SPAM detection: Naïve bayesian classification and RPN expression-based LGP approaches compared

An investigation is performed of a machine learning algorithm and the Bayesian classifier in the spam-filtering context. The paper shows the advantage of the use of Reverse Polish Notation (RPN) expressions with feature extraction compared to the traditional Naïve Bayesian classifier used for spam detection assuming the same features. The performance of the two is investigated using a public corpus and a recent private spam collection, concluding that the system based on RPN LGP (Linear Genetic Programming) gave better results compared to two popularly used open source Bayesian spam filters. © Springer International Publishing Switzerland 2016

Aisimam - An Artificial immune system based intelligent multiangent model

The goal of this thesis is to develop a biological model for multiagent systems. This thesis explores artificial immune systems, a novel evolutionary paradigm based on the immunological principles. Artificial Immune systems (AIS) are found to be powerful to solve complex computational tasks. The main focus of the thesis is to develop a generic mathematical model that uses the principles of the human immune system in multiagent systems (MAS). The components and properties of the human immune system are studied. On understanding the concepts of A/5, a literature survey of multiagent systems is performed to understand and compare the multiagent concepts and AIS concepts. An analogy between the immune system parameters and the agent theory was derived. Then, an intelligent multiagent model named AISIMAM is derived. It exploits several properties and features of the immune system in multiagent systems. In other words, the intelligence of the immune systems to kill the antigen and the characteristics of the agents are combined in the model. The model is expressed in terms of mathematical expressions. The model is applied to a specific application namely the mine detection and defusion. The simulations are done in MATLAB that runs on a PC. The experimental results of AISIMAM applied to the mine detection problem are discussed. The results are successful and shows that AISIMAM could be an alternative solution to agent based problems. Artificial Immune System is also applied to a pattern recognition problem. The problem experimented is a color image classification problem useful in a real time industrial application. The images are those of wooden components that need to be classified according to the color and type of wood. To solve the classification task, a simple negative selection and genetic algorithm based A/5 algorithm was developed and simulated. The results are compared with the radial basis function approach applied to the same set of input images

Swarm intelligence for clustering dynamic data sets for web usage mining and personalization.

Swarm Intelligence (SI) techniques were inspired by bee swarms, ant colonies, and most recently, bird flocks. Flock-based Swarm Intelligence (FSI) has several unique features, namely decentralized control, collaborative learning, high exploration ability, and inspiration from dynamic social behavior. Thus FSI offers a natural choice for modeling dynamic social data and solving problems in such domains. One particular case of dynamic social data is online/web usage data which is rich in information about user activities, interests and choices. This natural analogy between SI and social behavior is the main motivation for the topic of investigation in this dissertation, with a focus on Flock based systems which have not been well investigated for this purpose. More specifically, we investigate the use of flock-based SI to solve two related and challenging problems by developing algorithms that form critical building blocks of intelligent personalized websites, namely, (i) providing a better understanding of the online users and their activities or interests, for example using clustering techniques that can discover the groups that are hidden within the data; and (ii) reducing information overload by providing guidance to the users on websites and services, typically by using web personalization techniques, such as recommender systems. Recommender systems aim to recommend items that will be potentially liked by a user. To support a better understanding of the online user activities, we developed clustering algorithms that address two challenges of mining online usage data: the need for scalability to large data and the need to adapt cluster sing to dynamic data sets. To address the scalability challenge, we developed new clustering algorithms using a hybridization of traditional Flock-based clustering with faster K-Means based partitional clustering algorithms. We tested our algorithms on synthetic data, real VCI Machine Learning repository benchmark data, and a data set consisting of real Web user sessions. Having linear complexity with respect to the number of data records, the resulting algorithms are considerably faster than traditional Flock-based clustering (which has quadratic complexity). Moreover, our experiments demonstrate that scalability was gained without sacrificing quality. To address the challenge of adapting to dynamic data, we developed a dynamic clustering algorithm that can handle the following dynamic properties of online usage data: (1) New data records can be added at any time (example: a new user is added on the site); (2) Existing data records can be removed at any time. For example, an existing user of the site, who no longer subscribes to a service, or who is terminated because of violating policies; (3) New parts of existing records can arrive at any time or old parts of the existing data record can change. The user\u27s record can change as a result of additional activity such as purchasing new products, returning a product, rating new products, or modifying the existing rating of a product. We tested our dynamic clustering algorithm on synthetic dynamic data, and on a data set consisting of real online user ratings for movies. Our algorithm was shown to handle the dynamic nature of data without sacrificing quality compared to a traditional Flock-based clustering algorithm that is re-run from scratch with each change in the data. To support reducing online information overload, we developed a Flock-based recommender system to predict the interests of users, in particular focusing on collaborative filtering or social recommender systems. Our Flock-based recommender algorithm (FlockRecom) iteratively adjusts the position and speed of dynamic flocks of agents, such that each agent represents a user, on a visualization panel. Then it generates the top-n recommendations for a user based on the ratings of the users that are represented by its neighboring agents. Our recommendation system was tested on a real data set consisting of online user ratings for a set of jokes, and compared to traditional user-based Collaborative Filtering (CF). Our results demonstrated that our recommender system starts performing at the same level of quality as traditional CF, and then, with more iterations for exploration, surpasses CF\u27s recommendation quality, in terms of precision and recall. Another unique advantage of our recommendation system compared to traditional CF is its ability to generate more variety or diversity in the set of recommended items. Our contributions advance the state of the art in Flock-based 81 for clustering and making predictions in dynamic Web usage data, and therefore have an impact on improving the quality of online services

Integrating supercapacitors into a hybrid energy system to reduce overall costs using the genetic algorithm (GA) and support vector machine (SVM)

This research deals with optimising a supercapacitor-battery hybrid energy storage system (SB-HESS) to reduce the implementation cost for solar energy applications using the Genetic Algorithm (GA) and the Support Vector Machine (SVM). The integration of a supercapacitor into a battery energy storage system for solar applications is proven to prolong the battery lifespan. Furthermore, the reliability of the system was optimised using a GA within the Taguchi technique in the supercapacitor fabrication process. This is important to reduce the spread in tolerance of supercapacitors values (i.e. capacitance and Equivalent Series Resistance (ESR)) which affect system performance. One of the more important results obtained in this project is the net present cost (NPC) of the Supercapacitor-battery hybrid energy storage system is 7.51% lower than the conventional battery only system over a 20-years project lifetime. This NPC takes into account of components initial capital cost, replacement cost, maintenance and operational cost. The number of batteries is reduced from 40 (conventional – battery only system) to 24 (SB-HESS) with the inclusion of supercapacitors in the system. This leads to reduction cost in the implemented hybrid energy storage system. A greener renewable energy system is achievable as the number of battery is reduced significantly. An optimised combination of the number of components for renewable energy system is also found. The number of batteries is sized, based on the average power output instead of catering to the peak power burst as in a conventional battery only system. This allows for the reduction in the number of batteries as the peak power is catered for by the presence of the supercapacitor. Subsequent efforts have been focused on the energy management system which is coupled with a supervised learning machine – SVM, switches and sensors are used to forecast the load demand beforehand. This load predictive-energy management system is implemented on a lab-scaled hybrid energy storage system prototype. Results obtained also show that this load predictive system allows for accurate load classification and prediction. The supercapacitor in the hybrid energy storage system is able to switch on to cater for peak power without delay. This is crucial in maintaining an optimised battery depth-of-discharge (DOD) in order to reduce the rate of battery damage thru a degradation mechanism which is caused from particular stress factors (especially sulphation on the battery electrode and electrolyte stratification)

Integrating supercapacitors into a hybrid energy system to reduce overall costs using the genetic algorithm (GA) and support vector machine (SVM)

This research deals with optimising a supercapacitor-battery hybrid energy storage system (SB-HESS) to reduce the implementation cost for solar energy applications using the Genetic Algorithm (GA) and the Support Vector Machine (SVM). The integration of a supercapacitor into a battery energy storage system for solar applications is proven to prolong the battery lifespan. Furthermore, the reliability of the system was optimised using a GA within the Taguchi technique in the supercapacitor fabrication process. This is important to reduce the spread in tolerance of supercapacitors values (i.e. capacitance and Equivalent Series Resistance (ESR)) which affect system performance. One of the more important results obtained in this project is the net present cost (NPC) of the Supercapacitor-battery hybrid energy storage system is 7.51% lower than the conventional battery only system over a 20-years project lifetime. This NPC takes into account of components initial capital cost, replacement cost, maintenance and operational cost. The number of batteries is reduced from 40 (conventional – battery only system) to 24 (SB-HESS) with the inclusion of supercapacitors in the system. This leads to reduction cost in the implemented hybrid energy storage system. A greener renewable energy system is achievable as the number of battery is reduced significantly. An optimised combination of the number of components for renewable energy system is also found. The number of batteries is sized, based on the average power output instead of catering to the peak power burst as in a conventional battery only system. This allows for the reduction in the number of batteries as the peak power is catered for by the presence of the supercapacitor. Subsequent efforts have been focused on the energy management system which is coupled with a supervised learning machine – SVM, switches and sensors are used to forecast the load demand beforehand. This load predictive-energy management system is implemented on a lab-scaled hybrid energy storage system prototype. Results obtained also show that this load predictive system allows for accurate load classification and prediction. The supercapacitor in the hybrid energy storage system is able to switch on to cater for peak power without delay. This is crucial in maintaining an optimised battery depth-of-discharge (DOD) in order to reduce the rate of battery damage thru a degradation mechanism which is caused from particular stress factors (especially sulphation on the battery electrode and electrolyte stratification)

Research on Teaching and Learning In Biology, Chemistry and Physics In ESERA 2013 Conference

This paper provides an overview of the topics in educational research that were published in the ESERA 2013 conference proceedings. The aim of the research was to identify what aspects of the teacher-student-content interaction were investigated frequently and what have been studied rarely. We used the categorization system developed by Kinnunen, Lampiselkä, Malmi and Meisalo (2016) and altogether 184 articles were analyzed. The analysis focused on secondary and tertiary level biology, chemistry, physics, and science education. The results showed that most of the studies focus on either the teacher’s pedagogical actions or on the student - content relationship. All other aspects were studied considerably less. For example, the teachers’ thoughts about the students’ perceptions and attitudes towards the goals and the content, and the teachers’ conceptions of the students’ actions towards achieving the goals were studied only rarely. Discussion about the scope and the coverage of the research in science education in Europe is needed.Peer reviewe