Wenwu Tan¹, Jianjun Zhang^1,*, Xing Liu¹, Jiang Wu¹, Yifu Sheng¹, Ke Xiao², Li Wang², Haijun Lin¹, Guang Sun³, Peng Guo⁴

Journal on Big Data, Vol.5, pp. 85-97, 2023, DOI:10.32604/jbd.2022.030908

Abstract At present, water pollution has become an important factor affecting and restricting national and regional economic development. Total phosphorus is one of the main sources of water pollution and eutrophication, so the prediction of total phosphorus in water quality has good research significance. This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform. By constructing the attribute object mapping relationship, the correlation between the two indicators was analyzed and used to predict the future data. Firstly, the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers… More >

Krish R. Mehta, K. Jayant Naidu, Madhav Baheti, Dev Parmar, A. Sharmila^*

Journal on Internet of Things, Vol.5, pp. 45-55, 2023, DOI:10.32604/jiot.2023.043102

Abstract Farming has been the most prominent and fundamental activity for generations. As the population has been multiplying exponentially, the demand for agricultural yield is growing relentlessly. Such high demand in production through traditional farming methodologies often falls short in terms of efficiency due to the limitations of manual labour. In the era of digitization, smart agricultural solutions have been emerging through the windows of Internet of Things and Artificial Intelligence to improve resource management, optimize the process of farming and enhance the yield of crops, hence, ensuring sustainable growth of the increasing production. By implementing modern technologies in the field… More >

Mingyu Lee¹, Jiho Shin², Jung Taek Seo^3,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2427-2442, 2023, DOI:10.32604/cmc.2023.041774

Abstract Cyberattacks targeting industrial control systems (ICS) are becoming more sophisticated and advanced than in the past. A programmable logic controller (PLC), a core component of ICS, controls and monitors sensors and actuators in the field. However, PLC has memory attack threats such as program injection and manipulation, which has long been a major target for attackers, and it is important to detect these attacks for ICS security. To detect PLC memory attacks, a security system is required to acquire and monitor PLC memory directly. In addition, the performance impact of the security system on the PLC makes it difficult to… More >

Medhat A. Tawfeek^1,2, Nacim Yanes^3,4, Leila Jamel^5,*, Ghadah Aldehim⁵, Mahmood A. Mahmood^1,6

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2545-2564, 2023, DOI:10.32604/cmc.2023.042179

Abstract The trend towards smart greenhouses stems from various factors, including a lack of agricultural land area owing to population concentration and housing construction on agricultural land, as well as water shortages. This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers. The proposed model uses a one-dimensional convolutional neural network (CNN) deep learning model to control the growth of strategic crops, including cucumber, pepper, tomato, and bean. The proposed model uses the Internet of Things (IoT) to collect data on agricultural operations and then uses this data… More >

Muhammad Tahir^1,2,*, Mingchu Li^1,2, Irfan Khan^1,2, Salman A. Al Qahtani³, Rubia Fatima⁴, Javed Ali Khan⁵, Muhammad Shahid Anwar⁶

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2529-2544, 2023, DOI:10.32604/cmc.2023.042403

Abstract Real-time health data monitoring is pivotal for bolstering road services’ safety, intelligence, and efficiency within the Internet of Health Things (IoHT) framework. Yet, delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems. We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this. This strategy is devised to streamline the data retrieval path, subsequently diminishing network strain. Crafting an adept cache processing scheme poses its own set of challenges, especially given the transient nature of monitoring data and the imperative for swift data transmission, intertwined with resource allocation tactics.… More >

Zhicheng Liu¹, Long Zhao^1,*, Guanru Wen¹, Peng Yuan², Qiu Jin¹

Structural Durability & Health Monitoring, Vol.17, No.6, pp. 541-555, 2023, DOI:10.32604/sdhm.2023.029760

Abstract The displacement of transmission tower feet can seriously affect the safe operation of the tower, and the accuracy of structural health monitoring methods is limited at the present stage. The application of deep learning method provides new ideas for structural health monitoring of towers, but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning (DL). In this paper, we propose a DT-DL based tower foot displacement monitoring method, which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element method. Then the vibration signal… More > Graphic Abstract

Fadhil Mukhlif^1,*, Norafida Ithnin¹, Roobaea Alroobaea², Sultan Algarni³, Wael Y. Alghamdi², Ibrahim Hashem⁴

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 583-599, 2023, DOI:10.32604/cmc.2023.038757

Abstract The World Health Organization (WHO) refers to the 2019 new coronavirus epidemic as COVID-19, and it has caused an unprecedented global crisis for several nations. Nearly every country around the globe is now very concerned about the effects of the COVID-19 outbreaks, which were previously only experienced by Chinese residents. Most of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare systems. Every time the pandemic surprises them by providing new values for various parameters, all the connected research… More >

Mesfer Al Duhayyim¹, Majdy M. Eltahir², Ola Abdelgney Omer Ali³, Amani Abdulrahman Albraikan⁴, Fahd N. Al-Wesabi², Anwer Mustafa Hilal^5,*, Manar Ahmed Hamza⁵, Mohammed Rizwanullah⁵

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1355-1371, 2023, DOI:10.32604/cmc.2023.024198

Abstract Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development, regulatory measures, and their implementation elevating the environment. Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe. Therefore, the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent. Therefore, this paper focuses on the design of automated forest fire detection using a fusion-based deep learning (AFFD-FDL) model for environmental monitoring. The AFFD-FDL technique involves the design… More >

Yuling Dou¹, Fengqin Yao¹, Xiandong Wang¹, Liang Qu², Long Chen³, Zhiwei Xu⁴, Laihui Ding⁴, Leon Bevan Bullock¹, Guoqiang Zhong¹, Shengke Wang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 1001-1014, 2024, DOI:10.32604/cmes.2023.027764

Abstract UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience, low cost and convenient maintenance. In marine environmental monitoring, the similarity between objects such as oil spill and sea surface, Spartina alterniflora and algae is high, and the effect of the general segmentation algorithm is poor, which brings new challenges to the segmentation of UAV marine images. Panoramic segmentation can do object detection and semantic segmentation at the same time, which can well solve the polymorphism problem of objects in UAV ocean images. Currently, there are few studies on UAV marine image recognition… More >

Saqlain Abbas^1,2,*, Zulkarnain Abbas³, Xiaotong Tu⁴, Yanping Zhu²

Journal on Artificial Intelligence, Vol.5, pp. 43-56, 2023, DOI:10.32604/jai.2023.040948

Abstract Structural health monitoring (SHM) is considered an effective approach to analyze the efficient working of several mechanical components. For this purpose, ultrasonic guided waves can cover long-distance and assess large infrastructures in just a single test using a small number of transducers. However, the working of the SHM mechanism can be affected by some sources of variations (i.e., environmental). To improve the final results of ultrasonic guided wave inspections, it is necessary to highlight and attenuate these environmental variations. The loading parameters, temperature and humidity have been recognized as the core environmental sources of variations that affect the SHM sensing… More >