A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Know abnormal, find evil : frequent pattern mining for ransomware threat hunting and intelligence

Emergence of crypto-ransomware has significantly changed the cyber threat landscape. A crypto ransomware removes data custodian access by encrypting valuable data on victims’ computers and requests a ransom payment to reinstantiate custodian access by decrypting data. Timely detection of ransomware very much depends on how quickly and accurately system logs can be mined to hunt abnormalities and stop the evil. In this paper we first setup an environment to collect activity logs of 517 Locky ransomware samples, 535 Cerber ransomware samples and 572 samples of TeslaCrypt ransomware. We utilize Sequential Pattern Mining to find Maximal Frequent Patterns (MFP) of activities within different ransomware families as candidate features for classification using J48, Random Forest, Bagging and MLP algorithms. We could achieve 99% accuracy in detecting ransomware instances from goodware samples and 96.5% accuracy in detecting family of a given ransomware sample. Our results indicate usefulness and practicality of applying pattern mining techniques in detection of good features for ransomware hunting. Moreover, we showed existence of distinctive frequent patterns within different ransomware families which can be used for identification of a ransomware sample family for building intelligence about threat actors and threat profile of a given target

Towards Autonomous Robotic Valve Turning

In this paper an autonomous intervention robotic task to learn the skill of grasping and turning a valve is described. To resolve this challenge a set of different techniques are proposed, each one realizing a specific task and sending information to the others in a Hardware-In-Loop (HIL) simulation. To improve the estimation of the valve position, an Extended Kalman Filter is designed. Also to learn the trajectory to follow with the robotic arm, Imitation Learning approach is used. In addition, to perform safely the task a fuzzy system is developed which generates appropriate decisions. Although the achievement of this task will be used in an Autonomous Underwater Vehicle, for the first step this idea has been tested in a laboratory environment with an available robot and a sensor

Learning symbolic representations of actions from human demonstrations

In this paper, a robot learning approach is pro- posed which integrates Visuospatial Skill Learning, Imitation Learning, and conventional planning methods. In our approach, the sensorimotor skills (i.e., actions) are learned through a learning from demonstration strategy. The sequence of per- formed actions is learned through demonstrations using Visu- ospatial Skill Learning. A standard action-level planner is used to represent a symbolic description of the skill, which allows the system to represent the skill in a discrete, symbolic form. The Visuospatial Skill Learning module identifies the underlying constraints of the task and extracts symbolic predicates (i.e., action preconditions and effects), thereby updating the planner representation while the skills are being learned. Therefore the planner maintains a generalized representation of each skill as a reusable action, which can be planned and performed inde- pendently during the learning phase. Preliminary experimental results on the iCub robot are presented

Inequity in household's capacity to pay and health payments in Tehran-Iran-2013

Background: Health inequality monitoring especially in Health care financing field is very important. Hence, this study tends to assess the inequality in household's capacity to pay and out-ofpocket health carepaymentsin Tehran metropolis. Methods: This cross-sectional study was performed in 2013.Thestudy population was selected by stratified cluster sampling, and they constitute the typical households living in Tehran (2200 households). The required data were collected through questionnaires and analyzed using Excel and Stata v.11. Concentration Index on inequality was used for measuring inequality status in capacity to pay and household payments for health care expenses; and also the concentration index for out-of-pocket payments and capacity to pay was used to determine the extent of inequality. The recall period for inpatient care was one year and 1 month for outpatient. Results: The average of out-of-pocket payments for receiving the outpatient services was determined to be 44.33US and for each inpatient1861.11 US. Concentration index for household's outof- pocket payments for inpatient health care, out-of-pocket payments for outpatient health care and health prepayments were calculated 0.13, -0.10 and -0.11, respectively. Also, concentration index in household's capacity to pay was estimated to be 0.11whichindicatedinequality to the benefit of the rich. The households used financing strategies like savings, borrowing or lending to pay their health care expenditures. Conclusion: According to this study, the poor spend a greater portion of their capacity to pay for outpatient and inpatient health care costs and prepayment, in comparison to the rich. Thus, supporting the vulnerable groups of the society to decrease out-of-pocket payments and increasing the household's capacity to pay through government support in order to improve the household economic potential, must be considered very important

The parent play questionnaire: development of a parent questionnaire to assess parent–child play and digital media use

We introduce the Parent Play Questionnaire (PPQ), a parent-report measure designed to assess frequency of parent–infant play, parents’ attitudes towards play with their infant, and infants’ use of digital media. We describe measure development and empirical data across three samples of parent–infant dyads (total N = 414, offspring aged 0.3–2.5 years). Three latent factors explain the PPQ, corresponding with theoretically defined subscales. Summary scores showed good internal consistency and normally distributed results. Weak to moderate correlations were found between the frequency and attitude play scales, and with standardized measures of family social and emotional characteristics. Overall, frequency of digital media use was not correlated with play or broader family variables. Results suggest that the PPQ will be a useful tool for researchers interested in assessing parent–child play during early childhood

Deep dive into ransomware threat hunting and intelligence at fog layer

Ransomware, a malware designed to encrypt data for ransom payments, is a potential threat to fog layer nodes as such nodes typically contain considerably amount of sensitive data. The capability to efficiently hunt abnormalities relating to ransomware activities is crucial in the timely detection of ransomware. In this paper, we present our Deep Ransomware Threat Hunting and Intelligence System (DRTHIS) to distinguish ransomware from goodware and identify their families. Specifically, DRTHIS utilizes Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), two deep learning techniques, for classification using the softmax algorithm. We then use 220 Locky, 220 Cerber and 220 TeslaCrypt ransomware samples, and 219 goodware samples, to train DRTHIS. In our evaluations, DRTHIS achieves an F-measure of 99.6% with a true positive rate of 97.2% in the classification of ransomware instances. Additionally, we demonstrate that DRTHIS is capable of detecting previously unseen ransomware samples from new ransomware families in a timely and accurate manner using ransomware from the CryptoWall, TorrentLocker and Sage families. The findings show that 99% of CryptoWall samples, 75% of TorrentLocker samples and 92% of Sage samples are correctly classified