Image Classification of Traditional Indonesian Cakes Using Convolutional Neural Network (CNN)

Indonesia is one of the countries famous for its traditional culinary. Traditional cakes in Indonesia are traditional snacks typical of the archipelago's culture which have a variety of textures, shapes, colors that vary and some are similar so that there are still many people who do not know the name of the cake from the many types of traditional Indonesian cakes. The problem can be solved by creating a traditional cake image recognition system that can be programmed and trained to classify various types of traditional Indonesian cakes. The Convolutional Neural Network method with the AlexNet architecture model is used in this research to predict various kinds of traditional Indonesian cakes. The dataset used in this research is 1846 datasets with 8 classes of cake images. This study trained the AlexNet model with several optimizers, namely, Adam optimizer, SGD, and RMSprop. The best parameters from the model testing results are at batchsize 16, epoch 50, learning rate 0.01 for SGD optimizer and learning rate 0.001 for Adam and RMSprop optimizers. Each optimizer tested produces different accuracy, precision, recall, and f1_score values. The highest test results that have been carried out on the image dataset of typical Indonesian traditional cakes are obtained by the Adam optimizer with an accuracy value of 79%

The Turbofan Engine Remaining Useful Life Prediction Using 1-Dimentional Convolutional Neural Network

Turbofan engines have been the dominant type of engine in aircraft for the last forty years. Ensuring the quality of these engines is crucial for flight safety, particularly for long-distance flights. However, their performance degrades over time, impacting flight safety. To address this issue, it is essential to predict potential engine failures by estimating the Remaining Useful Life (RUL) of the engines Deep learning, especially Convolutional Neural Networks (CNNs), has demonstrated exceptional proficiency in handling intricate, non-linear data, leading to improved RUL predictionsdue to their ability to process complex and non-linear data. In this project, a 1-D CNN is used to predict RUL using the NASA C-MAPSS FD001 dataset, which consists of 3 settings and 21 sensors, though sensors with stagnant readings are excluded. The dataset is normalized using min-max and z-score methods, and then segmented into sequences for input into the 1-D CNN model. Various training scenarios were evaluated, with the best RMSE of 3.26 achieved using 10 epochs, a learning rate of 0.0001, and z-score normalization. The results indicate that feature selection can produce a lower RMSE compared to scenarios without feature selection

An Improved Myocardial Infarction Detection using Convolutional Neural Network and Graph Neural Network Algorithm

Myocardial infarction (MI) is a crucial health problem and its mortality rate is higher than that of cancer. It is the damage and death of heart muscle from the sudden blockage of a coronary artery by a blood clot. Although lots of researches have been carried out with impressive performance record for detection of MI, however, existing approaches for MI detection can be improved upon for better performance. A vital piece of medical technology that aids in the diagnosis of a number of heart-related disorders in patients is an electrocardiogram (ECG). To find significant episodes in long-term ECG data, an automated diagnostic method is needed. Cardiologists face a very difficult problem when trying to quickly examine long-term ECG records. To pinpoint critical occurrences, a computer-based diagnosing tool is necessary. In this study we employ Convolutional Neural Network (CNN) algorithm with Graph Neural Network (GNN) to select best features and make appropriate classifications. The result of the study gave f1 score of 99.58%, precision of 99.5% and an accuracy of 99.72%. Our proposed model have shown a significant improvement in the detection of MI, this will aid in effectively addressing the challenge of performance drawback in this domain of research

Electrical Energy Monitoring and Analysis System at Home using IoT-based Prophet Algorithm

Electrical energy is one of the necessities of human life, especially in modern society in urban areas. With a monitoring device for electrical energy consumption using IoT technology, the results of the development show that the monitoring system works well, but the results show that current and voltage measurements are still less accurate. Therefore, in this study, an Electrical Energy Analysis and Monitoring System were developed using the IoT-Based Prophet Algorithm. Data collection was obtained from electrical energy using the PZEM-004T module sensor device used at home and the energy data obtained were stored in a MySQL database. This PZEM data retrieval will appear in real time on the Monitoring Website. The dataset was processed by implementing the Prophet Algorithm, evaluating the model and visualizing the prediction results on the analysis website. Testing using Mean Absolute Percentage Error (MAPE). For design, this system uses energy data and data retrieval time as parameters in the monitoring system for the use of electrical energy at home. Analysis of data taken from electrical energy monitoring was predicted by the model created by the Prophet Algorithm and tested with MAPE to see how accurate the predicted value is in the Prophet Algorithm model. Predictions in this study get an error value of less than 10%, namely 6.87%, which means it is very accurate in predicting the prophet algorithm at home

A Hybrid of Fuzzy C-Means for the segmentation in CT scan and X-ray images for screening the COVID-19 patients

In this paper, using CT scan and X-ray images, we present a hybrid approach, based on combining fuzzy C-means with k-means clustering, to evaluate and determine pneumonia infection caused by the coronavirus disease (COVID-19). To achieve this objective, we introduce a hybrid method that combines fuzzy C-means clustering with K-means clustering. This hybrid approach is designed to effectively segment object boundaries within medical images, enabling the precise identification of pneumonia-related features. In addition to our hybrid method, we compare its performance with two other segmentation approaches: the Expectation Maximization (EM) algorithm and 2D Entropy segmentation. Which, the method we propose uses a comparison between the performances of the based on a database of medical imaging test. Experimental results showed that the proposed approach outperforms, it was found that the hybrid fuzzy C-means algorithm segmentation images methods give better performance in terms of accuracy, precision, and F-measure, which is effective in boundaries segmentation. Comparative results of the accuracy and image quality index demonstrate the robustness of AI. It also helps to improve work efficiency with accurate analysis of COVID-19 infection on CT scan and X-rays. In addition, the approach helps radiologists make clinical decisions for diagnosis, follow-up, and prognosis

Analysis and Implementation of Blowfish and LSB Algorithm on RGB Images using SHA-512

The growth of the internet globally keeps increasing as time goes. There's a big amount of data type saved there too. Those data need to be secured so anyone who doesn't have the right to access them can access it. The purpose of this article is to secure text information into image media using the Blowfish method for encrypting text information and securing it using the Hash function SHA-512 and then embedded it in image media using the Least Significant Bit (LSB) method. The result of implementing those methods using image media sized 138Kb and 39.85Kb with plaintext measuring 27 and 85 characters shows that integrity data is secured with SHA-512 method. The test result using PSNR method to get the score of image quality after embedding information to the image shows that the average number of PSNR’s score is 70,74 dB which means the quality is good and has less difference from the original image

Fake News Detection Using Optimized Convolutional Neural Network and Bidirectional Long Short-Term Memory

The spread of fake news in the digital age threatens the integrity of online information, influences public opinion, and creates confusion. This study developed and tested a fake news detection model using an enhanced CNN-BiLSTM architecture with GloVe word embedding techniques. The WELFake dataset comprising 72,000 samples was used, with training and testing data ratios of 90:10, 80:20, and 70:30. Preprocessing involved GloVe 100-dimensional word embedding, tokenization, and stopword removal. The CNN-BiLSTM model was optimized with hyperparameter tuning, achieving an accuracy of 96%. A larger training data ratio demonstrated better performance. Results indicate the effectiveness of this model in distinguishing fake news from real news. This study shows that the CNN-BiLSTM architecture with GloVe embedding can achieve high accuracy in fake news detection, with recommendations for further research to explore preprocessing techniques and alternative model architectures for further improvement

Optimization of Distributed RSA Encryption and Decription Processing Using Process Scheduling Method In Single Board Computer Cluster Architecture (SBC)

Data security is still a major issue regarding the need for data confidentiality. The encryption process using the RSA algorithm is still the most popular method used in securing data because the complexity of the mathematical equations used in this algorithm makes it difficult to hack. However, the complexity of the RSA algorithm is still a major problem that hinders its application in a more complex application. Optimization is needed in the processing of this RSA algorithm, one of which is by running it on a distributed system. In this paper, we propose an approach with a FIFO process scheduling algorithm that runs on a single board computer cluster. The test results show that the allocation of resources in a system that uses a FIFO process scheduling algorithm is more efficient and shows a decrease in the overall processing time of RSA encryption

The Eye and Nose Identification Chip Controller-Based on Robot Vision Using Weightless Neural Network Method

Increasingly advanced image analysis in computer vision, allowing computers to interpret, identify, and analyze pictures with accuracy comparable to humans. The availability of data sources in decimal, hexadecimal, or binary forms enables researchers to take the initiative in applying their study findings. Decimal formats are typically used on traditional computers like desktops and minicomputers, whereas hexadecimal and binary formats were utilized on single-chip controllers. Weightless Neural Network is a method that can be implemented in a single chip controller. The aim of this research is to develop a facial recognition system, for eye and mouth identification, that works in a single chip controller or also called a microcontroller. The suggested method is a Weightless Neural Network with Immediate Scan approach for processing and identifying eye and nose patterns. The data will be handled in many memory locations that are specifically designed to handle massive volumes of data. The data is made up of primary face data sheets and face input data. The data sets utilized are (x,y) pixels, and frame sizes range from 90x90 pixels to 110x110 pixels. Each face shot will be processed by selecting the region of the eyes and nose and saving it as an image file. The eye and nose will identify the face frame. Next, the photos will be converted to binary format. A magazine matrix will be used to transmit binary data from a minicomputer to a microcontroller via serial connection. Based on a known pattern, the resultant similarity accuracy is 83,08% for the eye and 84,09% for the sternum. In contrast, the similarity percentage for an eye ranges from 70% to 85% for an undefined pattern

Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering

Recommendation system always involves huge volumes of data, therefore it causes the scalability issues that do not only increase the processing time but also reduce the accuracy. In addition, the type of data used also greatly affects the result of the recommendations. In the recommendation system, there are two common types of data namely implicit (binary) rating and explicit (scalar) rating. Binary rating produces lower accuracy when it is not handled with the properly. Thus, optimized K-Means+ clustering and user-based collaborative filtering are proposed in this research. The K-Means clustering is optimized by selecting the K value using the Davies-Bouldin Index (DBI) method. The experimental result shows that the optimization of the K values produces better clustering than Elbow Method. The K-Means+ and User-Based Collaborative Filtering (UBCF) produce precision of 8.6% and f-measure of 7.2%, respectively. The proposed method was compared to DBSCAN algorithm with UBCF, and had better accuracy of 1% increase in precision value. This result proves that K-Means+ with UBCF can handle implicit feedback datasets and improve precision