In optical packet switching (OPS) networks wavelengths are shared on optical links and data packets are multiplexed statistically in all-optical domain. Quality of service concerns about management of resources and traffic control to deliver improved services to specific traffic classes. An artificial Intelligence-based dynamic wavelength grouping (AIDWG) scheme is proposed for OPS. In this scheme, available wavelengths are partitioned dynamically by Linear Regression Model (LRM) and allocated to traffic service classes at each network link. AIDWG tracks the load, blocking, wait time and utilization of each traffic class and schedules optical packets according to the assigned group of wavelengths. IBKSim simulator is used to examine the performance of NSFNet topology. DWG beats its previous static version(SWG) by a significant factor. Due to the flexibility, the proposed AIDWG technique gives good results in Blocking and Throughput even when the load share of one class is significantly less than other classes of traffic.

Accurate identification of snake species, particularly venom status, is important for emergency medical response and biodiversity monitoring. In practice, this task is challenging because visually similar species may differ in venom status, photographs may capture only part of the animal, and curated datasets are often limited in size. Existing image-based approaches commonly rely on fine-tuning deep networks on small regional datasets, while partial observation, probability calibration, and the relationship between species identity and venom status remain insufficiently addressed. This study proposes M2V-SnakeNet, which couples a frozen self-supervised vision foundation model, DINOv2, with presence-aware multi-view decomposition, cross-view attention fusion, and a prototypical metric-based head, together with a hierarchical venom head linked to the species distribution through a consistency constraint. The framework is designed to represent partial observations without fabricating absent views, while restricting task-specific training to 9.5 of 95.2 million parameters. On a balanced and leakage-audited dataset of seven Indian snake species comprising 1,776 images from a single geographic region, M2V-SnakeNet achieves 93.4% ± 1.5% species accuracy and 96.9% ± 1.4% venom accuracy under five-fold cross-validation. On the fixed held-out split, a five-seed ensemble reaches 97.4% species accuracy and 98.5% venom accuracy, with 99.3% recall for the venomous class. Ablation results identify the frozen DINOv2 encoder as the largest performance contributor, improving species accuracy by 17.4 percentage points over an equal-sized ImageNet-supervised backbone, while learned fusion and multi-view decomposition provide additional gains. External meta-pretraining on a large snake corpus does not improve performance in this setting. These findings indicate that frozen foundation features combined with lightweight task-specific adaptation can provide a competitive low-data approach to joint snake species and venom-status classification, while the limited species and geographic coverage remains an important constraint on generalization.

Automatic video surveillance systems have gained significant importance due to an increase in crime rate over the last two decades. Automatic baggage detection through surveillance camera can help in security and monitoring in public places. A detection algorithm for humans (with or without carrying baggage) is proposed in this paper. Detection in the proposed method can be achieved by employing spatial information of the baggage of various texture patterns with locus to the human body carrying it. To extract the features of body parts (such as head, trunk and limbs), the descriptor is exhibited and trained by the support vector machine classifier. The proposed approach has been widely assessed by using publically available datasets. The experimental results have shown that the proposed approach is viable for baggage detection and classification as compared to the other available approaches.