Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis

The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1–5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males). A group of MS patients included 365 patients (266 females and 99 males) with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients B3 and B4 of our mathematical model B3 ∗ exp(−B2  ∗  (X − 1)) + B4  ∗  (1 − exp(−B2  ∗  (X − 1))) because discriminant functions, calculated separately for B3 and B4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both

Intelligent Financial Fraud Detection Practices: An Investigation

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and Privacy in Communication Networks (SecureComm 2014

Video surveillance : past, present, and now the future

Video surveillance is a part of our daily life, even though we may not necessarily realize it. We might be monitored on the street, on highways, at ATMs, in public transportation vehicles, inside private and public buildings, in the elevators, in front of our television screens, next to our baby?s cribs, and any spot one can set a camera

An end-to-end system for content-based video retrieval using behavior, actions, and appearance with interactive query refinement

10.1109/AVSS.2015.7301807AVSS 2015 - 12th IEEE International Conference on Advanced Video and Signal Based Surveillance730180

Current Knowledge of Trichosporon spp. and Trichosporonosis

Summary: Trichosporon spp. are basidiomycetous yeast-like fungi found widely in nature. Clinical isolates are generally related to superficial infections. However, this fungus has been recognized as an opportunistic agent of invasive infections, mostly in cancer patients and those exposed to invasive medical procedures. It is possible that the ability of Trichosporon strains to form biofilms on implanted devices, the presence of glucuronoxylomannan in their cell walls, and the ability to produce proteases and lipases are all factors likely related to the virulence of this genus and therefore may account for the progress of invasive trichosporonosis. Disseminated trichosporonosis has been increasingly reported worldwide and represents a challenge for both diagnosis and species identification. Phenotypic identification methods are useful for Trichosporon sp. screening, but only molecular methods, such as IGS region sequencing, allow the complete identification of Trichosporon isolates at the species level. Methods for the diagnosis of invasive trichosporonosis include PCR-based methods, Luminex xMAP technology, and, more recently, proteomics. Treating patients with trichosporonosis remains a challenge because of limited data on the in vitro and in vivo activities of antifungal drugs against clinically relevant species of the genus. Despite the mentioned limitations, the use of antifungal regimens containing triazoles appears to be the best therapeutic approach