Indonesian Journal of Electrical Engineering and Computer Science
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Hybrid optimized multi-objective honey badger algorithm and NSGA-II for feature selection problems
Get PDFOne of the most important aspects of classification is choosing features in such a way as to get rid of redundant or irrelevant elements in the dataset. For the most part, multi-objective feature selection strategies have been offered by a number of scholars as a strategy for this aim. On the other hand, these techniques frequently fail to simultaneously improve classification accuracy while removing redundant feature combinations. This article presents a wrapper-based feature selection strategy that strikes a compromise between classification accuracy and redundancy reduction by combining features of the multi objective (MO) based honey badger algorithm (MO-HBA) and non-dominated sorting genetic algorithm-II (NSGA-II). The technique was developed as part of this investigation. Increasing the accuracy of the classification while simultaneously reducing the number of redundant characteristics is one of the optimizations aims of this approach. The MO-HBA shows excellent performance in exploration and exploitation. A Kernel version of the extreme learning machine (KELM) is used for the process of selecting the features to use. In order to evaluate how well this method of feature selection performs, eighteen benchmark datasets are utilized, and the results are compared to four established methods of multi-objective feature selection based on different metrics
Evaluating the efficacy of univariate LSTM approach for COVID-19 data prediction in Indonesia
Get PDFThe coronavirus disease 2019 (COVID-19) pandemic, originating in 2020, has emerged as a critical global issue due to its rapid and widespread transmission. Indonesia, among the affected nations, has taken measures to address the situation, including the development of a deep learning model for predicting future COVID-19 infection and spread. This predictive tool serves as a valuable reference for the government and stakeholders, aiding them in making informed decisions and implementing appropriate measures to contain the virus. The deep learning model employs the long short-term memory (LSTM) algorithm, chosen for its ability to recognize temporal patterns in the country’s COVID-19 data. The model creation process involves data collection, preprocessing, model architecture planning, modeling, training, and evaluation. Two LSTM models were developed: a univariate and a multivariate model. Following thorough training and evaluation, the univariate model emerged as the superior choice, boasting evaluation metrics of 16.72 for mean absolute percentage error (MAPE) and 66.36 for root mean squared error (RMSE). This model was then deployed on a publicly accessible website, presenting visualizations of past COVID-19 data and predictions of future cases through line graphs. This user-friendly platform enables the public to access and analyze the data easily
Modified back-line inset feed 1x4 array microstrip antenna for 5.8 GHz frequency band
Get PDFThis paper presents the design of 1x4 array microstrip antenna utilizing modified backline feeding technique at 5.8 GHz frequency band. The antenna, designed on flame retardant (FR-4) substrate with a dielectric constant of 4.4, aims to achieve reduced harmonics and mutual coupling between closely spaced antenna elements. The primary scope of the paper is investigating the performance of a single band microstrip antenna employing the proposed modified backline feeding method. Moreover, developed design came out with the result and critical analysis by various parameters such as, gain, return loss, voltage standing wave ratio (VSWR), and directivity. Therefore, the proposed design of microstrip antenna with backward linefeed (BLF) demonstrates a directivity of 10.29 dBi, return loss of -21.947 dB, and VSWR of 1.173; are significant improvement compared to recent literature shown in this paper. The adoption of proposed back line feeding technique (BLF) represents a promising alternative for addressing poor wireless connectivity issues in terms of antenna design, gain, and direction within microstrip technology
Hybrid model of convolutional neural network and long short term memory for heart disease prediction
Get PDFData mining is a process that assists in uncovering meaningful data from large, disorganized datasets. This research is being conducted to predict heart disorders by using available data to make predictions for the future. The approach is carried out in several stages, such as pre-processing the data, extracting relevant features, and classifying the data. all of these steps are essential for predicting heart disease. The deep learning models is already proposed by the researches for the heart disease prediction. This work introduces a hybrid deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) to predict heart disease. The proposed model has been implemented in python, and its accuracy, precision, and recall have been evaluated
Plant disease classification using novel integration of deep learning CNN and graph convolutional networks
Get PDFPlant diseases present substantial challenges to global agriculture, significantly affecting crop yields and jeopardizing food security. Accurate and timely detection of these diseases is paramount for mitigating their adverse effects. This paper proposes a novel approach for plant disease classification by integrating convolutional neural networks (CNNs) and graph convolutional networks (GCNs). The model aims to enhance classification accuracy by leveraging both visual features extracted by CNNs and relational information captured by GCNs. Using a Kaggle dataset containing images of diseased and healthy plant leaves from 31 classes, including apple, corn, grape, peach, pepper bell, potato, strawberry, and tomato. Standalone CNN models were trained on image data from each plant type, while standalone GCN models utilized graph-structured data representing plant relationships within each subset. The proposed integrated CNN-GCN model capitalizes on the complementary strengths of CNNs and GCNs to achieve improved classification performance. Through rigorous experimentation and comparative analysis, the effectiveness of the integrated CNN-GCN approach was evaluated alongside standalone CNN and GCN models across all plant types. Results demonstrated the superiority of the integrated model, highlighting its potential for enhancing plant disease classification accuracy
Exploring the intricacies of human memory and its analogous representation in ChatGPT
Get PDFHuman memory and ChatGPT both rely on associations and patterns to generate contextually relevant responses. We explore how they work in tandem. Both use associations to activate related information when prompted. Memory forms generic representations that become precise with added details, similar to ChatGPT's responses with specific prompts. Activation Through Cues: Memory and ChatGPT recall based on cues or prompts, influenced by input. Level of Detail: Memory constructs mental images based on information, just as ChatGPT responds to input details. Dynamic Nature: Both adapt to memorize repeated segments with diverse continuations. By understanding the dynamics of memory and its parallels with ChatGPT's response generation, researchers can further enhance the model's capabilities. Fine-tuning the model's ability to activate relevant information, generate specific responses, and adapt to varying levels of detail and specificity in the input can contribute to its overall performance and relevance in various language tasks
Fuzzy expert system design for detecting stunting
Get PDFStunting is a chronic nutritional problem that occurs in toddler due to lack of nutritional intake which results in impaired growth toddler. Usually, toddler who experience stunting are characterized by not increasing weight over a long period of time. Application utilization health which makes it easier for users to access information, one of which can be used to identify toddler who are stunted by selecting symptoms. The symptoms experienced by toddlers go through a system known as the system expert. In this research an expert system will be developed that is capable of early detection developmental disorders in toddlers using the Mamdani fuzzy method. The results obtained from this research are an expert system design for early detection of stunting using the Mamdani fuzzy method. The Mamdani fuzzy method was implemented to group the criteria for toddlers who fall into the stunting category or not from the initial data which is still gray because they are still unsure whether to categorize the toddler as having stunting or not. The detection accuracy rate using the Mamdani fuzzy method is 80.87% compared to expert diagnosis
Melanoma image synthesis: a review using generative adversarial networks
Get PDFMelanoma is a highly malignant skin cancer that may be fatal if not promptly detected and treated. The limited availability of high-quality melanoma images, which are needed for training machine learning models, is one of the obstacles to detecting melanoma. Generative adversarial networks (GANs) have grown in popularity as a strong technique for image synthesis. This research is also targeted at the sustainable development goal (SDG) for health care. In this study, we survey existing GAN-based melanoma image synthesis methods. In this work, we briefly introduce GANs and how they may be used for generating synthetic images. Ensuring healthy lifestyles and promoting well-being for everyone, regardless of age, is the main aim. A comparative study is carried out on how GANs are used in current research to generate melanoma images and how they improve the classification performance of neural networks. Various public and proprietary datasets for training GANs in melanoma image synthesis are also discussed. Lastly, we assess the examined studies' performance using measures like the Frechet Inception distance (FID), Inception score, structural similarity ındex (SSIM), and various classification performance metrics. We compare the evaluated findings and suggest further GAN-based melanoma image-creation research
MDVC corpus: empowering Moroccan Darija speech recognition
Get PDFAutomatic speech recognition (ASR) technology has significantly transformed human-machine interactions, but it remains limited in its representation of diverse languages and dialects. Moroccan Darija, the lively Moroccan dialect, has long been underrepresented in the realm of language technology. To address this gap, we present a novel corpus of audio files accompanied by meticulously transcribed Moroccan Darija speech. The corpus comprises 1,000 hours of diverse content, featuring multiple Moroccan accents, extracted from 80 YouTube channels. To standardize the representation of Moroccan Darija in our corpus, we made efforts to establish consistent writing norms and conventions. In addition to the dataset creation, we applied fine-tuning using the Wav2Vec2 model on the Moroccan Darija voice corpus (MDVC) dataset achieving a remarkable word error rate (WER) of 9%. This article discusses the current state of Moroccan Darija research, highlighting the scarcity of resources and the need for robust ASR systems. Our contribution offers a valuable resource for researchers and developers, and by standardizing the Darija language, we strive to improve ASR system for this low resource language
Identification of soluble solid content and total acid content using real-time visual inspection system
Get PDFThis paper presents the framework for identifying materials using a fused descriptor-based approach, leverage computer vision techniques. The system is structured into three phases: derivation, extraction, and portrayal. Initially, the system employs K-means gathering techniques for establishing derivation. Following derivation, the system utilizes variety, texture, and shape-based feature extraction methods to extract relevant features from the soluble solid content and total acid content using real-time visual inspection system. A “consolidating” fusion feature is explored in the final phase using classification algorithms like C4.5, support vector machines (SVM), and k-nearest neighbors (KNN). The performance evaluation of the recognition system demonstrates promising results, with accuracy rates of 97.89%, 94.60%, and 90.25% achieved by using C4.5, SVM, and KNN separately. This indicates that the proposed fusion strategy effectively supports accurately recognizing materials using a fused descriptor-based approach