Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%.

The Internet of Things (IoT) has revolutionized many domains. Due to the growing interconnectivity of IoT networks, several security challenges persist that need to be addressed. This research presents the application of deep learning techniques for Distributed Denial-of-Service (DDoS) attack detection in IoT networks. This study assesses the performance of various deep learning models, including Latent Autoencoders, LSTM Autoencoders, and convolutional neural networks (CNNs), for DDoS attack detection in IoT environments. Furthermore, a novel hybrid model is proposed, integrating CNNs for feature extraction, Long Short-Term Memory (LSTM) networks for temporal pattern recognition, and Autoencoders for dimensionality reduction. Experimental results on the CICIOT2023 dataset show the enhanced performance of the proposed hybrid model, achieving training and testing accuracy of 96.78% integrated with 96.60% validation accuracy. This presents its efficiency in addressing complex attack patterns within IoT networks. Results’ analysis shows that the proposed hybrid model outperforms the others. However, this research has limitations in detecting rare attack types and emphasizes the importance of addressing data imbalance challenges for further enhancement of DDoS attack detection capabilities in future.

Breast cancer is one of the major causes of women death worldwide. WHO organization has reported that 1 in every 12 women could be subjected to a breast abnormality during her lifetime. To increase survival rates, it is found that early detection of breast tumor is very critical. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with the cases where the tumor size less than 2mm. Thermography-based breast cancer detection methods can address this problem. In this paper, a breast cancer detection method is proposed. The proposed method is consists of four phases: (1) Image Pre-processing using homomorphic filtering, (2) Region of interest (ROI) Segmentation using K-mean clustering, (3) feature extraction using signature boundary, and (4) classification using Extreme Learning Machine (ELM). The proposed method is evaluated using the public dataset DMR-IR. Different activation functions in ELM are evaluated. The obtained results founded that “Tribas” is the best activation function under different experiments. It produced an accuracy result of 95.94% while talking 0.0469 second to detect the existence of malignant tumor, benign tumor or normal image. These promising results would be useful to develop thermography-based breast cancer detection system.

The prevalence of melanoma skin cancer has increased in recent decades. The greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and veins. Thus, the early diagnosis of melanoma is a key factor in improving the prognosis of the disease. Deep learning makes it possible to design and develop intelligent systems that can be used in detecting and classifying skin lesions from visible-light images. Such systems can provide early and accurate diagnoses of melanoma and other types of skin diseases. This paper proposes a new method which can be used for both skin lesion segmentation and classification problems. This solution makes use of Convolutional neural networks (CNN) with the architecture two-dimensional (Conv2D) using three phases: feature extraction, classification and detection. The proposed method is mainly designed for skin cancer detection and diagnosis. Using the public dataset International Skin Imaging Collaboration (ISIC), the impact of the proposed segmentation method on the performance of the classification accuracy was investigated. The obtained results showed that the proposed skin cancer detection and classification method had a good performance with an accuracy of 94%, sensitivity of 92% and specificity of 96%. Also comparing with the related work using the same dataset, i.e., ISIC, showed a better performance of the proposed method.