Support Vector Machine Technique Research Articles

Ion-pumping microbial rhodopsin protein classification by machine learning approach

BackgroundRhodopsin is a seven-transmembrane protein covalently linked with retinal chromophore that absorbs photons for energy conversion and intracellular signaling in eukaryotes, bacteria, and archaea. Haloarchaeal rhodopsins are Type-I microbial rhodopsin that elicits various light-driven functions like proton pumping, chloride pumping and Phototaxis behaviour. The industrial application of Ion-pumping Haloarchaeal rhodopsins is limited by the lack of full-length rhodopsin sequence-based classifications, which play an important role in Ion-pumping activity. The well-studied Haloarchaeal rhodopsin is a proton-pumping bacteriorhodopsin that shows promising applications in optogenetics, biosensitized solar cells, security ink, data storage, artificial retinal implant and biohydrogen generation. As a result, a low-cost computational approach is required to identify Ion-pumping Haloarchaeal rhodopsin sequences and its subtype.ResultsThis study uses a support vector machine (SVM) technique to identify these ion-pumping Haloarchaeal rhodopsin proteins. The haloarchaeal ion pumping rhodopsins viz., bacteriorhodopsin, halorhodopsin, xanthorhodopsin, sensoryrhodopsin and marine prokaryotic Ion-pumping rhodopsins like actinorhodopsin, proteorhodopsin have been utilized to develop the methods that accurately identified the ion pumping haloarchaeal and other type I microbial rhodopsins. We achieved overall maximum accuracy of 97.78%, 97.84% and 97.60%, respectively, for amino acid composition, dipeptide composition and hybrid approach on tenfold cross validation using SVM. Predictive models for each class of rhodopsin performed equally well on an independent data set. In addition to this, similar results were achieved using another machine learning technique namely random forest. Simultaneously predictive models performed equally well during five-fold cross validation. Apart from this study, we also tested the own, blank, BLAST dataset and annotated whole-genome rhodopsin sequences of PWS haloarchaeal isolates in the developed methods. The developed web server (https://bioinfo.imtech.res.in/servers/rhodopred) can identify the Ion Pumping Haloarchaeal rhodopsin proteins and their subtypes. We expect this web tool would be useful for rhodopsin researchers.ConclusionThe overall performance of the developed method results show that it accurately identifies the Ionpumping Haloarchaeal rhodopsin and their subtypes using known and unknown microbial rhodopsin sequences. We expect that this study would be useful for optogenetics, molecular biologists and rhodopsin researchers.

Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data

The present study is designed to monitor the spatio-temporal changes in forest cover using Remote Sensing (RS) and Geographic Information system (GIS) techniques from 1990 to 2017. Landsat data from 1990 (Thematic mapper [TM]), 2000 and 2010 (Enhanced Thematic Mapper [ETM+]), and 2013 to 2017 (Operational Land Imager/Thermal Infrared Sensor [OLI/TIRS]) were classified into the classes termed snow, water, barren land, built-up area, forest, and vegetation. The method was built using multitemporal Landsat images and the machine learning techniques Support Vector Machine (SVM), Naive Bayes Tree (NBT) and Kernel Logistic Regression (KLR). According to the results, forest area was decreased from 19,360 km2 (26.0%) to 18,784 km2 (25.2%) from 1990 to 2010, while forest area was increased from 18,640 km2 (25.0%) to 26,765 km2 (35.9%) area from 2013 to 2017 due to “One billion tree Project”. According to our findings, SVM performed better than KLR and NBT on all three accuracy metrics (recall, precision, and accuracy) and the F1 score was >0.89. The study demonstrated that concurrent reforestation in barren land areas improved methods of sustaining the forest and RS and GIS into everyday forestry organization practices in Khyber Pakhtun Khwa (KPK), Pakistan. The study results were beneficial, especially at the decision-making level for the local or provincial government of KPK and for understanding the global scenario for regional planning.

Automatic COVID-19 prediction using explainable machine learning techniques

The coronavirus is considered this century's most disruptive catastrophe and global concern. This disease has prompted extreme social, psychological and economic impacts affecting millions of people around the globe. COVID-19 is transmitted from one infected person's body to another through respiratory droplets. This virus proliferates when people breathe in air-contaminated space with droplets and microscopic airborne particles. This research aims to analyze automatic COVID-19 detection using machine learning techniques to build an intelligent web application. The dataset has been preprocessed by dropping null values, feature engineering, and synthetic oversampling (SMOTE) techniques. Next, we trained and evaluated different classifiers, i.e., logistic regression, random forest, decision tree, k-nearest neighbor, support vector machine (SVM), ensemble models (adaptive boosting and extreme gradient boosting) and deep learning (artificial neural network, convolutional neural network and long short-term memory) techniques. Explainable AI with the LIME framework has been applied to interpret the prediction results. The hybrid CNN-LSTM algorithm with the SMOTE approach performed better than the other models on the employed open-source dataset obtained from the Israeli Ministry of Health website, with 96.34% accuracy and a 0.98 F1 score. Finally, this model was chosen to deploy the proposed prediction system to a website, where users may acquire an instantaneous COVID-19 prognosis based on their symptoms.

A methodology for emotional intelligence testing in elderly people with low-cost EEG and PPG devices

Emotional intelligence (EI) is a basic concept in psychology that is gaining social importance. According to Goleman, it refers to the ability to be aware of and to handle one’s emotions in varying situations. Current research indicates that EI can be improved through training, leading to an increased awareness of how we can contribute to the emotional management. In this paper, a low-cost ElectroEncephaloGraphy (EEG) and PhotoPlethysmoGraphy (PPG) based proposal is introduced in order to assess the level of emotional intelligence among elderly people during cognitive stimulation sessions. Twenty-five older people , who were baby boomers (people born from 1946 to 1964), were recruited during 2 months, while they participated in a cognitive stimulation program. During those months, these participants were trained in different techniques of emotional management and they learned how to use several low-cost EEG and PPG devices. They were subjected to several emotional stimulation sessions where stress and anxiety scenarios were considered. By using our proposal, different supervised learning algorithms were evaluated in order to allow emotion detection, having the Support Vector Machine (SVM) technique as the one that reached better scores. Later, our solution supports emotional intelligence test and promising outcomes were achieved.

Morphologic brain network predicts levodopa responsiveness in Parkinson disease.

The levodopa challenge test (LCT) has been routinely used in Parkinson disease (PD) evaluation and predicts the outcome of deep brain stimulation (DBS). Guidelines recommend that patients with an improvement in Unified Parkinson's Disease Rating Scale (UPDRS)-III score > 33% in the LCT receive DBS treatment. However, LCT results are affected by many factors, and only provide information on the immediate effectiveness of dopamine. The aim of the present study was to investigate the relationship between LCT outcome and brain imaging features of PD patients to determine whether the latter can be used to identify candidates for DBS. A total of 38 PD patients were enrolled in the study. Based on improvement in UPDRS-III score in the LCT, patients were divided into low improvement (PD-LCT-L) and high improvement (PD-LCT-H) groups. Each patient's neural network was reconstructed based on T1-weighted magnetic resonance imaging data using the Jensen-Shannon divergence similarity estimation method. The network was established with the multiple kernel support vector machine technique. We analyzed differences in individual morphologic brain networks and their global and local metrics to determine whether there were differences in the connectomes of PD-LCT-L and PD-LCT-H groups. The 2 groups were similar in terms of demographic and clinical characteristics. Mean ± SD levodopa responsiveness was 26.52% ± 3.47% in the PD-LCT-L group (N = 13) and 58.66% ± 4.09% in the PD-LCT-H group (N = 25). There were no significant differences between groups in global and local metrics. There were 43 consensus connections that were affected in both groups; in PD-LCT-L patients, most of these connections were decreased whereas those related to the dorsolateral superior frontal gyrus and left cuneus were significantly increased. Morphologic brain network assessment is a valuable method for predicting levodopa responsiveness in PD patients, which can facilitate the selection of candidates for DBS.

Development of macro-defect-free PBF-EB-processed Ti–6Al–4V alloys with superior plasticity using PREP-synthesized powder and machine learning-assisted process optimization

The elimination of internal macro-defects is a key issue in Ti–6Al–4V alloys fabricated via powder bed fusion using electron beams (PBF-EB), wherein internal macro-defects mainly originate from the virgin powder and inappropriate printing parameters. This study compares different types powders by combining support vector machine techniques to determine the most suitable powder for PBF-EB and to predict the processing window for the printing parameters without internal macro-defects. The results show that powders fabricated via plasma rotating electrode process have the best sphericity, flowability, and minimal porosity and are most suitable for printing. Surface roughness criterion was also applied to determine the quality of the even surfaces, and support vector machine was used to construct processing maps capable of predicting a wide range of four-dimensional printing parameters to obtain macro-defect-free samples, offering the possibility of subsequent development of Ti–6Al–4V alloys with excellent properties. The macro-defect-free samples exhibited good elongation, with the best overall mechanical properties being the ultimate tensile strength and elongation of 934.7 MPa and 24.3%, respectively. The elongation of the three macro-defect-free samples was much higher than that previously reported for additively manufactured Ti–6Al–4V alloys. The high elongation of the samples in this work is mainly attributed to the elimination of internal macro-defects.

Business rule extraction using decision tree machine learning techniques: A case study into smart returnable transport items

Decision support systems are becoming increasingly sophisticated (e.g., being machine learning-based), attempting to automate decisions as much as possible. However, it remains challenging to extract meaningful value from large quantities of data while also maintaining transparency in seeking justification for the choices made. Instead of creating methods for increasing the interpretability of black box models, one way forward is to design models that are inherently interpretable in the first place. Rule-based methods can automate decisions with great transparency and accuracy, helping to ensure compliance with regulations and adherence to organizational guidelines. In this paper, we propose an approach that uses a decision tree machine learning classification technique for extracting business rules from IoT-generated data to predict the asset status of Smart Returnable Transport Items (SRTIs). We report on an industrial case study that uses two years of historical data, obtained from an SRTI provider in the Netherlands, to predict the status of smart pallets. We compare the performance with the results obtained by using a support-vector machine (SVM) technique. Our experiments show that our solution is both accurate and flexible in terms of business rule elicitation. The obtained decision trees are human-interpretable, can easily be combined with other decision-making techniques, and provide a prediction accuracy marginally higher than an SVM technique.

Using Data Mining Techniques to Predict Chronic Kidney Disease: A Review Study.

One of the growing global health problems is chronic kidney disease (CKD). Early diagnosis, control, and management of chronic kidney disease are very important. This study considers articles published in English between 2016 and 2021 that use classification methods to predict kidney disease. Data mining models play a vital role in predicting disease. Through our study, data mining techniques of support vector machine, Naive Bayes, and k-nearest neighbor had the highest frequency. After that, random forest, neural network, and decision tree were the most common data mining techniques. Among the risk factors associated with chronic kidney disease, respectively, risk factors of albumin, age, red blood cells, pus cells, and serum creatinine had the highest frequency in these studies. The highest number of best yields was allocated to random forest technique. Reviewing larger databases in the field of kidney disease can help to better analyze the disease and ensure the risk factors extracted.

Arabic Dialogue Processing and Act Classification using Support Vector Machine

Text classification is the technique of grouping documents according to their content into classes and groups. As a result of the vast amount of textual material available online, this procedure is becoming increasingly crucial. The primary challenge in text categorization is enhancing classification accuracy. This role is receiving more attention due to its importance in the development of these systems and the categorization of Arabic dialogue processing. In the research, attempts were made to define dialogue processing. It concentrates on classifying words that are used in dialogue. There are various types of dialogue processing, including hello, farewell, thank you, confirm, and apologies. The words are used in the study without context. The proposed approach recovers the properties of function words by replacing collocations with standard number tokens and each substantive keyword with a numerical approximation token. With the use of the linear support vector machine (SVM) technique, the classification method for this study was obtained. The act is classified using the linear SVM technique, and the anticipated accuracy is evaluated against that of alternative algorithms. This study encompasses Arabic dialogue acts corpora, annotation schema, and classification problems. It describes the outcomes of contemporary approaches to classifying Arabic dialogue acts. A custom database in the domains of banks, chat, and airline tickets is used in the research to assess the effectiveness of the suggested solutions. The linear SVM approach produced the best results.

The Application of Internet of Things for the Elderly Health Safety: A Systematic Review.

The elderly population is projected to increase from 8.5% in 2015 to 12% in 2030 and 16% in 2050. This growing demographic is chronically vulnerable to various age-related diseases and injuries like falling, leading to long-term pain, disability, or death. Thus, there is a need to use the potential of novel technologies to support the elderly regarding patient safety matters in particular. Internet of Things (IoT) has recently been introduced to improve the lifestyle of the elderly. This study aimed to evaluate the studies that have researched the use of the IoT for elderly patients' safety through performance metrics, accuracy, sensitivity, and specificity. We conducted a systematic review on the research question. To do this, we searched PubMed, EMBASE, Web of Science, Scopus, Google Scholar, and Science Direct databases by combining the related keywords. A data extraction form was used for data gathering through which English, full-text articles on the use of the IoT for the safety of elderly patients were included. The support vector machine technique has the most frequency of use compared to other techniques. Motion sensors were the most widely used type. The United States with four studies had the highest frequencies. The performance of IoT to ensure the elderly's safety was relatively good. It, however, needs to reach a stage of maturity for universal use.

Metaheuristics with Optimal Deep Transfer Learning Based Copy-Move Forgery Detection Technique

The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content. An effective technique for tampering the identification is the copy-move forgery. Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification. Contrastingly, deep learning (DL) models have demonstrated significant performance over the other statistical techniques. With this motivation, this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection (ODTL-CMFD) technique. The presented ODTL-CMFD technique aims to derive a DL model for the classification of target images into the original and the forged/tampered, and then localize the copy moved regions. To perform the feature extraction process, the political optimizer (PO) with Mobile Networks (MobileNet) model has been derived for generating a set of useful vectors. Finally, an enhanced bird swarm algorithm (EBSA) with least square support vector machine (LS-SVM) model has been employed for classifying the digital images into the original or the forged ones. The utilization of the EBSA algorithm helps to properly modify the parameters contained in the Multiclass Support Vector Machine (MSVM) technique and thereby enhance the classification performance. For ensuring the enhanced performance of the ODTL-CMFD technique, a series of simulations have been performed against the benchmark MICC-F220, MICC-F2000, and MICC-F600 datasets. The experimental results have demonstrated the improvised performance of the ODTL-CMFD approach over the other techniques in terms of several evaluation measures.

Machine Learning Techniques for Breast Cancer Prediction

Breast cancer is main reason for mortality in woman. Prediction of breast cancer is a challenging task in medical data analysis. Doctors and pathologist required some automated tools to take decision and to differentiate between malignant and benign tumour. A machine learning (ML) algorithm helps lot to take decisions and to perform diagnosis from the data collected by medical field. Various researches show that ML techniques are helpful for decision making in breast cancer prediction. In this paper, we used various ML Classification techniques: Naïve Bayes(NB), Logistic regression (LR),Support vector machine(SVM),K-Nearest Neighbor (KNN), Decision Tree(DT), and ensemble techniques: Random forest(RF), Adaboost, XGBoost on breast cancer dataset and evaluated by using different performance measure. It has been found that both decision tree and XGBoost classifier has highest accuracy 97% among all model and highest AUC 0.999 obtained for XGBoost classifier.

Efficient prediction and classification for cirrhosis disease using LBP, GLCM and SVM from MRI images

To enhance the specificity of Magnetic resonance imaging (MRI) based cirrhosis stage-diagnosis, a method of diagnosis incorporating the scan image texture discovery with classification techniques are suggested. First of all the liver MRI images are preprocessed and Region of Interest (ROI) image field is retrieved from the MRI images and the Lloyd method is compressed and quantified. Then a Local Binary Pattern (LBP) process filtering the ROI image and then extracts the texture features of the LBP images from four directions with a twenty directional Gray-Level Co-occurrence Matrix (GLCM). After that, the Support Vector Machine (SVM) technique has been used for MRI image classification and cirrhosis disease prediction. The experimental findings indicate that the procedure suggested will diagnose hepatic cirrhosis correctly.

A revolutionary acute subdural hematoma detection based on two-tiered artificial intelligence model.

The article was planned to make the first evaluation in terms of acute subdural hemorrhages, thinking that it can help in appropriate pathologies by tomography interpretation with the artificial intelligence (AI) method, at least in a way to quickly warn the responsible doctor. A two-level AI-based hybrid method was developed. The proposed model uses the mask-region convolutional neural network (Mask R-CNN) technique, which is a deep learning model, in the hemorrhagic region's mask generation stage, and a problem-specific, optimized support vector machines (SVM) technique which is a machine learning model in the binary classification stage. Furthermore, the bee colony algorithm was used for the optimization of SVM algorithms' parameters. In the first stage, the mean average precision (mAP) value was obtained as 0.754 when the intercept over union (IOU) value was taken as 0.5 with the Mask R-CNN architecture used. At the same time, when a 5-fold cross-validation was applied, the mAP value was obtained 0.736. With the hyperparameter optimization for both Mask R-CNN and the SVM algorithm, the accuracy of the two-level classification process was obtained as 96.36%. Furthermore, final false-negative rate and false-positive rate values were obtained as 6.20%, and 2.57%, respectively. With the proposed model, both the detection of hemorrhage and the presentation of the suspicious area to the physician were performed more successfully on two dimensional (2D) images with low cost and high accuracy compared to similar studies and today's interpretations with telemedicine techniques.

Supervised learning of Plasmodium falciparum life cycle stages using single-cell transcriptomes identifies crucial proteins

Vital gene expressions form the basis for the detection of malaria infection levels. Quantification of infected erythrocytes and classification of their life cycle stages are done at a macroscopic level by experts, for making informed decisions for diagnosis and treatment of malaria. Of late multiple computational approaches have been proposed to circumvent the problem of dimensionality leading to accurately predicted results. In this work, a dimensionality reduction technique based on Genetic Algorithm (GA) is applied to Plasmodium falciparum single cell transcriptomics to arrive at an optimized subset of features from the larger dataset. Features are chosen based on their class variants considering increased efficiency and accuracy, to separately transform the selected elements into a lower dimension. For the classification of the life cycle of malaria parasites based on single cell transcriptome data, a three-pronged approach employing the multiclass Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) technique is used. Further, we constructed protein interaction networks of the genes identified by the feature selection method and gene ontology analysis elucidated the role of the proteins in the progression of the parasite through its life cycle. Our approach presents a novel protocol to implement ML techniques on scRNA seq datasets and subsequently harness the extracted information for biomarker/drug target detection.

Precise Multi-Class Classification of Brain Tumor via Optimization Based Relevance Vector Machine

The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain tumors. The brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or malignant. Most tumors are misdiagnosed due to the variability and complexity of lesions, which reduces the survival rate in patients. Diagnosis of brain tumors via computer vision algorithms is a challenging task. Segmentation and classification of brain tumors are currently one of the most essential surgical and pharmaceutical procedures. Traditional brain tumor identification techniques require manual segmentation or handcrafted feature extraction that is error-prone and time-consuming. Hence the proposed research work is mainly focused on medical image processing, which takes Magnetic Resonance Imaging (MRI) images as input and performs preprocessing, segmentation, feature extraction, feature selection, similarity measurement, and classification steps for identifying brain tumors. Initially, the median filter is practically applied to the input image to reduce the noise. The graph-cut segmentation technique is used to segment the tumor region. The texture feature is extracted from the output of the segmented image. The extracted feature is selected by using the Ant Colony Optimization (ACO) algorithm to improve the performance of the classifier. This probabilistic approach is used to solve computing issues. The Euclidean distance is used to calculate the degree of similarity for each extracted feature. The selected feature value is given to the Relevance Vector Machine (RVM) which is a multi-class classification technique. Finally, the tumor is classified as abnormal or normal. The experimental result reveals that the proposed RVM technique gives a better accuracy range of 98.87% when compared to the traditional Support Vector Machine (SVM) technique.

An Optimized Deep Learning Approach for Improving Airline Services

The aviation industry is one of the most competitive markets. The most common approach for airline service providers is to improve passenger satisfaction. Passenger satisfaction in the aviation industry occurs when passengers’ expectations are met during flights. Airline service quality is critical in attracting new passengers and retaining existing ones. It is crucial to identify passengers’ pain points and enhance their satisfaction with the services offered. The airlines used a variety of techniques to improve service quality. They used data analysis approaches to analyze the passenger point data. These solutions have focused simply on surveys; consequently, deep-learning approaches have received insufficient attention. In this study, deep neural networks with the adaptive moment estimation Adam optimization algorithm were applied to enhance classification performance. In previous studies, the quality of the dataset has been ignored. The proposed approach was applied to the airline passenger satisfaction dataset from the Kaggle repository. It was validated by applying artificial neural networks (ANNs), random forests, and support vector machine techniques to the same dataset. It was compared with other research papers that used the same dataset and had a similar problem. The experimental results showed that the proposed approach outperformed previous studies. It has achieved an accuracy of 99.3%.

Static Pakistani Sign Language Classification using Support Vector Machine

In this study, a system is proposed that uses the Support Vector Machine (SVM) technique with Bag-of-Words (BoW) and recognizes static Pakistani Sign Language (PSL) alphabets. The application of the BoW technique with SVM, on a PSL images' dataset, has not been performed previously. Similarly, no publicly available dataset for PSL is available and previous studies have achieved a maximum classification accuracy of 91.98%. For this study, a total of 511 images are collected for 36 static PSL alphabet signs from a native signer. The Sign Language (SL) recognition system uses the collected images as input and converts them to grayscale. To segment the images, the system uses the thresholding technique and Speeded Up Robust Feature (SURF) to extract the features. The system uses K-means clustering to cluster the extracted features. To form the BoW, the system computes the Euclidean distance among SURF descriptors and clustered data. The system then uses 5-fold cross-validation to divide the codebooks obtained from the BoW into training and testing. The developed system yields an overall accuracy of 97.87% for the classification of static PSL signs at 1,500×1,500 image dimensions and 500 Bags.

Identifying Fake Digital Information Using Machine Learning Algorithms: Performance Analysis and Recommendation System

This work focuses on the detection of fake digital information using various machine learning and deep learning algorithms to prevent its spread through Internet of Things (IoT) devices and systems. The research highlights the significance of detecting and preventing false or misleading information in critical areas such as healthcare, public safety, and emergency response. The study compares the performance of several supervised machine learning algorithms and identifies logistic regression as the most accurate (98.03%). The empirical analysis used data from The Indian Express, PolitiFact, and Kaggle and leveraged natural language processing (NLP) to prepare, clean, and model the data. To detect fraudulent posts, the study employed random forest, a supervised machine learning algorithm, which achieved an impressive accuracy rate of 99.71% on a Kaggle dataset. The research also developed a model for detecting false reporting related to COVID-19, utilizing the support vector machine technique, which achieved an accuracy rate of 78.69%. The presented work also determined the authenticity of images through convolutional neural networks (CNNs). Lastly, a content-based recommendation system was developed to enhance people’s security and confidence.

Sustainable production using a hybrid IPSO optimized SVM-based technique: Fashion industry

The fashion industry is one of the most polluting industries in the world. Accordingly, it is crucial to gain a greater understanding of the products in this industry in order to prevent environmental degradation. Due to the rapid rate of transformation and the enormous volume of data, it is critical to develop an appropriate feature selection technique that improves managers' decision-making to predict the production of products with minimal environmental pollution. In this research, we proposed a new feature selection method based on Support Vector Machine (SVM) technique involving more than one environmental parameter, such as waste, pollution and energy. It served to predict the sustainable productions that achieve the highest number of customers and the lowest possible damage to the environment. Furthermore, we intended to optimize the SVM technique in feature selection by adopting the improved form of this technique through the Improved Particle Swarm Optimization (IPSO) algorithm and ultimately adjust the different kernel parameters involved in the SVM technique learning process. The overall objective of this study was to evaluate the performance of SVM in combination with IPSO algorithm and its impact on predicting high demand products and to enable smart decisions for managers to make sustainable production compared to other existing techniques. The results suggested that the accuracy of the test in the newly proposed hybrid method is higher than other similar methods owing to optimization of the parameters of SVM algorithm by IPSO as well as the adoption of kernels corresponding to the environmental parameter in SVM technique learning process.