0000000000173251
AUTHOR
Fazlullah Umar
A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks
An intrusion detection system, often known as an IDS, is extremely important for preventing attacks on a network, violating network policies, and gaining unauthorized access to a network. The effectiveness of IDS is highly dependent on data preprocessing techniques and classification models used to enhance accuracy and reduce model training and testing time. For the purpose of anomaly identification, researchers have developed several machine learning and deep learning-based algorithms; nonetheless, accurate anomaly detection with low test and train times remains a challenge. Using a hybrid feature selection approach and a deep neural network- (DNN-) based classifier, the authors of this re…
Internet of Things-Based Fire Alarm Navigation System: A Fire-Rescue Department Perspective
In the past few years, fire alarm systems have become increasingly sophisticated and more capable and reliable. The two main objectives are the protection of life and property. As a result of state and local codes, fire protection has become more concerned with life safety over the past two decades. Several safety measures have been implemented to address the problems caused by the fires and reduce the number of fatalities and property damage. Our project is to develop and review a fire alarm navigation system and application that uses the internet of things. Fire alarm systems are designed to warn people about fires in advance so that they can evacuate the fire-affected area and take immed…
A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream
In medical filed, predicting the occurrence of heart diseases is a significant piece of work. Millions of healthcare-related complexities that have remained unsolved up until now can be greatly simplified with the help of machine learning. The proposed study is concerned with the cardiac disease diagnosis decision support system. An OpenML repository data stream with 1 million instances of heart disease and 14 features is used for this study. After applying to preprocess and feature engineering techniques, machine learning approaches like random forest, decision trees, gradient boosted trees, linear support vector classifier, logistic regression, one-vs-rest, and multilayer perceptron are u…
In the Direction of Service Guarantees for Virtualized Network Functions
The trend of consolidating network functions from specialized hardware to software running on virtualization servers brings significant advantages for reducing costs and simplifying service deployment. However, virtualization techniques have significant limitations when it comes to networking as there is no support for guaranteeing that network functions meet their service requirements. In this paper, we present a design for providing service guarantees to virtualized network functions based on rate control. The design is a combination of rate regulation through token bucket filters and the regular scheduling mechanisms in operating systems. It has the attractive property that traffic profi…
Evaluation of Deep Learning and Conventional Approaches for Image Recaptured Detection in Multimedia Forensics
Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The attacker can recapture the manipulated images to fool image forensic system. As far as we know, ther…
A comprehensive skills analysis of novice software developers working in the professional software development industry
Measuring and evaluating a learner’s learning ability is always the focus of every person whose aim is to develop strategies and plans for their learners to improve the learning process. For example, classroom assessments, self-assessment using computer systems such as Intelligent Tutoring Systems (ITS), and other approaches are available. Assessment of metacognition is one of these techniques. Having the ability to evaluate and monitor one’s learning is known as metacognition. An individual can then propose adjustments to their learning process based on this assessment. By monitoring, improving, and planning their activities, learners who can manage their cognitive skills are better able t…
A New Correlation Coefficient for T-Spherical Fuzzy Sets and Its Application in Multicriteria Decision-Making and Pattern Recognition
The goal of this paper is to design a new correlation coefficient for T -spherical fuzzy sets (TSFSs), which can accurately measure the nature of correlation (i.e., positive and negative) as well as the degree of relationship between TSFS. In order to formulate our proposed idea, we had taken inspiration from the statistical concept of the correlation coefficient. While doing so, we firstly introduce the variance and covariance of two TSFS and then constructed our scheme using these two newly defined notions. The numerical value of our proposed correlation coefficient lies within the interval − 1 , + 1 , as it should be from a statistical point of view, whereas the existing methods cannot m…
An Efficient Convolutional Neural Network with Transfer Learning for Malware Classification
Rising prevalence of malicious software (malware) attacks represent a serious threat to online safety in the modern era. Malware is a threat to anyone who uses the Internet since it steals data and causes damage to computer systems. In addition, the exponential growth of malware hazards that affect many computer users, corporations, and governments has made malware detection, a popular issue in academic study. Current malware detection methods are slow and ineffectual because they rely on static and dynamic analysis of malware signatures and behavior patterns to detect unknown malware in real-time. Thus, this paper discusses the role of deep convolution neural networks in malware classifica…
An Enhanced Multifactor Multiobjective Approach for Software Modularization
Complex software systems, meant to facilitate organizations, undergo frequent upgrades that can erode the system architectures. Such erosion makes understandability and maintenance a challenging task. To this end, software modularization provides an architectural-level view that helps to understand system architecture from its source code. For modularization, nondeterministic search-based optimization uses single-factor single-objective, multifactor single-objective, and single-factor multiobjective, which have been shown to outperform deterministic approaches. The proposed MFMO approach, which uses both a heuristic (Hill Climbing and Genetic) and a meta-heuristic (nondominated sorting gene…
Applying Real-Time Dynamic Scaffolding Techniques during Tutoring Sessions Using Intelligent Tutoring Systems
An intelligent tutoring system (ITS) is a computer system or software application that is built to replicate human tutors by supporting the theory of “learning by doing.” Even though ITSs have been proven to be successful in academic studies, they still have not found large adoption by the industry due to the complexities of building such systems due to the high technical expertise and domain knowledge requirements. Attempts have been made to build authoring tools that can provide assistance in building tutoring systems; however, most of these tools are targeted toward authors that have considerable programming experience. This research proposes an authoring tool for ITS, which is targeted …