The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.

Lifelogging is the process of digital tracking of person’s daily experiences for a variety of purposes. In recent years, lifelogging has become an increasingly popular area of research due to not only the growing demands from many applications, such as wellbeing, entertainment, healthcare systems, and intelligent environments, but also to the advances in device technologies that offer the promise to record and store large volumes of personal data using inexpensive tools. However, getting insights from egocentric experience using huge deluge of unlabeled and unstructured data continues to pose major challenges. A large number of research have been conducted in recent years to cover these challenges but there is still a lack of studies that provide a comprehensive survey of the available literature, while most of the existing lifelogging surveys generally focus on only one aspect. This review highlights the advances of state-of-the-art in lifelogging from different angles, including its research history, current applications, activity recognition techniques, moment retrieval, storytelling, privacy and security issues, as well as challenges and future research trends.

Diabetes, in all of its types, costs countries of all income levels unacceptably enormous personal, societal, and economic expenses. To predict type-2 diabetes, this work aimed to develop an analytical predictive model based on machine learning techniques and a web-based personalized diabetes monitoring system. The history of a patient will be collected and ready for analysis purposes based on machine learning techniques by continuously monitoring the patient’s vital data. A diabetes monitoring system is proposed by utilizing the patient’s QR card that allows the patients and doctors to be connected to the Internet of Things. So, they can deliver real-time information (such as insulin records) about their health status and can visit different healthcare institutions. The proposed system can help doctors to make data-driven decisions and enhance patients’ treatment. Several machine learning algorithms that are Decision Tree, Support Vector Classifier, Random Forest, Gradient Boosting, Multi-layer Perceptron’s, Artificial Neural Network, k-Nearest Neighbors, Logistic Regression, and Naive Bayes are used. The proposed analytical model is evaluated based on two different datasets a synthetic dataset and PIMA Diabetes Dataset. The performance of the classification models was analyzed in terms of accuracy, recall, and precision based on the cross-validation strategy. The findings show that the ANN model has better prediction accuracy than other models. The evaluation findings are analyzed and compared with other existing models. The system has dashboard graphs displaying the number of patients in Saudi Arabia cities. It also contains visualized graphs that include more detailed classifications for patients’ states (Normal, Pre-Diabetes, and Diabetes).

Ongoing importance remains placed on parameter estimation in photovoltaic (PV) system design and simulation. Among the diode-based models utilized frequently are those consisting of a single diode, a double diode, and three diode. Minimizing the discrepancy between the calculated and measured values is often the primary aim when estimating parameters for these models. In recent years, the estimation of parameters in conventional PV models has been approached using various numerical, analytical, and hybrid techniques. However, these methods could be made more complex to produce credible results promptly and precisely. This article discusses the three fundamental PV models. A contemporary optimization algorithm, the Crayfish Optimisation algorithm (COA), is employed to extract the parameters for PV models. This includes the single-diode, double-diode, and three-diode models. An assessment uses to contrast the PV models. COA outperforms the subsequent competing algorithms, as demonstrated by the experimental results: Hunger Games Search, SOA, STOA, Synergistic Mimic Algorithm (SMA), TURBULAS Swarm Algorithm (TSA), and LAPOP (Lightning Attachment Procedure Optimisation) are all examples of optimization algorithms, along with HHO, HBO, LIPO, SOA, and STOA, respectively. By the negligible difference between measured and calculated data, this comparison illustrates that the parameters extracted by COA are optimal. As determined by the proposed COA algorithm, 0.00085477, 0.0010313, and 0.00092288 are the optimal RMSE values for SDM, DDM, and TDM.

Coverless image steganography conceals information without modifying the carrier image, addressing vulnerabilities in traditional methods. However, existing approaches often require transmitting metadata, raising suspicion and security risks. To overcome these limitations, we propose Coverless Hybrid Image Data Encryption (C-HIDE), a robust steganographic method integrating Advanced Encryption Standard (AES) for data confidentiality and Elliptic Curve Cryptography (ECC) for secure key exchange. The system ensures secure transmission without altering cover images, making embedded data harder to detect. C-HIDE eliminates metadata transmission by enabling both sender and receiver to independently generate synchronized coverless image datasets (CIDs) using random seeds. Encrypted secret data is mapped to images whose hash sequences correspond to segments of the message, with Speeded-Up Robust Features (SURF) ensuring reliable image matching. At the receiver’s end, ECC-decrypted AES keys recover the original message while SURF retrieves relevant images. Experimental results demonstrate that C-HIDE achieves an embedding capacity of 574 bits per image, significantly surpassing DCT (256 bits) and DWT (128 bits) techniques. The system maintains 98.5% accuracy under attacks such as noise addition, cropping, and geometric transformations. Furthermore, it enhances security by eliminating metadata transmission, achieving a zero additional information ratio, unlike conventional methods requiring up to 25% extra data. By integrating encryption, minimizing detection, and removing metadata transmission, C-HIDE provides a secure, efficient, and scalable solution for covert communication in real-world applications.