Dermoscopic skin lesion classification is a task of major clinical importance but is computationally expensive, making it inaccessible in resource-constrained healthcare settings. In this paper, we introduce a computationally efficient skin lesion classification framework for seven classes using EfficientNet-B0, complemented by Monte Carlo (MC) Dropout for uncertainty quantification. Our approach was trained and tested on the HAM10000 dataset containing 10,015 dermoscopic images across seven classes. To address the severe 67:1 class imbalance, we employ WeightedRandomSamplerand class-weighted cross-entropy loss as complementary corrections acting at the batch-composition level and the gradient-magnitude level respectively. By performing $T=50$ stochastic forward passes during inference, we decompose predictive uncertainty into aleatoric and epistemic components and apply an entropy-based referral threshold that flags uncertain predictions for specialist review. To validate spatial interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) is applied and quantitatively evaluated via Intersection over Union (IoU) against ISIC segmentation masks, yielding a mean IoU of 0.61 across all accepted predictions. Our experiments achieve a test macro AUROC of 0.9404and macro F1-score of 0.7308, with six of seven classes exceeding 70% per-class accuracy (melanocytic nevi: 69.8%). Referring the 30% most uncertain predictions to a clinician raises accepted-subset AUROC from 0.9404 to 0.9568 ($+1.64\%$). The framework is competitive with ResNet-50 and DenseNet-121 at one-fifth the parameter count, and the only lightweight method in the comparison providing calibrated uncertainty estimates. Inference latency benchmarks on an NVIDIA Jetson Nano (edge CPU mode) are reported to contextualize deployment feasibility.

The way software engineers approach coding, problem-solving, and teamwork has been completely altered by the incorporation of cutting-edge AI tools like ChatGPT. This study examines ChatGPT's advantages for software engineering, emphasizing how it can boost output, expedite development, and encourage creativity. Code generation, debugging, documenting, and validation are just a few of the phases of the program development lifecycle where ChatGPT helps engineers. Engineers may concentrate on more difficult problems because ChatGPT saves time on repetitive tasks by giving rapid access to technical assistance, code recommendations, and explanations. Companies can maintain high coding standards and enhance project outcomes by encouraging collaborative learning and knowledge sharing. This paper shows how ChatGPT is a useful tool through examples and real-life situations.