Functional Connectivity Evaluation for Infant EEG Signals based on Artificial Neural Network

The employment of the brain signals electroencephalography (EEG) could supply a deep intuitive understanding for infants behaviour and their alertness level within the living environment. The study of human brain through a computer-based approach has increased significantly as it aiming at the understanding of infants’ mind and measure their attention towards the surrounding activities. The artificial neural network achieved a significant level of success in different fields such as pattern classification, decision making, prediction, and adaptive control by learning from a set of data and construct weight matrices to represent the learning patterns. This research study proposes an artificial neural network based approach to predict the rightward asymmetry or leftward asymmetry which reflects higher frontal functional connectivity in the frontal right and frontal left, respectively within infant’s brain. In the traditional methods, the value of asymmetry of the frontal (FA) functional connectivity is used to determine the rightward or the leftward asymmetry. While the proposed approach is trying to predict that without going through all the levels of the calculation complexity. The achieved work will supply a deep understanding into the deployment of the functional connectivity to provide information on the interactions between different brain regions

Statistical Methods and Artificial Neural Networks

Artificial Neural Networks and statistical methods are applied on real data sets for forecasting, classification, and clustering problems. Hybrid models for two components are examined on different data sets; tourist arrival forecasting to Turkey, macro-economic problem on rescheduling of the countries’ international debts, and grouping twenty-five European Union member and four candidate countries according to macro-economic indicators

Direction set based Algorithms for adaptive least squares problems improvements and innovations.

The main objective of this research is to provide a mathematically tractable solutions to the adaptive filtering problem by formulating the problem as an adaptive least squares problem. This approach follows the work of Chen (1998) in his study of direction-set based CDS) adaptive filtering algorithm. Through the said formulation, we relate the DS algorithm to a class of projection method. Objektif utama penyelidikan ini ialah untuk menyediakan penyelesaian matematik yang mudah runut kepada masalah penurasan adaptif dengan memfonnulasikan masalah tersebut sebagai masalah kuasa dua terkecil adaptif. Pendekatan ini rnengikut hasil kerja oleh Chen (1998) dalam kajian beliau tentang algoritma penurasan adaptif berasaskan 'direction-set' (DS). Melalui fornulasi tersebut, kami menghubungkaitkan algoritma DS kepada satu kelas kaedah unjuran. Secara khususnya, versi rnudah aigoritma itu, iaitu algoritma 'Euclidean direction search' (EDS) ditunjukkan mempunyai hubungkait dengan satu kelas kaedah berlelaran yang dipanggil kaedah 'relaxation'. Penernuan ini rnembolehkan kami menambahbaik algoritma EDS kepada 'accelerated EDS' eli mana satu parameter pemecutan diperkenalkan untuk rnengoptirnumkan saiz langkah sernasa setiap pencarian garis