Simple Machine Learning Algorithms Research Articles

Classification and identification of perfume scents by an enantioselective colorimetric sensor array of chiral metal-organic-framework-based Fabry-Pérot films.

Many odors, like perfumes, are complex mixtures of chiral and achiral molecules where the cost-efficient (enantio-)selective sensing represents a major technical challenge. Here, we present a colorimetric sensor array of surface-mounted metal-organic-framework (SURMOF) films in Fabry-Pérot (FP) cavities. The optical properties of the FP-SURMOF films with different chiral and achiral structures are affected by the (enantio-)selective adsorption of the analytes in the SURMOF pores, resulting in different responses to the analyte molecules. The read-out of the sensor array is performed by the digital camera of a common smartphone, where the RGB values are determined. By analyzing the sensor array data with simple machine learning algorithms, the analytes are discriminated. After demonstrating the enantioselective response for a pair of pure chiral odor molecules, we apply the sensor array to detect and discriminate a large number (16) of common commercial perfumes and eau de toilettes. While our untrained human nose is not able to discriminate all perfumes, the presented colorimetric sensor array can classify all perfumes with great classification accuracy. Moreover, the sensor array was used to identify unlabeled samples correctly. We foresee such an FP-chiral-SURMOF-based sensor array as a powerful approach toward inexpensive selective odors sensing applications.

A Comprehensive Evaluation of Features and Simple Machine Learning Algorithms for Electroencephalographic-Based Emotion Recognition

The study of electroencephalographic (EEG) signals has gained popularity in recent years because they are unlikely to intentionally fake brain activity. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. Analyzing these signals involves three main processes: feature extraction, feature selection, and classification. The present study extensively evaluates feature sets across domains and their impact on emotion recognition. Feature selection improves results across the different domains. Additionally, hybrid models combining features from various domains offer a superior performance when applying the public DEAP dataset for emotion classification using EEG signals. Time, frequency, time–frequency, and spatial domain attributes and their combinations were analyzed. The effectiveness of the input vectors for the classifiers was validated using SVM, KNN, and ANN, which are simple classification algorithms selected for their widespread use and better performance in the state of the art. The use of simple machine learning algorithms makes the findings particularly valuable for real-time emotion recognition applications where the computational resources and processing time are often limited. After the analysis stage, feature vector combinations were proposed to identify emotions in four quadrants of the valence–arousal representation space using the DEAP dataset. This research achieved a classification accuracy of 96% using hybrid features in the four domains and the ANN classifier. A lower computational cost was obtained in the frequency domain.

Machine learning tools in the tasks of selecting the determinants of socio-economic situation and development potential of Russian regions

Subject. The article addresses the selection of determinants that are significant for assessing the level of socio-economic situation and development potential of Russian regions. Objectives. The aim is to study machine learning algorithms for the selection of determinants – predictors of the level of socio-economic situation and development potential of Russian regions, to build models of classification of regions, according to the level of socio-economic situation, using various machine learning algorithms. Methods. To build classification models, I used data from the Federal State Statistics Service, the Institute of Scientific Communications, the RIA Novosti news agency, and the TAdviser Internet portal. Procedures for data classification, model parameter estimation, selection of significant determinants and visualization of results are performed, using the basic functions of the PyCaret library. Cohen's Kappa statistics and Matthews correlation coefficient were employed as priority metrics for evaluating the model productivity. The algorithms for selecting determinants are implemented in the Google Colab analytical environment. Results. I constructed multiclass classification models based on simple and ensemble machine learning algorithms. Simple classification algorithms, including logistic and ridge regression models, naive Bayesian algorithm, decision tree, support vector machine, and k-nearest neighbor methods are characterized by accuracy values at 77%, however, Cohen's Kappa statistics and the Matthews correlation coefficient only show a satisfactory relationship between the actual and predicted value of the region class. Ensemble algorithms, including random forest, gradient boosting and extreme gradient boosting, are characterized by a close relationship between the actual and forecast estimates of the classifier at a level of more than 70%. Conclusions. The random forest algorithm is recognized as the most effective classification model. The gross regional product and investments in fixed assets are informative determinants for measuring the socio-economic status.

Utilising Machine Learning for the Automated Characterisation of In Situ Electron Microscopy Experiments with Catalytic Systems

The data analysis workflow for in situ electron microscopy experiments can require a lot of human-intensive and repetitive effort. The generation of Python-based scripts that incorporate simple machine learning algorithms are quite well established in biological sciences but not often utilised in the study of catalytic systems. Such scripted analysis is not only more efficient, but readily reproducible and allows a wide range of quantitative results to be reported, including but not limited to average and total particle size, particle counting and particle size distributions. In this work we utilise these tools to examine the effect of cycling reducing and oxidising atmospheres on CuO nanoparticles.

Creation and interpretation of machine learning models for aqueous solubility prediction

Aim: Solubility prediction is an essential factor in rational drug design and many models have been developed with machine learning (ML) methods to enhance the predictive ability. However, most of the ML models are hard to interpret which limits the insights they can give in the lead optimization process. Here, an approach to construct and interpret solubility models with a combination of physicochemical properties and ML algorithms is presented. Methods: The models were trained, optimized, and tested in a dataset containing 12,983 compounds from two public datasets and further evaluated in two external test sets. More importantly, the SHapley Additive exPlanations (SHAP) and heat map coloring approaches were used to explain the predictive models and assess their suitability to guide compound optimization. Results: Among the different ML methods, random forest (RF) models obtain the best performance in the different test sets. From the interpretability perspective, fragment-based coloring offers a more robust interpretation than atom-based coloring and that normalizing the values further improves it. Conclusions: Overall, for certain applications simple ML algorithms such as RF work well and can outperform more complex methods and that combining them with fragment-coloring can offer guidance for chemists to modify the structure with a desired property. This interpretation strategy is publicly available at https://github.com/Pharmacelera/predictive-model-coloring and could be further applied in other property predictions to improve the interpretability of ML models.

An electronic nose for harmful gas early detection based on a hybrid deep learning method H-CRNN

Among harmful gases, carbon monoxide (CO) has posed a significant threat to human safety due to its concealment and harmfulness. Therefore, it’s imperative for the preservation of human safety to implement early detection for CO concentration. However, traditional methods for gas detection are either plagued by the limitations of insufficient accuracy like electronic nose (E-nose) equipped with relatively simple machine learning algorithms such as vanilla SVM or RNN, or limited by the incapacity to make early prediction such as sensory analysis, which requires a large amount of manpower resources and time. To address this issue, a novel technique, namely H-CRNN, is presented for E-nose to perform early prediction of low concentration CO, leveraging the strengths of convolutional and recurrent neural network to efficiently capture long-term dependencies within data. Also, shortcut connection and a novel gated attention mechanism is applied to enhance the capacity to capture crucial features. Furthermore, a linear process unit has been incorporated to handle the scale insensitivity issue. The experimental results show that the proposed H-CRNN outperforms RNN with a notable average reduction of 50.51% in the root relative squared error and 53.8% in the relative absolute error, and exhibits varying degrees of improvement when compared to state-of-the-art algorithms (TCN, TPA-LSTM, STCN, LSTNet, LSTM). Additionally, the H-CRNN achieved an average accuracy rate of 96.42%, surpassing all other algorithms and demonstrating the highest level of performance. Therefore, our work substantiates the proposed H-CRNN as an applicable method for early prediction of CO concentration.

Convolutional Neural Networks: A Simple and Functional Approach for COVID-19 Severity Prediction

Background and Aims: The Coronavirus disease 2019 (COVID-19) pandemic began in 2020. A major problem during COVID-19 was determining the clinical severity. There are a variety of markers for assessing the COVID-19 severity and outcome. So, this study aims to introduce a new approach for determining the disease severity based on the laboratory data obtained by machine learning algorithms.
 Materials and Methods: In this study, we used 100 patients for modeling. We used demographical, background disease, and laboratory data of COVID-19 patients as parameters for training the convolutional neural network model to evaluate disease severity and tried to create a predictive algorithm for future data. The sequential neural network from the Keras library by TensorFlow was used for prediction. The clinical validation of prediction by model was evaluated by the receiver operating characteristic (ROC) curve.
 Results: The mean F1 score for our current model was 0.62 (in the range of 0-1). The F1 scores for the severe group and the mild group were 0.8 and 0.45, respectively. The ROC curve for clinical validity revealed an acceptable Area Under Curve (0.085) for both severe and mild categories.
 Conclusion: The current study introduces a simple machine learning algorithm as tool for determining COVID-19 severity of by acceptable ROC. This study can lead us to use such algorithms more often in laboratory medicine and clinical decision-making. Furthermore, the present study is just a preliminary study and highlights the need for further research to validate and refine the proposed model.

Supervised learning techniques for detection of Lung Carcinoma

Lung diseases are the most common ailments seen among people with the history of smoking. Prompt and timely recognition and diagnosis may help in saving many lives. In order to detect cancer at early stages machine learning algorithms can be employed. Use of simple machine learning algorithms will help identify the carcinoma faster with high accuracy and lesser expense. This work shows the use three of simple machine learning (ML) algorithms like Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). ML models were built using lung cancer patients’ dataset. The dataset was used to train the model as well as test the model. The three classifiers will detect the presence of lung cancer. For each classifier the Accuracy, Mean Square Error(MSE), precision, and Recall (R2) was calculated. A comparative study of the classifiers was done to identify which among the three was the best one. The main objective of the paper is to identify the best efficient machine-learning algorithm in terms of confusion matrices, accuracy, and precision for the prediction and diagnosis of lung cancer

Quantum and Quantum-Inspired Stereographic K Nearest-Neighbour Clustering.

Nearest-neighbour clustering is a simple yet powerful machine learning algorithm that finds natural application in the decoding of signals in classical optical-fibre communication systems. Quantum k-means clustering promises a speed-up over the classical k-means algorithm; however, it has been shown to not currently provide this speed-up for decoding optical-fibre signals due to the embedding of classical data, which introduces inaccuracies and slowdowns. Although still not achieving an exponential speed-up for NISQ implementations, this work proposes the generalised inverse stereographic projection as an improved embedding into the Bloch sphere for quantum distance estimation in k-nearest-neighbour clustering, which allows us to get closer to the classical performance. We also use the generalised inverse stereographic projection to develop an analogous classical clustering algorithm and benchmark its accuracy, runtime and convergence for decoding real-world experimental optical-fibre communication data. This proposed 'quantum-inspired' algorithm provides an improvement in both the accuracy and convergence rate with respect to the k-means algorithm. Hence, this work presents two main contributions. Firstly, we propose the general inverse stereographic projection into the Bloch sphere as a better embedding for quantum machine learning algorithms; here, we use the problem of clustering quadrature amplitude modulated optical-fibre signals as an example. Secondly, as a purely classical contribution inspired by the first contribution, we propose and benchmark the use of the general inverse stereographic projection and spherical centroid for clustering optical-fibre signals, showing that optimizing the radius yields a consistent improvement in accuracy and convergence rate.

Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms?

BackgroundThe worldwide society is currently facing an epidemiological shift due to the significant improvement in life expectancy and increase in the elderly population. This shift requires the public and scientific community to highlight successful aging (SA), as an indicator representing the quality of elderly people’s health. SA is a subjective, complex, and multidimensional concept; thus, its meaning or measuring is a difficult task. This study seeks to identify the most affecting factors on SA and fed them as input variables for constructing predictive models using machine learning (ML) algorithms.MethodsData from 1465 adults aged ≥ 60 years who were referred to health centers in Abadan city (Iran) between 2021 and 2022 were collected by interview. First, binary logistic regression (BLR) was used to identify the main factors influencing SA. Second, eight ML algorithms, including adaptive boosting (AdaBoost), bootstrap aggregating (Bagging), eXtreme Gradient Boosting (XG-Boost), random forest (RF), J-48, multilayered perceptron (MLP), Naïve Bayes (NB), and support vector machine (SVM), were trained to predict SA. Finally, their performance was evaluated using metrics derived from the confusion matrix to determine the best model.ResultsThe experimental results showed that 44 factors had a meaningful relationship with SA as the output class. In total, the RF algorithm with sensitivity = 0.95 ± 0.01, specificity = 0.94 ± 0.01, accuracy = 0.94 ± 0.005, and F-score = 0.94 ± 0.003 yielded the best performance for predicting SA.ConclusionsCompared to other selected ML methods, the effectiveness of the RF as a bagging algorithm in predicting SA was significantly better. Our developed prediction models can provide, gerontologists, geriatric nursing, healthcare administrators, and policymakers with a reliable and responsive tool to improve elderly outcomes.

A simulation and data informed approach to porosity partitioning

Information about pore shape and geometry form one of the significant inputs in determining the effective elastic properties of porous rocks. The size and structure of the pores determine the amount of fluid contents which in turn determine their bulk response. Conventionally, effective medium models have been used to study the macroscopic properties of these rocks. These models often use a fixed set of microscopic variables (like the shape parameter) to describe their bulk behavior. Barring the specification of the elastic properties of the starting host material, their original formulations often lack the provision to integrate any experimental data, later. In the context of carbonates, the rocks exhibit a large variation in pore shapes and sizes, hindering the development of rock physics models. Therefore, they eventually require calibration. This is an important issue as the size of the pores here range from nanoscale to meter scale due the presence of primary and secondary porosity systems. In this paper we develop a method that can integrate the pore shape information into differential effective medium models using simple statistical and machine learning algorithms. Avoiding the use of conceptual values for the interparticle and vug type pores, our technique facilitates a data-based inference of the shape parameters from both the image data and forward model simulations. Micro-tomography images were used to extract the pore shape information for relatively larger pores such as the vugs. The information about the interparticle pores was deduced using a simulation-based approach from well-logs. Both of these were used to specify the properties of the new inclusions, hence aiding in data informed porosity partitioning. Finally, we outline a simple regression-based scheme using a first order polynomial model that can be used to calibrate any general rock physics model.

530-P: Capillary Blood Ketone Level and the Prediction of Future Diabetic Ketoacidosis Risk in Type 1 Diabetes

Identification of those at risk of diabetic ketoacidosis (DKA) in T1D is challenging. Rather than during illness while DKA is developing, we aimed to determine if levels of routine point-of-care capillary blood ketones could predict DKA months prior to the event. In exploratory analysis, we examined 484 participants randomly assigned to placebo in an adjunct-to-insulin medication trial program (NCT02414958 and NCT02580591). Participants provided morning fasted capillary blood ketone levels twice per week via electronic logbook, and we calculated the maximum and mean level for each individual during a 2-month baseline period. Outcome was the 6 to 12-month occurrence of trial-adjudicated DKA. Area under the Curve (AUC) for the Receiver Operator Characteristic curves were generated. In sensitivity analysis we applied supervised machine learning methods (gradient-boosted trees). Participants had median age 43 [IQR 33, 54], mean HbA1c 8.2±0.6 percent, and provided 2 [1, 4] ketone measurements per week. Twelve DKA events occurred at median 105 [43, 199] days. Maximum and mean ketone levels were higher at baseline for the 12 cases compared to controls (for example, maximum ketone level 0.8 [0.6, 1.2] compared to 0.3 [0.2, 0.7] mmol/L, p=0.002). Maximum ketone level had AUC of 0.769 (95% CI 0.655-0.883). Maximum ketone ≥0.8 mmol/L had sensitivity 64%, specificity 78%, and likelihood ratios positive and negative of 2.9 and 0.5. Machine learning methods outperformed the single-metric analyses. Results provide proof-of-concept that routine capillary ketone surveillance can identify individuals at high-risk of future DKA. Simple or complex machine learning algorithms could be implemented in ketone meters or in future continuous ketone measurement technology. Disclosure C.Song: None. D.Cherney: Other Relationship; Boehringer Ingelheim-Lilly, Merck, AstraZeneca, Sanofi, Mitsubishi-Tanabe, Abbvie, Janssen, Bayer, Prometic, BMS, Maze, Gilead, CSL-Behring, Otsuka, Novartis, Youngene, Lexicon and Novo-Nordisk, Research Support; Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca, CSL-Behring and Novo-Nordisk. D.Mumford: None. G.Tomlinson: None. L.Lovblom: None. B.A.Perkins: Advisory Panel; Dexcom, Inc., Insulet Corporation, Novo Nordisk, Sanofi, Vertex Pharmaceuticals Incorporated, Other Relationship; Abbott, Medtronic, Sanofi, Research Support; Novo Nordisk, Bank of Montreal (BMO). S.Dhaliwal: None. P.Bapat: None. A.M.K.Bakhsh: None. D.R.Budhram: None. D.Scarr: None. A.Weisman: None. M.Fralick: None. N.Ivers: Consultant; Novo Nordisk Canada Inc., IQVIA Inc. Funding Diabetes Canada (OG-3-21-5572-BP)

Intra-operative desorption-electrospray ionization mass-spectrometry for real-time diagnosis, margin assessment, and maximization of brain cancer resection.

2071 Background: Despite advances in our understanding of brain cancer, the ability to discern between infiltrated and non-infiltrated tissue with current adjuncts, remains elusive. Isocitrate dehydrogenase (IDH) mutation is unique to IDH-mut glioma cells and can be used as a surrogate for tumor infiltration. Further, emerging evidence has shown unique signatures in brain cancer lipidomic profiling, with lipid metabolism playing a key role in brain cancer biology. Here, we aimed to evaluate the impact of desorption electrospray ionization-mass spectrometry (DESI-MS) as a near real-time method to determine infiltration status and the use of DESI-MS to perform intra-operative histological diagnosis. Methods: This is a prospective multi-institution cohort study using intra-operative DESI-MS on freshly obtained tumor samples to evaluate 2-hydroxyglutarate (2HG), with post-hoc validation by review by a board-certified neuropathologist. For tumor cell estimation and tissue diagnosis, high-throughput DESI-MS was used for rapid analysis of high-density arrays using lipidomic profiling. Analysis of the data was performed using a pre-written algorithm employing a combination of Python- and MATLAB-based custom software allowing results in less than a second per sample. Results: A total of 496 tumor biopsies from 72 patients have been analyzed. Earlier iterations focused on absolute quantitation of 2HG in 247 biopsies from 49 patients to determine IDH genotype yielding sensitivity, specificity, and accuracy values of 89, 100, and 94% for core biopsies (71). Improvement in accuracy was obtained with relative measurement of 2HG in comparison to glutamate allowing for detection of IDH-mut infiltration at the margin, resulting in 93% sensitivity, 100% specificity, and 98% accuracy with turnover times averaging 3 minutes in 183 biopsies from 23 patients. Margin assessment beyond the limits of neuro-navigation and surgeon assessment showed tumor infiltration in 88% of margin biopsies from IDH mutant tumors (30 of 34 biopsies) with 100% accuracy. Intra-operative tissue diagnosis was pursued in 66 unique tumor samples using DESI-MS lipidomic profiling, including samples of normal brain parenchyma, gliomas, meningiomas and pituitary adenomas, split into two microarray sets (36 and 40 samples). Using estimated principal components as input features, high accuracy ( > 90%; ROC AUCs ≥ 0.89) was obtained for supervised tissue classification using simple machine learning algorithms. Conclusions: We present a novel system for intra-operative evaluation of tumor infiltration at the margin in gliomas and for intra-operative tissue diagnosis of brain cancer. Our method identified tumor infiltration beyond the surgical margins and serves as a foundation for future uses employing DESI-MS to allow for molecularly guided surgery in IDH-mut gliomas.

Predicting missed health care visits during the COVID-19 pandemic using machine learning methods: evidence from 55,500 individuals from 28 European countries

BackgroundPandemics such as the COVID-19 pandemic and other severe health care disruptions endanger individuals to miss essential care. Machine learning models that predict which patients are at greatest risk of missing care visits can help health administrators prioritize retentions efforts towards patients with the most need. Such approaches may be especially useful for efficiently targeting interventions for health systems overburdened during states of emergency.MethodsWe use data on missed health care visits from over 55,500 respondents of the Survey of Health, Ageing and Retirement in Europe (SHARE) COVID-19 surveys (June – August 2020 and June – August 2021) with longitudinal data from waves 1–8 (April 2004 – March 2020). We compare the performance of four machine learning algorithms (stepwise selection, lasso, random forest, and neural networks) to predict missed health care visits during the first COVID-19 survey based on common patient characteristics available to most health care providers. We test the prediction accuracy, sensitivity, and specificity of the selected models for the first COVID-19 survey by employing 5-fold cross-validation, and test the out-of-sample performance of the models by applying them to the data from the second COVID-19 survey.ResultsWithin our sample, 15.5% of the respondents reported any missed essential health care visit due to the COVID-19 pandemic. All four machine learning methods perform similarly in their predictive power. All models have an area under the curve (AUC) of around 0.61, outperforming random prediction. This performance is sustained for data from the second COVID-19 wave one year later, with an AUC of 0.59 for men and 0.61 for women. When classifying all men (women) with a predicted risk of 0.135 (0.170) or higher as being at risk of missing care, the neural network model correctly identifies 59% (58%) of the individuals with missed care visits, and 57% (58%) of the individuals without missed care visits. As the sensitivity and specificity of the models are strongly related to the risk threshold used to classify individuals, the models can be calibrated depending on users’ resource constraints and targeting approach.ConclusionsPandemics such as COVID-19 require rapid and efficient responses to reduce disruptions in health care. Based on characteristics available to health administrators or insurance providers, simple machine learning algorithms can be used to efficiently target efforts to reduce missed essential care.

A machine-learning procedure to detect network attacks

Abstract The goal of this note is to assess whether simple machine-learning algorithms can be used to determine whether and how a given network has been attacked. The procedure is based on the k-Nearest Neighbour and the Random Forest classification schemes, using both intact and attacked Erdős–Rényi, Barabasi–Albert and Watts–Strogatz networks to train the algorithm. The types of attacks we consider here are random failures and maximum-degree or maximum-betweenness node deletion. Each network is characterized by a list of four metrics, namely the normalized reciprocal maximum degree, the global clustering coefficient, the normalized average path length and the degree assortativity: a statistical analysis shows that this list of graph metrics is indeed significantly different in intact or damaged networks. We test the procedure by choosing both artificial and real networks, performing the attacks and applying the classification algorithms to the resulting graphs: the procedure discussed here turns out to be able to distinguish between intact networks and those attacked by the maximum-degree of maximum-betweenness deletions, but cannot detect random failures. Our results suggest that this approach may provide a basis for the analysis and detection of network attacks.

MEMS piezoelectric resonant microphone array for lung sound classification

This paper reports a highly sensitive piezoelectric microelectromechanical systems (MEMS) resonant microphone array (RMA) for detection and classification of wheezing in lung sounds. The RMA is composed of eight width-stepped cantilever resonant microphones with Mel-distributed resonance frequencies from 230 to 630 Hz, the main frequency range of wheezing. At the resonance frequencies, the unamplified sensitivities of the microphones in the RMA are between 86 and 265 mV Pa−1, while the signal-to-noise ratios (SNRs) for 1 Pa sound pressure are between 86.6 and 98.0 dBA. Over 200–650 Hz, the unamplified sensitivities are between 35 and 265 mV Pa−1, while the SNRs are between 79 and 98 dBA. Wheezing feature in lung sounds recorded by the RMA is more distinguishable than that recorded by a reference microphone with traditional flat sensitivity, and thus, the automatic classification accuracy of wheezing is higher with the lung sounds recorded by the RMA than with those by the reference microphone, when tested with deep learning algorithms on computer or with simple machine learning algorithms on low-power wireless chip set for wearable applications.

Eco-Friendly High-Sensitive Piezoelectrets for Force Myography

Piezoelectrets (or ferroelectrets) are polarized cellular polymers exhibiting outstanding piezoelectric activity. Recently, a new manufacturing route has been introduced by combining 3-D-printed elastomeric soft grids and bulk fluorinated ethylene propylene (FEP) thin films to build an artificial ferroelectret sandwich exhibiting outstanding <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${d} _{{33}}$ </tex-math></inline-formula> coefficients. In this work, a similar approach is taken, however, replacing the environmentally harmful fluorinated polymer FEP by eco-friendly polylactic acid (PLA). The goal of this study is thereby threefold: 1) to investigate the temporal charge stability of PLA films charged at 25 °C and 80 °C, respectively; 2) to determine the stability of the piezoelectric activity when assembled as ferroelectret; and 3) to study the applicability of this easy assembled eco-friendly sensors for force myography (FMG). The surface potential of the PLA films was recorded over 180 days and showed promising stability. Because of the high flexibility of the spacer material, a large piezoelectric <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${d} _{{33}}$ </tex-math></inline-formula> coefficient of up to 2850pC/N was recorded directly after charging and stabilized at about 1500pC/N after <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 50$ </tex-math></inline-formula> days under ambient environmental conditions. The FMG was conducted using four similar ferroelectrets placed on a human forearm. The generated signal shapes and magnitudes of the sensors can be clearly distinguished from each other using simple machine learning algorithms known as support vector machine (SVM) with a classification accuracy (CA) of 89.5%. This work can be regarded as a milestone for these sensors to be accepted in the field of human–machine interfaces (HMIs) as an eco-friendly alternative to the conventional piezoelectric sensors.

Mutual Clustered Redundancy and Composite Learning for Intrusion Detection Systems

In the area of cyber space security, intrusion detection is a challenging task which aims at the provision of security from various malicious attacks. Hence, this paper proposes a two-stage hybrid intrusion detection system (IDS) mechanism to identify between normal and attack activities. The proposed mechanism is an integrated form of two simple and effective machine learning algorithms; they are support vector machine (SVM) and composite extreme learning machine (CELM). The first stage aims to distinguish the normal activities from abnormal activities and employed SVM. Next, the second stage employs CELM for the detection of different types of attacks . Further, aiming over training data, a clustering followed by duplicate connections removal and duplicate features removal is accomplished through fuzzy C-means clustering, correlation, and mutual information respectively. The proposed method applied eventually on the standard benchmark dataset NSL-KDD and the real modern UNSW-NB15 dataset. The performance analysis validates through accuracy, false alarm rate and computational time.

An Exploratory Analysis of Feature Selection for Malware Detection with Simple Machine Learning Algorithms

An Exploratory Analysis of Feature Selection for Malware Detection with Simple Machine Learning Algorithms

Clustering of massive datasets using an Adaptive and efficient K-Means approach

In today’s technology-driven and Internet-obsessed society, it can be challenging to go through huge amounts of information and find relevant knowledge for various educational contexts. Simple, fast, and adaptable machine learning algorithms make such tasks easier to complete. K-means is the most effective unsupervised learning technique for classifying data into meaningful groups. K-means groups data by shared characteristics. K-means clusters are determined by k. Unfortunately, standard k-means requires a lot of math. Scholars have suggested strategies to improve k-means grouping. This work recommends computing initial centroids and establishing a distance between data points that are unlikely to change their cluster in subsequent iterations and those that are extremely likely to do so to lessen the load of k-means clustering for very large data sets. This piece will find information digits whose cluster is statistically likely to alter in the following few cycles. After processing several datasets, it is compared to other K-Means methods