Contributing to Vision 2030, Saudi higher educational institutions (HEIs) must reform their education system, reevaluate their potentialities, and priorities to support higher education development in achieving the Vision theme. With this goal, several educational innovation projects have been introduced to attain the vision's higher educational development strategic objectives. This study investigates the HEIs' current practices and analyses their achievements and the progress toward achieving the Vision's higher education development goals for the first review cycle (2016–2020) of the Vision. Academic expert interviews and surveys were conducted involving participants from the top ten Saudi universities to know how these institutions contribute to the Vision progress using an innovative approach. Juxtaposing HEIs' potential and priorities with the Vision's higher educational objectives to determine the development progress. The findings reveal that the most sought priorities are the new modern curriculum, industry-based academic learning outcomes, skilled graduates, faculty development, innovative research, foreign universities collaborations, accreditations, and lifelong learning focusing on future skills. The tendency of these priorities works on higher education development, improving professional competency, filling the gaps between higher education outcomes and the growing market needs, revitalizing universities, and linking to knowledge base society. The presented approach will be an effective tool for understanding how specifically these entities contribute toward achieving the vision's targets. It is a significant model for future studies, useful for analyses of higher education potentialities' performances, and enhances readers' understanding.

With the growing number of cloud users, shared data auditing is becoming increasingly important. However, these schemes have issues with the certificate management. Although there is a certificate-shared auditing scheme, it is ineffective in dealing with dynamic data and protecting data privacy. The verifier cannot access the data content to ensure data integrity due to security concerns. This paper proposes a novel technique to ensure the integrity and improve the access control. A novel enhanced storage retrieval mechanism is used to improve the performance of the cloud’s storage and retrieval mechanisms to achieve this. The technique is evaluated in concern of the upload, download, encryption, and decryption time. As the file size grows, so does the time it takes to upload it. Similarly, the time taken to encrypt files of various formats and sizes evidenced that it depends on the file size and format. Thus, the encryption time increases as the file sizes increases, demonstrating the performance of the proposed system.

Vertical farming methods are gaining importance in the current era of urbanization and industrialization 5.0. These methods of farming enhance sustainability by consuming less space and reducing carbon emissions and greenhouse gas emissions. The Green Internet of Things (G-IoT) offers greater environmental sustainability by switching to a dormant mode while not in use, thereby consuming less energy. Each farming method has a different effect on the shoot and root growth of the plants. Thus, dedicated farming methods must be identified for each crop according to the type of crop under consideration. This leads to a need to compare and analyze the root as well as shoot growth trends of crops in different cultivation mediums, using different cultivation methods, thereby identifying the most suitable method for the cultivation of the crop. A comparative analysis of barley shoot and root growth in green IoT-embedded hydroponics and substrate cultivation methods has shown that hydroponics exhibits two times more shoot growth than substrate cultivation. Furthermore, the results were verified against the results obtained from the simulator, which confirmed that the hydroponic method of cultivation produced a year-round qualitative product with 17.112 tons of biomass and 8.556 tons of dry yield.

Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the health data from the patient are monitored using IoT technology. Arrhythmias may suddenly lead to death and the classification of arrhythmias is considered a complicated process. In this research, an effective classification model for the classification of heart disease is developed using flamingo optimization. Initially, the ECG signal from the heart is collected and then it is subjected to the preprocessing stage; to detect and control the electrical activity of the heart, the electrocardiogram (ECG) is used. The input signals collected using IoT nodes are collectively presented in the base station for the classification using flamingo-optimization-based deep convolutional networks, which effectively predict the disease. With the aid of communication technologies and the contribution of IoT, medical professionals can easily monitor the health condition of patients. The performance is analyzed in terms of accuracy, sensitivity, and specificity.

The traditional way of diagnosing malaria takes time, as physicians have to check about 5000 cells to produce the final report. The accuracy of the final report also depends on the physician’s expertise. In the event of a malaria epidemic, a shortage of qualified physicians can become a problem. In the manual method, the parasites are identified by visual identification; this technique can be automated with the use of new algorithms. There are numerous publicly available image datasets containing the intricate structure of parasites, and deep learning algorithms can recognize these complicated patterns in the images. This study aims to identify and localize malaria parasites in the photograph of blood cells using the YOLOv5 model. In this research, a publicly available malaria trophozoite dataset is utilized which contains 1182 data samples. YOLOv5, with the novel technique of weight ensemble and traditional transfer learning, is trained using this dataset, and the results were compared with the other object detection models—for instance, Faster RCNN, SSD net, and the hybrid model. It was observed that YOLOv5 with the ensemble weights yields better results in terms of precision, recall, and mAP values: 0.76, 0.78, and 0.79, respectively. The mAP score closer to 1 signifies a higher confidence in localizing the parasites. This study is the first implementation of ensemble YOLOv5 in the malaria parasite detection field. The proposed ensemble model can detect the presence of malaria parasites and localize them with bounding boxes better than previously used models.

In today’s multifaceted academic context, selecting, adopting, and adapting appropriate teaching methods (TMs) have been a pivotal concern for teachers. No study, to the researchers’ knowledge, has been conducted on compiling the maximum number of TMs in higher education. This study aims to list, describe, and provide a platform of the potential and the most practicing TMs in four major educational disciplines. This article, taking a cross-disciplinary lens, conducts an in-depth review of 90 articles and enumerates 110 TMs of higher education. It also identifies several TMs that are commonly used in each discipline. The article concludes that knowledge generated from this study fills up the existing literature gap. It calls attention to the current TM practices and provides teachers with an outline to employ available TMs in their respective disciplines.