Simplification of rules extracted from neural networks

Artificial neural networks (ANNs) have been proven to be successful general machine learning techniques for, amongst others, pattern recognition and classification. Realworld problems in agriculture (soybean, tea), medicine (cancer, cardiology, mammograms) and finance (credit rating, stock market) are successfully solved using ANNs. ANNs model biological neural systems. A biological neural system consists of neurons interconnected through neural synapses. These neurons serve as information processing units. Synapses carrt information to the neurons, which then processes or responds to the data by sending a signal to the next level of neurons. Information is strengthened or lessened according to the sign ..and magnitude of the weight associated with the connection. An ANN consists of cell-like entities called units (also called artificial neurons) and weighted connections between these units referred to as links. ANNs can be viewed as a directed graph with weighted connections. An unit belongs to one of three groups: input, hidden or output. Input units receive the initial training patterns, which consist of input attributes and the associated target attributes, from the environment. Hidden units do not interact with the environment whereas output units presents the results to the environment. Hidden and output units compute an output ai which is a function f of the sum of its input weights w; multiplied by the output x; of the units j in the preceding layer, together with a bias term fh that acts as a threshold for the unit. The output ai for unit i with n input units is calculated as ai = f("f:,'J= 1 x;w; - 8i ). Training of the ANN is done by adapting the weight values for each unit via a gradient search. Given a set of input-target pairs, the ANN learns the functional relationship between the input and the target. A serious drawback of the neural network approach is the difficulty to determine why a particular conclusion was reached. This is due to the inherit 'black box' nature of the neural network approach. Neural networks rely on 'raw' training data to learn the relationships between the initial inputs and target outputs. Knowledge is encoded in a set of numeric weights and biases. Although this data driven aspect of neural network allows easy adjustments when change of environment or events occur, it is difficult to interpret numeric weights, making it difficult for humans to understand. Concepts represent by symbolic learning algorithms are intuitive and therefore easily understood by humans [Wnek 1994). One approach to understanding the representations formed by neural networks is to extract such symbolic rules from networks. Over the last few years, a number of rule extraction methods have been reported (Craven 1993, Fu 1994). There are some general assumptions that these algorithms adhere to. The first assumption that most rule extraction algorithms make, is that non-input units are either maximally active (activation near 1) or inactive (activation near 0). This Boolean valued activation is approximated by using the standard logistic activation function /(z) = 1/( 1 + e-•z ) and setting s 5.0. The use of the above function parameters guarantees that non-input units always have non-negative activations in the range [0,1). The second underlying premise of rule extraction is that each hidden and output unit implements a symbolic rule. The concept associated with each unit is the consequent of the rule, and certain subsets of the input units represent the antecedent of the rule. Rule extraction algorithms search for those combinations of input values to a particular hidden or output unit that results in it having an optimal (near-one) activation. Here, rule extraction methods exploit a very basic principle of biological neural networks. That is, if the sum of its weighted inputs exceeds a certain threshold, then the biological neuron fires [Fu 1994). This condition is satisfied when the sum of the weighted inputs exceeds the bias, where (E'Jiz,=::l w; > 9i)• It has been shown that most concepts described by humans usally can be expressed as production rules in disjunctive normal form (DNF) notation. Rules expressed in this notation are therefore highly comprehensible and intuitive. In addition, the number of production rules may be reduced and the structure thereof simplified by using propositional logic. A method that extracts production rules in DNF is presented [Viktor 1995). The basic idea of the method is the use of equivalence classes. Similarly weighted links are grouped into a cluster, the assumption being that individual weights do not have unique importance. Clustering considerably reduces the combinatorics of the method as opposed to previously reported approaches. Since the rules are in a logically manipulatable form, significant simplifications in the structure thereof can be obtained, yielding a highly reduced and comprehensible set of rules. Experimental results have shown that the accuracy of the extracted rules compare favourably with the CN2 [Clark 1989] and C4.5 [Quinlan 1993] symbolic rule extraction methods. The extracted rules are highly comprehensible and similar to those extracted by traditional symfiolic methods

Reregistration of gynaecologists in South Africa - results of a 1-year trial run

Objective. Evaluation of an Australian system of reregistration with recommendations for a possible future South African system.Design. Cohort descriptive study.Setting. Gynaecologists from both private and full-time academic practices.Participants. One hundred and eighty volunteers participated for a period of 1 year.Intervention. Each participant had to obtain a minimum of 25 points and an additional subminimum in at least two of the following practice-related categories: audit, continuing medical education (CME), self-study and research or tuition.Outcome measures. Compliance with the rules of the system and participants' comments.Results. Ten of the 180 volunteers withdrew from the study. Only 42% of the remaining 170 participants retumed their logbooks and a mere 32% their self-studyquestionnaires. The majority were in favour of self-study programmes or CME as future methods of reregistration.Conclusion. A future system of reregistration must be based on self-study programmes and a well-structured and relevant CME curriculum

Knowledge and perceptions of nursing staff on the new Road to Health Booklet growth charts in primary healthcare clinics in the Tygerberg subdistrict of the Cape Town metropole district

Objectives: The objectives of the study were to assess the perceptions of nursing staff on the Road to Health Booklet (RTHB), to assess their knowledge of the RTHB growth charts, and to determine whether the level of knowledge was acceptable for successful utilisation of the RTHB growth charts.Design: A cross-sectional descriptive survey.Setting: Twelve primary healthcare clinics in the Tygerberg subdistrict.Subjects: Nursing staff who were going to work with the RTHB on a daily basis.Outcomes measures: The knowledge and perceptions of the nursing staff on the new RTHB were measured using a self-administered questionnaire.Results: The study highlighted that the majority of the nursing staff did not possess sufficient knowledge to successfully utilise the RTHB. The mean score percentage for the total 12 knowledge questions was 55%. Less than a third (n = 13) of participants could correctly interpret the cut-off value for mid-upper-arm circumference. Only 38% and 52% correctly knew that -2 standard deviation for weight-for-age and weight-for-length represents underweight and wasting, respectively. Fifty-five per cent could correctly interpret the growth faltering graph. Forty-three per cent of participants felt the change to the RTHB was unnecessary, and 55% thought that mothers or caregivers would not easily understand the RTHB. More than half (n = 22) of the participants said that they had adequate knowledge to work with the RTHB, while the rest reported that they did not.Conclusion: The RTHB has the potential to decrease the prevalence of malnutrition in children. However, to achieve this, effective usage and understanding of the RTHB is critical.Keywords: Road to Health booklet, growth monitoring, primary healthcare clinics, knowledg

Between male variation in semen characteristics and preliminary results on the dilution of semen in the ostrich

Abstract This study is part of an ongoing project on artificial insemination in ostriches. The physical output of neat semen from four ostrich males was investigated and the effect of reconstituting semen with: 1) seminal plasma of the same male (SPS); 2) seminal plasma of another male (SPD), and 3) Dulbecco's Modified Eagles Medium (DMEM). Semen was collected daily from one or two pairs of males using the dummy female method, each pair being replicated twice. Spermatozoa viability in neat semen, SPS, SPD and DMEM was assessed using nigrosin-eosin staining and the proportions of live normal, live abnormal and dead sperm were determined. Semen volume (mean ± SE) was 1.27 ± 0.13 mL, the concentration of spermatozoa 3.68 ± 0.17 x 10 9 /mL and the number of spermatozoa 4.92 ± 0.64 x 10 9 /ejaculate. Furthermore, the live normal, live abnormal and dead spermatozoa in the neat semen were 61.2 ± 4.5%, 21.2 ± 2.7% and 17.7 ± 4.3% respectively. The ejaculate volume and the number of dead spermatozoa were not affected by collection time. However, the number of live abnormal spermatozoa increased through the day causing a reduction in live normal spermatozoa. Furthermore, re-suspending spermatozoa in DMEM reduced the number of live normal (31.4 ± 4.6%) and live abnormal spermatozoa (11.0 ± 2.7%) and increased the number of dead spermatozoa (57.6 ± 4.4%). In contrast, numbers of live spermatozoa were higher when suspended in seminal plasma and similar in SPS (53.9 ± 4.6%) and SPD (50.7 ± 4.6%). These are the first crucial steps to determining the optimum semen collection time and to improving the viability of diluted spermatozoa

Universities and community-based research in developing countries: community voice and educational provision in rural Tanzania

The main focus of recent research on the community engagement role of universities has been in developed countries, generally in towns and cities and usually conducted from the perspectives of universities rather than the communities with which they engage. The purpose of this paper is to investigate the community engagement role of universities in the rural areas of developing countries, and its potential for strengthening the voice of rural communities. The particular focus is on the provision of primary and secondary education. The paper is based on the assumption that in order for community members to have both the capacity and the confidence to engage in political discourse for improving educational capacity and quality, they need the opportunity to become involved and well-versed in the options available, beyond their own experience. Particular attention is given in the paper to community-based research (CBR). CBR is explored from the perspectives of community members and local leaders in the government-community partnerships which have responsibility for the provision of primary and secondary education in rural Tanzania. The historical and policy background of the partnerships, together with findings from two case studies, provide the context for the paper

Meat quality, skin damage and reproductive performance of ostriches exposed to extensive human presence and interactions at an early age

The effect human presence and interactions performed after hatch to 3 months of age has on ostrich meat quality, skin damage and reproductive performance at a later age was investigated in 416-day-old ostrich chicks. The chicks were allocated to one of the three treatments, which varied with regard to exposure to human presence and care for 3 months post-hatch: HP1—extensive human presence with physical contact (touch, stroking), gentle human voice and visual contact; HP2—extensive human presence with gentle human voice and visual contact without physical contact; S—standard control treatment, where human presence and visual contact were limited to routine management, feed and water supply only. Carcass attributes (carcass weight, dressing percentage and drumstick weight), meat quality traits (pH, colour and tenderness) and skin traits (skin size, skin grading and number of lesions) were evaluated on twenty-four 1-year-old South African Black (SAB) ostriches

South African cardiovascular risk stratification guideline for non-cardiac surgery

The South African (SA) guidelines for cardiac patients for non-cardiac surgery were developed to address the need for cardiac risk assessment and risk stratification for elective non-cardiac surgical patients in SA, and more broadly in Africa. The guidelines were developed by updating the Canadian Cardiovascular Society Guidelines on Perioperative Cardiac Risk Assessment and Management for Patients Who Undergo Non-cardiac Surgery, with a search of literature from African countries and recent publications. The updated proposed guidelines were then evaluated in a Delphi consensus process by SA anaesthesia and vascular surgical experts. The recommendations in these guidelines are: 1. We suggest that elective non-cardiac surgical patients who are 45 years and older with either a history of coronary artery disease, congestive cardiac failure, stroke or transient ischaemic attack, or vascular surgical patients 18 years or older with peripheral vascular disease require further preoperative risk stratification as their predicted 30-day major adverse cardiac event (MACE) risk exceeds 5% (conditional recommendation: moderate-quality evidence). 2. We do not recommend routine non-invasive testing for cardiovascular risk stratification prior to elective non-cardiac surgery in adults (strong recommendation: low-to-moderate-quality evidence). 3. We recommend that elective non-cardiac surgical patients who are 45 years and older with a history of coronary artery disease, or stroke or transient ischaemic attack, or congestive cardiac failure or vascular surgical patients 18 years or older with peripheral vascular disease should have preoperative natriuretic peptide (NP) screening (strong recommendation: high-quality evidence). 4. We recommend daily postoperative troponin measurements for 48 - 72 hours for non-cardiac surgical patients who are 45 years and older with a history of coronary artery disease, or stroke or transient ischaemic attack, or congestive cardiac failure or vascular surgical patients 18 years or older with peripheral vascular disease, i.e. (i) a baseline risk >5% for MACE 30 days after elective surgery (if no preoperative NP screening), or (ii) an elevated B-type natriuretic peptide (BNP)/N-terminal-prohormone B-type natriuretic peptide (NT-proBNP) measurement before elective surgery (defined as BNP >99 pg/mL or a NT-proBNP >300 pg/mL) (conditional recommendation: moderate-quality evidence). Additional recommendations are given for the management of myocardial injury after non-cardiac surgery (MINS) and medications for comorbidities.The Global Surgery Fellowship grant.http://www.samj.org.zadm2022Anaesthesiolog

Water Contaminants Detection Using Sensor Placement Approach in Smart Water Networks

Incidents of water pollution or contamination have occurred repeatedly in recent years, causing significant disasters and negative health impacts. Water quality sensors need to be installed in the water distribution system (WDS) to allow real-time water contamination detection to reduce the risk of water contamination. Deploying sensors in WDS is essential to monitor and detect any pollution incident at the appropriate time. However, it is impossible to place sensors on all nodes of the network due to the relatively large structure of WDS and the high cost of water quality sensors. For that, it is necessary to reduce the cost of deployment and guarantee the reliability of the sensing, such as detection time and coverage of the whole water network. In this paper, a dynamic approach of sensor placement that uses an Evolutionary Algorithm (EA) is proposed and implemented. The proposed method generates a multiple set of water contamination scenarios in several locations selected randomly in the WDS. Each contamination scenario spreads in the water networks for several hours, and then the proposed approach simulates the various effect of each contamination scenario on the water networks. On the other hand, the multiple objectives of the sensor placement optimization problem, which aim to find the optimal locations of the deployed sensors, have been formulated. The sensor placement optimization solver, which uses the EA, is operated to find the optimal sensor placements. The effectiveness of the proposed method has been evaluated using two different case studies on the example of water networks: Battle of the Water Sensor Network (BWSN) and another real case study from Madrid (Spain). The results have shown the capability of the proposed method to adapt the location of the sensors based on the numbers and the locations of contaminant sources. Moreover, the results also have demonstrated the ability of the proposed approach for maximising the coverage of deployed sensors and reducing the time to detect all the water contaminants using a few numbers of water quality sensor