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A brain–computer interface (BCI) can be used for people with severe physical disabilities such as ALS or amyotrophic lateral sclerosis. BCI can allow these individuals to communicate again by creating a new communication channel directly from the brain to an output device. BCI technology can allow paralyzed people to share their intent with others, and thereby demonstrate that direct communication from the brain to the external world is possible and that it might serve useful functions. BCI systems include machine learning algorithms (MLAs). Their performance depends on the feature extraction and classification techniques employed. In this paper, we propose a system to exploit the P300 signal in the brain, a positive deflection in event-related potentials. The P300 signal can be incorporated into a spelling device. There are two benefits behind this kind of research. First of all, this work presents the research status and the advantages of communication via a BCI system, especially the P300 BCI system for disordered people, and the related literature review is presented. Secondly, the paper discusses the performance of different machine learning algorithms. Two different datasets are presented: the first dataset 2004 and the second dataset 2019. A preprocessing step is introduced to the subjects in both datasets first to extract the important features before applying the proposed machine learning methods: linear discriminant analysis (LDA I and LDA II), support vector machine (SVM I, SVM II, SVM III, and SVM IV), linear regression (LREG), Bayesian linear discriminant analysis (BLDA), and twin support vector machine (TSVM). By comparing the performance of the different machine learning systems, in the first dataset it is found that BLDA and SVMIV classifiers yield the highest performance for both subjects “A” and “B”. BLDA yields 98% and 66% for 15th and 5th sequences, respectively, whereas SVMIV yields 98% and 54.4% for 15th and 5th sequences, respectively. While in the second dataset, it is obvious that BLDA classifier yields the highest performance for both subjects “1” and “2”, it achieves 90.115%. The paper summarizes the P300 BCI system for the two introduced datasets. It discusses the proposed system, compares the classification methods performances, and considers some aspects for the future work to be handled. The results show high accuracy and less computational time which makes the system more applicable for online applications.

DNA microarray is a powerful tool and is widely used in genetics to monitor expression levels of thousands of genes in parallel. The gene expression process consists of three stages: gridding, segmentation and quantification. Gridding deals with finding areas in the microarray image which contain one spot using grid lines. This step can be done manually or automatically. In this paper, we propose an efficient and simple automatic gridding method for microarray image analysis. This method was implemented using MATLAB software and found very effective for gridding arrays with low intensity, poor quality spotsand tested by a number of microarray images. Results show that this method gives high accuracy of 76.9% improved to 98.6% when a preprocessing step is considered, rendering the method a promising technique for an efficient and automatic gridding the noisy microarray images.

DNA microarray is an innovative tool for gene studies in biomedical research, and its applications can vary from cancer diagnosis to human identification. Image processing is an important aspect of microarray experiments, the primary purpose of the image analysis step is to extract numerical foreground and background intensities for the red and green channels for each spot on the microarray. The background intensities are used to correct the foreground intensities for local variation on the array surface, resulting in corrected red and green intensities for each spot that can be considered as a primary data for subsequent analysis. Most techniques divide the overall microarray image processing into three steps: gridding, segmentation, and quantification. In this paper, a   simple automated gridding technique is developed with a great effect on noisy microarray images. A segmentation technique based on ‘edge-detection’ is applied to identify the spots and separate the foreground from the background is known as microarray image segmentation. Finally, a quantification technique is used to calculate the gene expression level from the intensity values of the red and green components of the image. Results revealed that the developed methods can deal with various kinds of noisy microarray images, with high  griddingaccuracy of 92.2% for low quality images and 100% for high quality images resulting in better spot quantification to get  more accurate gene expression values.Â

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