Manipal University Jaipur

Automatic glaucoma screening degrades when a model moves to another hospital and camera; a neuro-symbolic framework combines anatomical graph reasoning with differentiable fuzzy clinical rules in one network, trained on 9,100 fundus images drawn from a corpus of 12,619 images across eight datasets in eight countries and tested on 485 fully held-out RIM-ONE images from a different Spanish hospital and camera. On the in-distribution test set, EfficientNet-B4 with a graph attention network reaches an AUC of 0.9612 (95% CI 0.951–0.972) versus 0.9472 for EfficientNet-B4 alone, yet the combined full model scores 0.6155 cross-hospital, 8.1 points below the baseline.

Automatic glaucoma screening degrades when a model moves to another hospital and camera; a neuro-symbolic framework combines anatomical graph reasoning with differentiable fuzzy clinical rules in one network, trained on 9,100 fundus images drawn from a corpus of 12,619 images across eight datasets in eight countries and tested on 485 fully held-out RIM-ONE images from a different Spanish hospital and camera. On the in-distribution test set, EfficientNet-B4 with a graph attention network reaches an AUC of 0.9612 (95% CI 0.951–0.972) versus 0.9472 for EfficientNet-B4 alone, yet the combined full model scores 0.6155 cross-hospital, 8.1 points below the baseline.

Swarm synchronization for green IoT no longer broadcasts state on a fixed cycle. ET-S3 ties each agent's trigger threshold to its remaining battery, so low-energy nodes transmit less, and a control-barrier-function quadratic program filters the consensus input for safety. In a simulation of 100 dynamic agents over 1000 synchronization cycles, cumulative payload was 3.35 MB, 18.2% of the 18.40 MB of periodic consensus, with a 0.0% safety violation rate and convergence in 138 steps.

Swarm synchronization for green IoT no longer broadcasts state on a fixed cycle. ET-S3 ties each agent's trigger threshold to its remaining battery, so low-energy nodes transmit less, and a control-barrier-function quadratic program filters the consensus input for safety. In a simulation of 100 dynamic agents over 1000 synchronization cycles, cumulative payload was 3.35 MB, 18.2% of the 18.40 MB of periodic consensus, with a 0.0% safety violation rate and convergence in 138 steps.

A hybrid intrusion-detection framework that chains causal graph learning, entropy-guided compression, attention fusion, adversarial calibration and trust-weighted federated aggregation into one pipeline reaches 97.4% detection accuracy on CICIDS2017 while cutting the input from 78 features to 18.

A hybrid intrusion-detection framework that chains causal graph learning, entropy-guided compression, attention fusion, adversarial calibration and trust-weighted federated aggregation into one pipeline reaches 97.4% detection accuracy on CICIDS2017 while cutting the input from 78 features to 18.

Using experimentally determined fibril structures from the Amyloid Atlas, with ordered residues labeled as core and unresolved residues from the same proteins used as matched non-core controls, this work encoded core and non-core regions with physicochemical descriptors and protein language model embeddings (ESM-2, ANKH, ProtT5) and evaluated them under protein-grouped cross-validation to build AmyloCore-ML, a sequence-based predictor of structural amyloid-core propensity; protein language models consistently outperformed physicochemical descriptors, with the best models reaching AUROC about 0.88 and AUPRC about 0.85, locked full-length protein scans localized experimental cores with ESM-2/ExtraTrees W21 achieving AUROC 0.833, AUPRC 0.751 and a mean peak distance of 12.

Using experimentally determined fibril structures from the Amyloid Atlas, with ordered residues labeled as core and unresolved residues from the same proteins used as matched non-core controls, this work encoded core and non-core regions with physicochemical descriptors and protein language model embeddings (ESM-2, ANKH, ProtT5) and evaluated them under protein-grouped cross-validation to build AmyloCore-ML, a sequence-based predictor of structural amyloid-core propensity; protein language models consistently outperformed physicochemical descriptors, with the best models reaching AUROC about 0.88 and AUPRC about 0.85, locked full-length protein scans localized experimental cores with ESM-2/ExtraTrees W21 achieving AUROC 0.833, AUPRC 0.751 and a mean peak distance of 12.