Skip to content
G

Gajjala Lilly Rani

Publications (4)

Designing Interesting Opponents through Online Learning in Predator–Prey Environments

Gajjala Lilly Rani, Ankatwar Gajanan, Alurwad Tripat Venkatreddy, K. Krunal Yadav & Narote Preetham · Journal of Engineering Research and Reports · 2026

Predator–prey environments are widely used benchmarks for multi-agent reinforcement learning (MARL) because they capture simultaneous cooperation among predators and competition against prey, yet many deployed predator opponents rely on static or pre-trained policies that become...

Open access Research Article 10.9734/jerr/2026/v28i61929

Generative AI for Personalised Marketing Campaign Design and Content Automation

K. Krunal Yadav, Gajjala Lilly Rani, Ankatwar Gajanan, Narote Preetham & Alurwad Tripat Venkatreddy · Current Journal of Applied Science and Technology · 2026

The transition from mass communication to data-driven digital engagement has increased the importance of artificial intelligence in marketing practice and organisational decision-making. This study examines the role of Generative AI in personalised marketing campaign design and c...

Open access Research Article 10.9734/cjast/2026/v45i74714

Image Processing from Handcrafted Operators to Foundation Models: A Critical Review of Methods, Applications and Recent Advances

Ankatwar Gajanan, Narote Preetham, Gajjala Lilly Rani, Alurwad Tripat Venkatreddy & K Krunal Yadav · Advances in Research · 2026

Digital image processing has evolved from deterministic operators designed around explicit assumptions to data-driven systems that learn representations, priors and decision rules from large image collections. This transition has improved performance in restoration, segmentation,...

Open access Research Article 10.9734/air/2026/v27i51693

Multimodal Emotion Recognition Using Explainable Hybrid CNN-Transformer Networks

Narote Preetham, Alurwad Tripat Venkatreddy, K. Krunal Yadav, Ankatwar Gajanan & Gajjala Lilly Rani · Asian Journal of Research in Computer Science · 2026

Emotion recognition is central to affective computing because it enables intelligent systems to perceive and respond to human affective states in domains such as healthcare, education, customer service, and human–computer interaction. However, unimodal methods based solely on fac...

Open access Research Article 10.9734/ajrcos/2026/v19i7878