Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions

Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body. Previously, researchers have developed machine learning algorithms in breast cancer diagnosis such as Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, K-means, Fuzzy C-means, Neural Network, Principle Component Analysis (PCA) and Naive Bayes. Unfortunately these algorithms fall short in one way or another due to high levels of computational complexities. For instance, support vector machine employs feature elimination scheme for eradicating data ambiguity and detecting tumors at initial stage. However this scheme is expensive in terms of execution time. On its part, k-means algorithm employs Euclidean distance to determine the distance between cluster centers and data points. However this scheme does not guarantee high accuracy when executed in different iterations. Although the K-nearest Neighbor algorithm employs feature reduction, principle component analysis and 10 fold cross validation methods for enhancing classification accuracy, it is not efficient in terms of processing time. On the other hand, fuzzy c-means algorithm employs fuzziness value and termination criteria to determine the execution time on datasets. However, it proves to be extensive in terms of computational time due to several iterations and fuzzy measure calculations involved. Similarly, convolutional neural network employed back propagation and classification method but the scheme proves to be slow due to frequent retraining. In addition, the neural network achieves low accuracy in its predictions. Since all these algorithms seem to be expensive and time consuming, it necessary to integrate quantum computing principles with conventional machine learning algorithms. This is because quantum computing has the potential to accelerate computations by simultaneously carrying out calculation on many inputs. In this paper, a review of the current machine learning algorithms for breast cancer prediction is provided. Based on the observed shortcomings, a quantum machine learning based classifier is recommended. The proposed working mechanisms of this classifier are elaborated towards the end of this paper

An Enhanced Scheduling Algorithms Framework on Wi-Fi for Reducing Network Delay

Wi-Fi is a modern wireless network that provides several converged integration service. Due to remarkable growth there is a need to improve quality of service. This research focus on developing framework that enhanced the scheduling in Wi-Fi and improving QoS so as to allocate resources based on aggregation of frames, separation of different access categories. The problems which the Wi-Fi user’s face is delay of packets. This research focuses on Wi-Fi IEEE 802.11n that support a high bandwidth transmission rate and aggregation of Ethernet packets. A simulation was carried out to compare network QoS performance with and without the proposed framework. Simulations Shows that there is reduction of delay, the change in packet size in this Simulation control environment slightly alters the delay. There is reduction in delay when using Dynamic Aggregation Scheduler compared to without Dynamic Aggregation Scheduler. Generally as the packet size increases, Dynamic Aggregation Scheduler reduces the delay. Although, this scheduling algorithm produced better QoS results there were still some delay in the network