Intelligent Automation and Soft Computing最新文献

{"title":"Target Projection Feature Matching Based Deep ANN with LSTM for Lung Cancer Prediction","authors":"C. Thaventhiran, K. R. Sekar","doi":"10.32604/iasc.2022.019546","DOIUrl":"https://doi.org/10.32604/iasc.2022.019546","url":null,"abstract":"Prediction of lung cancer at early stages is essential for diagnosing and prescribing the correct treatment. With the continuous development of medical data in healthcare services, Lung cancer prediction is the most concerning area of interest. Therefore, early prediction of cancer helps in reducing the mortality rate of humans. The existing techniques are time-consuming and have very low accuracy. The proposed work introduces a novel technique called Target Projection Feature Matched Deep Artificial Neural Network with LSTM (TPFMDANNLSTM) for accurate lung cancer prediction with minimum time consumption. The proposed deep learning model consists of multiple layers to learn the given input patient data. Different processes are carried out at each layer to predict lung cancer at an earlier stage. The input layer of the deep neural network receives the data and associated features and sends them to the hidden layer. The first hidden layer performs the feature selection process using Target Projection matching to identify the relevant features for accurate disease prediction with minimum time consumption. Hidden layer 2 performs the patient Data Classification based on Czekanowski's dice similarity coefficient with the selected relevant features from the previous layer to predict lung cancer. The factors considered for performance evaluation of the proposed technique with the existing state of the art approaches include prediction accuracy, false-positive rate and prediction time. Lunar 16 Lung Cancer dataset consisting of patient data is used for evaluation. The obtained results show that the proposed TPFMDANN-LSTM technique achieves higher prediction accuracy with minimum time consumption and less false positive rate than the state-of-the-art methods. The experimental results reveal that the TPFMDANN-LSTM technique performs better with a 6% improvement in prediction accuracy, 36% reduction of false positives, and 16% faster prediction time for lung cancer detection compared to existing works.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"61 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90999917","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":"Process Optimization Method for Day Ward Based on Bayesian Decision-Tree","authors":"Ting Chen, Kai Pu, Lanzheng Bian, M. Rao, Jing Hu, Rugang Lu, Jinyue Xia","doi":"10.32604/iasc.2022.022510","DOIUrl":"https://doi.org/10.32604/iasc.2022.022510","url":null,"abstract":"The day surgery management mode is mainly decentralized management, with clinical departments as the unit, and with reference to the experience of inter project operation management in benchmark hospitals, the empirical management is implemented. With the development of day surgery, the extensive decentralized management mode has been unable to meet the needs of the current day surgery development situation. At first, the paper carefully analyzes the existing problems in the day surgery process in the day ward of the Children’s Hospital of Nanjing Medical University. And then, the concerns of doctors, nurses, anesthesiologists and other hospital staff in day ward, children and their parents are considered. Based on Bayesian decision-making theory, this paper optimizes the pre-admission evaluation of sick children and hospitalization process for day surgery in Nanjing Children’s Hospital. Moreover, the specific optimization process is designed. After the optimizations, it can be seen that the time consumption of each step of the hospitalization process in day surgery is reduced. Thus, the hospital stay of sick children are significantly reduced, and the operation cost is reduced. In addition, the first preoperative preparation time and the average time of receiving children were reduced in day ward. The satisfaction of children’s parents was significantly improved.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"8 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87089081","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":"Integration of Renewable Energy Sources into the Smart Grid Using Enhanced SCA","authors":"S. Karimulla, K. Ravi","doi":"10.32604/iasc.2022.022953","DOIUrl":"https://doi.org/10.32604/iasc.2022.022953","url":null,"abstract":"The usage of energy in everyday life is growing day by day as a result of the rapid growth in the human population. One solution is to increase electricity generation to the same extent as the human population, but this is usually practically impossible. As the population is increasing, the need for electrical usage is also increasing. Therefore, smart grids play an important role in making efficient use of existing energy sources like solar, wind and battery storage systems. By managing demand, the minimization of power consumption and its consequent costs. On the load side, residential and commercial types use a large amount of the total energy produced by renewable energy sources. As a result, in this work, we use DSM (Demand-side Management) to schedule various appliances on loads to minimize energy consumption. Smart grid plays a major role in the integration of renewable energy sources as well as in the minimization of cost of energy (COE). Smart meters like advanced metering infrastructure are also used to reduce load demand. Therefore, in this work, an Enhanced sine cosine algorithm (ESCA) is proposed to solve the optimization problem. The proposed method consists of loads like residential and commercial types. The proposed method considered the comparison with the Genetic Algorithm (GA) and Ant colony optimization (ACO). Simulation results were carried out by using MATLAB software. The results show the Enhanced sine cosine algorithm (ESCA) is best when compared to other algorithms like GA and ACO in the minimization of the cost of energy.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"18 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82098590","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":"Developing Secure Healthcare Video Consultations for Corona Virus (COVID-19) Pandemic","authors":"Mohammed A. Alzain, Jehad F. Al-Amri, Ahmed I. Sallam, Emad Sami Jaha, Sultan S. Alshamrani, Hala S. El-sayed, Osama S. Faragallah","doi":"10.32604/iasc.2022.020137","DOIUrl":"https://doi.org/10.32604/iasc.2022.020137","url":null,"abstract":"Many health networks became increasingly interactive in implementing a consulting approach to telemedicine before the COVID-19 pandemic. To mitigate patient trafficking and reduce the virus exposure in health centers, several GPs, physicians and people in the video were consulted during the pandemic at the start. Video and smartphone consultations will allow well-insulated and high-risk medical practitioners to maintain their patient care security. Video appointments include diabetes, obesity, hypertension, stroke, mental health, chemotherapy and chronic pain. Many urgent diseases, including an emergency triage for the eye, may also be used for online consultations and triages. The COVID-19 pandemic shows that healthcare option for healthy healthcare and the potential to increase to a minimum, such as video consultations, have grown quickly. The dissemination of COVID-19 viruses now aims at extending the use of Video-Health Consultations by exchanging insights and simulations of health consultations and saving costs and healthcare practices as a consequence of the COVID-19 pandemic. Our paper focuses on video consulting privacy. This essay further presents the advantages and inconveniences of video consultation and its implementation. This paper suggests the most recent video encryption method known as high efficiency video coding selective encryption (HEVC SE). Our video consultation schema has been improved to secure video streaming on a low calculation overhead, with the same bit rate and to ensure compatibility with the video format. The contribution is made with RC5, a low complexity computer, to encrypt subsets of bin-strings binarized in the HEVC sense using the context adaptive binary arithmetic coding (CABAC) method through the bypass binary arithmetic coding. This sequence of binstrings consists of a non-zero differential transforming cosine (DCT) coefficient bit, MVD sign bits, remainder absolute DCT suffixes and absolute MVD suffixes. This paper also examines the efficiency assessment of the use of the RC5 with its modes of operations in the HEVC CABAC SE proposed. This study chooses the best operating mode for RC5 to be used for the healthcare video consultation application. Security analysis, such as histogram analysis, correlation coefficient testing and key sensitivity testing, is presented to protect against brute force and statistical attacks for the proposed schema.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"15 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82146866","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":"Prevention of Runtime Malware Injection Attack in Cloud Using Unsupervised Learning","authors":"M. Prabhavathy, S. Umamaheswari","doi":"10.32604/iasc.2022.018257","DOIUrl":"https://doi.org/10.32604/iasc.2022.018257","url":null,"abstract":"Cloud computing utilizes various Internet-based technologies to enhance the Internet user experience. Cloud systems are on the rise, as this technology has completely revolutionized the digital industry. Currently, many users rely on cloud-based solutions to acquire business information and knowledge. As a result, cloud computing services such as SaaS and PaaS store a warehouse of sensitive and valuable information, which has turned the cloud systems into the obvious target for many malware creators and hackers. These malicious attackers attempt to gain illegal access to a myriad of valuable information such as user personal information, password, credit/debit card numbers, etc., from systems as the unsecured e-learning ones. As an important part of cloud services, security is needed to protect business customers and users from unauthorized threats. This paper aims to identify malware that attacks cloud-based software solutions using an unsupervised learning model with fixed-weight Hamming and Mexiannet. Different types of attack methodologies and various ways of malicious instructions targeting unknown files in cloud services are investigated. The result and analysis in this study provide an evolution of the unsupervised learning detection algorithm with an accuracy of 94.05%.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"2 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84629289","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":"Robust Speed Regulation of Induction Motor Subjected to Unknown Load Torque","authors":"H. Abdelfattah, A. Abouelsoud, F. Banakhr, M. I. Mosaad","doi":"10.32604/iasc.2022.018765","DOIUrl":"https://doi.org/10.32604/iasc.2022.018765","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"24 Sup10 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78041785","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":"Utilization of Deep Learning-Based Crowd Analysis for Safety Surveillance and Spread Control of COVID-19 Pandemic","authors":"Osama S. Faragallah, Sultan S. Alshamrani, Heba M. El-Hoseny, Mohammed A. Alzain, Emad Sami Jaha, Hala S. El-sayed","doi":"10.32604/iasc.2022.020330","DOIUrl":"https://doi.org/10.32604/iasc.2022.020330","url":null,"abstract":"Crowd monitoring analysis has become an important challenge in academic researches ranging from surveillance equipment to people behavior using different algorithms. The crowd counting schemes can be typically processed in two steps, the images ground truth density maps which are obtained from ground truth density map creation and the deep learning to estimate density map from density map estimation. The pandemic of COVID-19 has changed our world in few months and has put the normal human life to a halt due to its rapid spread and high danger. Therefore, several precautions are taken into account during COVID-19 to slowdown the new cases rate like maintaining social distancing via crowd estimation. This manuscript presents an efficient detection model for the crowd counting and social distancing between visitors in the two holy mosques, Al Masjid Al Haram in Mecca and the Prophet's Mosque in Medina. Also, the manuscript develops a secure crowd monitoring structure based on the convolutional neural network (CNN) model using real datasets of images for the two holy mosques. The proposed framework is divided into two procedures, crowd counting and crowd recognition using datasets of different densities. To confirm the effectiveness of the proposed model, some metrics are employed for crowd analysis, which proves the monitoring efficiency of the proposed model with superior accuracy. Also, it is very adaptive to different crowd density levels and robust to scale changes in several places.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"52 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78308159","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 Deep Learning to Distinguish COVID-19 from Others Pneumonia Cases","authors":"S. Gazzah, Rida Bayi, S. Kaloun, O. Bencharef","doi":"10.32604/iasc.2022.019360","DOIUrl":"https://doi.org/10.32604/iasc.2022.019360","url":null,"abstract":"A new virus called SARS-CoV-2 appeared at the end of space 2019 in Wuhan, China. This virus immediately spread throughout the world due to its highly contagious nature. Moreover, SARS-CoV-2 has changed the way of our life and has caused a huge economic and public health disaster. Therefore, it is urgent to identify positive cases as soon as possible and treat them as isolated. Automatic detection of viruses using computer vision and machine learning will be a valuable contribution to detecting and limiting the spread of this epidemic. The delay introduction of X-ray technology as diagnostic tool limited our ability to distinguish COVID-19 from other pneumonia cases. These images can be feed into a machine learning system that can be trained to detect lung infections, which can arise in acute pneumonia. However, some efforts attempt to detect SARS-CoV-2 using binary data and such combinations, which may leading to confusion. Indeed, in this case, the classifier's training minimizes the intra-class similarity between pneumonia in COVID-19 and typical pneumonia. Due to the fact that in addition to SARS-CoV-2 pneumonia, there are two other common types: viral and bacterial pneumonia. They all appear in similar shapes, thus making them difficult to distinguish. This research proposed a deep multi-layered classification of pneumonia based on a neural network using chest X-ray images. The experiment was conducted using the combination of two images from an open-source dataset collected by Cohen GP and Kagel. The data consists of 4 categories: normal pneumonia, bacterial pneumonia, viral pneumonia, and COVID19, for 2,433 images. We considered two architectures: Xception and ResNet50. In addition, a comparison was made between the two models. The pre-trained Xception model in 20 epochs provided a classification accuracy of 86%.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"15 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87757190","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":"An Enhanced Re-Ranking Model for Person Re-Identification","authors":"Jayavarthini Chockalingam, Malathy Chidambaranathan","doi":"10.32604/iasc.2022.024142","DOIUrl":"https://doi.org/10.32604/iasc.2022.024142","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"37 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90605737","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":"Cyber-Attack Detection and Mitigation Using SVM for 5G Network","authors":"Sulaiman Yousef Alshunaifi, Shailendra Mishra, Mohammed Alshehri","doi":"10.32604/iasc.2022.019121","DOIUrl":"https://doi.org/10.32604/iasc.2022.019121","url":null,"abstract":"5G technology is widely seen as a game-changer for the IT and telecommunications sectors. Benefits expected from 5G include lower latency, higher capacity, and greater levels of bandwidth. 5G also has the potential to provide additional bandwidth in terms of AI support, further increasing the benefits to the IT and telecom sectors. There are many security threats and organizational vulnerabilities that can be exploited by fraudsters to take over or damage corporate data. This research addresses cybersecurity issues and vulnerabilities in 4G (LTE) and 5G technology. The findings in this research were obtained by using primary and secondary data. Secondary data was collected by reviewing literature and conducting surveys. Primary data were obtained by conducting an experimental simulation using the support vector machine (SVM) approach. The results show that cybersecurity issues related to 4G and 5G need to be addressed to ensure integrity, confidentiality, and availability. All enterprises are constantly exposed to a variety of risks. Also implemented an efficient SVM-based attack detection and mitigation system for 5G network. The proposed intrusion detection system defends against security attacks in the 5G environment. The results show that the throughput and intrusion detection rate is higher while the latency, energy consumption, and packet loss ratio are low, indicating that the proposed intrusion detection and defense system has achieved better QoS. The security solutions are fast and effective in detecting and mitigating cyber-attacks.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"411 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75804426","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}