2692-4048ISSN (Online)
2770-0070ISSN (Print)
Breast cancer is one of the deadliest cancers among women worldwide and one of the main causes of mortality for women in the United States. Breast cancer can be detected earlier and with more accuracy, extending life expectancy at a lower cost. To do this, the efficiency and precision of early breast cancer detection can be increased by evaluating the large data that is currently available utilizing technologies like machine learning fusion-based decision support systems. In this paper, we investigate the prediction performance of various regression models and a decision support system based on these models that provided the predicted category along with a prediction confidence measure. The various machine learning (ML) algorithms applied include decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors regressor. The models are enhanced by average ensemble and ensemble using K-Neighbors regressor. We used the Breast Cancer Wisconsin Dataset from Wisconsin Prognostic Breast Cancer (WPBC) with 569 digitized images of a fine needle aspirate (FNA) of breast mass and 10 real-valued feature information. Among all five machine learning methods, K-Neighbors regressor had the best performance and ensemble using K-Neighbors regressor gave the best accuracy. The results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to the traditional methods.
Read MoreDoi: https://doi.org/10.54216/FPA.090202
Vol. 9 Issue. 2 PP. 19-26, (2022)
The increasing deep learning techniques used in multimedia and network/IoT solve many problems and increase performance. Securing the deep learning models, multimedia, and network/IoT has become a major area of research in the past few years which is considered to be a challenge during generative adversarial attacks over the multimedia or network/IoT. Many efforts and studies try to provide intelligent forensics techniques to solve security issues. This paper introduces a holistic organization of intelligent multimedia forensics that involve deep learning fusion, multimedia, and network/IoT forensics to attack detection. We highlight the importance of using deep learning fusion techniques to obtain intelligent forensics and security over multimedia or Network/IoT. Finally, we discuss the key challenges and future directions in the area of intelligent multimedia forensics using deep learning fusion techniques.
Read MoreDoi: https://doi.org/10.54216/FPA.090203
Vol. 9 Issue. 2 PP. 27-47, (2022)
Breast cancer is responsible for the deaths of the vast majority of women who succumb to the disease. By detecting and treating the disease at an earlier stage, it is feasible to reduce the mortality rate associated with breast cancer. Mammography is the technique of breast cancer screening that has received the most amount of approval from the medical community. Imaging of the ipsilateral projections of the bilateral (right and left) breasts, also known as mediolateral oblique (MLO) and craniocaudal (CC) views, is often necessary for this surgery. This imaging technique is also known as the craniocaudal projection. Sonography, which is also known as ultrasound imaging, is used in combination with mammography during the diagnostic phase (which occurs after the screening phase) to offer a more accurate examination of any abnormalities that may have been detected. Radiologists may be able to make a more precise diagnosis of breast cancer by carrying out an objective assessment with the assistance of CAD systems. CAD is an abbreviation that stands for computer-aided detection and diagnosis. Researchers have proposed computer-aided design (CAD) systems as a viable technique for increasing system performance. These CAD systems take information from a variety of sources and combine it into a single database. In the majority of occurrences, this necessitates the inclusion of qualities or evaluations that were collected from a wide range of information sources. Fusion of choices is effective when dealing with sources that are statistically independent, while fusion of characteristics is good when dealing with sources that have a significant degree of correlation with one another. However, sources often contain a mix of information that is associated with one another as well as information that is independent of one another; as a consequence, none of these approaches is the greatest choice available to choose from. The development of optimal fusion strategies for Multiview and multimodal breast CAD systems is the major focus of this thesis. Canonical correlation analysis is the name of the statistical approach that serves as the foundation for these tactics (CCA). The CCA algorithm alters two multivariate datasets in such a manner as to maximize the correlation that already exists between them. This, in turn, optimizes the feature fusion that occurs after the CCA method has been applied. On the other hand, the performance of benchmark fusion schemes that combine all three sources of information is only at most equivalent to the performance of benchmark schemes that fuse two information sources. In addition, the performance of benchmark fusion schemes that combine all three sources of information is worse than the performance of CCA-based feature fusion schemes that combine two sources of information. This indicates that even if increasing the number of sources could bring new information, only a fusion approach that is optimized to exploit its maximum potential would be able to make the most of this extra data. In conclusion, the CCA-based fusion schemes exhibit robustness when tested against a wide array of performance indicators, datasets, information sources, and diagnostic tasks that are related to the diagnosis of breast cancer. The benchmark fusion techniques, on the other hand, do not demonstrate this resilience.
Read MoreDoi: https://doi.org/10.54216/FPA.090204
Vol. 9 Issue. 2 PP. 48-61, (2022)
In this research, a unique multiband random chaotic key generator based provable authenticated encrypted technique for biomedical picture for the healthcare biomedical system, which can be used in 5G communication system is presented. In addition, the encryption method employed in this research is based on Multiband Random Chaotic Key Generator, and the proposed provable authenticated methodology is based on symmetric authenticated encryption data (MBRCKG). In the proposed proven Orthogonal Frequency Division Multiplexing (OFDM) communication system, the Authenticated Chaotic Encrypted Biomedical Image (ACE-BI) is utilized. This study uses discrete wavelet transformation (DWT) and discrete cosine transformation (DCT) to mask patient data and hospital watermarks in biological images. With various statistical and OFDM settings, channel analysis and statistical analysis have been examined for their effects on the collected hospital logo and patient data. The simulation studies demonstrate how resistant to communication signal processing the proposed ACE-BI method is. Additionally, the proposed algorithm is able to reduce encryption time to one quarter because the partial encryption based in one level DWT scheme.
Read MoreDoi: https://doi.org/10.54216/FPA.090201
Vol. 9 Issue. 2 PP. 08-18, (2022)
Healthcare transformation is becoming one of the highest priorities in a world whereby remarkable advances in technology are taking place. Recent healthcare data fusion management systems are centralized, which possess the probability of failure in case of a natural disaster. Blockchain has expanded fast to be the most widely spoken innovation that could address a large number of present data management problems in the health care sector. The usage of blockchain technology for the distribution of secure and safe health care datasets has received all the attention. This article presents a Bat Optimization Algorithm with Fuzzy Neural Network Based Classification (BOA-FNNC) Model for Blockchain Assisted Healthcare Data Fusion Environment. The presented BOA-FNNC technique mainly focuses on achieving security in the healthcare sector using BC technology. For accomplishing this, the BOA-FNNC technique performs BC assisted data transmission in the medical sector. Besides, the VGG-16 model is exploited for the creation of feature vectors. To classify healthcare data, the BOA with FNN model is utilized in this study, where the BOA fine tune the parameters related to the FNN model which in turn boosts the classifier efficiency. For illustrating the betterment of the BOA-FNNC technique, a series of experiments were performed. The comparison study reported the enhancements of the BOA-FNNC technique over other recent approaches.
Read MoreDoi: https://doi.org/10.54216/FPA.090205
Vol. 9 Issue. 2 PP. 62-73, (2022)