International Journal of Machine Learning
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Beyond Accuracy: A Bayesian Metric for Binary Classifier Evaluation in Imbalanced Crop Disease Data
When classes are imbalanced, the evaluation of binary classification algorithms presents a structural problem: the prevalence of the positive class varies widely across practical applications, from fraud detection and system monitoring to disease identification in medical and epidemiological settings. In these scenarios, classical accuracy yields misleadingly high values that fail to capture true discriminative capacity, as it is operates as a linear function of prevalence, dominated by the majority class and unable to penalize the trivial classifier that labels everything as the dominant class. To address this limitation, Bayesian Geometric Accuracy, defined as the geometric mean of the positive and negative predictive values, is proposed. Unlike classical accuracy, this metric is nonlinear in prevalence, vanishes at both extremes, and penalizes class imbalance by construction because predictive values explicitly depend on prevalence through Bayes' theorem. The equivalence condition between both measures yields a degree 3 polynomial in prevalence, whose coefficients are expressed in closed form in terms of sensitivity, specificity, and the Youden index. This polynomial has exactly two roots in the unit interval, and their separation constitutes a principled measure of the classifier's discriminative strength. The framework is illustrated through a multilayer perceptron trained on spectral indices for the binary classification of disease presence in potato crops, where classical accuracy is 0.868 while Bayesian Geometric Accuracy yields 0.655, a gap that reveals a substantial overestimation at the observed prevalence of 12.7%. The proposed metric defines a principled safe interval of class ratios that may guide a more robust model design for detecting early crop disease.
Topic: Machine Learning for Data Science and Analytics
Bayesian accuracy, positive predictive value, negative predictive value, class imbalance.
Copyright (c) 2026 AQUILES DARGHAN, Lizarazo, Carlos, Liliana, Jorge https://creativecommons.org/licenses/by/4.0
Framework Development of the Imaging Quality Inspection System for Automotive Metal Stamping Parts Using Machine Learning
Traditional quality control method for metal stamping parts relies on Checking Fixtures (C/Fs). The C/F is manufactured with metal parts and Araldite blocks. C/Fs are custom-engineered for individual stamping components, necessitating distinct design and fabrication processes for each part. This study proposes an imaging quality control framework utilizing a 3D laser scanner and machine learning method, specifically the k-Nearest Neighbors (kNN) method. This automated process includes data retrieval, segmentation, pre-processing, feature recognition, data analysis, post-processing, and final decision-making, enhancing both accuracy and speed. Compared to C/Fs, imaging quality control using machine learning offers versatility, lower ongoing labor costs due to automated process, faster, and increasing sustainability in the quality control process.
Metal stamping parts, checking fixture (C/F), quality control, kNN method
Copyright (c) 2026 Wahyono Widodo https://creativecommons.org/licenses/by/4.0
Long-term Series Forecasting via Conditional Variational Autoencoder Modeling Non-Stationarity
Accurate long-term time series forecasting can provide guidance for the rational allocation of public resources, optimization adjustments in industrial production, and modifications to social epidemic prevention and control measures. However, due to the highly complex temporal dynamics (non-stationarity, periodicity) of time series, long-term time series forecasting remains an unresolved challenge. Time series in the real world are typically non-stationary, and it is precisely the non-stationarity of sequences that increases the difficulty of long-term prediction. To achieve more accurate long-term time series forecasting, we intentionally design components or frameworks capable of modeling the non-stationarity of time series data, alleviate distribution drift issues, and aim to achieve precise long-term forecasting effects. Specifically, we propose a Conditional Variational Autoencoder (CVAE) framework, which introduces noise to enable the model to better adapt to different input distributions, thereby enhancing its adaptability to changes and mitigating distribution drift issues. Mixture of Experts is used to encode features to capture the periodic variations in time series patterns. The decoder is carefully designed with components such as Seasonal-Trend feature disentangler, adaptive Nadaraya-Watson regression, difference and cumsum operation, and hypergraph convolution to model the non-stationarity of time series patterns. Additionally, non-stationary information from the original sequence is reintroduced into the predicted sequence through sequence stabilization to mitigate distribution drift issues. The proposed CVAE is validated on multiple time series datasets, including real industrial settings and publicly available benchmarks, demonstrating the effectiveness of the proposed model.
Copyright (c) 2025 Baowen Xu, Song Gao, Hongxin Meng https://creativecommons.org/licenses/by/4.0
Malware Classification Using a Hybrid CNN-Autoencoder and Transformer Encoded Approach
Malware is presently a persistent and growing threat in the digital space, thereby, making it essential for the development of accurate and efficient detection techniques. This study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNN), Autoencoders, and Vision Transformers (ViT) for static malware classification using PE Portable Executable (PE) header metadata which is evaluated using the ClaMP_Integrated-5184 dataset. The CNN component extracts local spatial features, the Autoencoder compresses and denoises the feature space, and the Vision Transformer captures global dependencies for robust classification. Experimental results show that the proposed model achieved a classification accuracy of 97% and an F1-score of 97%, outperforming benchmark and state of the art models. The findings highlight the effectiveness of hybrid deep learning architectures in static malware detection. This work demonstrates a promising approach for enhancing real-time malware detection systems.
Copyright (c) 2025 Nanji Emmanuella Lakan, Dr. Oluwaseyi Ezekiel Olorunshola, Dr. Fatimah Adamu-Fika, Samaila Musa Abdullahi https://creativecommons.org/licenses/by/4.0
A Neural Network-based Diabetes Self-Management Chatbot System
The advances in mobile technology and natural language processing have made chatbots suitable for personal health care management. When the world's population is aging, diabetes becomes one of the most common chronic diseases in the world. After being discharged from a hospital, diabetes patients must conduct personal health care management such as monitoring blood glucose, professional diet advice, and regular exercise reminder to control the disease. Unfortunately, it is found that there are few chatbots designated for diabetes patients, especially for the Chinese language. To fulfill the need, this research proposes a diabetes self-management chatbot to assist patients in recording their blood glucose, exercise, and diet through conversation. The proposed chatbot system consists of four main components: a dialog controller, a neural network, a personal database, and a diabetic management rule base. Iterated Dilated Convolutional Neural Network with Conditional Random Field (ID-CNN-CRF) is applied for Named Entity Recognition (N.E.R.) to achieve high chatting quality. The experiments show that the ID-CNN-CRF outperforms the other three popular models of CNN, LSTM, and Bi-LSTM-CRF regarding intention prediction accuracy and response time. In addition, the chatbot can suggest proper diet and exercise advice to people with diabetes. The feedback from the diabetes caregivers and patients shows that the proposed chatbot is recognized as a convenient self-management tool.
Copyright (c) 2024 Chieh-Yuan Tsai, Hen-Yi Jen, Tai-Jung Chiang https://creativecommons.org/licenses/by/4.0
Instance Selection for MI-Support Vector Machines
Support vector machine (SVM) is a well-known algorithm in machine learning due to its superior performance, and it also functions well in multiple-instance (MI) problems. Our study proposes a schematic algorithm to select instances based on Hausdorff distance, which can be adapted to SVMs as input vectors under MI-setting. Based on experiments on five benchmark datasets, our strategy, for adapting representation, outperformed in comparison with original approach. In addition, task execution times (TETs) were reduced more than 80% based on MissSVM. Hence, it is noteworthy to consider this representation adaptation to SVMs under MI-setting
Copyright (c) 2025 Bokyung Amy Kwon https://creativecommons.org/licenses/by/4.0
Active Learning Approach from borderline on Imbalanced Transactional records in Alert -feedback system
Transactional records often exhibit highly imbalanced patterns, which can hinder the performance of data-driven models in alert-feedback systems. While oversampling techniques are commonly used to address this imbalance, they increase the total number of instances, leading to higher computational costs. Although the Active Learning (AL) approach is computationally expensive, it focuses only on the most informative samples, which can be more efficient for transactional records. Our experiments show that AL outperforms SMOTE and Borderline-SMOTE in terms of accuracy and AUPRC. Therefore, AL presents a promising approach for addressing the class imbalance problem in transactional records, without the added computational burden of synthetic samples.
Copyright (c) 2025 Bokyung Amy Kwon, Kyungtae Kang https://creativecommons.org/licenses/by/4.0
Hybrid Deep Learning and Genetic Algorithm Approach forDetecting Keratoconus Using Corneal Tomography
Nowadays, there are still significant challenges encountered in the accurate diagnosis of various eye diseases, such as Keratoconus (KCN) and cataracts. Early detection of Keratoconus is crucial in preventing its progression and ensuring the best treatment outcomes. Artificial intelligence (AI) is being widely applied in ophthalmology through the training of deep learning networks for the early detection of eye diseases. This research presents a novel, integrated machine learning approach for diagnosing Keratoconus disease by combining feature extraction through Convolutional Neural Networks (CNN) with a Support Vector Machine (SVM) and Artificial Neural Network (ANN) for classification. Employing a multi-objective genetic algorithm, the method optimizes feature selection, aiming to minimize both diagnostic error and the number of features. The study utilizes a dataset of 5,152 ophthalmic images (1288 samples) categorized into Normal (476), Suspect (453), and Keratoconus (359) cases. Combining a Convolutional Neural Network (CNN) for feature selection with a Genetic Algorithm (GA) significantly improved diagnostic accuracy. Consequently, by focusing on the most relevant features of Keratoconus, the model achieved an impressive 98.63% accuracy for ANN classification with a genetic algorithm, and 98.13% for SVM classification with a genetic algorithm. The accuracy of the algorithm exceeded that of when SVM and ANN were used without the genetic algorithm alone, which were 97.53% and 96.9% respectively, underscoring the benefit of combining Artificial Neural Networks (ANNs) with Genetic Algorithms (GAs) in KC diagnosis. Implementing this model can assist physicians in more accurate Keratoconus detection, providing better predictions regarding patients' eye conditions, and offering timely treatment recommendations.
Copyright (c) 2025 Amr Abdel-Dayem, Somayeh Ghasedi https://creativecommons.org/licenses/by/4.0
Optimizing the Topology of Transformer Networks Using Modified Clonal Selection Algorithm
This paper proposes the optimization of the Transformer model for analysis of sequential data using a modified clonal selection algorithm (mCSA). Transformers demonstrate better performance over Long Short-Term Memory (LSTM) deep networks when the input sequence is very long. They are good at capturing long-term dependencies in comparison to LSTM networks. However, this comparison is valid only if the hyperparameters are optimized correctly. Also, transformers are very sensitive to their hyperparameters. Designing the architecture of the transformer model for better performance is very complex and time-consuming. There have been other efforts using Bayesian, Grid Search, Blackbox, and metaheuristic optimization techniques for the optimization of the architecture of deep learning models. mCSA is a nature-inspired immunocomputing approach. The performance of the optimized transformer model has been compared with an unoptimized transformer model, genetic algorithm optimized transformer, Clonal Selection Algorithm optimized LSTM(CSA_LSTM), Clonal Selection Optimized Hybrid Convolutional Neural Network and LSTM network (CSA-CNN-LSTM), and Random Forest search algorithm. CSA optimized transformer model has consistently shown better performance in comparison to all other models for a variety of datasets such as IMDB-movie, SMS-Spam, and US Twitter Airline datasets. Here we also show that improper optimization of transformer hyperparameters can lead to degraded performance that cannot surpass even traditional ML approaches like random forest.
Copyright (c) 2024 Ashish Kharel https://creativecommons.org/licenses/by/4.0
Prediction of CD4 T-Lymphocyte count via machine Learning for HIV-positive patients
World Health Organization recommends routine immunological and virologic monitoring for all HIV-infected patients. However viral load and lymphocyte T CD4(LTCD4) count analysis requires sophisticated equipment and qualified human resources.This creates a financial burden, especially in limited resource settings.Thus,the need for alternative approaches.One such alternative is machine learning (ML),which offers a more cost-effective solution.In this study,five highly optimized data-driven models for LTCD4 prediction have been designed based on the popular ML techniques:support vector machine(SVM),random forest(RF),logistic regression(LR),artificial neural network(ANN) and naive bayes(NB).In order to guarantee the robust performance of the suggested algorithms,we meticulously scrutinized upon the optimal approach for constructing models.Furthermore,we conducted an analysis of the predictive capabilities of LTCD4 on multiple thresholds of the total lymphocyte count.Moreover,an imbalance-aware strategy to overcome the aforementioned issue was adopted using the Synthetic Minority Oversampling Technique.The cut-off points for the number of lymphocytes“1100”had the best performance to predict LTCD4 count.SVMs,RF,NB,LR,and AAN provided area under the curve of 97%,93,2%,90%,92,01%,93% respectively.SVMs achieved better results to predict LTCD4 in all metrics.The results offer novel perspectives on LTCD4 forecasting,presenting opportunities to enhance initiatives aimed at developing web-based systems.These systems could alleviate the financial burden associated with measuring LTCD4 in HIV-infected patients,particularly in resource-constrained settings.
Copyright (c) 2024 saad lamjadli, Oumayma Ouedrhiri, Zouhair Elamrani Abou Elassad, Oumayma Banouar, Ikram Souli, Safa Machraoui, Moulay Yassine Belghali, Raja Hazime, Noura Tassi, Said Raghay, Brahim Admou https://creativecommons.org/licenses/by/4.0
Auxiliary Classifier Generative Antagonist Network for the Detection of Pneumonia
Pneumonia is an inflammation of the lungs which is caused by bacteria, viruses, mold, and less commonly by environmental toxins. Pneumonia is extremely prevalent worldwide and is the number one cause of death among children under the age of five and is the most common reason for hospitalization for adults. Chest X-rays are a common medical tool for diagnosing this illness, but must be analyzed by trained radiologists, which is often time consuming and expensive. Therefore, it would be beneficial to have an accurate automated system for diagnosing pneumonia from radiological diagnostic imaging. A variety of machine learning techniques have been applied to the problem of medical image diagnostics and have exceeded the accuracy of the average radiologist. Medical datasets often suffer from a sparsity of training examples, so data augmentation is often necessary. Here we implement an auxiliary classifier generative adversarial network method which generates synthetic X-ray images which augment the training of a discriminator network. The described method has an accuracy of 97.7% when trained on the Pneumonia MNIST dataset, which is composed of low-resolution pediatric chest X-rays. Given the common difficulty of acquiring significantly sized medical datasets, the network was trained on a range of datasets sizes to determine the impact on performance given a smaller population of pneumonia examples. Even when trained on a subsample containing only 20 examples, the network achieves an impressive 84.54% accuracy. This system could be used in areas lacking proper medical personnel or act as a verification tool for diagnosticians.
auxiliary classifier generative adversarial network, AC-GAN, neural network, pneumonia, X-ray, pediatric, machine learning, medical diagnostics
Copyright (c) 2024 Robert Langenderfer, Ezzatollah Salari, Jared Olouch https://creativecommons.org/licenses/by/4.0
Long Short Term Memory (LSTM) Based Deep Learning Models for Predicting Univariate Time Series Data
This paper explores the three variants of Long Short Term Memory (LSTM) deep learning models for the analysis and prediction of univariate time series data to develop better understanding of the spread of Covid -19 pandemic. The Covid-19 pandemic continues to significantly impact public health, medical and industrial infrastructure, and the economy. Many researchers are working on various computational and mathematical models to analyze the underlying causes of transmission and spread of covid-19. Accurate predictive models for Covid-19 will be significant as they will help us better manage the resources of health care professionals and medication to combat the covid-19 pandemic. This thesis explores three deep learning models based on the Long-Short Term Memory (LSTM) cells to analyze and predict sequential data. These models are vanilla LSTM, bidirectional LSTM, and stacked LSTM. The models were trained and tested using the univariate time series data of daily trends in number of COVID-19 cases in the United States. Data was collected from the Centers for Disease Control and Prevention website (CDC) from January 23, 2020, to May 25, 2022. The models were trained using the first 743 samples and tested on the last 104 samples. The models were implemented using TensorFlow, and KerasTuner was used to optimize the hyperparameters of LSTM networks. The prediction accuracy of three models was compared using the metric of RMSE. It was found that bidirectional LSTM gave the best normalized accuracy of 0.002.
Copyright (c) 2024 Ashish Kharel, Devinder Kaur https://creativecommons.org/licenses/by/4.0
APPLICATION OF CLASSIFICATION METHODS IN FORECASTING BROADBAND INTERNET SUBSCRIBERS LEAVING THE NETWORK
The cancellation of subscribers are always a matter of special concern for service providers in general and VNPT An Giang in particular, because customers are the ones who bring in revenue and bring value to service providers. To achieve growth and maintain profitability, service providers must find ways to develop new subscribers while also maintaining a stable number of existing subscribers. Therefore, it is very important to research solutions to identify and forecast subscribers who are likely to withdraw from the data network in order to have a customer care strategy to reduce leaving the network. In this paper, we present an approach to exploiting broadband internet subscriber data from the available data warehouse at VNPT An Giang, building a forecasting model for broadband internet subscribers leaving the network before 1 month and 3 months. First, collect data about the 12-month usage history of broadband internet subscribers including 102,920 active subscribers and 24,376 disconnected subscribers with 12 related attributes per subscriber; then the data is preprocessed to remove null data, negative numeric data, duplicate data; In order to reduce the number of input attributes, select the attributes that are considered to be the most useful for the model, we use the SelectKBest method of the Sklearn library to evaluate and select 8 attributes with high scores. Based on historical data of 6 months/12 months, divide the data into 12 different data sets (in which 6 data sets are for building and evaluating the model to predict that subscribers leave the network before 1 month; 6 datasets for building and evaluating predictive models of subscribers leaving the network before 3 months) (see Table 6 and Table 7). To select the set of attributes and the most suitable model for the forecasting problem of broadband internet subscribers leaving the network, we propose to use 4 machine learning methods including Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Long short-term memory (LSTM). To evaluate the performance of machine learning methods on predictability, we use the Area Under the Curve measure (AUC).
Forecasting broadband internet; Decision Tree; Support Vector Machine; Multilayer Perceptron; Long short-term memory
Rotated Grid Search for Hyperparameter Optimization
This paper proposes a new hyperparameter search method involving elliptical grid transformations and rotations of a grid of probe points. This technique is termed “rotated grid search”. We begin by motivating the method by discussing the limitations of random search. A new formalism for more efficiently probing a hyperparameter search space is then proposed. Next, we build a theoretical framework to compare hyperparameter optimization performance of rotated grid search against random search. We then evaluate both search methods empirically to quantify the marginal benefit of using one over the other. Monte-Carlo simulations on various synthetic objective functions show that rotated grid search outperforms random search over the full range of anisotropy explored in this study. Finally, we conduct a case study on a real dataset, rectangles-images, and show that rotated grid search outperforms random search in a high dimensional space.
A Deep Regression Network with Key-joints Localization for Accurate Hand Pose Estimation.
In this paper, a new method of performing hand joint regression is proposed to estimate 3D hand pose estimation. The existing methods regress all joints together given a depthmap, causing misallocations of some hand joints, misuse of hand depth information, and ultimately difficulty in estimating 3D coordinates. In this paper, we perform joint regression in stages, such that highly flexible joints such as fingertip joints are regressed first followed by less flexible joints to avoid getting some errors while estimating all joints together. In practice, fingertip joints are highly flexible and therefore constitute relatively higher estimation errors than all other joints. Thus, we perform fingertip joint localization (2D joint estimation) after obtaining rough pose estimates from the pose estimator to locate fingertip joint positions. We then use these 2D joint estimates to generate the depth coordinates of the pose estimator. To further ensure the accuracy of the absolute pose hypothesis, we integrate a robust implicit shape-based hand detector with the deep regression pose estimator into one channel through a shared convolutional layer. Consequently, our system estimates hand pose more accurately regarding a prior knowledge of a well detected human hand and the properly located joint positions. Experiments were carried out on three publicly available datasets, ICVL, NYU, and MSRA. The hand pose estimation accuracy reached 96.4% at the threshold level of 40mm on the ICVL dataset, 92% on MSRA, and 89% on the NYU dataset illustrating the effectiveness of the proposed system over many state-of-art approaches.
Deep learning, vision computing, convolutional neural networks, human-machine interaction, image understanding, image analysis.
Copyright (c) 2022 Jamal Banzi, Stanley Tito, Alloys Mvuma
Forecasting Electricity Consumption in the Philippines using ARIMA Models
The electricity demand has been steadily increasing throughout the years. To prepare for future electricity consumption, a robust predictive model is required. This paper employed the ARIMA model in forecasting electricity consumption in the Philippines. Dataset being used is retrieved from the Philippine Institute for Development Studies website. It contains 48 data points, of which 43 are used in model building, and the remaining 5 data points are used for forecast evaluation. The order of the ARIMA (p,d,q) model is based on the ACF and PACF plots. Among the candidate models, the model that has the most negative AIC value is chosen, and the best-fitting model is identified. Based on the analysis results, ARIMA (0,2,1) is the statistically appropriate model to forecast electricity consumption in the Philippines. It is predicted that by 2030, the Philippines will consume 163,639.9 MWh of electricity. The R statistical software was used to do all of the calculations.
Generative Adversarial Networks (GANs): A Survey on Network Traffic Generation
Generating network traffic flows remains a critical aspect of developing cyber and network security systems. In this survey, we consider various network traffic generation models. We also introduce and review Generative Adversarial Networks (GANs) models used for generating traffic flows showing their architecture, training methods, generated results, identified limitations and prospects for further research, thus proving that GANs are key to future developments in network traffic generation and secured cyber and network systems.
Copyright (c) 2022 Mark Leeson, Tertsegha-Joseph Anande
A Machine Learning Ensemble Classifier for Cardiovascular Disease Taxonomy
This Paper presents an application of Machine Learning in cardiology and the role of ensemble classifiers for cardiovascular disease (CVD) taxonomy. Dataset from Kaggle on CVD was used. Data was cleaned and 5 feature reduction techniques were investigated. Furthermore, a statistical unbiased ensemble feature reduction is proposed by imposing a unitary weight on intersecting features. Considering only 7 features, the Recurrent Feature Elimination and the proposed unbiased-ensemble feature reduction techniques were effective for reducing variables. Here, 6 feature reduction methods are considered. Hence, from each feature reduction method; the diverse selected features are then fed into a set of 5 independent ML techniques to compose a corresponding classifier. This ML approach in turn considers the 5 resultant classifiers and one additional proposed Ensemble Classifier based on those 5 classifiers. This proposed Ensemble Classifier consisted of: Multi-Layer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and k- Nearest Neighbor (KNN), classifiers. The output of the machine learning (ML) Classifiers approach is a classification/taxonomy to determine an individual with cardiovascular disease; or an individual that is free from cardiovascular disease. By considering the effective Recursive Feature Elimination method and the proposed Ensemble Classifier it was demonstrated that the body weight of an individual, systolic and diastolic blood pressure, cholesterol level, glucose level, level of physical activity, and the age are decisive in diagnosing the CVD condition of an individual. It is relevant to mention that a genetic feature was not available from the considered database; therefore, this potentially important factor was not considered in this study.
Cardiovascular Disease, Classifiers, Machine Learning, Taxonomy
Copyright (c) 2022 Rene MAYORGA, Oyetunde Philip Oyelude
A New Approach to Neural Network Design for Fast Convergence via Feed-forward Loop
A feed-forward loop has been widely used in control systems to boost the performance without hurting the overall stability of the system. We propose a new neural network, FfcNet by adopting the idea from the control theory. The proposed network adds a pre-designed feed-forward loop in parallel with the existing regular blocks, which is similar to the identity mapping in ResNet network. The feed-forward loop helps the overall network to converge faster while keeping overall system stability and accuracy. It is also shown that the feed-forward loop is equivalent to setting a proper initial condition of the parameters in the network. The performance of the proposed design is verified through a simple pattern recognition problem of identifying the figures from the highly distorted 7-segment LED images.
Relaxed Training for a Binary Neural Network
The Binary neural networks (BNNs) have drawn attention of many researchers recently because of the most hopeful techniques to meet the desired requirements of memory footprint and inference speed. However, not only the inherently poor representation with only two possible values (-1 and +1), but the training process with quantization is also an important factor that causes them suffered from the severe intrinsic instability of the error convergence, leading to high prediction error and increase of its standard deviation. In this work, a new training procedure with relaxed quantization is proposed to address the above issue without incurring excessive costs, which firstly discusses the possibility of employing relaxed quantization on both weights and activations. It is shown through the experimental results that the proposed method with relaxed quantization on both weights and activations can reduce the error and its standard deviation by 1.71% and 13.08% on CIFAR-10, respectively, compared to the conventional BNN baseline.
Lifespan Prediction for Lung and Bronchus Cancer Patients via Machine Learning Techniques
Patients' accurate survival predictions can influence treatment planning and costs, in particular, lung cancer, which is one of the leading causes of cancer-related death. Machine Learning (ML) techniques are powerful in increasing the accuracy of such predictions. However, only a few studies have used an ML approach for actual lifespan prediction for cancer patients using the Surveillance, Epidemiology, and End Results (SEER) program database. This study intends to apply several well known ML models, namely, a developed Deep Neural Networks (DNN), Linear Regression, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Random Forest (RF), and Adaboost, to predict the actual survival time on monthly-basis for lung cancer patients. The results indicate that the models give better performance for low to average survival times (0 to 25 months) that make up the majority of the data. The best model was the considered DNN with a Root Mean Square Error (RMSE) value of 12.672. In contrast, the Adaboost model was the worst-performing technique; since it had weak discrete power for the data.
A Machine Learning Approach for the Classification of Lower Back Pain in the Human Body
The 21st century has been witnessing a high growth in technology in every field including the medical sector. Dynamic systems have been designed and implied for better and accurate diagnosis of a large variety of ailments; but, the growing number of patients makes it difficult to provide proper medical attention in time. To overcome this difficulty, Intelligent Systems techniques can be employed in the medical sector and help us overcome the huge difference in the ratio of doctors versus patients; along with reducing the examination and waiting time for the patients. Among all the variety of ailments prevailing in today’s world, “Lower Back Pain” has emerged as one of the most prevailing ailments which includes around 80% of the total population once in lifetime, making it to one of the prior concerns of medical sector. To act effectively onto it, many conventional methods have been used to diagnose lower back pain. This study aims to design a Non-Conventional technique to classify Lower back pain either Normal or Abnormal using Machine Learning techniques such as Naïve Bayes, Support Vector Machines, Decision Trees, Gradient Boosted Trees, Fast Large Margin, K Nearest Neighbor, Multilayer Perceptron, Random Forest, and Artificial Neural Networks. This research focuses upon the implementation of the above-mentioned techniques for the proper classification of Spine Dataset and for determining the best technique in terms of Accuracy, Precision, Sensitivity, Specificity, F-measure and Area under Curve.
Machine Learning, Lower Back Pain, Automatic Feature Engineering technique, Performance Evaluation
Forecasting international tourism demand using the recurrent neural network model with genetic algorithms and ARIMAX model in tourism supply chains
Forecasting is the basis of planning, and the key to tourism supply chain. The international tourists visiting Taiwan from Mainland China, Japan, and South Korea is the major international tourist source markets for Taiwan. Recurrent neural network model is a fairly new and promising neural network technology. Genetic algorithms can be optimized the neural network structure. The ARIMAX model is recently utilized time series model for tourism demand forecasting. Accordingly, this work presents recurrent neural network model to forecast numbers of international tourists to Taiwan from Mainland China, Japan, and South Korea to help the Taiwanese tourism industry. This work also compares the forecast accuracy of the ARIMAX model with that of the ARIMAX model. Empirical results demonstrate that the recurrent neural network model with genetic algorithms is better than the ARIMAX model for Mainland China and South Korea, and worse than for Japan. The findings of this study can contribute to management and policy-decision issues related to the tourism industry for Taiwan in tourism supply chains.
Recurrent neural network, Genetic algorithm, ARIMAX, Tourism demand, Forecasting.
Improvement in OCR Technologies in Postal Industry Using CNN-RNN Architecture: Literature Review
Convolutional Recurrent Neural Network (CRNN) based architecture is an attractive branch of Optical Character Recognition (OCR) studies. OCR is the process for transforming the image or the text obtained by scanning documents into machine-modifiable or editable format. [1]. It belongs to the domain of automatic identification of algorithms, modeled loosely after the animal brains, which are designed for pattern and character recognition. Hence, it falls under the neural networks category. An OCR system relies for the most part on the pre-processing, character/ image segmentation and feature extraction. This technology is an essential segment for automation processed in postal industry to read mail addresses and process mails. The first objective of this paper is to summarize the research that has been conducted in the field and further to present best in practice examples in this regard. Secondly, this research will also discuss about some gaps in the area and try to identify opportunities for future studies.
Optical Character Recognition, Convolutional Recurrent Neural Network, OCR, Postal industry, Automatic Mail Sorting, Neural Networks, CNN, RNN
Performance Impact of Minority Class Reweighting on XGBoost-based Anomaly Detection
This paper explores the impact of reweighting the minority class of an imbalanced fraud dataset on the performance of an XGBoost binary classifier. Classifier performance is measured here in terms of true positive rate, false positive rate, precision, accuracy, AUC-ROC and AUC-PR. Our results suggest that reweighting the minority class has significant impact on these four key performance metrics when the classification threshold is held fixed and the model bias is not corrected. However, this impact becomes insignificant when 1) classification threshold is held fixed and the bias is corrected, or 2) when the target number of predicted positives is held fixed. Since fraud detection often prescribes a target number of cases for special treatment, these findings suggest that reweighting a dataset offers performance advantage only under very specific conditions for XGBoost-based classifiers. These conclusions can also generalize to problems where certain resampling techniques are used instead of reweighting since the two approaches tend to converge for sufficiently large datasets.
Dockless Bike-sharing Demand Prediction with Deep Learnin
As a solution to the last mile problem in big metropolitan cities, dockless bike-sharing service is becoming a new choice for short travels all over the world. Unlike the docked bikes which requires the users to borrow and return at fixed stations, dockless bikes can be used everywhere. However, this feasibility also brings a higher management cost. The bikes should be scheduled from the regions with less demand to those with higher demand, based on a precise demand prediction. In this paper, we use deep learning techniques including Multi-Layer Perceptron and ConvLSTM networks for this task. We find that in the case of the insufficient training data, e.g., one-month data of Mobike, Multi-Layer Perceptron performs better than both ConvLSTM and two simple historical methods.
Dockless Bike-sharing; Demand Prediction; Deep learning
Vehicle Detection and Type Classification based on CNN-SVM
In this paper, vehicle detection and classification in a road environment are studied respectively using machine vision. Among the varies challenges, for the problem of poor robustness in extracting vehicle candidate regions through a single feature, the YOLO series algorithm of deep learning is used to propose potential regions, and in order to further improve the speed of detection, the lightweight network Yolov2-tiny is chosen as the location network. In the training process, anchor box clustering is performed based on the ground truth of training set, which improves its performance on the specific dataset. For the low classification accuracy problem after template-based feature extraction, the optimal feature description is extracted through convolution neural network learning. Moreover, based on AlexNet, through adjusting parameters, an improved algorithm was proposed whose model size is smaller and classification speed is faster than the original AlexNet. Spatial Pyramid Pooling (SPP) is added to the vehicle classification network, which solves the problem of low accuracy due to image distortion caused by image resizing. By combining CNN with SVM and normalizing features in SVM, the generalization ability of the model was improved. Experiments show that our method has advantages in vehicle detection and type classification.
Vehicle detection, Vehicle classification, Yolov2-tiny, AlexNet, Spatial Pyramid Pooling, CNN and SVM
A Joint Multi-task Architecture for Document-level Aspect-based Sentiment Analysis in Vietnamese
The increasing demands of e-commerce websites have caused a vast amount of opinions about Internet users’ products and services. Therefore, the aspect-based sentiment analysis (ABSA) has attracted much attention from academia and industry due to its application in many real-life problems in recent years. This problem is a complex task that aims to extract both the aspects and sentiments from the text input. Aspect Category Detection and Sentiment Polarity Classification are two of the challenging subtasks of ABSA, which detect a set of pre-defined categories and corresponding sentiment polarity for a given review. This paper presents an effective joint multi-task architecture based on neural network models to solve two tasks in the document-level ABSA datasets. Our model is designed to predict the whole mentioned aspect categories and corresponding sentiment polarities on the document-level datasets. We trained our model jointly on two tasks simultaneously and utilized the additional information of aspect category detection task for predicting the aspect categories and its sentiments for the specific domain. Our architecture can explore the hidden correlated information between categories and polarities in the review. Experiments on two Vietnamese language datasets in the restaurant domain and hotel domain demonstrate that our model outperforms the previous state of the art methods on two benchmark document-level.
Using Machine Learning to analyze Sudanese Opinions for Political Decision Making
Sentiment Analysis (SA) is the application of text mining techniques for extraction and identification of subjective opinions from textual data and categorizes them into positive, negative or neutral. SA has many applications ranging from financial analysis to political decision-making domains. Although a lot of works have been made in sentiment analysis and opinion mining, very limited work focused on Arabic text mining, particularly political opinion mining. In this paper, we leverage machine learning techniques to automatically analyzed Arabic tweets during Sudanese revolution based on several features for political decision-making process. In our methodology, we first start by building Arabic tweets dataset. Then, we propose to use two sets of features namely Term Frequency-Inverse Document Frequency (TF-IDF) and word embeddings. Lastly, we used several classifiers to classify the user’s comments/opinions into three classes. Experiments were conducted to evaluate our classifiers and the results showed superiority of the Ensemble learning model compared to other machine learning classifiers.
As data becomes more accessible, visualization methods are needed to help make it easier to understand the information. Analyzing and visualizing data helps the data better recognize the datasets, make it easier to understand without reading datasets, and connections between more than two different datasets. Tableau is one of the most popular interactive data visualization software. By using Tableau, it is simple and easy to clean up datasets, find correlation, and create visualization such as geo chart, map chart, table chart, line chart, pie chart, and treemap chart. This project aims to study the correlation between gross domestic product(GDP) and human immunodeficiency virus (HIV). Large data sets related to the GDP and HIV were gathered from open data sources. This data will be clean up with Tableau and Excel then show correlations between datasets with variable charts through Tableau.
GDP, HIV, Tableau, Dataset, Visualization, Correlation
Copyright (c) 2022 Ching-yu Huang, Eunbi Kim