Sung-Jung Hsiao¹, Wen-Tsai Sung^2,*

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 499-517, 2021, DOI:10.32604/iasc.2021.014911

Abstract The purpose of this research is to design an intelligent LED lighting control system with wireless remote control. Since the designed system is mainly based on Android applications, it controls wireless lighting modules through a sever by means of a smartphone, tablet computer, or another handheld mobile device, and the values displayed on the handheld device are used to monitor the establishment and design of the electricity state and multiple-LED light control scenarios in real time. The server is connected with a touch human-machine interface, digital meter, wireless lighting controller, infrared learning module, and other devices through RS-232 and RS-485… 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 >

Fatmah Baothman^*, Sarah Alssagaff, Bayan Ashmeel

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 519-531, 2021, DOI:10.32604/iasc.2021.014828

Abstract The National Center for Education Statistics study reported that 80% of students change their major or institution at least once before getting a degree, which requires a course equivalency process. This error-prone process varies among disciplines, institutions, regions, and countries and requires effort and time. Therefore, this study aims to overcome these issues by developing a decision support tool called TiMELY for automatic Arabic text recognition using artificial intelligence techniques. The developed tool can process a complete document analysis for several course descriptions in multiple file formats, such as Word, Text, Pages, JPEG, GIF, and JPG. We applied a comparative… 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 >

Fahd N. Al-Wesabi¹, Abdelzahir Abdelmaboud^2,*, Adnan A. Zain³, Mohammed M. Almazah⁴, Ammar Zahary⁵

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 483-498, 2021, DOI:10.32604/iasc.2021.014322

Abstract Text information depends primarily on natural languages processing. Improving the security and usability of text information shared through the public internet has therefore been the most demanding problem facing researchers. In contact and knowledge sharing through the Internet, the authentication of content and the identification of digital content are becoming a key problem. Therefore, in this paper, a fragile approach of zero-watermarking based on natural language processing has been developed for authentication of content and prevention of misuse of Arabic texts distributed over the Internet. According to the proposed approach, watermark embedding, and identification was technically carried out such that… More >

Musawwer Khan¹, Islam Ali¹, Waqar Mehmood¹, Wasif Nisar¹, Waqar Aslam², Muhammad Shafiq³, Jin-Ghoo Choi^3,*

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 311-331, 2021, DOI:10.32604/iasc.2021.014280

Abstract Legacy systems deteriorate due to changing business practices and recurrent problems such as high maintenance cost, lack of agility, and degraded performance. Modernization is highly desired especially for those organizations where key business processes are managed by legacy systems. During modernizing, two important aspects are usually ignored, i.e., the transformation of a legacy system to an enterprise solution, and considerations of quality concerns. This overlooking leads to a modernized information system that partially achieves the expected outcome. In this paper, we propose a Capability Maturity Model Integration (CMMI) Compliant Modernization Framework (CCMF) that addresses the problems of legacy systems by… 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 >

Ruijun Chai¹, Sujie Shao^1,*, Shaoyong Guo¹, Yuqi Wang¹, Xuesong Qiu¹, Linna Ruan²

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 471-482, 2021, DOI:10.32604/iasc.2021.013373

Abstract Long Range Radio (LoRa) has become one of the widely adopted Low-Power Wide Area Network (LPWAN) technologies in power Internet of Things (PIoT). Its major advantages include long-distance, large links and low power consumption. However, in LoRa-based PIoT, terminals are often deployed in the wild place and are easily affected by bad weather or disaster, which could easily lead to large-scale operation faults and could seriously affect the normal operation of the network. Simultaneously, the distribution characteristics of outdoor terminals with wide coverage and large links lead to a sharp increase in the difficulty and cost of fault recovery. Given… More >