Accessible Interface for Context Awareness in Mobile Devices for Users With Memory Impairment

2019 ◽  
Vol 8 (2) ◽  
pp. 1-30
Author(s):  
Iyad Abu Abu Doush ◽  
Sanaa Jarrah
Keyword(s):  
Mobile Application ◽  
Memory Impairment ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Memory Problems ◽  
User Context ◽  
Classification Technique ◽  
Learning Techniques ◽  
Context Detection ◽  
With Memory

Memory problems usually appear because of aging or may happen because of a brain injury. Such problems prevent the person from performing daily activities. In this paper, the authors propose a framework to develop a smartphone solution to detect and recognize the user context. In order to build the context detection framework, the authors compare three different machine learning techniques (C.4.5, random, and BFTree) in terms of context detection accuracy. Then, the authors use the classification technique with the highest accuracy in a mobile application to help users by detecting their context. The authors develop two interfaces based on the suggested accessibility features for users with memory impairment. Two scenarios are used to evaluate the user interface, and the results prove the applicability and the usability of the proposed context detection framework.

scholarly journals A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ali Soleymani ◽  
Fatemeh Arabgol
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Text Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Detection Accuracy ◽  
Domain Name ◽  
Botnet Detection ◽  
Learning Techniques

In today’s security landscape, advanced threats are becoming increasingly difficult to detect as the pattern of attacks expands. Classical approaches that rely heavily on static matching, such as blacklisting or regular expression patterns, may be limited in flexibility or uncertainty in detecting malicious data in system data. This is where machine learning techniques can show their value and provide new insights and higher detection rates. The behavior of botnets that use domain-flux techniques to hide command and control channels was investigated in this research. The machine learning algorithm and text mining used to analyze the network DNS protocol and identify botnets were also described. For this purpose, extracted and labeled domain name datasets containing healthy and infected DGA botnet data were used. Data preprocessing techniques based on a text-mining approach were applied to explore domain name strings with n-gram analysis and PCA. Its performance is improved by extracting statistical features by principal component analysis. The performance of the proposed model has been evaluated using different classifiers of machine learning algorithms such as decision tree, support vector machine, random forest, and logistic regression. Experimental results show that the random forest algorithm can be used effectively in botnet detection and has the best botnet detection accuracy.

scholarly journals Steel Bar Counting from Images with Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 402
Author(s):  
Ana Caren Hernández-Ruiz ◽  
Javier Alejandro Martínez-Nieto ◽  
Julio David Buldain-Pérez
Keyword(s):  
Machine Learning ◽  
Steel Industry ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Network Distance ◽  
Learning Techniques ◽  
Steel Bar ◽  
Steel Bars ◽  
Counting Time ◽  
Human Labour

Counting has become a fundamental task for data processing in areas such as microbiology, medicine, agriculture and astrophysics. The proposed SA-CNN-DC (Scale Adaptive—Convolutional Neural Network—Distance Clustering) methodology in this paper is designed for automated counting of steel bars from images. Its design consists of two Machine Learning techniques: Neural Networks and Clustering. The system has been trained to count round and squared steel bars, obtaining an average detection accuracy of 98.81% and 98.57%, respectively. In the steel industry, counting steel bars is a time consuming task which highly relies on human labour and is prone to errors. Reduction of counting time and resources, safety and productivity of employees and high confidence of the inventory are some of the advantages of the proposed methodology in a steel warehouse.

scholarly journals Parallel processing using big data and machine learning techniques for intrusion detection

2020 ◽  
Vol 9 (3) ◽  
pp. 553
Author(s):  
Alaeddine Boukhalfa ◽  
Nabil Hmina ◽  
Habiba Chaoni
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Processing Time ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
New Approach ◽  
Learning Techniques ◽  
Proposed Model ◽  
Multiple Devices

Currently, information technology is used in all the life domains, multiple devices produce data and transfer them across the network, these transfers are not always secured, they can contain new menaces invisible by the current security devices. Moreover, the large amount and variety of the exchanged data cause difficulties related to the detection time. To solve these issues, we suggest in this paper, a new approach based on storing the large amount and variety of network traffic data employing Big Data techniques, and analyzing these data with Machine Learning algorithms, in a distributed and parallel way, in order to detect new hidden intrusions with less processing time. According to the results of the experiments, the detection accuracy of the Machine Learning methods reaches 99.9 %, and their processing time has been reduced considerably by applying them in a parallel and distributed way, which proves that our proposed model is effective for the detection of new intrusions.

scholarly journals Leveraging Deep Learning Techniques for Malaria Parasite Detection Using Mobile Application

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Mehedi Masud ◽  
Hesham Alhumyani ◽  
Sultan S. Alshamrani ◽  
Omar Cheikhrouhou ◽  
Saleh Ibrahim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mobile Application ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Stochastic Gradient Descent ◽  
Cognitive Computing ◽  
Human Errors ◽  
Learning Techniques

Malaria is a contagious disease that affects millions of lives every year. Traditional diagnosis of malaria in laboratory requires an experienced person and careful inspection to discriminate healthy and infected red blood cells (RBCs). It is also very time-consuming and may produce inaccurate reports due to human errors. Cognitive computing and deep learning algorithms simulate human intelligence to make better human decisions in applications like sentiment analysis, speech recognition, face detection, disease detection, and prediction. Due to the advancement of cognitive computing and machine learning techniques, they are now widely used to detect and predict early disease symptoms in healthcare field. With the early prediction results, healthcare professionals can provide better decisions for patient diagnosis and treatment. Machine learning algorithms also aid the humans to process huge and complex medical datasets and then analyze them into clinical insights. This paper looks for leveraging deep learning algorithms for detecting a deadly disease, malaria, for mobile healthcare solution of patients building an effective mobile system. The objective of this paper is to show how deep learning architecture such as convolutional neural network (CNN) which can be useful in real-time malaria detection effectively and accurately from input images and to reduce manual labor with a mobile application. To this end, we evaluate the performance of a custom CNN model using a cyclical stochastic gradient descent (SGD) optimizer with an automatic learning rate finder and obtain an accuracy of 97.30% in classifying healthy and infected cell images with a high degree of precision and sensitivity. This outcome of the paper will facilitate microscopy diagnosis of malaria to a mobile application so that reliability of the treatment and lack of medical expertise can be solved.

scholarly journals Student Intervention System using Machine Learning Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2061-2065
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Student Records ◽  
Teaching Methodologies ◽  
Machine Learning Classification ◽  
Classification Technique ◽  
Learning Techniques ◽  
Vector Machines

Now a days, the educational institutes are adopting technologies for betterment of student’s quality, in respect to teaching methodologies etc. For which the huge information available with educational institutes can be used to predict student’s future in academics. The main objective of this paper is to predict the student performance in the examination and also to predict the student will graduate or not. Hence forth we are using statistical analytical method which is F1 score. F1 score or F measure is used to test the prediction accuracy by considering precision and recall to compute the score. To fulfill this requirement in machine learning, classification technique is used. The dataset used in this analysis contains 395 student records, having attributes, such as age, health, internet, school, father job, mother job etc. Using support vector machines (SVM), Decision Tree and Naïve Bayes (NB) classification algorithms F1 score is calculated for each algorithm. Based on the analysis done the F1 score of support vector machine is giving the better prediction compared to rest of the two algorithms.

scholarly journals Machine Learning Based Effective Classification of Distributed Denial of Service Attacks

2019 ◽  
Vol 9 (2) ◽  
pp. 1060-1064
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Learning Approach ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Learning Techniques ◽  
Machine Learning Approach

Distributed Denial of Service Attack (DDoS) is a deadliest weapon which overwhelm the server or network by sending flood of packets towards it. The attack disrupts the services running on the target thereby blocking the legitimate traffic accessing its services. Various advanced machine learning techniques have been applied for detection of different types of DDoS attacks but still the attack remains a potential threat to the world. There are mainly two broad categories of machine learning techniques: supervised machine learning approach and unsupervised machine learning approach. Supervised machine learning approach requires labelled attack traffic datasets whereas unsupervised machine learning approach analyses incoming network traffic and then categorizes it. In this paper we have attempted to apply four different classifiers for the detection of DDoS attacks. The four classifiers applied are Logistic Regression, Naïve Bayes, K- Nearest Neighbor and Artificial Neural Network. The chosen classifiers provide stable results when there is a large dataset. We compared their detection accuracy on KDD dataset which is a benchmark dataset in the field of network security. This paper is novel as it explains each pre-processing step with python conversion functions and explained in detail all the classifiers and detection accuracy with their functions in python as well.

Predicting mobile application breakout using sentiment analysis of Facebook posts

2020 ◽  
pp. 016555152091709 ◽  
Author(s):  
Moez Ben Hajhmida ◽  
Oumayma Oueslati
Keyword(s):  
Mobile Applications ◽  
Mobile Application ◽  
Prediction Accuracy ◽  
High Rate ◽  
Machine Learning Techniques ◽  
Statistical Features ◽  
User Interactions ◽  
Statistical Measures ◽  
User Comments ◽  
Learning Techniques

Publishing mobile applications on the official stores is becoming a big business. Many developers are charmed by the billion-dollar success of breakout applications. Thus, in order to ensure success, mobile applications need to sustain top ranking. Previous work on the predictability of mobile applications success aimed to extract from app stores relevant features that influence high rating. In this article, we propose an automated approach to exploit data available on Facebook platform that predicts mobile applications breakout. We collect data from Facebook graph API, then determine sentiment polarity of user comments. We design statistical features to score users sentiment for each post. Then, we compose posts scores with Facebook statistical measures to form a mobile applications breakout dataset. Finally, we use machine learning techniques to build our breakout prediction model. We evaluate our approach with 199 mobile applications and obtain a prediction accuracy of 83.78%. We find that Likes count on a Facebook page is decisive for climbing mobile applications ranking. However, a high rate of negative opinions declines application ranking and deprives mobile application of achieving a breakout. Based on these findings, we provide evidence that user interactions on social networks can influence the success of mobile applications.

scholarly journals Analysis and Prediction of Breast Cancer using Machine Learning Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 26-30
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Breast Cancer Dataset ◽  
Detection Accuracy ◽  
Vast Number ◽  
Learning Techniques ◽  
Number Of Patients

Rapid multiplication of cells in the human body leads to cancer. It is the foremost cause of death due to cancer in females, after lung cancer. As the breast cancer is one of the recurrent kinds of cancer, diagnosis of breast cancer recurring is extremelyessential to increase the survival rate of patient suffering from it. Although cancer is avertible and also treatable in primary/early stages yet a vast number of patients are diagnosed with cancer when it is very late. Almost 8% of females are detected with breast cancer. Its characteristics are mutation of genes, constant pain, changes in the size and redness of skin texture of breasts. With the development of technology and machine learning techniques, cancer diagnosis and detection accuracy has greatly improved. This paper presents an outline of evolved machine learning techniques in this medical field by applying machine learning algorithms on breast cancer dataset like Logistic regression, Random Forest, Decision Trees (DT) etc.

scholarly journals Machine Learning Techniques to Detecting and Preventing Jamming Attacks in Optical Networks

2019 ◽  
Author(s):  
Mounir Bensalem ◽  
Sandeep Kumar Singh ◽  
Admela Jukan
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Detection Accuracy ◽  
Resource Reallocation ◽  
Jamming Attacks ◽  
Learning Techniques ◽  
Artificial Neural

We study the effectiveness of various machine learning techniques, including artificial neural networks, support vector machine, logistic regression, K-nearest neighbors, decision tree and Naive Bayesian, for detecting and mitigating power jamming attacks in optical networks. Our study shows that artificial neural network is the most accurate in detecting out-of-band power jamming attacks in optical networks. To further mitigating the power jamming attacks, we apply a new resource reallocation scheme that utilizes the statistical information of attack detection accuracy, and propose a resource reallocation algorithm to lower the probability of successful jamming of lightpaths. Simulation results show that higher the accuracy of detection, lower is the likelihood of jamming a lightpath.

scholarly journals Improving the performance of Transposable Elements detection tools

2013 ◽  
Vol 10 (3) ◽  
pp. 40-50 ◽  
Author(s):  
Tiago Loureiro ◽  
Rui Camacho ◽  
Jorge Vieira ◽  
Nuno A. Fonseca
Keyword(s):  
Machine Learning ◽  
Transposable Elements ◽  
In Silico ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Learning Techniques ◽  
A Genome ◽  
Different Types ◽  
Species Evolution ◽  
Genome Alteration

Summary Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single tool achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an in silico evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning constructed classifiers.

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