With the emerging technologies of augmented reality (AR) and virtual reality (VR), the learning process in today’s classroom is much more effective and motivational. Overlaying virtual content into the real world makes learning methods attractive and entertaining for students while performing activities. AR techniques make the learning process easy, and fun as compared to traditional methods. These methods lack focused learning and interactivity between the educational content. To make learning effective, we propose to use handheld marker-based AR technology for primary school students. We developed a set of four applications based on students’ academic course of primary school level for learning purposes of the English alphabet, decimal numbers, animals and birds, and an AR Globe for knowing about different countries around the world. These applications can be played wherever and whenever a user wants without Internet connectivity, subject to the availability of a tablet or mobile device and the required target images. These applications have performance evaluation quizzes (PEQs) for testing students’ learning progress. Our study investigates the effectiveness of AR-based learning materials in terms of learning performance, motivation, attitude, and behavior towards different methods of learning. Our activity results favor AR-based learning techniques where students’ learning motivation and performance are enhanced compared to the non-AR learning methods.

In the modern technological era, Anti-cancer peptides (ACPs) have been considered a promising cancer treatment. It’s critical to find new ACPs to ensure a better knowledge of their functioning processes and vaccine development. Thus, timely and efficient ACPs using a computational technique are highly needed because of the enormous peptide sequences generated in the post-genomic era. Recently, numerous adaptive statistical algorithms have been developed for separating ACPs and NACPs. Despite great advancements, existing approaches still have insufficient feature descriptors and learning methods, limiting predictive performance. To address this, a trustworthy framework is developed for the precise identification of ACPs. Particularly, the presented approach incorporates four hypothetical feature encoding mechanisms namely: amino acid, dipeptide, tripeptide, and an improved version of pseudo amino acid composition are applied to indicate the motif of the target class. Moreover, principal component analysis (PCA) is employed for feature pruning, while selecting optimal, deep, and highly variated features. Due to the diverse nature of learning, experiments are performed over numerous algorithms to select the optimum operating method. After investigating the empirical outcomes, the support vector machine with hybrid feature space shows better performance. The proposed framework achieved an accuracy of 97.09% and 98.25% over the benchmark and independent datasets, respectively. The comparative analysis demonstrates that our proposed model outperforms as compared to the existing methods and is beneficial in drug development, and oncology.

The usage of media such as images and videos has been extensively increased in recent years. It has become impractical to store images and videos acquired by camera sensors in their raw form due to their huge storage size. Generally, image data is compressed with a compression algorithm and then stored or transmitted to another platform. Thus, image compression helps to reduce the storage size and transmission cost of the images and videos. However, image compression might cause visual artifacts, depending on the compression level. In this regard, performance evaluation of the compression algorithms is an essential task needed to reconstruct images with visually or near-visually lossless quality in case of lossy compression. The performance of the compression algorithms is assessed by both subjective and objective image quality assessment (IQA) methodologies. In this paper, subjective and objective IQA methods are integrated to evaluate the range of the image quality metrics (IQMs) values that guarantee the visually or near-visually lossless compression performed by the JPEG 1 standard (ISO/IEC 10918). A novel “Flicker Test Software” is developed for conducting the proposed subjective and objective evaluation study. In the flicker test, the selected test images are subjectively analyzed by subjects at different compression levels. The IQMs are calculated at the previous compression level, when the images were visually lossless for each subject. The results analysis shows that the objective IQMs with more closely packed values having the least standard deviation that guaranteed the visually lossless compression of the images with JPEG 1 are the feature similarity index measure (FSIM), the multiscale structural similarity index measure (MS-SSIM), and the information content weighted SSIM (IW-SSIM), with average values of 0.9997, 0.9970, and 0.9970 respectively.

The advancement in mobile communication and technologies has led to the usage of short-form digital content increasing daily. This short-form content is mainly based on images that urged the joint photographic experts’ group (JPEG) to introduce a novel international standard, JPEG Snack (International Organization for Standardization (ISO)/ International Electrotechnical Commission (IEC) IS, 19566-8). In JPEG Snack, the multimedia content is embedded into a main background JPEG file, and the resulting JPEG Snack file is saved and transmitted as a .jpg file. If someone does not have a JPEG Snack Player, their device decoder will treat it as a JPEG file and display a background image only. As the standard has been proposed recently, the JPEG Snack Player is needed. In this article, we present a methodology to develop JPEG Snack Player. JPEG Snack Player uses a JPEG Snack decoder and renders media objects on the background JPEG file according to the instructions in the JPEG Snack file. We also present some results and computational complexity metrics for the JPEG Snack Player.

Proteins perform critical functions, and their role is closely associated with their composition. Reliable prediction of protein function using computational techniques is becoming necessary because experimental limitations make it difficult to cover the massive rate of discovered proteins and their genetic transformation. This paper surveys the protein functions prediction, briefly discusses the employed literature's workflow and determines if the machine and deep learning techniques have been widely used for similar objectives. Furthermore, the paper discusses the trends of protein datasets and their features and summarises researchers' questions using these datasets. Many features have been identified and extracted, ranging from traditional physicochemical of amino acids and techniques for selecting features and reducing the dimensionality. Distinct from previous study manuscripts, we keep a detailed review of performance evaluation metrics and compare employed protein function prediction methods, thereby concluding the need for efficient, effective, and adaptable protein function prediction methods in real-world scenarios. We describe the machine learning processes and their development from elementary algorithms, such as logistic regression, to more sophisticated methods like conventional and highly developed sequential deep neural networks. The techniques of computing the hyper-parameters that are adopted to improve prediction efficiency have been discussed. Many studies reviewed implemented machine and deep learning approaches for protein function prediction compared with other methods. The critical challenge well noted in protein function prediction is getting relevant information. Then, approaches evaluated and provided future study prospects based on the results drawn from previous studies. This review provides important information as well as future prospects.