Talal Saad Alharbi

The developed world has focused on Web preservation compared to the developing world, especially news preservation for future generations. However, the news published online is volatile because of constant changes in the technologies used to disseminate information and the formats used for publication. News preservation became more complicated and challenging when the archive began to contain articles from low-resourced and morphologically complex languages like Urdu and Arabic, along with English news articles. The digital news story preservation framework is enriched with eighteen sources for Urdu, Arabic, and English news sources. This study presents challenges in low-resource languages (LRLs), research challenges, and details of how the framework is enhanced. In this paper, we introduce a multilingual news archive and discuss the digital news story extractor, which addresses major issues in implementing low-resource languages and facilitates normalized format migration. The extraction results are presented in detail for high-resource languages, i.e., English, and low-resource languages, i.e., Urdu and Arabic. LRLs encountered a high error rate during preservation compared to high-resource languages (HRLs), corresponding to 10% and 03%, respectively. The extraction results show that few news sources are not regularly updated and release few new news stories online. LRLs require more detailed study for accurate news content extraction and archiving for future access. LRLs and HRLs enrich the digital news story preservation (DNSP) framework. The Digital News Stories Archive (DNSA) preserves a huge number of news articles from multiple news sources in LRLs and HRLs. This paper presents research challenges encountered during the preservation of Urdu and Arabic-language news articles to create a multilingual news archive. The second part of the paper compares two bilingual linking mechanisms for Urdu-to-English-language news articles in the DNSA: the common ratio measure for dual language (CRMDL) and the similarity measure based on transliteration words (SMTW) with the cosine similarity measure (CSM) baseline technique. The experimental results show that the SMTW is more effective than the CRMDL and CSM for linking Urdu-to-English news articles. The precision improved from 46% and 50% to 60%, and the recall improved from 64% and 67% to 82% for CSM, CRMDL, and SMTW, respectively, with improved impact of common terms as well.

Retrieving a specific digital information object from a multi-lingual huge and evolving news archives is challenging and complicated against a user query. The processing becomes more difficult to understand and analyze when low-resourced and morphologically complex languages like Urdu and Arabic scripts are included in the archive. Computing similarity against a query and among news articles in huge and evolving collections may be inaccurate and time-consuming at run time. This paper introduces a Similarity Measure based on Transliteration Words (SMTW) from the English language in the Urdu scripts for linking news articles extracted from multiple online sources during the preservation process. The SMTW link Urdu-to-English news articles using an upgraded Urdu-to-English lexicon, including transliteration words. The SMTW was exhaustively evaluated to assess the effectiveness using different size datasets and the results were compared with the Common Ratio Measure for Dual Language (CRMDL). The experimental results show that the SMTW was more effective than the CRMDL for linking Urdu-to-English news articles. The precision improved from 50% to 60%, recall improved from 67% to 82%, and the impact of common terms also improved.

Disease detection, diagnosis, and treatment can all be done with the help of digitalized medical images. Macroscopic medical images are images obtained using ionizing radiation or magnetism that identify organs and body structures. In recent years, various computational tools such as databases, distributed processing, digital image processing, and pattern recognition in digital medical images have contributed to the development of Computer‐Aided Diagnosis (CAD), which serves as an auxiliary tool in health care. The use of various architectures based on convolutional neural networks (CNNs) for the automatic detection of diseases in medical images is proposed in this work. Different types of medical images are used in this work, such as chest tomography for identifying types of tuberculosis and chest X‐rays for detecting pneumonia to solve the same number of classification problems or detect patterns associated with diseases. Finally, an algorithm for automatic registration of thoracic regions is proposed, which intrinsically identifies the translation, scale, and rotation that align the thoracic regions in X‐ray images.

Chaotic systems are one of the most significant systems of the technological period because their qualities must be updated on a regular basis in order for the speed of security and information transfer to rise, as well as the system’s stability. The purpose of this research is to look at the special features of the nine‐dimensional, difficult, and highly nonlinear hyperchaotic model, with a particular focus on synchronization. Furthermore, several criteria for such models have been examined; Hamiltonian, synchronizing, Lyapunov expansions, and stability are some of the terms used. The geometrical requirements, which play an important part in the analysis of dynamic systems, are also included in this research due to their importance. The synchronization and control of complicated networks’ most nonlinear control is important to use and is based on two major techniques. The linearization approach and the Lyapunov stability theory are the foundation for attaining system synchronization in these two ways.

In recent years, in combination with technological advances, new paradigms of interaction with the user have emerged. This has motivated the industry to create increasingly powerful and accessible natural user interface devices. In particular, depth cameras have achieved high levels of user adoption. These devices include the Microsoft Kinect, the Intel RealSense, and the Leap Motion Controller. This type of device facilitates the acquisition of data in human activity recognition. Hand gestures can be static or dynamic, depending on whether they present movement in the image sequences. Hand gesture recognition enables human‐computer interaction (HCI) system developers to create more immersive, natural, and intuitive experiences and interactions. However, this task is not easy. That is why, in the academy, this problem has been addressed using machine learning techniques. The experiments carried out have shown very encouraging results indicating that the choice of this type of architecture allows obtaining an excellent efficiency of parameters and prediction times. It should be noted that the tests are carried out on a set of relevant data from the area. Based on this, the performance of this proposal is analysed about different scenarios such as lighting variation or camera movement, different types of gestures, and sensitivity or bias by people, among others. In this article, we will look at how infrared camera images can be used to segment, classify, and recognise one‐handed gestures in a variety of lighting conditions. A standard webcam was modified, and an infrared filter was added to the lens to create the infrared camera. The scene was illuminated by additional infrared LED structures, allowing it to be used in various lighting conditions.