Megha Arakeri

Bio:

Dr. Megha. P. Arakeri received her Ph.D from NITK, Surathkal, India during the year 2013. She is currently working as Professor in the Computer Science & Engineering department of Manipal Institute of Technology, Bangalore. She is having a total of 19 years of teaching and research experience.

She has more than 50 publications in reputed International conferences and journals and also written 9 book chapters with total of 504 citations to her credit. She is currently serving as CHAIR of IEEE Computational Intelligence Society, Bangalore Section. She also holds a global position in the EPICS in IEEE committee. She is a member of various professional bodies like IETE, CSI, ISTE and IAENG. She has also worked on several funded research and consultancy projects. Her research areas of interest are Computer Vision, Pattern Recognition, Machine Learning, Human-Computer Interaction and Data Analytics.

Abstracts:

Role of Green Technology in Climate Change and Sustainable Agriculture:

An increasing population has put a lot of pressure on agriculture to ensure the food and nutritional security of the world, which is further worsening with climate change. The key factors of climate, namely temperature, precipitation, and greenhouse gases, significantly hampered pest infestation, soil fertility, irrigation resources, physiology, and plants’ metabolic activities. Agriculture sector also contributes to the causes of climate change through the emission of greenhouse gasses (GHGs). Hence, adaptation-led mitigation measures are required to sustain agricultural productivity. Climate-smart agriculture can sustain climate change as well as mitigate the bad effects of agriculture on the environment. This is possible by using green IT in Agriculture. It helps in enhanced resilience, Reduced emissions and increased productivity. Technologies like AI, IoT, Robotics or drones used for smart agriculture have to be made green so that they have less carbon footprint in the environment and thus help in sustainable agriculture.

Computer-aided Medical Image Analysis for Clinical Decision Making:

Cancer is a life-threatening disease due to low survival rate. Hence, accurate diagnosis of tumors is necessary to provide effective treatment. Medical imaging techniques like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) help in acquiring images of the tumor. The visual analysis of these medical images by the radiologist is time consuming, subjective and inaccurate. The needle biopsy of the tumor provides accurate diagnosis but it is an invasive technique and generally not recommended. In order to overcome these drawbacks, there is a need for a Computer-Aided Diagnosis (CAD) system for assisting the radiologist in fast and accurate diagnosis of tumors. Effective and efficient CAD systems can perform automated tumor detection, classification, Content-Based Image Retrieval, 3D reconstruction etc to provide complete assistance to the radiologist in the diagnosis of tumors.

Advances in Artificial Intelligence for Genomic Medical Diagnosis:

Genomics is a new and very active application area of computer science. The past ten years there has been an explosion of genomics data -- the entire DNA sequences of several organisms, including human, are now available. These are long strings of base pairs (A,C,G,T) containing all the information necessary for an organism's development and life. Computer science is playing a central role in genomics: from sequencing and assembling of DNA sequences to analyzing genomes in order to locate genes, repeat families, similarities between sequences of different organisms, and several other applications. The area of computational genomics includes both applications of methods, and development of novel algorithms for the analysis of genomic sequences. Artificial intelligence (AI) and machine learning have significantly influenced many facets of the healthcare sector. Advancement in technology has paved the way for analysis of big datasets in a cost- and time-effective manner. Efforts to reduce mortality rates require early diagnosis for effective therapeutic interventions. However, metastatic and recurrent cancers evolve and acquire drug resistance. It is imperative to detect novel biomarkers that induce drug resistance and identify therapeutic targets to enhance treatment regimes. The introduction of the next generation sequencing (NGS) platforms address these demands, has revolutionised the future of precision oncology.

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