The cardiovascular disease risk in modern industrial societies and living with sedentary lifestyles, high-calorie meals, and mental stress is on the rise. Methods of diagnosing the disease, which is based on clinical ...
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The cardiovascular disease risk in modern industrial societies and living with sedentary lifestyles, high-calorie meals, and mental stress is on the rise. Methods of diagnosing the disease, which is based on clinical examinations and tests, are time-consuming and costly, and can also involve human error. Therefore, data mining methods have been used in recent years, each of which has its advantages and disadvantages. Due to the variety of features related to patient data, the use of a classifier alone cannot cover all the hidden sides of the problem. Thus, in the proposed method, a combined cascading learning model is used, which consists of two levels. In the first level, the Bayesian classifier is used, which adds two characteristics of the possibility of being sick or not to the data. In the second level, the decision tree and ripper classifiers are used in parallel. The model is based on the heart data set. The evaluation results based on the accuracy, recall, and precision parameters show that the proposed method compared to Miao and Yakala methods based on the accuracy parameter has improved performance by 9% and 2%, respectively.
Influenza has a negative sense, single-stranded, and segmented RNA. In the context of pandemic influenza research, most studies have focused on variations in the surface proteins (Hemagglutinin and Neuraminidase). How...
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Influenza has a negative sense, single-stranded, and segmented RNA. In the context of pandemic influenza research, most studies have focused on variations in the surface proteins (Hemagglutinin and Neuraminidase). However, new findings suggest that all internal and external proteins of influenza viruses can contribute in pandemic emergence, pathogenicity and increasing host range. The occurrence of the 2009 influenza pandemic and the availability of many external and internal segments of pandemic and non-pandemic sequences offer a unique opportunity to evaluate the performance of machine learning models in discrimination of pandemic from seasonal sequences using mutation positions in all segments. In this study, we hypothesized that identifying mutation positions in all segments (proteins) encoded by the influenza genome would enable pandemic and seasonal strains to be more reliably distinguished. In a large scale study, we applied a range of data mining techniques to all segments of influenza for rule discovery and discrimination of pandemic from seasonal strains. CBA (classification based on association rule mining), ripper and Decision tree algorithms were utilized to extract association rules among mutations. CBA outperformed the other models. Our approach could discriminate pandemic sequences from seasonal ones with more than 95% accuracy for PA and NP, 99.33% accuracy for NA and 100% accuracy, precision, specificity and sensitivity (recall) for M1, M2, PB1, NS1, and NS2. The values of precision, specificity, and sensitivity were more than 90% for other segments except PB2. If sequences of all segments of one strain were available, the accuracy of discrimination of pandemic strains was 100%. General rules extracted by rule base classification approaches, such as M1-V1471, NP-N334H, NS1-V1121, and PB1-L3641, were able to detect pandemic sequences with high accuracy. We observed that mutations on internal proteins of influenza can contribute in distinguishing the
Due to the vast popularity of sensors, cloud computing, mobile computing, and intelligent devices, the Internet of Things has seen tremendous growth in recent years. Operating system type recognition is the core techn...
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Due to the vast popularity of sensors, cloud computing, mobile computing, and intelligent devices, the Internet of Things has seen tremendous growth in recent years. Operating system type recognition is the core technology of network security assessment. Due to inherit security problems of Internet of Things such as the situation of risk and threat of information, the operating system recognition seeks research attention for Internet of Things network security. In view of the current identification method of active operating system, it is prone to be detected by intrusion detection system. The operating system identification technology based on transmission control protocol/Internet protocol fingerprint library is more complicated than to distinguish the operating system types of unknown fingerprints. In this work, a passive operating system identification method based on ripper model is proposed. Also, it is compared with the existing support vector machine and C45 decision tree classification algorithms. Experiments reveal that ripper-based algorithm has better recognition accuracy and recognition efficiency.
Recommender systems are widely used for recommending items based on the user's specific preference. They depict user choices in a manner that can be exploited to personalize search results. In this paper, a novel ...
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ISBN:
(纸本)9781665438070
Recommender systems are widely used for recommending items based on the user's specific preference. They depict user choices in a manner that can be exploited to personalize search results. In this paper, a novel rule-based model is proposed for recommending foods for Indian elderly diabetic population based on Glycemic Index (GI) of food items. Rules are extracted using ripper algorithm from a real clinical dataset which is enhanced with the help of Synthetic Minority Oversampling Technique (SMOTE) and the food dataset used for this work is constructed from real data as the requirements of the proposed system are definitive. The proposed system is evaluated by medical professionals and doctors who rated the system based on a variety of use cases presented to them. The system received an average rating of 8 out of 10 on its performance to accurately identify the test results, GI range and appropriately suggest food items based on user preferences.
This study presents the comparison of attribute selection techniques which used for classifying the bad behaviors of vocational education students. There are two classification methods: hybrid classification and singl...
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ISBN:
(纸本)9781424414895
This study presents the comparison of attribute selection techniques which used for classifying the bad behaviors of vocational education students. There are two classification methods: hybrid classification and single classification. Hybrid classification includes two steps, step one is attribute selection by search method using genetic search and results are compared by three evaluators: 1) Correlation-based Feature Selection (CFS) 2) Consistency-based Subset Evaluation and 3) Wrapper Subset Evaluation. Step two is the classification of data set by using selected attributed from step one and four classification algorithms. Next, Simple classification used classification algorithms only without attribute selection. The four classification algorithms that used in this experiment for comparing in two methods are : 1) Naive Bayes classifier 2) Baysian Belief Network 3) C4.5 algorithm and 4) ripper algorithm. The measurements of classification efficiency had been obtained by using the k-fold Cross Validation technique. From the experiment, it was found that hybrid classification technique using genetic search and CFS evaluator with C4.5 algorithm, gives the highest accuracy rate at 82.52%. However, results from F-measure evaluation showed that C4.5 algorithm did not fit for all data types. The hybrid classification technique using genetic search and wrapper subset with Baysian belief network can give a better precision value which can be seen in the F-measure, and it gives the accuracy rate at 82.42%.
During the operation of the distribution secondary system attackers will use the uncertainty of device identity to obtain device fingerprint information by scanning the network to associate vulnerabilities and invade ...
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ISBN:
(数字)9781665462976
ISBN:
(纸本)9781665462976
During the operation of the distribution secondary system attackers will use the uncertainty of device identity to obtain device fingerprint information by scanning the network to associate vulnerabilities and invade the system thus exposing the system to great risks. A device fingerprint is a set of characteristic information that uniquely identifies a device. Therefore, a device fingerprint can be used to discover malicious devices and system vulnerabilities. In this paper the passive device fingerprint identification method based on TCP/ IP protocol is proposed to solve the problem of inaccurate matching in the device fingerprint database of the distribution secondary system and the low efficiency of the existing device fingerprint identification method. The method combines the ripper algorithm to build the device fingerprint identification classification model. The automatic fingerprint library based on HTTP User-Agent is used to recognize the device fingerprint.
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