JOIV : International Journal on Informatics Visualization
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Comparison of VTOL UAV Battery Level for Propeller Faulty Classification Model
Get PDFThe degradation of batteries in UAVs may result in various problems, such as connectivity troubles, flight delays, and unexpected accidents. Flight safety and reliability are affected by propeller efficiency and performance. This study explores an acoustic-based method to classify propeller faulty conditions in Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL UAV). The main objective is to emphasize the difference between classifier models developed using different battery-level flight data. The sound generated by VTOL UAV provides valuable information about the flight performance, essential for effectively monitoring flying conditions and identifying potential faults. This study uses three classification algorithms-Medium Tree (MT), Linear Support Vector Machine (LSVM), and Linear Discriminant (LD), to classify propeller failures of VTOL UAVs. Datasets are collected from three simulated propeller faulty conditions using a wireless microphone connected to a smartphone in an indoor lab environment with a soundproofing mechanism. The Mel Frequency Cepstral Coefficients technique is implemented in MATLAB (R2020a) to extract valuable features from the recorded sound signals. Extracted features from high and low-battery flights are utilized to develop classification models. Classifiers' performance is analyzed to compare the difference between selected models developed using high and low-battery flight data. The accuracy was measured with other samples to test the robustness of classification models. LSVM and MT classification models developed using high-battery flight data produce better accuracy than low-battery flight data in the training and testing phases. LD classification model developed using high-battery flight data produces better accuracy than low-battery flight data in the testing phase only. These results show that battery degradation can affect the performance of the VTOL UAV faulty classification algorithm
Evaluation of the Performance of Kernel Non-parametric Regression and Ordinary Least Squares Regression
Get PDFResearchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors. This paper proposes a new nonparametric regression function for the kernel and employs it with the Nadaraya-Watson kernel estimator method and the Gaussian kernel function. The proposed kernel function (AMS) is then compared to the Gaussian kernel and the traditional parametric method, the ordinary least squares method (OLS). The objective of this study is to examine the effectiveness of nonparametric regression and identify the best-performing model when employing the Nadaraya-Watson kernel estimator method with the proposed kernel function (AMS), the Gaussian kernel, and the ordinary least squares (OLS) method. Additionally, it determines which method yields the most accurate results when analyzing nonparametric regression models and provides valuable insights for practitioners looking to apply these techniques in real-world scenarios. However, criteria such as generalized cross-validation (GCV), mean square error (MSE), and coefficient determination are used to select the most efficient estimated model. Simulated data was used to evaluate the performance and efficiency of estimators using different sample sizes. The results favorable the simulation illustrate that the Nadaraya-Watson kernel estimator using the proposed kernel function (AMS) exhibited favorable and superior performance compared to other methods. The coefficients of determination indicate that the highest values attained were 98%, 99%, and 99%. The proposed function (AMS) yielded the lowest MSE and GCV values across all samples. Therefore, this suggests that the model can generate precise predictions and enhance the performance of the focused data
DDOS Attack Analysis on IoT Device for Smart Home Environment and A Proposed Detection Technique
Get PDFThis study is grounded in a comprehensive review of literature on smart homes and Distributed Denial of Service (DDoS) attacks. To evaluate the defensive capabilities of pfSense and Suricata, a simulated Slowloris DDoS attack was performed on a smart home network, both with and without these security measures. Data was collected for each attack instance, followed by an analysis of the attack's effectiveness and the botnets' responses to refine DDoS assault strategies targeting smart home networks. The results revealed that the network was highly vulnerable without defense mechanisms, collapsing under the attack. In contrast, implementing pfSense and Suricata enabled swift detection and mitigation, neutralizing the attack within 15 seconds. Further testing involved five different scenarios, each assessing the ability of these systems to detect and block DDoS attacks. In all cases, the attacks were identified within 60 seconds. Attackers varied HTTP headers to flood IP-based cameras with packets ranging from 500 to 3000. The findings highlight the significant vulnerability of IoT devices in smart homes to cyber threats. However, deploying pfSense and Suricata proved to be a practical approach for detecting and mitigating DDoS attacks. The research underscores the importance of selecting high-quality hardware, evaluating IoT security features, and adopting proactive security practices to bolster smart home security
EEG Power Analysis of Children with Autism Spectrum Disorders (ASD) Based on EIBI Curriculum Levels
Get PDFEarly Intervention Behavioral Therapy as a method has been shown to aid children diagnosed with Autism in adjusting behavior through Applied Behavior Analysis. While there are three levels of ABA, EIBI does not provide a concrete metric of what separates between the individual levels. The current study focuses on differentiating the electrical patterns found in EEG in children and plans to explore how EIBI can serve across the ABA spectrum. The electrodes F3, F4, C3, C4, P3, P4, O1, and O2 were used to capture the EEG signals and were utilized in estimating the power, spectral density using the Welch method. It was observed during the statistical examination that there existed differences in the results of power across the frequency band amongst the groups. The higher levels of Alpha lead us to believe that there was better emotional management. The chronic group was shown to have more prominent Delta power reflecting weakened control. Comparatively, beginning level’s theta power was found to be higher across all groups showcasing change in attention requiring tasks. Due to greater focus being placed on the lower range frequency activity there existed no noteworthy changes in the Beta and Gamma portions. These findings highlight the role of EIBI in neuromodulation in the Alpha and Delta bands, and its application in the enhancement of emotional and neurological stability. EEG is an effective measure as it quantifies EIBI outcomes. Further studies should examine the long-term effects and enhance curriculum concepts to increase the efficacy of the interventions
Switching On/Off Air Conditioner and Fan Alternately based on IoT Motion Detection and Room Temperature
Get PDFThe Internet of Things (IoT) connects electrical appliances that enable data transfer for communication without human intervention. IoT has evolved, and its implementation has been extended to residential areas. It can be said that all residents use fans as cooling appliances. In Malaysia, having a fan is not sufficient due to its hot temperature throughout the year. Therefore, most of the residents use air conditioners as an additional cooling appliance. Using air conditioners regularly could contribute to high energy consumption. Furthermore, excessive energy consumption occurs when an occupant of a residential building forgets to switch off electrical appliances such as fans and air conditioners. In addition, leaving electrical appliances turning on when nobody is at home just wastes energy. This work aims to develop an IoT-based smart home controlling system for minimizing energy consumption. This system enables automatic control that depends on room temperature and motion detection. Various types of sensors, such as temperature sensor, humidity sensor, and motion sensor, are used to switch on/off the air conditioner and fan. The air conditioner and fan will be alternately switched on and off depending on the ideal room temperature. The testing results show a significant reduction in energy consumption and a promising decrease in the electricity bill. Future works should be focusing on determining the over limit energy consumption. On top of that, this research would be best to try on simulation to get better results
Datasets for Artificial Intelligence-based Spine Analysis: A Scoping Review
Get PDFThe advancement of artificial intelligence (AI) and intense learning is key to automating the diagnosis and inspection of spinal-related pathologies. This automation reduces the need for human manual analysis. Reducing the burden on the healthcare system and the risk of human error. Spine medical images have several modalities, such as X-rays, computed tomography (CT), and magnetic resonance imaging (MRI). Each modality captured the vertebral features differently. The choice of modality affects the performance of the applied algorithms. It is also important to note that a large amount of data is better for training AI algorithms, profound learning algorithms. However, medical images are often limited owing to privacy concerns and the lack of open-source databases. Therefore, it is essential to identify data sources to ensure the success of AI projects for spine analysis. This review discusses available datasets and their characteristics, such as modality, size, and labels. Additionally, the demographics and applications of the data were also discussed. The platform utilized to obtain related literature in this study is Lens. A scoping review was used in this study to extract information from related literature. The number of literature included in this study is 39. A total of 43 datasets, which include 32 private and 11 public datasets, are discussed in this review. This work will benefit researchers and developers developing an AI-based spinal analysis system
Handwritten Hiragana Letter Detection Using CNN
Get PDFHiragana is one of the primary alphabets used in Japanese. Hiragana is a phonetic symbol; each letter represents one syllable. Hiragana letters are formed from curved lines and strokes. However, detecting Hiragana letters causes many errors because people still rely on their vision to detect the letters, especially people familiar with them for the first time. It will be difficult and not very clear to read the letters. Therefore, a Convolutional Neural Network (CNN) method is used to detect handwritten Hiragana letters and help people who first get to know Hiragana letters when the letters are too complicated for human eyes to detect. This research uses the YOLOv8 model as a handwritten Hiragana letter detection algorithm. The Hiragana letters to be detected are basic letters with 46 characters. This research uses the YOLOv8 model run on Google Collaboratory with the Ultralytics library version 8.0.20 using the Python programming language. The dataset is collected from the internet and annotated using the Roboflow framework and dataset 4600 Hiragana letters. From the test results, the best model is YOLOv8l using SGD optimizer and learning rate 0.01 with a precision value of 98.5%, recall value of 95.7%, f1-score value of 97.1%, and mAP value of 95.5%. In the future, we aim to expand the number of datasets and employ a broader range of hyperparameter values to optimize the classification precision and accuracy of the Hiragana Letter Detection system
Performance Comparison of Zevenet Multi Service Load Balancing with Least Connection and Round Robin Algorithm
Get PDFAmikom Purwokerto University concentrates on Technology and Digital Business. This requires technology to be utilized optimally. The use of technology, especially internships, will make various jobs easier. KRS online is taking lecture schedules online via the AMIKOM Purwokerto Student website. There are several problems with the web server that arise due to the increasing need for information access, which causes the data traffic load to increase. Increased data traffic causes workload overload, resulting in server downtime. Experimental methods were used in this research to look for the causes of the web server's downtime. Then, implement the technology. The purpose is to evaluate the Zevenet load balancer performances by comparing the round-robin and least-connection algorithms. The decision is which algorithm will be used best to implement the Zevenet Load balancer to achieve a more efficient backend server traffic cluster distribution. The TIPHON standard Quality of Service parameters used in Zevenet Load Balancer performance testing are throughput, delay, jitter, packet loss, and CPU usage. The quality-of-service parameter test results show that the Zevenet Load Balancer with the round-robin algorithm has superior performance and shows less CPU usage. It is concluded that using the round-robin algorithm in implementing the Zevenet load balancer to overcome the problem of data traffic load sharing and minimize server downtime on the Student Amikom Purwokerto web server is more appropriate and more effective
Data Pre-processing of Website Browsing Records: To Prepare Quality Dataset for Web Page Classification
Get PDFThe increased usage of the internet worldwide has led to an abundance of web pages designed to supply information to internet users. The use of web page classification is becoming increasingly necessary to organize the growing number of web pages. This classification model serves as a tool to restrict internet usage to specific categories of web pages. To develop the classification model, it’s crucial to check the quality of the dataset, as it determines the performance of the web page classification model. Raw datasets are typically unreliable and subject to noise, which complicates data analysis. This is why data pre-processing is necessary to prepare the dataset properly. In this study, website browsing records serve as the dataset. The primary goal of this paper is to investigate data pre-processing techniques for website browsing records, focusing on Game and Online Video Streaming web pages. Data pre-processing involves two main steps: data cleaning and web content pre-processing. After completing the data cleaning process, the datasets are reduced from the original. This demonstrates that many datasets can be eliminated due to their inactivity or unsuitability as the datasets for Game and Online Video Streaming web pages. Meanwhile, web content pre-processing removes noise from an HTML document, retaining only relevant words that can represent the web page by creating a word cloud image. Convolutional Neural Networks (CNN) will be used to construct a model for categorizing web pages to determine whether they fall under Game or Online Video Streaming. The pre-processed data will be used as the input for this model
Impact of External Factors on Determining E-commerce Benefits among SMEs in Jakarta and Palembang
Get PDFTechnological trends have triggered a more advanced technology-based approach to influencing customers and encouraging the growth of the e-commerce industry in Indonesia. E-commerce is now considered a bridge for MSME players to market with a broader reach, even to international markets, which is one of the factors for the rise of Indonesian MSMEs, as well as the growth of the digital economy in Indonesia. However, very few MSMEs are still using technology to grow their business. This article examines how external factors, specifically customers and competitors, can encourage SMEs in Jakarta and Palembang to adopt e-commerce and promote e-commerce adoption. Small and medium enterprises benefit from applying this technology. The research method used was the quantitative conjoint type, using primary data in questionnaires distributed to 101 MSME owners in Jakarta and Palembang using a Google form. The data analysis technique in this study used structural equation modeling (SEM) based on partial least squares (PLS) by examining the measurement model and model structure. The results of this study indicate that perceived customer benefits have a significant influence on external relationships and are found to influence cost reduction, as well as a significant influence on loyalty. customer's status. At the same time, perceptions of competitive value increase relationships with external parties and customer loyalty. In contrast, competitive value only affects customer loyalty without significantly affecting cost reduction and external relation