41 research outputs found
Students’ academic performance and dropout predictions: a review / Ahmed O. Ameen, Moshood A. Alarape and Kayode S. Adewole
No full textStudents’ Academic Performance (SAP) is an important metric in determining the status of students in any academic institution. It allows the instructors and other education managers to get an accurate evaluation of the students in different courses in a particular semester and also serve as an indicator to the students to review their strategies for better performance in the subsequent semesters. Predicting SAP is therefore important to help learners in obtaining the best from their studies. A number of researches in Educational Psychology (EP), Learning Analytics (LA) and Educational Data Mining (EDM) has been carried out to study and predict SAP, most especially in determining failures or dropouts with the goal of preventing the occurrence of the negative final outcome. This paper presents a comprehensive review of related studies that deal with SAP and dropout predictions. To group the studies, this review proposes taxonomy of the methods and features used in the literature for SAP and dropout prediction. The paper identifies some key issues and challenges for SAP and dropout predictions that require substantial research efforts. Limitations of the existing approaches for SAP and dropout prediction are identified. Finally, the paper exposes the current research directions in the area
DFTMicroagg: a dual-level anonymization algorithm for smart grid data
No full textThe introduction of advanced metering infrastructure (AMI) smart meters has given rise to fine-grained electricity usage data at different levels of time granularity. AMI collects high-frequency daily energy consumption data that enables utility companies and data aggregators to perform a rich set of grid operations such as demand response, grid monitoring, load forecasting and many more. However, the privacy concerns associated with daily energy consumption data has been raised. Existing studies on data anonymization for smart grid data focused on the direct application of perturbation algorithms, such as microaggregation, to protect the privacy of consumers. In this paper, we empirically show that reliance on microaggregation alone is not sufficient to protect smart grid data. Therefore, we propose DFTMicroagg algorithm that provides a dual level of perturbation to improve privacy. The algorithm leverages the benefits of discrete Fourier transform (DFT) and microaggregation to provide additional layer of protection. We evaluated our algorithm on two publicly available smart grid datasets with millions of smart meters readings. Experimental results based on clustering analysis using k-Means, classification via k-nearest neighbor (kNN) algorithm and mean hourly energy consumption forecast using Seasonal Auto-Regressive Integrated Moving Average with eXogenous (SARIMAX) factors model further proved the applicability of the proposed method. Our approach provides utility companies with more flexibility to control the level of protection for their published energy data
Privacy Protection of Synthetic Smart Grid Data Simulated via Generative Adversarial Networks
No full textThe development in smart meter technology has made grid operations more efficient based on fine-grained electricity usage data generated at different levels of time granularity. Consequently, machine learning algorithms have benefited from these data to produce useful models for important grid operations. Although machine learning algorithms need historical data to improve predictive performance, these data are not readily available for public utilization due to privacy issues. The existing smart grid data simulation frameworks generate grid data with implicit privacy concerns since the data are simulated from a few real energy consumptions that are publicly available. This paper addresses two issues in smart grid. First, it assesses the level of privacy violation with the individual household appliances based on synthetic household aggregate loads consumption. Second, based on the findings, it proposes two privacy-preserving mechanisms to reduce this risk. Three inference attacks are simulated and the results obtained confirm the efficacy of the proposed privacy-preserving mechanisms
Energy disaggregation risk resilience through microaggregation and discrete Fourier transform
No full textProgress in the field of Non-Intrusive Load Monitoring (NILM) has been attributed to the rise in the application of artificial intelligence. Nevertheless, the ability of energy disaggregation algorithms to disaggregate different appliance signatures from aggregated smart grid data poses some privacy issues. This paper introduces a new notion of disclosure risk termed energy disaggregation risk. The performance of Sequence-to-Sequence (Seq2Seq) NILM deep learning algorithm along with three activation extraction methods are studied using two publicly available datasets. To understand the extent of disclosure, we study three inference attacks on aggregated data. The results show that Variance Sensitive Thresholding (VST) event detection method outperformed the other two methods in revealing households' lifestyles based on the signature of the appliances. To reduce energy disaggregation risk, we investigate the performance of two privacy-preserving mechanisms based on microaggregation and Discrete Fourier Transform (DFT). Empirically, for the first scenario of inference attack on UK-DALE, VST produces disaggregation risks of 99%, 100%, 89% and 99% for fridge, dish washer, microwave, and kettle respectively. For washing machine, Activation Time Extraction (ATE) method produces a disaggregation risk of 87%. We obtain similar results for other inference attack scenarios and the risk reduces using the two privacy-protection mechanisms
Effects of defects and reverse bending on tensile properties of tensile armour wires
No full textPhD ThesisFlexible pipes are used for risers and flowlines in the offshore oil and gas industry and in many other applications. As part of the construction of these pipes, tensile armour wires are incorporated to resist longitudinal stresses which arise during installation and service. Tensile armour wires also resist hoop stresses for pipes without a designated pressure armour layer.
The flexible pipeline manufacturing industry desires a better understanding of the tensile armour wire fracture mechanism, and especially the effects of defects with dimensions less than 0.2mm. Reverse bending operations (which arise due to the wire moving through paired rollers on unreeling during pipe manufacture) also affect the tensile properties of the tensile armour wires. Customarily, engineers estimate the safe load carrying capacity of defective wires solely by multiplying the ultimate strength obtained from a tension test by the original nominal area of the wire without any consideration for the fracture mechanisms of the wire. This approach may overestimate the strength of the wire. Recent research considering the fracture mechanisms of wires has employed a classical fracture mechanics approach, mainly using Linear Elastic Fracture Mechanics (LEFM) and/or Net Section Theory (NST).
Obtaining parameters for fracture mechanics analyses requires large/thick standard fracture mechanics test specimens which cannot be made out of tensile armour wires due to their small size. Also fracture mechanics analyses based on these parameters including the elastic plastic crack opening displacement (COD) and J-integral parameters are largely size and geometry dependent making transferability of the results obtained from full size specimens to actual structures questionable.
Laboratory tensile testing and tensile testing finite element simulations with mechanism-based fracture mechanics carried out on the as-received tensile armour wire and tensile armour wires with engineered defects reveal that the tensile armour wires fail by a shear mechanism. They also reveal that flat bottom scratches, pointed end scratches and dents identified from the Scanning Electron Microscope images of the as-received wire surface reduce the ultimate load and extension at fracture of the wires. In addition, denting was found to increase the wires yield load while scratching reduced the wire‟s yield load. The reduction in the tensile/ mechanical properties of tensile armour wires were found to depend largely on defect dimensions rather than defect locations with defects less than 0.2mm in any of its dimensions causing less than 0.072%, 0.238% and 10.946% reduction the yield load, the ultimate load and the displacement at fracture of tensile armour wires respectively.
Laboratory and finite element simulations of reverse bending, straightening and tensile testing of the reverse bent tensile armour wires reveal that reverse bending and straightening operations reduce the ultimate load and fracture displacement of the wires. This work also reveals that the reverse bending process can only reveal near surface laminations as wires with mid depth laminations or with scratches less than 1mm deep would pass through the reverse bending process without fracturing
Intrusion Detection Framework for Internet of Things with Rule Induction for Model Explanation
No full textAs the proliferation of Internet of Things (IoT) devices grows, challenges in security, privacy, and interoperability become increasingly significant. IoT devices often have resource constraints, such as limited computational power, energy efficiency, bandwidth, and storage, making it difficult to implement advanced security measures. Additionally, the diversity of IoT devices creates vulnerabilities and threats that attackers can exploit, including spoofing, routing, man-in-the-middle, and denial-of-service. To address these evolving threats, Intrusion Detection Systems (IDSs) have become a vital solution. IDS actively monitors network traffic, analyzing incoming and outgoing data to detect potential security breaches, ensuring IoT systems remain safeguarded against malicious activity. This study introduces an IDS framework that integrates ensemble learning with rule induction for enhanced model explainability. We study the performance of five ensemble algorithms (Random Forest, AdaBoost, XGBoost, LightGBM, and CatBoost) for developing effective IDS for IoT. The results show that XGBoost outperformed the other ensemble algorithms on two publicly available datasets for intrusion detection. XGBoost achieved 99.91% accuracy and 99.88% AUC-ROC on the CIC-IDS2017 dataset, as well as 98.54% accuracy and 93.06% AUC-ROC on the CICIoT2023 dataset, respectively. We integrate model explainability to provide transparent IDS system using a rule induction method. The experimental results confirm the efficacy of the proposed approach for providing a lightweight, transparent, and trustworthy IDS system that supports security analysts, end-users, and different stakeholders when making decisions regarding intrusion and non-intrusion events
Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis
No full text[EN] The importance of wireless path loss prediction and interference minimization studies in various environments cannot be over-emphasized. In fact, numerous researchers have done massive work on scrutinizing the effectiveness of existing path loss models for channel modeling. The difficulties experienced by the researchers determining or having the detailed information about the propagating environment prompted for the use of computational intelligence (CI) methods in the prediction of path loss. This paper presents a comprehensive and systematic literature review on the application of nature-inspired computational approaches in radio propagation analysis. In particular, we cover artificial neural networks (ANNs), fuzzy inference systems (FISs), swarm intelligence (SI), and other computational techniques. The main research trends and a general overview of the different research areas, open research issues, and future research directions are also presented in this paper. This review paper will serve as reference material for researchers in the field of channel modeling or radio propagation and in particular for research in path loss prediction.This work was funded by the Federal Ministry of Education, Federal Government of Nigeria, Tertiary Education Trust Fund (TETFUND), Institutional Based Research (IBR) Fund, 2018.Adebowale, QR.; Faruk, N.; Adewole, KS.; Abdulkarim, A.; Olawoyin, LA.; Oloyede, AA.; Chiroma, H.... (2021). Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis. Mobile Information Systems. 2021:1-20. https://doi.org/10.1155/2021/6619364S120202
Microarray cancer feature selection: Review, challenges and research directions
No full textMicroarray technology has become an emerging trend in the domain of genetic research in which many researchers employ to study and investigate the levels of genes’ expression in a given organism. Microarray experiments have lots of application areas in the health sector such as diseases prediction and diagnosis, cancer study and soon. The enormous quantity of raw gene expression data usually results in analytical and computational complexities which include feature selection and classification of the datasets into the correct class or group. To achieve satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is critical and sensitive in the classification task. Therefore, this paper presents a comprehensive survey of studies on microarray cancer classification with a focus on feature selection methods. In this paper, the taxonomy of the various feature selection methods used for microarray cancer classification and open research issues have been extensively discussed
Iris Feature Extraction for Personal Identification using Fast Wavelet Transform (FWT) 1
No full textIris is the annular region of the eye bounded by the pupil and the sclera(white of the eye) on either side. The iris has many interlacing features such as stripes, freckles, coronas, radial furrow, crypts, zigzag collarette, rings etc collectively referred to as texture of the iris. This texture is well known to provide a signature that is unique to each subject. All these features are extracted using different algorithms i.e features extraction is the process of extracting information from the iris image. Iris feature extraction is the crucial stage of the whole iris recognition process for personal identification. This is a key process where the two dimensional image is converted to a set of mathematical parameters. The significant features of the iris must be encoded so that comparisons between templates can be made. In this study the feature of the iris is extracted using Fast Wavelet Transform (FWT). The algorithm is fast and has a low complexity rate. The system encodes the features to generate its iris feature codes
A Systematic Literature Review of Privacy Related to Sensing in Smart Buildings
No full textThe concept of smart building is based on the deployment of Internet of Things (IoT) technologies to develop various building applications and services. Aided by the proliferation of smart devices, research in building automation has grown significantly. Nevertheless, these smart devices are integrated with sensors that can collect and share sensitive data and private information related to the building occupants, exposing them to a variety of privacy threats. Although research efforts to promote the development of privacy-aware solutions for smart buildings have been on the rise, a comprehensive review that summarizes these studies is lacking in the literature. This paper provides an extensive review of the studies related to sensing in smart buildings. It highlights privacy issues connected to sensing in smart buildings, provides mitigation strategies that can be deployed to minimize occupants’ privacy invasions, and discusses future research directions towards realising privacy-aware smart buildings. To fulfill the aim of this study, five research questions are formulated, which enable systematic navigation through existing studies related to the topic. These research questions are directed to providing answers to privacy related to data leakage, privacy connected to sensor types, privacy related to different applications, privacy concerns with sensor deployment locations and building types, privacy issues with data processing methods, and to highlight mitigation strategies for reducing privacy invasion. It further discusses the technical approaches, general principles, and design choices for privacy-aware applications which are relevant for guiding relevant stakeholders
