Search results

The social sciences are really the “hard sciences” and the physical sciences are the “easy” sciences. One of the great contributors to making the job of the social scientist very difficult is the lack of fundamental dimensions on the basis of which absolute (i.e. ratio) scales can be formulated and in which relationships could be realized as the [allegedly] coveted equations of physics. This deficiency leads directly to the uses of statistical methods of various types. However it is possible, as shown, to formulate equations and to use them to obtain ratio/absolute scales and relationships based on them. This paper uses differential/integral equations, fundamental ideas from the processing view of the brain‐mind, multiple scale approximation via Taylor series, and basic reasoning some of which may be formulated as infinite‐valued logic, and which is related to probability theory (the theoretical basis of statistics) to resolve some of the basic issues relating to learning theory, the roles of nature and nurture in intelligence, the measurement of intelligence itself, and leads to the correct formulation of the potential‐actual type behaviors (specifically intelligence) and dynamical‐temporal model of intelligence development. Specifically, it is shown that the: (1) basic model for intelligence in terms of genetics and environment has to be multiplicative, which corresponds to a logical‐AND, and is not additive; (2) related concept of “genetics” creating its own environment is simply another way of saying that the interaction of genetics and environment is multiplicative as in (1); (3) timing of environmental richness is critical and must be modeled dynamically, e.g. in the form of a differential equation; (4) path functions, not point functions, must be used to model such phenomena; (5) integral equation formulation shows that intelligence at any time t, is a a sum over time of the past interaction of intelligence with environmental and genetic factors; (6) intelligence is about 100 per cent inherited on a global absolute (ratio) scale which is the natural (dimensionless) scale for measuring variables in social science; (7) nature of the approximation assumptions implicit in statistical methods leads to “heritability” calculations in the neighborhood of 0.5. and that short of having controlled randomized experiments such as in animal studies these are expected sheerely due to the methods used; (8) concepts from AI, psychology, epistemology and physics coincide in many respects except for the terminology used, and these concepts can be modeled nonlinearly.

Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem

Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances.

The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results.

In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort.

Whiteness. We appropriate the word to erase it. We laugh – ha, ha – whiteness. I begin with my experiences as a white, upper-middle class girl raised up in a racist and racialized educational system. This authoethnography revolves around an epiphanic moment resulting from the impact of years of involvement in this system. I look at various ways educational practices that are meant to alleviate pain, inequity, and a legacy of racism can function to allow white people to distance ourselves from the ugliness of privilege, silence criticism, perpetuate inequity, and, ultimately, limit human growth and connection.

The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently.

In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices.

Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level.

This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.

This paper aims to review seven artificial intelligence tools that are useful in assembly automation: knowledge‐based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case‐based reasoning and ambient‐intelligence.

Each artificial intelligence tool is outlined, together with some examples of their use in assembly automation.

Artificial intelligence has produced a number of useful and powerful tools. This paper reviews some of those tools. Applications of these tools in assembly automation have become more widespread due to the power and affordability of present‐day computers.

Many new assembly automation applications may emerge and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed in the paper. The tools and methods reviewed in this paper have minimal computation complexity and can be implemented on small assembly lines, single robots or systems with low‐capability microcontrollers.

It may take another decade for engineers to recognize the benefits given the current lack of familiarity and the technical barriers associated with using these tools and it may take a long time for direct digital manufacturing to be considered commonplace… but it is expanding. The appropriate deployment of the new AI tools will contribute to the creation of more competitive assembly automation systems.

Other technological developments in AI that will impact on assembly automation include data mining, multi‐agent systems and distributed self‐organising systems.

The novel approaches proposed use ambient intelligence and the mixing of different AI tools in an effort to use the best of each technology. The concepts are generically applicable across all industrial assembly processes and this research is intended to prove that the concepts work in manufacturing.

“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.

As rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).

Results obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.

The result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

Product quality inspection is of importance in manufacturing industries to ensure that low quality or unqualified products are not delivered to the consumer. Human inspection has many limitations such as low accuracy or speed due to factors such as tiredness and boredom. Traditional 2D vision inspection also has limitations of product shape complexity or flexibility. Thus, automated 3D vision inspection is anticipated to meet the requirements of higher applicability. This paper seeks to address these issues.

In many product quality inspection problems, geometrical parameters of the industrial parts are commonly used as the basis of quality inspection. Machine vision is widely applied to acquire such kind of parameters. Comparing to traditional 2D vision, 3D vision can acquire 3D coordinates of the object directly, so that the inspection can be accomplished which is difficult to do with 2D vision. As an active vision technique, structure light system (SLS) is applied to acquire the 3D coordinate information of inspected object in this paper. On the basis of point cloud and regression analysis, features relative to quality are defined and extracted as the attributes for the product classification. Three data mining techniques are applied to accomplish the classification in this paper, which include decision trees, artificial neural networks and support vector machine.

A new intelligent automated 3D vision quality inspection for assembly lines has been developed, which comprises structure light system (SLS) and data mining approaches such as decision tree, artificial neutral networks and support vector machine.

The combination of structure light system (SLS) and data mining approaches makes the automated quality inspection available. The proposed system is easy to be implemented and flexible for different types of products.

Attention deficit hyperactivity disorder (ADHD) is a common disorder that is highly prevalent in children and frequently persists into adulthood. The purpose of this paper is to consider the need for practitioners to be aware of the disorder.

This paper reviews quantitative genetic findings in ADHD, primarily focussing on twin studies that describe the role of genetic influences throughout the lifespan and the associated overlap between ADHD and other syndromes, disorders and traits.

This paper concludes that ADHD is a lifespan condition that shares genetic risk factors with other psychiatric, neurodevelopmental disorders and intellectual disabilities.

This paper makes the case that clinicians working in the area of intellectual disability should be fully aware of the potential impact of ADHD and its associated impairments.

The purpose of this case study is to understand effect of Indian ethos, socio‐cultural setup, etc. on growth of family‐based business; impact of ethnicity and genetic intelligence on development of entrepreneurial traits, etc. in family business contexts in India.

The approach takes a single case study on an organized retail firm named Bothra Megabazar Private Limited in Rourkela, India to comprehend the established theories and literature on emergence and spread of business community/class in India known for its own ethos and values as a country. As a part of narrative enquiry method in qualitative research, it collects the narratives of central and peripheral characters in the respective business house through “story telling” and by “restorying” the same, understands and explains the family‐based entrepreneurial journey amidst business dynamics.

The important findings of this case study are manifold. It finds that there is inter‐connectedness of different aspects amounting for success/growth of family business entrepreneurs and enterprises. Some of these factors are deep‐seated Indian ethos and values, multiple family and social networks, joint and undivided family structure, inheritance of family business down the generations, financial backing from members of family and social networks, long standing experience in trade, genetic intelligence across generations, internal capacity building with unique style of leadership and high‐risk appetite, etc.

With its focus on one specific community like Bani(y)as or Marwaris in Indian business society, the case may not justify the understandings on genetic intelligence in case of other communities/class. However, the study elaborates scope of future studies in the same direction.

Practicing managers and research scholars can use this case for understanding of the key success/growth factors behind socio‐culturally guided family‐based business enterprises.

The paper presents a case that is original.

Multiple sequence alignment (MSA) is one of essential bioinformatics methods for decoding cis‐regulatory elements in gene regulation, predicting structure and function of proteins and RNAs, reconstructing phylogenetic tree, and other common tasks in biomolecular sequence analysis. The purpose of this paper is to describe briefly the basic concepts and formulations of gapped MSA and un‐gapped motif discovery approaches, and then review computational intelligence (CI) applications in MSA and motif‐finding problems.

This paper performs exhaustive literature review on the MSA and motif discovery using CI techniques.

Although CI‐based MSA algorithms were developed nearly a decade ago, most recent CI effort seems attempted to tackle the NP‐complete motif discovery problem. Applications of various CI techniques to solve motif discovery problem, including neural networks, self‐organizing map, genetic algorithms, swarm intelligence and combinations thereof, are surveyed. Finally, the paper concludes with discussion and perspective.

The algorithms and software discussed in this paper can be used to align DNA, RNA and protein sequences, discover motifs, predict functions and structures of protein and RNA sequences, and estimate phylogenetic tree.

The paper contributes to the first comprehensive survey of CI techniques that are applied to MSA and motif discovery.