Ovarian cancer among women is known as “The Silent Killer”. It is caused by the malignant ovarian cyst, which can spread to other organs if it is not treated at an early stage. Some are benign ovarian cyst which can be treated through medical procedures such as laparoscopic and laparotomy. The type of medical procedure that the patients have to undergo depends on the size of cyst. A few risk factors that can cause benign ovarian cyst are age, pregnancy, menopause and menstrual cycle. Apart from that, there are a few symptoms of benign ovarian cyst which are fever, nausea and abdominal pain, abdominal distension, dysmenorrhea and intermenstrual bleeding. The association between these 12 discrete categorical data variables (factors, symptoms, treatment and size) are measured using the log-linear analysis in this study. According to the analysis, the patients who have large benign ovarian cyst need laparoscopic procedure, while those with smaller cyst need either laparotomy procedure or they do not have to undergo any surgery at all. Among all of the factors, menopause gives the highest risk factor of benign ovarian cyst, followed by age, pregnancy and menstrual cycle. Meanwhile, the interaction between nausea, abdominal pain and intermenstrual bleeding give the highest symptom rate to the benign ovarian cyst.
One of the challenges in securing wireless sensor networks (WSNs) is the key distribution; that is, a single shared key must first be known to a pair of communicating nodes before they can proceed with the secure encryption and decryption of the data. In 1984, Blom proposed a scheme called the symmetric key generation system as one method to solve this problem. Blom’s scheme has proven to be λ-secure, which means that a coalition of λ+1 nodes can break the scheme. In 2021, a novel and intriguing scheme based on Blom’s scheme was proposed. In this scheme, elliptic curves over a finite field are implemented in Blom’s scheme for the case when λ=1. However, the security of this scheme was not discussed. In this paper, we point out a mistake in the algorithm of this novel scheme and propose a way to fix it. The new fixed scheme is shown to be applicable for arbitrary λ. The security of the proposed scheme is also discussed. It is proven that the proposed scheme is also λ-secure with a certain condition. In addition, we also discuss the application of this proposed scheme in distributed ledger technology (DLT).
Choosing the best attribute from a dataset is a crucial step in effective logic mining since it has the greatest impact on improving the performance of the induced logic. This can be achieved by removing any irrelevant attributes that could become a logical rule. Numerous strategies are available in the literature to address this issue. However, these approaches only consider low-order logical rules, which limit the logical connection in the clause. Even though some methods produce excellent performance metrics, incorporating optimal higher-order logical rules into logic mining is challenging due to the large number of attributes involved. Furthermore, suboptimal logical rules are trained on an ineffective discrete Hopfield neural network, which leads to suboptimal induced logic. In this paper, we propose higher-order logic mining incorporating a log-linear analysis during the pre-processing phase, the multi-unit 3-satisfiability-based reverse analysis with a log-linear approach. The proposed logic mining also integrates a multi-unit discrete Hopfield neural network to ensure that each 3-satisfiability logic is learned separately. In this context, our proposed logic mining employs three unique optimization layers to improve the final induced logic. Extensive experiments are conducted on 15 real-life datasets from various fields of study. The experimental results demonstrated that our proposed logic mining method outperforms state-of-the-art methods in terms of widely used performance metrics.
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