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Numerous cancer studies have combined different datasets for the prognosis of patients. This study incorporated four networks for significant directed random walk (sDRW) to predict cancerous genes and risk pathways. The study investigated the feasibility of cancer prediction via different networks. In this study, multiple micro array data were analysed and used in the experiment. Six gene expression datasets were applied in four networks to study the effectiveness of the networks in sDRW in terms of cancer prediction. The experimental results showed that one of the proposed networks is outstanding compared to other networks. The network is then proposed to be implemented in sDRW as a walker network. This study provides a foundation for further studies and research on other networks. We hope these finding will improve the prognostic methods of cancer patients. [ABSTRACT FROM AUTHOR]

As a context of this research, combinatorial testing (CT) in simplifications of the parameter interactions strategy is to grade or rank the best algorithm in order to eliminated the parameter interaction on various crucial issue. These will become the most interesting research finding so as to enhance the efficiency of the test cases generations. The objective of this research is to identify and analyze existing combinatorial testing technique in the context of the formulated research questions. Search terms with relevant keywords were used to identify primary research that was related to combinatorial testing technique classified under journal articles, conference papers, workshops, symposiums, book chapters and IEEE bulletins. In the end of this research, we provided a primarily result of comparison on efficiency of the simplifications strategy in order to enhance the combinatorial testing technique. [ABSTRACT FROM AUTHOR]

Poverty in Malaysia is a controversial economic issue. Although poverty alleviation strategies in Malaysia have been acclaimed success by United Nation Development Programme (UNDP, 2013), new form of poverty emerged in urban area as a result of rapid economic growth and development. Poverty is a multifaceted phenomenon and different societies have different perceptions of poverty. These will led to uncertainty in determining of poverty. Most of welfare institutions in Malaysia measure poverty from the monetary perspective using monthly income or expenditure. In practice, conventional institutions such as Jabatan Kebajikan Masyarakat Malaysia (JKMM) use monetary approach in determining poverty through the Poverty Line Income (PLI). While, Islamic institutions adopt the monetary approach in determining poverty using Had Al-Kifayah (HAK). The objective of this paper is to explain the concept and analyze the uncertainty factors that have contributed to the incidence of poverty in urban area using PLI and HAK method. This study would highlight the similarities and differences of both the methods. A survey aided by a structured questionnaire was carried out on 300 selected households in the state of Kuala Lumpur and 150 household datasets are obtained from Department of Zakat, Islamic Center, UTM, Johor. This empirical study will able to use in designing case representation for case-based reasoning that will be implemented in future work. [ABSTRACT FROM AUTHOR]

Satellite images taken in hazy conditions tend to have low brightness and contrast, and to address this problem, a simple and optimised method using near-infrared (NIR) histogram equalisation has been proposed, in order to improve the brightness and contrast of the SPOT-5 images taken by satellite. This helps to improve the detection of shoreline with a higher efficiency in edge detection, and produces results with a high success rate of more than 90%. In order to remove haze from SPOT-5 images, a false colour composite is used to prevent misclassified pixels of the shoreline, and ensure minimal error in image classification. The presence of an NIR channel is suitable for the detection of vegetation and waterbodies. The NIR channel is important for this study, in ensuring that contrast between vegetated areas and sea can be clarified, due to the high reflectance of vegetation leaves in the NIR wavelength region. This research used three areas of interest to show different results in shoreline detection. The results showed that the proposed method performed better than existing methods, when dealing with the low brightness or contrast ratio of SPOT-5 images. [ABSTRACT FROM PUBLISHER]

Objectives: To design and develop a classifier, named Sewing Driving Training based Optimization-Deep Residual Network (SDTO_DRN) for hand gesture recognition., Methods: The electrical activity of forearm muscles generates the signals that can be captured with Surface Electromyography (sEMG) sensors and includes meaningful data for decoding both muscle actions and hand movement. This research develops an efficacious scheme for hand gesture recognition using SDTO_DRN. Here, signal pre-processing is done through Gaussian filtering. Thereafter, desired and appropriate features are extracted. Following that, effective features are chosen using SDTO. At last, hand gesture identification is accomplished based on DRN and this network is effectively fine-tuned by SDTO, which is a combination of Sewing Training Based Optimization (STBO) and Driving Training Based Optimization (DTBO). The datasets employed for the implementation of this work are MyoUP Dataset and putEMG: sEMG Gesture and Force Recognition Dataset., Results: The designed SDTO_DRN model has gained superior performance with magnificent results by delivering a maximum accuracy of 0.943, True Positive Rate (TPR) of 0.929, True Negative Rate (TNR) of 0.919, Positive Predictive Value (PPV) of 0.924, and Negative Predictive Value (NPV) of 0.924., Conclusions: The hand gesture recognition using the proposed model is accurate and improves the effectiveness of the recognition., (© 2023 Walter de Gruyter GmbH, Berlin/Boston.)

Numerous cancer studies have combined different datasets for the prognosis of patients. This study incorporated four networks for significant directed random walk (sDRW) to predict cancerous genes and risk pathways. The study investigated the feasibility of cancer prediction via different networks. In this study, multiple micro array data were analysed and used in the experiment. Six gene expression datasets were applied in four networks to study the effectiveness of the networks in sDRW in terms of cancer prediction. The experimental results showed that one of the proposed networks is outstanding compared to other networks. The network is then proposed to be implemented in sDRW as a walker network. This study provides a foundation for further studies and research on other networks. We hope these finding will improve the prognostic methods of cancer patients.