Enamul Kabir

Exploring Sampling in the Detection of Multicategory EEG Signals

The paper presents a structure based on samplings and machine leaning techniques for the detection of multicategory EEG signals where random sampling (RS) and optimal allocation sampling (OS) are explored. In the proposed framework, before using the RS and OS scheme, the entire EEG signals of each class are partitioned into several groups based on a particular time period. The RS and OS schemes are used in order to have representative observations from each group of each category of EEG data. Then all of the selected samples by the RS from the groups of each category are combined in a one set named RS set. In the similar way, for the OS scheme, an OS set is obtained. Then eleven statistical features are extracted from the RS and OS set, separately. Finally this study employs three well-known classifiers: k-nearest neighbor (k-NN), multinomial logistic regression with a ridge estimator (MLR), and support vector machine (SVM) to evaluate the performance for the RS and OS feature set. The experimental outcomes demonstrate that the RS scheme well represents the EEG signals and the k-NN with the RS is the optimum choice for detection of multicategory EEG signals.

A novel statistical technique for intrusion detection systems

This paper proposes a novel approach for intrusion detection system based on sampling with Least Square Support Vector Machine (LS-SVM). Decision making is performed in two stages. In the first stage, the whole dataset is divided into some predetermined arbitrary subgroups. The proposed algorithm selects representative samples from these subgroups such that the samples reflect the entire dataset. An optimum allocation scheme is developed based on the variability of the observations within the subgroups. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted samples to detect intrusions. We call the proposed algorithm as optimum allocation-based least square support vector machine (OA-LS-SVM) for IDS. To demonstrate the effectiveness of the proposed method, the experiments are carried out on KDD 99 database which is considered a de facto benchmark for evaluating the performance of intrusions detection algorithm. All binary-classes and multiclass are tested and our proposed approach obtains a realistic performance in terms of accuracy and efficiency. Finally a way out is also shown the usability of the proposed algorithm for incremental datasets.

Epileptic seizure detection from EEG signals using logistic model trees

Reliable analysis of electroencephalogram (EEG) signals is crucial that could lead the way to correct diagnostic and therapeutic methods for the treatment of patients with neurological abnormalities, especially epilepsy. This paper presents a novel analysis system for detecting epileptic seizure from EEG signals, which uses statistical features based on optimum allocation technique (OAT) with logistic model trees (LMT). The analysis involves applying the OAT to select representative EEG signals that reflect the entire database. Then, some statistical features are extracted from these EEG signals and the obtained feature set is fed into the LMT classification model to detect epileptic seizure. To test the consistency of the proposed method, all experiments are carried out on a benchmark EEG dataset and repeated twenty times with the same parameters in the detection process, and the average values of the performance parameters are reported. The results show very high detection performances for each class, and also confirm the consistency of the proposed method in the repeating process. The proposed method outperforms some state-of-the-art methods of epileptic EEG signal detection using the same EEG dataset.

Cluster analysis of behavioural weight management strategies and associations with weight change in young women: a longitudinal analysis

Background/Objectives:Maintaining a healthy weight is important for the prevention of many chronic diseases. Little is known about the strategies used by young women to manage their weight, or the effectiveness of these in preventing weight gain. We aimed to identify clusters of weight control strategies used by women and to determine the average annual weight change among women in each cluster from 2000 to 2009.Methods:Latent cluster analysis of weight control strategies reported by 8125 participants in the Australian Longitudinal Study of Women’s Health. Analyses were performed in March–November 2014.Results:Weight control strategies were used by 79% of the women, and four unique clusters were found. The largest cluster group (39.7%) was named dieters as 90% had been on a diet in the past year, and half of these women had lost 5u2009kg on purpose. Women cut down on size of meals, fats and sugars and took part in vigorous physical activity. Additionally 20% had used a commercial programme. The next largest cluster (30.2%) was the healthy living group who followed the public health messages of ‘eat less and move more’. The do nothing group (20%) did not actively control their weight whereas the perpetual dieters group (10.7%) used all strategies, including unhealthy behaviours. On average women gained 700u2009g per year (over 9 years); however, the perpetual dieters group gained significantly more weight (210u2009g) than the do nothing group (P<0.001).Conclusions:Most women are actively trying to control their weight. The most successful approach was to follow the public health guidelines on health eating and physical activity.

Microaggregation Sorting Framework for K-Anonymity Statistical Disclosure Control in Cloud Computing

In cloud computing, there have led to an increase in the capability to store and record personal data (<italic>microdata</italic>) in the cloud. In most cases, data providers have no/little control that has led to concern that the personal data may be beached. Microaggregation techniques seek to protect microdata in such a way that data can be published and mined without providing any private information that can be linked to specific individuals. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a sorting framework for Statistical Disclosure Control (SDC) to protect microdata in cloud computing. It consists of two stages. In the first stage, an algorithm sorts all records in a data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage a microaggregation method is used to create <inline-formula><tex-math notation=LaTeX>

Maintaining a Healthy BMI: Data From a 16-Year Study of Young Australian Women

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Microdata Protection Method Through Microaggregation: A Median-Based Approach

</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=kabir-ieq1-2469649.gif/></alternatives></inline-formula>-anonymous clusters while minimizing the information loss. The performance of the proposed techniques is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithms perform significantly better than existing associate techniques in the literature.

Systematic Clustering-Based Microaggregation for Statistical Disclosure Control

INTRODUCTIONnThe aims of this prospective cohort study were to examine 16-year trajectories of weight and BMI in young adult women who had a healthy BMI in 1996 and determinants of remaining in the healthy BMI category.nnnMETHODSnA total of 4,881 women with healthy BMI at baseline and either healthy, overweight, or obese BMI at 16-year follow-up reported their weight, height, health, and health behaviors in six surveys of the Australian Longitudinal Study on Womens Health between 1996 (aged 18-23 years) and 2012 (aged 34-39 years). Determinants of BMI maintenance were estimated using binary logistic regression and generalized estimating equations in 2015.nnnRESULTSnAlmost 60% remained in the healthy BMI category from 1996 to 2012, (mean weight gain, 0.19 kg/year), 29% transitioned to overweight BMI (0.83 kg/year), and 11.6% transitioned to obese (1.73 kg/year). The mean rates of annual weight gain in each group were consistent over time. Only three factors (low alcohol, moderate/high physical activity, having a university degree) were positively associated with maintaining a healthy BMI. Additional behavioral factors (smoking, high sitting time, energy intake, dieting, takeaway food, and use of oral contraceptives), as well as blue collar occupation, separation/divorce/widowhood, and major illness were negatively associated with BMI maintenance.nnnCONCLUSIONSnTo prevent the transition from healthy to overweight/obese BMI, weight gain must be limited to <0.5 kg/year. Women with healthy BMI, but with higher rates of weight gain in their early 20s, could be identified by health professionals for assistance with prevention of becoming overweight/obese.

A computer aided analysis scheme for detecting epileptic seizure from EEG data

ABSTRACT Microaggregation for statistical disclosure control (SDC) is a family of methods to protect microdata from individual identification. SDC seeks to protect microdata in such a way that they can be published and mined without providing any private information that can be linked to specific individuals. The aim of SDC is to modify the original microdata in such a way that the modified data and the original data are similar. Microaggregation works by partitioning the microdata into groups, also called clusters of at least k records, and then replacing the records in each group with the centroid of the group. In this work we introduce a new microaggregation method where the centroid is considered as median. The new method guarantees that the microaggregated data and the original data are similar by using statistical tests. Another contribution of this work is that we propose a distance metric, called absolute deviation from median (ADM), to evaluate the amount of mutual information among records in microdata. We showed that ADM is always less than the most commonly used measure of distortion called sum of squares of errors (SSE) for any dataset. Thus, ADM causes the least information loss and can be used as a measure of information loss for a microaggregated microdata set.

Influence of distance between residence and health facilities on non-communicable diseases: an assessment over hypertension and diabetes in Bangladesh

Microdata protection in statistical databases has recently become a major societal concern. Micro aggregation for Statistical Disclosure Control (SDC) is a family of methods to protect microdata from individual identification. Micro aggregation works by partitioning the microdata into groups of at least k records and then replacing the records in each group with the centroid of the group. This paper presents a clustering-based micro aggregation method to minimize the information loss. The proposed technique adopts to group similar records together in a systematic way and then anonymized with the centroid of each group individually. The structure of systematic clustering problem is defined and investigated and an algorithm of the proposed problem is developed. Experimental results show that our method attains a reasonable dominance with respect to both information loss and execution time than the most popular heuristic algorithm called Maximum Distance to Average Vector (MDAV).