Skin Cancer Recognition by Using a Neuro-Fuzzy System

Skin cancer is the most prevalent cancer in the light-skinned population and it is generally caused by exposure to ultraviolet light. Early detection of skin cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) and a fuzzy inference system were used in this study as promising modalities for detection of different types of skin cancer. The accuracy rate of the diagnosis of skin cancer by using the hierarchal neural network was 90.67% while using neuro-fuzzy system yielded a slightly higher rate of accuracy of 91.26% in diagnosis skin cancer type. The sensitivity of NN in diagnosing skin cancer was 95%, while the specificity was 88%. Skin cancer diagnosis by neuro-fuzzy system achieved sensitivity of 98% and a specificity of 89%

ASSESSMENT AND PERFORMANCE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR GAS SENSING E-NOSE SYSTEMS

E-noses that combine machine learning and gas sensor arrays (GSAs) are widely used for the detection and identification of various gases. GSAs produce signals that provide vital information about the exposed gases for the machine learning algorithms, rendering them indispensable within the smart-gas sensing arena. In this work, we present a detailed assessment of several machine learning techniques employed for the detection of gases and estimation of their concentrations. The modeling and predictive analysis conducted in this paper are based on kNN, ANN, Decision Trees, Random Forests, SVM and other ensembling-based techniques. Predictive models are implemented and tested on three different MoX gas sensor-based experimental datasets as reported in the literature. The assessment includes a delineated analysis of the different models’ performance followed by a detailed comparison against results found in the literature. It highlights factors that play a pivotal role in machine learning for gas sensing and sheds light on the predictive capability of different machine learning approaches applied on experimental GSA datasets

Applying Artificial Intelligence Techniques on Cyber Security Datasets: Detecting Cyber Attacks.

The rapid expansion of government and corporate services to the online sphere has spurred a notable surge in internet usage among individuals. However, this increased connectivity also amplifies the risks posed by cyber threats, as hackers exploit external networking avenues and corporate networks for personal activities. Consequently, proactive measures must be taken to mitigate potential financial losses and resource drain from cyber attacks. To this end, numerous machine-learning techniques have been developed for cybercrime detection and threat mitigation. This study evaluates several prominent machine learning methods to identify and address significant cyber threats. The research scrutinizes the effectiveness of five techniques: Random Forest, Decision Tree, Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and Naive Bayes. Among these, Random Forest demonstrates superior performance with an accuracy rate of 99.69%, outperforming ensemble models such as Decision Tree, CNN, KNN, and Naive Bayes

Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious virus with overwhelming demand on healthcare systems, which require advanced predictive analytics to strategize COVID-19 management in a more effective and efficient manner. We analyzed clinical data of 2017 COVID-19 cases reported in the Dubai health authority and developed predictive models to predict the patient's length of hospital stay and risk of death. A decision tree (DT) model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed very good performance with a coefficient of determination R2 of 49.8% and a median absolute deviation of 2.85 days. Furthermore, another DT-based model was constructed to predict COVID-19 risk of death. The model showed excellent performance with sensitivity and specificity of 96.5 and 87.8%, respectively, and overall prediction accuracy of 96%. Further validation using unsupervised learning methods showed similar separation patterns, and a receiver operator characteristic approach suggested stable and robust DT model performance. The results show that a high risk of death of 78.2% is indicated for intubated COVID-19 patients who have not used anticoagulant medications. Fortunately, intubated patients who are using anticoagulant and dexamethasone medications with an international normalized ratio of <1.69 have zero risk of death from COVID-19. In conclusion, we constructed artificial intelligence–based models to accurately predict the length of hospital stay and risk of death in COVID-19 cases. These smart models will arm physicians on the front line to enhance management strategies to save lives

Resource and dependency based test case generation for RESTful Web services

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Automated Detection of Breast Cancer Using Artificial Neural Networks and Fuzzy Logic

Our aim was to develop a diagnostic system that could classify breast tumors as either malignant or benign to provide a faster and more reliable method for patients. In order to accomplish this, we built two systems: one is based on Artificial Neural Networks (ANN) with a resilient back propagation and the other is based on fuzzy logic. We used the dataset provided by the University of California Irvine (UCI) Machine Learning Repository: the Wisconsin Diagnostic Breast Cancer (WDBC) dataset which describes characteristics of the cell nuclei presented in the images. The dataset is composed of features computed from digitized images of a Fine Needle Aspirate (FNA) of the breast mass. The system is based on ANN and was built using a feed-forward neural network with a Resilient Back Propagation (Rprop) algorithm that used to train the network, the number of hidden layers and hidden neurons determined by performing experiments and selecting the highest architectural accuracy. In order to obtain general architecture and to identify the accuracy of this system, we used ten-folds cross validation. The second system is based on fuzzy logic, and we built a Fuzzy Inference System (FIS). The decision tree was used to define the membership functions and the rules. The experiments were performed on two types of FIS: Sugeno-type and Mamdani-type. For the system based on ANN, Feed-Forward Neural Network presented the highest accuracy at 97.6%. While for fuzzy system, Sugeno FIS showed the highest accuracy at 94.8%. Since breast tumors, both malignant and benign, share structural similarities, the process of their detection is extremely difficult and time consuming if it is to be manually classified. Laboratory analysis or biopsies of the tumor is a manual, time consuming process yet it is accurate system of prediction. It is, however, prone to human errors. Consequently, a need of creating an automated system to provide a faster and more reliable method of diagnosis and prediction for patients is rising. In this paper, we developed two kinds of artificial intelligence systems that can help physicians to classify breast cancer tumors as either malignant or benign

An intelligent rule-oriented framework for extracting key factors for grants scholarships in higher education

Education is a fundamental sector in all countries, where in some countries students com-pete to get an educational grant due to its high cost. The incorporation of artificial intelli-gence in education holds great promise for the advancement of educational systems and pro-cesses. Educational data mining involves the analysis of data generated within educational environments to extract valuable insights into student performance and other factors that enhance teaching and learning. This paper aims to analyze the factors influencing students' performance and consequently, assist granting organizations in selecting suitable students in the Arab region (Jordan as a use case). The problem was addressed using a rule-based tech-nique to facilitate the utilization and implementation of a decision support system. To this end, three classical rule induction algorithms, namely PART, JRip, and RIDOR, were em-ployed. The data utilized in this study was collected from undergraduate students at the University of Jordan from 2010 to 2020. The constructed models were evaluated based on metrics such as accuracy, recall, precision, and f1-score. The findings indicate that the JRip algorithm outperformed PART and RIDOR in most of the datasets based on f1-score metric. The interpreted decision rules of the best models reveal that both features; the average study years and high school averages play vital roles in deciding which students should receive scholarships. The paper concludes with several suggested implications to support and en-hance the decision-making process of granting agencies in the realm of higher education