Performance Evaluation Metrics Research Articles

An Enhanced Diabetic Foot Ulcer Classification Approach Using GLCM and Deep Convolution Neural Network

Diabetic Foot Ulcers (DFU) are considered to be a common complication of diabetes, usually resulting in the amputation of lower extremities. Therefore, diagnosing this disease at an early stage is necessary to avoid the accompanying treatment approach, and this results in a significant cost reduction for the patient. To achieve an early diagnosis of this disease, we need to classify a patient's skin as normal or abnormal. A classification process relies heavily on the extracted features. So, we proposed a new technique called CNN_GLCMNet for feature extraction. This technique relies on Convolution Neural Network (CNN) and the Gray-Level Co-Occurrence Matrix (GLCM) techniques to mine abstract features and second-order statistical texture features. Also, Singular Value Decomposition (SVD) is applied to reduce the dimensionality of the obtained features that result from CNN, Next, the GLCM method is applied to extract second-order statistical texture features. Then, these two kinds of features (abstract features and statistical features) are combined and used as input for the classifier. Two classification mechanisms have been adopted in the classification of images into normal and abnormal skin. First, the Deep Neural Network (DNN) classifier achieves the following performance evaluation metrics (accuracy 97.43%, recall 97.25%, specificity 97.59%, precision 97.53%, f1-score 97.38%). Second, the Support Vector Machine (SVM) classifier achieves the following performance evaluation metrics (accuracy 96.93%, recall 96.99%, specificity 96.94%, precision 96.76%, f1-score 96.85%). Since both classifiers have been validated against the DFU dataset using 10-fold cross-validation. The DNN classifiers with our new feature extraction technique achieve better results in terms of accuracy, specificity, precision, recall, and f1-score than in previous work. Furthermore, a comparison of DNN and SVM classifiers finds that DNN gives a better result according to performance metrics.

Identification of exon locations in DNA sequences using a fractional digital anti-notch filter

Identification of protein coding region (exon) locations in DNA sequences is a fundamental initial step in genomic signal processing (GSP). Several techniques have already been applied to achieve this challenging task. However, improvements are still needed. Transforms-based methods and digital filtering are among those techniques that have been widely used. These techniques exploit the period-3 property of protein coding regions. This paper proposes the application of a narrowband bandpass fractional digital filter to extract more selectively the single frequency component corresponding to the frequency f=1/3 from DNA sequences. The ideal fractional digital anti-notch filter has an infinite amplitude at the central frequency and two tuning parameters which may be used to independently adjust the central frequency and the amplitude frequency response. The ideal filter has been approximated and implemented efficiently as an infinite impulse response (IIR) filter. The effectiveness of the proposed method has been assessed in terms of common performance evaluation metrics computed from the results obtained using DNA sequences taken from the National Center for Biotechnology Information (NCBI) and HMR195 datasets using different numerical transformations including Voss mapping and electron–ion potential (EIIP) representation. In addition to overcome the problem of sliding window size encountered in transform-based methods, comparison with existing state-of-the-art methods for exon location identification has demonstrated superiority of the proposed method on benchmark datasets.

Differentiation of breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using deep transfer learning based on DenseNet201.

In order to achieve better performance, artificial intelligence is used in breast cancer diagnosis. In this study, we evaluated the efficacy of different fine-tuning strategies of deep transfer learning (DTL) based on the DenseNet201 model to differentiate malignant from benign lesions on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We collected 4260 images of benign lesions and 4140 images of malignant lesions of the breast pertaining to pathologically confirmed cases. The benign and malignant groups was randomly divided into a training set and a testing set at a ratio of 9:1. A DTL model based on the DenseNet201 model was established, and the effectiveness of 4 fine-tuning strategies (S0: strategy 0, S1: strategy; S2: strategy; and S3: strategy) was compared. Additionally, DCE-MRI images of 48 breast lesions were selected to verify the robustness of the model. Ten images were obtained for each lesion. The classification was considered correct if more than 5 images were correctly classified. The metrics for model performance evaluation included accuracy (Ac) in the training and testing sets, precision (Pr), recall rate (Rc), f1 score (f1), and area under the receiver operating characteristic curve (AUROC) in the validation set. The Ac of the 4 fine-tuning strategies reached 100.00% in the training set. The S2 strategy exhibited good convergence in the testing set. The Ac of S2 was 98.01% in the testing set, which was higher than those of S0 (93.10%), S1 (90.45%), and S3 (93.90%). The average classification Pr, Rc, f1, and AUROC of S2 in the validation set were (89.00%, 80.00%, 0.81, and 0.79, respectively) higher than those of S0 (76.00%, 67.00%, 0.69, and 0.65, respectively), S1 (60.00%, 60.00%, 0.60, 0.66, and respectively), and S3 (77.00%, 73.00%, 0.74, 0.72, respectively). The degree of coincidence between S2 and the histopathological method for differentiating between benign and malignant breast lesions was high (κ = 0.749). The S2 strategy can improve the robustness of the DenseNet201 model in relatively small breast DCE-MRI datasets, and this is a reliable method to increase the Ac of discriminating benign from malignant breast lesions on DCE-MRI.

An improved Gaussian Mixture Model with post-processing for multiple object detection in surveillance video analytics

Gaussian Mixture Model (GMM) is an effective method for extracting foreground objects from video sequences. However, GMM fails to detect the object in challenging scenarios like the presence of shadow, occlusion, complex backgrounds, etc. To handle these challenges, intrinsic and extrinsic enhancement is required in traditional GMM. This paper presents a novel framework that combines improved GMM with postprocessing for multiple object detection. In the proposed system, GMM with parameter initialization is considered an intrinsic improvement. Video preprocessing and postprocessing are considered extrinsic improvements. Integration of morphological operation with GMM helps for better segmentation than traditional GMM, and it also helps to increase detection performance by reducing false positives. Video preprocessing is the process of noise removal that prepares input video ready for further processing. In the final step gradient of morphological operations is used for postprocessing. The proposed approach was tested on challenging surveillance video sequences from benchmark datasets such as PETS 2009 and CD 2014(Change Detection). The experimental results are compared using ground truth and performance evaluation metrics. The results show that the proposed approach performs better than GMM, and the method can detect the object effectively even in illumination variation and partial occlusion.

Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition.

With a large number of Local Binary Patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called the Symmetric Inline Matrix generator method, which acts as a new variant of LBP. The key feature that separates our variant from existing counterparts is that our variant is very efficient in extracting facial expression features like eyes, eye brows, nose and mouth in a wide range of lighting conditions. For testing our model, we applied SIM-LBP on the JAFFE dataset to convert all the images to its corresponding SIM-LBP transformed variant. These transformed images are then used to train a Convolution Neural Network (CNN) based deep learning model for facial expressions recognition (FER). Several performance evaluation metrics, i.e., recognition accuracy rate, precision, recall, and F1-score, were used to test mode efficiency in comparison with those using the traditional LBP descriptor and other LBP variants. Our model outperformed in all four matrices with the proposed SIM-LBP transformation on the input images against those of baseline methods. In comparison analysis with the other state-of-the-art methods, it shows the usefulness of the proposed SIM-LBP model. Our proposed SIM-LBP variant transformation can also be applied on facial images to identify a person's mental states and predict mood variations.

Face presentation attack identification optimization with adjusting convolution blocks in VGG networks

• Empirical performance assessment of six pre-trained deep CNN architectures (VGG16, VGG19, InceptionV3, DenseNet121, MobileNet, and Xception) for the human face liveness detection application. • Modification of all considered pre-trained DCNN models to make them suitable for human face liveness detection. • Observation of the testing accuracy & HTER observed over a diverse number of epochs for assessing the pre-trained DCNN models. • Fine-tuning of the last two convolution blocks of VGG16 and VGG19 at the constant learning rate to get better test accuracy (100%). Advancement in deep learning is mapping to every field of life and applying it to almost all research problems. Numerous Deep Convolutional Neural Network (DCNN) architectures are being proposed, giving different results based on the depth and value of hyperparameters. The entire development of such DCNN architectures from scratch needs a lot of effort, and such architectures may not be used for other applications than the one they are structured for. Transfer learning is a way to modify these pre-trained networks to make them suitable for newer diverse applications. This paper attempts to empirically assess the performance and suitability of existing pre-trained DCNN architectures for human face liveness detection. Due to the advent of ambient computing for contactless identification of humans using their biometric traits, human face liveness detection proves to be an important research area. Six pre-trained DCNN models, alias VGG16, VGG19, DensNet121, Xception, MobileNet, and InceptionV3, are considered for empirical assessment in human face liveness detection. The method is explored using two face liveness detection datasets - NUAA and Replay-Attack. Face Liveness Accuracy, and Half Total Error Rate (HTER) are considered prime performance evaluation metrics. At a learning rate of 10 −4 , the VGG19 network with scenario “Original VGG” gives the highest face liveness detection accuracy, which is the outcome of current research.

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana.

COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situationculminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. Thepandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern,forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, havingknowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view toaccordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemicto both public health and economic sectors. Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory(LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm ConvolutionalNeural Network - Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, SouthAfrica and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developedfor 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively. The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAEand RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesserMSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively. Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africaand Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infectioncases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but alsoin terms of lesser MSE, MAE and RMSE.

Evaluation and screening of porous materials containing fluorine for carbon dioxide capture and separation

There is a significant challenge to discover porous materials that can effectively capture and separate CO2 from natural gas, refining gas, and flue gas, which has attracted attention in dealing with climate warming and energy purification. Recently, hybrid microporous materials with narrow pores and containing fluorine have been rapidly used in gas separation based on physisorption. In this contribution, molecular simulations combined with high-throughput calculations were performed to calculate structural parameters and performance evaluation metrics of 1015 promising adsorbents to rank and screen out the top candidates for CO2/CH4, CO2/H2 and CO2/N2 separation. To the best of our knowledge, this is the first time to unlock this kind of fluorinated material database in CO2 separation, in which statistical information indicates that a large number of interpenetrating structures lead to 64% of ultra-microporous materials and the number of particles per unit volume of fluorine we defined also has a positive effect on the heat of adsorption of CO2. The structural performance relationship reveals a clear picture of strong CO2 capture but poor energy gas (CH4 and H2) storage. The mathematical model established from the geometry and energy descriptors has a strong correlation with the mixture adsorption selectivity in CO2/N2 separation, and the ideal selectivity can be applied to save computing resources in CO2/H2 separation. Cadmium and vanadium with high frequency may represent the new characteristics of the next generation adsorbent for capturing CO2, among the 18 high-performance materials selected according to the adsorption performance score and mixture adsorption selectivity. The centroid density distribution and radial distribution function manifest that CO2 is preferentially close to fluorine atoms and metal atoms. Here, we established an online high-throughput calculation code (https://github.com/oddthinker/HTCS) for adsorption and separation. All of this will provide guidelines for experimental synthesis and large-scale screening of target materials.

The effect of Gaussian noise on pneumonia detection on chest radiographs, using convolutional neural networks

IntroductionChest X-rays (CXR) with under-exposure increase image noise and this may affect convolutional neural network (CNN) performance. This study aimed to train and validate CNNs for classifying pneumonia on CXR as normal or pneumonia acquired at different image noise levels. MethodsThe study used the curated and publicly available “Chest X-Ray Pneumonia” dataset of 5856 AP CXR classified into 1583 normal, 4273 viral and bacterial pneumonia cases. Gaussian noise with zero mean was added to the images, at 5 image noise variance levels, corresponding to decreasing exposure. Each noise-level dataset was split into 80% for training, 10% for validation, and 10% for test data and then classified using custom trained sequential CNN architecture. Six classification tasks were developed for five Gaussian noise levels and the original dataset. Sensitivity, specificity, predictive values and accuracy were used as evaluation performance metrics. ResultsCNN evaluation on the different datasets revealed no performance drop from the original dataset to the five datasets with different noise levels. Sensitivity, specificity and accuracy for the normal datasets were 98.7%, 76.1% and 90.2%. For the five Gaussian noise levels the sensitivity, specificity and accuracy ranged from 96.9% to 98.2%, 94.4%–98.7% and 96.8%–97.6%, respectively. A heat map was used for visual explanation of the CNNs. ConclusionThe CNNs sensitivity maintained, and the specificity increased in distinguishing between normal and pneumonia CXR with the introduction of image noise. Implications for practiceNo performance drops of CNNs in distinguishing cases with and without pneumonia CXR with different Gaussian noise levels was observed. This has potential for decreasing radiation dose to patients or maintaining exposure parameters for patients that require additional radiographs.

A Survey on Depth Ambiguity of 3D Human Pose Estimation

Depth ambiguity is one of the main challenges of three-dimensional (3D) human pose estimation (HPE). The recent strategies of disambiguating have brought significant progress and remarkable breakthroughs in the field of 3D human pose estimation (3D HPE). This survey extensively reviews the causes and solutions of the depth ambiguity. The solutions are systematically classified into four categories: camera parameter constraints, temporal consistency constraints, kinematic constraints, and image cues constraints. This paper summarizes the performance comparison, challenges, main frameworks, and evaluation metrics, and discusses some promising future research directions.

Computational discovery of Metal–Organic Frameworks for sustainable energy systems: Open challenges

Metal–Organic Frameworks (MOFs) are promising functional microporous materials for a variety of next-generation sustainable energy systems. Their large design space makes it impossible to synthesize, test, and screen them all to identify best candidates. The computational discovery of MOFs has thus become a popular research topic, with methodological advances in computational chemistry and data science heavily contributing to this. Structure databases, materials representation, property evaluation methodologies, performance metrics, and search algorithms all pose open challenges for the community to solve. These challenges are summarized and briefly discussed in this study, with a focus on the engineering aspects required for computational MOF discovery to become a reliable tool for industry. As computational discovery workflows are complicated and necessitate skills from a variety of disciplines, bridging the knowledge gap and enhancing collaboration are critical. Despite the challenges, we remain optimistic about the great potential of computational MOF discovery technology.

Analysis of Deep Learning Neural Networks for Seismic Impedance Inversion: A Benchmark Study

Neural networks have been applied to seismic inversion problems since the 1990s. More recently, many publications have reported the use of Deep Learning (DL) neural networks capable of performing seismic inversion with promising results. However, when solving a seismic inversion problem with DL, each author uses, in addition to different DL models, different datasets and different metrics for performance evaluation, which makes it difficult to compare performances. Depending on the data used for training and the metrics used for evaluation, one model may be better or worse than another. Thus, it is quite challenging to choose the appropriate model to meet the requirements of a new problem. This work aims to review some of the proposed DL methodologies, propose appropriate performance evaluation metrics, compare the performances, and observe the advantages and disadvantages of each model implementation when applied to the chosen datasets. The publication of this benchmark environment will allow fair and uniform evaluations of newly proposed models and comparisons with currently available implementations.

30 days mortality prediction and risk factor analysis of Asian patients with ACS using interpretable machine learning algorithm

Abstract Background Thrombolysis in Myocardial Infarction (TIMI) is used to predict the mortality rate in patients with acute coronary syndrome (ACS). TIMI was developed with limited data on the Asian cohort and was based on the Western cohort. STEMI and NSTEMI have separate TIMI scores. There has been limited research on Asian ACS patients using interpretable machine learning (ML) algorithms. Purpose To construct a single 30-day mortality risk scoring system, as well as identify and analyse risk factors in ASIAN patients with ACS, that is applicable to both STEMI and NSTEMI patients, using an interpretable ML algorithm. Methods The National Cardiovascular Disease Database registry data of 9054 patients was used. 70% of the data was used for algorithm development, with the remaining 30% used for validation Fifty-four parameters were considered, demographics, cardiovascular risk, medications, and clinical variables. To provide better guidance and advice for clinical judgement, the gradient boosting algorithm (XGBoost) for classification analysis and SHapley Additive exPlanation (SHAP) value analysis graphs were used. Each indicator's SHAP value indicates the impact on model output (mortality) and was calculated using the XGBoost model. The performance evaluation metric was the area under the curve (AUC). The model was validated with a validation dataset and compared to the conventional score TIMI for STEMI and NSTEMI. Results The performance on validation dataset of the XGBoost algorithm using the top ten predictors from SHAP for; STEMI (AUC = 0.8534, 95% CI: 0.8226–0.8842, Accuracy: 0.8053, Sensitivity: 0.73125, Specificity: 0.81355) and NSTEMI (AUC = 0.8145, 95% CI: 0.77–0.8589, Accuracy: 0.7972, Sensitivity: 0.64356, Specificity: 0.81232) outperformed TIMI score (STEMI AUC = 0.785, NSTEMI AUC = 0.543). Killip class, age, heart rate, fasting blood glucose, ACEI, creatine kinase, systolic blood pressure, HDLC, cardiac catheterization, and oralhypogly are the top ten predictors chosen by the SHAP feature selection in ascending order. Cardiac catheterization and pharmacotherapy drugs as selected predictors improve mortality prediction in STEMI and NSTEMI patients compared to TIMI. The variable names are displayed on the y-axis in ascending order of importance. The average SHAP value is shown next to them. The SHAP value is shown on the x-axis. The colour represents the value of the feature, ranging from small to large, allowing comprehension of the distribution of the SHAP values for each feature (Figure 1). We can see that having a high killip class and being older are linked to a lower survival rate in ACS patients. Cardiac catheterization procedures, as well as the use of ACEI and OHA, both improve patient mortality (Figure 2). Conclusions A single algorithm would classify ACS patients better than TIMI, which requires two distinct scores. In order to better predict 30-day mortality in an ASIAN population, interpretable ML can be used. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1

AN EFFECTIVE HYBRID STOCHASTIC GRADIENT DESCENT ARABIC SENTIMENT ANALYSIS WITH PARTIAL-ORDER MICROWORDS AND PIECEWISE DIFFERENTIATION

Social media networking sites, such as Instagram, Facebook, and Twitter, have become an inextricable part of our everyday lives. These social media networks are useful for sharing news, images, and other information. The study of social media sentiment has recently received a lot of attention, especially in Arabic sentiment analysis. Social media sites are distinguished by unusual language that differs from the traditional format of the language. As a result, there is a necessity for efficient ways for analyzing the massive amount of new word variants that appear regularly in the digital world and online world. This study proposes a piecewise Stochastic Gradient Descent (SGD)-based model for sentiment classification. The TF-IDF-based term weighting scheme is employed for textual feature representation. For enhancing the model performance, stemming and partially ordered microword representation of tweets with varying look ahead distances is employed. Also, various n-gram models are considered for textual feature representation, which also improves the model performance. The proposed model is simulated and evaluated with the help of publicly available tweet corpus9 which is a balanced tweet corpus. The effectiveness of the proposed model is estimated using various performance evaluation metrics. According to the experimental observations, the proposed method accurately categorizes the testing and validation tweets set with an accuracy of up to 92.23%.

Information sharing and fund performance: Evidence from the US mutual fund family

ABSTRACT In this paper, we investigate the information sharing within an organisation and its consequences on mutual fund performance. Using a sample from the US open-end mutual fund industry, information sharing inside an organisation is quantified via the dependence of individual funds from the fund family joint information set. The findings indicate a positive relationship between the degree of information sharing and the resulting fund performance. This remains robust across different performance evaluation metrics and various model specifications. Such superior performance can be attributed to the quality of information shared across affiliated fund members and timely information diffusion within the fund family.

Towards Real-Time Monocular Depth Estimation for Robotics: A Survey

As an essential component for many autonomous driving and robotic activities such as ego-motion estimation, obstacle avoidance and scene understanding, monocular depth estimation (MDE) has attracted great attention from the computer vision and robotics communities. Over the past decades, a large number of methods have been developed. To the best of our knowledge, however, there is not a comprehensive survey of MDE. This paper aims to bridge this gap by reviewing 197 relevant articles published between 1970 and 2021. In particular, we provide a comprehensive survey of MDE covering various methods, introduce the popular performance evaluation metrics and summarize publically available datasets. We also summarize available open-source implementations of some representative methods and compare their performances. Furthermore, we review the application of MDE in some important robotic tasks. Finally, we conclude this paper by presenting some promising directions for future research. This survey is expected to assist readers to navigate this research field.

Novel lightweight video encryption method based on ChaCha20 stream cipher and hybrid chaotic map

<span lang="EN-US">In the recent years, an increasing demand for securing visual resource-constrained devices become a challenging problem due to the characteristics of these devices. Visual resource-constrained devices are suffered from limited storage space and lower power for computation such as wireless sensors, internet protocol (IP) camera and smart cards. Consequently, to support and preserve the video privacy in video surveillance system, lightweight security methods are required instead of the existing traditional encryption methods. In this paper, a new light weight stream cipher method is presented and investigated for video encryption based on hybrid chaotic map and ChaCha20 algorithm. Two chaotic maps are employed for keys generation process in order to achieve permutation and encryption tasks, respectively. The frames sequences are encrypted-decrypted based on symmetric scheme with assist of ChaCha20 algorithm. The proposed lightweight stream cipher method has been tested on several video samples to confirm suitability and validation in term of encryption–decryption procedures. The performance evaluation metrics include visual test, histogram analysis, information entropy, correlation analysis and differential analysis. From the experimental results, the proposed lightweight encryption method exhibited a higher security with lower computation time compared with state-of-the-art encryption methods.</span>

Efficient feature descriptor selection for improved Arabic handwritten words recognition

<span lang="EN-US">Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the </span><em><span lang="EN-US">Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis</span></em><span lang="EN-US"> (IFN/ENIT). Then, we trained the k-nearest neighbor’s algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance evaluation results of the three k-NN generated models, the majority-voting algorithm is used to combine the prediction results. A high recognition rate of up to 99.88% is achieved, far exceeding the state-of-the-art results using the IFN/ENIT dataset. The obtained results highlight the reliability of the proposed system for the recognition of handwritten Arabic words.</span>

Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy

Forecasting renewable energy is essential for achieving a sustainable energy future. This study aimed to develop a hybrid deep-learning-based model for forecasting renewable electricity supply in a case study of South Korea in order to assist national-scale energy plan assessments. A generative model based on the variational auto-encoder (VAE) algorithm allows for the harnessing of numerous samples by solving problems such as a lack of sufficient time-series data and their uncertainties. Long short-term memory (LSTM) networks, which are well-suited to facilitate time-series problems, were used in forecasting, and they were compared to other machine learning-based models, such as the gated recurrent unit, deep neural network, and autoregressive integrated moving average models. Performance evaluation metrics, such as coefficient of determination (R2), root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and weighted absolute percentage error (WAPE), were used to determine the optimal model. Therefore, the developed forecasting model based on the LSTM and VAE is suitable for the case study because it presented the highest R2 score of 0.92 and a decrease of 17%, 24%, 13%, and 14% in the RMSE, MAE, MAPE, and WAPE, respectively. It can be stated that results from this study will have a significant impact on renewable energy planning.

Analysis of public opinion sentiment against COVID-19 in Indonesia on twitter using the k-nearest neighbor algorithm and decision tree

COVID-19 has become an ongoing disease pandemic across the globe. The need for information makes social media such as twitter a place to exchange information. This tweet can be used to see public sentiment towards COVID-19 in Indonesia. Sentiment analysis classifies opinions from tweets that have been processed and classified into different sentiments, namely negative, neutral, or positive. The aim of this paper is to find the algorithm that has the best accuracy. The researcher proposes to compare the K-Nearest Neighbors (KNN) and decision tree algorithms to be used in the classification of sentiment data from tweets related to COVID-19 that took place in Indonesia. The results of the evaluation of performance metrics concluded that the decision tree algorithm has a higher level of accuracy than KNN. Decision tree produces accuracy = 0.765, error = 0.235, recall = 0.76, and precision = 0.767 which is better when compared to KNN which produces accuracy = 0.69, error = 0.31, recall = 0.66, and precision = 0.702.