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Object tracking is one of the major tasks for mobile robots in many real-world applications. Also, artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation. In contrast to previous simulation studies, this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue (RGB) colored objects in a real experimental field. Moreover, a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative (PID) schemes to achieve accurate tracking results, considering robot command delay and tolerance errors. The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators. Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker. Classical PID control failed to handle some tracking scenarios in this study. The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets, while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm, respectively. These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads. [ABSTRACT FROM AUTHOR]

Cardiac pacemakers are used for handling bradycardia, which is a cardiac rhythm of usually less than 60 beats per minute. Therapeutic dual-sensor pacemakers aim to preserve or restore the normal electromechanical activity of the cardiac muscle. In this article, a novel intelligent controller has been developed for implanted dual-sensor cardiac pacemakers. The developed controller is mainly based on intuitionistic fuzzy logic (IFL). The main advantage of the developed IFL controller is its ability to merge the qualitative expert knowledge of cardiologists in the proposed design of controlled pacemakers. Additionally, the implication of non-membership functions with the uncertainty term plays a key role in the developed fuzzy controller for improving the performance of a cardiac pacemaker over other fuzzy control schemes in previous studies. Moreover, the proposed pacemaker control system is efficient for managing all health-status conditions and constraints during the different daily activities of cardiac patients. Consequently, the healthcare of patients with implanted dual-sensor pacemakers can be efficiently improved intuitively. [ABSTRACT FROM AUTHOR]

E-learning behavior data indicates several students' activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures. This article proposes a new analytics system to support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments. The proposed e-learning analytics system includes a new deep forest model. It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks. The developed forest model can analyze each student's activities during the use of an e-learning platform to give accurate expectations of the student's performance before ending the semester and/or the final exam. Experiments have been conducted on the Open University Learning Analytics Dataset (OULAD) of 32,593 students. Our proposed deep model showed a competitive accuracy score of 98.0% compared to artificial intelligence-based models, such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in previous studies. That allows academic advisors to support expected failed students significantly and improve their academic level at the right time. Consequently, the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework. [ABSTRACT FROM AUTHOR]

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability worldwide. This paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status conditions. The infusion of vasoactive drugs to patients endures various issues, such as variation of sensitivity and noise, which require effective and powerful systems to ensure robustness and good performance. The developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy (IT2F) logic and teaching-learning-based optimization (TLBO) algorithm. This networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs successfully. To avoid the manual selection of control parameter values, the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F controller. The simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances. A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy controllers.Moreover, the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients' sensitivity to infused vasoactive drugs in a noisy environment. [ABSTRACT FROM AUTHOR]

Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded random forest stages is automatically optimized. This study uses a public dataset of fall detection samples called SmartFall to validate the developed fall detection system. The SmartFall dataset is collected based on the acquired measurements of the three-axis accelerometer in a smartwatch. Each scenario in this dataset is classified and labeled as a fall or a non-fall. In comparison to the three machine learning models—K-nearest neighbors (KNN), decision tree (DT), and standard random forest (SRF), the proposed ensemble classifier outperformed the other models and achieved 98.4% accuracy. The developed healthcare IoHT framework can be realized for detecting fall accidents of older people by taking security and privacy concerns into account in future work. [ABSTRACT FROM AUTHOR]

This article introduces a new medical internet of things (IoT) framework for intelligent fall detection system of senior people based on our proposed deep forest model. The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks. Moreover, the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer. The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset, which is acquired from three-axis accelerometer in a smartwatch. It includes 92781 training samples and 91025 testing samples with two labeled classes, namely non-fall and fall. Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0% compared to three machine learning models, i.e., K-nearest neighbors, decision trees and traditional random forest, and two deep learning models, which are dense neural networks and convolutional neural networks. By considering security and privacy aspects in the future work, our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment. [ABSTRACT FROM AUTHOR]

The novel Coronavirus disease 2019 (COVID-19) pandemic has begun in China and is still affecting thousands of patient livesworldwide daily. AlthoughChest X-ray and Computed Tomography are the gold standardmedical imaging modalities for diagnosing potentially infected COVID-19 cases, applying Ultrasound (US) imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently. In this article, we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images, based on generative adversarial neural networks (GANs). The proposed image classifiers are a semi-supervised GAN and a modifiedGANwith auxiliary classifier. Each one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique, keeping its main function of defining the “realness” of tested images. Extensive tests have been successfully conducted on public dataset of US images acquired with a convex US probe. This study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 patients. The results of our proposed GAN models showed that high accuracy values above 91.0% were obtained under different sizes of limited training data, outperforming other deep learning-based methods, such as transfer learning models in the recent studies. Consequently, the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study. [ABSTRACT FROM AUTHOR]

Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural processes. Therefore, Internet of Things (IoT) technology can be applied to monitor and detect harmful insect pests such as red palm weevils (RPWs) in the farms of date palm trees. In this paper, we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier, namely InceptionResNet-V2. The sound sensors, namely TreeVibes devices are carefullymounted on each palm trunk to setup wireless sensor networks in the farm. Palm trees are labeled based on the sensor node number to identify the infested cases. Then, the acquired audio signals are sent to a cloud server for further on-line analysis by our fine-tuned deep transfer learning model, i.e., InceptionResNet-V2. The proposed infestation classifier has been successfully validated on the public TreeVibes database. It includes total short recordings of 1754 samples, such that the clean and infested signals are 1754 and 731 samples, respectively. Compared to other deep learning models in the literature, our proposed InceptionResNet-V2 classifier achieved the best performance on the public database of TreeVibes audio recordings. The resulted classification accuracy score was 97.18%. Using 10-fold cross validation, the fine-tuned InceptionResNet-V2 achieved the best average accuracy score and standard deviation of 94.53% and±1.69, respectively. Applying the proposed intelligent IoT-aided detection system of RPWs in date palm farms is the main prospect of this research work. [ABSTRACT FROM AUTHOR]

Lightweight deep convolutional neural networks (CNNs) present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients. Recently, advantages of portable Ultrasound (US) imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases. In this paper, a new framework of lightweight deep learning classifiers, namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images. Compared to traditional deep learning models, lightweight CNNs showed significant performance of real-time vision applications by usingmobile devices with limited hardware resources. Four main lightweight deep learning models, namely MobileNets, ShuffleNets, MENet and MnasNet have been proposed to identify the health status of lungs using US images. Public image dataset (POCUS) was used to validate our proposed COVID-LWNet framework successfully. Three classes of infectious COVID-19, bacterial pneumonia, and the healthy lungwere investigated in this study. The results showed that the performance of our proposedMnasNet classifier achieved the best accuracy score and shortest training time of 99.0% and 647.0 s, respectively. This paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases. [ABSTRACT FROM AUTHOR]

Compensation for the respiratory motion is a major challenge in the control design of medical robot ultrasound for accurately scanning the liver. Therefore, this paper presents an adaptive fuzzy proportional-integral-derivative (PID) force control of robot-assisted ultrasound to improve the guidance performance for anatomical or pathological structures of the liver under free breathing. A six degree-of-freedom robotic arm equipped with a 2-D ultrasound probe and a force sensor is simulated to perform the guidance procedure of the liver. The respiratory motion is also modeled and synchronized with retrospective liver ultrasound images. Without a priori knowledge of the nonlinear dynamic characteristics or mathematical modeling of the robot, the developed force controller exploits the advantages of fuzzy logic to directly auto-tune the PID controller gains for manipulating the robotic ultrasound probe on the patient’s abdomen at desired force levels in real time. A simulation framework has been developed to test the robotic force controller using four ultrasound image sequences of the liver. Compared to conventional PID controller with fixed gains, the adaptive fuzzy PID controller showed significantly better performance by resulting minimum tracked force errors of approximately 0.3 and 0.01 N with and without force sensor noise, respectively, for all tested image datasets. This simulation study presents potentially a good solution to accomplish the tracking task of desired probe forces during the robotic liver ultrasound guidance based on the developed fuzzy PID controller. [ABSTRACT FROM AUTHOR]

Objective: Aortic valve stenosis is one of the most frequently acquired valvular heart diseases, accounting for almost 70% of valvular cardiac surgery. Transapical transcatheter aortic valve implantation has recently become a suitable minimally invasive technique for high-risk and elderly patients with severe aortic stenosis. In this article, we aim to automatically define a target area of valve implantation, namely, the area between the coronary ostia and the lowest points of two aortic valve cusps. Therefore, we present a new image-based tracking method of these aortic landmarks to assist in the placement of aortic valve prosthesis under live 2D fluoroscopy guidance., Methods: We propose a rigid intensity-based image registration technique for tracking valve landmarks in 2D fluoroscopic image sequences, based on a real-time alignment of a contrast image including the initialized manual valve landmarks to each image of sequence. The contrast image is automatically detected to visualize aortic valve features when the aortic root is filled with a contrast agent., Results: Our registration-based tracking method has been retrospectively applied to 10 fluoroscopic image sequences from routine transapical aortic valve implantation procedures. Most of all tested fluoroscopic images showed a successful tracking of valve landmarks, especially for the images without contrast agent injections., Conclusions: A new intraoperative image-based method has been developed for tracking aortic valve landmarks in live 2D fluoroscopic images to assist transapical aortic valve implantations and to increase the overall safety of surgery as well.