https://vperifanis.gr/https://vperifanis.gr/about/https://vperifanis.gr/skills/https://vperifanis.gr/contact/https://vperifanis.gr/research/https://vperifanis.gr/projects/https://vperifanis.gr/papers/selmroute-probabilistic-semantic-evidence-for-large-language-model-routing/https://vperifanis.gr/papers/federated-learning-for-electric-vehicle-energy-demand-prediction-across-distributed-users/https://vperifanis.gr/papers/explainable-machine-learning-for-non-technical-loss-detection-in-power-systems/https://vperifanis.gr/papers/federated-learning-for-early-prediction-of-ev-charging-demand/https://vperifanis.gr/papers/duth-at-exist-2025-multilingual-sexism-detection-with-soft-labels-and-transformers/https://vperifanis.gr/papers/federated-learning-for-privacy-friendly-health-apps-a-case-study-on-ovulation-tracking/https://vperifanis.gr/papers/evaluation-of-bio-inspired-models-under-different-learning-settings-for-energy-efficiency-in-network-traffic-prediction/https://vperifanis.gr/papers/evaluating-the-defense-potential-of-machine-unlearning-against-membership-inference-attacks/https://vperifanis.gr/papers/federated-learning-for-recommender-systems-advances-and-perspectives/https://vperifanis.gr/papers/large-language-models-as-universal-predictors-an-empirical-study-on-small-tabular-datasets/https://vperifanis.gr/papers/bridging-the-gap-challenges-and-limitations-of-federated-learning-in-real-world-applications/https://vperifanis.gr/papers/justification-retrieval-with-llms-retrieval-augmented-generation-and-hybrid-labels/https://vperifanis.gr/papers/federated-learning-in-real-world-applications-mitigating-the-data-availability-problem/https://vperifanis.gr/papers/a-federated-explainable-ai-model-for-breast-cancer-classification/https://vperifanis.gr/papers/federated-learning-in-mobile-networks-a-comprehensive-case-study-on-traffic-forecasting/https://vperifanis.gr/papers/federated-anomaly-detection-for-early-stage-diagnosis-of-autism-spectrum-disorders-using-serious-game-data/https://vperifanis.gr/papers/towards-detecting-non-technical-losses-in-electrical-power-systems-with-machine-learning/https://vperifanis.gr/papers/carbon-aware-machine-learning-a-case-study-on-cellular-traffic-forecasting-with-spiking-neural-networks/https://vperifanis.gr/papers/sftc-machine-unlearning-via-selective-fine-tuning-and-targeted-confusion/https://vperifanis.gr/papers/federated-learning-for-5g-base-station-traffic-forecasting/https://vperifanis.gr/papers/towards-energy-aware-federated-traffic-prediction-for-cellular-networks/https://vperifanis.gr/papers/intelligent-client-selection-for-federated-learning-using-cellular-automata/https://vperifanis.gr/papers/duth-at-semeval-2023-task-9-an-ensemble-approach-for-twitter-intimacy-analysis/https://vperifanis.gr/papers/federated-learning-for-early-dropout-prediction-on-healthy-ageing-applications/https://vperifanis.gr/papers/an-extensive-overview-of-feature-representation-techniques-for-molecule-classification/https://vperifanis.gr/papers/predicting-early-dropouts-of-an-active-and-healthy-ageing-app/https://vperifanis.gr/papers/fedpoirec-privacy-preserving-federated-poi-recommendation-with-social-influence/https://vperifanis.gr/papers/federated-neural-collaborative-filtering/https://vperifanis.gr/teaching/