Predictive analytics systems are currently one of the most important areas of research and development within the Artificial Intelligence domain and particularly in Machine Learning. One of the "holy grails" of predictive analytics is the research and development of the "perfect" recommendation system. In our paper we propose an advanced pipeline model for the multi-task objective of determining product complementarity, similarity and sales prediction using deep neural models applied to big-data sequential transaction systems. Our highly parallelized hybrid pipeline consists of both unsupervised and supervised models, used for the objectives of generating semantic product embeddings and predicting sales, respectively. Our experimentation and benchmarking have been done using very large pharma-industry retailer Big Data stream.

Machine learning researcher Irina Nicolae is here to dispel a common misconception: You don't have to be a math whiz to end up working in technology. Growing up in Bucharest, Romania, Irina had relatively little interest in numerics. She was, however, captivated by machinery and how different parts fit together to perform a task. It was this fascination that eventually led her to programming. Today, Irina is turning her longtime passion into action in her role as a research scientist at IBM Research – Ireland.

Artificial intelligence (AI), in its many forms including self-driving cars, robo-advisors, mechanical baristas, and state-run facial recognition campaigns will go ahead and continue to redefine how individuals work and have their views about school, work, government, and daily life. AI startups have been marching ahead in the technology bandwagon gaining funding in a world where supergiant rounds are now quite common. The top well-funded AI startup to date, SenseTime has brought a funding in a total of $1.6 billion. Most recently, in May 2018 the company raised $620 million in a Series C round led by Tiger Global Management and Fidelity International. This round raised SenseTime's valuation to more than $4.5 billion, making it the world's most valuable artificial intelligence startup.

Digital transformation is a process that has been underway for some time. It is a wide-ranging overhaul of the way organisations do business both internally and externally. It includes changes to technology and culture from users to developers, operations teams to the C-Suite. On the technology side, organisations have taken advantage of APIs to integrate with suppliers and key customers. This removes paper from the workflow reducing both delays in entering data and the risk of keying errors.

In this book we introduce the plithogenic set (as generalization of crisp, fuzzy, intuitionistic fuzzy, and neutrosophic sets), plithogenic logic (as generalization of classical, fuzzy, intuitionistic fuzzy, and neutrosophic logics), plithogenic probability (as generalization of classical, imprecise, and neutrosophic probabilities), and plithogenic statistics (as generalization of classical, and neutrosophic statistics). Plithogenic Set is a set whose elements are characterized by one or more attributes, and each attribute may have many values. An attribute value v has a corresponding (fuzzy, intuitionistic fuzzy, or neutrosophic) degree of appurtenance d(x,v) of the element x, to the set P, with respect to some given criteria. In order to obtain a better accuracy for the plithogenic aggregation operators in the plithogenic set, logic, probability and for a more exact inclusion (partial order), a (fuzzy, intuitionistic fuzzy, or neutrosophic) contradiction (dissimilarity) degree is defined between each attribute value and the dominant (most important) attribute value. The plithogenic intersection and union are linear combinations of the fuzzy operators tnorm and tconorm, while the plithogenic complement, inclusion, equality are influenced by the attribute values contradiction (dissimilarity) degrees. Formal definitions of plithogenic set, logic, probability, statistics are presented into the book, followed by plithogenic aggregation operators, various theorems related to them, and afterwards examples and applications of these new concepts in our everyday life.

These days, there is no part of our lives that is unaffected via computerization. A few illustrations incorporate clothes washers, microwaves, autopilot mode for autos and planes, Nestlé utilizing Robots to offer espresso units in stores in Japan, Walmart testing automatons to convey items in the US, our bank checks being arranged to utilize Optical Character Recognition (OCR), and ATMs. Automation, in basic words, is innovation that arrangements with the utilization of machines and PCs to the generation of merchandise and enterprises. This aids in completing works with practically no human help. With the appearance of PCs, numerous product frameworks were created to achieve assignments that were beforehand done on paper to oversee organizations, or not being done at all because of the absence of devices.

Armed conflict has led to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when large fluxes of IDPs will cross into an area remains a major challenge for aid delivery organizations. Accurate forecasting of IDP migration would empower humanitarian aid groups to more effectively allocate resources during conflicts. We show that monthly flow of IDPs from province to province in both Syria and Yemen can be accurately forecasted one month in advance, using publicly available data. We model monthly IDP flow using data on food price, fuel price, wage, geospatial, and news data. We find that machine learning approaches can more accurately forecast migration trends than baseline persistence models. Our findings thus potentially enable proactive aid allocation for IDPs in anticipation of forecasted arrivals.

Paediatric and child trauma experts are sounding the alarm that separating migrant children from their parents at the US border can cause serious physical and psychological damage. As more stories emerge about children being separated from their parents at the border between Mexico and the US, doctors and scientists are warning that there could be long-term, irreversible health impacts on children if they're not reunited expediently. The head of the American Academy of Pediatrics went so far as to call the policy "child abuse" and against "everything we stand for as paediatricians". "This is completely ridiculous and I'm approaching that not as someone who's taking a position in the politics, but as a scientist," says Charles A Nelson III, a professor of paediatrics and neuroscience at Harvard Medical School. "We just know the science does not support that this is good for kids."

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We present for the first time an exhaustive enumeration of Williamson matrices of even order n < 65. The search method relies on the novel SAT+CAS paradigm of coupling SAT solvers with computer algebra systems so as to take advantage of the advances made in both the field of satisfiability checking and the field of symbolic computation. Additionally, we use a programmatic SAT solver which allows conflict clauses to be learned programmatically, through a piece of code specifically tailored to the domain area. Prior to our work, Williamson matrices had only been enumerated for odd orders n < 60, so our work increases the bounds that Williamson matrices have been enumerated up to and provides the first enumeration of Williamson matrices of even order. Our results show that Williamson matrices of even order tend to be much more abundant than those of odd orders. In particular, Williamson matrices exist for every even order n < 65 but do not exist in orders 35, 47, 53, and 59.