Intelligent Wireless Capsule Endoscopy for the Diagnosis of Gastrointestinal Diseases

Through a wireless capsule endoscope (WCE) fitted with a miniature camera (about an inch), this study aims to examine the role of wireless capsule endoscopy (WCE) in the diagnosis, monitoring, and evaluation of GI (gastrointestinal) disorders. In a wearable belt recorder, a capsule travels through the digestive tract and takes pictures. It attempts to find tiny components that can be used to enhance the WCE. To accomplish this, we followed the steps below: Researching current capsule endoscopy through databases, designing and simulating the device using computers, implanting the system and finding tiny components compatible with capsule size, testing the system and eliminating noise and other problems, and analyzing the results. In the present study, it was shown that a spherical WCE shaper and a smaller WCE with a size of 13.5 diameter, a high resolution, and a high frame rate (8-32 fps) could help patients with pains due to the traditional capsules and provide more accurate pictures as well as prolong the battery life. In addition, the capsule can also be used to reconstruct 3D images. Simulation experiments showed that spherical endoscopic devices are more advantageous than commercial capsule-shaped endoscopic devices for wireless applications. We found that the sphere's velocity through the fluid was greater than the capsule's.

Electrochemical Sensing of H2O2 by Employing a Flexible Fe3O4/Graphene/Carbon Cloth as Working Electrode

We report the synthesis of Fe₃O₄/graphene (Fe₃O₄/Gr) nanocomposite for highly selective and highly sensitive peroxide sensor application. The nanocomposites were produced by a modified co-precipitation method. Further, structural, chemical, and morphological characterization of the Fe₃O₄/Gr was investigated by standard characterization techniques, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscope (TEM) and high-resolution TEM (HRTEM), Fourier transform infrared (FTIR), and X-ray photoelectron spectroscopy (XPS). The average crystal size of Fe₃O₄ nanoparticles was calculated as 14.5 nm. Moreover, nanocomposite (Fe₃O₄/Gr) was employed to fabricate the flexible electrode using polymeric carbon fiber cloth or carbon cloth (pCFC or CC) as support. The electrochemical performance of as-fabricated Fe₃O₄/Gr/CC was evaluated toward H₂O₂ with excellent electrocatalytic activity. It was found that Fe₃O₄/Gr/CC-based electrodes show a good linear range, high sensitivity, and a low detection limit for H₂O₂ detection. The linear range for the optimized sensor was found to be in the range of 10-110 μM and limit of detection was calculated as 4.79 μM with a sensitivity of 0.037 µA μM^-1 cm^-2. The cost-effective materials used in this work as compared to noble metals provide satisfactory results. As well as showing high stability, the proposed biosensor is also highly reproducible.

Separation of Different Blogs from Skin Disease Data using Artificial Intelligence

A combination of environmental conditions may cause skin illness everywhere on the earth, and it is one of the most dangerous diseases that can develop as a result. A major goal in the selection of characteristics is to produce predictions about skin disease instances in connection with influencing variables, which is one of the most important tasks. As a consequence of the widespread usage of sensors, the amount of data collected in the health industry is disproportionately large when compared to data collected in other sectors. In the past, researchers have used a variety of machine learning algorithms to determine the relationship between illnesses and other disorders. Forecasting is a procedure that involves many steps, the most important of which are the preprocessing of any scenario and the selection of forecasting features. A major disadvantage of doing business in the health industry is a lack of data availability, which is particularly problematic when data is provided in an unstructured format. Filling in missing numbers and converting between various types of data take somewhat more than 70% of the total time. When dealing with missing data in machine learning applications, the mean, average, and median, as well as the stand mechanism, may all be employed to solve the problem. Previous research has shown that the characteristics chosen for a model's overall performance may have an influence on the overall performance of the model's overall performance. One of the primary goals of this study is to develop an intelligent algorithm for identifying relevant traits in models while simultaneously eliminating nonsignificant attributes that have an impact on model performance. To present a full view of the data, artificial intelligence techniques such as SVM, decision tree, and logistic regression models were used in conjunction with three separate feature combination methodologies, each of which was developed independently. As a consequence of this, their accuracy, F-measure, and precision are all raised by a factor of ten, respectively. We then have a list of the most important features, together with the weights that have been allocated to each of them.

Fuzzy PID Control for Respiratory Systems

This paper presents the implementation of a fuzzy proportional integral derivative (FPID) control design to track the airway pressure during the mechanical ventilation process. A respiratory system is modeled as a combination of a blower-hose-patient system and a single compartmental lung system with nonlinear lung compliance. For comparison purposes, the classical PID controller is also designed and simulated on the same system. According to the proposed control strategy, the ventilator will provide airway flow that maintains the peak pressure below critical levels when there are unknown parameters of the patient's hose leak and patient breathing effort. Results show that FPID is a better controller in the sense of quicker response, lower overshoot, and smaller tracking error. This provides valuable insight for the application of the proposed controller.

Adaptive Fuzzy Sliding Mode Control of a Pressure-Controlled Artificial Ventilator

This paper presents the application of adaptive fuzzy sliding mode control (AFSMC) for the respiratory system to assist the patients facing difficulty in breathing. The ventilator system consists of a blower-hose-patient system and patient's lung model with nonlinear lung compliance. The AFSMC is based on two components: singleton control action and a discontinuous term. The singleton control action is based on fuzzy logic with adjustable tuning parameters to approximate the perfect feedback linearization control. The switching control law based on the sliding mode principle aims to minimize the estimation error between approximated single fuzzy control action and perfect feedback linearization control. The proposed control strategy manipulated the airway flow delivered by the ventilator such that the peak pressure will remain under critical values in presence of unknown patient-hose-leak parameters and patient breathing effort. The closed-loop stability of AFSMC will be proven in the sense of Lyapunov. For comparative analysis, classical PID and sliding mode controllers are also designed and implemented for mechanical ventilation problems. For performance analysis, numerical simulations were performed on a mechanical ventilator simulator. Simulation results reveal that the proposed controller demonstrates better tracking of targeted airway pressure compared with its counterparts in terms of faster convergence, less overshoot, and small tracking error. Hence, the proposed controller provides useful insight for its application to real-world scenarios.

Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.

Design and Numerical Analysis of a Graphene-Coated SPR Biosensor for Rapid Detection of the Novel Coronavirus

In this paper, a highly sensitive graphene-based multiple-layer (BK7/Au/PtSe2/Graphene) coated surface plasmon resonance (SPR) biosensor is proposed for the rapid detection of the novel Coronavirus (COVID-19). The proposed sensor was modeled on the basis of the total internal reflection (TIR) technique for real-time detection of ligand-analyte immobilization in the sensing region. The refractive index (RI) of the sensing region is changed due to the interaction of different concentrations of the ligand-analyte, thus impacting surface plasmon polaritons (SPPs) excitation of the multi-layer sensor interface. The performance of the proposed sensor was numerically investigated by using the transfer matrix method (TMM) and the finite-difference time-domain (FDTD) method. The proposed SPR biosensor provides fast and accurate early-stage diagnosis of the COVID-19 virus, which is crucial in limiting the spread of the pandemic. In addition, the performance of the proposed sensor was investigated numerically with different ligand-analytes: (i) the monoclonal antibodies (mAbs) as ligand and the COVID-19 virus spike receptor-binding domain (RBD) as analyte, (ii) the virus spike RBD as ligand and the virus anti-spike protein (IgM, IgG) as analyte and (iii) the specific probe as ligand and the COVID-19 virus single-standard ribonucleic acid (RNA) as analyte. After the investigation, the sensitivity of the proposed sensor was found to provide 183.33°/refractive index unit (RIU) in SPR angle (θSPR) and 833.33THz/RIU in SPR frequency (SPRF) for detection of the COVID-19 virus spike RBD; the sensitivity obtained 153.85°/RIU in SPR angle and 726.50THz/RIU in SPRF for detection of the anti-spike protein, and finally, the sensitivity obtained 140.35°/RIU in SPR angle and 500THz/RIU in SPRF for detection of viral RNA. It was observed that whole virus spike RBD detection sensitivity is higher than that of the other two detection processes. Highly sensitive two-dimensional (2D) materials were used to achieve significant enhancement in the Goos-Hänchen (GH) shift detection sensitivity and plasmonic properties of the conventional SPR sensor. The proposed sensor successfully senses the COVID-19 virus and offers additional (1 + 0.55) × L times sensitivity owing to the added graphene layers. Besides, the performance of the proposed sensor was analyzed based on detection accuracy (DA), the figure of merit (FOM), signal-noise ratio (SNR), and quality factor (QF). Based on its performance analysis, it is expected that the proposed sensor may reduce lengthy procedures, false positive results, and clinical costs, compared to traditional sensors. The performance of the proposed sensor model was checked using the TMM algorithm and validated by the FDTD technique.

Fractional data-driven control for a rotary flexible joint system

As one of the most promising topics in complex control processes, data-driven techniques have been widely used in numerous industrial sectors and have developed over the past two decades. In addition, the fractional-order controller has become more attractive in applied studies. In this article, a fractional integral control is implemented for a rotary flexible joint system. Moreover, an adjusted virtual reference feedback tuning (VRFT) technique is used to tune the fractional-order integrator. In this method, fractional integral control is designed based on state feedback control. Then, VRFT is adjusted and applied to the fractional integral controller. The effectiveness of the proposed adjusted VRFT method is discussed and presented through simulation and experimental results. The tracking performance of the rotary arm and the minimization of the vibration tip is evaluated based on the proposed method. In this article, the comparison of our proposed VRFT fractional scheme is made with the classical state feedback as well as a recently developed state feedback-based fractional order integral (SF-FOI) controller. The current investigations determine the performance improvement of our proposed scheme of comparable structure to the recent SF-FOI, with the introduction of the VRFT to the SF-FOI scheme.

Surgical robotic arm control for tissue ablation

In the technology driven era, robot assisted surgery is gradually emerging as a revolutionized surgical procedure over traditional laparoscopic method. Despite the concerns about robotic surgery for minimally invasive surgical procedures, robotized surgical arms have been used in many hospitals. Certain surgical procedures require removal of a segment of an organ or body part like excision biopsy, linear thin layer of soft tissue, triangular mass, and tangential excision in burn management, where shaving-off at an angle of the tissue layer to be removed. For such minimally invasive procedures, we have designed a surgical arm governed by a rotary flexible joint. The surgical arm has a medical grade scalpel in its one end and the other end is connected to a D.C. servo motor. The motion of the surgical arm is controlled by the newly designed non-integer order controller. We have experimentally demonstrated the functioning of the surgical arm by ablating the tissue in-vitro. Our surgical robotic arm is cost effective, high precision and free from potential human errors.