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Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of the most reliable approaches for a radiologist to diagnosing many cancers, including lung tumor. In this work, we proposed stage classification of lung tumor which is a more challenging task in computer-aided diagnosis. As a result, a modified computer-aided diagnosis is being considered as a way to reduce the heavy workloads and second opinion to radiologists. In this paper, we present a strategy for classifying and validating different stages of lung tumor progression, as well as a deep neural model and data collection using cloud system for categorizing phases of pulmonary illness. The proposed system presents a Cloud-based Lung Tumor Detector and Stage Classifier (Cloud-LTDSC) as a hybrid technique for PET/CT images. The proposed Cloud-LTDSC initially developed the active contour model as lung tumor segmentation, and multilayer convolutional neural network (M-CNN) for classifying different stages of lung cancer has been modelled and validated with standard benchmark images. The performance of the presented technique is evaluated using a benchmark image LIDC-IDRI dataset of 50 low doses and also utilized the lung CT DICOM images. Compared with existing techniques in the literature, our proposed method achieved good result for the performance metrics accuracy, recall, and precision evaluated. Under numerous aspects, our proposed approach produces superior outcomes on all of the applied dataset images. Furthermore, the experimental result achieves an average lung tumor stage classification accuracy of 97%-99.1% and an average of 98.6% which is significantly higher than the other existing techniques.

With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms). Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.

© 2019 Elsevier Ltd The World Health Organization (WHO) recently reported that the lung tumor was the leading cause of death worldwide. In this study, a practical computer-aided diagnosis (CAD) system is developed to increase a patient's chance of survival. Segmentation is acritical analysis tool for dividing a lung image into several sub-regions. This work characterized an automated 3-D lung segmentation tool modeled by an active contour model for computed tomography (CT) images. The proposed segmentation model is used to integrate the local image bias field formulation with the active contour model (ACM). Here, a local energy term is specified by using the mean squared error to reconcile severely in homogeneous CT images and used to detect and segment tumor regions efficiently with intensity inhomogeneity. In addition, a Multiscale Gaussian distribution was applied to the CT images for smoothening the evolution process, and features were determined. For proposed model evaluation, were used the Lung Image Database Consortium (LIDC-IDRI) data set that consisted of 850 lung nodule-lesion images that were segmented and refined to generate accurate 3D lesions of lung tumor CT images. Tumor portions were extracted with 97% accuracy. Using continuous feature extraction of 3-D images leads to attributing the deformation and quantifies the centroid displacement. In this work, predict the centroid displacement and contour points by a curve evolution method which results in more accurate predictions of contour changes and than the extracted images were classified using an Enhanced Convolutional Neural Network (CNN) Classifier. The experimental result shows that the modified Computer Aided Diagnosis (CAD) system has a high ability to acquire good accuracy and assures automated diagnosis of a lung tumor.

Single Event Transients (SET’s) occur in analog integrated circuits due to the strike of a heavy-ion (or) heavy energy proton at the transistor junction. It produces electron–hole pairs, which propagates in the circuit and results in amplitude variation, phase displacement at the output voltage. This paper presents a Radiation Hardened By Design (RHBD) current starved inverter buffer Voltage Control Oscillator (VCO) for the application of Phase Locked Loop (PLL). The proposed VCO is composed of current starved inverter delay VCO and majority voter. The current starved inverter delay VCO is designed with the inverter delay cell, and a single current source is shared for each delay cell. The majority voter is designed by Gate Diffusion Input (GDI) logic to reduce power consumption. The Linear Energy Transfer (LET) ranging from 14.47 to 100 Mev − cm 2 ∕ mg is applied at the nodes of VCO. The proposed mitigation technique reduces the SET effect by 98%, and it operates at the frequency range from 0.5 GHz to 2.5 GHz with the power consumption of 290 μ W.

The diode laser has become an important dental armamentarium because of its exceptional ease of use and affordability. The lasers have many advantages with regard to periodontal treatment. For all soft tissue procedures, the diode laser functions as the handpiece, just like a dental handpiece for all hard tissue procedures. The main advantages of the diode laser for soft tissue applications are precise surgical procedure, bloodless surgery, sterilization of the surgical site, least possible swelling and scarring, negligible suturing, and practically no pain during and after surgery. In cosmetic dentistry, providing a desirable smile is one of the main concerns. Hyperpigmentation is one of the esthetic concerns especially in patients with black and discolored gums. Gingival depigmentation can be performed by means of surgical blade, electrosurgery, coarse diamond bur, cryosurgery, or lasers. Bearing in mind the advantages of lasers over other modalities of treatment, the present article will target on the management of such a case using diode lasers.