Thyroid is one of the vital diseases that influence individuals of any age group now a day. Infections of the thyroid, incorporate conditions related with extreme release of thyroid hormones (Hyper thyroidism) which is likewise called thyrotoxicosis and those related with thyroid hormone insufficiency (Hypothyroidism). Expectation of these two sorts of thyroid disease is critical for thyroid analysis. In this paper, support vector machines and logistic regression are proposed for predicting patients with thyrotoxicosis and without thyrotoxicosis. The outcomes demonstrate that, logistic regression perform well over support vector machine with 98.92% exactness.

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In recent years, the applications of diverse machine leaning algorithms and their fusion to the cybernetics and decisionmaking have been attracting more and more scholars from different disciplines. As a most frequently used technique of extracting knowledge from data, a feed-forwarded neural network (NN) has shown more advantages for the regression and classification problems. The main advantages come from the capability of good approximation of NNs and their corresponding highly nonlinear boundary. Due to the many merits and wide applications of NNs, the study on several fundamental issues of NNs, such as structure selection, overtraining, robustness and capability of resistance to noisy data, training on different types of data, and more importantly, the generalization ability, are still in progress although NNs have been an old topic in the areas of learning and reasoning. As a result, these extensive studies and significant improvements bring a number of new features anddevelopments to NNs. This issue makes an attempt to provide some latest advances of NN learning, the recent improvements of performance for NN learning systems, and new applications of NNs to different real fields.In this issue, 14 papers are accepted for publication. The 14 papers cover a variety of topics which include the stochastic stability NN with time delay, imbalanced and uncertain data, fuzzy NNs, bidirectional associative memory (BAM), deep learning of NNs, and other comparative and review studies. Categorization and brief description of these papers are given below.Four papers discuss the stability of NNs with delay time. Actually, the time delay is ubiquitous in most natural systems. Since the time delay is frequently encountered in NNs, the issue about stability of NNs with delay has been emerging as a challenging task for NN researchers. The paper authored by Cheng-De Zheng studied a class of stochastic reaction diffusion NNs with Markovian jumping parameters and time delays. Lyapunov functional method and stochastic analysis technique are used there to construct a new stochastic stability in the principle of the mean square. Continuing this topic, the paper authored by M. Syed Ali studied the stochastic stability analysis of uncertain recurrent NNs, also with Markovian jumping parameters and time varying delays. By using Lyapunov functional theory, Itô differential rule and matrix analysis techniques, a sufficient criterion in term of linear matrix inequality (LMI) is established such that, for all admissible parameter uncertainties and stochastic disturbances, the stochastic NN is perfectly stable. And then, a non-stationary problem on BAM NNs is discussed in Qingqing He's paper. An exponential stability in terms of LMIs is achieved for a class of interval C-G type (BAM) NNs with the mixed time delays and non-smooth behaved functions by using Homomorphic mapping theory, non-smooth analysis, LMIs, free-weighting matrix and Lyapunov-krasovskii functional approach. Furthermore, the paper authored by R. Raja derived the stability...

Physics for medical students is perceived as a sophisticated subject. The sophistication, however, does not lie in the physics concepts themselves or students’ comprehension of the subject, but it is more often related to the ineffectiveness of techniques applied to teach the subject. This study investigates the effect of the Jigsaw technique, a highly structured form of cooperative learning, on the academic achievement of first-year medical students in learning physics. A quasi-experimental research approach with a pretest-posttest design was employed to conduct the study with a purposive randomly selected sample of fifty students made up of twenty-five students in the control group and twenty-five students in the experimental group. The control group was taught using traditional lectures, while the experimental group was taught using the Jigsaw technique which involved students working actively to map the concepts of nuclear radiation in diagnosis and therapy. A comprehensive statistical analysis, which included a Shapiro’s test, paired sample t-test, independent sample t-test, average gain factor, and size effect calculations, was used to test the research hypotheses. The findings of this study showed that there was a statistically significant difference (P < 0.05) between the post-test scores of students exposed to the Jigsaw cooperative learning technique and those who were not. In addition, it was deduced by the educator (first author) that the students were actively engaged with the topic material, took more responsibility for their performance in the activity, learned how to map the radiation physics concepts, and explored a new learning environment that enabled them to use their higher-order thinking skills to solve medical physics problems.