A document’s keywords provide high-level descriptions of the content that summarize the document’s central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document’s content that can reflect the authors’ main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors’ provided keywords. The average similarity of our approach with author-assigned keywords is 51%.

Significant modifications have been seen in healthcare facilities over the past two decades. With the use of IoT-enabled devices, the monitoring and analysis of patient diagnostic parameters is made considerably easy. The new technology shift for medical field is IoMT. However, the problem of privacy for patient data and the security of information still a point to ponder. This research proposed a prototype model to integrate the blockchain and IoMT for providing better analysis of patient health factors. The authors suggested IoMT data to be collected over Edge Computing gateway devices and forward to Cloud Gateway. The three-layered decision making structure ensures the integrity of the data. The further analysis of information collected over sensor-based devices is done in the Cloud IoT Central Hub service. To ensure the secrecy and compliance of the patient data, Smart Contracts are integrated. After the exchange of smart contracts, a block of information is broadcast over the health blockchain. The P2P network makes it viable to retain all health statistics and further processing of information. The paper describes the scenario and experimental setup for a COVID-19 data-set analyzed in the proposed prototype mode. After the collection of information and decision making, the block of data is sent across all peer nodes. Thus, the power of IoMT and blockchain makes it easy for the healthcare worker to diagnose and handle patient data with privacy. The IoMT is integrated with artificial intelligence to enable decision making based on the classification of data. The results are saved as transactions in the blockchain hyperledger. This study demonstrates the prototype model with test data in a testing network with two peer nodes.

Validation of software requirements is a primary phase in requirements engineering that ensures requirements match the target system with the intended needs of the acquirer. It aims to detect and correct errors that prevail in the specified requirements. Although there are tremendous requirements validation approaches, some software may fail because of limited or ineffective requirements validation techniques and unreliable requirements’ quality characteristics. In this study, a systematic literature review of requirements validation is performed. The study analyzes the most adopted validation techniques, reports requirements quality characteristics, and discovers significant challenges of validation techniques. The review identified 66 relevant primary studies analyzed to derive deep insights into the following aspects of requirements validation: trends of requirements validation methods, including their subtechnique strengths and weaknesses, requirements quality characteristics categories, and adopted tools and datasets in these techniques. We grouped validation techniques into categories: prototyping, inspection, knowledge-oriented, test-oriented, modeling and assessment, and formal models. The analysis reported 19 validation techniques, 27 tools, new requirements validation characteristics, and several challenges that prevailed through validation techniques. The trend of validation techniques is to those methods that apply machine learning techniques with knowledge from dictionaries and ontologies. Most challenges are about how to express the requirements and how to revert clients’ feedback. There is a strong relationship between validation techniques, software application domain, and requirements validation quality attributes. Thus, there is an immense need to unify the quality characteristics and domain-specific validation methods.

Due to the COVID-19 pandemic, all Saudi universities have adopted e-learning systems to ensure that educational activities continue. Shaqra University adopted a platform called the Shaqra University e-learning platform. This study aimed to identify the factors contributing to the success of that platform in Shaqra University, based on students’ responses. This research has proposed an extension of well-known DeLone and McLean’s Information Systems Success (D&M ISS) model to check and validate the success factors of the Shaqra University platform. The questionnaire was adopted in this study to collect data from students currently enrolled at Shaqra University. One thousand online links to the questionnaire were randomly distributed among current students enrolled in Shaqra University. The results revealed that the instrument adopted in this study was valid and reliable. Also, the results showed that the model was a good fit for the Saudi context. The proposed factors of instructor’s quality, learner quality, and perceived usefulness positively impacted the e-learning platform. On the other hand, the factors information quality, system quality and service quality had no positive impact on the use of the e-learning platform.

Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with extensive data volumes. Smart grids provide a solution by enabling two-way electricity flow, thereby facilitating the detection, analysis, and implementation of new measures to address data flow issues. The key objective is to provide a deep learning-based amalgamated model to detect electricity theft and secure the smart grid. This research introduces an innovative approach to overcome the limitations of current electricity theft detection systems, which predominantly rely on analyzing one-dimensional (1-D) electric data. These approaches often exhibit insufficient accuracy when identifying instances of theft. To address this challenge, the article proposes an ensemble model known as the RNN-BiLSTM-CRF model. This model amalgamates the strengths of recurrent neural network (RNN) and bidirectional long short-term memory (BiLSTM) architectures. Notably, the proposed model harnesses both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data, thereby enhancing the effectiveness of the theft detection process. The experimental results showcase an impressive accuracy rate of 93.05% in detecting electricity theft, surpassing the performance of existing models in this domain.

New universities and educational organizations are increasing in Saudi Arabia with the increase in the need for high-quality education. This increased the need for a fast transformation to digitise the educational system in Saudi Arabia, which is one of the important pillars of the Saudi Vision 2030. The students who study in these organizations suffer the verification of academic records and other educational documents. Students who want to study at universities abroad also face the challenge of academic records and certificates verification. A secure, fast, and transparent model is required in the education sector in order to verify academic certificates issued by various educational organizations. Blockchain technology can be used with high data security to empower the educational sector of Saudi Arabia in the digital transformation and to help the educational organizations in verifying academic documents. In order to avoid any document fraud and forgery, along with the ease of verification of academic records and educational documents for the students. This research focuses on developing a model which will be helpful in achieving digital transformation in academic document verification by blockchain technology.