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

An improved bees algorithm local search mechanism for numerical dataset

Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. This algorithm performs a kind of exploitative neighbourhoods search combined with random explorative search. However, the main issue of BA is that it requires long computational time as well as numerous computational processes to obtain a good solution, especially in more complicated issues. This approach does not guarantee any optimum solutions for the problem mainly because of lack of accuracy. To solve this issue, the local search in the BA is investigated by Simple swap, 2-Opt and 3-Opt were proposed as Massudi methods for Bees Algorithm Feature Selection (BAFS). In this study, the proposed extension methods is 4-Opt as search neighbourhood is presented. This proposal was implemented and comprehensively compares and analyse their performances with respect to accuracy and time. Furthermore, in this study the feature selection algorithm is implemented and tested using most popular dataset from Machine Learning Repository (UCI). The obtained results from experimental work confirmed that the proposed extension of the search neighbourhood including 4-Opt approach has provided better accuracy with suitable time than the Massudi methods

新たな進化的及びニューロン計算による分類問題に関する研究

富山大学・富理工博甲第172号・銭孝孝・2020/3/24富山大学202

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

Mining Aircraft Telemetry Data With Evolutionary Algorithms

The Ganged Phased Array Radar - Risk Mitigation System (GPAR-RMS) was a mobile ground-based sense-and-avoid system for Unmanned Aircraft System (UAS) operations developed by the University of North Dakota. GPAR-RMS detected proximate aircraft with various sensor systems, including a 2D radar and an Automatic Dependent Surveillance - Broadcast (ADS-B) receiver. Information about those aircraft was then displayed to UAS operators via visualization software developed by the University of North Dakota. The Risk Mitigation (RM) subsystem for GPAR-RMS was designed to estimate the current risk of midair collision, between the Unmanned Aircraft (UA) and a General Aviation (GA) aircraft flying under Visual Flight Rules (VFR) in the surrounding airspace, for UAS operations in Class E airspace (i.e. below 18,000 feet MSL). However, accurate probabilistic models for the behavior of pilots of GA aircraft flying under VFR in Class E airspace were needed before the RM subsystem could be implemented. In this dissertation the author presents the results of data mining an aircraft telemetry data set from a consecutive nine month period in 2011. This aircraft telemetry data set consisted of Flight Data Monitoring (FDM) data obtained from Garmin G1000 devices onboard every Cessna 172 in the University of North Dakota\u27s training fleet. Data from aircraft which were potentially within the controlled airspace surrounding controlled airports were excluded. Also, GA aircraft in the FDM data flying in Class E airspace were assumed to be flying under VFR, which is usually a valid assumption. Complex subpaths were discovered from the aircraft telemetry data set using a novel application of an ant colony algorithm. Then, probabilistic models were data mined from those subpaths using extensions of the Genetic K-Means (GKA) and Expectation- Maximization (EM) algorithms. The results obtained from the subpath discovery and data mining suggest a pilot flying a GA aircraft near to an uncontrolled airport will perform different maneuvers than a pilot flying a GA aircraft far from an uncontrolled airport, irrespective of the altitude of the GA aircraft. However, since only aircraft telemetry data from the University of North Dakota\u27s training fleet were data mined, these results are not likely to be applicable to GA aircraft operating in a non-training environment

Multi-objective heuristics applied to robot task planning for inspection plants

Robotics are generally subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this regard the so-called cost of a mission must be considered as an additional criterion when designing optimal task schedules within the mission at hand. Such a cost can be conceived as the impact of the mission on the robotic resources themselves, which range from the consumption of battery to other negative effects such as mechanic erosion. This manuscript focuses on this issue by presenting experimental results obtained over realistic scenarios of two heuristic solvers (MOHS and NSGA-II) aimed at efficiently scheduling tasks in robotic swarms that collaborate together to accomplish a mission. The heuristic techniques resort to a Random-Keys encoding strategy to represent the allocation of robots to tasks whereas the relative execution order of such tasks within the schedule of certain robots is computed based on the Traveling Salesman Problem (TSP). Experimental results in three different deployment scenarios reveal the goodness of the proposed technique based on the Multi-objective Harmony Search algorithm (MOHS) in terms of Hypervolume (HV) and Coverage Rate (CR) performance indicators

An adaptive ant colony optimization algorithm for rule-based classification

Classification is an important data mining task with different applications in many fields. Various classification algorithms have been developed to produce classification models with high accuracy. Differing from other complex and difficult classification models, rules-based classification algorithms produce models which are understandable for users. Ant-Miner is a variant of ant colony optimisation and a prominent intelligent algorithm widely use in rules-based classification. However, the Ant-Miner has overfitting and easily falls into local optima problems which resulted in low classification accuracy and complex classification rules. In this study, a new Ant-Miner classifier is developed, named Adaptive Genetic Iterated-AntMiner (AGI-AntMiner) that aims to avoid local optima and overfitting problems. The components of AGI-AntMiner includes: i) an Adaptive AntMiner which is a prepruning technique to dynamically select the appropriate threshold based on the quality of the rules; ii) Genetic AntMiner that improves the post-pruning by adding/removing terms in a dual manner; and, iii) an Iterated Local Search-AntMiner that improves exploitation based on multiple-neighbourhood structure. The proposed AGI-AntMiner algorithm is evaluated on 16 benchmark datasets of medical, financial, gaming and social domains obtained from the University California Irvine repository. The algorithm’s performance was compared with other variants of Ant-Miner and state-of-the-art rules-based classification algorithms based on classification accuracy and model complexity. Experimental results proved that the proposed AGI-AntMiner algorithm is superior in two (2) aspects. Hybridization of local search in AGI-AntMiner has improved the exploitation mechanism which leads to the discovery of more accurate classification rules. The new pre-pruning and postpruning techniques have improved the pruning ability to produce shorter classification rules which are easier to interpret by the users. Thus, the proposed AGI-AntMiner algorithm is capable in conducting an efficient search in finding the best classification rules that balance the classification accuracy and model complexity to overcome overfitting and local optima problems

Enhanced Bees Algorithm with fuzzy logic and Kalman filtering

The Bees Algorithm is a new population-based optimisation procedure which employs a combination of global exploratory and local exploitatory search. This thesis introduces an enhanced version of the Bees Algorithm which implements a fuzzy logic system for greedy selection of local search sites. The proposed fuzzy greedy selection system reduces the number of parameters needed to run the Bees Algorithm. The proposed algorithm has been applied to a number of benchmark function optimisation problems to demonstrate its robustness and self-organising ability. The Bees Algorithm in both its basic and enhanced forms has been used to optimise the parameters of a fuzzy logic controller. The purpose of the controller is to stabilise and balance an under-actuated two-link acrobatic robot (ACROBOT) in the upright position. Kalman filtering, as a fast convergence gradient-based optimisation method, is introduced as an alternative to random neighbourhood search to guide worker bees speedily towards the optima of local search sites. The proposed method has been used to tune membership functions for a fuzzy logic system. Finally, the fuzzy greedy selection system is enhanced by using multiple independent criteria to select local search sites. The enhanced fuzzy selection system has again been used with Kalman filtering to speed up the Bees Algorithm. The resulting algorithm has been applied to train a Radial Basis Function (RBF) neural network for wood defect identification. The results obtained show that the changes made to the Bees Algorithm in this research have significantly improved its performance. This is because these enhancements maintain the robust global search attribute of the Bees Algorithm and improve its local search procedure.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Machine Learning-driven Optimization for SVM-based Intrusion Detection System in Vehicular Ad Hoc Networks

Machine Learning (ML) driven solutions have been widely used to secure wireless communications Vehicular ad hoc networks (VANETs) in recent studies. Unlike existing works, this paper applies support vector machine (SVM) for intrusion detection in VANET. The structure of SVM has many computation advantages, such as special direction at a finite sample and irrelevance between the complexity of algorithm and the sample dimension. Intrusion detection in VANETis nonconvex and combinatorial problem. Thus, three intelligence optimization algorithms are used for optimizing the accuracy value of SVM classifier. These optimization algorithms include Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Our results demonstrate that GA outperformed other optimization algorithms