Bhaveshkumar Dharmani

Thermography is a noncontact, noninvasive imaging technology that is commonly utilized in the medical profession. As early identification of cancer is critical, the computer-assisted method can enhance the diagnosis rate, curing, and survival of cancer patients. Early diagnosis is one of the major essential steps in decreasing the health and socioeconomic consequences of this condition, given the high cost of therapy and the large prevalence of afflicted people. Mammography is currently the majorly utilized procedure for detecting breast cancer. Yet, owing to the low contrast that occurs from a thick breast, mammography is not advised for young women, and alternate methods must be investigated. This work plans to develop a comparative evaluation of two well-performing heuristic-based expert systems for detecting thermogram breast cancer. The thermogram images are taken from the standard DMR dataset. Then, the given images are transferred to the pre-processing stage. Here, the input ...

Bandwidth parameter estimation in univariate Kernel Density Estimation has traditionally two approaches. Rule(s)-of-Thumb (ROT) achieve ‘quick and dirty’ estimations with some specific assumption for an unknown density. More accurate solve-the-equation-plug-in (STEPI) rules have almost no direct assumption for the unknown density but demand high computation. This article derives a balancing third approach. Extending an assumption of Gaussianity for the unknown density to be estimated in \textit{normal reference} ROT (NRROT) to near Gaussianity, and then expressing the density using Gram-Charlier A (GCA) series to minimize the asymptotic mean integrated square error, it derives GCA series based Extended ROT (GCAExROT). The performance analysis using the simulated and the real datasets suggests to replace NRROT by a modified GCAExROT rule achieving a balancing performance by accuracy nearer to STEPI rules at computation nearer to NRROT, specifically at small samples.

Information Theory (IT) has a concept of Information Potential analogues to that of electric or gravitational potential in Field Theory (FT). FT has a concept of reference potential, which is used to derive closed form expressions for the relative analysis of a potential in the field. This article extends the current analogy by introducing the concepts of Reference Information Potential and using it to provide a closed-form expressions, based on the least squares, for information field analysis. The closed-form expressions are applied to achieve direct single stage estimation of the Quadratic Mutual Information Euclidean Distance $(QMI_{ED})$ through multiplicative Gaussian kernels in two ways: (a) kernel basis placed at joint sample locations and (b) kernel basis placed at unpaired sample locations. The simulations provided the estimation methods.

There are various problems based on statistical inferences, such as, parameter estimation, detection, classification, prediction, pattern recognition and others. There are also various techniques to derive some statistical inferences. The article first reviews various problems based on statistical inferences with their differences. At the next level, the article compares the fundamental approaches towards the solutions of these problems. In the third part of the article, it explains the way various techniques for statistical may differ.

Existing Independent Component Analysis (ICA) algorithms are using varying independence measures derived through varying independence definitions and approximations. It will be interesting to study the effect of these variation on ICA solution and applications. This study require an ICA algorithm allowing use of varying independence measures as optimization criteria, assuring global solution and being truly blind. The article derives and verifies experimentally for the need, the Search for Rotation based ICA (SRICA) algorithm. It uses the fact that the independent components can be found by rotation of the whiten components. It uses Genetic Algorithm (GA), as a global search technique, to find the optimal angle of rotation. Also, the required study through SRICA finds minimization of sum of marginal entropies with kernel method for density estimation as the best independence measure, in terms of source matching, compare to the used four other independence measures.