Detecting Android Malware and Classifying Its Families in Large-scale Datasets

To relieve the burden of security analysts, Android malware detection and its family classification need to be automated. There are many previous works focusing on using machine (or deep) learning technology to tackle these two important issues, but as the number of mobile applications has increased in recent years, developing a scalable and precise solution is a new challenge that needs to be addressed in the security field. Accordingly, in this article, we propose a novel approach that not only enhances the performance of both Android malware and its family classification, but also reduces the running time of the analysis process. Using large-scale datasets obtained from different sources, we demonstrate that our method is able to output a high F-measure of 99.71% with a low FPR of 0.37%. Meanwhile, the computation time for processing a 300K dataset is reduced to nearly 3.3 hours. In addition, in classification evaluation, we demonstrate that the F-measure, precision, and recall are 97.5%, 96.55%, 98.64%, respectively, when classifying 28 malware families. Finally, we compare our method with previous studies in both detection and classification evaluation. We observe that our method produces better performance in terms of its effectiveness and efficiency.

Research on Intelligent Trash Can Garbage Classification Scheme Based on Improved YOLOv3 Target Detection Algorithm

In recent studies, YOLOv3, a deep learning-based target detection algorithm, becomes extensively used in object recognition, especially guiding the visually disabled. Current YOLOv3-based assistive technology for the disabled person can now achieve high-precision, real-time object recognition. Even though this algorithm has several flaws, including the failure to estimate distances and the difficulty of accurately recognizing points in fog or haze, it can perform well in waste management. Therefore, this study proposes an Intelligent Garbage Monitoring Scheme based on an improved YOLOv3 Target Detection Algorithm (IGMS-iYTDA) to classify the IoT’sgarbages (IoT) enabled trash can. The performance of the proposed scheme has been evaluated and illustrated for various classification evaluation metrics. The evaluation results show the highest classification accuracy of 99.9% compared to existing models for the proposed scheme.

Anatomy of a Data Science Software Toolkit That Uses Machine Learning to Aid ‘Bench-to-Bedside’ Medical Research—With Essential Concepts of Data Mining and Analysis Explained

Data science and machine learning are buzzwords of the early 21st century. Now pervasive through human civilization, how do these concepts translate to use by researchers and clinicians in the life-science and medical field? Here, we describe a software toolkit, just large enough in scale, so that it can be maintained and extended by a small team, optimised for problems that arise in small/medium laboratories. In particular, this system may be managed from data ingestion statistics preparation predictions by a single person. At the system’s core is a graph type database, so that it is flexible in terms of irregular, constantly changing data types, as such data types are common during explorative research. At the system’s outermost shell, the concept of ’user stories’ is introduced to help the end-user researchers perform various tasks separated by their expertise: these range from simple data input, data curation, statistics, and finally to predictions via machine learning algorithms. We compiled a sizable list of already existing, modular Python platform libraries usable for data analysis that may be used as a reference in the field and may be incorporated into this software. We also provide an insight into basic concepts, such as labelled-unlabelled data, supervised vs. unsupervised learning, regression vs. classification, evaluation by different error metrics, and an advanced concept of cross-validation. Finally, we show some examples from our laboratory using our blood sample and blood clot data from thrombosis patients (sufferers from stroke, heart and peripheral thrombosis disease) and how such tools can help to set up realistic expectations and show caveats.

E-Mail Spam Filtering

E-mail is that the most typical method of communication because of its ability to get, the rapid modification of messages and low cost of distribution. E-mail is one among the foremost secure medium for online communication and transferring data or messages through the net. An overgrowing increase in popularity, the quantity of unsolicited data has also increased rapidly. Spam causes traffic issues and bottlenecks that limit the quantity of memory and bandwidth, power and computing speed. To filtering data, different approaches exist which automatically detect and take away these untenable messages. There are several numbers of email spam filtering technique like Knowledge-based technique, Clustering techniques, Learning-based technique, Heuristic processes so on. For data filtering, various approaches exist that automatically detect and suppress these indefensible messages. This paper illustrates a survey of various existing email spam filtering system regarding Machine Learning Technique (MLT) like Naive Bayes, SVM, K-Nearest Neighbor, Bayes Additive Regression, KNN Tree, and rules. Henceforth here we give the classification, evaluation and comparison of some email spam filtering system and summarize the scenario regarding accuracy rate of various existing approaches. Keywords: e-mail spam, unsolicited bulk email, spam filtering methods.

Classification evaluation method for Chang 7 oil group of Yanchang formation in Ordos Basin

The Chang 7 oil group in the Ordos Basin has the characteristics of a tight lithology, a low formation pressure coefficient and strong reservoir heterogeneity. To better determine reasonable developmental technical countermeasures, oiliness, seepage capacity, and compressibility evaluations are combined. Using a combination of field practice and laboratory experiments, six types of sweetness classification evaluation parameters are screened: oil saturation, longitudinal oil layer structure coefficient, average pore throat radius, gas-oil ratio, brittleness index, and minimum horizontal principal stress. By combining the relationships among variables with the initial production from directional wells, the gray correlation method is used to quantify the weights of the contributions of evaluation parameters to production. On this basis, using the difference method for the curve slope, a sweetness evaluation and classification method for the Chang 7 oil group is constructed, and it solves the difficult problem of quality difference classification for the Chang 7 oil group and provides a reference basis for the optimal design of well patterns and fracturing reconstruction parameters.

Application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability

Longwall top coal caving technology is one of the main methods of thick coal seam mining in China, and the classification evaluation of top coal cavability in longwall top coal caving working face is of great significance for improving coal recovery. However, the empirical or numerical simulation method currently used to evaluate the top coal cavability has high cost and low-efficiency problems. Therefore, in order to improve the evaluation efficiency and reduce evaluation the cost of top coal cavability, according to the characteristics of classification evaluation of top coal cavability, this paper improved and optimized the fuzzy neural network developed by Nauck and Kruse and establishes the fuzzy neural network prediction model for classification evaluation of top coal cavability. At the same time, in order to ensure that the optimized and improved fuzzy neural network has the ability of global approximation that a neural network should have, its global approximation is verified. Then use the data in the database of published papers from CNKI as sample data to train, verify and test the established fuzzy neural network model. After that, the tested model is applied to the classification evaluation of the top coal cavability in 61,107 longwall top coal caving working face in Liuwan Coal Mine. The final evaluation result is that the top coal cavability grade of the 61,107 longwall top coal caving working face in Liuwan Coal Mine is grade II, consistent with the engineering practice.

Application of BigML in the Classification Evaluation of Top Coal Caving

Longwall top coal caving mining is one of the main methods of mining thick coal seams in China. Therefore, carrying out the classification evaluation of top coal caving is of great significance to ensure mining success and reduce the risk of mining technology. In order to realize the classification evaluation of top coal caving, this article introduces the method of using BigML to establish the classification evaluation model of top coal caving. Furthermore, using the data from the CNKI database as sample data, a classification evaluation model of top coal caving is established on BigML. After training, testing, and optimization, the model is used to evaluate the top coal caving in No. 3 coal seam of Gucheng Coal Mine, and the evaluation result is grade 1, which is consistent with the engineering practice. The final research results show that the application of BigML in the classification evaluation of top coal caving is successful; the evaluation of top coal caving through BigML is reliable; BigML provides another scientific reliability way for the classification evaluation of top coal caving.

Identify Attractive and Unattractive Individuals Based on Geometric Features Using Neural Network

This paper aims to explore the essence of facial attractiveness from the viewpoint of geometric features toward the classification and identification of attractive and unattractive individuals. We present a simple but useful feature extraction for facial beauty classification. Evaluation of facial attractiveness was performed with different combinations of geometric facial features using the deep learning method. In this method, we focus on the geometry of a face and use actual faces for our analysis. The proposed method has been tested on, image database containing 60 images of men's faces (attractive or unattractive) ranging from 20-50 years old. The images are taken from both frontal and lateral position. In the next step, principle components analysis (PCA) was applied to feature a reduction of beauty, and finally, the neural network was used for judging whether the obtained analysis of various faces is attractive or not. The results show that one of the indexes in identifying facial attractiveness base of science, is the values of the geometric features in the face, changing facial parameters can change the face from unattractive to attractive and vice versa. The experimental results are based on 60 facial images, high accuracy of 88%, and Sensitivity of 92% is obtained for 2-level classification (attractive or not).

Problems and prospects of using evidence-based medicine in hygiene (systematic review)

In the modern world, the principles and methods of Evidence-Based Medicine (EBM)) are the recognised basis for the development of Medicine despite the existing barriers to its implementation. EBM was formed and developed within the framework of its medical direction. In preventive medicine, the adoption of the term EBM was not accompanied by the development of appropriate definitions, standards, methods, and regulatory documents. This article discusses the problems and prospects for the development of EBM in hygienic science and practice. The authors conducted an independent screening of the frequency of publications on Preventive Medicine using the terms and provisions of EBM over the past ten years in the RSCI and MEDLINE (PubMed). The number of publications in English - language sources was found to exceed domestic ones by 45.5-139 times on all issues of EBM. In the RSCI, publications related to EBM in the preventive direction of Medicine accounted for 28 % of the total publications on EBM, in MEDLINE- 45 percentage. The data obtained indicate a more occasional use of the principles of EBM in domestic Medicine, especially in relation to preventive Medicine. Taking into account the experience of EBM in clinical Medicine, the article defines EBM, presents the stages of the EBM methodology, a variant of the hierarchy of evidence, and ideal characteristics of surrogate outcomes for preventive Medicine. For most hygiene problems, systematic reviews and meta-analyses will be the most evidence-based. The use of EBM was indicated to be impossible without understanding the fundamental principles and the correct application of biostatistics. Approaches to the classification, evaluation, development, and examination of clinical practice guidelines based on the principles of EBM, abroad and in Russia, and the possibility of their use for regulatory and methodological documents to ensure sanitary and epidemiological well-being are considered.