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%

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

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

Use of program and data-specific heuristics for automatic software test data generation

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Treatment of Diabetes Type II Using Genetic Algorithm

Chronic diseases is an important research field because of the growth of the number of affected people around the world. When someone has diabetes, the body either does not make enough insulin or cannot use its own insulin as well as it should. This causes sugar to build up in blood leading to complications like heart disease, stroke, and neuropathy. Poor circulation leading to loss of limbs, blindness, kidney failure, nerve damage, and death. Diagnosis plays vital role in diabetes treatment otherwise it leads to long term complications in terms of costs of the treatment of the patients and leads to many risks over the patient himself as mentioned above. In this research we propose new methodology to extract the best testing sequence evaluation mechanism for helping doctors to evaluate their patient’s cases and make the best decisions about the medicine being given. We managed to create chromosomes population each of which consists of binary decision tree, as this implementation considered being the best scenario of our problem. The system proves its efficiency by applying it on 50 patients and the results shows accuracy percentage of 95.4%.</p

Male and Female Hormone Reading to Predict Pregnancy Percentage Using a Deep Learning Technique: A Real Case Study

Diagnosing gynecological diseases is a significant difficulty for the medical sector. Numerous patients visit gynecological clinics for pregnancies as well as for other illnesses, such as polycystic ovarian syndrome, ovarian cysts, endometritis, menopause, and others. In relation to pregnancy, patients, whether they are men, women, or both, may experience a variety of issues. As a result, in this research, we developed a proposed method that makes use of artificial neural networks (ANN) to help gynecologists predict the success rate of a pregnancy based on the reading of the pregnancy hormone ratio in the blood. The ANN was used in this test in the lab as a group of multiple perceptrons or neurons at each layer; however, in the final hidden layer, the genetic algorithm (GA) and Bat algorithm were used instead. These two algorithms are fit and appropriate for optimizing the models that are aimed to estimate or predict a value. As a result, the GA attempts to determine the testing cost using equations and the Bat algorithm attempts to determine the training cost. To improve the performance of the ANN, the GA algorithm collaborates with the Bat algorithm in a hybrid approach in the hidden layer of ANN; therefore, the pregnancy prediction result of using this method can be improved, optimized, and more accurate. Based on the flexibility of each algorithm, gynecologists can predict the success rate of a pregnancy. With the help of our methods, we were able to run experiments using data collected from 35,207 patients and reach a classification accuracy of 96.5%. These data were gathered from the Department of Obstetrics and Gynecology at the Hospital University of Jordan (HUJ). The proposed method aimed to predict the pregnancy rate of success regardless of whether the data are comprised of patients whose pregnancy hormones are in the normal range or of patients that suffer from factors favoring sterility, such as infections, malformations, and associated diseases (e.g., diabetes)

Male and Female Hormone Reading to Predict Pregnancy Percentage Using a Deep Learning Technique: A Real Case Study

Diagnosing gynecological diseases is a significant difficulty for the medical sector. Numerous patients visit gynecological clinics for pregnancies as well as for other illnesses, such as polycystic ovarian syndrome, ovarian cysts, endometritis, menopause, and others. In relation to pregnancy, patients, whether they are men, women, or both, may experience a variety of issues. As a result, in this research, we developed a proposed method that makes use of artificial neural networks (ANN) to help gynecologists predict the success rate of a pregnancy based on the reading of the pregnancy hormone ratio in the blood. The ANN was used in this test in the lab as a group of multiple perceptrons or neurons at each layer; however, in the final hidden layer, the genetic algorithm (GA) and Bat algorithm were used instead. These two algorithms are fit and appropriate for optimizing the models that are aimed to estimate or predict a value. As a result, the GA attempts to determine the testing cost using equations and the Bat algorithm attempts to determine the training cost. To improve the performance of the ANN, the GA algorithm collaborates with the Bat algorithm in a hybrid approach in the hidden layer of ANN; therefore, the pregnancy prediction result of using this method can be improved, optimized, and more accurate. Based on the flexibility of each algorithm, gynecologists can predict the success rate of a pregnancy. With the help of our methods, we were able to run experiments using data collected from 35,207 patients and reach a classification accuracy of 96.5%. These data were gathered from the Department of Obstetrics and Gynecology at the Hospital University of Jordan (HUJ). The proposed method aimed to predict the pregnancy rate of success regardless of whether the data are comprised of patients whose pregnancy hormones are in the normal range or of patients that suffer from factors favoring sterility, such as infections, malformations, and associated diseases (e.g., diabetes)