In dermatological diagnosis, accurately and appropriately classifying skin diseases is crucial for timely treatment, thereby improving patient outcomes. Our goal is to develop transfer learning models that can detect skin disease from images. We performed our study in the “Skin Cancer: MNIST HAM10000” dataset. This dataset has seven categories, including melanocytic nevi, melanoma, benign keratosis (solar lentigo/seborrheic keratosis), basal cell carcinoma, actinic keratoses, intraepithelial carcinoma (Bowen’s disease), vascular lesions, and more. To leverage pre-trained feature extraction, we use five available models—ResNet50, InceptionV3, VGG16, VGG19, and MobileNetV2. Overall results from these models show that ResNet50 is the least time-intensive and has the best accuracy (99%) in comparison to other classification performances. Interestingly, with a notable accuracy of 97.5%, MobileNetV2 also seems to be adequate in scenarios with less computational power than ResNet50. Finally, to interpret our black box model, we have used LIME as an explainable AI technique (XAI) to identify how the model is classifying the disease. The results emphasize the utility of transfer learning for optimizing diagnostic accuracy in skin disease classification, blending performance and resource efficiency as desired. The findings from this study may contribute to the development of automated tools for dermatological diagnosis and enable clinicians to reduce skin conditions in a timely manner.

Smart contracts govern substantial financial value on public blockchains; however, because deployed contract code is immutable, a vulnerability that survives review cannot be patched in place. Pre-deployment auditing is the main safeguard for contract security; in addition to providing a warning, it must identify the flaw, explain why it is exploitable, and supply a candidate correction. Static analyzers match fixed patterns and emit many false alarms, while a single large language model asked to detect, explain, and repair a flaw in one pass tends to degrade on each subtask. We introduce SecureFlow, a multi-agent framework in which three agents share a Qwen3-8B backbone: SecureProbe detects vulnerable contracts, SecureAnalyze classifies them under the DASP Top-10, and SecureSynthesize writes a root-cause explanation and a minimally invasive patch through in-context learning. Against seventeen open and proprietary models on the SmartBug and Smart Contract Vulnerability datasets, SecureFlow performs on par with the strongest baselines, including fine-tuned GPT-4o, reaching accuracy and weighted F1 of 0.93 and 0.96 on the two datasets, respectively, while running locally on 8B open weights. It completes reports for 93.33% and 85.15% of instances; in blinded manual review of 50 sampled completed reports per dataset, patch and explanation validity are respectively 0.94 and 0.96 on SmartBug and 0.88 and 0.90 on the second dataset. A controlled prompt study shows that adding the predicted label and a knowledge-base hint improves generation and validity for every model, making prompt design a primary factor for language model auditing. An end-to-end auditor can match proprietary baselines while explaining and repairing flaws.

An application of software known as an Intrusion Detection System (IDS) employs machine algorithms to identify network intrusions. Selective logging, safeguarding privacy, reputation-based defense against numerous attacks, and dynamic response to threats are a few of the problems that intrusion identification is used to solve. The biological system known as IoT has seen a rapid increase in high dimensionality and information traffic. Self-protective mechanisms like intrusion detection systems (IDSs) are essential for defending against a variety of attacks. On the other hand, the functional and physical diversity of IoT IDS systems causes significant issues. These attributes make it troublesome and unrealistic to completely use all IoT elements and properties for IDS self-security. For peculiarity-based IDS, this study proposes and implements a novel component selection and extraction strategy (our strategy). A five-ML algorithm model-based IDS for machine learning-based networks with proper hyperparamater tuning is presented in this paper by examining how the most popular feature selection methods and classifiers are combined, such as K-Nearest Neighbors (KNN) Classifier, Decision Tree (DT) Classifier, Random Forest (RF) Classifier, Gradient Boosting Classifier, and Ada Boost Classifier. The Random Forest (RF) classifier had the highest accuracy of 99.39%. The K-Nearest Neighbor (KNN) classifier exhibited the lowest performance among the evaluated models, achieving an accuracy of 94.84%. This study's models have a significantly higher performance rate than those used in previous studies, indicating that they are more reliable.

AI and deep learning are two recent innovations that have made a big difference in helping to solve problems in the clinical space.