Exploring and exploiting a historical corpus for Arabic

This paper presents a historical Arabic corpus named HAC. At this early embryonic stage of the project, we report about the design, the architecture and some of the experiments which we have conducted on HAC. The corpus, and accordingly the search results, will be represented using a primary XML exchange format. This will serve as an intermediate exchange tool within the project and will allow the user to process the results offline using some external tools. HAC is made up of Classical Arabic texts that cover 1600 years of language use; the Quranic text, Modern Standard Arabic texts, as well as a variety of monolingual Arabic dictionaries. The development of this historical corpus assists linguists and Arabic language learners to effectively explore, understand, and discover interesting knowledge hidden in millions of instances of language use. We used techniques from the field of natural language processing to process the data and a graph-based representation for the corpus. We provided researchers with an export facility to render further linguistic analysis possible.     

Emotional Loneliness and Coping Strategies: A Reference to Older Malaysians at Nursing Homes

This article presents an in-depth qualitative study using a phenomenological approach to understand loneliness among elderly individuals in Malaysia. The objective of the study was to understand how the Malaysian elderly perceive and understand social isolation as well as loneliness, with the aim of identifying the factors that cause emotional loneliness among the elderly in nursing homes. In addition, this study also explored their coping strategies when dealing with loneliness. Semi-structured interviews were conducted with ten elderly participants from two different nursing homes in Kuala Lumpur and Selangor with representatives from the three major ethnic groups of Malaysia. Based on the results, there are several factors that cause the elderly to feel lonely – health factors, lack of family ties, and the lack of communication and cognitive factors, such as memory and perception. It was also found that internal (expectations and optimism) and external (work and activities) coping strategies play major roles in overcoming loneliness. In conclusion, some recommendations are made to respective party families and the government to consider when developing plans to help the elderly overcome loneliness, which could strengthen the family and social support system in Malaysia.     

Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22 %, 4.45 %, and 19.56 %, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35 % of that obtained in subjective evaluation by human judges.     

Enhanced fuzzy-filtered neural networks for material fatigue prognosis

Although fuzzy-filtered neural networks (FFNN) have been used in pattern classification because of their unique characteristics in feature extraction, they usually have poor performance in forecasting applications due to their structure complexities especially in their consequent reasoning part. In this paper, an enhanced FFNN, EFFNN, is proposed for time series forecasting and material fatigue prognosis. A novel neural network scheme is developed to facilitate computation implementation. A new conjugate technique is proposed to improve training efficiency. The effectiveness of the developed EFFNN scheme and the related training technique is demonstrated by a series of simulation tests. The EFFNN is also implemented for material fatigue prognosis. Test results show that the developed EFFNN predictor is an effective forecasting tool; it can capture system dynamics effectively and track system characteristics accurately.     

A multi-objective strategy in genetic algorithms for gene selection of gene expression data

A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Journal of Signal Processing Systems - Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful...     

A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19

The Journal of Supercomputing - This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19...     

An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.     

Simulation model for natural gas transmission pipeline network system

This paper focuses on developing a simulation model for the analysis of transmission pipeline network system (TPNS) with detailed characteristics of compressor stations. Compressor station is the key element in the TPNS since it provides energy to keep the gas moving. The simulation model is used to create a system that simulates TPNS with different configurations to get pressure and flow parameters. The mathematical formulations for the TPNS simulation were derived from the principles of flow of fluid through pipe, mass balance and compressor characteristics. In order to determine the unknown pressure and flow parameters, a visual C++ code was developed based on Newton–Raphson solution technique. Using the parameters obtained, the model evaluates the energy consumption for various configurations in order to guide for the selection of optimal TPNS. Results from the evaluations of the model with the existing TPNS and comparison with the existing approaches showed that the developed simulation model enabled to determine the operational parameters with less than 10 iterations. Hence, the simulation model could assist in decisions regarding the design and operations of the TPNS.