Technology such as cloud computing(CC) is constantly evolving and being adopted by the industries to manage their data and tasks. CC provides the resources for managing the tasks of the cloud users.... Show More

Technology such as cloud computing(CC) is constantly evolving and being adopted by the industries to manage their data and tasks. CC provides the resources for managing the tasks of the cloud users. The acceptance of the CC in healthcare industries is proven to be more cost-effective and convenient. CC manager has to manage the resources to provide services to the end-users of the healthcare sector. The SLAMMP framework discussed here shows how the resources are managed by using the concept of reinforcement learning (RL) and LSTM (long short-term memory) for monitoring and prediction of the cloud resources for healthcare organizations. The task(s) pattern and anti-pattern scenarios have been observed using HMM (hidden Markov model). These patterns will tune the SLA parameters (service level agreement) using blockchain-based smart contracts (SC). The result discussed here indicates that the variations in the cloud resource demand will be handled carefully using the SLAMMP framework. From the result obtained, it is identified that SLAMMP performs well with the parameter used here.

A large number of machine learning approaches are implemented in healthcare field for effective diagnosis and prediction of different diseases. The aim of these machine learning approaches is to... Show More

A large number of machine learning approaches are implemented in healthcare field for effective diagnosis and prediction of different diseases. The aim of these machine learning approaches is to build automated diagnostic tool for helping the physician as well as monitor the health status of patients. These diagnostic tools are widely adopted in intensive care unit for life expectancy of patients. In this study, an effort is made to design an automated diagnostic model for the diagnosis and prediction of diabetes patients. The proposed diagnostic model is designed using artificial bee colony (ABC) algorithm and deep neural network (DNN) technique, called ABC-DNN-based diagnostic model. The ABC algorithm is applied to determine the relevant features for diabetes prediction and diagnosis while DNN technique is adopted for the prediction and diagnosis of diabetes affected patients. The performance of proposed diagnostic model is tested over Pima Indian Diabetes dataset and evaluated using accuracy, sensitivity, specificity, F-measure, Kappa, and area under curve (AUC) parameters. Further, 10-fold and 50-50% training-testing method are considered to assess the performance of proposed diagnostic model. The experimental results of proposed ABC-DNN model is compared with DNN technique and several existing diabetes studies. It is observed that proposed ABC-DNN model achieves 94.74% accuracy rate using 10-fold method.

The medical imaging technique showed remarkable improvement in interventional treatment of computer-aided medical diagnosis system. Image processing techniques are broadly applied in detection and... Show More

The medical imaging technique showed remarkable improvement in interventional treatment of computer-aided medical diagnosis system. Image processing techniques are broadly applied in detection and exploring the abnormalities issues in tumor detection. The early stage of lung tumor detection is extremely important in medical research field. The proposed work uses image processing segmentation technique for detection of lung tumor and the support vector classifier learning technique for predicting stage of tumor. After performing preprocessing and segmentation the features are extracted from region of lung nodule. The classification is performed on dataset acquired from national cancer institute for the evaluation of lung cancer diagnosis. The multi-class machine learning classification technique SVM (support vector machine) identifies the tumor stage of lung dataset. The proposed methodology provides classification of tumor stages and improves the decision-making process. The performance is evaluated by measuring the parameters namely accuracy, sensitivity, and specificity.

Air pollution is increasing day by day, decreasing the world economy, degrading the quality of life, and resulting in a major productivity loss. At present, this is one of the most critical... Show More

Air pollution is increasing day by day, decreasing the world economy, degrading the quality of life, and resulting in a major productivity loss. At present, this is one of the most critical problems. It has a significant impact on human health and ecosystem. Reliable air quality prediction can reduce the impact it has on the nearby population and ecosystem; hence, improving air quality prediction is the prime objective for the society. The air quality data collected from sensors usually contains deviant values called outliers which have a significant detrimental effect on the quality of prediction and need to be detected and eliminated prior to decision making. The effectiveness of the outlier detection method and the clustering methods in turn depends on the effective and efficient choice of parameters like initial centroids and number of clusters, etc. The authors have explored the hybrid approach combining k-means clustering optimized with particle swarm optimization (PSO) to optimize the cluster formation, thereby improving the efficiency of the prediction of the environmental pollution.

One knows that smart transport is also an integrated part of healthcare technologies. To minimize the pollution for benefiting the healthcare, the traffic throughput on roundabout intersection has... Show More

One knows that smart transport is also an integrated part of healthcare technologies. To minimize the pollution for benefiting the healthcare, the traffic throughput on roundabout intersection has to be increased which will reduce wasted time and will also enhance passenger comfort. This paper presents a new approach by use of cooperative vehicular control utilizing VANET without compromising the safety of vehicles. This intersection side unit (ISU)-based system use lane change mechanism. The modular use of lane with lane change in newly designed protocol CARA (collision avoidance at roundabout algorithm) will greatly enhance the capacity utilization of roundabout. A new simulator ‘RoundSim' was also developed exclusively for simulation in roundabout. A new MAC protocol RMAC (roundabout MAC) is also designed which will suit the roundabout management utilizing lane change to minimize sudden jerk to passengers, thus enhancing healthcare of people. This RMAC utilizes message set with different prioritization scheme which results in better utilization of allotted frequency spectrum.

The human vision system is mimicked in the format of videos and images in the area of computer vision. As humans can process their memories, likewise video and images can be processed and perceptive... Show More

The human vision system is mimicked in the format of videos and images in the area of computer vision. As humans can process their memories, likewise video and images can be processed and perceptive with the help of computer vision technology. There is a broad range of fields that have great speculation and concepts building in the area of application of computer vision, which includes automobile, biomedical, space research, etc. The case study in this manuscript enlightens one about the innovation and future scope possibilities that can start a new era in the biomedical image-processing sector. A pre-surgical investigation can be perused with the help of the proposed technology that will enable the doctors to analyses the situations with deeper insight. There are different types of biomedical imaging such as magnetic resonance imaging (MRI), computerized tomographic (CT) scan, x-ray imaging. The focused arena of the proposed research is x-ray imaging in this subset. As it is always error-prone to do an eyeball check for a human when it comes to the detailing. The same applied to doctors. Subsequently, they need different equipment for related technologies. The methodology proposed in this manuscript analyses the details that may be missed by an expert doctor. The input to the algorithm is the image in the format of x-ray imaging; eventually, the output of the process is a label on the corresponding objects in the test image. The tool used in the process also mimics the human brain neuron system. The proposed method uses a convolutional neural network to decide on the labels on the objects for which it interprets the image. After some pre-processing the x-ray images, the neural network receives the input to achieve an efficient performance. The result analysis is done that gives a considerable performance in terms of confusion factor that is represented in terms of percentage. At the end of the narration of the manuscript, future possibilities are being traces out to the limelight to conduct further research.