A5013182850- Video Surveillance and Tracking Methods
- Spectroscopy and Chemometric Analyses
- Suicide and Self-Harm Studies
- Topic Modeling
- Water Quality Monitoring and Analysis
- Natural Language Processing Techniques
- Service-Oriented Architecture and Web Services
- Speech Recognition and Synthesis
- Nutrition, Health and Food Behavior
- Food Quality and Safety Studies
- Personality Disorders and Psychopathology
- Context-Aware Activity Recognition Systems
- Nanoparticle-Based Drug Delivery
- Mobile and Web Applications
- Software System Performance and Reliability
- IoT-based Smart Home Systems
- AI and Big Data Applications
- Software Testing and Debugging Techniques
- Advanced Technologies in Various Fields
- Identity, Memory, and Therapy
- Face recognition and analysis
- Human Mobility and Location-Based Analysis
- Culinary Culture and Tourism
- Spectroscopy Techniques in Biomedical and Chemical Research
- Mental Health Research Topics
Christ University
2022-2024
National Institute of Technology Tiruchirappalli
2015
Bharathidasan University
2014
Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift performance, lead to inaccurate predictions. Monitoring mitigating drift are vital maintain effectiveness. USFDA ICH regulate pharmaceutical variation with scientific risk-based approaches. this study, the hyperparameter optimization for Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data....
Nowadays with many services and applications focusing on mobile centric there is a need for an efficient robust advertisement system based location. Services can leverage the ubiquitous presence of systems to display advertisements at opportune situations. An architecture needed push consumers especially when considering fact that devices consumes lot energy GPS network connectivity. Traditional advertising just acquire location client nearby their which require internet access round clock....
Teenage suicidal ideation is on the rise, which emphasizes how crucial it to recognize and comprehend variables that contribute this problem. Convolutional neural networks (CNNs), are complex machine learning models capable of analysing intricate relationships within a network, one possible strategy for addressing issue. In our study, we employed CNN-LSTM hybrid model explore between teen suicide various risk variables, including depression, anxiety, social support by substantial dataset...
Suicide has been a prominent cause of death worldwide, regardless age, sex, geography, and so on, predominantly suicide among teens, increased as the years have passed. ideation, risk, attempts studied extensively, most common identified depression, followed by familial concerns, hereditary factors, stress, avoidance fear, variety other variables. When visited doctor, adolescents are unaware their mental state hence do not take action on own or assisted family peer members to overcome fear...
Recently, big data becomes evitable due to massive increase in the generation of real time application. Presently, object detection and tracking applications popular among research communities finds useful different namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based tracker using Automated Image Annotation with Inception v2 Faster RCNN (AIA-IFRCNN) model environment. The AIA-IFRCNN annotates images by Discriminative...