Skin cancer is the most common cancer in the USA, and it is a leading cause of death worldwide. Every year, more than five million patients are newly diagnosed in the USA. The deadliest and most serious form of skin cancer is called melanoma. Skin cancer can affect anyone, regardless of skin color, race, gender, and age. The diagnosis of melanoma has been done by visual examination and manual techniques by skilled doctors. It is a time-consuming process and highly prone to error. The skin images captured by dermoscopy eliminate the surface reflection of skin and give a better visualization of deeper levels of the skin. However, the existence of many artifacts and noise such as hair, veins, and water residue make the lesion images very complex. Due to the complexity of images, the border detection, feature extraction, and classification process are challenging. Without a proper mechanism, it is hard to identify and predict melanoma at an early stage. Therefore, there is a need to provide precise details, identify early skin cancer, and classify skin cancer with appropriate sensitivity and precision. This article aims to review and analyze two deep neural network-based classification algorithms (convolutional neural network, CNN; recurrent neural network, RNN) and a decision tree-based algorithm (XG-Boost) on skin lesion images (ISIC dataset) and find which of these provides the best classification performance metric. Also, the performance of algorithms is compared using six different metrics—loss, accuracy, precision, recall, F1 score, and ROC.

Due to availability of computational tools for data acquisition, it is very easy to collect many dimensions from an object. Nevertheless, data acquisition from an object in an experiment may have a low number of dimensions. The analysis of low dimensional data has break-through role. But raw and sparse nature of dataset imposes new challenges and requirements for data analysis due to their special and unique characteristics. In the process of overall characterization of low-dimensional data, the data pre-processing plays crucial role. One of the first processes is normalization and standardization process. Therefore, in this paper, I would like to propose novel standardization technique called SDFS (Standardization for Distribution Free Statistics) for nonparametric data analysis. This technique is robust for small sample size with missing values of data points, which commonly exist in real time experiments lead to sparse low-dimensional data. The comprehensive experimental evaluation shows that SDFS standardization is significantly outperforms on existing standardization methods.

A mobile agent is a small piece of software which works on direction of its source platform on a regular basis. Because mobile agents roam around wide area networks autonomously, the protection of the agents and platforms is a serious worry. The number of mobile agents-based software applications has increased dramatically over the past year. It has also enhanced the security risks associated with such applications. Most of the security mechanisms in the mobile agent architecture focus solely on platform security, leaving mobile agent safety to be a significant challenge. An efficient authentication scheme is proposed in this article to address the situation of protection and authentication of mobile agent at the hour of migration of across multiple platforms in malicious environment. An authentication mechanism for the mobile agent based on the Hopfield neural network proposed. The mobile agent's identity and password are authenticate using the specified mechanism at the moment of execution of assigned operation. An evaluative assessment has been offered, along with their complex character, in comparison to numerous agent authentication approaches. The proposed method has been put into practice, and its different aspects have been put to the test. In contrasted to typical client-server and code-on-demand approaches, the analysis shows that computation here is often more safe and simpler.

Melanoma remains the most lethal form of skin cancer, responsible for the majority of skin cancer-related fatalities despite its relatively low incidence \cite{milton2018}. Melanoma can spread to different parts of the body if it is not identified and treated in its early stages. In \cite{kumar2021, kumarVatsa2022} previous work on melanoma classification, the VGG16 architecture of the CNN performed better on the cancer dataset than any other popular network. % The early detection of melanoma by analyzing skin lesion images aims to enhance early diagnosis and accessibility. However, a significant challenge arises when incorporating demographic factors as biases in training data, as they can lead to disparities in model performance across different populations. These biases often stem from the underrepresentation of certain demographic groups in medical datasets, leading to lower accuracy for underserved communities. Such disparities can have serious consequences, including delayed diagnoses and inadequate treatment recommendations, further increasing healthcare inequities \cite{dietterich2000}. Therefore, this study examines the effects of demographic factors such as age, gender, and data scalability on the early detection of melanoma. It also evaluates and compares two distinct deep learning approaches for classifying melanoma from dermoscopic images and associated patient metadata. The first experiment establishes a baseline using a VGG-16 convolutional neural network (CNN) trained via transfer learning. The second, expanded experiment introduces a novel multimodal ensemble model that synergistically combines an EfficientNetB0 CNN with a Multi-Layer Perceptron (MLP) to process both image and tabular data concurrently.

Early detection of malignant melanoma is critical for patient survival, but even trained clinicians achieve only moderate accuracy with traditional visual inspection. Recent work demonstrates that deep convolutional neural networks (CNNs) can rival dermatologists in dermoscopy-based melanoma classification. Nevertheless, CNNs require large, balanced datasets, which are lacking for rare skin lesion classes. This study considered augmenting the ISIC 2019 skin lesion dataset with GAN-generated images to balance the benign and malignant classes and proposed a graph-based classification approach. The dermoscopic images are segmented into super pixels (using Simple Linear Iterative Clustering (SLIC)), a region-adjacency graph of super pixel nodes is constructed, and graph neural networks, including Graph Convolutional Network (GCN), Graph Attention Network (GAT), and a Dynamic Attention Variant graph, are trained. To mitigate the imbalance, we used focal loss and balanced resampling approaches. On the augmented dataset, GNN models achieve higher sensitivity and AUC than a CNN baseline (e.g., GAT ROC = 0.92 vs. CNN ROC = 0.87), indicating the benefit of graph-based relational features. Although based on other experimental results, all models tend to overfit to synthetic images, as performance drops when evaluated on a held-out set of real images. Additionally, training on a smaller, balanced, and real-only subset improves real-world performance and continues to favor GNNs over CNNs. Therefore, these findings suggest that graph representations can enhance melanoma detection in augmented data regimes but also highlight the pitfalls of synthetic augmentation and the need for robust evaluation.

Melanoma remains the most lethal form of skin cancer, responsible for the majority of skin cancer-related fatalities despite its relatively low incidence. Melanoma can spread to different parts of the body if it is not identified and treated in its early stages. In previous work on melanoma classification, the VGG16 architecture of the CNN performed better on the cancer dataset than any other popular network. The early detection of melanoma by analyzing skin lesion images aims to enhance early diagnosis and accessibility. However, a significant challenge arises when incorporating demographic factors as biases in training data, as they can lead to disparities in model performance across different populations. These biases often stem from the underrepresentation of certain demographic groups in medical datasets, leading to lower accuracy for underserved communities. Such disparities can have serious consequences, including delayed diagnoses and inadequate treatment recommendations, further increasing healthcare inequities. Therefore, this study examines the effects of demographic factors such as age, gender, and data scalability on the early detection of melanoma. It also evaluates and compares two distinct deep learning approaches for classifying melanoma from dermoscopic images and associated patient metadata. The first experiment establishes a baseline using a VGG-16 convolutional neural network (CNN) trained via transfer learning. The second, expanded experiment introduces a novel multimodal ensemble model that synergistically combines an EfficientNetB0 CNN with a Multi-Layer Perceptron (MLP) to process both image and tabular data concurrently.