Data Mining is one of the most motivating area of research that is become increasingly popular in health organization. Data Mining plays an important role for uncovering new trends in healthcare organization which in turn helpful for all the parties associated with this field. This survey explores the utility of various Data Mining techniques such as classification, clustering, association, regression in health domain. In this paper, we present a brief introduction of these techniques and their advantages and disadvantages. This survey also highlights applications, challenges and future issues of Data Mining in healthcare. Recommendation regarding the suitable choice of available Data Mining technique is also discussed in this paper.

The catastrophic outbreak of Severe Acute Respiratory Syndrome -Coronavirus (SARS-CoV-2) also known as COVID-2019 has brought the worldwide threat to the living society. The whole world is putting incredible efforts to fight against the spread of this deadly disease in terms of infrastructure, finance, data sources, protective gears, life-risk treatments and several other resources. The artificial intelligence researchers are focusing their expertise knowledge to develop mathematical models for analyzing this epidemic situation using nationwide shared data. To contribute towards the well-being of living society, this article proposes to utilize the machine learning and deep learning models with the aim for understanding its everyday exponential behaviour along with the prediction of future reachability of the COVID-2019 across the nations by utilizing the real-time information from the Johns Hopkins dashboard.

The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia. The current diagnostic procedure of COVID-19 follows reverse-transcriptase polymerase chain reaction (RT-PCR) based approach which however is less sensitive to identify the virus at the initial stage. Hence, a more robust and alternate diagnosis technique is desirable. Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment. These datasets have limited samples concerned with the positive COVID-19 cases, which raise the challenge for unbiased learning. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. Accuracy, precision, recall, loss, and area under the curve (AUC) are utilized to evaluate the performance of the models. Considering the experimental results, the performance of each model is scenario dependent; however, NASNetLarge displayed better scores in contrast to other architectures, which is further compared with other recently proposed approaches. This article also added the visual explanation to illustrate the basis of model classification and perception of COVID-19 in CXR images.

The erasure resilience of rateless codes, such as Luby-Transform (LT) codes, makes them particularly suitable to a wide variety of loss-prone wireless and sensor network applications, ranging from digital video broadcast to software updates. Yet, traditional rateless codes usually make no use of a feedback communication channel, a feature available in many wireless settings. As such, we generalize LT codes to situations where receiver(s) provide feedback to the broadcaster. Our approach, referred to as Shifted LT (SLT) code, modifies the robust soliton distribution of LT codes at the broadcaster, based on the number of input symbols already decoded at the receivers. While implementing this modification entails little change to the LT encoder and decoder, we show both analytically and through real experiments, that it achieves significant savings in communication complexity, memory usage, and overall energy consumption. Furthermore, we show that significant savings can be even achieved with a low number of feedback messages (on the order of the square root of the total number of input symbols) transmitted at a uniform rate. The practical benefits of Shifted LT codes are demonstrated through the implementation of a real over-the-air programming application for sensor networks, based on the Deluge protocol. A version of this paper appeared as:• A. Hagedorn, S. Agarwal, S. Starobinski, and A. Trachtenberg, "Rateless Coding with Feedback", IEEE INFOCOM 2009.I. INTRODUCTION Point-to-multipoint wireless data communication, i.e., from a broadcaster to multiple downstream receivers, is gaining popularity with emerging wireless broadcast channels like digital video broadcast and cellular data broadcast [1] that support digital data broadcasting to multiple receivers. Broadcast scenarios also appear naturally in wireless sensor networks, most notably during software updates. Unlike analog broadcast, digital broadcast may also allow a back channel for receivers to communicate with the broadcaster, enabling interactive applications and protocols such as (n)ack-based data dissemination.Point-to-multipoint wireless communication poses several unique challenges. First, wireless channels are prone to lost packets (packet erasures) due to interference, occlusion, multi-path, etc.; as a result, different receivers may, and often do, receive different subsets of the transmitted data packets. Second, energy constraints often require receivers to be off during various (often differing) time periods during a given broadcast, again leading to the reception of different subsets of broadcast packets at each receiver. The same energy constraints also typically limit computation and memory on receiver units, thus providing a natural limit on the complexity of error coding for the communication channel. Finally, receivers are usually heterogeneous, with the least capable device dictating, or at least heavily influencing, the broadcast protocols.Erasure codes, which have the rateless property of being applicable to any channel loss p...

Abstract-We study the problem of pricing uplink power in wide-band cognitive radio networks under the objective of revenue maximization for the service provider and while ensuring incentive compatibility for the users. User utility is modeled as a concave function of the signal-to-noise ratio (SNR) at the base station, and the problem is formulated as a Stackelberg game. Namely, the service provider imposes differentiated prices per unit of transmitting power and the users consequently update their power levels to maximize their net utilities. We devise a pricing policy and give conditions for its optimality when all the users are to be accommodated in the network. We show that there exist infinitely many Nash equilibrium points that reward the service provider with the same revenue. The pricing policy charges more from users that have better channel conditions and more willingness to pay for the provided service. We then study properties of the optimal revenue with respect to different parameters in the network. We show that for regimes with symmetric users who share the same level of willingness to pay, the optimal revenue is concave and increasing in the number of users in the network. We analytically obtain achievable SNRs for this special case, and finally present a numerical study in support of our results.