Background: Agriculture is one of the most crucial assets of any country, as it brings prosperity by alleviating poverty, food shortages, unemployment, and economic instability. The entire process of agriculture comprises many sectors, such as crop cultivation, water irrigation, the supply chain, and many more. During the cultivation process, the plant is exposed to many challenges, among which pesticide attacks and disease in the plant are the main threats. Diseases affect yield production, which affects the country’s economy. Over the past decade, there have been significant advancements in agriculture; nevertheless, a substantial portion of crop yields continues to be compromised by diseases and pests. Early detection and prevention are crucial for successful crop management. Methods: To address this, we propose a framework that utilizes state-of-the-art computer vision (CV) and artificial intelligence (AI) techniques, specifically deep learning (DL), for detecting healthy and unhealthy cotton plants. Our approach combines DL with feature extraction methods such as continuous wavelet transform (CWT) and fast Fourier transform (FFT). The detection process involved employing pre-trained models such as AlexNet, GoogLeNet, InceptionV3, and VGG-19. Implemented models performance was analysed based on metrics such as accuracy, precision, recall, F1-Score, and Confusion matrices. Moreover, the proposed framework employed ensemble learning framework which uses averaging method to fuse the classification score of individual DL model, thereby improving the overall classification accuracy. Results: During the training process, the framework achieved better performance when features extracted from CWT were used as inputs to the DL model compared to features extracted from FFT. Among the learning models, GoogleNet obtained a remarkable accuracy of 93.4% and a notable F1-score of 0.953 when trained on features extracted by CWT in comparison to FFT-extracted features. It was closely followed by AlexNet and InceptionV3 with an accuracy of 93.4% and 91.8% respectively. To further improve the classification accuracy, ensemble learning framework achieved 98.4% on the features extracted from CWT as compared to feature extracted from FFT. Conclusion: The results show that the features extracted as scalograms more accurately detect each plant condition using DL models, facilitating the early detection of diseases in cotton plants. This early detection leads to better yield and profit which positively affects the economy.

Analysing big data job posts in Saudi cyberspace to describe the future market need for sustainable skills, this study used the power of artificial intelligence, deep learning, and big data technologies. The study targeted three main stakeholders: students, universities, and job providers. It provides analytical insights to improve student satisfaction, retention, and employability, investigating recent trends in the essential skills pinpointed as enhancing the social effect of learning, and identifying and developing the competencies and talents required for the Kingdom of Saudi Arabia’s (KSA’s) digital transformation into a regional and global leader in technology-driven innovation. The methodological framework comprises smart data processing, word embedding, and case-based reasoning to identify the skills required for job positions. The study’s outcomes may promote the alignment of KSA’s business and industry to academia, highlighting where to build competencies and skills. They may facilitate the parameterisation of the learning process, boost universities’ ability to promote learning efficiency, and foster the labour market’s sustainable evolution towards technology-driven innovation. We believe that this study is crucial to Vision 2030’s realisation through a long-term, inclusive approach to KSA’s transformation of knowledge and research into new employment, innovation, and capacity.

With the advancement of technology, academics and curriculum developers are always under pressure to provide students with skills that match the market’s requirements. A systematic and continuous examination of the market is needed, to stay up to date with the required skills, and then to update the curriculum to train the students with required market skills. In this article, we present a framework referred to as Align My Curriculum (AMC). The AMC framework aims to facilitate alignment between acquired university curriculum outcomes and required market skills. It can be used to classify, compare and visualize the data of a university curriculum and job vacancies in the market. The presented framework benefits academics and curriculum developers by improving the courses and therefore bridging the skills gap. Stakeholders from both academia and industry can gain insights into the predominant required and acquired skills. In addition, it may be useful for analysts, students, and job applicants. This article describes the architecture, implementation and experimental results, with visual analysis to help decision and policy-makers.

Social Media (SM) platforms, particularly Twitter, have become useful tools for startup companies (henceforth startups) which use the latter to support most of their business activities. As a result, there is a need to gauge the performance of specific business initiatives vis-à-vis public sentiment, or more specifically the spread of such initiatives based on Twitter user-generated content. Previous research which makes use of Twitter analysis to analyze the business activities of startups is minimal, especially for Twitter user content in the Arabic language. Consequently, this paper proposes an analytics-based framework called Startup Initiatives Response Analysis (SIRA) designed to assess the performance of initiatives launched by startups via text classification, sentiment analysis, and statistical analysis techniques. To provide empirical evidence for the viability of the proposed research framework, this paper examined the case of an Arab transportation network startup, carrying out a SIRA analysis of an initiative undertaken by Careem to empower women by encouraging them to work for the company. The results confirm the effectiveness of the proposed framework for statistically measuring the initiative spread and the public feedback based on the user-generated content on the Twitter social platform.

The need for smart e‐learning environments is resulting in new challenges for researchers and practitioners to develop intelligent systems that can be used to automate the Higher Education (HE) activities in an intelligent way. Some common examples of such activities are “analyzing, finding, and ranking the right resource to teach a course,” “analyzing and finding the people with common research interests to start joint research projects,” and “using data analytics and machine reasoning techniques for conducting the exams with different levels of complexities.” Ontological reasoning and smart data analytics can play an important role in analyzing and automating these HE activities and processes. In this paper, we present a framework named as Higher Education Activities and Processes Automation Framework (HEAPAF). The HEAPAF framework can be used to identify, extract, process, and produce the semantically enriched data in machine understandable format from different educational resources. We also present the Higher Education Ontology (HEO) that we designed and developed to accommodate the HE data and then to perform analysis and reasoning on it. As a proof of concept, we present a case study on the topic, “analyzing, finding, and ranking the right resources to teach a course,” which can dramatically improve the learning patterns of students in the growing smart educational environment. Finally, we provide the evaluation of our framework as evidence of its competency and consistency in improving academic analytics for educational activities and processes by using machine reasoning.