Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques

Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data

Adaptive secure malware efficient machine learning algorithm for healthcare data

Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices. On the other hand, IoT‐based heartbeat digital healthcare applications have been steadily increasing in popularity. These applications make a lot of data, which they send to the fog cloud to be processed further. In healthcare networks, it is critical to examine healthcare data for malicious intent. The malware is a peace code with polymorphic and metamorphic attack forms. Existing malware analysis techniques did not find malware in the content‐aware heartbeat data. The Adaptive Malware Analysis Dynamic Machine Learning (AMDML) algorithm for content‐aware heartbeat data in fog cloud computing is described in this article. Based on heartbeat data from health records, an adaptive method can train both pre‐ and post‐ train malware models. AMDML is based on a rule called ‘federated learning,’ which says that malware analysis models are made at both the local fog node and the remote cloud to meet the performance workload safely. The simulation results show that AMDML out performs machine learning malware analysis models in terms of accuracy by 60%, delay by 50%, and detection of original heartbeat data by 66% compared to existing malware analysis schemes.Web of Scienc

A novel medical image data protection scheme for smart healthcare system

The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge-based learning systems. Smart healthcare systems leverage knowledge-based learning to become more context-aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X-rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge-based learning systems to foster structured decision-making and enhance the learning abilities of AI. Moreover, in knowledge-driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit-plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high-quality encryption, a large key space, key sensitivity, and resistance to statistical attacks

A novel medical image data protection scheme for smart healthcare system

The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge‐based learning systems. Smart healthcare systems leverage knowledge‐based learning to become more context‐aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X‐rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI. Moreover, in knowledge‐driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high‐quality encryption, a large key space, key sensitivity, and resistance to statistical attacks

An efficient deep learning model for brain tumour detection with privacy preservation

Internet of medical things (IoMT) is becoming more prevalent in healthcare applications as a result of current AI advancements, helping to improve our quality of life and ensure a sustainable health system. IoMT systems with cutting‐edge scientific capabilities are capable of detecting, transmitting, learning and reasoning. As a result, these systems proved tremendously useful in a range of healthcare applications, including brain tumour detection. A deep learning‐based approach for identifying MRI images of brain tumour patients and normal patients is suggested. The morphological‐based segmentation method is applied in this approach to separate tumour areas in MRI images. Convolutional neural networks, such as LeNET, MobileNetV2, Densenet and ResNet, are tested to be the most efficient ones in terms of detection performance. The suggested approach is applied to a dataset gathered from several hospitals. The effectiveness of the proposed approach is assessed using a variety of metrics, including accuracy, specificity, sensitivity, recall and F‐score. According to the performance evaluation, the accuracy of LeNET, MobileNetV2, Densenet, ResNet and EfficientNet is 98.7%, 93.6%, 92.8%, 91.6% and 91.9%, respectively. When compared to the existing approaches, LeNET has the best performance, with an average of 98.7% accuracy

An efficient deep learning model for brain tumour detection with privacy preservation

Internet of medical things (IoMT) is becoming more prevalent in healthcare applications as a result of current AI advancements, helping to improve our quality of life and ensure a sustainable health system. IoMT systems with cutting‐edge scientific capabilities are capable of detecting, transmitting, learning and reasoning. As a result, these systems proved tremendously useful in a range of healthcare applications, including brain tumour detection. A deep learning‐based approach for identifying MRI images of brain tumour patients and normal patients is suggested. The morphological‐based segmentation method is applied in this approach to separate tumour areas in MRI images. Convolutional neural networks, such as LeNET, MobileNetV2, Densenet and ResNet, are tested to be the most efficient ones in terms of detection performance. The suggested approach is applied to a dataset gathered from several hospitals. The effectiveness of the proposed approach is assessed using a variety of metrics, including accuracy, specificity, sensitivity, recall and F‐score. According to the performance evaluation, the accuracy of LeNET, MobileNetV2, Densenet, ResNet and EfficientNet is 98.7%, 93.6%, 92.8%, 91.6% and 91.9%, respectively. When compared to the existing approaches, LeNET has the best performance, with an average of 98.7% accuracy

Characterization of signaling and traffic in Joost

Peer-to-Peer (P2P) IPTV applications have increasingly been considered as a potential approach to online broadcasting. Recently, many applications such as PPlive, PPStream, and Sopcast have been deployed to deliver live streaming via P2P. One of the latest systems is Joost, which can deliver both Video-on-Demand and Real-Time services. Measuring and characterizing this application in terms of signaling overheads and traffic profiles helps to better understand the key limitations of current P2P IPTV systems. Therefore, the main purpose of this paper is firstly to study the impact of Joost on the network. Secondly, we wish to determine the underlying mechanisms of Joost, distinguishing between the Video-on-Demand and the Real-time services. Our study is carried out through a close investigation and analysis on the traffic of Joost in two types of streaming. Based upon the data tracing and collection, many different statistics have been derived. Our study unveils strengths (e.g. good resilience to end-to-end delay and jitter) and shortcomings (e.g. poor locality) and yields recommendations for future P2P IPTV systems

A Secure Data Dissemination in a DHT-Based Routing Paradigm for Wireless Ad Hoc Network

Over the past decade, distributed hash table- (DHT-) based routing protocols have been adopted in wireless ad hoc networks (WANETs) to achieve scalability in the route discovery phase by avoiding the flooding mechanism. The security aspects of the routing protocols based on the DHT mechanism are crucial to address and have not been discussed in the existing literature. Therefore, addressing the security issues in DHT-based routing protocols would prevent the service disruption, decrease the traffic overhead, and reduce the packet loss in the network. In this paper, several security issues are identified and elaborated through an example scenario. Moreover, a novel DHT-based routing protocol is proposed that uses a digital signature and the user’s trust in order to swap securely the logical identifiers (LIDs). Trust between nodes is established by the user’s acquaintance and the first visual contact. The proposed protocol vindicates its effectiveness via simulation results in terms of computation time, normalized overhead, percent improvement, and packet delivery ratio