Adaptive noise filtering based on artificial hydrocarbon networks: An application to audio signals

Many audio signal applications are corrupted by noise. In particular, adaptive filters are frequently applied to white noise reduction in audio. Recent work provides that there exist some insights on using an artificial intelligence method called artificial hydrocarbon networks (AHNs) for filtering audio signals. Thus, the scope of this paper is to design and implement a novel approach of artificial hydrocarbon networks on adaptive filtering for audio signals. Three experiments were developed. Results demonstrate that AHNs can reduce noise from audio signals. A comparison between the proposed algorithm and a FIR-filter is also provided. The short-time objective intelligibility value (STOI) and the signal-to-noise ratio (SNR) were used for evaluation. At last, the proposed training method for finding the parameters involved in the AHN-filter can also be used in other fields of application.     

Forecasting Thailand’s rice export: Statistical techniques vs. artificial neural networks

Forecasting the international trade of rice is difficult because demand and supply are affected by many unpredictable factors (e.g., trade barriers and subsidies, agricultural and environmental factors, meteorological factors, biophysical factors, changing demographics, etc.) that interact in a complex manner. This paper compares the performance of artificial neural networks (ANNs) with exponential smoothing and ARIMA models in forecasting rice exports from Thailand. To ascertain that the models can reproduce acceptable results on unseen future, we evaluated various aggregate measures of forecast error (MAE, MSE, MAPE, and RMSE) during the validation process of the models. The results reveal that while the Holt–Winters and the Box–Jenkins models showed satisfactory goodness of fit, the models did not perform as well in predicting unseen data during validation. On the other hand, the ANNs performed relatively well as they were able to track the dynamic non-linear trend and seasonality, and the interactions between them.     

Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images

Corneal images can be acquired using confocal microscopes which provide detailed views of the different layers inside a human cornea. Some corneal problems and diseases can occur in one or more of the main corneal layers: the epithelium, stroma and endothelium. Consequently, for automatically extracting clinical information associated with corneal diseases, identifying abnormality or evaluating the normal cornea, it is important to be able to automatically recognise these layers reliably. Artificial intelligence (AI) approaches can provide improved accuracy over the conventional processing techniques and save a useful amount of time over the manual analysis time required by clinical experts. Artificial neural networks (ANNs), adaptive neuro fuzzy inference systems (ANFIS) and a committee machine (CM) have been investigated and tested to improve the recognition accuracy of the main corneal layers and identify abnormality in these layers. The performance of the CM, formed from ANN and ANFIS, achieves an accuracy of 100% for some classes in the processed data sets. Three normal corneal data sets and seven abnormal corneal images associated with diseases in the main corneal layers have been investigated with the proposed system. Statistical analysis for these data sets is performed to track any change in the processed images. This system is able to pre-process (quality enhancement, noise removal), classify corneal images, identify abnormalities in the analysed data sets and visualise corneal stroma images as well as each individual keratocyte cell in a 3D volume for further clinical analysis.     

Applications of artificial neural network on signal processing of optical fibre pH sensor based on bromophenol blue doped with sol–gel film

In this paper, the applications of artificial neural network (ANN) in signal processing of optical fibre pH sensor is presented. The pH sensor is developed based on the use of bromophenol blue (BPB) indicator immobilized in a sol–gel thin film as a sensing material. A three layer feed-forward network was used and the network training was performed using the back-propagation (BP) algorithm. Spectra generated from the pH sensor at several selected wavelengths are used as the input data for the ANN. The bromophenol blue indicator, which has a limited dynamic range of 3.00–5.50 pH units, was found to show higher pH dynamic range of 2.00–12.00 and with low calibration error after training with ANN. The enhanced ANN could be used to predict the new measurement spectra from unknown buffer solution with an average error of 0.06 pH units. Changes of ionic strength showed minor effect on the dynamic range of the sensor. The sensor also demonstrated good analytical performance with repeatability and reproducibility characters of the sensor yield relative standard deviation (R.S.D.) of 3.6 and 5.4%, respectively. Meanwhile the R.S.D. value for this photostability test is 2.4% and it demonstrated no hysteresis when the sensor was cycled from pH 2.00–12.00–2.00 (acid–base–acid region) of different pH. Performance tests demonstrated a response time of 15–150 s, depending on the pH and quantity of the immobilized indicator.     

Blog-enhanced ICT courses: Examining their effects on prospective teachers’ ICT competencies and perceptions

Blogs are used as a technology support tool in teaching-learning processes in contemporary schools, especially in higher education. This paper examines the effects of using blogs on prospective teachers’ perceived ICT competencies and perceptions in blog-enhanced ICT courses. Both quantitative and qualitative research approaches were used in the data collection and analysis processes. The findings indicate that most of the participants perceived blogs to be an important tool that changed their ICT perceptions in a positive way and familiarized them with ICT. The participants also perceived the use of blogs as important for the acquisition of ICT competencies.     

Let’s play together: Effects of video-game play on intergenerational perceptions among youth and elderly participants

In this paper, we report on a longitudinal study that investigates the effects of video-game play on intergenerational perceptions amongst youths and the elderly after participating in a program involving regular interaction sessions over two months. Each pair of participants, consisting of one youth and one elderly, was randomly assigned to either the video-game condition (n = 38, 19 from each age group) or the non-video-game condition (n = 36, 18 each). Attraction, intergroup anxiety, attitudes, and game enjoyment were measured through pre-test and post-test surveys to investigate changes in perceptions. Results showed that participants in the video-game condition reported more positive changes in intergroup anxiety and attitudes, compared to participants in the non-video-game condition. Mediation analyses showed that specific attraction towards their interaction partner mediated the effects of video-game play on attitudes and intergroup anxiety towards the general members of the other age group. In addition, the results showed that game enjoyment played an important role in developing positive intergenerational perceptions only for the elderly, but not for the youth participants in the video-game condition. We discuss implications with respect to options to enhance intergenerational perceptions and communication for youth and elderly cohorts in today’s hyper-ageing society.     

Sometimes it’s hard to be a robot: A call for action on the ethics of abusing artificial agents

This is a call for informed debate on the ethical issues raised by the forthcoming widespread use of robots, particularly in domestic settings. Research shows that humans can sometimes become very abusive towards computers and robots particularly when they are seen as human-like and this raises important ethical issues.

The designers of robotic systems need to take an ethical stance on at least three specific questions. Firstly is it acceptable to treat artefacts – particularly human-like artefacts – in ways that we would consider it morally unacceptable to treat humans? Second, if so, just how much sexual or violent ‘abuse’ of an artificial agent should we allow before we censure the behaviour of the abuser? Thirdly is it ethical for designers to attempt to ‘design out’ abusive behaviour by users?

Conclusions on these and related issues should be used to modify professional codes as a matter of urgency.