The integration of artificial intelligence (AI) and big data technologies has the potential to revolutionize various industries, yet there are complexities and challenges associated with their implementation. This comprehensive study aims to investigate the combined impact of AI and big data on operational efficiency, precision, and security across multiple sectors. By utilizing a methodological analysis of 105 peer-reviewed articles sourced from reputable databases, we systematically explore the diverse applications, key innovations, and transformative potential of these technologies. Our findings uncover significant advancements in healthcare diagnostics, drug discovery, personalized education, and smart farming, highlighting how AI enhances big data analytics to drive notable improvements. Specifically, the study reveals the accuracy of AI in healthcare diagnostics, the efficiency of big data in drug discovery, the personalization of learning experiences through AI in education, and the sustainability advancements in agriculture through smart farming. These results underscore a substantial shift toward more sophisticated data-driven decision-making and operational processes facilitated by the integration of AI and big data. This shift addresses the initial research problem and makes a significant contribution to both academic and practical understanding of the role these technologies play in shaping the future of industry operations. The study concludes that while AI and big data integration offers substantial benefits, addressing associated challenges is crucial for maximizing their impact.

There is an increasing need for secure and fast encryption algorithms to support applications and communication protocols, and business models. In this paper, we present an alternative stream cipher (Strike) inspired by the stochastic behaviour of lightning strike phenomena. The novelty and originality of Strike stem from the utilisation of lightning strike behaviour as a source for generating random keystreams for encryption and decryption. Strike consists of three main functions: a function for setting up the security attributes, a function for generating lightning strikes and converting them to a keystream, and a function for plaintext encryption. The proposed stream cipher was tested against several cryptanalysis and statistical attacks in addition to other performance tests. The results show that Strike achieves high throughput on both high- and low-speed devices. Additionally, security analysis shows that our cipher is resistant to cryptanalysis and statistical attacks.

In standard iris recognition systems, a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture, look-and-stare constraints, and a close distance requirement to the capture device. When these conditions are relaxed, the system’s performance significantly deteriorates due to segmentation and feature extraction problems. Herein, a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments. First, the algorithm scans the whole iris image in the Hue Saturation Value (HSV) color space for local maxima to detect the sclera region. The image quality is then assessed by computing global features in red, green and blue (RGB) space, as noisy images have heterogeneous characteristics. The iris images are accordingly classified into seven categories based on their global RGB intensities. After the classification process, the images are filtered, and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ring. Finally, to characterize the pupil area, the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris ring. The experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.

Steganography refers to the manner in which secret data is concealed under a non-secret data. This non-secret data is known to be a digital form of media that are used as a cover. There are extensive researches regarding the images of steganographic methods. Nevertheless, the cover files did not receive enough attention. This study aims at providing a more inclusive approach for video steganography method. The difference in video steganography lies in the fact that they allow both sender and receiver to exchange hidden data through the matching between the secret text and the video frame RGB channels values presented in pixels, and manageable through different sizes of video files. The traditional manner contains the secret data under the video itself. The proposed method is designed over multithreading technique (parallelized method) for performance enhancement purposes. The results show that this developed steganographic method is more secure compared to the traditional steganographic method. In addition, results show that parallelism has impacted the proposed method positively.