The weight analysis research in developing a similarity classification problem of malicious code based on attributes

Data mining suggest an innovative way of prognostication stereotype of Patients health risks. Large amount of Electronic Health Records (EHRs) collected over the years have provided a rich base for risk analysis and prediction. An EHR contains digitally stored healthcare information about an individual, such as observations, laboratory tests, diagnostic reports, medications, procedures, patient identifying information and allergies. A special type of EHR is the Health Examination Records (HER) from annual general health check-ups. Identifying participants at risk based on their current and past HERs is important for early warning and preventive intervention. By “risk”, we mean unwanted outcomes such as mortality and morbidity. This approach is limited due to the classification problem and consequently it is not informative about the specific disease area in which a personal is at risk. Limited amount of data extracted from the health record is not feasible for providing the accurate risk prediction. The main motive of this project is for risk prediction to classify progressively developing situation with the majority of the data unlabeled.

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Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽

10.24160/1993-6982-2020-5-132-139 ◽

2020 ◽

Vol 5 (5) ◽

pp. 132-139

Author(s):

Ivan E. Kurilenko ◽

◽

Igor E. Nikonov ◽

Keyword(s):

Artificial Intelligence ◽

Classification Problem ◽

Subject Area ◽

Text Messages ◽

Case Based Reasoning ◽

Short Text ◽

Proposed Modification ◽

Voice Interaction ◽

Text Content ◽

Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

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TOOLS AND PECULIARITIES OF SOLVING THE CLASSIFICATION PROBLEM IN CREDIT SCORING SYSTEMS

CONTINUUM. MATHS. INFORMATICS. EDUCATION ◽

10.24888/2500-1957-2020-17-1-51-59 ◽

2020 ◽

Vol 17 (1) ◽

pp. 51-59

Author(s):

A.A. Grishin ◽

◽

S.P. Stroyev ◽

Keyword(s):

Credit Scoring ◽

Scoring Systems ◽

Classification Problem

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A Study of Malware Propagation Dynamics in Wireless Sensor Network using Spatially Correlated Security Model

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200613230323 ◽

2020 ◽

Vol 13 ◽

Author(s):

Satya Ranjan Biswal ◽

Santosh Kumar Swain

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Wireless Network ◽

Spatial Correlation ◽

Malicious Code ◽

Wireless Sensor ◽

Epidemic Theory ◽

Malware Propagation ◽

Proposed Model ◽

Propagation Dynamics

: Security is one of the important concern in both types of the network. The network may be wired or wireless. In case of wireless network security provisioning is more difficult in comparison to wired network. Wireless Sensor Network (WSN) is also a type of wireless network. And due to resource constraints WSN is vulnerable against malware attacks. Initially, the malware (virus, worm, malicious code, etc.) targets a single node of WSN for attack. When a node of WSN gets infected then automatically start to spread in the network. If nodes are strongly correlated the malware spreads quickly in the network. On the other hand, if nodes are weakly correlated the speed of malware spread is slow. A mathematical model is proposed for the study of malware propagation dynamics in WSN with combination of spatial correlation and epidemic theory. This model is based on epidemic theory with spatial correlation. The proposed model is Susceptible-Exposed-Infectious-Recover-Dead (SEIRD) with spatial correlation. We deduced the expression of basic reproduction number. It helps in the study of malware propagation dynamics in WSN. The stability analysis of the network has been investigated through proposed model. This model also helps in reduction of redundant information and saving of sensor nodes’ energy in WSN. The theoretical investigation verified by simulation results. A spatial correlation based epidemic model has been formulated for the study of dynamic behaviour of malware attacks in WSN.

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Circadian blood pressure and heart rate rhythms in mice

AJP Regulatory Integrative and Comparative Physiology ◽

10.1152/ajpregu.1999.276.2.r500 ◽

1999 ◽

Vol 276 (2) ◽

pp. R500-R504 ◽

Cited By ~ 28

Author(s):

Ping Li ◽

Steven H. Sur ◽

Ralph E. Mistlberger ◽

Mariana Morris

Keyword(s):

Heart Rate ◽

Sampling Rate ◽

Eating Patterns ◽

Cardiovascular Parameters ◽

C57bl Mice ◽

Weight Analysis ◽

Using Data ◽

Computerized Monitoring ◽

Carotid Arterial ◽

Mean Heart Rate

The circadian pattern of mean arterial pressure (MAP) and heart rate (HR) was measured in C57BL mice with carotid arterial catheters. Cardiovascular parameters were recorded continuously with a computerized monitoring system at a sampling rate of 100 Hz. The tethered animals were healthy, showing stabilized drinking and eating patterns within 2 days of surgery and little loss of body weight. Analysis of the 24-h pattern of MAP and HR was conducted using data from 3–6 consecutive days of recording. A daily rhythm of MAP was evident in all mice, with group mean dark and light values of 101.4 ± 7.3 and 93.1 ± 2.9 mmHg, respectively. The group mean waveform was bimodal, with peak values evident early and late in the dark period, and a trough during the middle of the light period. The phase of maximum and minimum values showed low within-group variance. Mean heart rate was greater at night than during the day (561.9 ± 22.7 vs. 530.3 ± 22.3 beats/min). Peak values generally occurred at dark onset, and minimum values during the middle of both the dark and the light periods. We conclude that it is possible to perform measurements of circadian cardiovascular parameters in the mouse, providing new avenues for the investigation of genetic models.

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Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽

10.3390/pr8091071 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1071

Author(s):

Lucia Billeci ◽

Asia Badolato ◽

Lorenzo Bachi ◽

Alessandro Tonacci

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Diffusion Tensor Imaging ◽

Magnetic Resonance ◽

Diffusion Tensor ◽

Classification Problem ◽

Computer Algorithms ◽

Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

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Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

Journal Of Big Data ◽

10.1186/s40537-021-00488-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Yahya Albalawi ◽

Jim Buckley ◽

Nikola S. Nikolov

Keyword(s):

Social Media ◽

Deep Learning ◽

Comprehensive Evaluation ◽

Classification Problem ◽

Data Sets ◽

Word Embeddings ◽

Data Set ◽

Lower Accuracy ◽

Health Related ◽

The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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Malware Detection Based on Code Visualization and Two-Level Classification

Information ◽

10.3390/info12030118 ◽

2021 ◽

Vol 12 (3) ◽

pp. 118

Author(s):

Vassilios Moussas ◽

Antonios Andreatos

Keyword(s):

Web Applications ◽

Detection System ◽

Malware Detection ◽

Image Features ◽

Malicious Code ◽

Malware Analysis ◽

Malicious Software ◽

Antivirus Software ◽

Malware Classification ◽

Artificial Neural Network Ann

Malware creators generate new malicious software samples by making minor changes in previously generated code, in order to reuse malicious code, as well as to go unnoticed from signature-based antivirus software. As a result, various families of variations of the same initial code exist today. Visualization of compiled executables for malware analysis has been proposed several years ago. Visualization can greatly assist malware classification and requires neither disassembly nor code execution. Moreover, new variations of known malware families are instantly detected, in contrast to traditional signature-based antivirus software. This paper addresses the problem of identifying variations of existing malware visualized as images. A new malware detection system based on a two-level Artificial Neural Network (ANN) is proposed. The classification is based on file and image features. The proposed system is tested on the ‘Malimg’ dataset consisting of the visual representation of well-known malware families. From this set some important image features are extracted. Based on these features, the ANN is trained. Then, this ANN is used to detect and classify other samples of the dataset. Malware families creating a confusion are classified by a second level of ANNs. The proposed two-level ANN method excels in simplicity, accuracy, and speed; it is easy to implement and fast to run, thus it can be applied to antivirus software, smart firewalls, web applications, etc.

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