Published Papers

Neurodegenerative disorders such as Alzheimer’s and Parkinson’s are challenging to detect early due to their gradual onset. This study investigates the use of machine learning algorithms to identify these conditions based on phonetic features in speech. By analyzing vocal attributes, such as fluency, articulation, and acoustic variation; this research aims to establish non-invasive diagnostic models. Principal Component Analysis (PCA) was used for feature selection, while Random Forest and Support Vector Machine (SVM) classifiers were deployed for detection accuracy. Results show promising accuracy levels, particularly in the Alzheimer’s model, highlighting the potential of AI in enhancing early clinical screening for cognitive decline. 

This case series examined the potential cognitive and neuro-physiological effects of daily auditory stimulation at a 40 Hz gamma frequency with dementia patients. In total, twenty older adults, ages 65–84 years and clinically diagnosed with mild to moderate dementia, completed a structural auditory stimulation procedure lasting for 15 minutes per day for 30 consecutive days. Assessments of cognition were completed using the standardized Mini-Cog test, and resulting neural responses  were quantitatively examined  with electroencephalography (EEG), focusing on gamma-band oscillatory activity. The results demonstrated 40% of participants showed statistically relevant improvements over pre stimulation Mini-Cog scores, demonstrating improvements in memory, attention, and executive functioning.

The relentless pursuit of alpha has driven the creation of several quantitative trading strategies, including pairs trading. The recent emergence of reinforcement learning frameworks, such as Q-learning, has led to the development of advanced statistical arbitrage models. This study presents an empirical investigation into the application of ensemble Q- learning, a reinforcement learning method, to pairs trading in the Indian equity market. Utilising hourly data from the NIFTY 50 index constituents, which covers over 128,400 data points, a novel composite scoring framework was devised to select optimal asset pairs, balancing both long-term cointegration and mean-reversion criteria. A custom trading environment was constructed using OpenAI’s gym library to realistically simulate capital constraints, transaction costs, and position management. The agent maintained multiple independently trained Q-tables, aggregating their estimates to reduce variance and improve stability in trading decisions. Hyperparameters for the agent were optimised via a Bayesian search, and strategy performance was evaluated on out-of-sample data across multiple independent runs. Results demonstrate that the ensemble Q-learning agent delivered an average six-month portfolio return of 105.2% with a Sharpe ratio of 2.08, substantially outperforming both traditional pairs trading benchmarks and major global indices. These findings provide rigorous evidence that reinforcement learning, when coupled with well-designed pairs trading strategies, can drive superior alpha generation in financial markets. 

This research paper aims to investigate the activity of the catalase enzyme under varying environmental conditions, including pH value, glucose concentration, and temperature. Catalase is a crucial antioxidant enzyme that breaks down hydrogen peroxide (H₂O₂), a highly reactive oxygen species (ROS), into water and oxygen. In diabetic cells, activities like glycation can impair catalase activity. This stimulates how hyperglycemia in diabetes reduces catalase efficiency. We hypothesize that high glucose and extreme pH or temperature will lower enzyme activity and produce less O₂ gas. Our experiment involves Solanum tuberosum (potato) derived catalase extract, observing its reactions with H₂O₂ and measuring O₂ evolution as an indicator for catalase activity through biological assays under varying environments. The results showed the expected trends: maximum activity at neutral pH and moderate temperature, and reduced activity with high glucose (mimicking diabetic glycation of catalase). 

This paper reviews literature where AI chatbots have been experimented with in psychotherapy. In 2015, nearly half the  population lived in countries with fewer than one psychiatrist per 100,000 people. Considering the global shortage, AI chatbots may provide more economic and accessible solutions, though they struggle to replicate the values of human therapists. A conversational AI chatbot can strengthen the therapeutic bond. However, they cannot imitate the emotional depth of a therapist. Of the three dimensions of empathy— cognitive, emotional, and motivational—AI chatbots currently lack two (emotional and motivational). Statistically, chatbots are more effective at reducing symptoms of certain mental conditions, but discontinued use causes a relapse, which is also possible with therapists. Qualitatively, there are mixed opinions on which is better. Overall, all the studies agree that chatbots lack the human touch and sensitivity required to deal with emotions. 

Creatine monohydrate is one of the most widely researched and utilized supplements in the fitness industry, known for its role in enhancing athletic performance, muscle growth, and recovery. Naturally obtained through dietary sources such as red meat and seafood, as well as being produced by the liver, creatine plays a critical role in adenosine triphosphate (ATP) regeneration, providing energy for high intensity exercise. This paper explores the physical impacts of creatine supplementation, its benefits in resistance training, and potential reasons to not supplement creatine into one’s diet. A review of existing literature indicates that consistent creatine intake, paired with structured training, leads to increased muscular strength, enhanced training capacity, and reduced injury risk due to improved hydration and muscle integrity. However, some studies present conflicting evidence, suggesting minimal performance enhancement in certain exercises, while others raise concerns about potential adverse effects, particularly for individuals with renal complications. This paper synthesizes these findings to provide a comprehensive evaluation of creatine monohydrate's impact on athletic performance and overall muscle health