The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an image. An Auto-Encode-based support vector machine approach is proposed in this work to estimate the image geo-site to address the issue of misclassifying the estimations. The proposed method for geo-site estimation is conducted using a dataset consisting of 125 classes of various images captured within 125 countries. The proposed work uses a convolutional Auto-Encode for training and dimensionality reduction. After that, the acquired preprocessed input dataset is further processed by a multi-label support vector machine. The performance assessment of the proposed approach has been accomplished using accuracy, sensitivity, specificity, and F1-score as evaluation parameters. Eventually, the proposed approach for image geo-site estimation presented in this article outperforms Auto-Encode-based K-Nearest Neighbor and Auto-Encode-Random Forest methods.

The purpose of the present study is to explore the prospective teacher’s awareness about Technological Pedagogical and Content Knowledge (TPACK) framework in their ongoing teacher education programme. The sample comprised 80 B.Ed. students from two colleges of Jammu division and 20 M.Ed. students from one prominent university of the Jammu division. The investigator employed a self-constructed semi-structured tool as per the objective of the study. The study employed a descriptive exploratory design. The findings revealed that both at UG and PG level, students have individualised knowledge about technology, pedagogy and content but, the least number of students were familiar with this framework. The study included some concrete suggestions and recommendations for meaningful integration of Technology-mediated pedagogy in the ongoing teacher education programme at the regional and national level. The findings of the study help in strengthening the policy formulation for teacher education in the current perspective.

COVID-19 has changed the teaching-learning scenario all around the globe as educational institutions are functioning in a hybrid mode. Blended Learning or Hybrid Learning approach has been a great boon during the pandemic. The blended learning approach mixes online teaching with traditional offline teaching methods to get the best of both approaches. Scientific attitude is a requirement of today's multicultural society for a peaceful and meaningful living. The present research determines the scientific attitude of undergraduate students in blended learning situations. A module of one week duration for the course 'Assessment of Learning' was prepared and implemented for the students of Integrated BA-B.Ed. programme using the Blended Learning approach. The findings indicate a significant enhancement in the scientific attitude of undergraduate students after implementing the blended learning approach. The present research recommends the blended learning approach in the classroom to maximize learning outcomes. The results also provide an idea to implement flexible teaching methods to cater to the diverse learning needs of learners.

<span lang="EN-US">The efficient finding of common patterns: a group of items that appear frequently in a dataset is a critical task in data mining, especially in transaction datasets. The goal of this paper is to look into the efficiency of various algorithms for frequent pattern mining in terms of computing time and memory consumption, as well as the problem of how to apply the algorithms to different datasets. In this paper, the algorithms investigated for mining the frequent patterns are; Pre-post, Pre-post+, FIN, H-mine, R-Elim, and estDec+ algorithms. These algorithms have been implemented and tested on four real-life datasets that are: The retail dataset, the Accidents dataset, the Chess dataset, and the Mushrooms dataset. From the results, it has been observed that, for the Retail dataset, estDec+ algorithm is the fastest among all algorithms in terms of run time as well as consumes less memory for its execution. Pre-post+ algorithm performs better than all other algorithms in terms of run time and maximum memory for the Mushrooms dataset. Pre-Post outperforms other algorithms in terms of performance. And for Accident datasets, in terms of execution time and memory consumption, the FIN method outperforms other algorithms.</span>

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