Recommender systems (RSs) are increasingly recognized as intelligent software for predicting users’ opinions on specific items. Various RSs have been developed in different domains, such as e-commerce, e-government, e-resource services, e-business, e-library, e-tourism, and e-learning, to make excellent user recommendations. In e-learning technology, RSs are designed to support and improve the learning practices of a student or an organization. This survey aims to examine the different works of literature on RSs that corroborate e-learning and classify and provide statistics of the reviewed articles based on their recommendation goals, recommendation techniques used, the target user, and the application platforms. The survey makes a prominent contribution to the e-learning RSs field by providing an overview of current research and traditional and nontraditional recommendation techniques to provide different recommendations for future e-learning. One of the most significant findings to emerge from this survey is that a substantial number of works followed either deep learning or context-aware recommendation techniques, which are considered more efficient than any traditional methods. Finally, we provided comprehensive observations from the quantitative assessment of publications, which can guide and support researchers in understanding the current development for potential future trends and the direction of deep learning-based RSs in e-learning.

Potatoes are an important crop heavily consumed by Indian food products. It is produced on a massive scale, with China, India, Russia, Poland, and the USA being the main producers. Numerous leaf diseases harm the crop during its production. A typical Indian farmer lacks the tools necessary to detect Leaf Disease before damage is done. On a dataset of potato leaf images retrieved from Kaggle, we employed the EfficientNetB0 of Deep Learning to address this problem. This model uses width scaling and resolution scaling apart from depth scaling to perform the classification. Our work mainly focuses on the diseases Early Blight and Late Blight, two serious potato diseases. Early blight Spots start off as tiny, dry, dark, and papery specks that develop into brown to black, circular to oval-shaped regions. Veins that round the spots frequently give them an angular appearance. Late blight syntoms appear as small, light to dark green and round to irregularly shaped. Water-soaked patches are the first signs of late blight. The Data Collection has 2152 pictures in total, 2000 of which are diseased and 152 of which are healthy. The deep learning model provides a testing accuracy of 99.05%, which is higher than several widely used techniques available to provide farmers with knowledge about correct diseases well in time.

Natural Language Processing (NLP) has transformed machine translation, sentiment analysis, information retrieval, and conversation systems. NLP applications rely on complete linguistic resources, which might be difficult for low-resource languages. NLP solutions for every language require a language-specific dataset. Dataset in a language is essential for NLP solution creation. Over 7000 languages are spoken worldwide. Only around 20 languages have text corpora for NLP applications. English has the most datasets, then Chinese and Spanish. Japanese has several Western European language datasets. For an accurate NLP system, most Asian and African languages lack training datasets. To address this challenge, we propose a methodology for building a lexicon-based sentiment analysis model for languages with limited resources. The Hausa language was used as training and evaluation language. The methodology combines lexicon creation; augmentation, annotation, and fine-tuning model, and has been tested on a corpus of Hausa tweets achieving an accuracy of 98 %. The results suggest that our proposed model is a promising tool for sentiment analysis in a variety of applications, such as social media monitoring, customer service, and market research. Our methodology can be used for any low-resource language. The outline of the work done in this paper can be shown as follows: • We propose a methodology for building a lexicon-based sentiment analysis model for languages with limited resources, using the Hausa language as a case study. • The methodology combines lexicon creation, augmentation, annotation, and fine-tuning model, and achieves an accuracy of 98 % on a corpus of Hausa tweets. • The results suggest that the proposed model is a promising tool for sentiment analysis in a variety of applications for low-resource languages.

The COVID-19 pandemic has affected more than 192 countries. The condition results in a respiratory illness (e.g., influenza) with signs and symptoms such as cold, cough, fever, and breathing difficulties. Predicting new instances of COVID-19 is always a challenging task. This study improved the autoregressive integrated moving average (ARIMA)-based time series prediction model by incorporating statistical significance for feature selection and k-means clustering for outlier detection. The accuracy of the improved model (ARIMAI) was examined using World Health Organization's official data on the COVID-19 pandemic worldwide and compared with that of many modern, cutting-edge algorithms. The ARIMAI model (RSS score = 0.279, accuracy = 97.75%) outperformed the current ARIMA model (RSS score = 0.659, accuracy = 93%). The ARIMAI model is not only an efficient but also a rapid and simple technique to forecast COVID-19 trends. The usage of this model enables the prediction of any disease that will affect patients in the future pandemics.

The need for appropriate applications of the various similarity measures for clustering has arisen over the years as data massively keep on increasing. The issue of deciding which similarity measure is the best and on what kind of dataset have been a very cumbersome task in the field of data mining, data science, other related fields, and organizations that highly depends on the knowledge outcome from a huge set of data to make some vital / crucial decisions. This is because various datasets portray some common features associated with them; the need for clearer understanding of the various similarity measures for clustering different datasets is needed. This paper presents a critical review of various similarity measures applied in text and data clustering. A theoretical comparison has been made to check the suitability of the measures on different kind of data sets.