Well-known Machine Learning Classifiers Research Articles

Impact of Tweet Sentiments on the Return of Cryptocurrencies: Rule-Based vs. Machine Learning Approaches

In an attempt to assess the appropriateness of the best-practice lexicon-based approaches as opposed to novel learning-based models to extract the sentiment of textual content in the context of the cryptocurrency market, the current study provides further insights into the association between digital activity and price movement of cryptocurrencies. Using a sample of Bitcoin and Ethereum trade data, this study compares the performance of Harvard IV-4 and BERT models in conjunction with the well-known machine learning classifiers. It examines to what extent learning-based sentiment models can enhance the price movement prediction, compared to lexicon-based approaches, and whether the prediction is improved or impaired by introducing different features as input to the classifiers. Results indicate that the contribution of the selected learning-based model varies across the two cryptocurrencies, and predictions are better in the absence of trade volume as an input feature to the classifiers.

Structural health monitoring via a group-theoretic WSA for optimal feature selection and data fusion

In this study, two binary versions of the Water Strider Algorithm (WSA) are proposed and applied to optimal feature selection in classification problems. In the new binary versions, the formulations of WSA in continuous space are converted into binary space using group-theoretic operators (in AWSA) and sigmoid function (in BWSA). AWSA, BWSA, genetic algorithm (GA), and binary particle swarm optimization (BPSO) are selected and compared over eighteen well-known datasets from the University of California, Irvine repository. The results of AWSA indicate its satisfactory performance compared to those of other algorithms. Then, they are applied to find optimal features of a structural health monitoring classification problem using two well-known machine learning classifiers, namely k-Nearest Neighbor (KNN) and Naïve Bayes (NB) algorithms. To further improve the accuracy of the classification models, a decision-level data fusion technique is proposed based on the improved Dempster-Shafer theory. It is demonstrated that the AWSA presents superior results compared to the other algorithms and the suggested decision-level data fusion provides a reliable detection of damage.

Classification of Severe Maternal Morbidity from Electronic Health Records Written in Spanish Using Natural Language Processing

One stepping stone for reducing the maternal mortality is to identify severe maternal morbidity (SMM) using Electronic Health Records (EHRs). We aim to develop a pipeline to represent and classify the unstructured text of maternal progress notes in eight classes according to the silver labels defined by the ICD-10 codes associated with SMM. We preprocessed the text, removing protected health information (PHI) and reducing stop words. We built different pipelines to classify the SMM by the combination of six word-embeddings schemes, three different approaches for the representation of the documents (average, clustering, and principal component analysis), and five well-known machine learning classifiers. Additionally, we implemented an algorithm for typos and misspelling adjustment based on the Levenshtein distance to the Spanish Billion Word Corpus dictionary. We analyzed 43,529 documents constructed by an average of 4.15 progress notes from 22,937 patients. The pipeline with the best performance was the one that included Word2Vec, typos and spelling adjustment, document representation by PCA, and an SVM classifier. We found that it is possible to identify conditions such as miscarriage complication or hypertensive disorders from clinical notes written in Spanish, with a true positive rate higher than 0.85. This is the first approach to classify SMM from the unstructured text contained in the maternal EHRs, which can contribute to the solution of one of the most important public health problems in the world. Future works must test other representation and classification approaches to detect the risk of SMM.

Emotional Climate Recognition in Conversations using Peers' Speech-based Bispectral Features and Affect Dynamics.

Emotion recognition in conversations using artificial intelligence (AI) has recently gained a lot of attention, as it can provide additional emotion cues that can be correlated with human social behavior. An extension towards an AI-based emotional climate (EC) recognition, i.e., the recognition of the joint emotional atmosphere dynamically created and perceived by the peers throughout a conversation, is proposed here. In our approach, namely MLBispeC (Machine Learning Based Bispectral Classification), the peers' speech signals during their conversation are subjected to time-windowed bispectral analysis, allowing for feature extraction related to dynamic harmonics nonlinear interactions. In addition, peers' affect dynamics, derived from their same time-windowed emotion labeling, are combined to form an extended feature vector, inputted into two well-known machine learning classifiers (Support Vector Machine, K-Nearest Neighbor). MLBispeC was evaluated on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) open access dataset, which contains 2D emotions, i.e., Arousal (A) and valence (V) that are divided into (low/high) classes. The experimental results have shown that MLBispeC outperforms previous state-of-the-art techniques, achieving an accuracy of 0.826A/0.754V, sensitivity of 0.864A/0.774V, and area under the curve (AUC) of 0.821A/0.799V. This demonstrates the effectiveness of MLBispeC to objectively recognize peers' EC during their conversation, allowing for insights into their emotional and social interactions.Clinical relevance-Unobtrusive, objective and dynamic recognition of the EC built during peers' conversation can scaffold effective assessment of patients with physiological, psychological, and mental diseases, at various age ranges (children, adults, and older adults).

Neural Network-Based Prediction of Potential Ribonucleic Acid Aptamers to Target Protein

Aptamers are short strands of nucleic acid with a single strand that may unite to target a certain molecule in a selective and specific manner. SELEX experiments are the typical method used for identifying aptamers in vitro (systematic evolution of ligands by exponential enrichment). Several different computational methods have been developed to locate aptamers. The purpose of this research is to identify and make predictions on the possible RNA aptamers that may be used to target the protein. To do this, we propose the use of a multi-layer perceptron neural network with sixteen layers that are trained to locate possible aptamers of a protein target. This network is trained by extracting the main properties of RNA sequences. The outcome of our proposed model is compared to the output of two well-known machine learning classifiers, namely random forest (RF) and support vector machine (SVM). Additionally, we undertake the independent testing of our model on the benchmark dataset, which allows us to reach the highest accuracy possible. As a consequence of this, our model obtains an accuracy of 98.44% and an MCC of 0.9123 during the 15-fold cross-validation, and it achieves an accuracy of 98.10% and an MCC of 0.9354 when the leave-one-out cross-validation is performed. We are certain that our approach will contribute to a reduction in the amount of money and time spent on in vitro testing. Therefore, restricting the length of the initial pool of potential nucleic acid pattern combinations.

Carp-DCAE: Deep convolutional autoencoder for carp fish classification

The fisheries industry relies heavily on automatic fish species identification for its socio-economic well-being. Due to the similarity in shape and size of the major carps , it can be difficult to recognise them using morphological features. To recognise these species automatically, our proposed autoencoder network models have been applied to a fish dataset containing 1500 images of three major carps of India. As a feature, the autoencoder model’s latent representation is used. After the training phase is complete, the decoder is removed and fish species are categorised using several classifiers. Different variations of autoencoders, such as the Simple Autoencoder, the Deep Autoencoder, and the Deep Convolutional Autoencoder are applied with different hyper parameters. An encouraging maximum accuracy rate of 97.33% is obtained in 250 epochs with a learning rate of 0.0001 using Deep Convolutional Autoencoder. Some well-known machine learning classifiers, such as Logistic Regression, Naive Bayes, K-Nearest Neighbor, Support Vector Machine, and Random Forest, are also used to evaluate the latent representation’s effectiveness using the latent representation as a feature vector. The Support Vector Machine-based latent representation of the Deep Convolutional Autoencoder outperformed all other approaches significantly. The models’ performance is compared to that of Hu moments, Haralick texture, Weber local descriptor, HOG descriptor etc. with best classifiers along with different deep learning models, such as InceptionV3, InceptionResNetV2, MobileNet, VGG16 and VGG19. The Deep Convolutional Autoencoder outperforms the other models by 52%, 43.55%, 13.77%, 6.67%, 22.22%, 15.11%, 6.66%, 4.89%, and 9.78% respectively. It demonstrates the efficacy of this systematic study in identifying major carps.

Stuttering Disfluency Detection Using Machine Learning Approaches

Stuttering is a neurodevelopmental speech disorder wherein people suffer from disfluency in speech generation. Recent research has applied machine learning and deep learning approaches to stuttering disfluency recognition and classification. However, these studies have focussed on small datasets, generated by a limited number of speakers and within specific tasks, such as reading. This paper rigorously investigates the effective use of eight well-known machine learning classifiers, on two publicly available datasets (FluencyBank and SEP-28k) to automatically detect stuttering disfluency using multiple objective metrics, i.e. prediction accuracy, recall, precision, F1-score, and AUC measures. Our experimental results on the two datasets show that the Random Forest classifier achieves the best performance, with an accuracy of 50.3% and 50.35%, a recall of 50% and 42%, a precision of 42% and 46%, and an F1 score of 42% and 34%, against the FluencyBank and SEP-28K datasets, respectively. Moreover, we show that the machine learning-based approaches may not be effective in accurate stuttering disfluency evaluation, due to diverse variations in speech rate, and differences in vocal tracts between children and adults. We argue that the use of deep learning approaches and Automatic Speech Recognition (ASR) with language models may improve outcomes, specifically for large scale and imbalanced datasets.

Swarm Optimization and Machine Learning for Android Malware Detection

Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. Three swarm optimization methods, viz., Ant Lion Optimization (ALO), Cuckoo Search Optimization (CSO), and Firefly Optimization (FO) are applied to API calls using auto-encoders for identification of most influential features. The nature-inspired wrapper-based algorithms are evaluated using well-known Machine Learning (ML) classifiers such as Linear Regression (LR), Decision Tree (DT), Random Forest (RF), K–Nearest Neighbor (KNN) & Support Vector Machine (SVM). A hybrid Artificial Neuronal Classifier (ANC) is proposed for improving the classification of android malware. The experimental results yielded an accuracy of 98.87% with just seven features out of hundred API call features, i.e., a massive 93% of data optimization.

Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis.

Breast cancer is one of the leading causes of death among women, more so than all other cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the disease, changing treatment procedures and different patient population samples. Diagnostic techniques with better performance are very important for personalized care and treatment and to reduce and control the recurrence of cancer. The main objective of this research was to select feature selection techniques using correlation analysis and variance of input features before passing these significant features to a classification method. We used an ensemble method to improve the classification of breast cancer. The proposed approach was evaluated using the public WBCD dataset (Wisconsin Breast Cancer Dataset). Correlation analysis and principal component analysis were used for dimensionality reduction. Performance was evaluated for well-known machine learning classifiers, and the best seven classifiers were chosen for the next step. Hyper-parameter tuning was performed to improve the performances of the classifiers. The best performing classification algorithms were combined with two different voting techniques. Hard voting predicts the class that gets the majority vote, whereas soft voting predicts the class based on highest probability. The proposed approach performed better than state-of-the-art work, achieving an accuracy of 98.24%, high precision (99.29%) and a recall value of 95.89%.

Deepfake tweets classification using stacked Bi-LSTM and words embedding.

The spread of altered media in the form of fake videos, audios, and images, has been largely increased over the past few years. Advanced digital manipulation tools and techniques make it easier to generate fake content and post it on social media. In addition, tweets with deep fake content make their way to social platforms. The polarity of such tweets is significant to determine the sentiment of people about deep fakes. This paper presents a deep learning model to predict the polarity of deep fake tweets. For this purpose, a stacked bi-directional long short-term memory (SBi-LSTM) network is proposed to classify the sentiment of deep fake tweets. Several well-known machine learning classifiers are investigated as well such as support vector machine, logistic regression, Gaussian Naive Bayes, extra tree classifier, and AdaBoost classifier. These classifiers are utilized with term frequency-inverse document frequency and a bag of words feature extraction approaches. Besides, the performance of deep learning models is analyzed including long short-term memory network, gated recurrent unit, bi-direction LSTM, and convolutional neural network+LSTM. Experimental results indicate that the proposed SBi-LSTM outperforms both machine and deep learning models and achieves an accuracy of 0.92.

Land use land cover mapping using advanced machine learning classifiers

Abstract Due to the recent climate changes such as floods and droughts, there is a need for Land Use Land Cover (LULC) mapping to monitor environmental changes that have effects on ecology, policy management, health and disaster management. As such, in this study, two well-known machine learning classifiers, namely, Support Vector Machine (SVM) and Random Forest (RF), are used for land cover mapping. In addition, two advanced deep learning algorithms, namely, the GAMLP and FSMLP, that are based on the Multi-layer Perceptron (MLP) function are developed in MATLAB programming language. The GAMLP uses a Genetic Algorithm (GA) to optimise parameters of the MLP function and, on the other hand, the FSMLP uses a derivative-free function for optimisation of the MLP function parameters. Three different scenarios using Landsat-8 imagery with spatial resolutions of 30 and 15 m are defined to investigate the effects of data pre-processing on the final predicted LULC map. Results based on the statistical indices, including overall accuracy (OA) and kappa index, show that the developed MLP-based algorithms have relatively high accuracies with higher than 98% correct classification. Besides the statistical indices, final LULC maps are interpreted visually where the GAMLP and FSMLP give the best results for the pre-processed Landsat-8 imagery with a spatial resolution of 15 m, but they have the worst outcomes for the unprocessed Landsat-8 imagery compared to SVM and RF classifiers visually and statistically.

IPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer

Radical hysterectomy is a recommended treatment for early-stage cervical cancer. However, the procedure is associated with significant morbidities resulting from the removal of the parametrium. Parametrial cancer invasion (PMI) is found in a minority of patients but the efficient system used to predict it is lacking. In this study, we develop a novel machine learning (ML)-based predictive model based on a random forest model (called iPMI) for the practical identification of PMI in women. Data of 1112 stage IA-IIA cervical cancer patients who underwent primary surgery were collected and considered as the training dataset, while data from an independent cohort of 116 consecutive patients were used as the independent test dataset. Based on these datasets, iPMI-Econ was then developed by using basic clinicopathological data available prior to surgery, while iPMI-Power was also introduced by adding pelvic node metastasis and uterine corpus invasion to the iPMI-Econ. Both 10-fold cross-validations and independent test results showed that iPMI-Power outperformed other well-known ML classifiers (e.g., logistic regression, decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes, support vector machine, and extreme gradient boosting). Upon comparison, it was found that iPMI-Power was effective and had a superior performance to other well-known ML classifiers in predicting PMI. It is anticipated that the proposed iPMI may serve as a cost-effective and rapid approach to guide important clinical decision-making.

Human Activity Recognition With Accelerometer and Gyroscope: A Data Fusion Approach

This paper compares the three levels of data fusion with the goal of determining the optimal level of data fusion for multi-sensor human activity data. Using the data processing pipeline, gyroscope and accelerometer data was fused at the sensor-level, feature-level and decision-level. For each level of data fusion four different techniques were used with varying levels of success. This analysis was performed on four human activity publicly-available datasets along with four well-known machine learning classifiers to validate the results. The decision-level fusion (Acc = 0.7443±0.0850) outperformed the other two levels of fusion in regards to accuracy, sensor level (Acc = 0.5934 ± 0.1110) and feature level (Acc = 0.6742 ± 0.0053), but, the processing time and computational power required for training and classification were far greater than practical for a HAR system. However, Kalman filter appear to be the more efficient method, since it exhibited both good accuracy (Acc = 0.7536 ± 0.1566) and short processing time (time = 61.71ms ± 63.85); properties that play a large role in real-time applications using wearable devices. The results of this study also serve as baseline information in the HAR literature to compare future methods of data fusion.

Using an Ensemble to Identify and Classify Macroalgae Antimicrobial Peptides.

The rapid spread of multi-drug resistant microbes has lead researchers to discover natural alternative remedies such as antimicrobial peptides (AMPs). In the first line of defense, AMPs display a broad spectrum of potent activity against multi-resistant pathogenic bacteria, viruses, fungi, and even cancer. AMPs can be further characterised into families according to amino acid composition, secondary structure, and function. However, despite recent advancements in rapid computational methods for AMP prediction from various mammalian, aquatic, and terrestrial species, there is limited information regarding their presence, functional roles, and family type from marine macroalgae. In this paper, we present a promising two-tier ensemble of heterogeneous machine learning models that integrates seven well-known machine learning classifiers to predict AMPs from macroalgae. The first tier of the ensemble consists of a suite of binary classifiers that identify AMPs from protein sequence data which are then forwarded to a second-tier multi-class ensemble to characterise their functional family type. The two-tier ensemble was successfully used to identify 39 putative AMP sequences in 12 macroalgae species from three different phyla groups. The approach we describe is not limited to AMPs and can also be applied to search sequence data for other types of proteins.

Predicting a Risk of Diabetes at Early Stage using Machine Learning Approach

Diabetes, known also as Diabetes mellitus, is a prolonged disease that distresses a way body uses to take up sugar or glucose. It can be described as a condition occurs when the blood sugar of a body is too high. The cases of diabetes are rapidly raising in low- and middle-income countries as compare to high income countries. Advancement in the field of Information and Communication Technology (ICT) are revolutionizing many sectors including health care and medical technologies. It plays a critical role in improvising services offered to patients and hospitals. Machine learning is a field of Artificial Intelligence that makes computer system to learn from data, identify the patterns and take decisions without human intervention. Machine learning can be applied on medical datasets to detect and diagnose diseases in an effective and accurate manner. This research focuses on applying machine learning classifiers on the publicly available dataset that contains signs and a symptom of either a person is diabetic or non-diabetic. For that, a dataset for early-stage diabetes risk prediction is acquired from UCI machine learning repository. The well-known machine learning classifiers i.e., Naive Byes, Random Forest, Support Vector Machine and Multilayer Perceptron are experimented on the dataset and the results are analysed. Finally, the result shows that the Random Forest provides the highest values i.e. 0.975 for precision, recall and F-measure respectively. Multiplayer perceptron also works well with 0.96 precision value, 0.963 recall value and 0.964 F-measure value, respectively.

Phonocardiogram Classification Based on Machine Learning with Multiple Sound Features

In this study the heartbeat sound signals were tackled by classifying them into heart disease categories such as normal, artifact, murmur and extrahals in an attempt for early detection of heart defects. Phonocardiogram (i.e., PCG) is used to obtain the digital recording dataset of the heart sounds using an electronic stethoscope or mobile device. Multiple features are extracted from the digital recording dataset such as MFCC, Delta MFCC, FBANK and a combination between MFCC and FBANK features. Moreover, to classify the heartbeat sound signals, multiple well-known machine learning classifiers were used such as Naive Bays (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The evaluation processes went through five metrics: Confusion matrix, accuracy, F1 score, precision and recall evaluating the recognition rate. Comparative experimental results show that the correctness of the feature with a best accuracy 99.2% adopted by MFCC and FBANK combination features which reduce false detection.

Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China

The objective of this study is to investigate different ensemble learning techniques namely Bagging, Boosting, and Stacking for LSM at the Jinping county, Southwest China. Two well-known machine learning classifiers such as C4.5 decision tree (C4.5) and artificial neural network (ANN) were served as base-learners. A total of five ensemble models, including the Bag-C4.5 model, the Boost-C4.5 model, the Bag-ANN model, the Boost-ANN model, and the Stacking C4.5-ANN model, were constructed by using various ensemble techniques and base-learners. A landslide inventory map and 12 landslide-related factors have been prepared as the spatial database for landslide modeling. The importance of factors was verified using the information gain (IG) method. It turns out that the distance to roads has the greatest contribution to landslide susceptibility assessment. Subsequently, various landslide models were evaluated regarding the goodness of fit, generalization capability, and robustness. The area under the ROC curve (AUC), statistical analysis, and stability index (SI) were used as performance metrics. Evaluation results showed that ensemble learning techniques significantly refined individual landslide models such as the C4.5 (AUC = 0.832) and ANN (AUC = 0.870). In particular, Boosting-based models, e.g., the Boost-C4.5 model (AUC = 0.945) and the Boost-ANN model (AUC = 0.903), gained a higher performance than the Stacking C4.5-ANN model (AUC = 0.900), the Bag-ANN (AUC = 0.892), and the Bag-C4.5 (AUC = 0.878). Additionally, the best modeling robustness was achieved by the Stacking C4.5-ANN method (SI = 1). The results indicate that the Boosting technique has great confidence in strengthening the predictive accuracy for LSM. Also, the Stacking can provide a promising method for stable and improved landslide modeling. Findings from this study may assist to refine the quality of LSM and facilitate risk management for the study area or other similar regions.

An Efficient Method for Detection of Fake Accounts on the Instagram Platform

In recent years, social media platforms such as Instagram, Twitter, and Facebook have gradually become important ways to disseminate information. One of these social platforms that have attracted more attention in past years is Instagram. Instagram has widely used for sharing photos and videos and is profitable for celebrities, businesses, and people with a considerable number of followers. In the meantime, this high profit made this platform prone to be the potential place to be used for malicious activities. One of the essential malicious activities in the Instagram platform is fake accounts. However, in this paper, an efficient method for identifying Instagram fake accounts is proposed. In the presented model. First, a dataset of legitimate and fake accounts is created. Then, the collected dataset has been used as input of the bagging classifier to classify fake users on the dataset. Furthermore, the proposed method compared to the five well-known machine-learning classifiers in terms of classification accuracy to better evaluate effectiveness of the method. The experimental results show that the proposed method performs better than other considered algorithms and correctly classified over 98% of the accounts with a low error rate.

IDPPIV-SCM: A Sequence-Based Predictor for Identifying and Analyzing Dipeptidyl Peptidase IV (DPP-IV) Inhibitory Peptides Using a Scoring Card Method

The inhibition of dipeptidyl peptidase IV (DPP-IV, E.C.3.4.14.5) is well recognized as a new avenue for the treatment of Type 2 diabetes (T2D). Until now, peptide-like DDP-IV inhibitors have been shown to normalize the blood glucose concentration in T2D subjects. To the best of our knowledge, there is yet no computational model for predicting and analyzing DPP-IV inhibitory peptides using sequence information. In this study, we present for the first time a simple and easily interpretable sequence-based predictor using the scoring card method (SCM) for modeling the bioactivity of DPP-IV inhibitory peptides (iDPPIV-SCM). Particularly, the iDPPIV-SCM was developed by employing the SCM method together with the propensity scores of amino acids. Rigorous independent test results demonstrated that the proposed iDPPIV-SCM was found to be superior to those of well-known machine learning (ML) classifiers (e.g., k-nearest neighbor, logistic regression, and decision tree) with demonstrated improvements of 2-11, 4-22, and 7-10% for accuracy, MCC, and AUC, respectively, while also achieving comparable results to that of the support vector machine. Furthermore, the analysis of estimated propensity scores of amino acids as derived from the iDPPIV-SCM was performed so as to provide a more in-depth understanding on the molecular basis for enhancing the DPP-IV inhibitory potency. Taken together, these results revealed that iDPPIV-SCM was superior to those of other well-known ML classifiers owing to its simplicity, interpretability, and validity. For the convenience of biologists, the predictive model is deployed as a publicly accessible web server at http://camt.pythonanywhere.com/iDPPIV-SCM. It is anticipated that iDPPIV-SCM can serve as an important tool for the rapid screening of promising DPP-IV inhibitory peptides prior to their synthesis.

Novel Artificial Immune Networks-based optimization of shallow machine learning (ML) classifiers

Artificial Immune Networks (AIN) is a population-based evolutionary algorithm that is inspired by theoretical immunology. It applies ideas and metaphors from the biological immune system to solve multi-disciplinary problems. This paper presents a novel application of the AIN for optimizing shallow machine learning (ML) classification algorithms. AIN accomplishes this task by searching the best hyper-parameter set for a specific classification algorithm (also termed model selection), which minimizes training error and enhances the generalization capability of the algorithm. We present a convergence analysis of the proposed algorithm and employ it in conjunction with selected, well-known ML classifiers, namely, an extreme learning machine (ELM), a support vector machine (SVM) and an echo state network (ESN). The performance is evaluated in terms of classification accuracy and learning time, using a range of benchmark datasets, and compared against grid search as well as evolutionary strategy (ES)-based optimization techniques. An empirical study with different datasets demonstrates improved classification accuracy of SVM, from 2% to 5%, for ESN from 3% to 6%, whereas in the case of ELM from 3% to 9%. Comparative simulation results demonstrate the potential of AIN as an alternative optimizer for shallow ML algorithms.