Yanyun Jiang¹, Yuanjie Zheng^1,2,*, Xiaodan Sui¹, Wanzhen Jiao³, Yunlong He⁴, Weikuan Jia¹

Computer Systems Science and Engineering, Vol.36, No.3, pp. 537-549, 2021, DOI:10.32604/csse.2021.014578

Abstract Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep learning framework called “Adversarial Segmentation… More >

Guang Sun¹, Jingjing Lin^1,*, Chen Yang¹, Xiangyang Yin¹, Ziyu Li¹, Peng Guo^1,2, Junqi Sun³, Xiaoping Fan¹, Bin Pan¹

Computer Systems Science and Engineering, Vol.36, No.3, pp. 509-520, 2021, DOI:10.32604/csse.2021.014189

Abstract Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean… More >

Wajdi Alhakami¹, Abdullah Baz², Hosam Alhakami³, Masood Ahmad⁴, Raees Ahmad Khan^4,*

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 409-424, 2021, DOI:10.32604/iasc.2021.015351

Abstract Healthcare devices play an essential role in tracking and managing patient’s safety. However, the complexities of healthcare devices often remain ambiguous due to hardware, software, or the interoperable healthcare system problems. There are essentially two critical factors for targeting healthcare: First, healthcare data is the most valuable entity on the dark web; and the second, it is the easiest to hack. Data pilferage has become a major hazard for healthcare organizations as the hackers now demand ransom and threaten to disclose the sensitive data if not paid within the stipulated timeline. The present study enlists a thorough research on the… More >

Sung-Jung Hsiao¹, Wen-Tsai Sung^2,*

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 359-377, 2021, DOI:10.32604/iasc.2021.014905

Abstract Traditional agriculture has to face different climatic factors, resulting in unstable crop yields, whereas a plant factory is indoors, thus avoiding different natural disasters and insect pests. It is also available for bulk production in all-weather all year-round, guaranteeing crop quality. This study uses a wireless sensor network (WSN) architecture for multi-monitoring of a plant environment, aiming to improve its growing environment quality. In terms of monitoring environment, the sensor modules at the end device nodes are combined with Arduino for capturing the sensor values, which are transmitted by wireless signal transmission. The data of wireless signals are further transmitted… More >

Rajesh Kaluri¹, Dharmendra Singh Rajput¹, Qin Xin^2,*, Kuruva Lakshmanna¹, Sweta Bhattacharya¹, Thippa Reddy Gadekallu¹, Praveen Kumar Reddy Maddikunta¹

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 453-469, 2021, DOI:10.32604/iasc.2021.014369

Abstract Internet of Things (IoT) and related applications have successfully contributed towards enhancing the value of life in this planet. The advanced wireless sensor networks and its revolutionary computational capabilities have enabled various IoT applications become the next frontier, touching almost all domains of life. With this enormous progress, energy optimization has also become a primary concern with the need to attend to green technologies. The present study focuses on the predictions pertinent to the sustainability of battery life in IoT frameworks in the marine environment. The data used is a publicly available dataset collected from the Chicago district beach water.… More >

Arif Iqbal^*, Abdul Basit, Imran Ali, Junaid Babar, Ihsan Ullah

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 299-309, 2021, DOI:10.32604/iasc.2021.014250

Abstract Automated meter reading has recently been adopted by utility service providers for improving the reading and billing process. Images captured during meter reading are incorporated in consumer bills to prevent reporting false reading and ensure transparency. The availability of images captured during the meter reading process presents the potential of completely automating the meter reading process. This paper proposes a convolutional network-based multi-box model for the automatic meter reading. The proposed research leverages the inception model with a single shot detector to achieve high accuracy and efficiency compared to the existing state-of-the-art machine learning methods. We tested the multi-box detector… More >

Huaning Song¹, Jiansheng Peng^1,2,*, Nianyang Tuo¹, Haiying Xia², Yiyun Peng³

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 581-592, 2021, DOI:10.32604/iasc.2021.014152

Abstract Accurate identification of sugarcane buds, as one of the key technologies in sugarcane plantation, becomes very necessary for its mechanized and intelligent advancements. In the traditional methods of sugarcane bud recognition, a significant amount of algorithms work goes into the location and recognition of sugarcane bud images. A Convolutional Neural Network (CNN) for classifying the bud conditions is proposed in this paper. Firstly, we convert the colorful sugarcane images into gray ones, unify the size of them, and make a TFRecord format data set, which contains 1100 positive samples and 1100 negative samples. Then, a CNN mode is developed to… More >

Yajun Zhang^1,*, Jianjun Yi¹, Jiahao Zhang¹, Yuanhao Chen¹, Liang He²

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 425-439, 2021, DOI:10.32604/iasc.2021.013795

Abstract Image recognition algorithms based on deep learning have been widely developed in recent years owing to their capability of automatically capturing recognition features from image datasets and constantly improving the accuracy and efficiency of the image recognition process. However, the task of training deep learning networks is time-consuming and expensive because large training datasets are generally required, and extensive manpower is needed to annotate each of the images in the training dataset to support the supervised learning process. This task is particularly arduous when the image scenes involve randomly stacked objects. The present work addresses this issue by developing a… More >

S. Praveen Kumar^1,*, S. Appavu alias Balamurugan², S. Kannan³

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 333-346, 2021, DOI:10.32604/iasc.2021.013310

Abstract The establishment of multi-channel routing systems in WSNs has shown the reliability of the transmission across several channels. Meeting the service quality needs of all the applications is still a challenging task. Various surveys carried out, have suggested for the development of user-oriented Wireless Sensor Network (WSN) architectures. Throughout WSNs, the Optimized Link State Routing (OLSR) and the Adhoc On Demand Vector (AODV) are the multi-way routing systems that exhibit the reliability of the multipath delivery to fulfill the customer service quality requirements. The topology is modified only on requests, by selecting the best route based on the route weights… More >

Aolin Sun, Xiang Cao^*, Xu Xiao, Liwen Xu

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 551-564, 2021, DOI:10.32604/iasc.2021.01008

Abstract An essential issue in a target search is safe navigation while quickly finding targets. In order to improve the efficiency of a target search and the smoothness of AUV’s (Autonomous Underwater Vehicle) trajectory, a fuzzy-based bio-inspired neural network approach is proposed in this paper. A bio-inspired neural network is applied to a multi-AUV target search, which can effectively plan search paths. In the meantime, a fuzzy algorithm is introduced into the bio-inspired neural network to make the trajectory of AUV obstacle avoidance smoother. Unlike other algorithms that need repeated training in the parameters selection, the proposed approach obtains all the… More >