Hire the Experts: Combinatorial Auction Based Scheme for Experts Selection in E-Healthcare

During the last decade, scheduling the healthcare services (such as staffs and OTs) inside the hospitals have assumed a central role in healthcare. Recently, some works are addressed in the direction of hiring the expert consultants (mainly doctors) for the critical healthcare scenarios from outside of the medical unit, in both strategic and non-strategic settings under monetary and non-monetary perspectives. In this paper, we have tried to investigate the experts hiring problem with multiple patients and multiple experts; where each patient reports a preferred set of experts which is private information alongwith their private cost for consultancy. To the best of our knowledge, this is the first step in the direction of modeling the experts hiring problem in the combinatorial domain. In this paper, the combinatorial auction based scheme is proposed for hiring experts from outside of the hospitals to have expertise by the preferred doctors set to the patients.Comment: 7 Page

Review Paper on Enhanced Image Captioning with Deep Learning: Encoder-Decoder and Attention Mechanism

Image captioning involves the generation of textual descriptions that describe the content within an image. This process finds extensive utility in diverse applications, including the analysis of large, unlabelled image datasets, uncovering concealed patterns to facilitate machine learning applications, guiding self-driving vehicles, and developing software solutions to aid visually impaired individuals. The implementation of image captioning relies heavily on deep learning models, a technological frontier that has simplified the task of generating captions for images. This paper focuses on the utilisation of encoder-decoder model with attention mechanism for image captioning. In classic image captioning model, the words usually describe only a part of the image, however with attention mechanism special attention is given to the low level and high level features of the image. Object detection using attention mechanism has shown to have increased the CIDEr score by 15%. With the use of stable dataset of MSCOCO through keras datasets, it is possible to score more on caption generation and accurate description of image

Machine Learning Techniques For Detecting Untrusted Pages on the Web

The Web is both an excellent medium for sharing information, as well as an attractive platform for delivering products and services. This platform is, to some extent, mediated by search engines in order to meet the needs of users seeking information. Search engines are the “dragons” that keep a valuable treasure: information. Many web pages are unscrupulous and try to fool search engines to get to the top of ranking. The goal of this project is to detect such spam pages. We will particularly consider content spam and link spam, where untrusted pages use link structure to increase their importance. We pose this as a machine learning problem and build a classifier to classify pages into two category - trustworthy and untrusted .We use different link features, in other words structural characteristics of the web graph and content based features, as input to the classifier. We propose link-based techniques and context based techniques for automating the detection of Web spam, a term referring to pages which use deceptive techniques to obtain undeservedly high scores in search engines. We propose Naïve Bayesian Classifier to detect the content Spam and PageRank and TrustRank to detect the link spam

Efficient Implementation of Multilevel inverter with new modulation scheme for Reducing THD

This paper proposed an improved phase disposition pulse width modulation (PDPWM) for a modular multilevel inverter which is used for Photovoltaic grid connection. This new modulation method is based on selective virtual loop mapping, to achieve dynamic capacitor voltage balance without the help of an extra compensation signal. The concept of virtual submodule (VSM) is first established, and by changing the loop mapping relationships between the VSMs and the real sub-modules, the voltages of the upper/lower arm?s capacitors can be well balanced. This method does not requiring sorting voltages from highest to lowest, and just identify the MIN and MAX capacitor voltage?s index which makes it suitable for a modular multilevel converter with a large number of sub-modules in one arm. Compared to carrier phase-shifted PWM (CPSPWM), this method is more easily to be realized in field-programmable gate array and has much stronger dynamic regulation ability, and is conducive to the control of circulating current and Power quality injected into the grid. The maximum power point tracking is achieved with a fuzzy logic controller. The validity of the proposed system is confirmed by simulations

Impact of flood on rural population and strategies for mitigation: A case study of Darbhanga district, Bihar state, India

Floods are one of the most disastrous acts of nature and impact human life in multiple ways. Damages by floods in rural areas are more severe compared to urban counterparts due to poverty, limited infrastructures and access to resources and health care services. The Province of Bihar in India, with a population of 104.1 million, has 76 per cent of the population living under recurring threat of floods. In 2008, Bihar experienced severe floods in the northern region that affected more than 2.3 million people; and in 2013, they affected more than 5.9 million in 3768 villages across 20 rural districts. Floods damage property, infrastructure and further decreases access to health care and social services. This paper draws from the data collected for the primary author’s master’s thesis, along with his personal experience on floods as an inhabitant of a flooded community. It outlines the impact of floods in the rural areas of Bihar and highlights the continuous marginalization and exclusion of flood-affected communities. This paper will raise awareness of the issue and call for global support to advocate for more effective flood mitigation strategies

A mechanism design framework for hiring experts in e-healthcare

We investigate the problem of hiring experts (motivated socially and monetarily) from outside of the hospital(s) in e-healthcare through the lens of mechanism design with and without money. This paper presents the mechanisms that handle the following scenarios: 1) Multiple patients and multiple experts with patients having zero budget, 2) Single or multiple patients and multiple experts with patient(s) having some positive budget. In this paper, for the first scenario, we have proposed algorithms based on the theory of mechanism design without money that satisfies several economic properties such as truthfulness, pareto optimality, and core allocation. Considering the second scenario, the truthful and budget feasible mechanisms are proposed. Through simulations, we evaluate the performance and validate our proposed mechanismsPeer ReviewedPostprint (author's final draft