Search results for: bootstrap aggregation

Authors: Hannah F. Opayinka, Adedayo A. Adepoju

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This study is an extension of a prior study on the modification of the existing m-out-of-n (moon) bootstrap method for heavy-tailed distributions in which modified m-out-of-n (mmoon) was proposed as an alternative method to the existing moon technique. In this study, both moon and mmoon techniques were applied to two real income datasets which followed Lognormal and Pareto distributions respectively with finite variances. The performances of these two techniques were compared using Standard Error (SE) and Root Mean Square Error (RMSE). The findings showed that mmoon outperformed moon bootstrap in terms of smaller SEs and RMSEs for all the sample sizes considered in the two datasets.

Keywords: Bootstrap, income data, lognormal distribution, Pareto distribution

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Authors: Brahim Boukabcha, Amar Rebbouh

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The bootstrap method based on the idea of exploiting all the information provided by the initial sample, allows us to study the properties of estimators. In this article we will present a theoretical study on the different methods of bootstrapping and using the technique of re-sampling in statistics inference to calculate the standard error of means of an estimator and determining a confidence interval for an estimated parameter. We apply these methods tested in the regression models and Pareto model, giving the best approximations.

Keywords: bootstrap, error standard, bias, jackknife, mean, median, variance, confidence interval, regression models

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Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

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Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

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Authors: A. Brener, B. Ismailov, G. Berdalieva

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In the paper we submit the modification of kinetic Smoluchowski equation for binary aggregation applying to systems with chemical reactions of first and second orders in which the main product is insoluble. The goal of this work is to create theoretical foundation and engineering procedures for calculating the chemical apparatuses in the conditions of joint course of chemical reactions and processes of aggregation of insoluble dispersed phases which are formed in working zones of the reactor.

Keywords: binary aggregation, clusters, chemical reactions, insoluble phases

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Authors: Edlira Donefski, Tina Donefski, Lorenc Ekonomi

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Edgeworth Approximation, Bootstrap, and Monte Carlo Simulations have considerable impacts on achieving certain results related to different problems taken into study. In our paper, we have treated a financial case related to the effect that has the components of a cash-flow of one of the most successful businesses in the world, as the financial activity, operational activity, and investment activity to the cash and cash equivalents at the end of the three-months period. To have a better view of this case, we have created a vector autoregression model, and after that, we have generated the impulse responses in the terms of asymptotic analysis (Edgeworth Approximation), Monte Carlo Simulations, and residual bootstrap based on the standard errors of every series created. The generated results consisted of the common tendencies for the three methods applied that consequently verified the advantage of the three methods in the optimization of the model that contains many variants.

Keywords: autoregression, bootstrap, edgeworth expansion, Monte Carlo method

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Authors: Rasha M. Hussein, Reem M. Hashem, Laila A. Rashed

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Alzheimer’s disease is the most common dementia disease in the elderly. It is characterized by the accumulation of extracellular amyloid β (Aβ) peptides and intracellular hyper-phosphorylated tau protein. In addition, recent evidence indicates that accumulation of intracellular amyloid β peptides may play a role in Alzheimer’s disease pathogenesis. This suggests that intracellular Heat Shock Proteins (HSP) that maintain the protein quality control in the cell might be potential candidates for disease amelioration. DNAJB6, a member of DNAJ family of HSP, effectively prevented the aggregation of poly glutamines stretches associated with Huntington’s disease both in vitro and in cells. In addition, DNAJB6 was found recently to delay the aggregation of Aβ42 peptides in vitro. In the present study, we investigated the ability of DNAJB6 to prevent the aggregation of both intracellular and extracellular Aβ peptides using transfection of HEK293 cells with Aβ-GFP and recombinant Aβ42 peptides respectively. We performed western blotting and immunofluorescence techniques. We found that DNAJB6 can prevent Aβ-GFP aggregation, but not the seeded aggregation initiated by extracellular Aβ peptides. Moreover, DNAJB6 required interaction with HSP70 to prevent the aggregation of Aβ-GFP protein and its J-domain was essential for this anti-aggregation activity. Interestingly, overexpression of other DNAJ proteins as well as HSPB1 suppressed Aβ-GFP aggregation efficiently. Our findings suggest that DNAJB6 is a promising candidate for the inhibition of Aβ-GFP mediated aggregation through a canonical HSP70 dependent mechanism.

Keywords: Aβ, Alzheimer’s disease, chaperone, DNAJB6, aggregation

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Authors: O. Ikpotokin

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In any production process, every product is aimed to attain a certain standard, but the presence of assignable cause of variability affects our process, thereby leading to low quality of product. The ability to identify and remove this type of variability reduces its overall effect, thereby improving the quality of the product. In case of a univariate control chart signal, it is easy to detect the problem and give a solution since it is related to a single quality characteristic. However, the problems involved in the use of multivariate control chart are the violation of multivariate normal assumption and the difficulty in identifying the quality characteristic(s) that resulted in the out of control signals. The purpose of this paper is to examine the use of non-parametric control chart (the bootstrap approach) for obtaining control limit to overcome the problem of multivariate distributional assumption and the p-value method for detecting out of control signals. Results from a performance study show that the proposed bootstrap method enables the setting of control limit that can enhance the detection of out of control signals when compared, while the p-value method also enhanced in identifying out of control variables.

Keywords: bootstrap control limit, p-value method, out-of-control signals, p-value, quality characteristics

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Xianpei Li, Yanmin Yuan, Tracy Lin Huan

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Researchers are doing enchanting research into the inherited features of Alzheimer’s disease and probable consistent therapies. In Alzheimer’s, memories are extinct in reverse order; memories formed lately are more transitory than those from formerly. Reminiscence therapy includes the conversation of past actions, trials and knowledges with another individual or set of people, frequently with the help of perceptible reminders such as photos, household and other acquainted matters from the past, music and collection of tapes. In this manuscript, the competence of reminiscence therapy for Alzheimer’s disease is measured using logistic regression based linear bootstrap aggregating. Logistic regression is used to envisage the experiential features of the patient’s memory through various therapies. Linear bootstrap aggregating shows better stability and accuracy of reminiscence therapy used in statistical classification and regression of memories related to validation therapy, supportive psychotherapy, sensory integration and simulated presence therapy.

Keywords: Alzheimer’s disease, linear bootstrap aggregating, logistic regression, reminiscence therapy

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Authors: S. Phanyaem

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This paper presents the confidence intervals for the effect size base on bootstrap resampling method. The meta-analytic confidence interval for effect size is proposed that are easy to compute. A Monte Carlo simulation study was conducted to compare the performance of the proposed confidence intervals with the existing confidence intervals. The best confidence interval method will have a coverage probability close to 0.95. Simulation results have shown that our proposed confidence intervals perform well in terms of coverage probability and expected length.

Keywords: effect size, confidence interval, bootstrap method, resampling

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Authors: Karima Kimouche

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In this paper, we consider the asymptotic problems in spectral analysis of stationary causal random fields. We impose conditions only involving (conditional) moments, which are easily verifiable for a variety of nonlinear random fields. Limiting distributions of periodograms and smoothed periodogram spectral density estimates are obtained and applications to the spectral domain bootstrap are given.

Keywords: spatial nonlinear processes, spectral estimators, GMC condition, bootstrap method

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Authors: Edlira Donefski, Lorenc Ekonomi, Tina Donefski

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Edgeworth approximation is one of the most important statistical methods that has a considered contribution in the reduction of the sum of standard deviation of the independent variables’ coefficients in a Quantile Regression Model. This model estimates the conditional median or other quantiles. In this paper, we have applied approximating statistical methods in an economical problem. We have created and generated a quantile regression model to see how the profit gained is connected with the realized sales of the cosmetic products in a real data, taken from a local business. The Linear Regression of the generated profit and the realized sales was not free of autocorrelation and heteroscedasticity, so this is the reason that we have used this model instead of Linear Regression. Our aim is to analyze in more details the relation between the variables taken into study: the profit and the finalized sales and how to minimize the standard errors of the independent variable involved in this study, the level of realized sales. The statistical methods that we have applied in our work are Edgeworth Approximation for Independent and Identical distributed (IID) cases, Bootstrap version of the Model and the Edgeworth approximation for Bootstrap Quantile Regression Model. The graphics and the results that we have presented here identify the best approximating model of our study.

Keywords: bootstrap, edgeworth approximation, IID, quantile

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Authors: A. Al Drees, A. Alahmari, R. Almuwayshir

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The advanced information technology is becoming an important factor in the development of financial services industry, especially the banking industry. It has introduced new ways of delivering banking to the customer, such as Internet Banking. Banks began to look at electronic banking (e-banking) as a means to replace some of their traditional branch functions using the Internet as a new distribution channel. Some consumers have at least more than one account, and across banks, and access these accounts using e-banking services. To look at the current net worth position, customers have to login to each of their accounts and get the details and work on consolidation. This not only takes ample time but it is a repetitive activity at a specified frequency. To address this point, an account aggregation concept is added as a solution. E-banking account aggregation, as one of the e-banking types, appeared to build a stronger relationship with customers. Account Aggregation Service generally refers to a service that allows customers to manage their bank accounts maintained in different institutions through a common Internet banking operating a platform, with a high concern to security and privacy. This paper presents an overview of an e-banking account aggregation approach as a new service in the e-banking field.

Keywords: e-banking, account aggregation, security, enterprise development

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Authors: Lutful Karim, Mohammed S. Al-kahtani

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Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay.

Keywords: big data, clustering, tree topology, data aggregation, sensor networks

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Gubhinder Kundhi, Paul Rilstone

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It is well established that Instrumental Variable (IV) estimators in the presence of weak instruments can be poorly behaved, in particular, be quite biased in finite samples. Finite sample approximations to the distributions of these estimators are obtained using Edgeworth and Saddlepoint expansions. Departures from normality of the distributions of these estimators are analyzed using higher order analytical corrections in these expansions. In a Monte-Carlo experiment, the performance of these expansions is compared to the first order approximation and other methods commonly used in finite samples such as the bootstrap.

Keywords: bootstrap, Instrumental Variable, Edgeworth expansions, Saddlepoint expansions

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Authors: Sandrine Dourdain, Cesar Lopez, Tamir Sukhbaatar, Guilhem Arrachart, Stephane Pellet-Rostaing

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A promising challenge in solvent extraction is to replace the conventional organic solvents, with ionic liquids (IL). Depending on the extraction systems, these new solvents show better efficiency than the conventional ones. Although some assumptions based on ions exchanges have been proposed in the literature, these properties are not predictable because the involved mechanisms are still poorly understood. It is well established that the mechanisms underlying solvent extraction processes are based not only on the molecular chelation of the extractant molecules but also on their ability to form supra-molecular aggregates due to their amphiphilic nature. It is therefore essential to evaluate how IL affects the aggregation properties of the extractant molecules. Our aim is to evaluate the influence of IL structure and polarity on solvent extraction mechanisms, by looking at the aggregation of the extractant molecules in IL. We compare extractant systems that are well characterized in common solvents and show thanks to SAXS and SANS measurements, that in the absence of IL ion exchange mechanisms, extraction properties are related to aggregation.

Keywords: solvent extraction in Ionic liquid, aggregation, Ionic liquids structure, SAXS, SANS

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Authors: Ayman Baklizi

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Based on type II censored data, we consider interval estimation of the quantiles of the two-parameter exponential distribution and the difference between the quantiles of two independent two-parameter exponential distributions. We derive asymptotic intervals, Bayesian, as well as intervals based on the generalized pivot variable. We also include some bootstrap intervals in our comparisons. The performance of these intervals is investigated in terms of their coverage probabilities and expected lengths.

Keywords: asymptotic intervals, Bayes intervals, bootstrap, generalized pivot variables, two-parameter exponential distribution, quantiles

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Authors: Syeda Qamarunnisa, Ishrat Jamil, Abid Azhar, Zabta K. Shinwari, Syed Irtifaq Ali

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A phylogenetic analysis of family Solanaceae was conducted using sequence data from the chloroplast intergenic atpB-rbcL spacer. Sequence data was generated from 17 species representing 09 out of 14 genera of Solanaceae from Pakistan. Cladogram was constructed using maximum parsimony method and results indicate that Solanaceae is mainly divided into two subfamilies; Solanoideae and Cestroideae. Four major clades within Solanoideae represent tribes; Physaleae, Capsiceae, Datureae and Solaneae are supported by high bootstrap value and the relationships among them are not corroborating with the previous studies. The findings established that subfamily Cestroideae comprised of three genera; Cestrum, Lycium, and Nicotiana with high bootstrap support. Position of Nicotiana inferred with atpB-rbcL sequence is congruent with traditional classification, which placed the taxa in Cestroideae. In the current study Lycium unexpectedly nested with Nicotiana with 100% bootstrap support and identified as a member of tribe Nicotianeae. Expanded sampling of other genera from Pakistan could be valuable towards improving our understanding of intrafamilial relationships within Solanaceae.

Keywords: systematics, solanaceae, phylogenetics, intergenic spacer, tribes

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Authors: Ashok V. Sutagundar, Sunilkumar S. Manvi

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Energy, delay and bandwidth are the prime issues of wireless sensor network (WSN). Energy usage optimization and efficient bandwidth utilization are important issues in WSN. Event triggered data aggregation facilitates such optimal tasks for event affected area in WSN. Reliable delivery of the critical information to sink node is also a major challenge of WSN. To tackle these issues, we propose an event driven dynamic clustering and data aggregation scheme for WSN that enhances the life time of the network by minimizing redundant data transmission. The proposed scheme operates as follows: (1) Whenever the event is triggered, event triggered node selects the cluster head. (2) Cluster head gathers data from sensor nodes within the cluster. (3) Cluster head node identifies and classifies the events out of the collected data using Bayesian classifier. (4) Aggregation of data is done using statistical method. (5) Cluster head discovers the paths to the sink node using residual energy, path distance and bandwidth. (6) If the aggregated data is critical, cluster head sends the aggregated data over the multipath for reliable data communication. (7) Otherwise aggregated data is transmitted towards sink node over the single path which is having the more bandwidth and residual energy. The performance of the scheme is validated for various WSN scenarios to evaluate the effectiveness of the proposed approach in terms of aggregation time, cluster formation time and energy consumed for aggregation.

Keywords: wireless sensor network, dynamic clustering, data aggregation, wireless communication

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Authors: Citra Pertiwi, Muhammad Fikruzzaman Rahawarin

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Government of Indonesia had committed to increasing its national defense the budget up to 1,5 percent of GDP. However, the budget increase does not necessarily allocate efficiently and effectively. Using Data Envelopment Analysis (DEA), the operational units of Indonesian Armed Forces are considered as a proxy to measure those two aspects. The bootstrap technique is being used as well to reduce uncertainty in the estimation. Additionally, technological change is being measured as a nonstationary component. Nearly half of the units are being estimated as fully efficient, with less than a third is considered as effective. Longer and larger sets of data might increase the robustness of the estimation in the future.

Keywords: bootstrap, effectiveness, efficiency, DEA, military, Malmquist, technological change

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Authors: Erfan Niazi, Marianne Fenech

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Red Blood Cells (RBCs) or erythrocytes tend to form chain-like aggregates under low shear rate called rouleaux. This is a reversible process and rouleaux disaggregate in high shear rates. Therefore, RBCs aggregation occurs in the microcirculation where low shear rates are present but does not occur under normal physiological conditions in large arteries. Numerical modeling of RBCs interactions is fundamental in analytical models of a blood flow in microcirculation. Population Balance Modeling (PBM) is particularly useful for studying problems where particles agglomerate and break in a two phase flow systems to find flow characteristics. In this method, the elementary particles lose their individual identity due to continuous destructions and recreations by break-up and agglomeration. The aim of this study is to find RBCs aggregation in a dynamic situation. Simplified PBM was used previously to find the aggregation rate on a static observation of the RBCs aggregation in a drop of blood under the microscope. To find aggregation rate in a dynamic situation we propose an experimental set up testing RBCs sedimentation. In this test, RBCs interact and aggregate to form rouleaux. In this configuration, disaggregation can be neglected due to low shear stress. A high-speed camera is used to acquire video-microscopic pictures of the process. The sizes of the aggregates and velocity of sedimentation are extracted using an image processing techniques. Based on the data collection from 5 healthy human blood samples, the aggregation rate was estimated as 2.7x10³(±0.3 x10³) 1/s.

Keywords: red blood cell, rouleaux, microfluidics, image processing, population balance modeling

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Authors: Manju Pandey, Nilay Khare, S. C. Shrivastava

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This paper is the final in a series, which has defined two new classes of aggregation operators for triangular and trapezoidal fuzzy numbers based on the geometrical characteristics of their fuzzy membership functions. In the present paper, a new aggregation operator for trapezoidal fuzzy numbers has been defined. The new operator is based on the geometric mean of the membership lines to the left and right of the maximum possibility interval. The operator is defined and the analytical relationships have been derived. Computation of the aggregate is demonstrated with a numerical example. Corresponding arithmetic and geometric aggregates as well as results from the recent work of the authors on TrFN aggregates have also been computed.

Keywords: LR fuzzy number, interval fuzzy number, triangular fuzzy number, trapezoidal fuzzy number, apex angle, left apex angle, right apex angle, aggregation operator, arithmetic and geometric mean

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Authors: Shuqi Zhang

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Federated learning allows users to participate in collaborative model training without sending data to third-party servers, reducing the risk of user data privacy leakage, and is widely used in smart finance and smart healthcare. However, the distributed architecture design of federation learning itself and the existence of secure aggregation protocols make it inherently vulnerable to backdoor attacks. To solve this problem, the federated learning backdoor defense framework FLEX based on group aggregation, cluster analysis, and neuron pruning is proposed, and inter-compatibility with secure aggregation protocols is achieved. The good performance of FLEX is verified by building a horizontal federated learning framework on the CIFAR-10 dataset for experiments, which achieves 98% success rate of backdoor detection and reduces the success rate of backdoor tasks to 0% ~ 10%.

Keywords: federated learning, secure aggregation, backdoor attack, cluster analysis, neuron pruning

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Authors: Mouad Zouina, Benaceur Outtaj

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The proposed work falls within the context of improving data security for m-commerce systems. In this context we have placed under the light some flaws encountered in HTTPS the most used m-commerce protocol, particularly the man in the middle attack, shortly MITM. The man in the middle attack is an active listening attack. The idea of this attack is to target the handshake phase of the HTTPS protocol which is the transition from a non-secure connection to a secure connection in our case HTTP to HTTPS. This paper proposes a solution to fix those flaws based on the upgrade of HSTS standard handshake sequence using the DNSSEC standard.

Keywords: m-commerce, HTTPS, HSTS, DNSSEC, MITM bootstrap vulnerability

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Authors: Bo Ram Choi, Ji Su Kim, Juyeon Cho, Hyukjin Lee

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Food contaminated by bacteria (E.coli), causes food poisoning, which occurs to many patients worldwide annually. We have introduced an application of rolling circle amplification (RCA) as a versatile biosensor and developed a diagnostic platform composed of capillary tube and microbeads for rapid and easy detection of Escherichia coli (E. coli). When specific mRNA of E.coli is extracted from cell lysis, rolling circle amplification (RCA) of DNA template can be achieved and can be visualized by beads aggregation in capillary tube. In contrast, if there is no bacterial pathogen in sample, no beads aggregation can be seen. This assay is possible to detect visually target gene without specific equipment. It is likely to the development of a genetic kit for point of care testing (POCT) that can detect target gene using microbeads.

Keywords: rolling circle amplification (RCA), Escherichia coli (E. coli), point of care testing (POCT), beads aggregation, capillary tube

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Authors: Merad Boudia Omar Rafik, Feham Mohammed

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Data aggregation in wireless sensor networks (WSNs) provides a great reduction of energy consumption. The limited resources of sensor nodes make the choice of an encryption algorithm very important for providing security for data aggregation. Asymmetric cryptography involves large ciphertexts and heavy computations but solves, on the other hand, the problem of key distribution of symmetric one. The latter provides smaller ciphertexts and speed computations. Also, the recent researches have shown that achieving the end-to-end confidentiality and the end-to-end integrity at the same is a challenging task. In this paper, we propose (SA-SPKC), a novel security protocol which addresses both security services for WSNs, and where only the base station can verify the individual data and identify the malicious node. Our scheme is based on stateful public key encryption (StPKE). The latter combines the best features of both kinds of encryption along with state in order to reduce the computation overhead. Our analysis

Keywords: secure data aggregation, wireless sensor networks, elliptic curve cryptography, homomorphic encryption

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Authors: Kushagra Khandelwal, Chinmay Anand, Sharmistha Banerjee

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This paper presents an approach to news aggregation that is aimed at solving the issues centered on depolarization and manipulation of news information and stories. Largest democracies across the globe face numerous issues related to news democratization. With the advancements in technology and increasing outreach, web has become an important information source which is inclusive of news. Research was focused on the current millennial population consisting of modern day internet users. The study involved literature review, an online survey, an expert interview with a journalist and a focus group discussion with the user groups. The study was aimed at investigating problems associated with the current news system from both the consumer as well as distributor point of view. The research findings helped in producing five key potential opportunity areas which were explored for design intervention. Upon ideation, we identified five design features which include opinion aggregation. Categorized opinions, news tracking, online discussion and ability to take actions that support news democratization.

Keywords: citizen journalism, democratization, depolarized news, napsterization, news aggregation, opinions

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Authors: M. Zayoud, S. Oueida, S. Ionescu, P. AbiChar

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Background: Wireless sensor network is encompassed of diversified tools of information technology, which is widely applied in a range of domains, including military surveillance, weather forecasting, and earthquake forecasting. Strengthened grounds are always developed for wireless sensor networks, which usually emerges security issues during professional application. Thus, essential technological tools are necessary to be assessed for secure aggregation of data. Moreover, such practices have to be incorporated in the healthcare practices that shall be serving in the best of the mutual interest Objective: Aggregation of encrypted data has been assessed through homomorphic stream cipher to assure its effectiveness along with providing the optimum solutions to the field of healthcare. Methods: An experimental design has been incorporated, which utilized newly developed cipher along with CPU-constrained devices. Modular additions have also been employed to evaluate the nature of aggregated data. The processes of homomorphic stream cipher have been highlighted through different sensors and modular additions. Results: Homomorphic stream cipher has been recognized as simple and secure process, which has allowed efficient aggregation of encrypted data. In addition, the application has led its way to the improvisation of the healthcare practices. Statistical values can be easily computed through the aggregation on the basis of selected cipher. Sensed data in accordance with variance, mean, and standard deviation has also been computed through the selected tool. Conclusion: It can be concluded that homomorphic stream cipher can be an ideal tool for appropriate aggregation of data. Alongside, it shall also provide the best solutions to the healthcare sector.

Keywords: aggregation, cipher, homomorphic stream, encryption

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Authors: Min Kyung An

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In Wireless Sensor Networks which consist of tiny wireless sensor nodes with limited battery power, one of the most fundamental applications is data aggregation which collects nearby environmental conditions and aggregates the data to a designated destination, called a sink node. Important issues concerning the data aggregation are time efficiency and energy consumption due to its limited energy, and therefore, the related problem, named Minimum Latency Aggregation Scheduling (MLAS), has been the focus of many researchers. Its objective is to compute the minimum latency schedule, that is, to compute a schedule with the minimum number of timeslots, such that the sink node can receive the aggregated data from all the other nodes without any collision or interference. For the problem, the two interference models, the graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR), have been adopted with different power models, uniform-power and non-uniform power (with power control or without power control), and different antenna models, omni-directional antenna and directional antenna models. In this survey article, as the problem has proven to be NP-hard, we present and compare several state-of-the-art approximation algorithms in various models on the basis of latency as its performance measure.

Keywords: data aggregation, convergecast, gathering, approximation, interference, omni-directional, directional

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Authors: Tarek Aboueldahab, Amin Mohamed Nassar

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Wind energy is a fluctuating energy source unlike conventional power plants, thus, it is necessary to accurately predict short term wind speed to integrate wind energy in the electricity supply structure. To do so, we present a hybrid artificial intelligence model of short term wind speed prediction based on passive aggregation of the particle swarm optimization and neural networks. As a result, improvement of the prediction accuracy is obviously obtained compared to the standard artificial intelligence method.

Keywords: artificial intelligence, neural networks, particle swarm optimization, passive aggregation, wind speed prediction

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