NUML International Journal of Engineering and Computing

An IoT and Machine Learning-based Neonatal Sleep Stage Classification

Awais Abbas — 2024-02-21

Sleep, in neonates, is used to access the quality of brain and physical development. Typically, neonatal sleep has been divided into three stages: active sleep (AS), quiet sleep (QS), and intermediate sleep (IS). Polysomnography (PSG) is considered a gold standard to classify sleep. To address this issue, over the past two decades, researchers proposed multiple algorithms for automatic sleep stage classification. These algorithms work achieved outstanding results i.e. quiet sleep detection still, lacks in many aspects. One major drawback of the existing research is amalgamation of awake and active sleep into low voltage irregular (LVI) state. This amalgamation corrupts 40% of the overall EEG signal. For this reason, we proposed an algorithm for neonatal sleep-wake classification using machine learning. The proposed research is divided into three steps. Firstly, the EEG signal was pre-processed using finite impulse response filter to remove the noise and artifacts. Clean EEG signal is then divided into 4560 30-sec segments. Then, twenty prominent EEG features were extracted from time, frequency, and spatial domain. After feature extraction, support vector machine was used for sleep stage classification. The propounded study outperformed all the existing algorithms for sleep-wake classification with a mean accuracy of 83.7%. Four-fold cross-validation was used to validate the overall dataset. Multiple other performance matrices i.e. sensitivity, specificity, Kappa were calculated to prove the efficacy of the proposed study. Statistical results show that the proposed study can be used as a real-time neonatal sleep and Awake classification algorithm, as this did not use prior post-processing techniques.

Solving Traffic congestion using Artificial Intelligence: A review

Muhammad Usman — 2024-02-21

Artificial intelligence(AI) is the new electricity, that can assist any system present in the world. In this paper, we have discussed AI and its associated technologies currently available for traffic management. AI can be incorporated into traffic lights to make them smart and to meet new challenges. AI can also be used in combination with IOTs and in autonomous cars to make traffic flow easier, lower time wastage, decrease fuel consumption, and benefit the environment.

A Systematic Literature Review on SQL Injection Attacks

Maryam Mehmood — 2024-02-21

With the increasing use of web applications, concerns for data integrity and security have increased manifolds in the current time. The growth in quantity of internet clients and sites has made the web security circumstances progressively extreme. Structured Query Language Injection Attack (SQLIA) is a major threat to web applications. Over the time, many studies have explored the reasons and techniques of these attacks, and also ways to detect and prevent them from happening. This study presents a Systematic Literature Review (SLR) based on the methodology proposed by Kitchenham in 2007. The focus of study is on determining how and why SQLIA are done and how can they be avoided or mitigated. The literature is considered for a time period of four years; 2016 to 2023. Moreover, evaluation has been done, based on limitations and priorities proposed by each technique studied. Attack types with their severity has been reviewed that may help researchers propose new techniques in order to make web applications more secure against SQLIAs.

A Comprehensive Survey of Cutting-Edge Methods for Software Architecture Evaluation

Amna Sajid — 2024-02-21

The crucial responsibility of assessing software architecture is of utmost importance in ensuring that a software system conforms to superior qualities. It is a crucial tool for cutting expenses and labor during the course of the software development lifecycle. The main goal of software architecture evaluation is to provide reliable methods for determining and improving the quality characteristics that are innate in software. This study is deeply devoted to investigating the wide range of techniques used in software architecture assessment. One of the main focuses of these evaluation techniques is scenario-based assessments, which offer a comprehensive picture of the software's behavior under different circumstances. Within the scope of this study, a thorough analysis is carried out on eighteen different methods. Thirteen of these fall into the category of early evaluation techniques, which are positioned to detect and address architectural problems at the outset of development. The other five strategies are classified as late assessment techniques and focus on validating and optimizing the software architecture in the latter stages of the development process.

Detection of Emulation Attacks in Cognitive Radio Networks using Heuristic Techniques

Jabbar Mahmood — 2024-02-21

This paper discusses the issue of primary user emulation attacks (PUEAs) in cognitive radio networks (CRNs). These attackers imitate the signal characteristics of primary users (PUs), preventing secondary users (SUs) from accessing their assigned spectrum. The research focuses on detecting PUEAs using the time difference of arrivals (TDOA) to detect the attacker and reduce detection errors using heuristic techniques. Differential evolution (DE) and cuckoo search (CS) algorithms are utilized to optimize detection accuracy. Simulation results confirm that the proposed CS algorithm outperforms and gives better performance as compared to the DE algorithm.