85 research outputs found
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
Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities
This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development
Detection of energy waste in French households thanks to a co-clustering model for multivariate functional data
The exponential growth of smart devices in all aspects of everyday life leads to make common the collection of high frequency data. Those data can be seen as multivariate functional data: quantitative entities evolving along time, for which there is a growing needs of methods to summarize and understand them. The database that have motivated our project is supplied by the historical French electricity provider whose aim is to detect poorly insulated buildings, anomalies or long periods of absence. Their motivation is to answer COP24 requirements to reduce energy waste and to adapt electric load. To this end, a novel co-clustering model for multivariate functional data is defined. The model is based on a functional latent block model which assumes for each block a probabilistic distribution for multivariate functional principal component scores. A Stochastic EM algorithm, embedding a Gibbs sampler is proposed for model inference, as well as model selection criteria for choosing the number of co-clusters
Benne: A Modular and Self-Optimizing Algorithm for Data Stream Clustering
In various real-world applications, ranging from the Internet of Things (IoT)
to social media and financial systems, data stream clustering is a critical
operation. This paper introduces Benne, a modular and highly configurable data
stream clustering algorithm designed to offer a nuanced balance between
clustering accuracy and computational efficiency. Benne distinguishes itself by
clearly demarcating four pivotal design dimensions: the summarizing data
structure, the window model for handling data temporality, the outlier
detection mechanism, and the refinement strategy for improving cluster quality.
This clear separation not only facilitates a granular understanding of the
impact of each design choice on the algorithm's performance but also enhances
the algorithm's adaptability to a wide array of application contexts. We
provide a comprehensive analysis of these design dimensions, elucidating the
challenges and opportunities inherent to each. Furthermore, we conduct a
rigorous performance evaluation of Benne, employing diverse configurations and
benchmarking it against existing state-of-the-art data stream clustering
algorithms. Our empirical results substantiate that Benne either matches or
surpasses competing algorithms in terms of clustering accuracy, processing
throughput, and adaptability to varying data stream characteristics. This
establishes Benne as a valuable asset for both practitioners and researchers in
the field of data stream mining
Detection of energy waste in French households thanks to a co-clustering model for multivariate functional data
The exponential growth of smart devices in all aspects of everyday life leads to make common the collection of high frequency data. Those data can be seen as multivariate functional data: quantitative entities evolving along time, for which there is a growing needs of methods to summarize and understand them. The database that have motivated our project is supplied by the historical French electricity provider whose aim is to detect poorly insulated buildings, anomalies or long periods of absence. Their motivation is to answer COP24 requirements to reduce energy waste and to adapt electric load. To this end, a novel co-clustering model for multivariate functional data is defined. The model is based on a functional latent block model which assumes for each block a probabilistic distribution for multivariate functional principal component scores. A Stochastic EM algorithm, embedding a Gibbs sampler is proposed for model inference, as well as model selection criteria for choosing the number of co-clusters
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