In this research paper, we concentrated on the development of a smart system for the preservation of Government of Iraq records in digital format. In literature, many existing preservation techniques and models have been discussed and presented with their detailed comparison as preservation rate of digital data growth has increased. Many western countries have already upgraded their paper-based systems to the smart system for preservation of government records including Turkey. In methodology, we used python language for the implementation of smart system application with OAIS framework to preserve archived paper-based records, It also serves as a blueprint for the new digital ecology. It provides a framework for identifying roles and responsibilities in a constrained system. Iraq's major ministries (such as finance, power, and science and technology) are working on guidelines for preserving digital artifacts and data. During the preservation phase, records were also subjected to accession, normalization, and transformation in order to clean them up and convert them to a new format. In results, we achieved good preservation of most of the records from 2015 to 2019, the goal is to preserve which most of the records it belongs to digital format. The distribution of records preservation for major ministries of Iraq government in term of paper based and digitally preserved records from year 2015 to 2019. The preservation of digital records was recorded very low in 2015 as much of the record archival were paper based in all major ministries of the government. However, year-by-year the smart system tends preserve the records in digital format from old paper-based format and till 2019 large number of records are being converted to the digital format. To have such smart system can be very helpful for preserving the records of the government, and useful for understanding how records are being preserved and their functionality.

Building a Smart System for Preservation of Government Records in Digital Form

We investigated the usage of hand motion recognition using data mining approaches. In gesture detection applications, I've discovered that employing data mining to recognize hand motions has several advantages. To begin, we employ data mining to recognize bandwidth hand motions. Second, we have a high temporal resolution, which is great for data mining. A short-range, close-pulse broadcast signal is required in most cases. The frequency components of the data mining for hand gesture recognition are the same as the pulse, but the phase components are different. Our vision must be strong enough to differentiate a far hand from distant things. Data mining is equal to recognizing hand motions by heart rate divided by length in these categorized articles. Obtaining significant power for a brief period. 

Survey On Recognition Hand Gesture By Using Data Mining Algorithms

A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology

One of the influential models in the artificial neural network (ANN) research field for addressing the issue of knowledge in the non-systematic logical rule is Random k Satisfiability. In this context, knowledge structure representation is also the potential application of Random k Satisfiability. Despite many attempts to represent logical rules in a non-systematic structure, previous studies have failed to consider higher-order logical rules. As the amount of information in the logical rule increases, the proposed network is unable to proceed to the retrieval phase, where the behavior of the Random Satisfiability can be observed. This study approaches these issues by proposing higher-order Random k Satisfiability for k ≤ 3 in the Hopfield Neural Network (HNN). In this regard, introducing the 3 Satisfiability logical rule to the existing network increases the synaptic weight dimensions in Lyapunov’s energy function and local field. In this study, we proposed an Election Algorithm (EA) to optimize the learning phase of HNN to compensate for the high computational complexity during the learning phase. This research extensively evaluates the proposed model using various performance metrics. The main findings of this research indicated the compatibility and performance of Random 3 Satisfiability logical representation during the learning and retrieval phase via EA with HNN in terms of error evaluations, energy analysis, similarity indices, and variability measures. The results also emphasized that the proposed Random 3 Satisfiability representation incorporates with EA in HNN is capable to optimize the learning and retrieval phase as compared to the conventional model, which deployed Exhaustive Search (ES).

https://www.mdpi.com/2227-9717/9/8/1292/pdf

Novel Hopfield Neural Network Model with Election Algorithm for Random 3 Satisfiability

A novel method for classification of multi-class motor imagery tasks based on feature fusion.

Sign language recognition is one of the most challenging applications in machine learning and human-computer interaction. Many researchers have developed classification models for different sign languages such as English, Arabic, Japanese, and Bengali; however, no significant research has been done on the general-shape performance for different datasets. Most research work has achieved satisfactory performance with a small dataset. These models may fail to replicate the same performance for evaluating different and larger datasets. In this context, this paper proposes a novel method for recognizing Bengali sign language (BSL) alphabets to overcome the issue of generalization. The proposed method has been evaluated with three benchmark datasets such as `38 BdSL’, `KU-BdSL’, and `Ishara-Lipi’. Here, three steps are followed to achieve the goal: segmentation, augmentation, and Convolutional neural network (CNN) based classification. Firstly, a concatenated segmentation approach with YCbCr, HSV and watershed algorithm was designed to accurately identify gesture signs. Secondly, seven image augmentation techniques are selected to increase the training data size without changing the semantic meaning. Finally, the CNN-based model called BenSignNet was applied to extract the features and classify purposes. The performance accuracy of the model achieved 94.00%, 99.60%, and 99.60% for the BdSL Alphabet, KU-BdSL, and Ishara-Lipi datasets, respectively. Experimental findings confirmed that our proposed method achieved a higher recognition rate than the conventional ones and accomplished a generalization property in all datasets for the BSL domain.

/pdf/bensignnet-bengali-sign-language-alphabet-recognition-using-2ak8qzpc.pdf

BenSignNet: Bengali Sign Language Alphabet Recognition Using Concatenated Segmentation and Convolutional Neural Network

Motor imagery (MI) from human brain signals can diagnose or aid specific physical activities for rehabilitation, recreation, device control, and technology assistance. It is a dynamic state in learning and practicing movement tracking when a person mentally imitates physical activity. Recently, it has been determined that a brain–computer interface (BCI) can support this kind of neurological rehabilitation or mental practice of action. In this context, MI data have been captured via non-invasive electroencephalogram (EEGs), and EEG-based BCIs are expected to become clinically and recreationally ground-breaking technology. However, determining a set of efficient and relevant features for the classification step was a challenge. In this paper, we specifically focus on feature extraction, feature selection, and classification strategies based on MI-EEG data. In an MI-based BCI domain, covariance metrics can play important roles in extracting discriminatory features from EEG datasets. To explore efficient and discriminatory features for the enhancement of MI classification, we introduced a median absolute deviation (MAD) strategy that calculates the average sample covariance matrices (SCMs) to select optimal accurate reference metrics in a tangent space mapping (TSM)-based MI-EEG. Furthermore, all data from SCM were projected using TSM according to the reference matrix that represents the featured vector. To increase performance, we reduced the dimensions and selected an optimum number of features using principal component analysis (PCA) along with an analysis of variance (ANOVA) that could classify MI tasks. Then, the selected features were used to develop linear discriminant analysis (LDA) training for classification. The benchmark datasets were considered for the evaluation and the results show that it provides better accuracy than more sophisticated methods.

https://www.mdpi.com/2079-9292/9/10/1584/pdf

Motor-Imagery Classification Using Riemannian Geometry with Median Absolute Deviation

In recent years, hand gesture recognition (HGR) systems have been extensively developed with the technologies of human-computer interaction (HCI), which enables regular interaction with machines. However, the significance of progress in HGR continues to advance, although the problem of hand segmentation and recognition is challenging due to the unfavorable environment, background illumination, hand size, and shape. To overcome this, we propose an optimal segmentation method for identifying hand gestures from input images, improving recognition performance. For segmenting hand gestures, we compared the segmentation methods of YCbCr, SkinMask, and HSV (hue, saturation, and value). The CR component is extracted from YCbCr, then binarization, erosion, and hole filling are performed. Color segmentation is applied to the SkinMask process that detects pixels that match the color of the hand. In the HSV process, threshold masking determines the dominant features. The Softmax classification is used to classify hand gestures where features are extracted through convolutional neural network (CNN). The proposed segmentation methods are applied to a benchmark dataset and the result shows an improvement in recognition accuracy over state-of-the-art systems.

Hand Gesture Recognition Based on Optimal Segmentation in Human-Computer Interaction

We proposed a classification technique of EEG motor imagery signals using Logistic regression and feature extraction algorithm using filter bank common spatial pattern (FBCSP). Main theme of FBCSP is that the signals decomposed into 5 sub band then calculated CSP for each sub band, this algorithm also allows automated frequency band selection. We combined each subband CSP feature vector, feed this feature vector into machine learning algorithm. In the paper Logistic regression is used to classify among multiple classes. To evaluate this method, we used here publicly available dataset namely Brain-Computer Interface competition IV-2a. Because of high accuracy and kappa that shown in accuracy table that proposed method is promising.

/pdf/multiclass-mi-task-classification-using-logistic-regression-v003n22qfo.pdf

Multiclass MI-Task Classification Using Logistic Regression and Filter Bank Common Spatial Patterns

Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious task. One of the main functions of sign language is to communicate with each other through hand gestures. Recognition of hand gestures has become an important challenge for the recognition of sign language. There are many existing models that can produce a good accuracy, but if the model test with rotated or translated images, they may face some difficulties to make good performance accuracy. To resolve these challenges of hand gesture recognition, we proposed a Rotation, Translation and Scale-invariant sign word recognition system using a convolutional neural network (CNN). We have followed three steps in our work: rotated, translated and scaled (RTS) version dataset generation, gesture segmentation, and sign word classification. Firstly, we have enlarged a benchmark dataset of 20 sign words by making different amounts of Rotation, Translation and Scale of the original images to create the RTS version dataset. Then we have applied the gesture segmentation technique. The segmentation consists of three levels, i) Otsu Thresholding with YCbCr, ii) Morphological analysis: dilation through opening morphology and iii) Watershed algorithm. Finally, our designed CNN model has been trained to classify the hand gesture as well as the sign word. Our model has been evaluated using the twenty sign word dataset, five sign word dataset and the RTS version of these datasets. We achieved 99.30% accuracy from the twenty sign word dataset evaluation, 99.10% accuracy from the RTS version of the twenty sign word evolution, 100% accuracy from the five sign word dataset evaluation, and 98.00% accuracy from the RTS version five sign word dataset evolution. Furthermore, the influence of our model exists in competitive results with state-of-the-art methods in sign word recognition.

/pdf/rotation-translation-and-scale-invariant-sign-word-1tdafp0u.pdf

Abu Saleh Musa Miah

Papers

BenSignNet: Bengali Sign Language Alphabet Recognition Using Concatenated Segmentation and Convolutional Neural Network

Motor-Imagery Classification Using Riemannian Geometry with Median Absolute Deviation

Hand Gesture Recognition Based on Optimal Segmentation in Human-Computer Interaction

Multiclass MI-Task Classification Using Logistic Regression and Filter Bank Common Spatial Patterns

Rotation, Translation and Scale Invariant Sign Word Recognition Using Deep Learning