Education plays an important role in development of any country that is why this study was conducted in 2018 regarding online live classes at primary level in Khyber Pakhtunkhwa on pilot based. This paper draws on the challenges faced by the facilitators, co-facilitators and teachers in the pilot project. Multiple tools were used for collecting data from facilitators, co-facilitators, and teachers. Data were collected from a total of four facilitators, sixteen co-facilitators, two IT experts, and four observers participated to develop this paper. The data were analyzed using thematic analysis technique. The study highlighted challenges like technology, delivery, and contents related issues. Apart from these Adaptability issue, Time Management and Motivation were also identified as challenges in live classes. The respondents used quick fixed remedies to address the challenges, and highlighted various recommendations for its solution.

Health, Technology, education, and food production are the four main issues facing developing nations like Pakistan, and it is undeniable that agriculture is the most important factor behind economic growth. In addition, implementing a strategy for food production is crucial for citizens to ensure their survival, and it is assumed that these initiatives will result in sufficient farm productivity. One strategy to make a field productive is to take significant care of its components, which starts with cultivating healthy plants or crops. Wheat leaf rust is a fatal condition that attacks young seedlings. It is a significant fungi disease. Leaf rust has 25% effect on the productivity of wheat. To mitigate this issue, a Multi-Scale Discrete Wavelet Transform (MsclDWT) using hybrid fusion rules method is proposed to obtain the complementary information from multiple input images. In second phase, Lab color space followed by color thresholding method is applied to detect and segment wheat leaf rust disease in wheat crop. The proposed model also computes the rust-affected area of the wheat crop, which assists the farmers in the post-medication (anti rust spray) process. The empirical results show that the proposed model achieved 97% of accuracy in rusted pixels detection and classification and outperformed the existing comparative methods.

Historical documents such as newspapers, invoices, contract papers are often difficult to read due to degraded text quality. These documents may be damaged or degraded due to a variety of factors such as aging, distortion, stamps, watermarks, ink stains, and so on. Text image enhancement is essential for several document recognition and analysis tasks. In this era of technology, it is important to enhance these degraded text documents for proper use. To address these issues, a new bi-cubic interpolation of Lifting Wavelet Transform (LWT) and Stationary Wavelet Transform (SWT) is proposed to enhance image resolution. Then a generative adversarial network (GAN) is used to extract the spectral and spatial features in historical text images. The proposed method consists of two parts. In the first part, the transformation method is used to de-noise and de-blur the images, and to increase the resolution effects, whereas in the second part, the GAN architecture is used to fuse the original and the resulting image obtained from part one in order to improve the spectral and spatial features of a historical text image. Experiment results show that the proposed model outperforms the current deep learning methods.