Every patient has health record, it was written as statement of patient's conditions, treatments, and medications, and nowadays it is become digitalized, it can be copied and shared easily, but the nature of EHR i...
详细信息
This study addresses the Wireless Sensor Network Planning Problem with Multiple Sources/Destinations (WSNPMSD), an optimization challenge focused on reducing the sensor count within a network topology for a specified ...
详细信息
ISBN:
(数字)9781737749769
ISBN:
(纸本)9798350371420
This study addresses the Wireless Sensor Network Planning Problem with Multiple Sources/Destinations (WSNPMSD), an optimization challenge focused on reducing the sensor count within a network topology for a specified area, considering numerous sources and destinations. We introduce a hybrid strategy for tackling WSNP-MSD, particularly effective for large-scale scenarios, combining a Biased Random-key Genetic Algorithm with a Local Branching Technique. This methodology is justified by the limitations exact methods may encounter when the number of variables increases. Through computational experiments, we demonstrate the superiority of our proposed method over conventional exact methods in managing large instances of the WSNP-MSD.
Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today,...
Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today, techniques that can automatically detect malware before it infects the system is required. This study aims to develop malware detection using machine learning approach with Principal Component Analysis (PCA) as feature reduction. PCA (Principal Component Analysis) is expected to be able to reduce the number of features which then could also reduce the learning time but do not reduce its accuracy significantly. There were four machine learning classifiers used in this study, i.e. K-Nearest Neighbor, Decision Tree, Naïve bayes, and Random Forest. The n-components used in this study were 20 and 34 and the ratio of test and train in the dataset was 35% for test and 65% for training. The results have shown that the best performance come from the detection using random forest with 34 n-component and 100 n-estimator with the average accuracy was 0.991688.
Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers’ opinions on using such platform. Sentim...
Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers’ opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users’ opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, $\mathrm{N}=2$ for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with $K=11$ as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms $(N=2)$ with Split Validation (8:2), which is $\mathbf{97.26\%}$.
Network security is a crucial component of Information Technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of ne...
Network security is a crucial component of Information Technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it’s practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953.
Because of the advancement of new technologies and the popularity of mobile devices, this study was designed to identify whether apps have a representative influence on companies' brand image. To fulfill this obje...
详细信息
Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. A...
详细信息
This full paper describes a complete article in the research category. This work presents a Bibliometric Review that aims to characterize the current scenario of academic research in Artificial Intelligence in educati...
详细信息
ISBN:
(数字)9798350351507
ISBN:
(纸本)9798350363067
This full paper describes a complete article in the research category. This work presents a Bibliometric Review that aims to characterize the current scenario of academic research in Artificial Intelligence in education to support educators in the teaching and learning processes in Special Education in different contexts, becoming a tool with potential use in cases of students with Autism Spectrum Disorder (ASD). In this sense, this work aims to contribute to a better understanding of research associated with the area of Artificial Intelligence aimed at educational processes involving students with autism. To do this, we used the EndNote reference manager, an online tool designed to support researchers in conducting Literature Reviews, following the bibliometric protocol proposed by Guedes and Borschiver (2005). From the definition of search strings, (“artificial intelligence”) AND (“autism” OR “ASD” OR “autism spectrum disorder”) AND (“education” OR “teaching”), scientific databases were explored like Web of science (WoS), Scopus, ERIC, Emerald, Scielo, Portal CAPES, and IEEE, to locate existing studies on the topic, between 2019 and 2024. As a result, 298 articles were mapped and 20 scientific works were selected that address the aforementioned specific theme, written by 89 authors belonging to 50 institutions in 19 countries on four continents, enabling the creation of a significant theoretical basis. By analyzing the information contained in scientific publications in this specific field, it was possible to infer that research is divided into distinct areas of concentration: 1) the exploration of algorithms and data analysis based on Artificial Intelligence for analytical-predictive issues in the field of special education; 2) the use of robots in the teaching and learning processes of students with autism; 3) the development of personalized educational intervention models for learning pedagogical, social and communication skills. The data show that research on
In order to reduce Polyethylene Terephthalate (PET) bottle plastic waste, Universitas Brawijaya (UB) provided reverse osmosis drinking water in some buildings. Water monitoring is needed to keep the water quality. The...
详细信息
The use of superabsorbent polymers (SAPs) in combination with supplementary cementitious materials like metakaolin presents an opportunity to enhance concrete performance while addressing environmental concerns in con...
详细信息
暂无评论