Technical Terms Used in General English Textbooks Across Disciplines

The study aimed to analyze lexical items underpinned in the textbooks used in the current teaching of ESP and GE. Using content analysis, a systematic evaluation of texts to examine nuances to bridge the gap between quantitative and qualitative data. This was such of importance, however, difficult to study due to issues of interest like in the study, frequency of lexical items in ESP, and GE textbooks. Results found 13,713 lexical items in Hospitality Management, 17,561 in Criminology, 4576 in Tourism, 7167 in Marine Engineering, and 512 in Information Technology. Furthermore, the overall percentage of ESP lexical items fell in Tier 2 (with multiple-meaning while the least was in Tier 3, specifically on context-specific vocabulary. It is the core of vocabulary learning to ensure English language teaching. It is its goal to help learners better understand language, allowing them to understand others as they want to express themselves as well. This applies not only in speaking but also in writing and reading. Wilkins (1972, p. 111-112) stated that without grammar very little understanding can be acquired and without vocabulary there can be no learning at all. Thus, even without good grammar, so long as you equipped with useful words and expression, one can still manage to communicate. Lewis (1993) argued that “lexis is the heart of language” and that it develops a better fluency and expression in English. He also added the significance to the learners of acquiring a more productive vocabulary knowledge, also, their eagerness to develop their own personal vocabulary strategies. Thus, a proposed bridge model program was recommended to highlight the study findings using the lexicons found from the different respective courses.

The Classification of Multispectral and Statistical Texture Data Using Data Mining Techniques

The real-time information for land use/land cover (LU/LC) data is very important for resource management, future prediction, and crops growth assessment. Although conventionally LU/LC data is collected through field survey but remote sensing data collection has its own importance due to time, accuracy and transparency factors. During the last decade, advancement in spaceborne multispectral data has proven to be beneficial over airborne data for land monitoring due to their increased spectral resolution. The objective of this research is to compare and analyze the five types (Fertile, Green pasture, Desert-rangeland, Bare and Sutlej-river land) of LU/LC multispectral data (five bands) acquired by multispectral radiometer (MSR5) and digital photographic data acquired from high resolution 10.1 megapixel Nikon camera. All experimentation has been performed using MaZda software version 4.6 with WEKA data mining tool version 3.6.12 on Intel® Core i3 processor 2.4 gigahertz (GHz) with the the 64-bit operating system. This research is conducted at The Islamia University of Bahawalpur province Punjab (Pakistan), located at 29°23′44″N and 71°41′1″E. For photographic data, image pre-processing techniques are applied, i.e., grayscale conversion, enhanced the contrast and sharpening procedure. Extract the 229 statistical texture features of the LU/LC data of each 512×512 image size. Three feature selection techniques fisher (F), the probability of error plus average correlation coefficient (POE+ACC) and mutual information (MI) are combined together (F+PA+M) and extract thirty most discriminant features out of 229 features space of each photographic image. For feature reduction, non-linear discriminant analysis (NDA) for photographic data (texture data) and linear discriminant analysis (LDA) for remote sensing data (multispectral data) have shown better clustering as compared to principal component analysis (PCA) and raw data analysis (RDA). Finally, we have employed different data mining classifiers namely, Artificial Neural Network (ANN), Random Forest (RF), Naive Bayes (NB) and J48 for classification. It is observed that artificial neural network (ANN: n class) is applied for training and testing by cross-validation (80-20) on these texture and multispectral data. It showed comparative better 91.332% accuracy for texture dataset and 96.40% for multispectral (MSR5) dataset respectively among all the employed classifiers.