IoT Based Virtual Reality Game for Physio-therapeutic Patients

Biofeedback therapy trains the patient to control voluntarily the involuntary process of their body. This non-invasive and non-drug treatment is also used as a means to rehabilitate the physical impairments that may follow a stroke, a traumatic brain injury or even in neurological aspects within occupational therapy. The idea behind this study is based on using immersive gaming as a tool for physical rehabilitation that combines the idea of biofeedback and physical computing to get a patient emotionally involved in a game that requires them to do the exercises in order to interact with the game. This game is aimed towards addressing the basic treatment for ‘Frozen Shoulder’. In this work, the physical motions are captured by the wearable ultrasonic sensor attached temporarily to the various limbs of the patient. The data received from the sensors are then sent to the game via serial wireless communication. There are two main aspects to this study: motion capturing and game design. The current status of the application is a single ultrasonic detector. The experimental result shows that physio-therapeutic patients are benefited through the IoT based virtual reality game

Multi-Criteria Service Selection Agent for Federated Cloud

Federated cloud interconnects small and medium-sized cloud service providers for service enhancement to meet demand spikes. The service bartering technique in the federated cloud enables service providers to exchange their services. Selecting an optimal service provider to share services is challenging in the cloud federation. Agent-based and Reciprocal Resource Fairness (RRF) based models are used in the federated cloud for service selection. The agent-based model selects the best service provider using Quality of Service (quality of service). RRF model chooses fair service providers based on service providers\u27 previous service contribution to the federation. However, the models mentioned above fail to address free rider and poor performer problems during the service provider selection process. To solve the above issue, we propose a Multi-criteria Service Selection (MCSS) algorithm for effectively selecting a service provider using quality of service, Performance-Cost Ratio (PCR), and RRF. Comprehensive case studies are conducted to prove the effectiveness of the proposed algorithm. Extensive simulation experiments are conducted to compare the proposed algorithm performance with the existing algorithm. The evaluation results demonstrated that MCSS provides 10% more services selection efficiency than Cloud Resource Bartering System (CRBS) and provides 16% more service selection efficiency than RPF

Super-resolution reconstruction of brain magnetic resonance images via lightweight autoencoder

Magnetic Resonance Imaging (MRI) is useful to provide detailed anatomical information such as images of tissues and organs within the body that are vital for quantitative image analysis. However, typically the MR images acquired lacks adequate resolution because of the constraints such as patients’ comfort and long sampling duration. Processing the low resolution MRI may lead to an incorrect diagnosis. Therefore, there is a need for super resolution techniques to obtain high resolution MRI images. Single image super resolution (SR) is one of the popular techniques to enhance image quality. Reconstruction based SR technique is a category of single image SR that can reconstruct the low resolution MRI images to high resolution images. Inspired by the advanced deep learning based SR techniques, in this paper we propose an autoencoder based MRI image super resolution technique that performs reconstruction of the high resolution MRI images from low resolution MRI images. Experimental results on synthetic and real brain MRI images show that our autoencoder based SR technique surpasses other state-of-the-art techniques in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Information Fidelity Criterion (IFC), and computational time

Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation

Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules near other lung structures, and similar visual aspects. This study proposes a new model named Lung_PAYNet, a pyramidal attention-based architecture, for improved lung nodule segmentation in low-dose CT images. In this architecture, the encoder and decoder are designed using an inverted residual block and swish activation function. It also employs a feature pyramid attention network between the encoder and decoder to extract exact dense features for pixel classification. The proposed architecture was compared to the existing UNet architecture, and the proposed methodology yielded significant results. The proposed model was comprehensively trained and validated using the LIDC-IDRI dataset available in the public domain. The experimental results revealed that the Lung_PAYNet delivered remarkable segmentation with a Dice similarity coefficient of 95.7%, mIOU of 91.75%, sensitivity of 92.57%, and precision of 96.75%

Automated sleep stage classification in sleep apnoea using convolutional neural networks

A sleep disorder is a condition that adversely impacts one\u27s ability to sleep well on a regular schedule. It also occurs as a consequence of numerous neurological sicknesses. These types of disorders can be investigated using laboratory-based polysomnography (PSG) signals. The detection of neurological disorders is exact and efficient thanks to the automated monitoring of sleep relegation stages. This automation method publicly presents a flexible deep learning model and machine learning approach utilizing raw electroencephalogram (EEG) signals. The deep learning model is a Deep Convolutional Neural Network (CNN) that analyses invariant time capacities and frequency actualities and collects assessment adaptations. It also captures the inviolate and long brief length setting conditions between the epochs and the degree of sleep stage relegation. This method uses an innovative function to calculate data loss and misclassified errors found while training the network for the sleep stage, considering the restrictions found in the publicly available sleep datasets. It is used in conjunction with machine learning techniques to forecast the best approach for the process. Its effectiveness is determined by using two open-source, public databases available from PhysioNet: two recordings with 5402 epoch counts. The technique used in this approach achieves an accuracy of 90.70%, precision of 90.50%, recall of 92.70%, and F-measure of 90.60%. The proposed method is more significant than existing models like AlexNet, ResNet, VGGNet, and LeNet. The comparative study of the models could be adopted for clinical use and modified based on the requirements

Achieving Longevity in Wireless Body Area Network by Efficient Transmission Power Control for IoMT Applications

The application of tiny body sensors to collect, process, store, analyze, and retrieve medical information from a human body is a part of the Internet of Medical Things (IoMT).  IoMT helps to monitor and track human vital health parameters, predict disease, notify the patients and the health care professionals with relevant data for analyzing the problems before they become severe and for earlier invention. By 2022, more than 60 % of IoT applications will be health-related. The convergence of biomedical sensors, wireless body area networks (WBAN), Information technology, and bioinformatics will help improve the efficiency of saving human lives. In a WBAN, network longevity is challenging because of the limited supply of low power battery energy in tiny body sensor nodes. Here, we proposed an energy-efficient transmission power control (TPC) algorithm to extend the network lifetime in IoMT networks for healthcare applications by eliminating the transceiver overhearing problem. In TPC, human tissue resistivity properties are considered to adjust the transmission power, which reduces the communication power and extends the network lifetime. The simulation results show that network power consumption is reduced by 35%

Machine Learning Frameworks in Cancer Detection

Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer

Unmanned Aerial Vehicles and Multidisciplinary Applications Using AI Techniques

Используемые программы Adobe AcrobatБеспилотные летательные аппараты (БПЛА) и искусственный интеллект (ИИ) привлекают внимание академических и промышленных исследователей ,благодаря свободам, которые предоставляют БПЛА при удаленном управлении и мониторинге деятельности. Применение методов машинного обучения и глубокого обучения может привести к быстрым и надежным результатам и помогло в мониторинге в режиме реального времени, сборе и обработке данных, а также прогнозировании. Беспилотные летательные аппараты, использующие эти технологии, могут стать незаменимыми инструментами для компьютерных / беспроводных сетей, умных городов, военного применения, сельского хозяйства и добычи полезных ископаемых. Беспилотные летательные аппараты и междисциплинарные приложения с использованием методов искусственного интеллекта являются важным справочным источником, который охватывает методы распознавания образов, машинного и глубокого обучения, а также другие методы искусственного интеллекта и влияние, которое они оказывают при применении к различным приложенUnmanned aerial vehicles (UAVs) and artificial intelligence (AI) are gaining the attention of academic and industrial researchers due to the freedoms that UAVs afford when operating and monitoring activities remotely. Applying machine learning and deep learning techniques can result in fast and reliable outputs and have helped in real-time monitoring, data collection and processing, and prediction. UAVs utilizing these techniques can become instrumental tools for computer/wireless networks, smart cities, military applications, agricultural sectors, and mining. Unmanned Aerial Vehicles and Multidisciplinary Applications Using AI Techniques is an essential reference source that covers pattern recognition, machine and deep learning-based methods, and other AI techniques and the impact they have when applied to different real-time applications of UAVs. It synthesizes the scope and importance of machine learning and deep learning models in enhancing UAV capabilities, solutions to problems, and numerous applicatio

Artificial Intelligence in dentistry: Concepts, Applications and Research Challenges

Artificial Intelligence (AI) has the ability to process huge datasets, disclose human essence computationally, and perform like humans as technology advances. Because of the necessity for precise diagnosis and improved patient care, AI technology has greatly influenced the healthcare industry. In the domains of dentistry and medicine, artificial intelligence has yet to come a long way. As a result, dentists must be aware of the potential implications for a profitable clinical practise in the future. In this paper, we present the current applications of AI in dentistry. The different types of AI techniques are introduced and summarized. The state-of-the-art literature is studied analysed. A comparative analysis on the different AI techniques in dentistry is presented. Further, the research challenges in the field of dentistry and future directions are also provided