-01 DOI: 10.4018/ijismd.300778
Xunyou Min, Xiaofei Xu, Zhongjie Wang, Zhizhong Liu
{"title":"A Diffraction Service Composition Approach Based on S-ABCPC: An Improved ABC Algorithm","authors":"Xunyou Min, Xiaofei Xu, Zhongjie Wang, Zhizhong Liu","doi":"10.4018/ijismd.300778","DOIUrl":"https://doi.org/10.4018/ijismd.300778","url":null,"abstract":"In recent years, the research on the QoS-aware service composition problem often assume that each component service in the process to be solved is equally essential, they do not consider the impact of core component services and other component services on problem-solving, or even though their impact is considered, they are not fully considered. So this paper first proposes a diffractive method based on them. Considering the advantages of Artificial Bee Colony (ABC) such as simplicity, this paper chooses it as the basic algorithm. In addition, with the continuous development of service ecosystem, it gradually formed a variety of domain features. They have an important influence on problem-solving, but the existing research has not explored this influence in-depth. Therefore, this paper digs deep into this influence. Given the characteristics of the problem to be solved in this paper, the S-ABCPC algorithm is designed. At last, experiments have proved the effectiveness of the method proposed in this paper. The impact factors of this method have been studied.","PeriodicalId":289800,"journal":{"name":"Int. J. Inf. Syst. Model. Des.","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124894499","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}
引用次数: 0
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