Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier

The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.

Life Can’t Be Any Easier than This—Introduction of the Portable and Disposable V.A.C. Machines

The application of controlled levels of negative pressure on to a wound has been shown to accelerate evacuation of dead cells, debris and fluid which eventually encourages wound healing in a verity of surgical wounds. Vacuum Assisted Closure (V.A.C.) therapy—KCI Medical Limited, the terminology by which this is widely known, became popular, especially among the plastic surgery professionals in America and soon gained recognition worldwide. It is now widely used in the UK to manage and assist healing in a wide variety of wounds. Although KCI’s V.A.C. machines were the only ones on the market for a number of years, several wound management companies have now brought out their own machines and these are now known collectively as topical negative pressure therapy (TNPT). Traditional TNPT is often considered a relatively costly procedure. It is often used in patients with large wounds to facilitate dressing management and promote rapid cleaning and granulation. This may also allow them to be discharged to the community when they would otherwise remain inpatients, thereby saving bed days. Capital purchase of the machines is expensive and hospitals often rent or lease them on a short or long term basis. This can lead to difficulties in arranging the finances for discharge to the community. Subsequent dressing changes (recommended every 48 - 72 hrs) also incur high costs and involvement of the trained medical or nursing staff. As we all know;“Need is the mother of invention”. The disposable TNPT machine (V.A.C. ViaTM KCI Medical Ltd) has been introduced to help to solve these problems. It is a single use machine, inclusive of a dressing and canister and available off the shelf. It is very cost effective, easy to use and is used for small to moderate sized wounds. Senior author is using this machine which excellent results and illustrated the use of this machine with pictures in this paper.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Use of portable X-ray fluorescence in the analysis of surficial sediments in the exploration of hydrothermal vents on the Southwest Indian Ridge

Hydrothermal plumes released from the eruption of sea floor hydrothermal fluids contain large amounts of oreforming materials. They precipitate within certain distances from the hydrothermal vent. Six surficial sediment samples from the Southwest Indian Ridge（SWIR） were analyzed by a portable X-ray fluorescence（PXRF） analyzer on board to find a favorable method fast and efficient enough for sea floor sulfide sediment geochemical exploration. These sediments were sampled near, at a moderate distance from, or far away from hydrothermal vents. The results demonstrate that the PXRF is effective in determining the enrichment characteristics of the oreforming elements in the calcareous sediments from the mid-ocean ridge. Sediment samples（〉40 mesh） have high levels of elemental copper, zinc, iron, and manganese, and levels of these elements in sediments finer than 40 mesh are lower and relatively stable. This may be due to relatively high levels of basalt debris/glass in the coarse sediments, which are consistent with the results obtained by microscopic observation. The results also show clear zoning of elements copper, zinc, arsenic, iron, and manganese in the surficial sediments around the hydrothermal vent. Sediments near the vent show relatively high content of the ore-forming elements and either high ratios of copper to iron content and zinc to iron content or high ratios of copper to manganese content and zinc to manganese content. These findings show that the content of the ore-forming elements in the sediments around hydrothermal vents are mainly influenced by the distance of sediments to the vent, rather than grain size. In this way, the PXRF analysis of surface sediment geochemistry is found to satisfy the requirements of recognition geochemical anomaly in mid-ocean ridge sediments. Sediments with diameters finer than 40 mesh should be used as analytical samples in the geochemical exploration for hydrothermal vents on mid-oceanic ridges. The results concerning copper, zinc, arsenic, iron, and manganese and their ratio features can be used as indicators in sediment geochemical exploration of seafloor sulfides.

Application of a Portable XRF Spectrometer for <i>In-Situ</i>and Nondestructive Investigation of Pigments in Two 15th Century Icons

A simple portable X-Ray Fluorescence (XRF) spectrometer was successfully used for in-situ and nondestructive identification of the painting materials in two 15th century icons from the Onufri Museum in Beart, Albania. The spectrometer is based on a low power X-ray tube, a thermoelectrically cooled Si PIN detector and the spectrum acquisition system. It was assembled and adjusted at our laboratory for the investigation of the icons. A small number of pigments were clearly identified by X-Ray Fluorescence (XRF) measurements in both icons. This includes Lead white for the white color, gold and yellow ochre for the yellow color, red lead, cinnabar and red ochre for the red color, as well as cooper based pigments for the green color. At the same time, the investigation raised some new questions that need further investigations by the use of additional analytical techniques. The results show that in both icons are used similar pigments, which are in accordance with the Byzantine icon painting tradition.

Automatic Detection and Measurement of Fetal Biparietal Diameter and Femur Length —Feasibility on a Portable Ultrasound Device

An automatic method able to recognize a presented section through the biparietal plane of the fetal head and a section through the fetal femur in ultrasound images is developed. Once the correct anatomical section for measurement is identified by the machine, the placement of the measurement calipers is automatically determined by fitting an active contour model to the structure of interest. The fetal biparietal diameter (BPD) and femur length (FL) are then measured automatically. The validation data set contained 167 and 197 B-mode images for BPD and FL measurements, respectively. The images were acquired using 4 different ultrasound scanners, which resulted in varied image quality and gain settings. The mean gestational age (GA) of the fetuses was 19.4 weeks, range 16 to 41 weeks. A measurement success rate of 90% was achieved for both BPD and FL. The correlation coefficients between the manual and automatic measurements were 0.995 (BPD) and 0.967 (FL), mean errors were 0.5 mm (BPD) and -1.7 mm (FL) and error range with 95% confidence interval (CI) were ﹣3.8 - 4.8 mm (BPD) and ﹣11.4 - 8.1 mm (FL). The automatic measurement results were consistent in both high and low gain settings. The intraclass correlation coefficients between manual and automatic measurements were 0.995 (95% CI;0.981 - 0.999) for BPD in high gain, 1.0 (95% CI;0.998 - 1.0) for BPD in low gain, 0.998 (95% CI;0.991 - 0.999) for FL in high gain and 0.999 (95% CI;0.996 - 1.0) for FL in low gain settings. The method was implemented on a prototype, portable ultrasound machine designed to be used in low- and middle-income countries (LMIC). The overall performance of the method supports our hypothesis that automated methods can be used and are beneficial in a clinical setting.

Efficiency of Portable Electronic Vulcanizer

This research was aimed at finding out the efficiency of the portable electronic vulcanizer. The old vulcanizing equipment was upgraded to save time, investment, manpower and to eliminate the problem of gas emission in vulcanization. The study also determined the accurate temperature setting and duration of vulcanizing process using electronic vulcanizer which eliminated the problem of gas emission produced by the conventional (gas fired) vulcanizer of about 2.772 kg of carbon dioxide for 1 liter of diesel fuel and/or 2.331 kg of carbon dioxide for 1 liter of petrol into the atmosphere. In constructing this vulcanizer, a letter G body configuration made of GI pipe with 31.5 cm long lag bolt with some electronic parts were installed, like the analog temperature gauge, digital timer, relay, LED, buzzer, switch, and heating element. Specifically, the product is divided into three components: base or body, control panel board and the heating unit. The effectiveness level of the equipment was tested utilizing five different temperatures at a constant and variable time. For Class A gum, the best temperature which bonded the gum exactly to the rubber tire was 60℃ in 1 minute while Class B gum was bonded at 60℃ in 2 minutes. The rate of energy consumed by the electronic vulcanizer for Class A gum was Php 0.0757 with an efficiency of 85.22% and for Class B gum was Php 0.15 with an efficiency of 85.22% and for conventional vulcanizer for Class A gum was Php 1.08 with an efficiency of 43.38% and for Class B gum was Php 1.52, with an efficiency of 78.08%. The study revealed that more tires could be vulcanized in a short period of time, therefore providing greater income over time. It is also environment-friendly since it does not emit carbon dioxide as compared to the conventional vulcanizing.

A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography

Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.

手持式近红外光谱仪测定梨三种品质指标通用模型建模方法研究

梨是生活中非常常见的水果,梨的糖度、酸度(pH)、硬度是评价梨品质的重要指标。近红外光谱技术因快速、无损和高效等优点,广泛应用于检测水果品质指标。手持式近红外光谱仪可以应用于现场无损检测梨品质,梨的大小不同会对梨的光谱和建模产生一定影响。因此,采集大小不同的5个品种梨果(雪梨、红香酥、蜜梨、红肖梨、酸梨)的近红外光谱,最大的雪梨平均赤道周长27.64 cm,重量为362.84 g,最小的蜜梨平均赤道周长18.35 cm,重量为112.67 g,共197个样品。光谱范围为900~1700 cm^(-1),并在梨的赤道上选取三个点测量梨果的可溶性固形物、酸度(pH)与硬度三个化学指标。采集光谱发现,小梨吸光度较大,而大梨吸光度较小。采用三点平均光谱代表样品光谱和一阶导预处理,改善了光谱的一致性,解决了样品不均匀性、不同梨大小不同等因素的影响。线性回归模型PLS可溶性固形物、酸度(pH)和硬度的校正集决定系数依次为0.7394、0.9335、0.8866,预测集决定系数依次为0.7559、0.8734、0.7874,校正集RMSEC依次为0.5504、0.1941、0.5181。预测集RMSEP依次为0.6564、0.2420、0.6692。非线性回归LSSVM模型可溶性固形物、酸度(pH)和硬度的校正集决定系数依次为0.9763、0.9999、0.9960,预测集决定系数依次为0.9234、0.9777、0.9394,校正集RMSEC依次为0.1949、0.0033、0.0894。预测集RMSEP依次为0.3169、0.1089、0.3613。对比线性算法和非线性算法,LS-SVM建模效果明显优于PLS,LS-SVM算法适用于更多的品种、更宽的品质指标范围的样品预测,模型的准确度和稳定性有了显著提高,可以建立不同品种大小的梨的通用模型。手持式近红外光谱仪可用于梨果的糖度、硬度和pH值的快速无损高效检测,并摆脱了实验室的限制,可以实现现场快速检测。

便携式麻醉机应用及研究进展

重大灾害事故瞬间将产生批量危重症伤员,开展损伤控制手术是灾害事故现场有效降低伤员死亡率和致残率的关键。便携式麻醉机是灾害事故现场实施损伤控制手术的重要医疗装备。本文回顾了国内外麻醉机的研究现状,结合灾害救援特点,重点分析了便携式麻醉机的性能特点和关键技术,并对未来发展方向进行了展望。

Radiography Image Classification Using Deep Convolutional Neural Networks

Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.

基于机器学习的恶意PNG图像识别方法

为避免网络病毒借助便携式网络图形(Portable Network Graphics,PNG)传播恶意代码,应用机器学习技术,提出一套行之有效的恶意PNG图像识别方法。该识别方法采用动态分析的方式,评估PNG图像和加载器行为,同时结合基于传统调用特征的机器学习算法,分类并识别恶意PNG图像。结果表明,提出的恶意PNG图像识别方法具有较高的可靠性和可行性,可以保证对多种恶意PNG图像的识别效果,还能避免用户隐写处理健康图像信息,完全符合图像水印隐秘传输应用需求。

基于SPEI指数的河北省夏秋两季干旱预测研究

为获得河北省夏、秋两季干旱预测模型,本文以标准化降雨蒸散指数(SPEI)为基础,基于极限学习机模型(ELM),采用麻雀搜索算法(SSA)、蜂群算法(ABC)、蝙蝠算法(BA)、粒子群算法(PSO)、遗传算法(GA)共5种优化算法构建了优化ELM模型用于建立区域SPEI指数估算模型,结果表面:SSA-ELM模型在所有模型中精度最高,同时可移植性较好,可作为河北省夏、秋两季的干旱预测模型使用。

便携式材料拉伸试验机结构及其控制系统的设计与研究

材料拉伸试验机是检测和测试材料力学性能的重要设备。然而,传统设备体积大、重量大,难以在工程现场搬运和使用。为此,文章设计了一种便携式材料拉伸试验仪。在机械结构设计过程中,根据试验机的参数选定电机、减速器、丝杠等标准件,并根据标准件尺寸使用三维设计软件进行建模;在设计两个重要的非标准零件支撑杆、支撑板过程中,先根据经验进行主体设计,然后用有限元分析软件对其结构强度进行校核,确保非标准零件的结构强度可靠性。

Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic loss.But unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and diagnostics.Data analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported patients.This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques.Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task.We used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 split.This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.

Multi-Label Chest X-Ray Classification via Deep Learning

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in general. Deep learning is increasingly being applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical method for diagnosing a number of diseases such as pneumonia, lung cancer and many other abnormalities like lesions and fractures. Proper diagnosis of a disease from X-ray images is often challenging task for even expert radiologists and there is a growing need for computerized support systems due to the large amount of information encoded in X-Ray images. The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. Along with the image features, we are also going to use non-image features available in the data such as X-ray view type, age, gender etc. The original study conducted Stanford ML Group is our base line. Original study focuses on predicting 5 diseases. Our aim is to improve upon previous work, expand prediction to 14 diseases and provide insight for future chest radiography research.

便携式X射线荧光光谱法结合支持向量回归算法定量分析土壤中的砷含量

研究了基于统计学习理论的支持向量机(SVM)回归法在X射线荧光光谱定量分析中的应用。采用39个农田土壤样品作为实验材料,以其中32个土壤样品作为校正集,选用SVM模型中Linear、Poly和RBF 3种核函数对As元素含量与荧光光谱数据进行回归建模。用3种不同模型对预测集中7个土壤样品的As元素含量进行预测分析,结果显示模型预测As元素含量与电感耦合等离子体质谱法测定的As元素含量之间的相关系数R2均大于0.99,相对分析误差(RPD)均大于3,表明所建立的SVM模型具有较好的实用价值。为了进一步考察SVM回归模型的预测效果,同应用较成熟的PLS回归模型的预测结果进行对比,结果显示SVM法的预测结果更好,表明SVM回归模型亦可用于便携式X射线荧光光谱法的定量预测分析。

Chest Radiographs Based Pneumothorax Detection Using Federated Learning

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest wall.The most persistent disease,as well as one that necessitates particular patient care and the privacy of their health records.The radiologists find it challenging to diagnose pneumothorax due to the variations in images.Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems.However,it is challenging to employ it in the medical field due to privacy issues and a lack of data.To address this issue,a federated learning framework based on an Xception neural network model is proposed in this research.The pneumothorax medical image dataset is obtained from the Kaggle repository.Data preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s performance.Min-max normalization technique is used to normalize the data,and the features are extracted from chest Xray images.Then dataset converts into two windows to make two clients for local model training.Xception neural network model is trained on the dataset individually and aggregates model updates from two clients on the server side.To decrease the over-fitting effect,every client analyses the results three times.Client 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%accuracy.The experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.

A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries.The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission.There is a signifi-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most countries.To overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of Covid-19.How-ever,this method burdens the radiologist to examine each report.Therefore,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of COVID.We proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 cases.The proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays images.The dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical repositories.The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.