Aida Ali

Particle Swarm Optimization for NURBS Curve Fitting

This paper discusses an alternative solution for curve fitting based on particle swarm optimization (PSO). The implementation of this method is conducted by generating randomly weight and control points of the NURBS curve. The weight and generated control points are used to calculate the NURBS point. The results are compared with the example data points to find the minimum error. The implementation results have shown that the proposed method yield better solution compared to the conventional methods with minimum error generated.

An integrated formulation of Zernike representation in character images

Many studies have been done on improving Geometric Moment Invariants proposed by Hu since 1962 for pattern recognition. However, many researchers have found that there are some drawbacks with this geometric moment. Hence, this paper presents an integrated formulation of United Moment and Aspect Moment into Zernike Moment Invariant to seek the invarianceness of the solutions. The proposed method will be validated mathematically and experimentally. The validity invarianceness of the proposed method is measured by conducting the intra-class and inter-class analysis. The results of the proposed method are promising and feasible in identifying the similarity and differences of the images accordingly.

Automated kinship verification and identification through human facial images: a survey

Face is the most considerable constituent that people use to recognize one another. Humans can quickly and easily identify each other by their faces and since facial features are unobtrusive to lighting condition and pose, face remains as a dynamic recognition approach to human. Kinship recognition refers to the task of training a machine to recognize the blood relation between a pair of kin and non-kin faces (verification) based on features extracted from facial images, and to determine the exact type or degree of that relation (identification). Automatic kinship verification and identification is an interesting areas for investigation, and it has a significant impact in many real world applications, for instance, forensic, finding missing family members, and historical and genealogical research. However, kinship recognition is still not largely explored due to insufficient database availability. In this paper we present a survey on issues and challenges in kinship verification and identification, related previous works, current trends and advancements in kinship recognition, and potential applications and research direction for the future. We also found that Deep Learning (DL) has mostly outperformed numerous methods using manually designed features by automatically learning and extracting important information from facial features, and enable significant visual recognition functions by improving accuracy in most applications.

Hybrid intelligent systems in survival prediction of breast cancer

Hybrid intelligent systems play an important role in the survival prediction of breast cancer. The life-expectancy prediction of a patient is highly significant in decision making for treatments, medications and therapies. This paper addresses the motivation behind the need of hybrid model approach to survival prediction for breast cancer. The conventional approach of survival prediction faces difficulties in handling complex non-linear correlation between the prognostic factors and tumor progression, the censoring issue in medical data and the need to process the growing number of macro-scale and molecular-scale prognostic factors. The issues in breast cancer survivability are discussed with some examples of prominent works from machine learning approaches. Current trends and advancements of hybrid intelligent system are also presented.

Fuzzy classifier for classification of medical data

Survival analysis is a procedure of data analysis focusing on time until an event occurs. In the medical field, predicted life expectancy is a highly significant factor in the decision making process for both the patient and the medical practitioner i.e. when making decision on palliative care and hospice referral, initiation of medications, and avoidance of aggressive therapies. The conventional statistical approach faces many challenges in handling the nature of the survival analysis datasets which often are censored data, and the difficulties in managing the complex, non-linear relationships between the prognostic factors and the patients tumor progression. Also the statistical approach omits the need in prediction of the patients prognosis since it does not take into account that all patients are individual and unique cases. The aim of this study is to develop a survival prediction model for breast cancer patients using Fuzzy Classifier (FC). The FC method applied is a new approach to classifying datasets with imbalanced and overlapping problems which is particularly effective in managing survival data since the data is widely known as imbalanced in nature and very rarely normally distributed. The results from a comparative study on FC, PNN and CART using Wisconsin breast cancer datasets are presented, where FC classification yields better results than the other two methods.

Human action invarianceness for invarianceness using integration moment for human action recognition in video

The uniqueness of the human action shape or silhouette can be used for the human action recognition. Acquiring the features of human silhouette to obtained the concept of human action invarianceness have led to an important research in video surveillance domain. This paper discusses the investigation of this concept by extracting individual human action features using integration moment invariant. Experiment result have shown that human action invarianceness are improved with better recognition accuracy. This has verified that the integration method of moment invariant is worth explored in recognition of human action in video surveillance.