The emergence in computing and the latest hardware technologies realized the use of natural interaction with computers. Gesture-based interaction is one of the prominent fields of natural interactions. The recognition and application of hand gestures in virtual environments (VEs) need extensive calculations due to the complexities involved, which directly affect the performance and realism of interaction. In this paper, we propose a new interaction technique that uses single fingertip-based gestures for interaction with VEs. The objective of the study is to minimize the computational cost, increase performance, and improve usability. The interaction involves navigation, selection, translation, and release of objects. For this purpose, we propose a low-cost camera-based system that uses a colored fingertip for the fastest and accurate recognition of gestures. We also implemented the proposed interaction technique using the Leap Motion controller. We present a comparative analysis of the proposed system with the Leap Motion controller for gesture recognition and operation. A VE was developed for experimental purposes. Moreover, we conducted a comprehensive analysis of two different recognition setups including video camera and the Leap Motion sensor. The key parameters for analysis were task accuracy, interaction volume, update rate, and spatial distortion of accuracy. We used the Standard Usability Scale (SUS) for system usability analysis. The experiments revealed that camera implementation was found with good performance, less spatial distortion of accuracy, and large interaction volume as compared to the Leap Motion sensor. We also found the proposed interaction technique highly usable in terms of user satisfaction, user-friendliness, learning, and consistency.

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks.

Collaborative Virtual Environments (CVEs) are used for training, education, tele- conferencing and assembly of synthetic objects. Network latency, loose coordination, no task distribution strategy and less awareness are the main factors that affect user performance in CVEs. The effect of network latency on user performance in CVE was analyzed. Visual and audio feedback was used for communication between the collaborators. Based on task distribution model, a simulated environment for collaborative assembly task was developed to conduct the experiments. The experiments were performed on different college students comprising of 20 virtual teams of 40 individuals using the latency from 0 to 5000 msec in LAN. The result showed that the user‟s performance was better in dynamic with audio with no latency, but whenever the lag was increased the task completion time increased than dynamic with textual based. At 1000 msec latency, the dynamic with audio and textual modality values were almost the same. Above 1000 msec of latency, the user performance in dynamic with textual was better as compared to dynamic with audio.

In virtual environments (VEs), whether collaborative or single-user, numerous interaction strategies have been developed to facilitate task execution. However, due to the diverse nature of tasks and applications in VEs, these interaction techniques often vary significantly and lack standardization. Consequently, there are no universally accepted or well-organized interaction techniques that can be effectively applied across all VEs. This limitation becomes especially evident in Single User Virtual Environments (SUVEs), where effective communication modalities are essential for task execution. Despite their importance, there has been limited research on systematically comparing communication modalities such as arrows-casting, textual guidance, audio cues, and 3D Map-Liner (3DML) to assess their impact on user performance during task completion in SUVEs.This study aims to address the above gap by evaluating user performance with different communication modalities in SUVEs. Specifically, it compares the effectiveness of arrows-casting, textual guidance, audio cues, and 3DML for task execution in a VE designed for assembly tasks. A virtual environment was developed where the Dijkstra algorithm was implemented to calculate the shortest distance, ensuring optimized navigation. To conduct the study, 20 undergraduate students were selected to test these navigational aids. The results highlight that arrows-casting demonstrated the highest user performance among the tested modalities, while audio navigation aids showed the lowest performance.The findings of this study provide valuable insights into the design and selection of communication modalities in SUVEs. The superior performance of arrows-casting suggests that visual navigation aids are particularly effective in guiding users during task execution. On the other hand, the low performance of audio navigation aids indicates the need for further refinement and integration of audio cues in VEs. These results can inform the development of more efficient and user-friendly navigation aids, contributing to improved task completion and overall user experience in VEs. Additionally, the methodology and findings can serve as a foundation for future research on interaction techniques and task optimization in diverse virtual environments.

This systematic literature review (SLR) investigates search-based strategies for generating combinatorial test suites using covering arrays (CAs) to efficiently test system interactions. Conducted following PRISMA guidelines, the review analyzes 91 primary studies published between 2003 and 2025, selected through a rigorous process from major academic databases. The identified strategies are categorized into five types: standard, mix, adaptive, hybrid, and hyper-heuristic, based on their underlying algorithmic approaches, including swarm intelligence, evolutionary algorithms, and hyper-heuristic techniques. Each strategy is examined in depth, evaluating its effectiveness in generating high-quality combinatorial test suites. The review also highlights challenges in applying these strategies to varying software testing scenarios. Based on the findings, it provides practical insights to enhance their application and effectiveness in real-world contexts. This work supports broader adoption of search-based testing to improve software quality and reduce defect rates.