This paper focuses on the provision of public transport information in Dublin, Ireland. It examines both existing and potential methods of accessing information, with particular focus on the implementation of various intelligent transport systems applications. One of the main objectives of this paper is examining the stages a passenger goes through when deciding to undertake a public transport trip and in what form they require information at each stage. This paper defines these stages as "pre-trip to destination," "at-stop," "onboard," and "pre-trip to origin" (this is the return journey). Each of these four stages is examined in this paper. A web-based survey was used to collect data on passenger preferences and describes the methods of information delivery each passenger requires at each stage. This paper primarily deals with the respondents' stated preference for public transport information and does not examine revealed preferences. The survey also details results of passengers' opinions of the different information provision formats such as call centers, mobile phones, the Internet, and paper-based methods. This paper concludes with the results of this exploratory research.

An examination of the public transport information requirements of users

A BIM-based approach for predicting corrosion under insulation

The feature selection is an essential data preprocessing stage in data mining The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly associated redundant features In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets Among the main disadvantages of present meta-heuristic based approaches is that they are often neglecting the correlation between a set of selected features In this article, for the purpose of feature selection, the authors propose a genetic algorithm based on community detection, which functions in three steps The feature similarities are calculated in the first step The features are classified by community detection algorithms into clusters throughout the second step In the third step, features are picked by a genetic algorithm with a new community-based repair operation Nine benchmark classification problems were analyzed in terms of the performance of the presented approach Also, the authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection Comparing the performance of the proposed method with three new feature selection methods based on PSO, ACO, and ABC algorithms on three classifiers showed that the accuracy of the proposed method is on average 052% higher than the PSO, 120% higher than ACO, and 157 higher than the ABC algorithm

/pdf/a-novel-community-detection-based-genetic-algorithm-for-1dgx4nhwnt.pdf

A novel community detection based genetic algorithm for feature selection

Towards reinforcing telemedicine adoption amongst clinicians in Nigeria.

With the development of the Internet, network security has aroused people’s attention. It can be said that a secure network environment is a basis for the rapid and sound development of the Internet. Phishing is an essential class of cybercriminals which is a malicious act of tricking users into clicking on phishing links, stealing user information, and ultimately using user data to fake logging in with related accounts to steal funds. Network security is an iterative issue of attack and defense. The methods of phishing and the technology of phishing detection are constantly being updated. Traditional methods for identifying phishing links rely on blacklists and whitelists, but this cannot identify new phishing links. Therefore, we need to solve how to predict whether a newly emerging link is a phishing website and improve the accuracy of the prediction. With the maturity of machine learning technology, prediction has become a vital ability. This paper offers a state-of-the-art survey on methods for phishing website detection. It starts with the life cycle of phishing, introduces common anti-phishing methods, mainly focuses on the method of identifying phishing links, and has an in-depth understanding of machine learning-based solutions, including data collection, feature extraction, modeling, and evaluation performance. This paper provides a detailed comparison of various solutions for phishing website detection.

A Survey of Machine Learning-Based Solutions for Phishing Website Detection

The wide acceptability of Advanced Encryption Standard (AES) as the most efficient of all of the symmetric cryptographic techniques has further opened it up to more attacks. Efforts that were aimed at securing information while using AES is still being undermined by the activities of attackers This has further necessitated the need for researchers to come up with ways of enhancing the strength of AES. This article presents an enhanced AES algorithm that was achieved by modifying its SubBytes and ShiftRows transformations. The SubBytes transformation is modified to be round key dependent, while the ShiftRows transformation is randomized. The rationale behind the modification is to make the two transformations round key dependent, so that a single bit change in the key will produce a significant change in the cipher text. The conventional and modified AES algorithms are both implemented and evaluated in terms avalanche effect and execution time. The modified AES algorithm achieved an avalanche effect of 57.81% as compared to 50.78 recorded with the conventional AES. However, with 16, 32, 64, and 128 plain text bytes, the modified AES recorded an execution time of 0.18, 0.31, 0.46, and 0.59 ms, respectively. This is slightly higher than the results obtained with the conventional AES. Though a slightly higher execution time in milliseconds was recorded with the modified AES, the improved encryption and decryption strength via the avalanche effects measured is a desirable feat.

https://www.mdpi.com/2073-8994/11/12/1484/pdf

Modified Advanced Encryption Standard Algorithm for Information Security

Over the last decades, various methods have been employed in medical images analysis. Some state-of-the-arts techniques such as deep learning have been recently applied to medical images analysis. This research proposes the application of deep learning technique in performing segmentation of retinal blood vessels. Analyzing and segmentation of retina vessels has assisted in diagnosis and monitoring of some diseases. Diseases such as age-related fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arteriosclerosis and choroidal neovascularization can be effectively managed by the analysis of retinal vessels images. In this work, a Deep Convolutional Encoder-Decoder Architecture for the segmentation of retinal vessels images is proposed. The proposed method is a deep learning system composed of an encoder and decoder mechanism allows a low resolution image set of retinal vessels to be analyzed by set of convolutional layers in the encoder unit before been sent into a decoder unit for final segmented output. The proposed system was evaluated using some evaluation metrics such as dice coefficient, jaccard index and mean of intersection. The review of the existing works was also carried out. It could be shown that the proposed system outperforms many existing methods in the segmentation of retinal vessels images.

/pdf/a-deep-convolutional-encoder-decoder-architecture-for-4vwkplutzo.pdf

A Deep Convolutional Encoder-Decoder Architecture for Retinal Blood Vessels Segmentation.

Purpose Breast cancer remains a serious public health problem that results in the loss of lives among women. However, early detection of its signs increases treatment options and the likelihood of cure. Although mammography has been established to be a proven technique of examining symptoms of cancer in mammograms, the manual observation by radiologists is demanding and often prone to diagnostic errors. Therefore, computer aided diagnosis (CADx) systems could be a viable alternative that could facilitate and ease cancer diagnosis process; hence this study. Methodology The inputs to the proposed model are raw mammograms downloaded from the Mammographic Image Analysis Society database. Prior to the classification, the raw mammograms were preprocessed. Then, gray level co-occurrence matrix was used to extract fifteen textural features from the mammograms at four different angular directions: θ={0°, 45°, 90°, 135°}, and two distances: D={1,2}. Afterwards, a two-stage support vector machine was used to classify the mammograms as normal, benign and malignant. Results All of the 37 normal images used as test data were classified as normal (no false positive) and all 41 abnormal images were correctly classified to be abnormal (no false negative), meaning that the sensitivity and specificity of the model in detecting abnormality is 100%. After the detection of abnormality, the system further classified the abnormality on the mammograms to be either "benign" or "malignant". Out of 23 benign images, 21 were truly classified as benign. Also, out of 18 malignant images, 17 were truly classified to be malignant. From these findings, the sensitivity, specificity, positive predictive value, and negative predictive value of the system are 94.4%, 91.3%, 89.5%, and 95.5%, respectively. Conclusion This article has further affirmed the prowess of automated CADx systems as a viable tool that could facilitate breast cancer diagnosis by radiologists.

/pdf/an-automated-mammogram-classification-system-using-modified-3fyoasdgd8.pdf

An automated mammogram classification system using modified support vector machine

The adverse effects of website phishing attacks are often damaging and dangerous as the information gathered from unsuspecting users are used inappropriately and recklessly Several solutions have been proposed to curb website phishing attacks and to mitigate its impact However, most of these solutions are rather ineffective due to the evolving and dynamic processes used for phishing attacks Recently, machine learning (ML)-based solutions are deployed in addressing the phishing attacks due to its ability to deal with the dynamic nature of phishing attacks Nonetheless, ML solutions suffer drawbacks in the case of high false alarm rates and the need to further improve the detection accuracies of existing ML solutions as proposed in the literature Considering the dynamism of phishing attacks, there is a continuous need for novel and effective ML-based methods for detecting phishing websites This study proposed an ensemble-based Logistic Model Trees (LMT) for website phishing attack detection LMT is the combination of logistic regression and tree induction methods into a single model tree Experimental results showed that the proposed methods (ABLMT: AdaBoostLMT and BGLMT: BaGgingLMT) are highly effective for website phishing attack detection with the least accuracy of 9718% and 0996 AUC values Besides, the proposed methods outperform some ML-based phishing attack models from recent existing studies Hence, the proposed methods are recommended for addressing website phishing attacks with dynamic properties

Ensemble-Based Logistic Model Trees for Website Phishing Detection

Purpose: Breast cancer remains a serious public health problem that results in the loss of
lives among women. However, early detection of its signs increases treatment options and
the likelihood of cure. Although mammography has been established to be a proven
technique of examining symptoms of cancer in mammograms, the manual observation by
radiologists is demanding and often prone to diagnostic errors. Therefore, computer aided
diagnosis (CADx) systems could be a viable alternative that could facilitate and ease cancer
diagnosis process; hence this study.
Methodology: The inputs to the proposed model are raw mammograms downloaded from
the Mammographic Image Analysis Society database. Prior to the classification, the raw
mammograms were preprocessed. Then, gray level co-occurrence matrix was used to extract
fifteen textural features from the mammograms at four different angular directions: θ={0°,
45°, 90°, 135°}, and two distances: D={1,2}. Afterwards, a two-stage support vector
machine was used to classify the mammograms as normal, benign and malignant.
Results: All of the 37 normal images used as test data were classified as normal (no false
positive) and all 41 abnormal images were correctly classified to be abnormal (no false
negative), meaning that the sensitivity and specificity of the model in detecting abnormality
is 100%. After the detection of abnormality, the system further classified the abnormality on
the mammograms to be either “benign” or “malignant”. Out of 23 benign images, 21 were
truly classified as benign. Also, out of 18 malignant images, 17 were truly classified to be
malignant. From these findings, the sensitivity, specificity, positive predictive value, and
negative predictive value of the system are 94.4%, 91.3%, 89.5%, and 95.5%, respectively.
Conclusion: This article has further affirmed the prowess of automated CADx systems as a
viable tool that could facilitate breast cancer diagnosis by radiologists.

Noah Oluwatobi Akande

Papers

Modified Advanced Encryption Standard Algorithm for Information Security

A Deep Convolutional Encoder-Decoder Architecture for Retinal Blood Vessels Segmentation.

An automated mammogram classification system using modified support vector machine

Ensemble-Based Logistic Model Trees for Website Phishing Detection

An automated mammogram classification systemusing modified support vector machine