Artificial Intelligence has touched each and every sector. We all know about Facebook suggesting friends or Siri managing our calendars. Even the stocks are traded by computers. There are cars which can park on their own. From education to military to even manufacturing there is hardly a field where artificial intelligence does not pay a role.

Journal of Neurology and Neuroscience (ISSN: 2171-6625) is an international circulating peer-reviewed Open Access journal presenting original research contributions and scientific advances in the field of Neurology and Neuroscience. Journal of Neurology & Neuroscience aims to promote research communications and provide a forum for doctors, researchers, physicians and healthcare professionals to find most recent advances in all areas of Neurology & Neurological Sciences. Neurology & Neurosciences strongly supports the scientific up gradation and fortification in related scientific research community by enhancing access to peer reviewed scientific literary works. Neurology is a specialized area of medicine that concerns disorders and diseases of the nervous system. Neurology involves diagnosing and treating conditions of the central, peripheral and autonomic nervous systems.

When American novelist David Foster Wallace delivered the commencement address at Kenyon College in 2005, he urged the graduating class to "exercise some control over how and what you think." If you don't at least try to regulate your thoughts and behaviors, Wallace cautioned, you will go through life "dead, unconscious, a slave to your head." Wallace himself long suffered with unwanted negative thoughts and crippling self-doubt--and took his own life three years after that speech. But can our mind become a "terrible master," as Wallace described? Kessler, the former commissioner of the U.S. Food and Drug Administration, has considered that question for the past two decades, studying how substances such as food, alcohol and tobacco can hijack our brain chemistry and compel us to act against our own best intentions--bingeing on brownies, booze or cigarettes.

The word Artificial Intelligence is increasing. The latest example is Picturesqe, a tool for photographers that uses AI-powered automation to help pick out the best snaps and filter out the dross. Founders of Picturesqe, a machine-learning powered piece of software, are confident that it can select the good photos from your large stack. But unlike similar mobile Apps, Picturesqe is targeted specifically at professional photographers and semi-pros. Features of the application include smart grouping, which automatically groups similar photos based on visual content, intelligent zoom so that you can quickly compare the same spot on multiple shots, and aesthetic ranking.

One of the key insights that legendary physicist and Nobel Prize laureate Richard Feynman had was that quantum mechanics (the branch of physics that deals with subatomic particles, uncertainty principle, and many other concepts beyond classic physics) is just way too complicated to simulate using traditional computers. Nature, of course, can handle these complex calculations -- computers however can't do those same calculations (or would take a prohibitively long time and amount of resources to do so). But this isn't just about being able to do more with computers in a faster (or smaller) way: It's about solving problems that we couldn't solve with traditional computers; it's about a difference of kind not just degree. So what is a quantum computer and "qubits" -- especially as compared to a traditional computer and bits? And besides speed of processing, what are some of the new applications that wouldn't have been possible before? From how traditional computers work and quantum computers will work to why this all matters, a16z Deal and Research team head Frank Chen walks us through the basics of quantum computing in this slide presentation.

This is an excellent book which is very accessible to readers in several fields. It gives a very good summary of different statistical techniques which are used for data mining. It also gives some good large data sets and show how the tools can be implemented. It begins with a chapter on summarizing the data to have an initial feel about the data. Then it gives discussion on regression (linear, polynomial, nonparametric).

High predictive performance and ease of use and interpretability are important requirements for the applicability of a computer-aided diagnosis (CAD) to human reading studies. We propose a CAD system specifically designed to be more comprehensible to the radiologist reviewing screening breast MRI studies. Multiparametric imaging features are combined to produce a CAD system for differentiating cancerous and non-cancerous lesions. The complete system uses a rule-extraction algorithm to present lesion classification results in an easy to understand graph visualization.

This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Density-based Outlier Score (RDOS) is introduced to measure the local outlierness of objects, in which the density distribution at the location of an object is estimated with a local KDE method based on extended nearest neighbors of the object. Instead of using only $k$ nearest neighbors, we further consider reverse nearest neighbors and shared nearest neighbors of an object for density distribution estimation. Some theoretical properties of the proposed RDOS including its expected value and false alarm probability are derived. A comprehensive experimental study on both synthetic and real-life data sets demonstrates that our approach is more effective than state-of-the-art outlier detection methods.

Relational logistic regression (RLR) is a representation of conditional probability in terms of weighted formulae for modelling multi-relational data. In this paper, we develop a learning algorithm for RLR models. Learning an RLR model from data consists of two steps: 1- learning the set of formulae to be used in the model (a.k.a. structure learning) and learning the weight of each formula (a.k.a. parameter learning). For structure learning, we deploy Schmidt and Murphy's hierarchical assumption: first we learn a model with simple formulae, then more complex formulae are added iteratively only if all their sub-formulae have proven effective in previous learned models. For parameter learning, we convert the problem into a non-relational learning problem and use an off-the-shelf logistic regression learning algorithm from Weka, an open-source machine learning tool, to learn the weights. We also indicate how hidden features about the individuals can be incorporated into RLR to boost the learning performance. We compare our learning algorithm to other structure and parameter learning algorithms in the literature, and compare the performance of RLR models to standard logistic regression and RDN-Boost on a modified version of the MovieLens data-set.

This paper proposes a novel dynamic Hierarchical Dirichlet Process topic model that considers the dependence between successive observations. Conventional posterior inference algorithms for this kind of models require processing of the whole data through several passes. It is computationally intractable for massive or sequential data. We design the batch and online inference algorithms, based on the Gibbs sampling, for the proposed model. It allows to process sequential data, incrementally updating the model by a new observation. The model is applied to abnormal behaviour detection in video sequences. A new abnormality measure is proposed for decision making. The proposed method is compared with the method based on the non- dynamic Hierarchical Dirichlet Process, for which we also derive the online Gibbs sampler and the abnormality measure. The results with synthetic and real data show that the consideration of the dynamics in a topic model improves the classification performance for abnormal behaviour detection.