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  • Open Access

    ARTICLE

    A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

    Md. Moddassir Alam1, Md Mottahir Alam2, Muhammad Moinuddin2,3, Mohammad Tauheed Ahmad4, Jabir Hakami5, Anis Ahmad Chaudhary6, Asif Irshad Khan7, Tauheed Khan Mohd8,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4553-4571, 2023, DOI:10.32604/cmc.2023.032118

    Abstract Artificial Intelligence (AI) is finding increasing application in healthcare monitoring. Machine learning systems are utilized for monitoring patient health through the use of IoT sensor, which keep track of the physiological state by way of various health data. Thus, early detection of any disease or derangement can aid doctors in saving patients’ lives. However, there are some challenges associated with predicting health status using the common algorithms, such as time requirements, chances of errors, and improper classification. We propose an Artificial Krill Herd based on the Random Forest (AKHRF) technique for monitoring patients’ health and eliciting an optimal prescription based… More >

  • Open Access

    ARTICLE

    Health Monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms

    Sonali S. Patil, Sujit S. Pardeshi, Abhishek D. Patange*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 177-199, 2023, DOI:10.32604/cmes.2023.025516

    Abstract In-process damage to a cutting tool degrades the surface finish of the job shaped by machining and causes a significant financial loss. This stimulates the need for Tool Condition Monitoring (TCM) to assist detection of failure before it extends to the worse phase. Machine Learning (ML) based TCM has been extensively explored in the last decade. However, most of the research is now directed toward Deep Learning (DL). The “Deep” formulation, hierarchical compositionality, distributed representation and end-to-end learning of Neural Nets need to be explored to create a generalized TCM framework to perform efficiently in a high-noise environment of cross-domain… More >

  • Open Access

    ARTICLE

    Outlier Detection and Forecasting for Bridge Health Monitoring Based on Time Series Intervention Analysis

    Bing Qu*, Ping Liao, Yaolong Huang

    Structural Durability & Health Monitoring, Vol.16, No.4, pp. 323-341, 2022, DOI:10.32604/sdhm.2022.021446

    Abstract The method of time series analysis, applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior, stands out as a novel and viable research direction for bridge state assessment. However, outliers inevitably exist in the monitoring data due to various interventions, which reduce the precision of model fitting and affect the forecasting results. Therefore, the identification of outliers is crucial for the accurate interpretation of the monitoring data. In this study, a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory, and the forecasting… More >

  • Open Access

    ARTICLE

    Experimental Investigation of Performance Characteristics of PZT-5A with Application to Fault Diagnosis

    Saqlain Abbas1,2, Zulkarnain Abbas3,4,*, Yanping Zhu2, Waqas Tariq Toor5, Xiaotong Tu6

    Structural Durability & Health Monitoring, Vol.16, No.4, pp. 307-321, 2022, DOI:10.32604/sdhm.2022.015266

    Abstract In the previous couple of decades, techniques to reap energy and empower low voltage electronic devices have received outstanding attention. Most of the methods based on the piezoelectric effect to harvest the energy from ambient vibrations have been revolutionized. There’s an absence of experiment-based investigation which incorporates the microstructure analysis and crystal morphology of those energy harvest home materials. Moreover, the impact of variable mechanical and thermal load conditions has seldom been studied within the previous literature to utilize the effectiveness of those materials in several practical applications like structural health monitoring (SHM), etc. In the proposed research work, scanning… More >

  • Open Access

    ARTICLE

    Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

    P. M. Arunkumar1, Mehedi Masud2, Sultan Aljahdali2, Mohamed Abouhawwash3,4,*

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2265-2280, 2023, DOI:10.32604/csse.2023.032571

    Abstract Nowadays, the cloud environment faces numerous issues like synchronizing information before the switch over the data migration. The requirement for a centralized internet of things (IoT)-based system has been restricted to some extent. Due to low scalability on security considerations, the cloud seems uninteresting. Since healthcare networks demand computer operations on large amounts of data, the sensitivity of device latency evolved among health networks is a challenging issue. In comparison to cloud domains, the new paradigms of fog computing give fresh alternatives by bringing resources closer to users by providing low latency and energy-efficient data processing solutions. Previous fog computing… More >

  • Open Access

    ARTICLE

    Development of IoT-Based Condition Monitoring System for Bridges

    Sheetal A. Singh, Suresh S. Balpande*

    Sound & Vibration, Vol.56, No.3, pp. 209-220, 2022, DOI:10.32604/sv.2022.014518

    Abstract As of April 2019, India has 1,42,126 kilometres of National Highways and 67,368 kilometres of railway tracks that reach even the most remote parts of the country. Bridges are critical for both passenger and freight movement in the country. Because bridges play such an important part in the transportation system, their safety and upkeep must be prioritized. Manual Condition Monitoring has the disadvantage of being sluggish, unreliable, and ineffi- cient. The Internet of Things has given structural monitoring a boost. Significant decreases in the cost of electronics and connection, together with the expansion of cloud platforms, have made it possible… More >

  • Open Access

    ARTICLE

    Data Reliability and Sensors Lifetime in Bridge Health Monitoring using LoRaWAN-Zigbee

    Awad Ali1,*, Reyazur Rashid Irshad1, Ahmed Abdu Alattaab1, Aamir Fatahayab2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2663-2678, 2022, DOI:10.32604/cmc.2022.028503

    Abstract The Wireless Sensor Network (WSN) is regarded as the fastest expanding technological trend in recent years due its application in a variety of sectors. In the monitoring region, several sensor nodes with various sensing capabilities are installed to gather appropriate data and communicate it to the gateway. The proposed system of the heterogeneous WSN employing LoRaWAN-Zigbee based hybrid communication is explored in this research study. To communicate in a network, two Long–Range Wide Area Network (LoRaWAN) sensor clusters and two Zigbee sensor clusters are employed, together with two Zigbee and LoRaWAN converters. The suggested Golden eagle shepherd optimization (GESO) method… More >

  • Open Access

    ARTICLE

    A Smart Room to Promote Autonomy of Disabled People due to Stroke

    Moeiz Miraoui1,2,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 677-692, 2023, DOI:10.32604/csse.2023.025799

    Abstract A cerebral vascular accident, known as common language stroke, is one of the main causes of mortality and remains the primary cause of acquired disabilities in adults. Those disabled people spend most of their time at home in their living rooms. In most cases, appliances of a living room (TV, light, cooler/heater, window blinds, etc.) are generally controlled by direct manipulation of a set of remote controls. Handling many remote controls can be disturbing and inappropriate for these people. In addition, in many cases these people could be alone at home and must open the door for visitors after their… More >

  • Open Access

    ARTICLE

    Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

    G. Ravikumar1, K. Venkatachalam2, Mohammed A. AlZain3, Mehedi Masud4, Mohamed Abouhawwash5,6,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 945-959, 2023, DOI:10.32604/csse.2023.024605

    Abstract Sleep apnea syndrome (SAS) is a breathing disorder while a person is asleep. The traditional method for examining SAS is Polysomnography (PSG). The standard procedure of PSG requires complete overnight observation in a laboratory. PSG typically provides accurate results, but it is expensive and time consuming. However, for people with Sleep apnea (SA), available beds and laboratories are limited. Resultantly, it may produce inaccurate diagnosis. Thus, this paper proposes the Internet of Medical Things (IoMT) framework with a machine learning concept of fully connected neural network (FCNN) with k-nearest neighbor (k-NN) classifier. This paper describes smart monitoring of a patient’s… More >

  • Open Access

    ARTICLE

    IoT Based Disease Prediction Using Mapreduce and LSQN3 Techniques

    R. Gopi1,*, S. Veena2, S. Balasubramanian3, D. Ramya4, P. Ilanchezhian5, A. Harshavardhan6, Zatin Gupta7

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1215-1230, 2022, DOI:10.32604/iasc.2022.025792

    Abstract In this modern era, the transformation of conventional objects into smart ones via internet vitality, data management, together with many more are the main aim of the Internet of Things (IoT) centered Big Data (BD) analysis. In the past few years, significant augmentation in the IoT-centered Healthcare (HC) monitoring can be seen. Nevertheless, the merging of health-specific parameters along with IoT-centric Health Monitoring (HM) systems with BD handling ability is turned out to be a complicated research scope. With the aid of Map-Reduce and LSQN3 techniques, this paper proposed IoT devices in Wireless Sensors Networks (WSN) centered BD Mining (BDM)… More >

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