DFTMicroagg: a dual-level anonymization algorithm for smart grid data

The 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

Microarray cancer feature selection: Review, challenges and research directions

Microarray 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

Iris 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

Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis

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

Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection

Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant re-training of new training instances. Hence, this paper seeks to conduct a comparative study using selected batch learning and data streaming algorithms. The batch learning and data streaming algorithms considered are J48, projective adaptive resonance theory (PART), Hoeffding tree (HT) and OzaBagAdwin (OBA). Furthermore, binary and multiclass classification problems are considered for the tested algorithms. Experimental results show that data streaming algorithms achieved considerably higher performance in binary classification problems when compared with batch learning algorithms. Specifically, binary classification produced J48 (94.73), PART (92.83), HT (98.38), and OBA (99.67), and multiclass classification produced J48 (87.66), PART (87.05), HT (71.98), OBA (82.80) based on accuracy. Hence, the use of data streaming algorithms to solve the scalability issue and allow real-time detection of network intrusion is highly recommended

Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection

Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant re-training of new training instances. Hence, this paper seeks to conduct a comparative study using selected batch learning and data streaming algorithms. The batch learning and data streaming algorithms considered are J48, projective adaptive resonance theory (PART), Hoeffding tree (HT) and OzaBagAdwin (OBA). Furthermore, binary and multiclass classification problems are considered for the tested algorithms. Experimental results show that data streaming algorithms achieved considerably higher performance in binary classification problems when compared with batch learning algorithms. Specifically, binary classification produced J48 (94.73), PART (92.83), HT (98.38), and OBA (99.67), and multiclass classification produced J48 (87.66), PART (87.05), HT (71.98), OBA (82.80) based on accuracy. Hence, the use of data streaming algorithms to solve the scalability issue and allow real-time detection of network intrusion is highly recommended

Secular trend in interobserver agreement of VIA diagnosis for cervical cancer screening in Nigeria.

ObjectiveIn low resource settings, visual inspection with acetic acid (VIA) by allied health workers, has been suggested as an alternative for cervical cancer screening. However, there are concerns about the objectivity and time to diagnostic concordance with specialists. We evaluated the secular trend in interobserver agreement between nurse providers and a gynecologist/colposcopist over a five-year period.MethodsNurses provided VIA screening with digital cervivography to 4,961 participants in five screening clinics from October 2010 to May 2014 in Nigeria in this observational study. Cervigraphs were reviewed at meetings where a gynaecologist/colposcopist made an assessment from the cervigraphs. We used weighted kappa statistics to calculate agreement in diagnosis between nurse providers and the gynecologist/colposcopist; linear regression models to examine overall trend and investigate potential clinic characteristics that may influence agreement; and time series models to characterize month to month variations.ResultsMean age of participants was 37±8 years. Overall agreement was 0.89 at Site D, 0.78 and 0.73 at Sites A and C respectively, 0.50 for Site E and 0.34 for Site C. The number of trainings attended by nurse providers(β = 0.47,95%CI:0.02-0.93, p = 0.04), high level of engagement by site gynecologists(β = 0.11,95%CI:0.01-0.21,p = 0.04) were associated with increased agreement; while increasing distance from the coordinating site(β = -0.47,95%CI:-0.92-0.02,p = 0.04) was associated with decreased agreement. There were no associations between number of years screening clinics were operational(β = 0.01,95%CI: -0.01-0.03,p = 0.29), cumulative experience of nurse providers(β = 0.04,95%CI:-0.03-0.12,p = 0.19) and agreement. There were no significant increases in weighted kappa statistics over time for all sites considered. Monthly variations were significant for only one of two sites considered in time series models (AR1 term = -0.40, 95%CI:-0.71-0.09,p = 0.01).ConclusionOur results showed a lack of objectivity, persistent variation and lack of convergence of diagnostic capabilities of nurse led VIA cervical cancer screening with the diagnostic capabilities of a specialist in a cervical cancer screening program in Nigeria

A review of dust-induced electromagnetic waves scattering theories and models for 5G and beyond wireless communication systems

Dust particles and sand storms can cause attenuation and cross-polarization of electromagnetic wave propagation, especially at high frequencies above 10 GHz. Dust attenuation has been the focus of many research works, mainly with the deployment of a 5G wireless network in the FR-2 band (mmWave band, 23–53 GHz with TDD). This has led to the development of novel models to accurately predict and estimate attenuation. However, the existing review works have not adequately provided extensive taxonomies for these models to show the state-of-art and future research directions. This paper aims to bridge this gap by providing a comprehensive review of all electromagnetic scattering models in terms of their strengths, weaknesses, and applications. Lessons learned from the detailed survey have been stated and discussed extensively. Key findings from this review indicate that all the models developed were limited to the region where they were developed, with frequency and visibility levels as the two main parameters. The survey across regions showed no model was developed for Region 2, including the Americas, Greenland, and some of the eastern Pacific Islands. Among the dry regions of the globe, where dust and sand storms can occur either occasionally or frequently, it can be seen that only a few parts of these desert regions of Africa (Region 1) and Asia (Region 3) have been considered by authors for the development of prediction models for attenuation due to dust storms. Thus, this also shows the limitations of the overall deterministic models and presents the crucial need to develop new models or modify existing models to accurately predict dust attenuation in other regions, particularly in Africa