An integrated approach is necessary for implementing a model that involves multiple actors. One effective way to achieve this is through a service-oriented approach. The objective of the study was to develop a service...
An integrated approach is necessary for implementing a model that involves multiple actors. One effective way to achieve this is through a service-oriented approach. The objective of the study was to develop a service-oriented fuzzy model, which combines the functional-structural plant modelling (FSPM) method for the plant computational model (PCM) and the fuzzy logic method for both PCM and the decision support model (DSM). This combined method aims to model the plant behaviour morphologically and propose investment decisions in agriculture, specifically for the hydroponic cultivation of Bok Choy, a green-leaf vegetable. The accuracy of the interconnected model application based on the service concept, known as the service-oriented fuzzy smart model (SOFSM), reached 94.33%.
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.
Deep learning models require large amounts of data to be trained to fulfill their potential. To solve this problem, we propose a novel method for creating high-quality photorealistic synthetic training data and compar...
Deep learning models require large amounts of data to be trained to fulfill their potential. To solve this problem, we propose a novel method for creating high-quality photorealistic synthetic training data and compare its performance to real data for object detection. We introduce the RealStreet and SynthStreet datasets, which were designed to enhance a safety analysis for road object detection. The objective of the project is to provide a useful synthetic environment for learning and building a road traffic experience by imitating the real environment as nearly as possible. This will improve the safety of road users and enable testing and planning before actual, dangerous events arise. The RealStreet data-set was collected in real-world scenarios of an urban city, while the SynthStreet closely recreates the RealStreet scenes: field of view, road objects, such as pedestrians, cyclists, vehicles, and background information buildings are matched. We study the performance and behavior of a network model trained on real and synthetic data-sets and both with various ratio mixes. Our approach is to evaluate data-set performances using a state-of-the-art method for object detection tasks in learning from synthetic data. We also compare the performance of each dataset, which is evaluated on real-world data, to determine the possibility of synthetic data-set benefits and analyze the effect of limited real-world data. However, it is important to note that synthetic data-sets may not always accurately represent the variability and complexity of real-world environments and may not generalize well to real-world scenarios. Therefore, it is important to carefully evaluate the performance of deep learning models trained on synthetic data and validate their results using real-world data.
A new three dimensional approach to the chaos game representation of protein sequences is explored in this thesis. The basics of DNA, the synthesis of proteins from DNA, protein structure and functionality and sequenc...
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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%.
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.
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.
Real-world practical systems inherently exhibit non-linearities in their dynamics. Also, it is known that a time-varying delay exists in the system state or input-output. Combined, it affects the stability of the clos...
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Real-world practical systems inherently exhibit non-linearities in their dynamics. Also, it is known that a time-varying delay exists in the system state or input-output. Combined, it affects the stability of the closed-loop system. It also increases the complexity of the controller design. In-depth controller design research on the class of Nonlinear Systems with Time-Varying Delay (NSTVD) has been the focus of the control community for many years. However, there is a lack of Systematic Literature Review (SLR) and classifications of the papers on this topic. This paper aims to review controller design utilizing a neural network model for the class of NSTVD systems. The study employs Kitchenham’s SLR method to gather, analyze and synthesize published papers from reliable databases between 2017 and 2021. The bibliometric analysis for the selected 38 papers reveals the prolific authors, countries, affiliations, publishers, co-authorship network, co-occurrences of keywords, and ten most-cited papers. Finally, this paper developed a conceptual map outlining six multi-layered findings: the addressed problem, control design method, nonlinear system properties, time-varying delay properties, system constraint properties, and actuator limit properties. A brief qualitative analysis of the ten most-cited papers is performed based on the map. The findings highlighted that the proposed methods have shown encouraging results in the simulation domain and can be used as a source of inspiration for future studies and implementation of the neural controller design of the NSTVD system.
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.
The point of Agile Methodology is continuous improvement, delivering a small feature quickly without sacrificing the feature quality; every sprint must be better than the previous sprint, and better can be fewer bugs,...
The point of Agile Methodology is continuous improvement, delivering a small feature quickly without sacrificing the feature quality; every sprint must be better than the previous sprint, and better can be fewer bugs, faster development, and testing. We will present how we reduce production bugs by customizing our sprint iteration. As we know, bugs are unavoidable, there is no software engineer that can make software without a bug; however, we can reduce bugs in production if we can find bugs in lower environments as early as possible. The case study in this paper was taken from one of technology company in Indonesia, the activity was done by the Quality Engineer (QA) Team. We will show that shift-left testing can help us reduce bugs in production. Testing is part of agile methodology, and the main idea of shift-left testing is to move testing early and could be done by any team member, not only QA. We include shift-left testing in our agile methodology for one year in 2022 and compare the result in the previous year.
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