Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate.

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IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning

Realizing secure and fast communication in smart homes is still a challenging task due to slow and imperfect information reception from end nodes for proper traffic control. The image data captured through high-resolution cameras demand high-speed data link nodes having a capacity of more than 10 Gbps. The advancement in technologies makes it possible to send accurate and timely images data to store in clouds. LiFi is a wireless technology that uses lights for data transmission between devices. The speed of LiFi is 100 times greater than Conventional WiFi Technology. A Proposed Architecture for Traffic Monitoring & Control systems via LiFi Technology in Smart Homes is presented in this paper. The proposed model is the multi-purposed model like we can control, manage and identify traffic vehicles on time.

A Proposed Architecture for Traffic Monitoring & Control System via LiFi Technology in Smart Homes

https://www.techscience.com/cmc/v67n1/41171/pdf

Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

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Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

The world is digitizing at an unprecedented rate. Information systems and technology are changing or evolving very fast. With advances in technology, the speed of processes, and the amount of data generated daily, it is almost impossible for systems to be protected and monitored by human beings without the help of applications or intelligent systems that facilitate the work. Due to the large number of electronic devices connected to the internet, cybersecurity experts face many challenges inadequately protecting systems. At present, all the support that can be obtained is needed to prevent and mitigate cyber-attacks and data breaches. In this paper, we discussed AI security trends and data breaches caused by malicious attacks.

Applied Artificial Intelligence as Event Horizon Of Cyber Security

The developing countries are still starving for the betterment of health sector. The disease commonly found among the women is breast cancer, and past researches have proven results that if the cancer is detected at a very early stage, the chances to overcome the disease are higher than the disease treated or detected at a later stage. This article proposed cloud-based intelligent BCP-T1F-SVM with 2 variations/models like BCP-T1F and BCP-SVM. The proposed BCP-T1F-SVM system has employed two main soft computing algorithms. The proposed BCP-T1F-SVM expert system specifically defines the stage and the type of cancer a person is suffering from. Expert system will elaborate the grievous stages of the cancer, to which extent a patient has suffered. The proposed BCP-SVM gives the higher precision of the proposed breast cancer detection model. In the limelight of breast cancer, the proposed BCP-T1F-SVM expert system gives out the higher precision rate. The proposed BCP-T1F expert system is being employed in the diagnosis of breast cancer at an initial stage. Taking different stages of cancer into account, breast cancer is being dealt by BCP-T1F expert system. The calculations and the evaluation done in this research have revealed that BCP-SVM is better than BCP-T1F. The BCP-T1F concludes out the 96.56 percentage accuracy, whereas the BCP-SVM gives accuracy of 97.06 percentage. The above unleashed research is wrapped up with the conclusion that BCP-SVM is better than the BCP-T1F. The opinions have been recommended by the medical expertise of Sheikh Zayed Hospital Lahore, Pakistan, and Cavan General Hospital, Lisdaran, Cavan, Ireland.

/pdf/cloud-based-breast-cancer-prediction-empowered-with-soft-1cfl3ykbyn.pdf

Farrukh Khan

Papers

Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches.

Smart occupancy detection for road traffic parking using deep extreme learning machine