Interview with Dr. Chetanpal Singh—Winner of the Applied Sciences 2022 Best Paper Award
1. Congratulations on winning the 2022 Best Paper Award! Could you briefly introduce yourself and the main content of the winning paper to our readers?
Hello, my name is Dr. Chetana Singh. I am currently engaged in the research field of machine-learning and artificial intelligence at Central Queensland University, Australia. I collaborate with my professors, Santoso and Shariman Grandes, on various projects. Our work primarily focuses on advancing smart crop technologies and developing machine learning applications for diagnosing skin diseases. My background is in computer science, which has significantly contributed to our exploration of these emerging technologies. Looking ahead, my research interests lie in two main areas. Firstly, I am interested in developing AI applications designed to support and assist people, ensuring that these tools provide accurate solutions rather than causing confusion. Secondly, our publications have been instrumental in highlighting the potential of these technologies in medical sciences, particularly in diagnosing and treating skin diseases, as well as addressing agricultural challenges. In agriculture, we have teams at Central Queensland University who have made significant strides in understanding and combating crop diseases. But what we are trying to achieve is solutions for the future, for our generations and for our kids. Thank you for supporting us and helping us to highlight the importance of this research field and publish our research.
2. Do you have any advice for aspiring researchers looking to make a meaningful impact in their respective fields?
Research is driven by personal passion and dedication. My advice to aspiring researchers is to remain committed to their interests and seek mentorship and support. It is important to collaborate and connect with experienced academics. For instance, my professors were instrumental in guiding me toward the right vision and field. Researchers should not hesitate to reach out to established academics through university emails or contact details available in research papers. Collaboration often yields better results than working in isolation. MDPI’s open access platform facilitates such connections, enabling researchers to collaborate and share their work more effectively.
3. Do you have any suggestions for our journal as to how we could further support researchers and the academic community?
MDPI offers significant support to the academic community. They provide a platform for researchers to submit proposals and abstracts, which are reviewed by experts in the field. This process ensures that only high-quality research is published. Additionally, MDPI’s open access nature allows for greater visibility and collaboration opportunities.
From my experience, MDPI has been supportive not only in publishing my work but also in recognizing it through awards and monetary prizes. This recognition motivates researchers to continue their work and strive for excellence.
One area where MDPI could enhance support for researchers is by providing access to specialized software and applications. For example, during my research, I faced challenges with processing data on standard CPUs and required GPUs. Having access to such resources would greatly benefit researchers, especially during times like the COVID-19 pandemic when travel and access to facilities were limited.
Furthermore, creating a blog-like environment within the research community could foster more interaction and collaboration. Researchers could discuss their projects, share insights, and seek advice, creating a more dynamic and supportive research ecosystem.
4. What do you think, how can we improve the visibility of this award?
I always feel that our discussions, if we could have them live with everyone involved, would be incredibly valuable. When we bring people together to talk about new fields and ideas, it can lead to groundbreaking research. It's always a pleasure for me because people are genuinely interested in these new topics and conversations.
I’ve published in a lot of journals, but MDPI stands out. The way they recognize, and honor research is something special. When an award is given, it’s truly an honorable moment for us. It brings immense joy. Personally, I was overjoyed, but my professors were even happier than I was. They insisted that I share this achievement widely and encouraged me to publish it. I joked that it was my first time experiencing something like this, but my professors reminded me of the hard work I put in. We worked diligently on this project for about a year and a half. It required immense effort, persistence, and dedication, in addition to lots of hard work, tears, and even blood. It wasn't easy at all. It's not easy, and when you receive an award after all that effort, the happiness you feel is indescribable. It's a deep, profound joy that stays with you.
5. As the winner of this award, is there something you want to express or someone you wish to thank the most?
MDPI has been an invaluable partner in my research journey. Their support and recognition have greatly contributed to my work. I encourage young researchers to take advantage of the resources and opportunities provided by MDPI, to collaborate, and to remain dedicated to their research interests. Together, we can achieve remarkable advancements in our respective fields. Thank you.
6. Have you ever encountered any difficulties conducting research and how did you overcome them?
Conducting research in the field of sentiment analysis using deep learning presents several challenges, particularly due to the inherent complexity and variability of human language. One major difficulty is handling the nuances of sentiment such as sarcasm, idioms and contextual polarity, which can significantly skew the results. Deep learning models often struggle with these subtleties because they rely heavily on the data they are trained on, and if the training data is not diverse or comprehensive enough, the models may fail to generalize well to real-world scenarios. Additionally, the requirement for large, labeled datasets to train deep learning models is another significant hurdle. Obtaining and labeling sufficient data, especially for specific domains, can be time-consuming and resource-intensive, making it difficult to build robust models.
To overcome these challenges, I focused on enhancing the quality and diversity of the training data by leveraging data augmentation techniques and incorporating domain-specific knowledge into the models. By using techniques like transfer learning, I was able to adapt pre-trained models to specific sentiment analysis tasks, reducing the need for extensive labeled datasets. Additionally, I experimented with advanced neural architectures, such as the attention mechanism, which are better at capturing long-range dependencies and context, thus improving the model’s ability to understand nuanced sentiments. Combining these approaches allowed me to build more accurate and reliable sentiment analysis models, capable of handling the complexities of human language more effectively.
7. What appealed to you about the journal that made you want to submit your paper? What benefits do you think authors can gain when publishing their articles in Applied Sciences?
What appealed to me about Applied Sciences as a journal was its broad interdisciplinary focus and its commitment to advancing research that bridges the gap between fundamental science and practical applications. The journal’s emphasis on cutting-edge developments across a wide range of scientific disciplines, including engineering, computer science, and applied physics, aligns perfectly with the innovative nature of my research. Additionally, the rigorous peer-review process and the journal's reputation for publishing high-quality, impactful research were significant factors that motivated me to submit my paper. The global visibility and accessibility provided by the open-access model of Applied Sciences also assured me that my work would reach a diverse and extensive audience, further contributing to the scientific community and beyond.
Authors who publish in Applied Sciences can gain several substantial benefits. The journal’s interdisciplinary scope allows researchers to reach a broader audience, facilitating cross-disciplinary collaborations and enhancing the impact of their work. Moreover, the open access nature of the journal ensures that their research is widely accessible, increasing citation potential and visibility within the scientific community. The journal’s commitment to a swift and thorough peer review process also means that authors can expect timely feedback and publication, which is crucial for maintaining the momentum of their research projects. Overall, Applied Sciences provides a platform that not only showcases research but also amplifies its reach and impact, making it an ideal choice for authors looking to contribute to both their specific field and the broader scientific discourse.
8. Which research topics do you think will be of particular interest to the research community in the coming years?
In the coming years, the research community in deep learning is likely to focus on several emerging and critical topics that push the boundaries of current capabilities and address key challenges. These topics include the development of more efficient and interpretable models, the integration of deep learning with other areas such as neuroscience and quantum computing, and the ethical implications of deploying AI systems. As deep learning continues to evolve, there will be a strong interest in models that require less computational power while maintaining or even improving accuracy, addressing the growing concern over the environmental impact and accessibility of AI technologies. Additionally, enhancing the interpretability and explainability of deep learning models will be crucial for increasing trust and adoption in critical sectors like healthcare and finance. The intersection of deep learning with other scientific domains also holds great promise, particularly in areas like personalized medicine and autonomous systems, where these techniques can lead to groundbreaking innovations.
Research Topics of Interest:
- Interpretability and Explainability: Improving the transparency of deep learning models to enhance trust and usability in critical applications;
- Quantum Machine Learning: Exploring the integration of quantum computing with deep learning for solving complex problems more efficiently;
- Ethical AI and Bias Mitigation: Addressing the ethical implications and biases in AI systems to ensure fairness and accountability;
- Personalized Medicine: Applying deep learning to healthcare for the development of personalized treatment plans and diagnostics;
- AI for Climate Change: Utilizing deep learning to model, predict, and mitigate the effects of climate change.