Query: A Biomedical Information Extraction Primer for NLP Researchers

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Bootstrapping Biomedical Ontologies for Scientific Text using NELL <=SEP=> We describe an open information extraction system for biomedical text based on NELL (the NeverEnding Language Learner) [7], a system designed for extraction from Web text. NELL uses a coupled semi-supervised bootstrapping approach to learn new facts from text, given an initial ontology and a small number of “seeds” for each ontology category. In contrast to previous applications of NELL, in our task the initial ontology and seeds are automatically derived from existing resources. We show that NELL’s bootstrapping algorithm is susceptible to ambiguous seeds, which are frequent in the biomedical domain. Using NELL to extract facts from biomedical text quickly leads to semantic drift. To address this problem, we introduce a method for assessing seed quality, based on a larger corpus of data derived from the Web. In our method, seed quality is assessed at each iteration of the bootstrapping process. Experimental results show significant improvements over NELL’s original bootstrapping algorithm on two types of tasks: learning terms from biomedical categories, and named-entity recognition for biomedical entities using a learned lexicon.

Exploring Recurrent Neural Networks to Detect Named Entities from Biomedical Text <=SEP=> Biomedical named entity recognition (bio-NER) is a crucial and basic step in many biomedical information extraction tasks. However, traditional NER systems are mainly based on complex hand-designed features which are derived from various linguistic analyses and maybe only adapted to specified area. In this paper, we construct Recurrent Neural Network to identify entity names with word embeddings input rather than hand-designed features. Our contributions mainly include three aspects: 1) we adapt a deep learning architecture Recurrent Neural Network (RNN) to entity names recognition; 2) based on the original RNNs such as Elman-type and Jordan-type model, an improved RNN model is proposed; 3) considering that both past and future dependencies are important information, we combine bidirectional recurrent neural networks based on information entropy at the top layer. The experiments conducted on the BioCreative II GM data set demonstrate RNN models outperform CRF and deep neural networks (DNN), furthermore, the improved RNN model performs better than two original RNN models and the combined method is effective.

Extracting Complex Biological Events with Rich Graph-Based Feature Sets <=SEP=> We describe a system for extracting complex events among genes and proteins from biomedical literature, developed in context of the BioNLP’09 Shared Task on Event Extraction. For each event, its text trigger, class, and arguments are extracted. In contrast to the prevailing approaches in the domain, events can be arguments of other events, resulting in a nested structure that better captures the underlying biological statements. We divide the task into independent steps which we approach as machine learning problems. We define a wide array of features and in particular make extensive use of dependency parse graphs. A rule-based post-processing step is used to refine the output in accordance with the restrictions of the extraction task. In the shared task evaluation, the system achieved an F-score of 51.95% on the primary task, the best performance among the participants.

Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks <=SEP=> Two problems arise when using distant supervision for relation extraction. First, in this method, an already existing knowledge base is heuristically aligned to texts, and the alignment results are treated as labeled data. However, the heuristic alignment can fail, resulting in wrong label problem. In addition, in previous approaches, statistical models have typically been applied to ad hoc features. The noise that originates from the feature extraction process can cause poor performance. In this paper, we propose a novel model dubbed the Piecewise Convolutional Neural Networks (PCNNs) with multi-instance learning to address these two problems. To solve the first problem, distant supervised relation extraction is treated as a multi-instance problem in which the uncertainty of instance labels is taken into account. To address the latter problem, we avoid feature engineering and instead adopt convolutional architecture with piecewise max pooling to automatically learn relevant features. Experiments show that our method is effective and outperforms several competitive baseline methods.

Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking <=SEP=> Discriminative reranking is one method for constructing high-performance statistical parsers (Collins, 2000). A discriminative reranker requires a source of candidate parses for each sentence. This paper describes a simple yet novel method for constructing sets of 50-best parses based on a coarse-to-fine generative parser (Charniak, 2000). This method generates 50-best lists that are of substantially higher quality than previously obtainable. We used these parses as the input to a MaxEnt reranker (Johnson et al., 1999; Riezler et al., 2002) that selects the best parse from the set of parses for each sentence, obtaining an f-score of 91.0% on sentences of length 100 or less.

Controlling electromagnetic fields. <=SEP=> Using the freedom of design that metamaterials provide, we show how electromagnetic fields can be redirected at will and propose a design strategy. The conserved fields-electric displacement field D, magnetic induction field B, and Poynting vector B-are all displaced in a consistent manner. A simple illustration is given of the cloaking of a proscribed volume of space to exclude completely all electromagnetic fields. Our work has relevance to exotic lens design and to the cloaking of objects from electromagnetic fields.

Memory reduction method for deep neural network training <=SEP=> Training deep neural networks requires a large amount of memory, making very deep neural networks difficult to fit on accelerator memories. In order to overcome this limitation, we present a method to reduce the amount of memory for training a deep neural network. The method enables to suppress memory increase during the backward pass, by reusing the memory regions allocated for the forward pass. Experimental results exhibit our method reduced the occupied memory size in training by 44.7% on VGGNet with no accuracy affection. Our method also enabled training speedup by increasing the mini batch size up to double.

Requirements of Quality of Service in Wireless Sensor Network <=SEP=> Sensor networks are distributed networks made up of small sensing devices equipped with processors, memory, and short-range wireless communication. They differ from traditional computer networks in that they have resource constraints, unbalanced mixture traffic, data redundancy, network dynamics, and energy balance. Work within wireless sensor networks (WSNs) Quality of service (QoS) has been isolated and specific either on certain functional layers or application scenarios. However the area of sensor network quality of service (QoS) remains largely open. In this paper we define WSNs QoS requirements within a WSNs application, and then analyzing Issues for QoS Monitoring.

The critical role of retrieval practice in long-term retention <=SEP=> Learning is usually thought to occur during episodes of studying, whereas retrieval of information on testing simply serves to assess what was learned. We review research that contradicts this traditional view by demonstrating that retrieval practice is actually a powerful mnemonic enhancer, often producing large gains in long-term retention relative to repeated studying. Retrieval practice is often effective even without feedback (i.e. giving the correct answer), but feedback enhances the benefits of testing. In addition, retrieval practice promotes the acquisition of knowledge that can be flexibly retrieved and transferred to different contexts. The power of retrieval practice in consolidating memories has important implications for both the study of memory and its application to educational practice.

An analysis of unreliability and asymmetry in low-power wireless links <=SEP=> Experimental studies have demonstrated that the behavior of real links in low-power wireless networks (such as wireless sensor networks) deviates to a large extent from the ideal binary model used in several simulation studies. In particular, there is a large transitional region in wireless link quality that is characterized by significant levels of unreliability and asymmetry, significantly impacting the performance of higher-layer protocols. We provide a comprehensive analysis of the root causes of unreliability and asymmetry. In particular, we derive expressions for the distribution, expectation, and variance of the packet reception rate as a function of distance, as well as for the location and extent of the transitional region. These expressions incorporate important environmental and radio parameters such as the path loss exponent and shadowing variance of the channel, and the modulation, encoding, and hardware variance of the radios.

An LED Driver With Pulse Current Driving Technique <=SEP=> A novel driving technique, called pulse current driving technique for LED driver, is proposed. A complete introduction and analysis of the proposed driving technique and the conventional driving technique will be presented in this paper. The LED driver supply maximum peak current is 220 mA and operating frequency is between 500 kHz and1 MHz. Simulation and experimental results are presented to verify the analytical results. The LED driver is fabricated with CMOS 0.35-μm technology and chip area with pads is 0.89 mm 2. The design of the proposed circuit structure is simple, without additional output capacitance compensation for stability and with small power consumption, and is implemented in integrated circuit. As a result of the above benefits, the proposed approach is believed to be suitable in portable application.

Flow Motifs in Soccer : What can passing behavior tell us ? <=SEP=> In soccer, both individual and team performance is crucial to win matches. Passing is the backbone of the game and forms the basis of important decisions made by managers and owners; such as buying players, picking offensive or defensive strategies or even defining a style of play. These decisions can be supported by analyzing how a player performs and how his style affects team performance. The flow of a player or a team can be studied by finding unique passing motifs from the patterns in the subgraphs of a possession-passing network of soccer games. These flow motifs can be used to analyze individual players and teams based on the diversity and frequency of their involvement in different motifs. Building on the flow motif analyses, we introduce an expected goals model to measure the effectiveness of each style of play. We also make use of a novel way to represent motif data that is easy to understand and can be used to compare players, teams and seasons. Further, we exploit the relationship between play style and the pass probability matrix to support our analysis. Our data set has the last 4 seasons of 6 big European leagues with 8219 matches, 3532 unique players and 155 unique teams. We will use flow motifs to analyze different events, such as for example the transfer of Claudio Bravo to Pep Guardiola’s Manchester City, who Jean Seri is and why he must be an elite midfielder and the difference in attacking style between Lionel Messi and Cristiano Ronaldo. Ultimately, an analysis of Post-Fàbregas Arsenal is conducted wherein different techniques are combined to analyze the impact the acquisition of Mesut Özil and Alexis Sánchez had on the strategies implemented at Arsenal.

An Alternative to NCD for Large Sequences, Lempel-Ziv Jaccard Distance <=SEP=> The Normalized Compression Distance (NCD) has been used in a number of domains to compare objects with varying feature types. This flexibility comes from the use of general purpose compression algorithms as the means of computing distances between byte sequences. Such flexibility makes NCD particularly attractive for cases where the right features to use are not obvious, such as malware classification. However, NCD can be computationally demanding, thereby restricting the scale at which it can be applied. We introduce an alternative metric also inspired by compression, the Lempel-Ziv Jaccard Distance (LZJD). We show that this new distance has desirable theoretical properties, as well as comparable or superior performance for malware classification, while being easy to implement and orders of magnitude faster in practice.

Number 693 December 2000 ON THE EFFECT OF THE INTERNET ON INTERNATIONAL TRADE <=SEP=> The Internet stimulates trade. Using a gravity equation of trade among 56 countries, we find no evidence of an effect of the Internet on total trade flows in 1995 and only weak evidence of an effect in 1996. However, we find an increasing and significant impact from 1997 to 1999. Specifically, our results imply that a 10 percent increase in the relative number of web hosts in one country would have led to about 1 percent greater trade in 1998 and 1999. Surprisingly, we find that the effect of the Internet on trade has been stronger for poor countries than for rich countries, and that there is little evidence that the Internet has reduced the impact of distance on trade. The evidence is consistent with a model in which the Internet creates a global exchange for goods, thereby reducing market-specific sunk costs of exporting.

Detecting Faulty Nodes with Data Errors for Wireless Sensor Networks <=SEP=> Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functional faults, this article, proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank. Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.

Impact of IEEE 802.11n/ac PHY/MAC High Throughput Enhancements over Transport/Application Layer Protocols - A Survey <=SEP=> Since the inception of Wireless Local Area Networks (WLANs) in the year 1997, it has tremendously grown in the last few years. IEEE 802.11 is popularly known as WLAN. To provide the last mile wireless broadband connectivity to users, IEEE 802.11 is enriched with IEEE 802.11a, IEEE 802.11b and IEEE 802.11g. More recently, IEEE 802.11n, IEEE 802.11ac and IEEE 802.11ad are introduced with enhancements to the physical (PHY) layer and medium access control (MAC) sublayer to provide much higher data rates and thus these amendments are called High Throughput WLANs (HT-WLANs). In IEEE 802.11n, the data rate has increased up to 600 Mbps, whereas IEEE 802.11ac/ad is expected to support a maximum throughput of 1 to 7 Gbps over wireless media. For both standards, PHY is enhanced with multiple-input multiple-output (MIMO) antenna technologies, channel bonding, short guard intervals (SGI), enhanced modulation and coding schemes (MCS). At the same time, MAC layer overhead is reduced by introducing frame aggregation and block acknowledgement technologies. However, existing studies reveal that although PHY and MAC enhancements promise to improve physical data rate significantly, they yield negative impact over upper layer protocols – mainly for reliable end-to-end transport/application layer protocols. As a consequence, a large number of schools have focused researches on HT-WLANs to improve the coordination among PHY/MAC and upper layer protocols and thus, boost up the performance benefit. In this survey, we discuss the impact of enhancements of PHY/MAC layer in HT-WLANs over transport/application layer protocols. Several measures have been reported in the literature that boost up data rate of WLANs and use aforesaid enhancements effectively for performance benefit of end-to-end protocols. We also point out limitations of existing researches and list down different open challenges that can be explored for the development of next generation HT-WLAN technologies. Keywords-IEEE 802.11n, IEEE 802.11ac, MIMO, MU-MIMO, Channel bonding, Short-guard interval (SGI), Frame aggregation, Block Acknowledgement, TCP/UDP Throughput.

A Lower-Back Robotic Exoskeleton: Industrial Handling Augmentation Used to Provide Spinal Support <=SEP=> A lower-back exoskeleton prototype designed to provide back support for industrial workers who manually handle heavy materials is presented in this article. Reducing spinal loads during these tasks can reduce the risk of work-related back injuries. Biomechanical studies show that compression of the lumbar spine is a key risk factor for musculoskeletal injuries. To address this issue, we present a wearable exoskeleton designed to provide back support and reduce lumbar spine compression. To provide effective assistance and avoid injury to muscles or tendons, we apply a continuous torque of approximately 40 Nm on both hip joints to actively assist both hip abduction/adduction (HAA) and hip flexion/extension (HFE). Each actuation unit includes a modular and a compact series-elastic actuator (SEA) with a clutch. The SEA provides mechanical compliance at the interface between the exoskeleton and the user, and the clutches can automatically disengage the torque between the exoskeleton and the user. These experimental results show that the exoskeleton can lower lumbar compression by reducing the need for muscular activity in the spine. Furthermore, powering both HFE and HAA can effectively reduce the lumbar spinal loading user experience when lifting and lowering objects while in a twisted posture.

News-based trading strategies <=SEP=> The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted by investors. Quite recently, researchers have started to automatically determine news sentiment in order to explain stock price movements. Interestingly, this so-called news sentiment works fairly well in explaining stock returns. In this paper, we design trading strategies that utilize textual news in order to obtain profits on the basis of novel information entering the market. We thus propose approaches for automated decisionmaking based on supervised and reinforcement learning. Altogether, we demonstrate how news-based data can be incorporated into an investment system.

A Passive Gait-Based Weight-Support Lower Extremity Exoskeleton With Compliant Joints <=SEP=> This paper presents the design and analysis of a passive body weight (BW)-support lower extremity exoskeleton (LEE) with compliant joints to relieve compressive load in the knee. The biojoint-like mechanical knee decouples human gait into two phases, stance and swing, by a dual snap fit. The knee joint transfers the BW to the ground in the stance phases and is compliant to free the leg in the swing phases. Along with a leg dynamic model and a knee biomechanical model, the unmeasurable knee internal forces are simulated. The concept feasibility and dynamic models of the passive LEE design have been experimentally validated with measured plantar forces. The reduced knee forces confirm the effectiveness of the LEE in supporting human BW during walking and also provide a basis for computing the internal knee forces as a percentage of BW. Energy harvested from the hip spring reveals that the LEE can save human walking energy.

Lnc2Cancer: a manually curated database of experimentally supported lncRNAs associated with various human cancers <=SEP=> Lnc2Cancer (http://www.bio-bigdata.net/lnc2cancer) is a manually curated database of cancer-associated long non-coding RNAs (lncRNAs) with experimental support that aims to provide a high-quality and integrated resource for exploring lncRNA deregulation in various human cancers. LncRNAs represent a large category of functional RNA molecules that play a significant role in human cancers. A curated collection and summary of deregulated lncRNAs in cancer is essential to thoroughly understand the mechanisms and functions of lncRNAs. Here, we developed the Lnc2Cancer database, which contains 1057 manually curated associations between 531 lncRNAs and 86 human cancers. Each association includes lncRNA and cancer name, the lncRNA expression pattern, experimental techniques, a brief functional description, the original reference and additional annotation information. Lnc2Cancer provides a user-friendly interface to conveniently browse, retrieve and download data. Lnc2Cancer also offers a submission page for researchers to submit newly validated lncRNA-cancer associations. With the rapidly increasing interest in lncRNAs, Lnc2Cancer will significantly improve our understanding of lncRNA deregulation in cancer and has the potential to be a timely and valuable resource.

AN AUTOMATIC SYSTEM FOR DETECTING AND COUNTING RBC AND WBC USING FUZZY LOGIC <=SEP=> Blood cell detection and counting is the initial process for detecting and diagnosing diseases. Several image processing algorithms are there for the blood cell classification and counting. The processed image helps to detect different blood related diseases. In all those algorithms several pre-processing steps are there for the process of detection and counting. Though all the algorithms give accurate results, the pre-processing steps are complex and time-consuming. This paper discusses about the RBC and WBC detection using fuzzy logic. Fuzzy logic toolbox software in MATLAB is used to develop the model on virtual platform.

Scalable Gaussian Process Regression Using Deep Neural Networks <=SEP=> We propose a scalable Gaussian process model for regression by applying a deep neural network as the feature-mapping function. We first pre-train the deep neural network with a stacked denoising auto-encoder in an unsupervised way. Then, we perform a Bayesian linear regression on the top layer of the pre-trained deep network. The resulting model, Deep-Neural-Network-based Gaussian Process (DNN-GP), can learn much more meaningful representation of the data by the finite-dimensional but deep-layered feature-mapping function. Unlike standard Gaussian processes, our model scales well with the size of the training set due to the avoidance of kernel matrix inversion. Moreover, we present a mixture of DNN-GPs to further improve the regression performance. For the experiments on three representative large datasets, our proposed models significantly outperform the state-of-the-art algorithms of Gaussian process regression.

Bridging Neural Machine Translation and Bilingual Dictionaries <=SEP=> Neural Machine Translation (NMT) has become the new state-of-the-art in several language pairs. However, it remains a challenging problem how to integrate NMT with a bilingual dictionary which mainly contains words rarely or never seen in the bilingual training data. In this paper, we propose two methods to bridge NMT and the bilingual dictionaries. The core idea behind is to design novel models that transform the bilingual dictionaries into adequate sentence pairs, so that NMT can distil latent bilingual mappings from the ample and repetitive phenomena. One method leverages a mixed word/character model and the other attempts at synthesizing parallel sentences guaranteeing massive occurrence of the translation lexicon. Extensive experiments demonstrate that the proposed methods can remarkably improve the translation quality, and most of the rare words in the test sentences can obtain correct translations if they are covered by the dictionary.

Modeling and controller design for a voice coil actuated engine valve <=SEP=> This paper presents a physical model of a spark ignition (SI) engine valve actuated by a voice coil actuator (VCA) and proposes a novel control strategy that achieves fast transitions and low seating velocities with high precision position control. The control strategy is based on applying two different pulse width modulated (PWM) voltages for transient and steady state performance. This novel switching control strategy performs better than current strategies using electromechanical valve actuators to implement variable valve timing (VVT). The main improvement over electromechanical actuators is the fact that the novel control strategy achieves the conflicting performance requirements of very fast transition times while simultaneously exhibiting low contact velocities

Extension of heuristic evaluation method: a review and reappraisal <=SEP=> As one of the major discount methods, the heuristic evaluation method (HE) is the most commonly used usability inspection method. We introduced the history and procedure of this method, as well as its strengths and weaknesses. We then reviewed the applications of this method to different Human-Computer Interaction systems with the adapted heuristic sets. We also reviewed many studies that extended the traditional HE method in different ways. Finally, the paper ends with a reappraisal of these extension methods and future research direction to improve the HE method.

On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems <=SEP=> The ability to compute an accurate reward function is essential for optimising a dialogue policy via reinforcement learning. In real-world applications, using explicit user feedback as the reward signal is often unreliable and costly to collect. This problem can be mitigated if the user’s intent is known in advance or data is available to pre-train a task success predictor off-line. In practice neither of these apply for most real world applications. Here we propose an on-line learning framework whereby the dialogue policy is jointly trained alongside the reward model via active learning with a Gaussian process model. This Gaussian process operates on a continuous space dialogue representation generated in an unsupervised fashion using a recurrent neural network encoder-decoder. The experimental results demonstrate that the proposed framework is able to significantly reduce data annotation costs and mitigate noisy user feedback in dialogue policy learning.

The Analysis of the Disease Spectrum in China <=SEP=> Analysis of the related risks of disease provides a scientific basis for disease prevention and treatment, hospital management, and policy formulation by the changes in disease spectrum of patients in hospital. Retrospective analysis was made to the first diagnosis, age, gender, daily average cost of hospitalized patients, and other factors in the First Affiliated Hospital of Nanjing Medical University during 2006-2013. The top 4 cases were as follows: cardiovascular disease, malignant tumors, lung infections, and noninsulin dependent diabetes mellitus. By the age of disease analysis, we found a younger age trend of cardiovascular disease, and the age of onset of cancer or diabetes was somewhat postponed. The average daily cost of hospitalization and the average daily cost of the main noncommunicable diseases were both on the rise. Noncommunicable diseases occupy an increasingly important position in the constitution of the disease, and they caused an increasing medical burden. People should pay attention to health from the aspects of lifestyle changing. Hospitals should focus on building the appropriate discipline. On the other hand, an integrated government response is required to tackle key risks. Multiple interventions are needed to lower the burden of these diseases and to improve national health.

Mechanics and energetics of level walking with powered ankle exoskeletons. <=SEP=> Robotic lower limb exoskeletons that can alter joint mechanical power output are novel tools for studying the relationship between the mechanics and energetics of human locomotion. We built pneumatically powered ankle exoskeletons controlled by the user's own soleus electromyography (i.e. proportional myoelectric control) to determine whether mechanical assistance at the ankle joint could reduce the metabolic cost of level, steady-speed human walking. We hypothesized that subjects would reduce their net metabolic power in proportion to the average positive mechanical power delivered by the bilateral ankle exoskeletons. Nine healthy individuals completed three 30 min sessions walking at 1.25 m s(-1) while wearing the exoskeletons. Over the three sessions, subjects' net metabolic energy expenditure during powered walking progressed from +7% to -10% of that during unpowered walking. With practice, subjects significantly reduced soleus muscle activity (by approximately 28% root mean square EMG, P<0.0001) and negative exoskeleton mechanical power (-0.09 W kg(-1) at the beginning of session 1 and -0.03 W kg(-1) at the end of session 3; P=0.005). Ankle joint kinematics returned to similar patterns to those observed during unpowered walking. At the end of the third session, the powered exoskeletons delivered approximately 63% of the average ankle joint positive mechanical power and approximately 22% of the total positive mechanical power generated by all of the joints summed (ankle, knee and hip) during unpowered walking. Decreases in total joint positive mechanical power due to powered ankle assistance ( approximately 22%) were not proportional to reductions in net metabolic power ( approximately 10%). The ;apparent efficiency' of the ankle joint muscle-tendon system during human walking ( approximately 0.61) was much greater than reported values of the ;muscular efficiency' of positive mechanical work for human muscle ( approximately 0.10-0.34). High ankle joint ;apparent efficiency' suggests that recoiling Achilles' tendon contributes a significant amount of ankle joint positive power during the push-off phase of walking in humans.

Comparison among three analytical methods for knowledge communities group-decision analysis <=SEP=> Knowledge management can greatly facilitate an organization’s learning via strategic insight. Assessing the achievements of knowledge communities (KC) includes both a theoretical basis and practical aspect; however, a cautionary word is in order, because using improper measurements will increase complexity and reduce applicability. Group decision-making, the essence of knowledge communities, lets one considers multi-dimensional problems for the decision-maker, sets priorities for each decision factor, and assesses rankings for all alternatives. The purpose of this study is to establish the objective and measurable patterns to obtain anticipated achievements of KC through conducting a group-decision comparison. The three multiple-criteria decision-making methods we used, simple average weight (SAW), ‘‘Technique for Order Preference by Similarity to an Ideal Solution’’ (TOPSIS) and ‘‘VlseKriterijumska Optimizacija I Kompromisno Resenje’’ (VIKOR), are based on an aggregating function representing ‘‘closeness to the ideal point’’. The TOPSIS and VIKOR methods were used to highlight our innovative idea, academic analysis, and practical appliance value. Simple average weight (SAW) is known to be a common method to get the preliminary outcome. Our study provides a comparison analysis of the above-three methods. An empirical case is illustrated to demonstrate the overall KC achievements, showing their similarities and differences to achieve group decisions. Our results showed that TOPSIS and simple average weight (SAW) had identical rankings overall, but TOPSIS had better distinguishing capability. TOPSIS and VIKOR had almost the same success setting priorities by weight. However, VIKOR produced different rankings than those from TOPSIS and SAW, and VIKOR also made it easy to choose appropriate strategies. Both the TOPSIS and VIKOR methods are suitable for assessing similar problems, provide excellent results close to reality, and grant superior analysis. 2006 Elsevier Ltd. All rights reserved.

E-commerce customer churn prediction based on improved SMOTE and AdaBoost <=SEP=> E-commerce customer churn rate is high and the customer churn dataset is seriously imbalanced. In order to improve the prediction accuracy of churn customers as well as strengthen to identify non-churn customers, this paper presents e-commerce customer churn prediction model based on improved SMOTE and AdaBoost. First, processing the churn data with improved SMOTE, which combines oversampling and undersampling methods to address the imbalance problem and subsequently integrates AdaBoost algorithm to predict. Finally, the empirical study on B2C E-commerce platform proves that this model has better efficiency and accuracy compared with the mature customer churn prediction algorithms.