The distribution of data plays an important role in determining the successfulness of learning process in machine learning. Data sets with imbalanced distribution may lead to biased results, especially in clustering. If the data is insufficient, the clustering will not be able to cluster and this will add randomness to the grouping. Therefore, the KSOM algorithm is modified to improve the clustering process. This modification is done based on the exploration and exploitation procedures in Ant Clustering Algorithm (ACA). To investigate the effectiveness of the modified algorithm, three imbalanced data sets are chosen; glass, Wisconsin diagnostic breast cancer and tropical wood data set. From the result, the modified KSOM has able to produce accurate number of clusters, reduce the number of overlapped cluster and slightly improve the percentage of accuracy.

It is crucial to differentiate between venomous and non-venomous snake whereby an immediate and effective medical care can be instituted to the victims. However, early identification is not easy. We developed a snake bite identification system (N'viteR) to differentiate the snake using Neuro-GA technique. Based on 200 cases, this work had revealed that Neuro-GA has yield a high accuracy in identifying the snake. A number of experiments have been done which based on number of epoch, momentum, learning rate, number of generation, population and chromosome. This hybrid technique has achieved 97.6% of accuracy which enables early identification of snake and immediate specific anti-venom can be administered. Hence, reduces the rate of morbidity and mortality.

Surface defect detection plays a pivotal role in ensuring product quality in industrial production, as defects like cracks, scratches, and dents can compromise product performance and durability. Traditional detection methods, such as manual inspection and Non-Destructive Testing (NDT), are limited by inefficiency, reliance on human expertise, and susceptibility to errors, which restrict their application in large-scale production. With advancements in artificial intelligence, deep learning models, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have emerged as promising solutions for automated surface defect detection. This paper provides a comprehensive review of surface defect detection technologies, starting from traditional methods to modern deep learning-based techniques. The advantages and limitations of each approach are analyzed, highlighting key advancements in deep learning, including recent models like Faster R-CNN, Cascade R-CNN, and YOLOv4. Furthermore, challenges such as handling complex defects and improving detection accuracy in real-world industrial environments are discussed, along with potential directions for future research. Experimental evaluations using the Few Steels Classification (FSC) dataset demonstrate the effectiveness of modern detection methods in industrial applications, offering insights into enhancing defect detection systems.