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Abnormal behaviors of tumors pose a risk to human survival. Thus, the detection of cancers at their initial stage is beneficial for patients and lowers the mortality rate. However, this can be difficult due to various factors related to imaging modalities, such as complex background, low contrast, brightness issues, poorly defined borders and the shape of the affected area. Recently, computer-aided diagnosis (CAD) models have been used to accurately diagnose tumors in different parts of the human body, especially breast, brain, lung, liver, skin and colon cancers. These cancers are diagnosed using various modalities, including computed tomography (CT), magnetic resonance imaging (MRI), colonoscopy, mammography, dermoscopy and histopathology. The aim of this review was to investigate existing approaches for the diagnosis of breast, brain, lung, liver, skin and colon tumors. The review focuses on decision-making systems, including handcrafted features and deep learning architectures for tumor detection.

Network security is the main issue in the Internet of Things networks because most manufacturers do not focus on security standards during design. The performance of firewalls, intrusion prevention systems, and intrusion detection systems depend upon accuracy, thus its essentials, to enhance the detection rate to lessen false alarms. The objective of the firewall is to find violations as per predefined rules and block the incoming risky traffic. However, it is very tough to differentiate between malicious and regular traffic due to advanced techniques of attack. To address these issues, a two-stage hybrid method is proposed. First, the genetic algorithm (GA) is applied to select appropriate features to improve the accuracy of the proposed framework. Next, the well-known machine learning (ML) algorithm, including the support vector machine (SVM), ensemble classifier, and decision tree are employed. The achieved accuracy is 99.8% through 10-fold cross-validation using a multiclass NSL-KDD database.

The novel coronavirus named COVID-19 has quickly spread among humans worldwide, and the situation remains hazardous to the health system. The existence of this virus in the human body is identified through sputum or blood samples. Furthermore, computed tomography (CT) or X-ray has become a significant tool for quick diagnoses. Thus, it is essential to develop an online and real-time computer-aided diagnosis (CAD) approach to support physicians and avoid further spreading of the disease. In this research, a convolutional neural network (CNN) -based Residual neural network (ResNet50) has been employed to detect COVID-19 through chest X-ray images and achieved 98% accuracy. The proposed CAD system will receive the X-ray images from the remote hospitals/healthcare centers and perform diagnostic processes. Furthermore, the proposed CAD system uses advanced load balancer and resilience features to achieve fault tolerance with zero delays and perceives more infected cases during this pandemic.