Water filtration by using apple and banana peels as activated carbon

Water filter is an important devices for reducing the contaminants in raw water. Activated from charcoal is used to absorb the contaminants. Fruit peels are some of the suitable alternative carbon to substitute the charcoal. Determining the role of fruit peels which were apple and banana peels powder as activated carbon in water filter is the main goal. Drying and blending the peels till they become powder is the way to allow them to absorb the contaminants. Comparing the results for raw water before and after filtering is the observation. After filtering the raw water, the reading for pH was 6.8 which is in normal pH and turbidity reading recorded was 658 NTU. As for the colour, the water becomes more clear compared to the raw water. This study has found that fruit peels such as banana and apple are an effective substitute to charcoal as natural absorbent

A novel approach to data mining using simplified swarm optimization

Data mining has become an increasingly important approach to deal with the rapid growth of data collected and stored in databases. In data mining, data classification and feature selection are considered the two main factors that drive people when making decisions. However, existing traditional data classification and feature selection techniques used in data management are no longer enough for such massive data. This deficiency has prompted the need for a new intelligent data mining technique based on stochastic population-based optimization that could discover useful information from data. In this thesis, a novel Simplified Swarm Optimization (SSO) algorithm is proposed as a rule-based classifier and for feature selection. SSO is a simplified Particle Swarm Optimization (PSO) that has a self-organising ability to emerge in highly distributed control problem space, and is flexible, robust and cost effective to solve complex computing environments. The proposed SSO classifier has been implemented to classify audio data. To the author’s knowledge, this is the first time that SSO and PSO have been applied for audio classification. Furthermore, two local search strategies, named Exchange Local Search (ELS) and Weighted Local Search (WLS), have been proposed to improve SSO performance. SSO-ELS has been implemented to classify the 13 benchmark datasets obtained from the UCI repository database. Meanwhile, SSO-WLS has been implemented in Anomaly-based Network Intrusion Detection System (A-NIDS). In A-NIDS, a novel hybrid SSO-based Rough Set (SSORS) for feature selection has also been proposed. The empirical analysis showed promising results with high classification accuracy rate achieved by all proposed techniques over audio data, UCI data and KDDCup 99 datasets. Therefore, the proposed SSO rule-based classifier with local search strategies has offered a new paradigm shift in solving complex problems in data mining which may not be able to be solved by other benchmark classifiers

Optimal feature selection and machine learning for high-level audio classification : a random forests approach

Content related information, metadata, and semantics can be extracted from soundtracks of multimedia files. Speech recognition, music information retrieval and environmental sound detection techniques have been developed into a fairly mature technology enabling a final text mining process to obtain semantics for the audio scene. An efficient speech, music and environmental sound classification system, which correctly identify these three types of audio signals and feed them into dedicated recognisers, is a critical pre-processing stage for such a content analysis system. The performance and computational efficiency of such a system is predominately dependent on the selected features. This thesis presents a detailed study to identify the suitable classification features and associate a suitable machine learning technique for the intended classification task. In particular, a systematic feature selection procedure is developed to employ the random forests classifier to rank the features according to their importance and reduces the dimensionality of the feature space accordingly. This new technique avoids the trial-and-error approach used by many authors researchers. The implemented feature selection produces results related to individual classification tasks instead of the commonly used statistical distance criteria based approaches that does not consider the intended classification task, which makes it more suitable for supervised learning with specific purposes. A final collective decision-making stage is employed to combine multiple class detectors patterns into one to produce a single classification result for each input frames. The performance of the proposed feature selection technique has been compared with the techniques proposed by MPEG-7 standard to extract the reduced feature space. The results show a significant improvement in the resulted classification accuracy, at the same time, the feature space is simplified and computational overhead reduced. The proposed feature selection and machine learning technique enable the use of only 30 out of the 47 features without degrading the classification accuracy while the classification accuracy lowered by 1.7% only while just 10 features were utilised. The validation shows good performance also and the last stage of collective decision making was able to improve the classification result even after selecting only a small number of classification features. The work represents a successful attempt to determine audio feature importance and classify the audio contents into speech, music and environmental sound using a selected feature subset. The result shows a high degree of accuracy by utilising the random forests for both feature importance ranking and audio content classification