Offline arabic character recognition using genetic approach

Many optical character recognition (OCR) techniques and tools have been developed for plurality of languages. A successful OCR system improves interactivity between humans and computers in many applications such as digitising and recognising written content. With regard to Arabic OCR, the problem of handwriting recognition is challenging because Arabic letters are cursive and shapechangeable depending on their positions. OCR systems have reached nearly perfect acknowledgement of Arabic printed text, yet still in its inception and needs to be greatly improved with handwritten text. Therefore in this study, an approach to recognize Arabic characters based on genetic algorithms (GA) is proposed. The approach requires two separate stages; feature extraction and GA for character recognition development. In the feature extraction stage, six features are detected for each character and denoted as a feature vector of 6 integer numbers. The feature vectors are then utilised in the next stage. Three genetic operators namely selection, crossover and mutation are implemented to search for the similar vectors with the best fitness value to recognise the character. The data used in this study were collected from different resources and stored in a database. It consists of 12,500 printed text words in 50 paragraphs and 15,000 words written by 100 different writers, males and females aged 5 to 60 years. Pre-processing operations are conducted including segmenting paragraphs into lines, segmenting line into words, segmenting words into characters, detecting skeleton, and determining baseline and other horizontal zones. The experimental results have shown that the proposed method has achieved promising accuracy recognition rate with 90.46% for printed text and handwritten characters

Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study

The maxillary sinus is the most prominent in humans. Maxillary Sinus Volume (MSV) has grown in popularity as a tool to predict the success of various dental procedures and oral surgeries and determine a person's gender in cases such as forensic investigations when only partial skulls are available. Because it is an irregularly shaped cavity that may be difficult to measure manually, robust imaging techniques such as cone-beam computed tomography (CBCT) used in conjunction with machine learning (ML) algorithms may offer quick and vigorous ways to make accurate predictions using sinus data. In this retrospective study, we used data from 150 patients with normal maxillary sinuses to train and evaluate a Python ML algorithm for its ability to predict MSV from basic patient demographics (age, gender) and sinus length measurements in three directions (anteroposterior, mediolateral, and superoinferior). The model found sinus length measurements had significantly higher predictive values than either age or gender and could predict MSVs from these length measurements with almost linear accuracy indicated by R-squared values ranging from 0.97 to 0.98% for the right and left sinuses

Methodology for Exploring Patterns of Epigenetic Information in Cancer Cells Using Data Mining Technique

Epigenetic changes are a necessary characteristic of all cancer types. Tumor cells usually target genetic changes and epigenetic alterations as well. It is most beneficial to identify epigenetic similar features among cancer various types to be able to discover the appropriate treatments. The existence of epigenetic alteration profiles can aid in targeting this goal. In this paper, we propose a new technique applying data mining and clustering methodologies for cancer epigenetic changes analysis. The proposed technique aims to detect common patterns of epigenetic changes in various cancer types. We demonstrated the validation of the new technique by detecting epigenetic patterns across seven cancer types and by determining epigenetic similarities among various cancer types. The experimental results demonstrate that common epigenetic patterns do exist across these cancer types. Additionally, epigenetic gene analysis performed on the associated genes found a strong relationship with the development of various types of cancer and proved high risk across the studied cancer types. We utilized the frequent pattern data mining approach to represent cancer types compactly in the promoters for some epigenetic marks. Utilizing the built frequent pattern item set, the most frequent items are identified and yield the group of the bi-clusters of these patterns. Experimental results of the proposed method are shown to have a success rate of 88% in detecting cancer types according to specific epigenetic pattern

Secure Patient Data Transfer Using Information Embedding and Hyperchaos

Health 4.0 is an extension of the Industry standard 4.0 which is aimed at the virtualization of health-care services. It employs core technologies and services for integrated management of electronic health records (EHRs), captured through various sensors. The EHR is processed and transmitted to distant experts for better diagnosis and improved healthcare delivery. However, for the successful implementation of Heath 4.0 many challenges do exist. One of the critical issues that needs attention is the security of EHRs in smart health systems. In this work, we have developed a new interpolation scheme capable of providing better quality cover media and supporting reversible EHR embedding. The scheme provides a double layer of security to the EHR by firstly using hyperchaos to encrypt the EHR. The encrypted EHR is reversibly embedded in the cover images produced by the proposed interpolation scheme. The proposed interpolation module has been found to provide better quality interpolated images. The proposed system provides an average peak signal to noise ratio (PSNR) of 52.38 dB for a high payload of 0.75 bits per pixel. In addition to embedding EHR, a fragile watermark (WM) is also encrypted using the hyperchaos embedded into the cover image for tamper detection and authentication of the received EHR. Experimental investigations reveal that our scheme provides improved performance for high contrast medical images (MI) when compared to various techniques for evaluation parameters like imperceptibility, reversibility, payload, and computational complexity. Given the attributes of the scheme, it can be used for enhancing the security of EHR in health 4.0

Sentiment analysis of extremism in social media from textual information

Uncertainty in political, religious, and social issues causes extremism among people that are depicted by their sentiments on social media. Although, English is the most common language used to share views on social media, however, other vicinity based languages are also used by locals. Thus, it is also required to incorporate the views in such languages along with widely used languages for revealing better insights from data. This research focuses on the sentimental analysis of social media multilingual textual data to discover the intensity of the sentiments of extremism. Our study classifies the incorporated textual views into any of four categories, including high extreme, low extreme, moderate, and neutral, based on their level of extremism. Initially, a multilingual lexicon with the intensity weights is created. This lexicon is validated from domain experts and it attains 88% accuracy for validation. Subsequently, Multinomial Naïve Bayes and Linear Support Vector Classifier algorithms are employed for classification purposes. Overall, on the underlying multilingual dataset, Linear Support Vector Classifier out-performs with an accuracy of 82%

An Iterative Filtering Based ECG Denoising Using Lifting Wavelet Transform Technique

This research article explores a hybrid strategy that combines an adaptive iterative filtering (IF) method and the fast discrete lifting-based wavelet transform (LWT) to eliminate power-line noise (PLI) and baseline wander from an electrocardiogram (ECG) signal. Due to its correct mathematical basis and its guaranteed a priori convergence, the iterative filtering approach was preferred over empirical mode decomposition (EMD). The noisy modes generated from the IF are fed to an LWT system so as to be disintegrated into the detail and the approximation coefficients. These coefficients are then scaled using a threshold method to generate a noise-free signal. The proposed strategy improves the quality and allows us to precisely preserve the vital components of the signal. The method’s potency has been established empirically by calculating the improvement in signal-to-noise ratio, cross-correlation coefficient and percent root-mean-square difference for different recordings available on the MIT-BIH arrhythmia database and then compared to numerous existing methods

Abstractive text summarization of low-resourced languages using deep learning

Background Humans must be able to cope with the huge amounts of information produced by the information technology revolution. As a result, automatic text summarization is being employed in a range of industries to assist individuals in identifying the most important information. For text summarization, two approaches are mainly considered: text summarization by the extractive and abstractive methods. The extractive summarisation approach selects chunks of sentences like source documents, while the abstractive approach can generate a summary based on mined keywords. For low-resourced languages, e.g., Urdu, extractive summarization uses various models and algorithms. However, the study of abstractive summarization in Urdu is still a challenging task. Because there are so many literary works in Urdu, producing abstractive summaries demands extensive research. Methodology This article proposed a deep learning model for the Urdu language by using the Urdu 1 Million news dataset and compared its performance with the two widely used methods based on machine learning, such as support vector machine (SVM) and logistic regression (LR). The results show that the suggested deep learning model performs better than the other two approaches. The summaries produced by extractive summaries are processed using the encoder-decoder paradigm to create an abstractive summary. Results With the help of Urdu language specialists, the system-generated summaries were validated, showing the proposed model’s improvement and accuracy

Fabrication of Flexible Role-Based Access Control Based on Blockchain for Internet of Things Use Cases

The Internet of Things (IoT) connects many objects and allows continuous communication and data sharing has emerged as a revolutionary technology. However, expanding IoT devices has raised concerns regarding data security and access control. Traditional access control mechanisms face challenges in managing access rights, particularly in scenarios where multiple users with the same roles try to access several resources which may lead to conflicting roles. Additionally, there is also an overhead of system performance using traditional approaches. In existing studies, the main problem of conflict roles is not addressed or not even identified appropriately. This paper proposes a framework to address these challenges using blockchain technology and role-based access control with a smart contract implementation on the hyperledger fabric framework. The proposed methodology introduces a role management system that resolves conflicts based on predefined rules and user preferences. It employs a consensus mechanism to determine access permissions, ensuring fairness and accountability. The findings demonstrate that applying the suggested framework eliminates conflicting problems, improves system security and also provides better results in response times for concurrent user requests