Jay Darji
Jr. Technical researcher
Jay is an engineer with well-developed skills in Data analytics, research, and software development. He is also a good communicator with good interpersonal skills and is used to working in a team. He is always enthusiastic to learn and undertake new challenges. At Rhenix Lifesciences, he works as a junior engineer working in a technical research department for digital medicine. He is also responsible for developing various statistical methods for anomaly detection with human heart rate data captured with the help of wearable devices.
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He is an electrical engineer. He did his bachelor's degree in electrical engineering from the Institute of Infrastructure Technology Research and Management along with voluntary work at the blind people's association. Moreover, he has done research in biomedical signal processing and machine learning as well.
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Since the completion of his studies, he has wanted to become a data scientist thus, he began learning data analytics and machine learning through a summer internship. At Rhenix, he hopes to increase his technical knowledge while providing solutions and improvements to common industry problems. In the long run, he aspires to contribute significantly to industry development and optimization to create something that would genuinely improve the lives of thousands.
Summary "Lung cancer prediction using robust machine learning and image enhancement methods on extracted gray-level co-occurrence matrix features."
In the present era, cancer is the leading cause of demise in both men and women world-wide, with low survival rates due to inefficient diagnostic techniques. In the year 2021, about 1,898,160 new cancer cases...
Summary "DeepMAge: A methylation aging clock developed with deep learning."
Human longevity refers to the actual duration of an individual’s life, and it has been a subject of fascination, study, and speculation throughout human history. The quest for understanding and extending the human lifespan...
Summary "Systematic Evaluation of machine learning algorithms for neuroanatomically-based age prediction in youth."
Human longevity refers to the actual duration of an individual’s life, and it has been a subject of fascination, study, and speculation throughout human history. Application of machine learning (ML) algorithms to structural...
Videos
A comparative analysis of machine learning algorithms to predict Alzheimer’s disease | Cell talk
Alzheimer's disease is a progressive neurological disorder that causes brain cells to die and the brain to shrink (atrophy). Alzheimer's disease is the most common cause of dementia, which is defined as a progressive decline...
A hybrid CNN-GLCM classifier for detection and grade classification of brain tumor | Cell talk
A tumor is a volume of irregular and abnormal cells affecting the function of nearby healthy cells in the human body. Meningioma is the most commonly occurring tumor in adults with high-risk factors. Meningiomas are seen in...
Lung cancer prediction using robust machine learning and image enhancement methods on extracted gray-level co-occurrence matrix features | Cell talk
In the present era, cancer is the leading cause of demise in both men and women world-wide, with low survival rates due to inefficient diagnostic techniques. In the year 2021, about 1,898,160 new cancer cases were encountered, and 608,570 deaths were...
DeepMAge: A methylation aging clock developed with deep learning | Cell talk
Human longevity refers to the actual duration of an individual’s life, and it has been a subject of fascination, study, and speculation throughout human history. The quest for understanding and extending the human lifespan has driven scientific research...
Systematic Evaluation of machine learning algorithms for neuroanatomically-based age prediction in youth | Cell talk
Human longevity refers to the actual duration of an individual’s life, and it has been a subject of fascination, study, and speculation throughout human history. Application of machine learning (ML) algorithms to structural...
