Lung cancer has emerged as a major cause of death among all demographics worldwide, largely caused by a proliferation of smoking habits. However, early detection and diagnosis of lung cancer through technological improvements can save the lives of millions of individuals affected globally. Computerized tomography (CT) scan imaging is a proven and popular technique in the medical field, but diagnosing cancer with only CT scans is a difficult task even for doctors and experts. This is why computer-assisted diagnosis has revolutionized disease diagnosis, especially cancer detection. This study looks at 20 CT scan images of lungs. In a preprocessing step, we chose the best filter to be applied to medical CT images between median, Gaussian, 2D convolution, and mean. From there, it was established that the median filter is the most appropriate. Next, we improved image contrast by applying adaptive histogram equalization. Finally, the preprocessed image with better quality is subjected to two optimization algorithms, fuzzy c-means and k-means clustering. The performance of these algorithms was then compared. Fuzzy c-means showed the highest accuracy of 98%. The feature was extracted using Gray Level Cooccurrence Matrix (GLCM). In classification, a comparison between three algorithms—bagging, gradient boosting, and ensemble (SVM, MLPNN, DT, logistic regression, and KNN)—was performed. Gradient boosting performed the best among these three, having an accuracy of 90.9%.

Cyber-security intelligence have made a great impact over healthcare industry where several researchers are developing new techniques to improve security for healthcare systems. Besides, Artificial Intelligence (AI) become the tremendous technology in recent decades to improve the existing methods to be more intelligent. In this paper, we proposed cyber attack detection system for healthcare sector with centralized and federated transfer learning mode. Edge of Things (EoT) framework is developed in connection with cloud and healthcare sectors to transmit the data efficiently and the proposed Centralized with Multi-Source Transfer Learning (CMTL) algorithm which is used for detection and classification of various threats such as information gathering, DoS/DDoS attacks, Malware attacks, Injection attacks, and Man in the Middle attacks. Performance of the proposed framework is evaluated using various datasets such as EMNIST, X-IIoTID, and Federated TON_IoT. Our framework outperforms with the analysis of execution time and obtains high level accuracy when compared with different algorithms.

The Internet of Things, sometimes known as IoT, is a relatively new kind of Internet connectivity that connects physical objects to the Internet in a way that was not possible in the past. The Internet of Things is another name for this concept (IoT). The Internet of Things has a larger attack surface as a result of its hyperconnectivity and heterogeneity, both of which are characteristics of the IoT. In addition, since the Internet of Things devices are deployed in managed and uncontrolled contexts, it is conceivable for malicious actors to build new attacks that target these devices. As a result, the Internet of Things (IoT) requires self-protection security systems that are able to autonomously interpret attacks in IoT traffic and efficiently handle the attack scenario by triggering appropriate reactions at a pace that is faster than what is currently available. In order to fulfill this requirement, fog computing must be utilised. This type of computing has the capability of integrating an intelligent self-protection mechanism into the distributed fog nodes. This allows the IoT application to be protected with the least amount of human intervention while also allowing for faster management of attack scenarios. Implementing a self-protection mechanism at malicious fog nodes is the primary objective of this research work. This mechanism should be able to detect and predict known attacks based on predefined attack patterns, as well as predict novel attacks based on no predefined attack patterns, and then choose the most appropriate response to neutralise the identified attack. In the environment of the IoT, a distributed Gaussian process regression is used at fog nodes to anticipate attack patterns that have not been established in the past. This allows for the prediction of new cyberattacks in the environment. It predicts attacks in an uncertain IoT setting at a speedier rate and with greater precision than prior techniques. It is able to effectively anticipate both low-rate and high-rate assaults in a more timely manner within the dispersed fog nodes, which enables it to mount a more accurate defence. In conclusion, a fog computing-based self-protection system is developed to choose the most appropriate reaction using fuzzy logic for detected or anticipated assaults using the suggested detection and prediction mechanisms. This is accomplished by utilising a self-protection system that is based on the development of a self-protection system that utilises the suggested detection and prediction mechanisms. The findings of the experimental investigation indicate that the proposed system identifies threats, lowers bandwidth usage, and thwarts assaults at a rate that is twenty-five percent faster than the cloud-based system implementation.

Due to improper postures, and unhealthy lifestyle of millennials, there has been an exponential increase in spinal cord related diseases. These include Slip Discs, Spine Injuries, Tumours, etc. each of which has multiple side-effects on the human body. To analyze these conditions, a wide variety of image processing models are developed by researchers. But most of these models do not analyze side-effects of spinal cord tumours on other body parts, due to which their applicability is limited when used for clinical trials. The main novelty of this work is to analyze side effects resulting due to spinal cord tumours, and to perform this task a novel Bioinspired Reinforcement Learning Model for Side-Effect Analysis of Spinal Cord Tumours is discussed in this text. The proposed model initially uses a Recurrent Neural Network (RNN) based on combination of Long-Short-Term Memory (LSTM) & Gated Recurrent Unit (GRU) for extraction of highly dense image features. These features allow the model to estimate tumour positions in Computer Tomography (CT) scans. The extracted features are classified via the RNN Model, which assists in high accuracy classification & localization of spinal cord tumours. These classification & localization results are linked with blood reports to estimate side-effects on kidney, lungs, heart activity and vitamin levels. To perform this correlation, a Grey Wolf Optimization (GWO) Model is used, which assists in linking tumour type, and size with blood report parameters. The GWO Model evaluates a fitness function, that fuses tumour levels with its side-effects on individual body parts. This fusion is done via analysis of temporal blood reports, which evaluates effects of different tumour types-and-sizes on individual body parameters. Due to a combination of GWO with LSTM & GRU based RNN, the model is capable of showcasing high accuracy of tumour classification, with better precision of correlation with side effects when compared with state-of-the-art models. It was observed that the proposed model was able to achieve 98.5% accuracy for tumour classification, 96.4% correlation precision with kidney diseases, 95.8% correlation precision with lung diseases, 96.2% correlation precision with heart diseases, and 91.5% correlation precision with vitamin deficiencies. Due to such a high performance, the model is capable of deployment for real-time clinical applications.