Today, cancer is one of the leading causes of death in humans in the world. Cancers affect different parts of the human anatomy in different ways. There are significantly more deaths associated with gastrointestinal cancers such as colorectal cancer than with all other cancers. With computer-aided diagnosis systems, medical practitioners can recognize a variety of illnesses more quickly than with manual procedures. It is possible to reduce mortality significantly by detecting and removing precancerous lesions early. Due to the time-consuming and challenging nature of manual diagnosis, researchers have developed computational algorithms that are used to identify and classify diseases such as gastrointestinal (GI) diseases. Computer-aided diagnosis relies heavily on the classification of medical images, which is a challenging task. Therefore, this study presents a less sophisticated but still effective pre-processing technique for identifying endoscopic images known as denoising capsule networks (Dn-CapsNets). Moreover, we constructed activation maps (AM) using the feature representations to visualize the results. As a result of these evaluations, the trained model achieved 94.16%, 83.1%, 86.7%, 96.1%, 86.6%, and +0.69 respectively in terms of accuracy, precision, sensitivity, specificity, F1-score, and Matthew’s correlation. Comparisons between the proposed method and the current state-of-the-art have shown improved accuracy.

In this paper we use a statistical mechanical model as a paradigm for educational choices when the reference population is partitioned according to the socioeconomic attributes of gender and residence. We study how educational attainment is influenced by socioeconomic attributes of gender and residence for five selected developing countries. The model has a social and a private incentive part with coefficients measuring the influence individuals have on each other and the external influence on individuals, respectively. The methods of partial least squares and the ordinary least squares are, respectively, used to estimate the parameters of the interacting and the noninteracting models. This work differs from the previous work that motivated this work in the following sense: (a) the reference population is divided into subgroups with unequal subgroup sizes, (b) the proportion of individuals in each of the subgroups may depend on the population size N, and (c) the method of partial least squares is used for estimating the parameters of the model with social interaction as opposed to the least squares method used in the earlier work.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

This paper studies large deviations properties of vectors of empirical means and measures generated as follows. Consider a sequence X1,X2,…,Xn of independent and identically distributed random variables partitioned into d-subgroups with sizes n1,…,nd. Further, consider a d-dimensional vector mn whose coordinates are made up of the empirical means of the subgroups. We prove the following. (1) The sequence of vector of empirical means mn satisfies large deviations principle with rate n and rate function I, when the sequence X1,X2,…,Xn is Rl valued, with l≥1. (2) Similar large deviations results hold for the corresponding sequence of vector of empirical measures Ln if Xi’s, i=1,2,…,n, take on finitely many values. (3) The rate functions for the above large deviations principles are convex combinations of the corresponding rate functions arising from the large deviations principles of the coordinates of mn and Ln. The probability distributions used in the convex combinations are given by α=(α1,…,αd)=limn→∞1/n(n1,…,nd). These results are consequently used to derive variational formula for the thermodynamic limit for the pressure of multipopulation Curie-Weiss (I. Gallo and P. Contucci (2008), and I. Gallo (2009)) and mean-field Pott’s models, via a version of Varadhan’s integral lemma for an equicontinuous family of functions. These multipopulation models serve as a paradigm for decision-making context where social interaction and other socioeconomic attributes of individuals play a crucial role.