Self-Diagnosis with Advanced Hospital Management

In the case of an epidemic, it is impossible to consult a doctor about minor ailments. This program helps to get recommendations from doctors. So that people can be treated for minor ailments at home. The main purpose of self-examination in a high-quality hospital management system is to provide self-examination to patients, if hospitals are not available in nearby areas. This program will be developed using one of the heuristic search methods in artificial intelligence which means greedy local search. The program provides additional space, which means that the patient can communicate with the appropriate physician by chatting online. The patient can receive appropriate suggestions for his or her problem. In fact, in self-examination this app makes a report about the patient's health status. That report may include the patient's disease name, level of symptoms and strength and the recommendation provided by the program. This proposal can be in two ways. The first suggestion would be a suitable drug based on the level of Weight Loss; the second proposal would be a meeting of specialist doctors which means that his illness may be in serious condition. It offers suggestions on some of the preventable diseases that mean minor problems with some pills. This can be helpful for patients who are far away from the hospital and for doctors and for working with patients inside or outside the hospital. The administrator can update medications in the database regularly. We can create a suggestion box for patients to improve the app

Distributional Metareasoning for Heuristic Search

Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this work, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning.

Comics in the Practical Activity of Ukraine Libraries

The amount increasing of produced information raises the issue of its effective perception and assimilation and increases attention to graphic information products as effective communication tools, including, in particular, comic books, and library practices built around comics. The article is devoted to the disclosure of modern domestic library experience in activities related to the comics use, outlining the system of service formed around comics in the libraries of Ukraine. Based on the study of funds and official websites of domestic libraries, professional publications, media materials, and the use of a set of research methods – especially bibliographic and heuristic search methods, analysis of content – it was found out that comic books are presented in the funds of domestic libraries of different levels and types: from large national-level institutions to small district book collections. The beginning of the comics history in the libraries of Ukraine dates back to the 20-the 30s of the twentieth century and today we can already talk about the formation of a comprehensive innovative library service around comics as an information product, the main components of which are the organization of educational events dedicated to comics, informing about the receipt of comics in the library, organizing exhibitions of comics, compiling lists and preparing bibliographic indexes and organization of meetings with authors and publishers of comics, workshops, and competitions. It has been found that comics as an information product is not only the subject of work of domestic librarians but also an effective tool for solving educational, social, and internal narrow professional tasks to promote the library profession and library business by them.

A Study on Darwinian Crow Search Algorithm for Multilevel Thresholding

Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for Crow Search Algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently.

Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service

This paper formulates a new problem for the optimal placement of Unmanned Aerial Vehicles (UAVs) geared towards wireless coverage provision for Voice over WiFi (VoWiFi) service to a set of ground users confined in an open area. Our objective function is constrained by coverage and by VoIP speech quality and minimizes the ratio between the number of UAVs deployed and energy efficiency in UAVs, hence providing the layout that requires fewer UAVs per hour of service. Solutions provide the number and position of UAVs to be deployed, and are found using well-known heuristic search methods such as genetic algorithms (used for the initial deployment of UAVs), or particle swarm optimization (used for the periodical update of the positions). We examine two communication services: (a) one bidirectional VoWiFi channel per user; (b) single broadcast VoWiFi channel for announcements. For these services, we study the results obtained for an increasing number of users confined in a small area of 100 m2 as well as in a large area of 10,000 m2. Results show that the drone turnover rate is related to both users’ sparsity and the number of users served by each UAV. For the unicast service, the ratio of UAVs per hour of service tends to increase with user sparsity and the power of radio communication represents 14–16% of the total UAV energy consumption depending on ground user density. In large areas, solutions tend to locate UAVs at higher altitudes seeking increased coverage, which increases energy consumption due to hovering. However, in the VoWiFi broadcast communication service, the traffic is scarce, and solutions are mostly constrained only by coverage. This results in fewer UAVs deployed, less total power consumption (between 20% and 75%), and less sensitivity to the number of served users.

Discovering dependencies with reliable mutual information

We consider the task of discovering functional dependencies in data for target attributes of interest. To solve it, we have to answer two questions: How do we quantify the dependency in a model-agnostic and interpretable way as well as reliably against sample size and dimensionality biases? How can we efficiently discover the exact or $$\alpha $$ α -approximate top-k dependencies? We address the first question by adopting information-theoretic notions. Specifically, we consider the mutual information score, for which we propose a reliable estimator that enables robust optimization in high-dimensional data. To address the second question, we then systematically explore the algorithmic implications of using this measure for optimization. We show the problem is NP-hard and justify worst-case exponential-time as well as heuristic search methods. We propose two bounding functions for the estimator, which we use as pruning criteria in branch-and-bound search to efficiently mine dependencies with approximation guarantees. Empirical evaluation shows that the derived estimator has desirable statistical properties, the bounding functions lead to effective exact and greedy search algorithms, and when combined, qualitative experiments show the framework indeed discovers highly informative dependencies.

Optimal Power Flow Management System for a Power Network with Stochastic Renewable Energy Resources Using Golden Ratio Optimization Method

An optimal operation system is a potential solution to increase the energy efficiency of a power network equipped with stochastic Renewable Energy Sources (RES). In this article, an Optimal Power Flow (OPF) problem has been formulated as a single and multi-objective problems for a conventional power generation and renewable sources connected to a power network. The objective functions reflect the minimization of fuel cost, gas emission, power loss, voltage deviation and improving the system stability. Considering the volatile renewable generation behaviour and uncertainty in the power prediction of wind and solar power output as a nonlinear optimization problem, this paper uses a Weibull and lognormal probability distribution functions to estimate the power output of renewable generation. Then, a new Golden Ratio Optimization Method (GROM) algorithm has been developed to solve the OPF problem for a power network incorporating with stochastic RES. The proposed GROM algorithm aims to improve the reliability, environmental and energy performance of the power network system (IEEE 30-bus system). Three different scenarios, using different RES locations, are presented and the results of the proposed GROM algorithm is compared to six heuristic search methods from the literature. The comparisons indicate that the GROM algorithm successfully reduce fuel costs, gas emission and improve the voltage stability and outperforms each of the presented six heuristic search methods.

Feature-Based Diversity Optimization for Problem Instance Classification

Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Travelling Salesperson Problem (TSP). In this article, we present a general framework that is able to construct a diverse set of instances which are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances which are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT.