Remote sensing technology has penetrated all the natural resource segments as it provides precise information in an image mode. Remote sensing satellites are currently the fastest-growing source of geographic area information. With the continuous change in the earth’s surface and the wide application of remote sensing, change detection is very useful for monitoring environmental and human needs. So, it is necessary to develop automatic change detection techniques to improve the quality and reduce the time required by manual image analysis. This work focuses on the improvement of the classification accuracy of the machine learning techniques by reviewing the training samples and comparing the post-classification comparison with the image differencing in the algebraic technique. Landsat data are medium spatial resolution data; that is why pixel-wise computation has been applied. Two change detection techniques have been studied by applying a decision tree algorithm using a separability matrix and image differencing. The first change detection, e.g., the separability matrix, is a post-classification comparison in which individual images are classified by a decision tree algorithm. The second change detection is, e.g., the image differencing change detection technique in which changed and unchanged pixels are determined by applying the corner method to calculate the threshold on the changing image. The performance of the machine learning algorithm has been validated by 10-fold cross-validation. The experimental results show that the change detection using the post-classification method produced better results when compared to the image differencing of the algebraic change detection technique.

An accurate identification of objects from the acquisition system depends on the clear segmentation and classification of remote sensing images. With the limited financial resources and the high intra-class variations, the earlier proposed algorithms failed to handle the sub-optimal dataset. The building of an efficient training set iteratively in active learning (AL) approaches improves classification performance. The heuristics-based AL provides better results with the inheritance of contextual information and the robustness to noise variations. The uncertainty exists pixel variations make the heuristics-based AL fail to handle the remote sensing image classification. Previously, we focused on the extraction of clear textural pattern information by using the extended differential pattern-based relevance vector machine (EDP-AL). This paper extends that work into the novel pixel-certainty activity learning (PCAL) based on the information about textural patterns obtained from the extended differential pattern (EDP). Initially, distributed intensity filtering (DIF) is used to eliminate noise from the image, and then histogram equalization (HE) is used to improve the image quality. The EDP is used to merge and classify different labels for each image sample, and this algorithm expresses the textural information. The PCAL technique is used to classify the HSI patterns that are important in remote sensing applications using this pattern collection. Pavia University and Indian Pines (IP) are the datasets used to validate the performance of the proposed PCAL (PU). The ability of PCAL to accurately categorize land cover types is demonstrated by a comparison of the proposed PCAL with existing algorithms in terms of classification accuracy and the Kappa coefficient.

Adding gaming elements to conventional teaching methodologies has gained a lot of attention because of its ability to incorporate an engaging, motivating, and fun-based environment. As a result, learners' dedication and performance are also better. Unfortunately, current gamification models do not consider the effect of different levels of gamification. Therefore, this study provides deeper insight into the three levels of gamification on the motivation, engagement, and performance of 450 undergraduates enrolled in an online course. The level of gamification is experimentally manipulated based on different gaming elements and the presentation of learning content. The outcomes were measured at three points. Quantitative methods were used to analyze defined measures, and qualitative methods were used to analyze open-ended measures. The results revealed no change in outcomes between all groups during pre-course and mid-course assessments. However, motivation, engagement, and performance are improved in gamified environments, and these effects are more noticeable towards the end of the course. It was discovered that the gamification level was a significant determinant of motivation and performance but not engagement, which highlights the importance of implementing gamification in educational platforms. The gamification appeared to be a pedagogically profound way of engaging students in the online course. The whole setup triggered the learner’s motivation to learn and perform in the course. We conclude that gamification does help in motivation, engagement and performance if considered properly. Thus, educators and educational institutions seeking to enhance student motivation and performance may look at the ‘right level’ of gamification as an appropriate methodology.

Gamification has been gaining increasing acceptability in recent times in educational and commercially related activities, as a tool that encourages and improves the motivation of digital native learners. Since learners can easily engage, educationists have explored gamification as a tool for remediation of engagement, motivation, and collaboration. However, the literature showed that the structural and contextual deployment of game elements is defined only partially in practice. Subsequently, gamification success and failure factors should be explored to identify the required enhancement to achieve improved efficiency in current systems. This research extracts the relevant aspects of gamification that need due consideration to make a guided choice through existing theories. This study is based on an online gamified study that uses well-founded concepts in teaching and evaluation of students in a university. Although badges earned and time spent indicated an increase in engagement, the results show that further work needs to be done by incorporating feedback elements, social interaction, and interactive guidance. The underlying impression is that timely, frequent feedback and personalized guidance, avenues for collaboration and interactivity need to be explored towards the better utility of gamification. Therefore, learning culture in the current learner-centered environment should be further studied to infuse better productivity.

The present research focused on combining Particle Swarm Optimization (PSO) based hybrid deep learning models to classify heart disease images and patient sequences. This study employs Convolutional Neural Networks (CNNs), including VGG 16, VGG 19 and ResNet 50, as well as Recurrent Neu-ral Networks (RNNs), whereby their performance is optimized by PSO to im-prove the accuracy in diagnosing heart disease from CT images together with associated medical history. The models experienced a significant increase in classification performance, using manual hyperparameters tuning by PSO. The combined algorithm PSO with VGG 19 and the RNN model performed best, achieving a precision of 97.78% and becoming the highest recall on testing. The model that we propose uses the modern feature extraction of VGG 19 and an RNN to take into consideration the sequential nature of data, making it very accurate while keeping loss minimal. PSO with VGG 16 and RNN model is also another robust performance with an accuracy of 94.5%.

In many serious games, psychological concepts function as narrative backdrop rather than as structures players can actively engage with. Cognitive dissonance is a clear example: players may sense the tension between belief and action, yet the dynamics through which that tension forms and resolves remain implicit in the mechanics. This work operationalises cognitive dissonance as playable dynamics within an interactive narrative system. Belief Strength tracks how firmly a player holds a declared stance, Cognitive Strain accumulates from the divergence between that belief and in-game actions, and three labelled reduction pathways (behavior change, belief revision, and justification) are instantiated as choosable options rather than abstract explanations. A Persona Dominance Ratio captures which coping pattern has stabilised across the session. The prototype examines whether experiencing dissonance through interactive decision-making supports conceptual understanding of the theory, and whether engaging with these mechanics prompts reflection on personal patterns of rationalization and belief change.