Smart agriculture, with the aid of Artificial Intelligence (AI), is playing a pivotal role to ensure agriculture sustainability. AI techniques are employed in soil and irrigation management, weather forecasting, plant growth, disease prediction and livestock management that are considered to be significant domains of agriculture. We review recent AI techniques that have been deployed in these domains. We focus on the various AI algorithms used as well as their performance impact. This review not only highlights the effective use of AI at different layers of a smart agriculture architecture but also identifies future research directions in this field. We found that the deep learning algorithms that have been used in recent studies have performed far better than the conventional machine learning algorithms due to recent technological advances that can efficiently process vast amount of data and enable timely intelligent decisions similar to human decisions.

Sustainability incorporation within the field of Software Engineering is an emerging research area. Sustainability, from an academic perspective, has been addressed to a large extent. However, when it comes to the software industry, the topic has not received much-needed attention. Software, being designed and developed in the industry, can benefit society at large, if sustainability is taken into account by the software professionals during the software design and development process. To develop a sustainable software application, knowledge and awareness about sustainability by professional software developers is one of the key elements. This study is an attempt to examine sustainability knowledge, importance, and support from the perspective of South Asian software professionals. Additionally, this study also proposes sustainability guidelines for certain software applications and also a catalog for the identification of sustainability requirements for different software applications. The queries such as ‘What does sustainability mean to a professional software developer?’, ‘How does the software industry identify sustainability requirements?’, ‘How do software developers incorporate the sustainability parameters within software during software development?’, and many other such queries are addressed in this study. To achieve this goal, a survey was carried out among 221 industry practitioners involved in software projects in various application domains such as banking, finance, and management applications. The results pinpoint that even though sustainability is deemed important by 91% of practitioners, still there is a lack of understanding regarding sustainability incorporation in software development. A total of 48% of professionals often misunderstand “Green software” as “sustainable software”. The technical aspect of sustainability is considered most important by professionals (67%) as well as companies (77%). One of the key findings of this study is that 92% of software practitioners are not able to identify sustainability requirements for software applications. The outcomes of the study may be regarded as an initial attempt towards how sustainability is comprehended in software by the South Asian software industry.

With the increase in the generation and spread of textual content on social media, natural language processing (NLP) has become an important area of research for detecting underlying threats, racial abuse, violence, and implied warnings in the content. The subtlety and ambiguity of language make the development of effective models for detecting threats in text a challenging task. This task is further complicated when the threat is not explicitly conveyed. This study focuses on the task of implied threat detection using an explicitly designed machine-generated dataset with both linguistic and lexical features. We evaluated the performance of different machine learning algorithms on these features including Support Vector Machines, Logistic Regression, Naive Bayes, Decision Tree, and K-nearest neighbors. The ensembling approaches of Adaboost, Random Forest, and Gradient Boosting were also explored. Deep learning modeling was performed using Long Short-Term Memory, Deep Neural Networks (DNN), and Bidirectional Long Short-Term Memory (BiLSTM). Based on the evaluation, it was observed that classical and ensemble models overfit while working with linguistic features. The performance of these models improved when working with lexical features. The model based on logistic regression exhibited superior performance with anF₁score of 77.13%. While experimenting with deep learning models, DNN achieved anF₁score of 91.49% while the BiLSTM achieved anF₁score of 91.61% while working with lexical features. The current study provides a baseline for future research in the domain of implied threat detection.