Agriculture 4.0 is a term for the next big trends facing the industry, including a greater focus on precision agriculture, the internet of things (IoT), and the use of big data to drive greater business efficiencies in the face of rising populations and climate change. Agriculture 4.0 is the next step forward: a smarter, more efficient industry that uses big data and new technologies to benefit the whole supply chain. This talk aims to understand the concept of Agriculture 4.0 and to know about different trends and technologies under Agriculture 4.0. Discussion about emerging research opportunities and business use cases in smart agriculture.



Internet of things (IoT) is the network of interconnected physical objects or “things”, connected to the Internet, all communicating and collecting data. Artificial Intelligence (AI) is the intelligence demonstrated by machines as compared to the natural intelligence of human beings or animals. AI-enabled IoT produces intelligent machines that instill smart behavior and decision-making with little-to-no human intervention. The artificial intelligence of things (AIoT) has the potential to transform major segments of IoT, i.e., smart cities, smart agriculture, wearables, and smart homes. The convergence of AI and IoT means the smarter future is nearer than we expect, which is the main agenda of this session


Natural Language Processing (NL) is a subfield of Computer Science and Artificial Intelligence, concerned with interactions between computers and human (natural) languages. It is used to apply machine learning algorithms to text and speech for wide-ranging applications. Thus, this webinar introduces NLP, its applications, steps required for text preprocessing, features extraction, and model training for the text classification task. The discussion is followed by a step-by-step demonstration of the code for text preprocessing, feature extraction, and model training.


Pathology detection has always been a significant challenge for the medical community and is crucial to carry out the treatment. Many diseases have similar kinds of symptoms, which may lead to erroneous human decision-making and thus treatments. To avoid this, medical doctors ask patients for chemical tests. Sometimes multiple tests are required to be conducted, which leads to a waste of resources at both ends. In this aspect, AI-based models can be used as better diagnostic tools for monitoring various diseases. Thus, this talk focuses on discussing how AI applications can provide benefits in disease analysis and diagnoses

Retinal Degenerative Diseases (RDD) are among the leading causes of blindness in the adult population worldwide. It involves gradual and irreversible degeneration of retinal cells. Unfortunately, to-date, there is no significant cure possible for RDD that can reverse the damage. Therefore, the focus of the medical and research community is on rehabilitation, during which patients learn how to use their residual vision. The focus of this talk is to introduce previous work in the field of RDD rehabilitation and discuss how AI can help in formulating better rehabilitation training for individuals suffering from RDD.