Protection of data transmitted over the network from illegal access is one of the major challenges being posed by exponential growth of data in online digital communication. Modern cryptosystems assist in data sanctuary by utilizing substitution-boxes (S-boxes). This paper presents a modest and novel technique to erect dynamic and key dependent S-boxes with the help of a novel linear trigonometric transformation. A new optimization plan is also suggested to improvise the nonlinearity characteristic of the preliminary S-box generated through trigonometric transformation. The proposed technique has the competence to create significant quantity of cryptographic strong S-boxes with the help of projected scheme. A specimen S-box is procreated, and standard performance criteria is applied to appraise the cryptographic strength of the resultant S-box and other known S-boxes available in the literature. Comparative performance analyses validate the noteworthy contribution of the proposed scheme for the generation of dynamic and secure S-boxes. An image privacy preserving scheme based on the proposed S-box is also suggested to validate the fact that it holds strong candidature for modern cryptosystems to protect multimedia data.

Economies of the world and workforce level of skills are changing rapidly. Industrial revaluations and knowledge economies have raised the demand for a knowledgeable and digitally equipped skilled workforce. Technical and Vocational Education and Training (TVET) is an educational stream that provides vocational skills to youth and produces a skilled workforce for the industry. The role of Information and Communication Technologies (ICT) in the TVET pedagogy, training delivery, teachers training, curriculum, and labs is need of the day to meet current era challenges. In the TVET education training cycle, the level of ICT enablement is required to be reviewed. This Systematic Literature Review (SLR) evaluates literature for innovation of ICT in TVET education for the last ten years. This study aims to identify and present ICT-based technology innovations, research, and applications used in TVET training cycle system components/functional areas to identify gaps for future research directions/agenda. This gap identification will help (i) TVET training institutes to upgrade their teachers, curriculum, labs and, equipment, (ii) policymakers to devise technology-oriented skilled workforce development policies, (iii) to provide guidelines to industry, researchers, and academics to focus on gaps to address future challenges of workforce development. Based on the search string, we found 2,445 relevant documents and after applying quality assurance and inclusion/exclusion criteria, finally, 134 documents were selected for the study and analyzed. The result of this systematic literature review identifies that ICT technologies and application dispersion into TVET training cycle system components/functional areas is very low particularly in monitoring and evaluation, career guidance and job placement, trainee's assessment, and teacher's training. TVET Technology index suggests that much focus is needed on IoT, Robotics, Data Science, Artificial intelligence, cloud computing, and other similar technology induction to all TVET training.

As the motivations and capabilities of threat actors continue to evolve, providing data security has become more important than ever. For this purpose, different ciphers using various techniques are being developed. Currently, chaotic maps are designed and applied in the development of these ciphers. Modern ciphers utilize a substitution box (S-Box) as a core module to provide data security. In this article, an innovative chaotic map is suggested for the design of new and dynamic S-Box. Criteria like Bijectiveness, Nonlinearity (NL), Strict Avalanche Criterion (SAC), Bit Independence Criterion (BIC), Linear Approximation Probability (LP), and Differential Approximation Probability (DP) are used to critically analyze and evaluate the proposed S-Box performance against various attacks. The cryptanalytic strength of the proposed S-Box is equated with freshly designed S-Boxes for its customization in real-life security applications. The comparative analysis gratifies the true potential of the proposed S-Box for its solicitation in data security domain.

Fall induced damages are serious incidences for aged as well as young persons. A real-time automatic and accurate fall detection system can play a vital role in timely medication care which will ultimately help to decrease the damages and complications. In this paper, we propose a fast and more accurate real-time system which can detect people falling in videos captured by surveillance cameras. Novel temporal and spatial variance-based features are proposed which comprise the discriminatory motion, geometric orientation and location of the person. These features are used along with ensemble learning strategy of boosting with J48 and Adaboost classifiers. Experiments have been conducted on publicly available standard datasets including Multiple Cameras Fall (with 2 classes and 3 classes) and UR Fall Detection achieving percentage accuracies of 99.2, 99.25 and 99.0, respectively. Comparisons with nine state-of-the-art methods demonstrate the effectiveness of the proposed approach on both datasets.

Personality represents enduring patterns, providing insights into an individual’s aptitude and behavior. Integrating these insights with learning tendencies shows promise in enhancing learning outcomes, optimizing returns on investment, and reducing dropout rates. This interdisciplinary study integrates techniques in advanced artificial intelligence (AI) with human psychology by analyzing data from the trades of Technical and Vocational Education and Training (TVET) education, by combining them with individual personality traits. This research aims to address dropout rates by providing personalized trade recommendations for TVET, with the goal of optimizing outcome-based personalized learning. The study leverages advanced AI techniques and data from a nationwide TVET program, including information on trades, trainees’ records, and the Big Five personality traits, to develop a Personality-Aware TVET Course Recommendation System (TVET-CRS). The proposed framework demonstrates an accuracy rate of 91%, and a Cohen’s Kappa score of 0.84, with an NMAE at 0.04 and an NDCG at 0.96. TVET-CRS can be effectively integrated into various aspects of the TVET cycle, including dropout prediction, career guidance, on-the-job training assessments, exam evaluations, and personalized course recommendations.