ENSEMBLE LEARNING MODEL FOR SCREENING AUTISM IN CHILDREN
Mofleh Al Diabat1, Najah Al-Shanableh2 , 1Al Albayt University , 2Al Mafraq- Jordan
Autistic Spectrum Disorder (ASD) is a neurological condition associated with communication, repetitive, and social challenges. ASD screening is the process of detecting potential autistic traits in individuals using tests conducted by a medical professional, a caregiver, or a parent. These tests often contain large numbers of items to be covered by the user and they generate a score based on scoring functions designed by psychologists and behavioural scientists. Potential technologies that may improve the reliability and accuracy of ASD tests are Artificial Intelligence and Machine Learning. This paper presents a new framework for ASD screening based on Ensembles Learning called Ensemble Classification for Autism Screening (ECAS). ECAS employs a powerful learning method that considers constructing multiple classifiers from historical cases and controls and then utilizes these classifiers to predict autistic traits in test instances. ECAS performance has been measured on a real dataset related to cases and controls of children and using different Machine Learning techniques. The results revealed that ECAS was able to generate better classifiers from the children dataset than the other Machine Learning methods considered in regard to levels of sensitivity, specificity, and accuracy.
Artificial Neural Network, Autism Screening, Classification, Ensemble Learners, Predictive Models, Machine Learning
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INTRUSION DETECTION SYSTEM CLASSIFICATION USING DIFFERENT MACHINE LEARNING ALGORITHMS ON KDD-99 AND NSL-KDD DATASETS - A REVIEW PAPER
Ravipati Rama Devi1 and Munther Abualkibash2 , 1,2Eastern Michigan University, Michigan, USA
Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.
Intrusion Detection System, KDD-99 cup, NSL-KDD, Machine learning algorithms
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IMAGE GENERATION WITH GANS-BASED TECHNIQUES: A SURVEY
Shirin Nasr Esfahani1and Shahram Latifi2 , 1,2UNLV, Las Vegas, USA
In recent years, frameworks that employ Generative Adversarial Networks (GANs) have achieved immense results for various applications in many fields especially those related to image generation both due to their ability to create highly realistic and sharp images as well as train on huge data sets. However, successfully training GANs are notoriously difficult task in case ifhigh resolution images are required. In this article, we discuss five applicable and fascinating areas for image synthesis based on the state-of-the-art GANs techniques including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Manipulation, 3D Image Synthesis and DeepMasterPrints. We provide a detailed review of current GANs-based image generation models with their advantages and disadvantages.The results of the publications in each section show the GANs based algorithmsAREgrowing fast and their constant improvement, whether in the same field or in others, will solve complicated image generation tasks in the future
Conditional Generative Adversarial Networks (cGANs), DeepMasterPrints, Face Manipulation, Text-to-Image Synthesis, 3D GAN
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DETECTION OF FAKE ACCOUNTS IN INSTAGRAM USING MACHINE LEARNING
Ananya Dey1, Hamsashree Reddy2,Manjistha Dey3 and Niharika Sinha4
, 1 National Institute of Technology, India , 2PES University, Bangalore, India , 3RV College of Engineering, India , 4Manipal Institute of Technology,India
With the advent of the Internet and social media, while hundreds of people have benefitted from the vast sources of information available, there has been an enormous increase in the rise of cyber-crimes, particularly targeted towards women. According to a 2019 report in the  Economics Times, India has witnessed a 457% rise in cybercrime in the five year span between 2011 and 2016. Most speculate that this is due to impact of social media such as Facebook, Instagram and Twitter on our daily lives. While these definitely help in creating a sound social network, creation of user accounts in these sites usually needs just an email-id. A real life person can create multiple fake IDs and hence impostors can easily be made. Unlike the real world scenario where multiple rules and regulations are imposed to identify oneself in a unique manner (for example while issuing one’s passport or driver’s license), in the virtual world of social media, admission does not require any such checks. In this paper, we study the different accounts of Instagram, in particular and try to assess an account as fake or real using Machine Learning techniques namely Logistic Regression and Random Forest Algorithm.
Logistic Regression, Random Forest Algorithm, median imputation, Maximum likelihood estimation, k cross validation, overfitting, out of bag data, recall, identity theft, Angler phishing.
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