Artificial intelligence capability: Conceptualization,measurement calibration,and empirical study on its impact on organizational creativity and firm performance

Artificial intelligence (AI) has been heralded by many as the next source of business value. Grounded on the resource-based theory of the firm and on recent work on AI at the organizational context, this study (1) identifies the AI-specific resources that jointly create an AI capability and provides a definition, (2) develops an instrument to capture the AI capability of the firms, and (3) examines the relationship between an AI capability and organizational creativity and performance. Findings empirically support the suggested theoretical framework and corresponding instrument and provide evidence that an AI capability results in increased organizational creativity and performance.     

Least squares large margin distribution machine for regression

Better prediction ability is the main objective of any regression-based model. Large margin Distribution Machine for Regression (LDMR) is an efficient approach where it tries to reduce both loss functions, i.e. ε-insensitive and quadratic loss to diminish the effects of outliers. However, still, it has a significant drawback, i.e. high computational complexity. To achieve the improved generalization of the regression model with less computational cost, we propose an enhanced form of LDMR named as Least Squares Large margin Distribution Machine-based Regression (LS-LDMR) by transforming the inequality conditions alleviate to equality conditions. The elucidation is attained by handling a system of linear equations where we need to measure the inverse of the matrix only. Hence, there is no need to solve the large size of the quadratic programming problem, unlike in the case of other regression-based algorithms as SVR, Twin SVR, and LDMR. The numerical experiment has been performed on the benchmark real-life datasets along with synthetically generated datasets by using the linear and Gaussian kernel. All the experiments of presented LS-LDMR are analyzed with standard SVR, Twin SVR, primal least squares Twin SVR (PLSTSVR), ε-Huber SVR (ε-HSVR), ε-support vector quantile regression (ε-SVQR), minimum deviation regression (MDR), and LDMR, which shows the effectiveness and usability of LS-LDMR. This approach is also statistically validated and verified in terms of various metrics.

     

A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals

Neural Computing and Applications - This work presents an efficient hybridized approach for the classification of electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat...     

Variation resilient low-power memristor-based synchronous flip-flops: design and analysis

Microsystem Technologies - Flip-flops are the basic digital components for all types of complex digital electronics systems and sequential logic circuits. In this paper, new nonvolatile, low power,...     

Theory of edge perception for computer vision feedback control

In this paper, the authors address the problem of edge-perception for its applications to vision-feedback control in robotic systems.In natural vision, the recognition of objects takes place through the process consisting of eye system, neural networks and cognition. The cognitive process, in turn yields a phenomenon known as perception. This is the phenomenon of perception of physical attributes, such as edges, color and texture, etc., which is responsible for the recognition of objects through the natural vision processes.In this paper, we make an attempt to postulate the theory of perception for gray-level images. The gray-level images, when going through the cognitive and perception processes, are contaminated by the uncertainty; here we call it cognitive uncertainty.The studies in this paper are confined to the phenomenon of edge-perception for two-dimensional gray-level images, however, these studies can be extended to other types of visual attributes both in two-dimensional and three-dimensional spaces. Indeed, the perception of these attributes, which attempts to emulate the human vision system, may help in the design of a truly robust computer vision-feedback control system for robotic applications.     

Barium ferrite: Preparation by liquid mix technique and its characterization

Barium ferrite has been prepared by a novel non-conventional preparation method known as the liquid mix technique which involves the preparation of citrates of the metals concerned. The decomposition of the citrates indicated a gradual transition from an amorphous material to a crystalline phase leading to the formation of barium ferrite at 600° C as indicated by the X-ray diffraction and Mössbauer measurements. Submicrometre-sized particles of the ferrite could be obtained directly on decomposition of the citrates, thus avoiding the milling or grinding operations normally employed to reduce the particles to the size required for obtaining good magnetic properties. The best properties (coercivity and remanence) were obtained for the ferrite sintered around 990° C.     

Lotka's law and productivity patterns of entomological research in Nigeria for the period, 1900–1973

A bibliography of entomological research in Nigeria, 1900–1973 totally 1720 publications was analysed to study the author productivity patterns and to test the applicability of Lotka's law for the obtained distributions. Four different files were generated, one for the publications of all the authors, second for the publications by first authors, third for single authors and fourth for coauthors. Lotka's law in its original form as inverse square law does not apply to any of the four data sets. However, it does apply in its generalised form with the calculated values of characteristic exponent . The values of were found to be 1.9, 1.8, 2.2 and 2.4 for the four different data sets. K-S statistical test was aplied to test the applicability of generalised form of Lotka's law. The maximum difference in the observed and estimated values of the proportions of authors was found to be highly insignificant at 0.01 level of significance in each of the four cases.