Hybrid classification of pulmonary nodules

Automated classification of lung nodules is challenging because of the variation in shape and size of lung nodules, as well as their associated differences in their images. Ensemble based learners have demonstrated the potentialof good performance. Random forests are employed for pulmonary nodule classification where each tree in the forest produces a classification decision, and an integrated output is calculated. A classification aided by clustering approach is proposed to improve the lung nodule classification performance. Three experiments are performed using the LIDC lung image database of 32 cases. The classification performance and execution times are presented and discussed.

A semiotic view of Dewey's times and Habermas's lifeworlds

John Dewey (1859-1952) explained how life was ‘corporatised’ at the time of rampant, laissez faire capitalism in early 20th century America. This paper refers Dewey’s observations to Habermas’s notions of the colonisation of the lifeworld. The semiotic and pragmatist approaches of Charles Saunders Peirce are then enlisted to look further into these lifeworld changes. The
paper suggests modifications to Habermas’s schema to bring it more in line with Dewey’s empirical account. It puts together a theoretically and empirically informed picture of the contemporary disruption to ways of living and the accompanying social and political instability. The paper then goes on to suggest how that instability appears to have been quelled by communicative means. These stages of: (1) stability; (2) disruption/instability; and (3) the regaining of stability are compared to Habermas’s notions of: (1) an original lifeworld; (2) colonisation of that lifeworld by the consequences of purposive rational activity; then (3) communicative action which ‘rebuilds’— that is which replaces or modifies or reforms or repairs—the disrupted lifeworld in order to create a new lifeworld. ‘Colonisation’ could be said to have provoked social instability. Notions of building a new ‘lifeworld’—a new cultural and psychic reference—could be said to correspond with attempts to resume social and political stability. The implication is that whatever the degree of purposive rationalism there is always a need for a return to some level of shared values and
understandings which imply communicative rationality. This ‘return’ or ‘counter-colonisation’ can be thought of as operating via a ‘lifeworld negotiation’ which might best be understood with reference to a Peircean based pragmatism-semiotic theory of human subjectivity. This paper
has been criticised for discussing “arguments” which: “would justify those who accommodated themselves to Nazism.” What this paper in fact tries to do is to use the concepts of the above three philosophers to try to account for the ways people think. This paper is not about justifying what philosophies people should hold. It is presumed that most readers are sensible and ethical and can make their own minds up in that respect. Rather it attempts to draw from Dewey, Habermas and Peirce to offer a characterisation of what philosophies might be argued to be held and to offer an explanation about how these modes of thinking might be said to have come into existence.
This paper rejects the notion that ones ‘will’ and thus the way one is able to think, is totally free and beyond the formative influences of the social-cultural context—including the influences of public relations and other persuasive discourse industries.

Multi-classifier classification of spam email on an ubiquitous multi-core architecture

This paper presents an innovative fusion based multi-classifier email classification on a ubiquitous multi-core architecture. Many approaches use text-based single classifiers or multiple weakly trained classifiers to identify spam messages from a large email corpus. We build upon our previous work on multi-core by apply our ubiquitous multi-core framework to run our fusion based multi-classifier architecture. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our proposed multi-classifier based filtering system. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at the average cost of 1.4 ms. We also reduced the instance of false positive, which is one of the key challenges in spam filtering system, and increases email classification accuracy substantially compared with single classification techniques.

Email categorization using (2+1)-tier classification algorithms

In this paper we have proposed a spam filtering technique using (2+1)-tier classification approach. The main focus of this paper is to reduce the false positive (FP) rate which is considered as an important research issue in spam filtering. In our approach, firstly the email message will classify using first two tier classifiers and the outputs will appear to the analyzer. The analyzer will check the labeling of the output emails and send to the corresponding mailboxes based on labeling, for the case of identical prediction. If there are any misclassifications occurred by first two tier classifiers then tier-3 classifier will invoked by the analyzer and the tier-3 will take final decision. This technique reduced the analyzing complexity of our previous work. It has also been shown that the proposed technique gives better performance in terms of reducing false positive as well as better accuracy.

Identification and classification of animal fibres using artificial neural networks

It has been an important and challenging task to classify and evaluate the contents in wool blends. Quantitative characterisation of animal fibre scale patterns has attracted considerable attention, since it is the major evidence for identification and subsequent classification purpose. Although techniques such as imaging processing and linear demarcation functions have been used to identify unknown fibre type with some success, a more comprehensive approach is required to perform this task. In this paper, a new approach is presented, which employs non-linear demarcation functions by using an artificial neural network (ANN). Based on scale pattern features extracted by using image processing techniques the artificial neural network (ANN) model is to classify mohair and merino fibres. It is observed that the techniques developed in this work are very effective and have the potential to be applied to other animal fibres.

Microarray classification using statistical learning and decision tree algorithms

This considers the challenging task of cancer prediction based on microarray data for the medical community. The research was conducted on mostly common cancers (breast, colon, long, prostate and leukemia) microarray data analysis, and suggests the use of modern machine learning techniques to predict cancer.