A Survey on Malware detection using ML

This Malware detection is a field of computer security that deals with the study and prevention of malicious software. It is not the only way to defend a company against a cyber- attack. In order to be effective, companies should analyse their risk and identify the vulnerabilities. In this paper, we will examine different techniques used to detect computer malware and malicious websites as well as future directives in this area of study and also, we will discuss the growth in computer malware and how traditional methods of detection are being replaced by innovative techniques like behavioural-based model and Signature-based model. Future directives involve developing better security products in order to fight against cyber fraud which is on a rise in recent years especially in Asia Pacific region. With this increase in cyber frauds and other malicious activities, traditional methods are not enough to block computers from it as this method has many drawbacks. In order to tackle these issues, researchers have been developing new techniques such as heuristic analysis, static & dynamic analysis which can detect more than 90% of malware samples without any false positives or negatives. Keywords: Behaviour-based approach, Dynamic analysis, Heuristic, Malware, Ransomware, Signature-based model, Static analysis, Vulnerability.

Nanomaterials-Based Urinary Extracellular Vesicles Isolation and Detection for Non-invasive Auxiliary Diagnosis of Prostate Cancer

Extracellular vesicles (EVs) are natural nanoparticles secreted by cells in the body and released into the extracellular environment. They are associated with various physiological or pathological processes, and considered as carriers in intercellular information transmission, so that EVs can be used as an important marker of liquid biopsy for disease diagnosis and prognosis. EVs are widely present in various body fluids, among which, urine is easy to obtain in large amount through non-invasive methods and has a small dynamic range of proteins, so it is a good object for studying EVs. However, most of the current isolation and detection of EVs still use traditional methods, which are of low purity, time consuming, and poor efficiency; therefore, more efficient and highly selective techniques are urgently needed. Recently, inspired by the nanoscale of EVs, platforms based on nanomaterials have been innovatively explored for isolation and detection of EVs from body fluids. These newly developed nanotechnologies, with higher selectivity and sensitivity, greatly improve the precision of isolation target EVs from urine. This review focuses on the nanomaterials used in isolation and detection of urinary EVs, discusses the advantages and disadvantages between traditional methods and nanomaterials-based platforms, and presents urinary EV-derived biomarkers for prostate cancer (PCa) diagnosis. We aim to provide a reference for researchers who want to carry out studies about nanomaterial-based platforms to identify urinary EVs, and we hope to summarize the biomarkers in downstream analysis of urinary EVs for auxiliary diagnosis of PCa disease in detail.

TRADITIONAL METHODS OF PRODUCING NATURAL DYES AND WOOL DYEING AMONG THE INGUSH IN THE 19TH - EARLY 20TH CENTURIES

В статье рассматриваются традиционные способы производства природных красителей, раскрываются особенности технологии правильного крашения с применением натуральных ингредиентов. Антропология цвета в культуре ингушей базировалась на двухуровневой основе: с одной стороны, она была связана с естественно-средовыми условиями окружения, с другой - формировалась под влиянием исторической практики и жизнедеятельности народа. Рассматриваются технологические издержки, которые возникают при производстве натуральных красителей в домашних условиях. Отмечается, что к началу ХХ в. на территории Северного Кавказа происходит постепенный переход от применения натуральных красителей к более дешевым и менее трудоемким фабричным аналогам. Автором представлен систематизированный перечень местных красильных растений и деревьев, дается информация о красильных свойствах минералов и технологии их использования в кустарном производстве ингушей в XIX-XX вв. в процессе крашения шерсти. На основе проведенного анализа делается вывод о том, что в современных условиях происходит утрата традиционного опыта производства натуральных красителей и крашения шерсти у ингушей. The paper discusses traditional methods for the production of natural dyes, the features of the technology of correct dyeing. The anthropology of color in the culture of the Ingush was based on a two-level basis: on the one hand, it was associated with the natural environmental conditions, and on the other hand, it was formed under the influence of historical practice and life. The technological costs that arise in the production of natural dyes at home are considered. The research shows that by the beginning of the twentieth century in the North Caucasus there is a gradual transition from the use of natural dyes to cheaper and less laborious factory analogues. The author presents a systematized list of local dyeing plants and trees, provides information on the dyeing properties of minerals and the technology of their use in the handicraft production of the Ingush in the 19th - 20th centuries in the process of dyeing wool. Based on the analysis, the author concludes that in modern conditions there is a loss of traditional experience in the production of natural dyes and wool dyeing among the Ingush.

Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example

Background There are often many missing values in medical data, which directly affect the accuracy of clinical decision making. Discharge assessment is an important part of clinical decision making. Taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example, this study adopted the missing data processing evaluation criteria more suitable for clinical decision making, aiming at systematically exploring the performance and applicability of single machine learning algorithms and ensemble learning (EL) under different data missing scenarios, as well as whether they had more advantages than traditional methods, so as to provide basis and reference for the selection of suitable missing data processing method in practical clinical decision making. Methods The whole process consisted of four main steps: (1) Based on the original complete data set, missing data was generated by simulation under different missing scenarios (missing mechanisms, missing proportions and ratios of missing proportions of each group). (2) Machine learning and traditional methods (eight methods in total) were applied to impute missing values. (3) The performances of imputation techniques were evaluated and compared by estimating the sensitivity, AUC and Kappa values of prediction models. (4) Statistical tests were used to evaluate whether the observed performance differences were statistically significant. Results The performances of missing data processing methods were different to a certain extent in different missing scenarios. On the whole, machine learning had better imputation performance than traditional methods, especially in scenarios with high missing proportions. Compared with single machine learning algorithms, the performance of EL was more prominent, followed by neural networks. Meanwhile, EL was most suitable for missing imputation under MAR (the ratio of missing proportion 2:1) mechanism, and its average sensitivity, AUC and Kappa values reached 0.908, 0.924 and 0.596 respectively. Conclusions In clinical decision making, the characteristics of missing data should be actively explored before formulating missing data processing strategies. The outstanding imputation performance of machine learning methods, especially EL, shed light on the development of missing data processing technology, and provided methodological support for clinical decision making in presence of incomplete data.

An Improved Fast Estimation of Satellite Phase Fractional Cycle Biases

Satellite phase fractional cycle biases (FCBs) are crucial to precise point positioning with ambiguity resolution (PPP–AR), and they can improve the accuracy and reliability of a solution. Traditional methods need multiple iterations and need to keep the same reference when estimating satellite phase fractional cycle biases. In this paper, we propose an improved fast estimation of FCB, which does not need any iterations and can select any reference when estimating FCB. We compare the suitability and precision of a traditional and a proposed method by BDS-3 experiments. The results of the FCB experiments show that the calculated time of the proposed method is less than the traditional method and that computation efficiency is increased by 34.71%. These two methods have a similar rate of fixed epochs and ambiguities in the static and dynamic models. However, the time to first fix (TTFF) of the proposed method decreased by 19.69% and 28.83% for the static and dynamic models, respectively. The results show that the proposed method has a better convergence time in PPP–AR.

A method for determining stoichiometric coefficients using minors of a matrix

Spinel materials often have complex structures and as a result, balancing of reactions with these compounds by traditional methods become very time consuming. A method to calculate the stoichiometric coefficients for chemical reactions using first a modified matrix-inverse method and then an optimised method is proposed. Both methods are explored using linear algebra and the result demonstrated using a typical chromite reduction reaction.

A METHOD FOR DETERMINING STOICHIOMETRIC COEFFICIENTS USING MINORS OF A MATRIX

Spinel materials often have complex structures and as a result, balancing of reactions with these compounds by traditional methods become very time consuming. A method to calculate the stoichiometric coefficients for chemical reactions using first a modified matrix-inverse method and then an optimised method is proposed. Both methods are explored using linear algebra and the result demonstrated using a typical chromite reduction reaction.

Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness Using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals

Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Three different classes were defined for drowsiness based on video observation of driving tests performed in a simulator for manual and automated modes. The Bayesian optimization method is employed to optimize the hyperparameters of the designed neural networks, such as the learning rate and the number of neurons in every layer. To assess the results of the deep network method, heart rate variability (HRV) data is derived from the ECG signals, some features are extracted from this data, and finally, random forest and k-nearest neighbors (KNN) classifiers are used as two traditional methods to classify the drowsiness levels. Results show that the trained deep network achieves balanced accuracies of about 77% and 79% in the manual and automated modes, respectively. However, the best obtained balanced accuracies using traditional methods are about 62% and 64%. We conclude that designed deep networks working with wavelet scalogram images of ECG signals significantly outperform KNN and random forest classifiers which are trained on HRV-based features.

Background Speech Synchronous Recognition Method of E-commerce Platform Based on Hidden Markov Model

In order to improve the effect of e-commerce platform background speech synchronous recognition and solve the problem that traditional methods are vulnerable to sudden noise, resulting in poor recognition effect, this paper proposes a background speech synchronous recognition method based on Hidden Markov model. Combined with the principle of speech recognition, the speech feature is collected. Hidden Markov model is used to input and recognize high fidelity speech filter to ensure the effectiveness of signal processing results. Through the de-noising of e-commerce platform background voice, and the language signal cache and storage recognition, using vector graph buffer audio, through the Ethernet interface transplant related speech recognition sequence, thus realizing background speech synchronization, so as to realize the language recognition, improve the recognition accuracy. Finally, the experimental results show that the background speech synchronous recognition method based on Hidden Markov model is better than the traditional methods.