The insurance industry faces a significant challenge concerning insurance claims, particularly due to the prevalence of fraudulent insurance claims. To address this issue, one potential solution is the implementation ...
The insurance industry faces a significant challenge concerning insurance claims, particularly due to the prevalence of fraudulent insurance claims. To address this issue, one potential solution is the implementation of a computer-based decision model. This research presents a fuzzy decision model based on object-oriented method development. The study involves seven stages (i.e. case analyzing, parameter analyzing, objects-parameters linking, detail object relation constructing, parameter exchange analyzing, OOFDM constructing, and model verifying and validating), with an object-oriented approach serving as the foundational method for constructing the model, and fuzzy logic as the primary method for assessing claim risks in proposing the best decision. The model has the capability to simulate insurance claims and offers objective decisions based on 19,611 claims data, categorizing them into two decision categories: acceptance and pending.
The increasing number of articles available in the digital library will require quite a long time and accuracy in sorting articles according to needs. The use of artificial neural networks in finding the right article...
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Artificial intelligence (AI) refers to human-like intelligence exhibited by computers, robots, or other machines. In popular use, artificial intelligence refers to the ability of a computer or machine to mimic the abi...
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ISBN:
(纸本)9789819916238
Artificial intelligence (AI) refers to human-like intelligence exhibited by computers, robots, or other machines. In popular use, artificial intelligence refers to the ability of a computer or machine to mimic the ability of the human mind to learn from examples and experiences, recognize objects, understand and respond to language, make decisions, solve problems, and combine these and other abilities to perform functions that humans might perform. Artificial intelligence requires experience and data to smarten up the technology. The most important things in making artificial intelligence are learning, reasoning, and self-correction. At the learning stage, AI gives machines the ability to learn tasks without requiring a defined programming language. Then, the reasoning stage is the stage where the reason AI is applied in a technology. The self-correction stage is the stage where the AI is refining itself from and learning from experience in order to minimize errors or problems that exist. Then, this matter is concerned with the parking lot to be discussed. Parking can be interpreted as public facilities available in agencies or offices that serve to store vehicles. Vehicles entering the parking area become tens or even thousands, because it requires a parking system and management area. Such arrangements are capable of parking procedures and even other support systems such as adequate parking facilities and infrastructure, and another function is to create and develop parking systems in general to provide safety and comfort. The methodology obtained from this problem is checking the research model to check and find out the comparison of vehicle user’s Parking System Security between cars or motorcycles, as well as the usual parking lots visited. Then, we created a questionnaire. This study used questionnaires with the aim to get respondents’ results about the Parking Security System. Our sampling method is based on respondents aged 17–20 years. Questionnaires are su
The concept of object-oriented (OO) serves is a fundamental approach in the development of models. The stages associated with this method contribute significantly to ensuring that the resulting model is both lucid and...
The concept of object-oriented (OO) serves is a fundamental approach in the development of models. The stages associated with this method contribute significantly to ensuring that the resulting model is both lucid and transparent. The primary objective of the study is to create a decision model for evaluating student performance. Floating fuzzy logic (FFL) is employed as a technique to handle fluctuating data within the model. Moreover, OO conception plays a central role in analyzing, designing, and constructing the model through the utilization of four distinct types of Unified Modeling Language (UML) diagrams: object, activity, state-machine, and sequence diagrams. The model itself is crafted using the Python programming language and executed in the Google Colab platform. Additionally, this model has the capability to simulate changes in students' performance on a semester-by -semester basis, exhibiting a variance of 15 % when compared to the conventional fuzzy logic model.
In real life, many activities are performed sequentially. These activities must be carried out sequentially, such as the assembly process in the manufacturing production process. This series of activities cannot be re...
In real life, many activities are performed sequentially. These activities must be carried out sequentially, such as the assembly process in the manufacturing production process. This series of activities cannot be reduced or added so that the main goal of the series of activities is achieved. Apart from that, there are also time series events that occur naturally, such as rainy and hot conditions in a certain area. The classification process of time series activities is very important to see the possibility of anomalies occurring. The significant development of machine learning models in recent years has made the process of classifying time series data increasingly researched. Several previous studies stated that deep learning models were more accurate in classifying time series data. In this paper, we will compare Convolutional Neural Network (CNN) and Transformer deep learning models in classifying time series data. Experimental results using the same public datasets for CNN and Transformer model show that the CNN model is more accurate than the Transformer model. The results of measuring accuracy using confusion matrix show that CNN has an accuracy of 92% and Transformer has an accuracy of 80%.
Refasctoring is a technique used in software development to improve the quality of code without changing its functionality. One metric that is often used to measure code quality is Code Coverage. This study aims to ex...
Refasctoring is a technique used in software development to improve the quality of code without changing its functionality. One metric that is often used to measure code quality is Code Coverage. This study aims to examine refactoring techniques that can maximize Code Coverage Metric. Through the study, identification, evaluation, and summary of empirical evidence from various literature sources are carried out. The results of this study provide guidance on effective refactoring techniques to improve Code Coverage as well as other positive impacts for software development. There are ten refactoring techniques that can be used to improve Code Coverage Metrics in software testing.
Opening or closing dam-gate activities manually conducted in Manggarai dam to control the dam water level. The controlling action operated to avoid the flood possibility occurring in Jakarta city (the Indonesian capit...
Opening or closing dam-gate activities manually conducted in Manggarai dam to control the dam water level. The controlling action operated to avoid the flood possibility occurring in Jakarta city (the Indonesian capital). The study was conducted to develop a smart model for flood controlling based on service or called a service-oriented smart model (SOSM). The water-flow algorithm (WFA), fuzzy logic, object and service-oriented are four main methods operated in the study. The WFA is a central method to model the real water flow in the river coming from Katulampa dam (in Bogor city) until Manggarai dam (in Jakarta). The fuzzy logic functioned to simulate the dam’s water level and the gate open/close decision should be decided by avoiding the bias value. The object-oriented model analysis and design approach, where the unified modelling language (UML) tools are operated to analyze and design the constructed model. Then, the service-oriented conception is used to integrate all sides in implementing the model. Finally, the constructed model can simulate the flood status in Jakarta via status value in decimal numbers with 6 numbers behind the point.
From the year 2019, every aspect of our lives has been negatively impacted as a result of the spread of a deadly disease known as COVID-19, particularly the health and business domains. The primary goal of t...
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Image classification has been instrumental in the interpretation and labeling of images in the field of remote sensing, computer vision, and in robotics applications. Machine learning and artificial intelligence algor...
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ISBN:
(数字)9798350391084
ISBN:
(纸本)9798350391091
Image classification has been instrumental in the interpretation and labeling of images in the field of remote sensing, computer vision, and in robotics applications. Machine learning and artificial intelligence algorithms, particularly artificial neural networks, are extensively utilized for this purpose. In this work we propose the Expanded Latent Space Autoencoder (ELSA) with a case use application to classify land cover data. The main idea on the ELSA network structure is to utilize the latent spaces of multiple internal autoencoders in order to create an expanded latent space. This expanded latent space extracts more information from the input data, and serves as input features for a more simpler classifier network. In order to evaluate the proposed network's ability to extract features and classify complex and multispectral images we employed it to the EuroSAT dataset. The results demonstrate a remarkable capacity for feature extraction using the ELSA network, with lower complexity, trained with a reduced number of images. The classifier network achieved a final accuracy of 98.7%, matching or exceeding the performance of more complex state-of-the-art models.
With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous st...
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