Journal of Information Technology Management (مدیریت فناوری اطلاعات)

The special issue aims to cover the latest research topics in designing and deploying visual information analytics and management techniques using deep learning. It is intended to serve as a platform to researchers who want to present research in deep learning. The special issue focuses explicitly on deep learning and its application in visual computing and signal processing. It emphasizes on the extent to which Deep Learning can help specialists in understanding and analyzing complex images and signals. The field of Visual Information Analytics and Management is considered in its broadest sense and covers both digital and analog aspects. This involves development of techniques for image analysis, understanding and restoration. Deep learning techniques are effective for visual analytics. Deep learning is a fast growing area and is gaining impetus for application in various fields. Therefore, in this special issue, the objective is to publish articles related to deep learning in various problems of visual information analytics and management.

Handwriting Analysis has been used for a very long time to analyze an individual’s suitability for a job, and is in recent times, gaining popularity as a valid means of a person’s evaluation. Extensive Research has been done in the field of determining the Personality Traits of a person through handwriting. We intend to analyze an individual’s personality by breaking it down into the Big Five Personality Traits using their handwriting samples. We present a dataset that links personality traits to the handwriting features. We then propose our algorithm - consisting of one ANN based model and PersonaNet, a CNN based model. The paper evaluates our algorithm’s performance with baseline machine learning models on our dataset. Testing our novel architecture on this dataset, we compare our algorithm based on various metrics, and show that our novel algorithm performs better than the baseline Machine Learning models.

In this paper a novel digital watermarking algorithm is proposed. The proposed method comprises of a watermarking embedding and extraction algorithm using bio inspired optimization technique. Dragonfly algorithm (DA) is based on the static and dynamic swarming behaviors of dragonflies in nature. The dragonfly algorithm is used to optimize the scaling factor of the watermarking so that an optimal watermark is embedded. Watermarking algorithms take as input a cover image and the message. The cover image in the proposed method is decomposed into sub bands using discrete wavelet transform (DWT). Thereafter, it is converted to discrete cosine blocks (DCT). An optimal scaling factor is required for performing the watermarking. In this paper, DA is used for computing the scaling factor. The DA generated scaling factor is optimal and improves the performance of the watermarking. The inverse DWT and DCT are computed to extract the watermarked image from the cover image. The proposed method is applied on different images to evaluate the performance. The results obtained are compared with other state of the art methods.

The accuracy of brain tumor detection and segmentation are greatly affected by tumors’ location, shape, and image properties. In some situations, brain tumor detection and segmentation processes are greatly complicated and far from being completely resolved. The accuracy of the segmentation process significantly influences the diagnosis process, such as abnormal tissue detection, disease classification, and assessment. However, medical images, in particular, the Magnetic Resonance Imaging (MRI), often include undesirable artefacts such as noise, density inhomogeneity, and partial volume effects. Although many segmentation methods have been proposed, the accuracy of the segmentation results can be further improved. Subsequently, this study attempts to provide very important properties about the size, initial location and shape of tumors known as Region of Interest (RoI) to kick-start the segmentation process. The MRI consists of a sequence of images (MRI slices) of a particular person and not one image. Our method chooses the best image among them based on the tumor size, initial location and shape to avoid the partial volume effects. The selected algorithms to test our method are Active Contour and Otsu Thresholding algorithms. Several experiments are conducted in this research using the BRATS standard dataset that consist of 100 samples. These experiments comprised of MRI slices of 65 patients. The proposed method is evaluated by the similarity coefficient as a standard measure using Dice, Jaccard, and BF scores. The results revealed that the Active Contour algorithm has higher segmentation accuracy when tested across the three different similarity coefficients. Moreover, the achieved results of the two algorithms verify the ability of the proposed method to choose the best RoIs of the MRI samples.

Electrocardiogram (ECG) is a tool used for the electrical analysis of the status of human heart activity. When the ECG signal is recorded, it gets contaminated with different types of noises. So, for accurate analysis, noises must be eliminated from the ECG signal. There are different types of noises that contaminate the characteristics of ECG signal i.e Power line interference, baseline wander, Electromyogram (EMG). In this paper, different techniques have implemented for the removal of noises. A median filter is used for removal of DC component and Savitzky-Golay filter (SG) is used for smoothing noised waveform and then wavelet transform (db4) is used to decompose the ECG signal for removal of various artifacts. Wavelet transform provides the information in frequency and time domain and then thresholding has been applied for the implementation of algorithms in MATLAB. The measured results i.e. SNR(Signal to Noise ratio) and MSE(Mean square error) have been calculated using different databases like MIT-BIH, Long-term ST database, European ST-T database. The results are examined with proposed methods that are better than those reported in the literature.