Fires often occur, and the damage caused by them is often irreversible.Fire-prone environments can be identified through historical data, and predictive models are recommended to prevent fires in advance.This study uses a variety of machine learning techniques to build fire prediction models and perform a comparative analysis to predict fires.We use data from local fire departments in South Korea to build fire prediction models using decision trees, random forest, XGBoost, extra tree classification, artificial neural networks, and more.Before creating the fire prediction models, we analyze and significant predictive features of a structural fire.We compared the fire prediction models and showed accuracy, F1-score, precision, and recall.The prediction model built with the random forest is the most accurate, but there is a little difference in the accuracy of each model trained with the extra tree classifier, XGBoost, and neural network.For the F1 Score, the model with a neural network shows the best value.
The advantages of cloud computing for organizations are self-evident, but for human resources experts cloud computing applications might be especially valuable. In bigger companies, HR groups are typically spread out over various regions of a building, various area of the nation or different time zones. This is probably going to bring about an absence of communication, where recruitment specialists can only with significant effort converse with team member or the other way around, and HR team member don't generally have prepared access to information observed and updated by another division. A cloud-based HR system can help remove the administrative worry from a developing HR group and permit them to focus on their business.
In this paper, an attempt has been made to understand the importance of a neural network-based chatbot system for movie-related queries.
Cloud technology has changed the distribution method to software development and distribution, and not only its own cloud services and solutions provided by cloud service providers, but also the services registered by the developers through the marketplace for each service provider can do.Based on the convenient advantages of the cloud marketplace, many cloud-based software vendors are expected to grow their domestic and international markets.Many cloud service providers (CSPs) have the convenience of registering and deploying their software to the marketplace, billing and technical support to attract customers for their services and continually improve their stable sales.I'm making a lot of effort to this study analyzes the optimal cloud platform based on software marketing strategy for domestic and foreign cloud market changes.
A typical mental problem that develops over a long period of time after experiencing a physically and mentally shocking event is known as post-traumatic stress disorder.Posttraumatic stress disorder is a complex psychological trauma that can lead to self-harm and self-harm and suicidal thoughts at risk, even in serious cases to suicide.The root cause of this phenomenon can be found in many places, but repeated trauma exposure and inadequate medical support in regard to the occupational specificity of fire fighters can be cited as the main cause.As such, the psychological post-traumatic stress disorder needs to be dealt with as a mental health problem in the public domain, beyond the mental health level of the individual.Even for stability, research on advancement of analysis tools for high-risk groups and preemptive prediction methods should be carried out along with the shift of awareness of the actual risk factors in modern society to encompass social problems in a broad sense.The purpose of this study is to analyze the risk group of post-traumatic stress disorders exposed to various types of trauma such as accidents, damages and disasters based on NEMA (National Emergency Management Association) data.
Cloud computing is the process of allocating the network access admission to a group of selected users having advanced and smart pattern of computing facilities on the plan of usefulness of the network permission for accessing the network resources whenever there's a demand for the facility to be provided from the cloud.It may be a customary term and thus the regular service that was delivering the required services to the hosts among net.Here, the cloud computing mechanism is employed for describing each the list of platforms that were out there to the users for operating and additionally the many styles of applications which will be processed.The current technique was being thought about by most of the analyzers because of the most potential and therefore the most helpful space for the analysis and also for analysis in academe like universities and major research laboratories.Solely few notable works are revealed with regards to performance analysis in cloud computing.Generally the analytical models were geared toward coming up with the models that use the cloud and its services through that the performance of the model was analyzed and evaluated below numerous configurations and assumptions.These assumptions were based on the queuing theory and its accuracy is verified with numerical calculations and simulations.Present paper deals with the performance evaluation in-terms of steady-state parameters of a small cloud server farm using single and multi-server queuing models.Single server model includes M/G/1 and M/Er/1.
Korean education-related evaluation agencies utilize a centralized system that directly manages learner data. This leaves the intellectual property of the organizations and the personal information of the students vulnerable to leakage should the central server be attacked. In this study, the researchers propose a multilateral personal portfolio authentication system that guarantees the reliability, integrity, and transparency of the data such as learner's schooling history. The system uses the features of blockchain in a distributed network wherein a learner submits schooling data to a peer in the network. The data is then verified through an agreement among the peers and recorded in a chronologically encrypted ledger. The proposed system is implemented based on Hyperledger Fabric and the analysis thereof conducted by evaluating its processing speed, capacity, and security. The analysis indicates that the system is able to successfully intercept the transactions of unvalidated users, thereby preventing the recording of incorrect data by an unauthorized user. Furthermore, it provides a record of previously made changes to a learner's profile, thus improving the integrity and reliability of the data. This system provides a platform to share learner information safely and promptly among schools, certification authorities, and higher learning institutions.
The usage of the MANET network models had increased a lot in the recent years due to various advantages of these networks.The major advantage was the size of the model and the network topology.The network of these models changes from time to time due to the reason that the addition of nodes and separation of nodes to the network model is always possible and very frequent in these network models.Hence, it is very important and required to analyze the performance of these networks with the help of various routing protocols.The performance had analyzed with various performance metrics like the end to end delay of the network model, output of the network etc.The results are displayed and discussed in detail in the results section.
Cloud-based HRM permit associations to store employee data on a single, effectively available, secure online location. The data can be gotten to right away, whenever, anyplace, from any device having internet connection. With the regularly changing prerequisites of HR and with progressions in smart HR innovation, software merchants are currently offering cloud based Human Resource management that are simple to implement and map to the association present HR process.
The HR managers cooperate with various directors to prompt each other about how to disperse the endeavor among employees. This community oriented undertaking improves proficiency bringing about better productivity in the association. Organizations are confronting another wave of technology that will affect the manner in which we work. The human resources division is in a unique position to set up the workforce for this better approach for working and to use the enormous data produced by IoT. This paper depicts the Effect of IoT on Human Resources.
Data mining is the computational procedure of discovering styles in huge statistics sets regarding strategies on the intersection of artificial intelligence, gadget studying, facts, and database systems.It is an interdisciplinary subfield of computer technological know-how.In now a day's lifestyle illnesses are increasing increasingly more.Data mining is one of the solutions for it, it helps us to overcome this problem by exploring old datasets.For any disease if it is identified at early stage treatment can be done easily.A wide range of data is produced in health care institutions, we will use that data to get some useful information.Data mining in medical sector helps doctors for diagnosis and treatment of diseases, this paper makes an effort to study and find interesting patterns from the data of patients.
In Korea, weather forecasts for fundamental weather factors, such as temperature, precipitation, wind direction and speed, humidity, and cloudiness, are provided for a three-day period in each region. This can facilitate predicting photovoltaic power generation based on weather forecasting. For this purpose, in the present paper, we aim to propose corresponding model. However, the Korea Meteorological Administration does not forecast the amount of solar radiation and sunshine that mostly influence the results of photovoltaic power generation prediction. In this study, we predict these parameters considering various input/output (I/O) variables and learning algorithms applied to weather forecasts on hourly weather data. Finally, we predict photovoltaic power generation based on the best sunshine and solar radiation prediction results. The data structure underlying all predictions relies on four models applied to fundamental weather factors on sunshine and solar radiation data two hours ago. Then, the photovoltaic power generation prediction is implemented using four models depending on whether to add the predicted sunshine and solar radiation data obtained at the previous step. The prediction algorithm relies on an adaptive neuro-fuzzy inference system and artificial neural network (ANN) techniques, including dynamic neural network (DNN), recurrent neural network (RNN), and long short-term memory (LSTM). The results of the conducted experiment indicate that ANN perform better than the neuro-fuzzy approach. Moreover, we demonstrate that RNN and LSTM are more suitable for the time series data structures compared with DNN. Furthermore, we report that the weather forecast structure and the model 4 structure, which includes sunshine and solar radiation data two hours ago, achieve the best prediction results.
With the massive growth of the organizations files, the needs for archiving system become a must.A lot of time is consumed in collecting requirements from the organization to build an archiving system.Sometimes the system does not meet the organization needs.This paper proposes a domain-based requirement engineering system that efficiently and effectively develops different archiving systems based on new suggested technique that merges the two best used agile methodologies: extreme programming (XP) and SCRUM.The technique is tested on a real case study.The results shows that the time and effort consumed during analyzing and designing the archiving systems decreased significantly.The proposed methodology also reduces the system errors that may happen at the early stages of the development of the system.
The reliability of safety-critical systems is of concern, a failure of which would result ininjury, death, damage to the environment or financial loss. Such systems have evolved frombeing largely mechanical to computer driven. The approach to analysing failure ofmechanical or hardware architecture of the system is well established in literature andpractice. Although the hardware architecture could be adequately analysed for failureusing traditional safety analysis techniques, the manner in which the software architectureis to be analysed for failures is fuzzy. This creates additional concern to the reliability ofcontemporary safety-critical systems. This paper defines an approach to analysing softwarefailure at class diagram using one of traditional safety analysis techniques, failure modesand effects analysis (FMEA). It also demonstrates how to apply the approach to analysingsoftware failure.
