An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans

To be diagnostically effective, structural magnetic resonance imaging (sMRI) must reliably distinguish a depressed individual from a healthy individual at individual scans level. One of the tasks in the automated diagnosis of depression from brain sMRI is the classification. It determines the class to which a sample belongs (i.e., depressed/not depressed, remitted/not-remitted depression) based on the values of its features. Thus far, very limited works have been reported for identification of a suitable classification algorithm for depression detection. In this paper, different types of classification algorithms are compared for effective diagnosis of depression. Ten independent classification schemas are applied and a comparative study is carried out. The algorithms are: Naïve Bayes, Support Vector Machines (SVM) with Radial Basis Function (RBF), SVM Sigmoid, J48, Random Forest, Random Tree, Voting Feature Intervals (VFI), LogitBoost, Simple KMeans Classification Via Clustering (KMeans) and Classification Via Clustering Expectation Minimization (EM) respectively. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. A classification accuracy evaluation method was employed for evaluation and comparison of the performance of the examined classifiers.

Classification and optimization of product review information using soft computing models

A soft computing framework to classify and optimize text-based information extracted from customers' product reviews is proposed in this paper. The soft computing framework performs classification and optimization in two stages. Given a set of keywords extracted from unstructured text-based product reviews, a Support Vector Machine (SVM) is used to classify the reviews into two categories (positive and negative reviews) in the first stage. An ensemble of evolutionary algorithms is deployed to perform optimization in the second stage. Specifically, the Modified micro Genetic Algorithm (MmGA) optimizer is applied to maximize classification accuracy and minimize the number of keywords used in classification. Two Amazon product reviews databases are employed to evaluate the effectiveness of the SVM classifier and the ensemble of MmGA optimizers in classification and optimization of product related keywords. The results are analyzed and compared with those published in the literature. The outputs potentially serve as a list of impression words that contains useful information from the customers' viewpoints. These impression words can be further leveraged for product design and improvement activities in accordance with the Kansei engineering methodology.

An examination of a proposed DSM-IV pathological gambling hierarchy in a treatment seeking population: similarities with substance dependence and evidence for three classification systems

Toce-Gerstein et al. (Addiction 98:1661–1672, 2003) investigated the distribution of Diagnostic and Statistical Manual for Mental Disorders, 4th edition (DSM-IV) pathological gambling criteria endorsement in a U.S. community sample for those people endorsing a least one of the DSM-IV criteria (n = 399). They proposed a hierarchy of gambling disorders where endorsement of 1–2 criteria were deemed ‘At-Risk’, 3–4 ‘Problem gamblers’, 5–7 ‘Low Pathological’, and 8–10 ‘High Pathological’ gamblers. This article examines these claims in a larger Australian treatment seeking population. Data from 4,349 clients attending specialist problem gambling services were assessed for meeting the ten DSM-IV pathological gambling criteria. Results found higher overall criteria endorsement frequencies, three components, a direct relationship between criteria endorsement and gambling severity, clustering of criteria similar to the Toce-Gerstein et al. taxonomy, high accuracy scores for numerical and criteria specific taxonomies, and also high accuracy scores for dichotomous pathological gambling diagnoses. These results suggest significant complexities in the frequencies of criteria reports and relationships between criteria.

Medulloblastoma Down Under 2013 : a report from the third annual meeting of the International Medulloblastoma Working Group.

Medulloblastoma is curable in approximately 70% of patients. Over the past decade, progress in improving survival using conventional therapies has stalled, resulting in reduced quality of life due to treatment-related side effects, which are a major concern in survivors. The vast amount of genomic and molecular data generated over the last 5-10 years encourages optimism that improved risk stratification and new molecular targets will improve outcomes. It is now clear that medulloblastoma is not a single-disease entity, but instead consists of at least four distinct molecular subgroups: WNT/Wingless, Sonic Hedgehog, Group 3, and Group 4. The Medulloblastoma Down Under 2013 meeting, which convened at Bunker Bay, Australia, brought together 50 leading clinicians and scientists. The 2-day agenda included focused sessions on pathology and molecular stratification, genomics and mouse models, high-throughput drug screening, and clinical trial design. The meeting established a global action plan to translate novel biologic insights and drug targeting into treatment regimens to improve outcomes. A consensus was reached in several key areas, with the most important being that a novel classification scheme for medulloblastoma based on the four molecular subgroups, as well as histopathologic features, should be presented for consideration in the upcoming fifth edition of the World Health Organization's classification of tumours of the central nervous system. Three other notable areas of agreement were as follows: (1) to establish a central repository of annotated mouse models that are readily accessible and freely available to the international research community; (2) to institute common eligibility criteria between the Children's Oncology Group and the International Society of Paediatric Oncology Europe and initiate joint or parallel clinical trials; (3) to share preliminary high-throughput screening data across discovery labs to hasten the development of novel therapeutics. Medulloblastoma Down Under 2013 was an effective forum for meaningful discussion, which resulted in enhancing international collaborative clinical and translational research of this rare disease. This template could be applied to other fields to devise global action plans addressing all aspects of a disease, from improved disease classification, treatment stratification, and drug targeting to superior treatment regimens to be assessed in cooperative international clinical trials.

Internet traffic classification using constrained clustering

Statistics-based Internet traffic classification using machine learning techniques has attracted extensive research interest lately, because of the increasing ineffectiveness of traditional port-based and payload-based approaches. In particular, unsupervised learning, that is, traffic clustering, is very important in real-life applications, where labeled training data are difficult to obtain and new patterns keep emerging. Although previous studies have applied some classic clustering algorithms such as K-Means and EM for the task, the quality of resultant traffic clusters was far from satisfactory. In order to improve the accuracy of traffic clustering, we propose a constrained clustering scheme that makes decisions with consideration of some background information in addition to the observed traffic statistics. Specifically, we make use of equivalence set constraints indicating that particular sets of flows are using the same application layer protocols, which can be efficiently inferred from packet headers according to the background knowledge of TCP/IP networking. We model the observed data and constraints using Gaussian mixture density and adapt an approximate algorithm for the maximum likelihood estimation of model parameters. Moreover, we study the effects of unsupervised feature discretization on traffic clustering by using a fundamental binning method. A number of real-world Internet traffic traces have been used in our evaluation, and the results show that the proposed approach not only improves the quality of traffic clusters in terms of overall accuracy and per-class metrics, but also speeds up the convergence.

Applying classification controls to internet content in Australia

Purpose – The purpose of this study is to explore Australian public and stakeholders views towards the regulation of the Internet and its content. The federal government called for submissions addressing their proposal, and this paper analyses these submissions for themes and provides clarity as to the Australian public and stakeholders key concerns in regards to the proposed policy. Design/methodology/approach – The paper uses a qualitative approach to analyse the public consultations to the Australian Federal Government. These documents are coded and analysed to determine negative and positive viewpoints. Findings – The research has shown, based upon the analysis of the consultation, that there was no public support for any of the measures put forward, that the Australian Federal Government in its response has not recognised this public feedback and instead has only utilised some of the qualitative feedback obtained through the public consultation process to try to justify its case to proceed with its proposals. Research limitations/implications – The study is focussed on Australia. Practical implications – The paper analyses a proposed national approach to filtering the content of the Internet and discussed the public reaction to such an approach. Social implications – The paper looks at how different parts of Australian society view Internet filtering in a positive or negative manner. Originality/value – The only study that directly looks at the viewpoint of the Australian public.

A practical guide to the application of the IUCN Red List of Ecosystems criteria

The newly developed IUCN Red List of Ecosystems is part of a growing toolbox for assessing risks to biodiversity, which addresses ecosystems and their functioning. The Red List of Ecosystems standard allows systematic assessment of all freshwater, marine, terrestrial and subterranean ecosystem types in terms of their global risk of collapse. In addition, the Red List of Ecosystems categories and criteria provide a technical base for assessments of ecosystem status at the regional, national, or subnational level. While the Red List of Ecosystems criteria were designed to be widely applicable by scientists and practitioners, guidelines are needed to ensure they are implemented in a standardized manner to reduce epistemic uncertainties and allow robust comparisons among ecosystems and over time. We review the intended application of the Red List of Ecosystems assessment process, summarize 'best-practice' methods for ecosystem assessments and outline approaches to ensure operational rigour of assessments. The Red List of Ecosystems will inform priority setting for ecosystem types worldwide, and strengthen capacity to report on progress towards the Aichi Targets of the Convention on Biological Diversity. When integrated with other IUCN knowledge products, such as the World Database of Protected Areas/Protected Planet, Key Biodiversity Areas and the IUCN Red List of Threatened Species, the Red List of Ecosystems will contribute to providing the most complete global measure of the status of biodiversity yet achieved.

Heuristic approaches for multi-criteria optimisation in kidney exchange programs

The Kidney Exchange Problem (KEP) is an optimisation problem that was first discussed in Rapaport (1986) but has only more recently been the subject of much work by combinatorial optimisation re-searchers. This has been in parallel with its increased prevalence in the medical community. In the basic formulation of a KEP, each instance of the problem features a directed graph D = (V,A) . Each node i ∈ V represents an incompatible pair wherein the patient needs to trade kidneys with the patient of another incompatible pair. The goal is to find an optimal set of cycles such that as many patients as possible receive a transplant. The problem is further complicated by the imposition of a cycle-size constraint, usually considered to be 3 or 4. Kidney exchange programs around the world implement different algorithms to solve the allocation problem by matching up kidneys from potential donors to patients. In some systems all transplants are considered equally desirable, whereas in others, ranking criteria such as the age of the patient or distance they will need to travel are applied, hence the multi-criteria nature of the KEP. To address the multi-criteria aspect of the KEP, in this paper we propose a two-stage approach for the kidney exchange optimisation problem. In the first stage the goal is to find the optimal number of exchanges, and in the second stage the goal is to maximise the weighted sum of the kidney matches, subject to the added constraint that the number of exchanges must remain optimal. The idea can potentially be extended to multiple-objectives, by repeating the process in multiple runs. In our preliminary numerical experiments, we first find the maximum number of kidney matches by using an existing open source exact algorithm of Anderson et al. (2015). The solution will then be used as an initial solution for the stage two optimisation problem, wherein two heuristic methods, steepest ascent and random ascent, are implemented in obtaining good quality solutions to the objective of maximizing total weight of exchanges. The neighbourhood is obtained by two-swaps. It is our intention in the future to implement a varying neighbourhood scheme within the same two heuristic framework, or within other meta-heuristic framework.

Application of multi-criteria decision making methods to compression ignition engine efficiency and gaseous, particulate and greenhouse gas emissions

Compression ignition (CI) engine design is subject to many constraints which presents a multi-criteria optimisation problem that the engine researcher must solve. In particular, the modern CI engine must not only be efficient, but must also deliver low gaseous, particulate and life cycle greenhouse gas emissions so that its impact on urban air quality, human health, and global warming are minimised. Consequently, this study undertakes a multi-criteria analysis which seeks to identify alternative fuels, injection technologies and combustion strategies that could potentially satisfy these CI engine design constraints. Three datasets are analysed with the Preference Ranking Organization Method for Enrichment Evaluations and Geometrical Analysis for Interactive Aid (PROMETHEE-GAIA) algorithm to explore the impact of 1): an ethanol fumigation system, 2): alternative fuels (20 % biodiesel and synthetic diesel) and alternative injection technologies (mechanical direct injection and common rail injection), and 3): various biodiesel fuels made from 3 feedstocks (i.e. soy, tallow, and canola) tested at several blend percentages (20-100 %) on the resulting emissions and efficiency profile of the various test engines. The results show that moderate ethanol substitutions (~20 % by energy) at moderate load, high percentage soy blends (60-100 %), and alternative fuels (biodiesel and synthetic diesel) provide an efficiency and emissions profile that yields the most “preferred” solutions to this multi-criteria engine design problem. Further research is, however, required to reduce Reactive Oxygen Species (ROS) emissions with alternative fuels, and to deliver technologies that do not significantly reduce the median diameter of particle emissions.