Previous studies analyzed errors in English as a second language writing in school or university; no work has been conducted on Indian Madrasa (Islamic institution) students’ errors in English writing. The current study analyzes Madrasa students’ English writing errors. The students were grouped into an experimental group (EG) and control group (CG) and engaged for twenty-eight days, where only EG learners received blended learning (BL) treatment. The investigation used a pre-and post-test purposive design across all the groups. The errors were spotted from their write-ups belonging to morphological, syntactical, and orthographical categories. Next, errors were analyzed both quantitatively and qualitatively. Though the results revealed that both groups committed errors in all seven categories: morphological (article and preposition), syntactical (tense and word order), and orthographic (capitalization, spelling, and punctuation) types, EG’s errors were fewer than CG’s. This implies that BL can lead to effective remedial writing in Madrasa classrooms. In addition, EG’s pre-test scores were also greater than post-test scores, which has implications for adopting BL at different Madaris in India.

In order to reduce the labor cost pressure of telecom operators' customer service and improve the service quality, the natural language analysis technology based on artificial intelligence technology will realize the automatic question and answer customer service.This paper proposes to obtain word vectors based on Word2vec model. By comparing the word vectors under different training model parameters, the results show that the low-frequency word threshold plays a better role in controlling the number of the final trained word vectors. The training results of SKIP-GRAM model are better than that of CBOW and the word list is more regular. Under the condition of making full use of the existing customer service knowledge resources, the new system will realize the goal of innovating service means, expanding customer service channels, diverting customer service pressure and improving service efficiency.

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The most pressing concern in the world since independence has been producing enough food to feed an expanding population. The mix of high-yielding production techniques has helped the globe to generate a food surplus while also raising worries about soil health and environmental pollution. Though, scientists and policy makers are rethinking agricultural systems that rely heavily on biological inputs. Organic farming can provide high-quality food without compromising the health of the land or the environment; nevertheless, it is unclear if large-scale organic farming would be able to feed world’s vast population. Adoption of this emerging approach “organic vegetable farming” plays a vital role in development of agricultural sustainability through avoiding indiscriminate use of synthetic chemicals. There are numerous organic sources for organic vegetable farming but various type of composts (especially vermi-compost) and biochar are most famous among all other organic sources as they improved soil healthy and vegetables productions through improving soil physico-chemical and biological attributes. In addition, demand and prices of organically produced vegetables are much higher in market and evidence showed that organically grown vegetables are enriched with nutrients and safe for consumption because of their less exposure with residues of in-organic pesticides.

In order to quickly extract the visual navigation line of farmland robot, an extraction algorithm for dark primary agricultural machinery is proposed. The application of dark primary color principle in new farmland is made clearer by gray scale method, and the soil and crops are obviously separated, and the image processing technology of visual navigation line image of farmland is realized. In binary filtering of gray scale images, the maximum interclass variance method and morphological method are used respectively. The researchers use vertical projection method and least square method to the farmland interval extracted by navigation line. The farmland that needs the guide line image will be accurately located. It is found that the visual navigation extraction algorithm of farmland robot is widely used in the image extraction of navigation lines of various farmland roads and scenes compared with the traditional gray scale algorithm. Image processing has the advantages of clearer image processing.