Over the course of the last 2-3 decades, researchers have made great strides in applying Machine Learning (ML), Optimization, and Artificial Intelligence (AI) to agriculture, food, and biomedical sciences. Recently, there has been important progress in using these technologies to ensure high-quality drinking water, sustainable food supply, and disease prediction through analysis of food and water supply. This Article Collection solicits papers related to the use of Machine Learning, Optimization, and/or Artificial Intelligence for managing food and water supply and predicting disease.
Approximately three billion people worldwide survive on groundwater for drinking, and providing high-quality drinking water has become one of the key challenges for human society. Water quality also has a substantial affect on non-human animal and plant ecosystems. The prediction of water quality (including drinking water, wastewater, groundwater, surface water, seawater, and freshwater) is an emerging research topic of particular importance, as these different types of water have different characteristics. Support vector machine (SVM), artificial neural network (ANN), and group method of data handling (GMDH) are utilized for predicting water quality components.
ML/AI is also being used for agricultural purposes. For instance, the Digital Agriculture, Food and Wine (DAFW) group utilizes various ML/Optimization applications (such as decision tree classification techniques, sensor networks, and genetic algorithm-based multi-objective optimization techniques) to obtain meaningful information for optimal agricultural decision making. Researchers are also exploring fertilizer management using sensor networks through ML/AI.
Further, ML plays important role in the food industry. Toxic chemicals, such as arsenic, can be found both in water and in in human food sources (e.g. chicken, pork), threatening both animal and human health. ML is an efficient and effective approach to resolve the critical challenges of food and water contamination, as the complex, nonlinear relational data can be easily controlled by ML. Researchers have been using different learning models based on the fuzzy inference systems and multilayer perceptron (MLP) methodologies to ensure food safety and to predict the future of food supply. ML/AI can also be used to detect or predict disease (such as cancer) that result from contaminated food and water.
This article collection calls for papers related (but not limited to) the following topics:
- Performance of ML/AI/optimization methods in predicting water quality.
- Data management challenges using AI in plant and agricultural research.
- Multi-class classification system for the continuous water quality monitoring.
- Water level monitoring using single/multi-class/subtype classification techniques.
- Water Quality Classification through Data Mining approaches.
- Water supply clusters and few-shot learning methodologies for the plant disease recognition using semi-supervised learning.
- ML for Big Data analytics in plants or food (e.g., chicken, pork) related data profile.
- Big Data and ML in Water Sciences.
- Water-level forecasting through fuzzy logic and AI-based neural network approaches.
- Various tissue-type or subtype cancer prediction collected on geographical area-based or food/water-based Microarray or Sequence (patient) data analysis.
- River Flow Estimation through AI and Fuzzy Techniques.
- Evaluation of surface water quality for drinking by using fuzzy inference system.
- Plant Leaf Disease Detection by Neuro-Fuzzy classification.
- Regression analysis in plant cell, tissue, and organ culture experiments as well as in Prediction of Crop Yield.
- Diagnosis of Poultry Disease using ML or Deep Learning or regression models.
- ML/DL Models of Groundwater Arsenic Spatial Distribution and applied to water pollution detection.
- Applications of Machine Learning in Food Safety.
- Biosensor application for water/food supply.
About the Guest Advisors:
Dr. Saurav Mallik is currently working as a Research Scientist in the Department of Pharmacology and Toxicology, The University of Arizona, USA. Previously, he worked as Postdoctoral Fellow at Harvard T.H. Chan School of Public Health, Boston, MA, USA, the Center of Precision Health at University of Texas Health Science Center, and in the Division of Bio-statistics at the University of Miami Miller School of Medicine, Miami, Florida, USA. He obtained his Ph.D. degree in the Department of Computer Science and Engineering (C.S.E.) from Jadavpur University, Kolkata, India in 2017. Dr. Mallik has more than 75 research papers in different top high-impact factor peer-reviewed International Journals, Conferences, and Book Chapters. He is working as an active member of the Institute of Electrical and Electronics Engineers (IEEE), USA, and the American Association for Cancer Research (AACR), USA. His research interest includes Computational Biology, Knowledge Retrieval, Data Mining, Bioinformatics, Bio-Statistics, and Machine Learning/Deep Learning.
Dr. Anirban Mukhopadhyay is currently a Professor of the Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal. He is the recipient of a Fulbright-Nehru Academic and Professional Excellence Fellowship 2017-2018, as well as the Institution of Engineers, India (IEI) Young Engineers Award 2013-14 in Computer Engineering and the Indian National Academy of Engineering (INAE) Young Engineer Award 2014. He has coauthored one book and over 150 research papers in various International Journals and Conferences. His research interests include soft and evolutionary computing, data mining, multi-objective optimization, pattern recognition, bioinformatics, and optical networks.
Dr. Mingqiang Wang is currently working as Postdoctoral Fellow in Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA. He obtained his PhD degree from Chinese University of Hong Kong in 2018. Dr. Wang has more than 12 research papers in different top high impact factor peer-reviewed International Journals, Conferences and Book Chapters. He has also worked with section editors and reviewers with several well-reputed high impact journals. His research interest includes Computational Biology, Bioinformatics, Bio-Statistics and Machine Learning/Deep Learning.
Dr. Amit Kumar Mishra is currently working as an Associate Professor and the Head of the School of Computing, DIT University, Dehradun, India. He is part of the Artificial Intelligence, Machine Learning, and Robotics (AIMLR) Research Group. His research interests include artificial intelligence, big data, machine learning, information retrieval, text mining, natural language processing, computational technologies, complex networks, and linguistics. He is the author of numerous peer-reviewed papers and also a guest editor of several highly reputed journals.
Dr. Tapas Bhadra is currently serving as an Assistant Professor in the Department of Computer Science and Engineering of Aliah University, Kolkata, India. Dr. Bhadra was an INSPIRE Fellow at the Machine Intelligence Unit of Indian Statistical Institute Kolkata and has has more than 35 research papers in peer-reviewed International Journals, Conferences and Book Chapters. His research interests include pattern recognition, data mining, computational biology, and bioinformatics.
Soumita Seth is currently serving as an Assistant Professor in the Department of Computer Science and Engineering of Pailan College of Management and Technology, Kolkata, India. Ms. Seth has more than 20 research papers in different top high impact factor peer-reviewed International Journals, Conferences and Book Chapters. Her research interests include Computational Biology, Data Mining, Bioinformatics, Pattern Recognition, Biological Regulatory Networks, Statistical application on bioinformatics, Machine learning/Deep learning.
