Intelligent Automation and Soft Computing最新文献

{"title":"Robust Node Localization with Intrusion Detection for Wireless Sensor Networks","authors":"R. Punithavathi, R. Thanga Selvi, R. Latha, G. Kadiravan, V. Srikanth, Neeraj Kumar Shukla","doi":"10.32604/iasc.2022.023344","DOIUrl":"https://doi.org/10.32604/iasc.2022.023344","url":null,"abstract":"Wireless sensor networks comprise a set of autonomous sensor nodes, commonly used for data gathering and tracking applications. Node localization and intrusion detection are considered as the major design issue in WSN. Therefore, this paper presents a new multi-objective manta ray foraging optimization (MRFO) based node localization with intrusion detection (MOMRFO-NLID) technique for WSN. The goal of the MOMRFO-NLID technique is to optimally localize the unknown nodes and determine the existence of intrusions in the network. The MOMRFO-NLID technique encompasses two major stages namely MRFO based localization of nodes and optimal Siamese Neural Network (OSNN) based intrusion detection. The OSNN technique involves the hyperparameter tuning of the traditional SNN using the MRFO algorithm and consequently increases the detection rate. In order to assess the enhanced performance of the MOMRFONLID technique, a series of simulations take place and the results reported superior performance compared to existing techniques interms of distinct evaluation parameters.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"21 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73429864","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}

{"title":"PCN2: Parallel CNN to Diagnose COVID-19 from Radiographs and Metadata","authors":"A. Baz, M. Baz","doi":"10.32604/iasc.2022.020304","DOIUrl":"https://doi.org/10.32604/iasc.2022.020304","url":null,"abstract":"COVID-19 constitutes one of the devastating pandemics plaguing humanity throughout the centuries;within about 18 months since its appearing, the cumulative confirmed cases hit 173 million, whereas the death toll approaches 3.72 million. Although several vaccines became available for the public worldwide, the speed with which COVID-19 is spread, and its different mutant strains hinder stopping its outbreak. This, in turn, prompting the desperate need for devising fast, cheap and accurate tools via which the disease can be diagnosed in its early stage. Reverse Transcription Polymerase Chain Reaction (RTPCR) test is the mainstay tool used to detect the COVID-19 symptoms. However, due to the high false-negative rate of this test, physicians usually use chest radiographs as an adjunct or alternative tool. Although radiographs screening is wide-available, low-cost, and its results are timely, relying on radiologists to interpret them manually stands against using radiographs as a diagnostic tool. Motivated by the need to speed up the radiographic diagnosis of COVID-19 and to improve its reliability, this paper proposes a novel deep-learning framework dubbed Parallel Deep Neural Networks for COVID-19 Diagnosis (PCN2). PCN2 treats the radiographs and their metadata simultaneously by running two CNNs in parallel. Firstly: a 2-dimensional CNN ( 2DCNN) to capture the spatial information from the radiographs due to its super competency in this domain. Secondly, a 1-dimensional CNN (1DCNN) to extract the medical knowledge presented in the metadata. By this integration, PCN2 can make perfect classifications even for those cases in which the infection signs in radiographs are unclear due to being the disease in early-stage, confounded by other markers or overlapped by other diseases. Extensive assessments of PCN2 carried out using several datasets demonstrate average diagnostic accuracy of 99.9 and 0.99 F1-score.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"50 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74024507","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}

{"title":"A Novel Classification Method with Cubic Spline Interpolation","authors":"Husam Ali Abdulmohsin, H. A. Wahab, A. Hossen","doi":"10.32604/IASC.2022.018045","DOIUrl":"https://doi.org/10.32604/IASC.2022.018045","url":null,"abstract":"Classification is the last, and usually the most time-consuming step in recognition. Most recently proposed classification algorithms have adopted machine learning (ML) as the main classification approach, regardless of time consumption. This study proposes a statistical feature classification cubic spline interpolation (FC-CSI) algorithm to classify emotions in speech using a curve fitting technique. FC-CSI is utilized in a speech emotion recognition system (SERS). The idea is to sketch the cubic spline interpolation (CSI) for each audio file in a dataset and the mean cubic spline interpolations (MCSIs) representing each emotion in the dataset. CSI interpolation is generated by connecting the features extracted from each file in the feature extraction phase. The MCSI is generated by connecting the mean features of 70% of the files of each emotion in the dataset. Points on the CSI are considered the new generated features. To classify each audio file according to emotion, the Euclidian distance (ED) is found between each CSI and all MCSIs of all emotions in the dataset. Each audio file is classified according to the nearest MCSI to the CSI representing it. The three datasets used in this work are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Berlin (Emo-DB), and Surrey Audio-Visual Expressed Emotion (SAVEE). The proposed work shows fast classification and high accuracy of results. The classification accuracy, i.e., the proportion of samples assigned to the correct class, using FC-CSI without feature selection (FS), was 69.08%, 92.52%, and 89.1% with RAVDESS, Emo-DB, and SAVEE, respectively. The results of the proposed method were compared to those of a designed neural network called SER-NN. Comparisons were made with and without FS. FC-CSI outperformed SER-NN on Emo-DB and SAVEE, and underperformed on RAVDESS, without using an FS algorithm. It was noticed from experiments that FC-CSI operated faster than the same system utilizing SER-NN.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"31 1","pages":"339-355"},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69778240","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}

{"title":"Classification Framework for COVID-19 Diagnosis Based on Deep CNN Models","authors":"W. El-shafai, Abeer D. Algarni, Ghada M. El Banby, Fathi E. Abd El-Samie, Naglaa. F. Soliman","doi":"10.32604/iasc.2022.020386","DOIUrl":"https://doi.org/10.32604/iasc.2022.020386","url":null,"abstract":"Automated diagnosis based on medical images is a very promising trend in modern healthcare services. For the task of automated diagnosis, there should be flexibility to deal with an enormous amount of data represented in the form of medical images. In addition, efficient algorithms that could be adapted according to the nature of images should be used. The importance of automated medical diagnosis has been maximized with the evolution of COVID-19 pandemic. COVID-19 first appeared in China, Wuhan, and then it has exploded in the whole world with a very bad impact on our daily life. The third wave of COVID-19 in the third world is really a disaster in current days, especially with the emergence of the delta variant of COVID-19 that is widespread. Required inspections should be carried out to monitor the COVID-19 spread in daily life and allow primary diagnosis of suspected cases, and long-term clinical laboratory monitoring. Healthcare professionals or radiologists can exploit AI (Artificial Intelligence) tools to quickly and reliably identify the cases of COVID-19. This paper introduces a DCNN (Deep Convolutional Neural Network) framework for chest X-ray and CT image classification based on TL (Transfer Learning). The objective is to perform multi-class and binary classification of the images in order to determine pneumonia and COVID-19 case. The TL is feasible, when using a small dataset by transferring knowledge from natural image classification to medical image classification. Two types of TL are used. The first type is fine-tuning of the DenseNet121, Densenet169, DenseNet201, ResNet50, ResNet152, VGG16, and VGG19 models. The second type is deep tuning of the LeNet-5, AlexNet, Inception naive v1, and VGG16 models. Extensive tests have been carried out on datasets of chest X-ray and CT images with different training/testing ratios of 80%:20%, 70%:30%, and 60%:40%. Experimental results on 9,270 chest X-ray ray and 2,762 chest CT images acquired from different institutions show that the TL is effective with an average accuracy of 98.49%.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"30 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75576308","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}

{"title":"Intelligent Computing and Control Framework for Smart Automated System","authors":"R. Manikandan, G. Ranganathan, V. Bindhu","doi":"10.32604/iasc.2022.023922","DOIUrl":"https://doi.org/10.32604/iasc.2022.023922","url":null,"abstract":"This paper presents development and analysis of different control strategies for smart automated system. The dynamic role of an electrical motor and sensor interfacing with wireless module becomes an essential element in a smart agriculture system to monitor various environmental parameters. The various key parameters such as temperature, humidity, air pressure, soil health and solar radiation are widely used to analyze the growth of plants and soil health based on different climate conditions. However, the smart development of an automatic system to measure these vital parameters provides a feasible approach and helps the farmers to monitor their crops productivity. In this paper, a smart sensor based intelligent and automatic control strategies such as fuzzy logic controller and PID (Proportional Integral Derivative) controller is developed to collect the real time environmental parameters and to adapt any environmental conditions by updating their membership functions automatically with the help of sensor outputs. This paper targets to bring the usage of sensor-based intelligent and automatic control methods in the field of an agriculture system which includes automatic solar panel tracking and control, environmental vital parameters measurement, monitoring and implementation of intelligent control methods. The different experiments have been carried out with the support of graphical and microcontroller programming environment. Experimental results provide that the proposed system effectively measure the environmental parameters and provide an accurate transmission of data for continuous monitoring. The advantage of the proposed system is simple, easy to implement and maintain the dynamic environment for plants with respect to any climate conditions.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"180 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80165469","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}

{"title":"COVID-19 Pandemic Prediction and Forecasting Using Machine Learning Classifiers","authors":"Jabeen Sultana, Anjani Kumar Singha, Shams Tabrez Siddiqui, G. Nagalaxmi, Anil Kumar Sriram, Nitish Pathak","doi":"10.32604/iasc.2022.021507","DOIUrl":"https://doi.org/10.32604/iasc.2022.021507","url":null,"abstract":"COVID-19 is a novel virus that spreads in multiple chains from one person to the next. When a person is infected with this virus, they experience respiratory problems as well as rise in body temperature. Heavy breathlessness is the most severe sign of this COVID-19, which can lead to serious illness in some people. However, not everyone who has been infected with this virus will experience the same symptoms. Some people develop cold and cough, while others suffer from severe headaches and fatigue. This virus freezes the entire world as each country is fighting against COVID-19 and endures vaccination doses. Worldwide epidemic has been caused by this unusual virus. Several researchers use a variety of statistical methodologies to create models that examine the present stage of the pandemic and the losses incurred, as well as considered other factors that vary by location. The obtained statistical models depend on diverse aspects, and the studies are purely based on possible preferences, the pattern in which the virus spreads and infects people. Machine Learning classifiers such as Linear regression, Multi-Layer Perception and Vector Auto Regression are applied in this study to predict the various COVID-19 blowouts. The data comes from the COVID-19 data repository at Johns Hopkins University, and it focuses on the dissemination of different effect patterns of Covid-19 cases throughout Asian countries. © 2022, Tech Science Press. All rights reserved.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"6 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80480124","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}

{"title":"A Wireless ECG Monitoring and Analysis System Using the IoT Cloud","authors":"Anas Bushnag","doi":"10.32604/iasc.2022.024005","DOIUrl":"https://doi.org/10.32604/iasc.2022.024005","url":null,"abstract":"A portable electrocardiogram (ECG) monitoring system is essential for elderly and remote patients who are not able to visit the hospital regularly. The system connects a patient to his/her doctor through an Internet of Things (IoT) cloud server that provides all the information needed to diagnose heart diseases. Patients use an ECG monitoring device to collect and upload information regarding their current medical situation via the Message Queue Telemetry Transport (MQTT) protocol to the server. The IoT cloud server performs further analysis that can be useful for both the doctor and the patient. Moreover, the proposed system has an alert mechanism that sends notifications when a certain threshold is reached. The monitoring system accepts two types of input data: real-time data that are collected by an ECG device and benchmark data from the PhysioNet ECG-ID database. The system framework has four components: input, embedded device, IoT cloud server, and interface. Herein, two experiments are conducted using both types of input data. The results show that the proposed system provides reliable and trusted results that might reduce the number of required hospital visits. A comparison between the proposed system and several techniques previously reported in the literature is conducted. Finally, an implementation of the proposed system is presented to illustrate its operation.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"13 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82356796","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}

{"title":"A Framework for Mask-Wearing Recognition in Complex Scenes for Different Face Sizes","authors":"Hanan A. Hosni Mahmoud, Amal H. Alharbi, Norah S. Alghamdi","doi":"10.32604/iasc.2022.022359","DOIUrl":"https://doi.org/10.32604/iasc.2022.022359","url":null,"abstract":"People are required to wear masks in many countries, now a days with the Covid-19 pandemic. Automated mask detection is very crucial to help identify people who do not wear masks. Other important applications is for surveillance issues to be able to detect concealed faces that might be a safety threat. However, automated mask wearing detection might be difficult in complex scenes such as hospitals and shopping malls where many people are at present. In this paper, we present analysis of several detection techniques and their performances. We are facing different face sizes and orientation, therefore, we propose one technique to detect faces of different sizes and orientations. In this research, we propose a framework to incorporate two deep learning procedures to develop a technique for mask-wearing recognition especially in complex scenes and various resolution images. A regional convolutional neural network (R-CNN) is used to detect regions of faces, which is further enhanced by introducing a different size face detection even for smaller targets. We combined that by an algorithm that can detect faces even in low resolution images. We propose a mask-wearing detection algorithms in complex situations under different resolution and face sizes. We use a convolutional neural network (CNN) to detect the presence of the mask around the detected face. Experimental results prove our process enhances the precision and recall for the combined detection algorithm. The proposed technique achieves Precision of 94.5%, and is better than other techniques under comparison.","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":"89493553","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}

{"title":"A Novel Hybrid MPPT Control Strategy for Isolated Solar PV Power System","authors":"D. Sabaripandiyan, H. Habeebullah Sait, G. Aarthi","doi":"10.32604/iasc.2022.021950","DOIUrl":"https://doi.org/10.32604/iasc.2022.021950","url":null,"abstract":"The main aspiration of this paper is to improve the efficiency of Solar Photovoltaic (SPV) power system with a new Hybrid controller for standalone/ isolated Solar PV applications is proposed. This controller uses the merits of both Adapted Neuro-Fuzzy Inference System (ANFIS) and Perturbation & Observation (P&O) control techniques to concede rapid recovery at dynamic change of environment conditions such as solar irradiation and temperature. The ANFIS strategy itself has the merits over Fuzzy Logic and ANN methods. Conversely, P&O has its simplicity in implementation. Hence a case study for rapid recovery with the proposed controller and conventional P&O control strategy is carried out in this work. A SPV Module is associated to a load resistance with an interface of DC-DC step-up converter. A pattern of solar irradiation comprises of different static, dynamic, slow but sure increase with positive and negative slope are applied to the system and the response is observed. The proposed method is having the benefits of both P&O and ANFIS respectively to get better results on rapid change over conditions. The performance comparison of various MPPT algorithm of existing methods. The outcome demonstrates that the proposed hybrid-controller converges so rapid than the conventional P&O controller at dynamic situations and obeys at static and gradually varying environment conditions.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"21 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84144213","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}

{"title":"CGraM: Enhanced Algorithm for Community Detection in Social Networks","authors":"Kalaichelvi Nallusamy, K. S. Easwarakumar","doi":"10.32604/iasc.2022.020189","DOIUrl":"https://doi.org/10.32604/iasc.2022.020189","url":null,"abstract":"Community Detection is used to discover a non-trivial organization of the network and to extract the special relations among the nodes which can help in understanding the structure and the function of the networks. However, community detection in social networks is a vast and challenging task, in terms of detected communities accuracy and computational overheads. In this paper, we propose a new algorithm Enhanced Algorithm for Community Detection in Social Networks – CGraM, for community detection using the graph measures eccentricity, harmonic centrality and modularity. First, the centre nodes are identified by using the eccentricity and harmonic centrality, next a preliminary community structure is formed by finding the similar nodes using the jaccard coefficient. Later communities are selected from the preliminary community structure based on the number of inter-community and intra-community edges between them. Then the selected communities are merged till the modularity improves to form the better resultant community structure. This method is tested on the real networks and the results are evaluated using the evaluation metrics modularity and Normalized Mutual Information (NMI). The results are visualized and also compared with the state-of-the-art algorithms that covers louvian, walktrap, infomap, label propagation, fast greedy and eigen vector for more accurate analysis. CGraM achieved the better modularity and improved NMI values comparatively with other algorithms and gives improved results collaboratively when compared to previous methods.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"133 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":"86477739","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}