Disaster management information system are essential for handling emergencies and saving lives through swift responses and coordination. This study aims to analyse and design an efficient backend technology for such a...
详细信息
Decision making in an organization is needed a system. Decision support system is a system that helps company management and education in decision making that provides information, modeling and analyzing data with the...
详细信息
Disaster management information system are essential for handling emergencies and saving lives through swift responses and coordination. This study aims to analyse and design an efficient backend technology for such a...
Disaster management information system are essential for handling emergencies and saving lives through swift responses and coordination. This study aims to analyse and design an efficient backend technology for such apps. Research methods involve literature review, interviews, and observation with stakeholders. Load balancing is found effective for optimizing disaster management, efficiently distributing workloads and ensuring high system availability. The analysis identifies functional and non-functional requirements, examining technologies like API, WebSocket, Caching, and Relational Database. The design section creates a 3-tier architecture with caching for enhanced performance and scalability. Integration with Mobile Cognitive Radio Base Station (MCRBS) ensures emergency communication in affected areas.
This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and...
详细信息
This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.
Decision making in an organization is needed a system. Decision support system is a system that helps company management and education in decision making that provides information, modeling and analyzing data with the...
详细信息
ISBN:
(纸本)9798350399080
Decision making in an organization is needed a system. Decision support system is a system that helps company management and education in decision making that provides information, modeling and analyzing data with the application of a method. The management, especially higher education, must maximize sophisticated technology. For example, to analyze scholarship data. Problems are found in manual data analysis so that human errors often occur in addition to using more time than the target, losing old data, resulting in inaccurate analysis of scholarship sustainability every semester. The research was conducted to overcome the problems that have been found by analyzing the existing data with the simple additive weighting method, building a system by applying the method and testing the system. The result of this research is to produce an application for the development of a decision support system in managing student data to determine the sustainability of the scholarship every semester. Applications that are built can help the management in managing scholarships.
The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smel...
详细信息
ISBN:
(数字)9798331517601
ISBN:
(纸本)9798331517618
The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.
The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of tho...
详细信息
ISBN:
(数字)9798331529376
ISBN:
(纸本)9798331529383
The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.
Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier st...
详细信息
Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.
The present study aims at comparing different ways of conducting lesson on two-digits multiplication for primary school students using algorithm and Realistic Mathematics Education (RME) approach. Qualitative experime...
The present study aims at comparing different ways of conducting lesson on two-digits multiplication for primary school students using algorithm and Realistic Mathematics Education (RME) approach. Qualitative experiment with user observation method had deliberately chosen as the design of the study as the intention is to figure out the impact of treatment while the subjects stay in natural setting of their habitat. There are three third grade classes consist of 61 primary school students in Singaraja, Bali – Indonesia who participated in this study. The participants were experienced different learning method, which are: (1) conventional class with multiplication algorithm followed by area model, (2) RME followed by multiplication algorithm and (3) RME. The data were gathered from students’ written work on the literacy test. Afterwards, a descriptive quantitative method was performed to analyze the data. The result showed that the third group in which RME approach was employed gained the best average scores to solve the literacy problems. Meanwhile, the second group in which the RME lesson was followed by multiplication algorithm gained the best average scores in solving routine problems. The study implies that RME approach contributes to the better mathematical literacy skills for young students.
In this paper, the Szemerédi's Regularity Lemma and its application are studied. This lemma is used to partition a large enough graph into almost equal parts so that the number of edges across the parts is fa...
In this paper, the Szemerédi's Regularity Lemma and its application are studied. This lemma is used to partition a large enough graph into almost equal parts so that the number of edges across the parts is fairly random. On the other hand, Roth's Theorem states that there exists an arithmetic progression with length 3 in a subset in integer with positive upper density. We shall see that it can be proved by using triangle removal lemma, which is an application of Szemerédi's Regularity Lemma. The regularity lemma does not seem to be a direct tool to be used on Roth's theorem. The lemma deals with the graphs while Roth's theorem states about subsets on integers. But however, the two are connected through the construction of an auxiliary graph, where 3-arithmetic progression of integers subsets corresponds to triangles in this such graph. At the end of this explanation, we will find that the whole trivial triangles formed by such graph partition are all disjoint. This is at the point we can conclude that there exists an arithmetic progression with length 3 in a subset in integer with positive upper density.
暂无评论