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  • Open Access

    ARTICLE

    Vibration-Based Fault Diagnosis Study on a Hydraulic Brake System Using Fuzzy Logic with Histogram Features

    Alamelu Manghai T Marimuthu1, Jegadeeshwaran Rakkiyannan2,*, Lakshmipathi Jakkamputi1, Sugumaran Vaithiyanathan1, Sakthivel Gnanasekaran2

    Structural Durability & Health Monitoring, Vol.16, No.4, pp. 383-396, 2022, DOI:10.32604/sdhm.2022.011396 - 03 January 2023

    Abstract The requirement of fault diagnosis in the field of automobiles is growing higher day by day. The reliability of human resources for the fault diagnosis is uncertain. Brakes are one of the major critical components in automobiles that require closer and active observation. This research work demonstrates a fault diagnosis technique for monitoring the hydraulic brake system using vibration analysis. Vibration signals of a rotating element contain dynamic information about its health condition. Hence, the vibration signals were used for the brake fault diagnosis study. The study was carried out on a brake fault diagnosis More >

  • Open Access

    ARTICLE

    CNN Based Multi-Object Segmentation and Feature Fusion for Scene Recognition

    Adnan Ahmed Rafique1, Yazeed Yasin Ghadi2, Suliman A. Alsuhibany3, Samia Allaoua Chelloug4,*, Ahmad Jalal1, Jeongmin Park5

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4657-4675, 2022, DOI:10.32604/cmc.2022.027720 - 28 July 2022

    Abstract Latest advancements in vision technology offer an evident impact on multi-object recognition and scene understanding. Such scene-understanding task is a demanding part of several technologies, like augmented reality-based scene integration, robotic navigation, autonomous driving, and tourist guide. Incorporating visual information in contextually unified segments, convolution neural networks-based approaches will significantly mitigate the clutter, which is usual in classical frameworks during scene understanding. In this paper, we propose a convolutional neural network (CNN) based segmentation method for the recognition of multiple objects in an image. Initially, after acquisition and preprocessing, the image is segmented by using… More >

  • Open Access

    ARTICLE

    Single Point Cutting Tool Fault Diagnosis in Turning Operation Using Reduced Error Pruning Tree Classifier

    E. Akshay1, V. Sugumaran1,*, M. Elangovan2

    Structural Durability & Health Monitoring, Vol.16, No.3, pp. 255-270, 2022, DOI:10.32604/sdhm.2022.0271 - 18 July 2022

    Abstract Tool wear is inevitable in daily machining process since metal cutting process involves the chip rubbing the tool surface after it has been cut by the tool edge. Tool wear dominantly influences the deterioration of surface finish, geometric and dimensional tolerances of the workpiece. Moreover, for complete utilization of cutting tools and reduction of machine downtime during the machining process, it becomes necessary to understand the development of tool wear and predict its status before happening. In this study, tool condition monitoring system was used to monitor the behavior of a single point cutting tool… More >

  • Open Access

    ARTICLE

    Tyre Pressure Supervision of Two Wheeler Using Machine Learning

    Sujit S. Pardeshi1, Abhishek D. Patange1, R. Jegadeeshwaran2,*, Mayur R. Bhosale3

    Structural Durability & Health Monitoring, Vol.16, No.3, pp. 271-290, 2022, DOI:10.32604/sdhm.2022.010622 - 18 July 2022

    Abstract The regulation of tyre pressure is treated as a significant aspect of ‘tyre maintenance’ in the domain of autotronics. The manual supervision of a tyre pressure is typically an ignored task by most of the users. The existing instrumental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires regular manual conduct. Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition. In this perspective, the Machine Learning (ML) approach acts appropriate as it exhibits comparison of specific performance in the past with present, intended for predicting the… More >

  • Open Access

    ARTICLE

    Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

    Hadil Shaiba1, Radwa Marzouk2, Mohamed K Nour3, Noha Negm4,5, Anwer Mustafa Hilal6,*, Abdullah Mohamed7, Abdelwahed Motwakel6, Ishfaq Yaseen6, Abu Sarwar Zamani6, Mohammed Rizwanullah6

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3367-3382, 2022, DOI:10.32604/cmc.2022.030067 - 16 June 2022

    Abstract The agricultural sector’s day-to-day operations, such as irrigation and sowing, are impacted by the weather. Therefore, weather constitutes a key role in all regular human activities. Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from disasters. Rainfall, wind speed, humidity, wind direction, cloud, temperature, and other weather forecasting variables are used in this work for weather prediction. Many research works have been conducted on weather forecasting. The drawbacks of existing approaches are that they are less effective, inaccurate, and time-consuming. To overcome these issues,… More >

  • Open Access

    ARTICLE

    An Efficient Ensemble Model for Various Scale Medical Data

    Heba A. Elzeheiry*, Sherief Barakat, Amira Rezk

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1283-1305, 2022, DOI:10.32604/cmc.2022.027345 - 18 May 2022

    Abstract Electronic Health Records (EHRs) are the digital form of patients’ medical reports or records. EHRs facilitate advanced analytics and aid in better decision-making for clinical data. Medical data are very complicated and using one classification algorithm to reach good results is difficult. For this reason, we use a combination of classification techniques to reach an efficient and accurate classification model. This model combination is called the Ensemble model. We need to predict new medical data with a high accuracy value in a small processing time. We propose a new ensemble model MDRL which is efficient… More >

  • Open Access

    ARTICLE

    Intelligent Sign Language Recognition System for E-Learning Context

    Muhammad Jamil Hussain1, Ahmad Shaoor1, Suliman A. Alsuhibany2, Yazeed Yasin Ghadi3, Tamara al Shloul4, Ahmad Jalal1, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5327-5343, 2022, DOI:10.32604/cmc.2022.025953 - 21 April 2022

    Abstract In this research work, an efficient sign language recognition tool for e-learning has been proposed with a new type of feature set based on angle and lines. This feature set has the ability to increase the overall performance of machine learning algorithms in an efficient way. The hand gesture recognition based on these features has been implemented for usage in real-time. The feature set used hand landmarks, which were generated using media-pipe (MediaPipe) and open computer vision (openCV) on each frame of the incoming video. The overall algorithm has been tested on two well-known ASL-alphabet More >

  • Open Access

    ARTICLE

    Self-Care Assessment for Daily Living Using Machine Learning Mechanism

    Mouazma Batool1, Yazeed Yasin Ghadi2, Suliman A. Alsuhibany3, Tamara al Shloul4, Ahmad Jalal1, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1747-1764, 2022, DOI:10.32604/cmc.2022.025112 - 24 February 2022

    Abstract Nowadays, activities of daily living (ADL) recognition system has been considered an important field of computer vision. Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders. Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth (distance information) and visual cues has greatly enhanced the performance of activity recognition. In this paper, an RGB-D-based ADL recognition system has been presented. Initially, human silhouette has been extracted from the noisy background of RGB and depth images to track More >

  • Open Access

    ARTICLE

    Malware Detection Using Decision Tree Based SVM Classifier for IoT

    Anwer Mustafa Hilal1,*, Siwar Ben Haj Hassine2, Souad Larabi-Marie-Sainte3, Nadhem Nemri2, Mohamed K. Nour4, Abdelwahed Motwakel1, Abu Sarwar Zamani1, Mesfer Al Duhayyim5

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 713-726, 2022, DOI:10.32604/cmc.2022.024501 - 24 February 2022

    Abstract The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology… More >

  • Open Access

    ARTICLE

    Decision Tree Based Key Management for Secure Group Communication

    P. Parthasarathi1,*, S. Shankar2

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 561-575, 2022, DOI:10.32604/csse.2022.019561 - 04 January 2022

    Abstract Group communication is widely used by most of the emerging network applications like telecommunication, video conferencing, simulation applications, distributed and other interactive systems. Secured group communication plays a vital role in case of providing the integrity, authenticity, confidentiality, and availability of the message delivered among the group members with respect to communicate securely between the inter group or else within the group. In secure group communications, the time cost associated with the key updating in the proceedings of the member join and departure is an important aspect of the quality of service, particularly in the… More >

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