UGC-Registered (Research Topics-Synopsis-Thesis)
These fillers achieve comparable curing behavior and durability to petroleum-based options, with life cycle assessments showing reduced environmental burdens, like zero agro-waste impact beyond transport, and economic viability via lower costs. Tyres with bio-fillers exhibit enhanced abrasion resistance and sustainability, supporting up to 50% renewable content without safety compromises. Pyrolyzed tire waste and biomass blends cut fossil dependency while preserving properties like elongation and modulus.
AI-Driven-Novel-Drug-Formulations-Revolutionizing-Pharmaceutical-Development. Neural networks analyze vast datasets for nonlinear correlations in compositions like cocrystals and solid dispersions, generating personalized formulations via generative models like ReLeaSE. AlphaFold predicts protein structures for targeted designs, while reinforcement learning optimizes ligand-receptor interactions and PBPK models for delivery systems. Robotic automation enables self-optimizing manufacturing with predictive analytics.
Zero-shot and few-shot prompts enable rapid diagnostics from EHR data, while CoT fosters step-by-step reasoning for treatment planning, and adaptive prompting integrates hierarchical context from vitals to imaging via reinforcement learning. Principles include explicitness, contextual relevance, ethical bias mitigation, and evidence-based integration with SMART on FHIR for seamless workflows. Meta-prompting and generated knowledge prompting enhance output reliability in real-time CDS.
Building on AI/ML in cloud-based BFSI and blockchain fraud detection interests, explore hybrid models combining DID with anomaly detection for predictive onboarding risks, validated via simulations in permissioned networks like Hyperledger. Key challenges include scalability, interoperability standards, and regulatory frameworks for SSI adoption. Empirical studies could measure metrics like false positives and ROI in pilots, extending to supplier-like reputation in financial networks.
Despite the immense potential, the field faces challenges including the need for high-quality, standardized data, issues of model interpretability, and the development of clear regulatory frameworks. The research will focus on: Integrating data from multi-omics (genomics, proteomics, etc.) to gain a more comprehensive understanding of disease and patient response. Developing Explainable AI (XAI) frameworks to build trust among clinicians and comply with regulatory requirements. Leveraging new technologies like the Internet of Things (IoT) and blockchain for secure data sharing and real-time monitoring. Ultimately, AI-driven predictive formulation is set to revolutionize pharmaceuticals, leading to safer, more effective, and precisely tailored treatments.
Addressing sector-specific volatility from rapid innovation. Empirical data shows the lower failure rates in AI-supported IT deals versus non-AI, with enhanced ESG integration boosting long-term value in tech acquisitions. Tools automate end-to-end risk evaluation, from financial modeling to compliance, enabling faster decisions in competitive IT markets. Focus on IT-specific metrics like cybersecurity risks or scalability synergies, comparing empirical outcomes against benchmarks like 90% success prediction.
Comprehensive research examining psychological effects, behavioural changes, social development challenges, and cyberbullying exposure associated with social media use in children aged 10-15 years. Includes evidence-based recommendations for healthy social media practices covering parental guidance, digital literacy education, time management, reporting mechanisms, and positive online communities.
Edge computing represents a paradigm shift in how we process IoT-generated data by moving computation closer to data sources. This research explores the development of robust frameworks that enable real-time analytics at the network edge, reducing latency and bandwidth consumption. The investigation encompasses architectural patterns, resource allocation algorithms, and fault-tolerance mechanisms essential for distributed edge environments. Key challenges include dynamic workload distribution, energy efficiency optimization, and seamless integration with cloud infrastructures. The research aims to establish design principles for scalable edge computing systems that can handle the exponential growth of IoT devices while maintaining performance guarantees and security standards.
Financial fraud detection systems increasingly rely on complex machine learning models whose decision-making processes remain opaque to human analysts. This research develops explainable AI techniques specifically adapted for fraud detection contexts, balancing predictive performance with interpretability requirements. The study investigates local and global explanation methods, counterfactual reasoning for fraud scenarios, and visualization techniques for complex feature interactions. Critical research areas include developing domain-specific explanation metrics, ensuring explanations are actionable for fraud investigators, and maintaining model performance while adding interpretability constraints. The investigation addresses regulatory compliance requirements for algorithmic decision-making in financial services and explores how explanations can improve model trust and adoption among fraud analysts. The work contributes to responsible AI deployment in high-stakes financial applications.