Diabetic retinopathy (DR) is a complication of diabetes that leads to blindness. The manual screening of color fundus images to detect DR at early stages is expensive and time consuming. Deep learning (DL) techniques have been employed for automatic DR screening on fundus images due to their outstanding performance in many applications. However, training a DL model needs a huge amount of data, which are usually unavailable in the case of DR, and overfitting is unavoidable. Employing a two-stage transfer learning method, we developed herein an intelligent computer-aided system using a pre-trained convolutional neural network (CNN) for automatic DR screening on fundus images. A CNN model learns the domain-specific hierarchy of low- to high-level features. Given this, using the regions of interest (ROIs) of lesions extracted from the annotated fundus images, the first layer of a pre-trained CNN model is re-initialized. The model is then fine-tuned, such that the low-level layers learn the local structures of the lesion and normal regions. As the fully connected layer (FC) layers encode high-level features, which are global in nature and domain specific, we replace them with a new FC layer based on the principal component analysis PCA and use it in an unsupervised manner to extract discriminate features from the fundus images. This step reduces the model complexity, significantly avoiding the overfitting problem. This step also lets the model adopt the fundus image structures, making it suitable for DR feature detection. Finally, we add a gradient boosting-based classification layer. The evaluation of the proposed system using a 10-fold cross-validation on two challenging datasets (i.e., EyePACS and Messidor) indicates that it outperforms state-of-the-art methods. It will be useful for the initial screening of DR patients and will help graders in deciding quickly as regards patient referral to an ophthalmologist for further diagnosis and treatment.

Fingerprints are gaining in popularity, and fingerprint datasets are becoming increasingly large. They are often captured utilizing a variety of sensors embedded in smart devices such as mobile phones and personal computers. One of the primary issues with fingerprint recognition systems is their high processing complexity, which is exacerbated when they are gathered using several sensors. One way to address this issue is to categorize fingerprints in a database to condense the search space. Deep learning is effective in designing robust fingerprint classification methods. However, designing the architecture of a CNN model is a laborious and time-consuming task. We proposed a technique for automatically determining the architecture of a CNN model adaptive to fingerprint classification; it automatically determines the number of filters and the layers using Fukunaga–Koontz transform and the ratio of the between-class scatter to within-class scatter. It helps to design lightweight CNN models, which are efficient and speed up the fingerprint recognition process. The method was evaluated two public-domain benchmark datasets FingerPass and FVC2004 benchmark datasets, which contain noisy, low-quality fingerprints obtained using live scan devices and cross-sensor fingerprints. The designed models outperform the well-known pre-trained models and the state-of-the-art fingerprint classification techniques.

The incorporation of renewable energy sources into the power grid has necessitated the development of sophisticated forecasting models that can effectively handle the inherent fluctuation and uncertainty associated with renewable energy generation. In this study, an adaptive principal component analysis (PCA)-enhanced transformer architecture, hereinafter referred to as PCA–Transformer, is developed to enhance the performance of transformer models in predicting renewable energy output. The proposed model uses PCA to dynamically determine and adapt the transformer architecture and prioritize the most informative features from time series data, thereby improving the model’s attention on relevant information and reducing computational burden. This is essential for accurately capturing the intricate temporal patterns and nonlinear relationships that are typical present in renewable energy time series data. The PCA–Transformer enhances the performance of transformer models in sequence-to-sequence predictions by incorporating an adaptive mechanism that customizes their structure based on the best PCA eigenvectors. The architecture adaptively aligns with the underlying patterns in data by adjusting the number of attention heads and critical dimensions within each transformer block. The adaptability of the proposed architecture is crucial for effectively simulating the complex nature of renewable energy generation patterns. The efficiency of the proposed model was evaluated using the Alice Springs Australia DKASC-ASA and EIA Energy datasets. The proposed model has superior forecasting performance than traditional transformer models and cutting-edge renewable energy forecasting methodologies.

Line outage contingencies in power distribution systems pose critical challenges, leading to disruptions, reduced reliability, and potential cascading failures. These problems include increased vulnerability, limited resilience, and the need for efficient mitigation strategies to enhance the overall system reliability and quality. This study aims to investigate, analyze, and evaluate the renewable distributed generator (RDG) allocation and sizing under N-1 line outage conditions in terms of the reliability and quality for the IEEE 30-bus benchmark power system as a case study. Under all possible N-1 line outage conditions, there were four critical N-1 line outage conditions, 19–20, 10–20, 27–29, and 27–30, which caused overloading on at least one line. The Severity Performance Index (SPI) recorded the highest value of 0.715 during the line 10–20 outage, followed by 0.683, 0.606, and 0.476 during the line F27–30 outage, line F19–20 outage, and line F27–29 outage, respectively. This indicates that the line 10–20 outage is the most critical among the line outages followed by the line 27–30 outage. During the line 10–20 outage, the crow search integrated with the particle swarm optimizer recommends allocating renewable distributed generators (RDGs) at optimal or feasible buses 14, 15, 17, 20, and 30, with suggested sizes of 26.8127 MW, 38.8986 MW, 27.9600 MW, 21.6300 MW, and 27.0184 MW, respectively. The obtained finding revealed that allocating five RDGs at optimal busbars helped keep the line loading below maximum limits and improved the voltage profiles during the N-1 line outages in the IEEE 30-bus benchmark power system. This approach enhanced the power system reliability and quality across all four N-1 scenarios.

Diabetic retinopathy (DR) is a leading cause of blindness in middle-aged diabetic patients. Regular screening for DR using fundus imaging aids in detecting complications and delays the progression of the disease. Because manual screening takes time and is subjective, deep learning has been used to help graders. Pre-trained or brute force CNN models are used in existing DR grading CNN-based approaches that are not suited to fundus image complexity. To solve this problem, we present a method for automatically customizing CNN models based on fundus image lesions. It uses k-medoid clustering, principal component analysis (PCA), and inter-class and intra-class variations to determine the CNN model’s depth and width. The designed models are lightweight, adapted to the internal structures of fundus images, and encode the discriminative patterns of DR lesions. The technique is validated on a local dataset from King Saud University Medical City, Saudi Arabia, and two challenging Kaggle datasets: EyePACS and APTOS2019. The auto-designed models outperform well-known pre-trained CNN models such as ResNet152, DenseNet121, and ResNeSt50, as well as Google’s AutoML and Auto-Keras models based on neural architecture search (NAS). The proposed method outperforms current CNN-based DR screening methods. The proposed method can be used in various clinical settings to screen for DR and refer patients to ophthalmologists for further evaluation and treatment.

Nowadays, technology is increasingly being adopted in different kinds of businesses to process, store, and share sensitive information in digital environments that include enormous numbers of users. However, this has also increased the likelihood of cyberattacks and misuse of information, potentially causing severe damage. One promising technology, which can provide the required security services with an improved level of efficiency, is blockchain. This research explores the use of Ethereum blockchain and smart contracts to create a secure and efficient quality assurance system (QAS) for academic programs. By utilizing blockchain and smart contracts, the proposed approach improves the integrity and reliability of sensitive information processed by the QAS, promotes transparency and governance, and reduces the time and effort required for quality operations. The current approach uses an additional access control layer to further enhance user privacy. Smart contracts automate various quality transactions and saves time and resources, and hence increases the efficiency of the QAS. The interplanetary file system (IPFS) is used to address the challenge of size limitations in blockchain. Additionally, this research investigates the use of various cryptographic schemes to provide robust security services at the application layer. The experimental results showed that the use of a hybrid cryptosystem relying on an Elliptic curve digital signature and AES encryption (AES_ECCDSA) outperforms other counterparts’ cryptosystems using an RSA digital signature and AES encryption (AES_RSADSA) and Elliptic Curve Integrated Encryption Scheme (ECIES) in terms of speed. The performance results showed that AES_ECCDSA consumes 188 ms to perform the required cryptographic operations for a standard-quality document with a size of 8088 KB, compared to the 231 ms and 739 ms consumed by the AES_RSADSA and ECIES schemes, respectively. This study presents a prototype implementation of the blockchain-based QAS, which outlines the processing model and system requirements for key QAS processes. It has been found that the cost and time required for blockchain operations vary depending on the size of the input data—a larger data size requires more time and costs more to process. The results of the current study showed that the time delay for blockchain transactions ranges from 15 to 120 s, while the cost ranges from USD 50 to USD 400. This research provides evidence that blockchain and smart contract technologies have the potential to create a secure, efficient, and trustworthy QAS environment for academic programs.