A total of 8.46 million tons of date fruit are produced annually around the world. The date fruit is considered a high-valued confectionery and fruit crop. The hot arid zones of Southwest Asia, North Africa, and the Middle East are the major producers of date fruit. The production of dates in 1961 was 1.8 million tons, which increased to 2.8 million tons in 1985. In 2001, the production of dates was recorded at 5.4 million tons, whereas recently it has reached 8.46 million tons. A common problem found in the industry is the absence of an autonomous system for the classification of date fruit, resulting in reliance on only the manual expertise, often involving hard work, expense, and bias. Recently, Machine Learning (ML) techniques have been employed in such areas of agriculture and fruit farming and have brought great convenience to human life. An automated system based on ML can carry out the fruit classification and sorting tasks that were previously handled by human experts. In various fields, CNNs (convolutional neural networks) have achieved impressive results in image classification. Considering the success of CNNs and transfer learning in other image classification problems, this research also employs a similar approach and proposes an efficient date classification model. In this research, a dataset of eight different classes of date fruit has been created to train the proposed model. Different preprocessing techniques have been applied in the proposed model, such as image augmentation, decayed learning rate, model checkpointing, and hybrid weight adjustment to increase the accuracy rate. The results show that the proposed model based on MobileNetV2 architecture has achieved 99% accuracy. The proposed model has also been compared with other existing models such as AlexNet, VGG16, InceptionV3, ResNet, and MobileNetV2. The results prove that the proposed model performs better than all other models in terms of accuracy.

In the digital era, social media has become a cornerstone for educational institutions, driving public engagement and enhancing institutional communication. This study utilizes AI-driven image processing and Web-enhanced Deep Learning (DL) techniques to investigate the effectiveness of King Faisal University’s (KFU’s) social media strategy as a case study, particularly on Twitter. By categorizing images into five primary event management categories and subcategories, this research provides a robust framework for assessing the social media content generated by KFU’s administrative units. Seven advanced models were developed, including an innovative integration of Vision Transformers (ViTs) with Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, VGG16, and ResNet. The AI-driven ViT-CNN hybrid model achieved perfect classification accuracy (100%), while the “Development and Partnerships” category demonstrated notable accuracy (98.8%), underscoring the model’s unparalleled efficacy in strategic content classification. This study offers actionable insights for the optimization of AI-driven digital communication strategies and Web-enhanced data collection processes, aligning them with national development goals and Saudi Arabia’s Vision 2030, thereby showcasing the transformative power of DL in event-centric management and the broader higher education landscape.

The Web of Things (WoT) represents a significant advancement on the Internet of Things (IoT), where web technologies are integrated to enhance device interoperability and accessibility. While this integration offers numerous benefits, it also introduces new and complex security challenges. This paper presents a critical analysis of WoT security, examining the ecosystem’s vulnerabilities and associated threats. Our contributions include an in-depth analysis of existing threat enumeration methodologies, highlighting misconceptions and inefficiencies that may weaken security measures. We further conduct a comprehensive survey of critical threats within the WoT environment, detailing potential attack vectors and misuses linked to these threats. To address these security gaps, we propose a set of defenses tailored to each identified threat, providing a holistic view of the WoT’s security landscape. We also develop abstract architectural models of the WoT using UML, serving as foundational tools for understanding the interactions and risks within WoT systems. Finally, we model a specific attack scenario, demonstrating how attacks unfold in real-world WoT environments and the importance of defense strategies. These findings aim to guide the development of secure WoT systems, ensuring robust defenses against evolving security threats.

Universities increasingly leverage social media platforms, especially Twitter, for news dissemination, audience engagement, and feedback collection. King Faisal University (KFU) is dedicated to enhancing its institutional identity (ID), grounded in environmental sustainability and food security, encompassing nine critical areas. This study aims to assess the impact of KFU’s Twitter interactions on public awareness of its institutional identity using systematic analysis and machine learning (ML) methods. The objectives are to: (1) Determine the influence of KFU’s Twitter presence on ID awareness; (2) create a dedicated dataset for real-time public interaction analysis with KFU’s Twitter content; (3) investigate Twitter’s role in promoting KFU’s institutional identity across 9-ID domains and its changing impact over time; (4) utilize k-means clustering and sentiment analysis (TFIDF and Word2vec) to classify data and assess similarities among the identity domains; and (5) apply the categorization method to process and categorize tweets, facilitating the assessment of word meanings and similarities of the 9-ID domains. The study also employs four ML models, including Logistic Regression (LR) and Support Vector Machine (SVM), with the Random Forest (RF) model combined with Word2vec achieving the highest accuracy of 100%. The findings underscore the value of KFU’s Twitter data analysis in deepening the understanding of its ID and guiding the development of effective communication strategies.