Intelligent Automation and Soft Computing最新文献_第6页

Aquarium Monitoring System Based on Internet of Things 基于物联网的水族馆监控系统

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.022501

Wen-Tsai Sung, Shuo-Chen Tasi, Sung-Jung Hsiao

{"title":"Aquarium Monitoring System Based on Internet of Things","authors":"Wen-Tsai Sung, Shuo-Chen Tasi, Sung-Jung Hsiao","doi":"10.32604/iasc.2022.022501","DOIUrl":"https://doi.org/10.32604/iasc.2022.022501","url":null,"abstract":"With the ever-increasing richness of social resources, the number of devices using the Internet of Things is also increasing. Currently, many people keep pets such as fish in their homes, and they need to be carefully taken care of. In particular, it is necessary to create a safe and comfortable environment for them and to maintain this environment continuously. An adverse environment can affect the growth of fish and may even result in their death. This study used the LinkIt 7697 module and the BlocklyDuino editor to produce a control system for a smart aquarium. The purpose of this system is to monitor the temperature, light intensity, and water level in an aquarium, as well as to provide alerts to presence of intruders; therefore, temperature, light, ultrasonic, and infrared sensing modules are used. The system has set aquarium environment thresholds, and it processes the signals obtained by the sensors to control and optimize the outputs to loads using data fusion calculations so that the aquarium has the most comfortable environment for the fish. An automatic feeder is also included in the system, and this uses a servo motor. The data from the system is uploaded to a back-end computer through the built-in Wi-Fi system of the LinkIt 7697 module. The Cloud Sandbox platform is used to display the results in real time, achieving the purpose of remote network monitoring.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"7 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75437541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Arrhythmia and Disease Classification Based on Deep Learning Techniques 基于深度学习技术的心律失常和疾病分类

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.019877

Ramya G. Franklin, B. Muthukumar

{"title":"Arrhythmia and Disease Classification Based on Deep Learning Techniques","authors":"Ramya G. Franklin, B. Muthukumar","doi":"10.32604/iasc.2022.019877","DOIUrl":"https://doi.org/10.32604/iasc.2022.019877","url":null,"abstract":"Electrocardiography (ECG) is a method for monitoring the human heart’s electrical activity. ECG signal is often used by clinical experts in the collected time arrangement for the evaluation of any rhythmic circumstances of a topic. The research was carried to make the assignment computerized by displaying the problem with encoder-decoder methods, by using misfortune appropriation to predict standard or anomalous information. The two Convolutional Neural Networks (CNNs) and the Long Short-Term Memory (LSTM) fully connected layer (FCL) have shown improved levels over deep learning networks (DLNs) across a wide range of applications such as speech recognition, prediction etc., As CNNs are suitable to reduce recurrence types, LSTMs are reasonable for temporary displays and DNNs are appropriate for preparing highlights for a more divisible area. CNN, LSTM, and DNNs are appropriate to view. The complementarity of CNNs, LSTMs, and DNNs was explored in this paper by consolidating them through a single architecture firm. Our findings show that the methodology suggested can expressively explain ECG series and of detection of anomalies through scores that beat other techniques supervised as well as unsupervised technique. The LSTM-Network and FL also showed that the imbalanced data sets of the ECG beat detection issue have been consistently solved and that they have not been prone to the accuracy of ECG-Signals. The novel approach should be used to assist cardiologists in their accurate and unbiased analysis of ECG signals in telemedicine scenarios.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"9 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75487887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Hybrid Multi-Criteria Collaborative Filtering Model for Effective Personalized Recommendations 有效个性化推荐的混合多准则协同过滤模型

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.020132

Abdelrahman H. Hussein, Qasem M. Kharma, Faris M. Taweel, Mosleh M. Abualhaj, Qusai Y. Shambour

{"title":"A Hybrid Multi-Criteria Collaborative Filtering Model for Effective Personalized Recommendations","authors":"Abdelrahman H. Hussein, Qasem M. Kharma, Faris M. Taweel, Mosleh M. Abualhaj, Qusai Y. Shambour","doi":"10.32604/iasc.2022.020132","DOIUrl":"https://doi.org/10.32604/iasc.2022.020132","url":null,"abstract":"Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. One way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the multi-criteria ratings, implicit similarity, similarity transitivity and global reputation concepts to expand the space of potential recommenders. This expansion will enhance the prediction accuracy and coverage of the proposed model when applied to sparse data situations. To show effectiveness of the proposed model, a set of experiments are conducted on two real-world multi-criteria datasets, Yahoo! Movies and TripAdvisor. The experimental results demonstrate the superiority of the proposed model compared to a number of existing collaborative filtering-based recommendation methods under a variety of evaluation metrics.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"67 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81605461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Enhancing Detection of Malicious URLs Using Boosting and Lexical Features 使用增强和词法特征增强恶意url的检测

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.020229

Mohammad Atrees, Ashraf Ahmad, Firas Alghanim

引用次数: 2

Efficient Key Management System Based Lightweight Devices in IoT 物联网中基于轻量级设备的高效密钥管理系统

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.020422

T. Chindrella Priyadharshini, D. Mohana Geetha

引用次数: 6

Performance Analysis of Two-Stage Optimal Feature-Selection Techniques for Finger Knuckle Recognition 两阶段最优特征选择技术在指关节识别中的性能分析

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.022583

P. Jayapriya, K. Umamaheswari

{"title":"Performance Analysis of Two-Stage Optimal Feature-Selection Techniques for Finger Knuckle Recognition","authors":"P. Jayapriya, K. Umamaheswari","doi":"10.32604/iasc.2022.022583","DOIUrl":"https://doi.org/10.32604/iasc.2022.022583","url":null,"abstract":"Automated biometric authentication attracts the attention of researchers to work on hand-based images to develop applications in forensics science. Finger Knuckle Print (FKP) is one of the hand-based biometrics used in the recognition of an individual. FKP is rich in texture, less in contact and known for its unique features. The dimensionality of the features, extracted from the image, is one of the main problems in pattern recognition. Since selecting the relevant features is an important but challenging task, the feature subset selection is an optimization problem. A reduced number of features results in enhanced classification accuracy. The proposed FKP system presents a mulitalgorithm fusion based on subspace algorithms at feature level fusion technique. In this paper, a new feature-selection algorithm, which is a Modified Magnetotatic bacterium Optimization Algorithm (MMBOA), is proposed for finger knuckle recognition to select relevant and useful features that increase the classification accuracy. The distinct characteristic of this bacterium influences the design of a new optimization technique. The hybrid features such as Eigen and Fisher (EiFi) are extracted from the finger knuckle. The fusion of this feature vector is optimized using newly proposed MMBOA_mr optimization algorithm. The results demonstrate a significant improvement compared with unimodal identifiers, and the proposed approach significantly outperforms with a recognition accuracy of 99.7% with 22 features with the reduction rate of 72%. Additionally, the proposed approach is compared with the state-of-the-art methods.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"77 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79512695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Novel Hybrid Deep Learning Framework for Intrusion Detection Systems in WSN-IoT Networks 一种用于WSN-IoT网络入侵检测系统的混合深度学习框架

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.022259

M. Maheswari, R. A. Karthika

引用次数: 5

Deep Learning-Based Skin Lesion Diagnosis Model Using Dermoscopic Images 基于深度学习的皮肤镜图像诊断模型

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.019117

G. Reshma, Chiai Al-Atroshi, Vinay Kumar Nassa, B. T. Geetha, G. Sunitha, Mohammad Gouse Galety, S. Neelakandan

{"title":"Deep Learning-Based Skin Lesion Diagnosis Model Using Dermoscopic Images","authors":"G. Reshma, Chiai Al-Atroshi, Vinay Kumar Nassa, B. T. Geetha, G. Sunitha, Mohammad Gouse Galety, S. Neelakandan","doi":"10.32604/iasc.2022.019117","DOIUrl":"https://doi.org/10.32604/iasc.2022.019117","url":null,"abstract":"In recent years, intelligent automation in the healthcare sector becomes more familiar due to the integration of artificial intelligence (AI) techniques. Intelligent healthcare systems assist in making better decisions, which further enable the patient to provide improved medical services. At the same time, skin lesion is a deadly disease that affects people of all age groups. Skin lesion segmentation and classification play a vital part in the earlier and precise skin cancer diagnosis by intelligent systems. However, the automated diagnosis of skin lesions in dermoscopic images is challenging because of the problems such as artifacts (hair, gel bubble, ruler marker), blurry boundary, poor contrast, and variable sizes and shapes of the lesion images. This study develops intelligent multilevel thresholding with deep learning (IMLT-DL) based skin lesion segmentation and classification model using dermoscopic images to address these problems. Primarily, the presented IMLT-DL model incorporates the Top hat filtering and inpainting technique for the pre-processing of the dermoscopic images. In addition, the Mayfly Optimization (MFO) with multilevel Kapur’s thresholding-based segmentation process is involved in determining the infected regions. Besides, an Inception v3 based feature extractor is applied to derive a valuable set of feature vectors. Finally, the classification process is carried out using a gradient boosting tree (GBT) model. The presented model’s performance takes place against the International Skin Imaging Collaboration (ISIC) dataset, and the experimental outcomes are inspected in different evaluation measures. The resultant experimental values ensure that the proposed IMLT-DL model outperforms the existing methods by achieving higher accuracy of 0.992.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"87 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80782181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 37

An Improved Genetic Algorithm for Automated Convolutional Neural Network Design 一种用于自动卷积神经网络设计的改进遗传算法

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.020975

Rahul Dubey, Jitendra Agrawal

{"title":"An Improved Genetic Algorithm for Automated Convolutional Neural Network Design","authors":"Rahul Dubey, Jitendra Agrawal","doi":"10.32604/iasc.2022.020975","DOIUrl":"https://doi.org/10.32604/iasc.2022.020975","url":null,"abstract":"Extracting the features from an image is a cumbersome task. Initially, this task was performed by domain experts through a process known as handcrafted feature design. A deep embedding technique known as convolutional neural networks (CNNs) later solved this problem by introducing the feature learning concept, through which the CNN is directly provided with images. This CNN then learns the features of the image, which are subsequently given as input to the further layers for an intended task like classification. CNNs have demonstrated astonishing performance in several practicable applications in the last few years. Nevertheless, the pursuance of CNNs primarily depends upon their architecture, which is handcrafted by domain expertise and type of investigated problem. On the other hand, for researchers who do not have proficiency in using CNNs, it has been very difficult to explore this topic in their problem statements. In this paper, we have come up with a rank and gradient descent-based optimized genetic algorithm to automatically find the architecture design of CNNs that is vigorously competent in exploring the best CNN architecture for maneuvering the tasks of image classification. In the proposed algorithm, there is no requirement for handcrafted preand post-processing, which implies that the algorithm is fully mechanized. The validation of the proposed algorithm on conventional benchmarked datasets has been done by comparing the run time of a graphics processing unit (GPU) throughout the training process and assessing the accuracy of various measures. The experimental results show that the proposed algorithm accomplishes better and more persistent ‘classification accuracy’ than the original genetic algorithm on the CIFAR datasets by using fifty percent less intensive computing resources for training the individual CNN and the entire population.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"72 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86319049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Coronavirus Decision-Making Based on a Locally -Generalized Closed Set 基于局部广义闭集的冠状病毒决策

IF 2 4区计算机科学

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI: 10.32604/iasc.2022.021581

M. A. El Safty, S. A. Alblowi, Y. Almalki, M. E. El Sayed

引用次数: 0