Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework is based on the Endsley situational awareness model, and integrates automated capabilities for metrics discovery, pattern recognition, prediction techniques and rule-based reasoning to infer anomalous situations in the current operational context. Those situations should then be mitigated, either proactive or reactively, by a more complex decision-making process. The framework is driven by a use case methodology, where the network administrator is able to customize the knowledge inference rules and operational parameters. The proposal has also been instantiated to prove its adaptability to a real use case. To this end, a reference network traffic dataset was used to identify suspicious patterns and to predict the behavior of the monitored data volume. The preliminary results suggest a good level of accuracy on the inference of anomalous traffic volumes based on a simple configuration. The rapid growth of network devices connected to mobile network infrastructures and the increasing demand of online Internet services have pushed new challenges for telecommunication operators. Future generation networks should guarantee several outstanding attributes  such as With the aim to contribute to a smarter 5G network management, this paper proposes the Reasoning and Knowledge Acquisition Framework Analytics designed with a machine learning approach. It is composed by functional elements with data discovery features, prediction capabilities, pattern recognition methods and knowledge inference techniques to acquire and generate knowledge about the underlying mobile network with the purpose of analyzing and inferring potential events or situations that might affect the desired operational levels of a 5G network. The introduced software-based proposal is aligned with the principles and supportive technologies of 5G, and brings analytical capabilities to deploy a self-organized autonomic network. The research has been conducted under the SELFNET Horizon 2020 project , bearingIn depth study of recent 5G-related research contributions. Several research topics related with the definition of the 5G technology, European research projects and network incidence management approaches have been studied with the purpose of laying the foundations, design principles and architectural components of the introduced framework. On the one hand, 5G provides research challenges to be addressed by innovative network architectures that are still under development, many of them targeted by recent European research projects. In particular, the SELFNET project offers a baseline architecture for network self-management in 5G mobile networks, under which the proposed framework was developed. On the other hand, advances on network incidence management in dynamic scenarios have been considered. Special interest has been put on the Endsley inspired architectures, since they motivated the adoption of a situational-awareness model in the proposed framework.A novel reasoning 5G-oriented architecture. A novel framework composed by functional elements arranged on an orchestrated workflow is proposed to enable reasoning capabilities in a 5G network. As a result, the framework generates conclusions about the 5G network status. The introduced architecture distinguishes two types of knowledge: procedural and factual. Procedural knowledge corresponds to the use case configuration loaded to the system. Initial factual knowledge is acquired by discovery methods applied on data previously collected by several sensors distributed along the 5G network, whereas factual knowledge is generated by the prediction, pattern recognition and knowledge inference modules introduced in this proposal. Thereby, the framework approaches the perception, comprehension and projection steps of the Endsley model.Instantiation of the framework. An instance of the proposed framework has been created to enhance the understanding of the proposal. To this end, well-known multiplatform open source technologies and a battery of prediction and machine learning algorithms have been integrated in accordance with the framework design. In addition, publicly available datasets were applied to allow its experimental replicability. The generation of knowledge was successfully demonstrated in a datacenter-oriented use case, even though the current instance of the framework can be applied to several use cases just by modifying its configuration.Comprehensive experimentation on a real use case. To assess the accuracy of the instantiation, a set of experiments have been conducted. They were oriented either for the evaluation of the pattern recognition and prediction modules; and for the evaluation of a real use case. Prediction and pattern recognition features exposed good accuracy levels when applied over the reference datasets. Likewise, a particular use case configuration to generate conclusions about traffic behaviour has been tested. The experiments were conducted on real network traffic samples where the inference of suspicious network traffic volumes in a datacenter exposed good precision rates contrasted with the real reference scenario.The major contributions of this paper to the sensor and multi-platform information processing, and the advances on knowledge-based management approaches upon monitored 5G infrastructures are summarized below:The paper is structured in six sections, this one being the introduction. The following describes the key elements and the background to be kept in mind in order to properly understand the rest of the paper. Among them it is worth highlighting the recent progress toward the deployment of fifth generation networking technologies, the role of the SELFNET project in their development, and some of the most common approaches focused on the management of network incidents.Research initiatives in 5G have been conducted to cope with the challenges of new generation mobile networks. These approaches are worldwide fostered and funded by different organizations, such as the European Horizon 2020 programme   was proposed with the main goal to develop a smart autonomic network management framework to provide self-organization capabilities in mobile 5G networks. This project status is in an ongoing stage and it is funded by the Horizon 2020 programme. SELFNET reference architecture  relies oMonitoring has the main objective of collecting a wide range of low level metrics and events from the physical and virtual network infrastructure, as well as from deployed sensors.Aggregation and Correlation methods reduce the amount of monitored information, by inferring aggregated metrics about a specific network domain. Meanwhile, events are correlated and filtered to avoid redundant or non-sensitive information.Analysis is aimed for reasoning and knowledge acquisition about the operational network context deducted from the analysis of aggregated monitored metrics. This process is accomplished by pattern recognition capabilities, prediction methods, and knowledge inference procedures in order to deduce conclusions regarding potential network failure or degradation scenarios projected from the observations.SELFNET embraces the autonomic management paradigm by integrating enhanced monitoring features, prediction algorithms, pattern recognition strategies, machine learning capabilities, orchestration of virtual functions and service chaining to conduct a SON approach focused on the identification of the current network behavior, the decision of the best mitigation responses against the identified network problems, and the deployment of the corresponding actuators in the infrastructure. To facilitate the operational context comprehension based on the Endsley situational awareness model, a three-step schema of Monitoring, Aggregation and Correlation, and Analysis is proposed, which, maps the Monitor and Analysis component of the SON Autonomic Layer:Through the rest of the paper, the proposed reasoning and knowledge acquisition framework is aligned with the SELFNET Analyzer component. Its design and implementation is deeply covered in In the last four decades, a great variety of contributions related with incident management have been published. Some of them are classified and reviewed in depth in ,37, wherThe introduced framework is aimed to grant analysis capabilities to a 5G network by the proper instantiation of its components at the management plane, decoupling data analysis logic from specific data extraction procedures carried on at the lowest architectural levels of the network. It allows the insertion of several prediction algorithms, pattern recognition capabilities and production rules to generate meaningful knowledge that will enhance decision-making processes from the performance and efficiency perspective. Thereby, the framework provides the advantage to express metrics gathered by sensors  according to a knowledge representation language in order to deduce conclusions about possible network scenarios driven by the Endsley approach . PerceptThe following design requirements and assumptions have been kept in mind at both design and implementation stages of the reasoning and knowledge acquisition framework.Scalability. The proposed framework must accommodate the 5G design principles, and in particular, those associated with scalability, such as “Extensibility by design”, “Expandability by design” or “Multi-level scalability by design” [ design” , throughSupport of use case onboarding. The knowledge acquisition framework adopts a use case driven research methodology. Because of this it is required that from design, it must support the onboarding of new different use case specifications. Given the heavy reliance of the tasks performed with the characteristics of use cases, the basic definition of the observations to be studied  must be provided as factual knowledge by use case operators, thus being the framework scalable to alternative contexts. In addition, use case operators must provide procedural knowledge, thus configuring the analytic tasks to be performed per use case. More information about these knowledge representations is detailed in [ailed in .Reference datasets. Laskov et al. [v et al.  realizedGranularity. 5G environments are complex monitoring scenarios where large amounts of sensors collect information about the state of the network in real time. In SELFNET all this information is processed in the Aggregation sub-layer, which provides the necessary metrics to infer knowledge from them. However, this information is not raw processed. As described in [ribed in , it is cStationary monitoring environment. By definition, the features monitored on a stationary scenario are similar to those considered when building data mining models. The assumption of operating on a stationary monitoring environment entails ignoring in terms of learning process any variation in the characteristics of the information to be studied, such as dimensionality or distribution. The main disadvantage of this approach is the loss of precision when such changes occur, in large part because the initially performed calibrations are not adapted to the current status of the network. On the other hand, their proper accommodation tends to retain the acquired calibration at the expense of addressing many other issues, emphasizing among them to discover relevant variations in the data nature, calibration upgrades based on the new features, or improvement of the original datasets [datasets . Being aHigh dimensional data. When the dimensions of the data to be studied are more extensive than usual, it is possible that some reasoning and knowledge acquisitions implementations lose effectiveness, either in terms of efficiency or accuracy. Because of this, the bibliography provides a wide variety of publications focused on the optimization of this kind of processes, as is discussed in [ussed in . Note thThe proposed framework is composed by the functional elements illustrated in The Discovery component obtains information, represented as facts, gathered by network sensors in the lower levels of a 5G architecture by monitoring, data aggregation and correlation procedures. It exempts the framework to the need of dealing with network technology-dependent protocols or interfaces, and allows assuming that the constraints inherent in the monitoring environment  have no impact on the effectiveness of the proposal, since they have been previously managed at lower data processing stages. New knowledge is acquired by the Inference Engine based on the collected facts stored in the Working Memory, and permits the inference of conclusions about the network status by applying production rules configured in the Knowledge Base. Conclusions are expressed as symptoms, reflecting situations that might affect or compromise network operability or a degradation of the agreed service levels. The framework also facilitates a situational awareness projection of the network through the Prediction and Adaptive Thresholding components, by calculating predictive metrics and forecasting intervals that allow pro-action responses over the predicted scenarios. Likewise, the Pattern Recognition component implements some of the recent paradigms of Artificial Intelligence, among them machine learning, data mining, classification or novelty detection methods.To deal with scalability, each analytic component is designed to be executed independently, exchanging only input and output data between them. From the instantiation perspective, it is accomplished through the implementation of buffering data structures or by the deployment of message broker tools  into the Working Memory. Note that this data processing stage depends on synchronous ADB loading where time series are fed with observations fetched from the ADBs. It requires two types of knowledge: procedural and factual. Procedural knowledge is provided via use case onboarding in their data descriptors. Likewise, factual knowledge is acquired by the Discovery component, when new ADBs are loaded. Once the time series with the required length are built, several prediction methods are evaluated to decide the most accurate algorithm fitted to the given time series. The selection of the best forecasting methods entails several steps. Once data is acquired, a time series of size Throughout the tasks involved in the reasoning process, it is necessary to define under what circumstances an observation about the monitoring environment must conditionate the inference of new knowledge. This is a complicated challenge, which must take into account both the situational awareness of the monitoring scenario and the specific use cases on which the self-organizing deployment for incident management has been implemented. Therefore, the instantiated adaptive thresholding strategies must pose dynamic solutions, subject to the changes in the different features of the scenario on which they operate, and must be configurable according to the level of constraint that operators decide . Because of this, the calculated thresholds act on any source of knowledge of the Knowledge Inference engine , or may be part of the production rules. This makes the results they provide considered as factual knowledge by the knowledge-based of the framework, being Adaptive Thresholding and additional knowledge acquisition step dependent of the rest of the components of the proposal.The Pattern Recognition component of the proposed framework operate at two different stages: training and discovery. At training, the knowledge representation to be taken into account, as well as the description of the pattern recognition actions are included into the procedural knowledge according to the use case specifications. This step involves generating/loading reference datasets and construction of the best models in function of the most relevant data features on the sets of metrics to be analyzed. The training step may take place in two moments of the analytic process. Firstly, models from reference data can be built before operating on real monitored samples. On the other hand the training step may operate at runtime, so the reference samples are gathered from the first observations on the monitoring environment. At the discovery stage, the knowledge acquisition framework launches the pattern recognition actions defined by the use case operators. Samples are constructed from the aggregated metrics observed, and they are analyzed based on the models built at training stage. The framework at least allows two pattern recognition actions: classification and novelty detection. When classifying, a reference dataset is loaded before the monitoring of the protected environment. A battery of classification algorithms is executed in concurrency, which are properly calibrated and combined as an ensemble of models ,63. ThenThe knowledge inference component allows deducing information from previous observations (facts) based on procedural knowledge represented as rule sets. In order to align the decision strategy of which rules should be activated and when, with the previously assumed design principles and requirements, the implemented approach is driven by production rules. This facilitates the deployment of a modus ponens (forward chaining) decision scheme where attributes enable the deduction of goals, which are final facts encapsulated as symptoms before their report to the decision-making layer. It should be kept in mind that throughout the bibliography, Rete algorithms are the most popular and proved proposals to address the efficient implementation and execution of forward chaining on complex monitoring environments. Created by Forgy , these mAs an illustrative example of instantiation of the proposed framework, this subsection describes how it has been deployed with the aim on contributing with the management of a real network. Its contribution is focused on the recognition of discordant behaviors based on analyzing the variations of the traffic volume, which borne in mind the prediction of their evolution, the construction of adaptive thresholding to decide when they may be considered unexpected, and novelty detection based on several distances and similarity measures. It is important to emphasize that the instantiated solution could be replaced by an alternative implementation and still achieving similar results. Nevertheless, with this subsection it is intended to describe a simple, didactic and scalable solution, that provides a greater understanding of the proposed framework and draft several basic guidelines for its adaptation to other problems. With this purpose, the following introduces the implementation of each of the aforementioned reasoning and knowledge acquisition components considered at the experimentation.unexpected. On the other hand, the pattern recognition component is configured for novelty detection based in analyzing difference distances and similarity measures between each monitored observation and that of an immediately preceding monitoring period. Note that his action requires building one-class classification models, which demand a reference dataset. It is obtained from the first observations made, so directly loading an external dataset is not required. The discordant observations are in this stage labeled as fluctuations. On the other hand, the Knowledge Inference component is configured by production rules to conclude that an observation marked as unexpected and fluctuation is a suspicious event, hence being notified as a symptom. The findings are reported through a message broker software to be consumed by external sources, i.e., to perform more complex decision-making procedures. The initial knowledge of the instantiated framework is directly provided by the use case operators, hence postponing auto-calibration and other machine learning approaches for future work. It includes specific information about what activities must be monitored, what knowledge acquisition actions should be accomplished and what kind of reasoning must be carried out; the latter is guided by production rules and the inference of conclusions about the state of the network. Therefore, it can be said that the initial knowledge is the configuration of the framework and the strategy of acquiring the initial facts to be analyzed. These facts arrive to the system compiled as ADBs, which gather the information monitored in certain time periods. As stated before, the different sensors deployed on the 5G infrastructure are considered the most important information providers. For this framework instantiation the initial metric to be studied is the traffic volume per observation, which is assumed to be already reported by the sensors. With this collected data, the framework creates the required time series, enabling the possibility to apply prediction methods, i.e., to estimate the traffic volume at the coming observations according to a given forecasting horizon. The system is also configured to build adaptive thresholds based on the forecasts. They allow to identify if the observed traffic volume differs significantly from the predictions. Herein, those traffic observations are labeled as X and the T forecasted values as inputs. It is described by the following expression:X represents the real time series values and F are the forecasted values estimated for the given observations.The current instantiation of the proposed framework does not support objects with multiples values. Because of this, the adapted battery of forecasting algorithms only considers univariate time series. This does not mean that this capability cannot be included in future instantiations, but it has been considered that working with a simpler instantiation facilitates the comprehension of the prototype, as well as the specification of new use cases. Two families of well-known forecasting methods are implemented: moving averages and exponential smoothing, as detailed in t and t observation. The variance t . In addition, the thresholds include a parameter K, from which use case operators can adjust the sensitivity of the upper and lower limits.To evaluate the prediction errors, two adaptive thresholds are constructed: an upper threshold iography , hence aThe instantiation of the framework considered at the performed experimentation assumes that the use case operators provide the collection of reference samples to be taken into account throughout the pattern recognition process. This implementation embraces the Attribute-Relation File Format (ARFF), Comma-Separated Values (CSV) or Packet Capture (pacp) feature descriptions in order to represent the reference datasets required for the construction of data mining models , in thisIt is well known that one of the classical problems with the software that implements Rete algorithms is the lack of interoperability with other high-level languages and complex data structures, such as class hierarchies, complex knowledge representations or abstract data. Nowadays there are few open source implementations with these capabilities, as is the case of Drools . Given tThe following describes the evaluation scenario, reference datasets and the use cases considered throughout the performed experimentation.Since there are no collections of 5G network traffic, the performed experimentation is focused on the study of traffic traces gathered as public domain datasets, hence facilitating the replication of the obtained results. In particular, the evaluation scenario is focused on the study of real traffic monitored on high-speed Internet backbone links published at 2016 within the CAIDA anonymized Internet Traces Dataset . With thThe performed experimentation applied three collections of reference data: NSL-KDD, M3-Competition and CAIDA’16. They are described below.normal, 97,277 (19.69%)) samples and the following simulated threats:back, land, neptune, pod, smurf and teardrop; 391,458 (79.24%) instances.Denial of Service attack (DoS): classes Buffer overfow, loadmodule, perl and rootkit; 52 (0.01%) instances.User to Root attack (U2R): classes Guess_passwd, ftp_write, imap, phf, multihop, warezmaster, warezclient and spy; 1126 (0.23%) instances.Remote to Local attack (R2L); classes satan, ipsweet, nmap and portsweep; 4107 (0.83%) instances.Probing attacks: classes NSL-KDD is a dataset suggested to solve some of the inherent problems of the KDD’99 dataset, which were reviewed by Tavallaee et al. in , among ta et al. , KDD’99 Their samples are characterized by 41 different features usually divided into three groups: basic features, traffic features and content features. The first group gathers all the attributes that can be extracted from a TCP/IP connection . On the other hand, the traffic features are computed with respect to a window interval, and describe host features  and server features . Finally, a group of features provides information able to unmask suspicious behaviors in the data portion, i.e., independent of the time period . KDD’99 proposed as evaluation methodology to split the dataset into two groups: a 20% subset as training samples and the rest for testing. Note that NSL-KDD sanitized the original collection eliminating 78.05% of training samples , and 75.15% of the test set . Given that most of the discards where instances repeated in training and evaluation samples, the evaluation of classifiers with NSL-KDD displays considerably less precise results than KDD’99, posing much greater difficulty to the evaluated proposals. The SELFNET Pattern Recognition testbed adapts this scheme for evaluating the accuracy of the classification and novelty detection actions.To the best of the author’s knowledge, there are not standardized methodologies to assess the effectiveness of forecasting algorithms on 5G environments; in fact, there are also no collections of samples of these monitoring scenarios. In view of this, the most reliable way of demonstrating the capacity of the SELFNET prediction framework is to evaluate it from general purpose methodologies adapted to time series prediction. Among them it is worth considering a well-known scheme such as the M3-Competition . It provThe Center for Applied Internet Data Analysis (CAIDA) has published the Anonymized Internet Traces 2016 Dataset , which cThe first part of the experimentation was conducted using a three-minutes sample of traffic traces extracted from Ds-Jan. They correspond to network data packets captured from 14:00:00 to 14:02:59. In order to measure traffic volume, a time series of 180 elements was constructed by accumulating the total number of bytes per second. At this stage of the research, it was not feasible to extend the length of the time series due to storage and parsing time limitations. Henceforth, this sample data is referred as CAIDA’16-sample.In the second part of the experimentation, network traffic measures were gathered from the statistics files published by CAIDA. Each statistics file corresponds to a one-minute traffic observed in a one-hour dataset. Thereby, every dataset has 60 statistics files. Unlike the described CAIDA’16-sample, there was no need to parse pcap files since every one-minute statistics file provides the total number of transmitted bytes. This is exactly the same metric used in the first part of the experimentation, being the only difference the granularity. Consequently, four time series of 60 elements were constructed from Ds-Jan, Ds-Feb, Ds-Mar and Ds-Apr, being each element of the time series the observed traffic volume expressed in bytes per minute. Henceforth, the generated time series are referred as CAIDA’16-monthly.This use case goal is to infer if the observed network traffic volume presents an anomalous behavior. For this purpose, the knowledge inference framework components are instantiated, contributing to the generation of new facts that are used to evaluate the production rules configured to infer knowledge. The process is triggered once the novelty detection capabilities of the Pattern Recognition component identify a change in the network behavior. This task requires building a model of the normal traffic behavior, which is created by assuming the first observations as reference samples and their following six attributes: Euclidean, Squared iewed in .Provided by the generated facts about possible anomalous traffic pattern, the Prediction component calculates the forecasting values for the time series considering 1, 5 and 10 time horizons, and the results are also inserted in the working memory. With the forecasted metrics, the Adaptive Thresholding component deduces the prediction intervals (PI) for each observation, registering them in the working memory as new acquired facts. The upper and lower thresholds are computed upon the forecasting error. Previously generated facts about abnormal traffic patterns, forecasting values and thresholds allow the Inference Engine to deduce the existence of anomalous traffic volume variations when two conditions are met: traffic volume has been labeled as abnormal and the observation is either exceeding the upper prediction interval or below the lower bound. Note that combining both of them allows considering the presence of outliers regarding the general traits of the behavior observed in the monitored environment, as well as unexpected variations from the latest observations. In this way, the incidents will be reported with greater certainty about their nature.The following describes the results obtained when analyzing the aforementioned datasets.The M3 dataset described in the previous section led to the evaluation of the framework under different time series; being Yearly, Monthly, Quarterly and Others the time series classifications as described in ries see . As compThe results obtained for the different classifiers at pattern recognition actions considering NSL-KDD’9-Pereira  was applThe following sections describe the two experiments carried on upon the CAIDA’16 reference dataset, analyzed under different levels of data granularity for each: per second and per minute.The first step on the CAIDA traffic volume analysis according to the aforementioned use case is novelty detection. With this purpose, the first 35 observations on the monitored environment are considered as reference samples for building the normal network usage model. The evaluation of the model demonstrated 91.4894% accuracy when tested via cross-validation. The best selected pattern recognition setting was the combination of generating synthetic data as counterexample  and its The next knowledge acquisition step is to infer new facts from predictions. The obtained results are summarized in K adjustment value at the decisions taken. This parameter regulates the restraint of the adaptive thresholds. K. Regardless of the prediction horizon, when K shows lower values the number of observations labeled as unexpected is higher; hence the level of restriction on which the framework operates is higher. Conversely, as K grows the normal labeling rate increases, in this way overlooking situations that in the previous cases were considered discordant.Another aspect to keep in mind is the impact of the K = 1, Finally, letting a forecasting horizon of 1 observation and This experiment was performed upon CAIDA’16-monthly data, by following the same approach conducted in the previous section. Since CAIDA’16-monthly is composed by four time series , they are individually analyzed by the framework. Being novelty detection and forecasting the set of actions in the knowledge acquisition process, the conclusions deduced by the framework are summarized in When analyzing CAIDA’16 at January and February it can be concluded that even when discordant observations are detected in the novelty detection stage, unexpected traffic volumes are not observed in any of the time series. Thereby, there are no anomalous traffic volume variations reported by the framework. It is explained due to the required conditions (novelty and unexpected traffic) do not occur simultaneously at any observation.On the other hand, March and April CAIDA’16 datasets detected both unexpected and discordant traffic volume observations in the analyzed time series. By contrasting the traffic labeled as “unexpected” with the novelty detection results at the March dataset f, the inTo validate the current reasoning framework, the architecture was instantiated adapted to the introduced design. It allowed the acquisition of facts and generation of knowledge in the analysis of a real use case. The instanced framework demonstrated the interaction of the components involved to deduce conclusions regarding anomalous network traffic volumes. The different steps followed in the knowledge generation process were also explained in the paper, being the prediction and patter recognition actions more extensively detailed since they exposed the more complex efforts. The suitable integration of the instantiated components validates the applicability of the proposed framework, and its effectiveness for the evaluation of a use case.In order to determine the accuracy of the prediction and patter recognition capabilities, the individual evaluation of each component has been performed. To this end, publicly available datasets were taken as a baseline. On the one hand, accuracy was measured in the prediction module, in comparison with the forecasting errors extracted from the M3-competition dataset. The prediction capabilities have proven acceptable accuracy levels when contrasted with other methods in the majority of forecasted scenarios of the M3-Competition. It was accomplished by the efficient selection of the right prediction algorithms included in the module, where parameter calibration played an important role when minimizing the forecasting error. Consequently, the decision of the best method has been determined by the evaluation of the lowest prediction error. On the other hand, the accuracy of patter recognition capabilities was also evaluated. With this purpose, the NSL-KDD’9With the instantiated framework, the CAIDA’16 dataset allowed to analyze anomalous traffic on real internet traces. It was proven that the implemented reasoning and knowledge acquisition capabilities facilitate the comprehension of the network status. Traffic analysis was performed at different granularity levels exposing the effectiveness of the framework to deduce symptoms of anomalous traffic. The prediction component obtained precise estimations of the traffic volume at different forecasting horizons. It led to the accurate construction of adaptive thresholds that determined the discovery of unexpected traffic volumes. Moreover, the pattern recognition module performed novelty detection actions to find fluctuations of the monitored data. The generated model was also effective when identifying network traffic fluctuations. Hence, the outcomes of the prediction and patter recognition modules allowed an overall accuracy of the whole use case.Throughout the development of the proposal several design principles and restrictions were introduced. They were carefully taken into account not only in the design but also in the instantiation of the framework. Use case onboarding is supported by the insertion of procedural knowledge in the framework. Reference datasets were also applied for evaluation purposes, but it must be recalled that the validity of the results is tightly dependent on the datasets accuracy as show in this paper. The framework also considered a stationary environment in the experimentation, hence demonstrating its effectiveness. The main goal of this proposal is to generate useful knowledge following an orchestrated information flow, which was accomplished by the definition and instantiation of this framework.Finally, it is important to bear in mind that the framework itself is a modular architecture which separates analytical processes into different sets of actions coordinated by an orchestration strategy. These processes are completely independent and only share their input and output data, hence accommodating vertical and horizontal scalability, execution of tasks in concurrence and their deployment on different machines. Likewise, each set of actions can deploy different analytical tools, and so far, it was not encountered any incompatibility involving the incorporation of additional capabilities such as big data mechanisms. These criteria are compliant with the most recent network “softwarization” trends grounding the development of 5G networks.This paper introduced a reasoning and knowledge acquisition framework adapted to support analytic actions on 5G monitoring environments toward providing self-organized autonomic networks. Because of this, the framework assumes the design principles of the new generation networks and the technologies they implement, being instantiated as part of the solutions brought by the SELFNET Horizon 2020 project. This involves the implementation of different techniques and algorithms based, inter alia, on pattern recognition, prediction, adaptive thresholding or knowledge inference; which are orchestrated as a modular architecture, easily expandable and scalable, adapted to the large volume of information flowing through these challenging scenarios.In order to facilitate the understanding of the conducted research, an instantiation of this framework has been performed. For this purpose, every component has been equipped with very basic capabilities, which has led to the implementation of well-known data mining algorithms and machine learning schemes within each of them. The experimentation has focused on two fundamental aspects: demonstrating that each component operates properly and testing the potential of the proposal in a real use case. At the first stage, the functional standards NSL-KDD and M3-competition have been considered. The obtained results corroborated the efficacy of the deployed components by comparison with similar proposals. On the other hand, the defined use case allowed the acquisition of knowledge related to variations of traffic volume on the monitoring environment. This has been tested with real traffic, in particular with traces provided by the CAIDA’16 collection. The results demonstrated that regardless of the granularity in which the observed information is studied, the proposal is able of generating useful information for the management of the occurred incidents.Notwithstanding as part of the contributions aimed on the development of 5G networks, the proposal rises new research lines. The clearest of them is to delve into how the knowledge acquisition framework can be instantiated in order to face the challenges posed by the different use cases. These may have very different requirements; for example, a use case focused on bandwidth optimization may require information that facilitates deciding proactive actions, and therefore must be primarily based on prediction; but the reactive responses have greater impact on mitigating threats such as botnets or denial of service attacks. Other interesting topics, such as inclusion of data protection policies or the communication ways between the proposed framework and the rest of network components , have not been detailed throughout the article, in this way postponing their development for future work.


In this article, we examined the productivity and welfare effects of the program using household-level data from rural Nigeria. The article employed instrumental variable regression approach to control for the potential endogeneity of the input subsidy program. Our results suggest that the program is effective in improving productivity and welfare outcomes of beneficiary smallholders. The size of the estimated effects suggests a large improvement in productivity and welfare outcomes. Moreover, the distributional effects of the program suggest no heterogeneity effects based on gender and farm land size. These results are robust to using alternative measurements of program participation. The benefit–cost ratio of 1.11 suggests that the program is marginally cost-effective. Overall, our results suggest that while improving average productivity is a good outcome for improving food security, improving the distributional outcome of the program by targeting the most disadvantaged groups would maximize the program’s contribution to food security and poverty reduction.In an attempt to go beyond the so called “ In an effort to promote adoption of yield-increasing technologies such as inorganic fertilizer and improved seed, many sub-Saharan African (SSA) countries implemented universal large-scale input subsidy programs throughout the 1970s and 1980s . However1Like many SSA governments, Nigeria has since then implemented a large-scale targeted input subsidy program called the Growth Enhancement Support Scheme (GES) in 2012. The program was implemented with the broad official objective of promoting agricultural productivity and food security by making fertilizer and improved seed more affordable and accessible to smallholders . The GESSince the GES program outsources the subsidy to private agro-dealers, it is safe to assume that the scheme will have a crowding-in effect on the supply side of fertilizer and improved seed markets in terms of private sector sales. However, farmers could still use subsidized inputs provided through the GES in place of some or all of their commercial purchases, leading to crowding out of commercial inputs on the demand side of the market. Regardless, there is little empirical evidence on the impact of the program to inform on-going debates on how effectively the GES program increases agricultural productivity and welfare of poor and food insecure households. In this study, we empirically test whether a mobile-based e-voucher system for fertilizer and improved seed subsidies in Nigeria improves productivity and welfare outcomes. Maize yield and income from maize production are used as a proxy for productivity outcomes in this article, while per-capita food, total, and non-food expenditure are used as indicators for welfare outcomes. We focus on productivity and welfare outcomes as they are the most important indicators given the stated objectives of the GES.ceteris paribus, only improve the income of smallholders. However, leakages and imperfect targeting may divert subsidies away from the intended beneficiaries. Under such circumstances, the GES may become beneficial on average by improving the productivity of commercial farmers albeit ineffective in addressing the needs of smallholders. This paper therefore addresses this issue by estimating the overall average effect of the program as well as its distributional effects. In estimating the overall average and distributional impacts of the GES program, we control for the potential endogeneity of participation in the GES using Instrumental Variable (IV) regression approach. We also examined the robustness of estimated impacts by constructing alternative measures of participation in the GES program. Finally, in an attempt to provide an overall effectiveness of the program, the paper undertakes a benefit–cost analysis based on observed direct costs and benefits. The rest of the paper is organized as follows: Section . Data sources and the econometric strategy are presented in Section This article contributes to the literature on input subsidies in the following ways: First, by focusing on a country that implemented the most expensive input subsidy program in SSA, it investigates the enduring question of whether and to what extent a smart input subsidy program such as the GES impacts productivity and welfare outcomes. To date, there is not a single study that evaluated the impact of the GES program on productivity and welfare of smallholders in Nigeria. In addition, this article is the first to evaluate the effectiveness of an e-voucher input subsidy program. Second, in an attempt to provide beyond average treatment effects, we examine the distributional impacts of the GES program focusing on two sources of heterogeneity: gender and farm land size. In principle, the GES should, 2Although the vast majority of Nigeria’s rural population engages in agriculture, the sector has been in the periphery of government priorities over the past 30 years. As a result, the rate of rural poverty and food insecurity has increased substantially over that time period. For instance, rural poverty measured at the food poverty line has increased from 33.6% in 2004 to 48.3% in 2010 . MoreoveThe GES has been implemented with the intention to improve the efficiency of fertilizer and improved seed distribution to smallholders. By deregulating seed and fertilizer markets as well as through well-targeted fertilizer and improved seed subsidies, the GES aims to increase the productivity of smallholders. It was specifically designed to minimize leakages and to ensure that subsidies are provided only to intended farmers. In addition, the procurement and distribution of fertilizer and improved seeds is outsourced to private agro-dealers. The GES intends to provide improved seed and fertilizer to 20 million farmers within five years . In addismarter”, the initial list compiled at the village level was transferred to an electronic database. Moreover, each farmer was given a unique identifier with the help of National Identity Management Commission (NIMC). After registration was completed, eligible farmers received an e-wallet (an electronic voucher) notification through their phone. The e-wallet specifies the total quantity of fertilizer and improved seed allocated to the farmer as well as the designated redemption center for collection. According to In principle, the GES targets only fulltime and non-commercial farmers. However, there is no database of farmers to verify eligibility and most of the demographic information provided by farmers was self-reported. Registration of eligible farmers was carried out manually, with completion of forms at village level with the help of village leaders . In orde3(a)This study uses a household survey data collected by the International Institute of Tropical Agriculture (IITA) in 2015 as part of an on-going effort to evaluate the impact of adoption of drought tolerant maize varieties and the GES on productivity and welfare outcomes. A multi-stage-stratified sampling procedure was employed to select Enumeration Areas (EAs) from each Local Government Areas (LGAs), and households from each of the selected EAs. The list of all EAs was obtained from the National Population Commission (NPC) of Nigeria. The EAs were then divided by the number of LGAs in each of the selected states to obtain the number of EAs per LGA. Following the National Bureau of Statistics (NBS) recommendation for a nationally representative data collection, 10% of the LGAs in each of the selected States and 5% of the total EAs per LGA were randomly selected. Finally, from the households in each of the selected EAs, five farming households were randomly selected, resulting in a sample size of 2,305 households.surveybe” software.The data were collected using structured questionnaire which was pre-tested twice by trained and experienced enumerators. The survey questionnaire was designed to gather detailed information on socio-economic characteristics of households, expenditure on food, and non-food items, input use and allocation, crop and livestock production, output for maize, and other notable crops and participation in the GES program. In addition, extensive village-level data were collected on the incidence of shocks, prices of key inputs and crops, among others. In terms of participation in the GES, relevant data were collected on the level of awareness about the GES program as well as on farmers' decision to register for the GES program. In addition, data were collected on the quantity of seed and fertilizer allocated to registered farmers as well as the final quantity collected and the price paid by the participants of the GES program. To minimize errors usually encountered with the use of paper questionnaire, the data for this study were collected electronically using the “(b)Like any evaluation program, establishing the causal impacts of an input subsidy program on various outcomes of interest is in fact a “wicked problem”  is then computed as the difference between Eqns. However, casual identification requires controlling for both observable and unobservable factors that influence participation in the GES and productivity and welfare outcomes. Hence, estimates of Eq. However, finding an instrument that satisfies the orthogonality condition is not a trivial matter. Most of the studies that evaluated the impacts of input subsidies on such outcome indicators have used the variable “number of years the household head has lived in a village” as an instrument . Followi4i’s characteristics such as household size, age, education that affect participation in the GES. iS captures participation in social network activities such as membership in cooperatives and labor-sharing arrangements The outcome indicators are related to productivity and welfare. Our first productivity outcome-related indicator is maize yield. Although the GES does not require participating farmers to use the subsidized inputs on a particular crop, we opt to consider maize yield as a productivity outcome variable as maize is one of the most important food crops in Nigeria. In fact, Nigeria is the largest maize producer in West Africa. In addition, maize also stands to benefit the most from fertilizer subsidies as response rate of improved maize is higher than most other crops. According to our data, average maize yield in the study area stands at 2,006 kg/ha. However, average maize yield for GES participants 2,205 kg/ha) is significantly higher than for non-participants  and the difference is statistically significant at 1% significance level see . Our sec kg/ha isOur welfare-related indicators include food expenditure, non-food expenditure, and total expenditure, all measured on per-capita basis. In addition to expenditure indicators, we also used headcount poverty ratio as an additional welfare indicator. Total expenditure is calculated by summing food and non-food expenditure values. A household’s food consumption expenditure is comprised of monetary expenditures on purchased food and the imputed values of consumption from own harvest. Looking into the distribution of consumption expenditures, the average per capita total consumption expenditure is about ₦112,136 per year.r capita .7I(.) i(b)In the household survey, we collected detailed information regarding the awareness and registration process of the GES. More specifically, first we asked households if they were aware of the GES program. Second, we asked those who responded in the affirmative if they had registered in the GES program. Third, we asked those who responded to the registration question in the affirmative if they had received mobile alert. Finally, we asked those who were notified of the e-voucher through the mobile alert if they had actually collected the subsidized inputs and the quantity of seed and fertilizer allocated to them as well as the final quantity they collected. We used the above sequence of questions to construct dummy variables for our sequential probit model. According to the survey, about 65.5% of the households were aware of the GES program and only 64% of them registered for the GES. However, our survey further revealed that only 76% of the registered households actually received mobile alerts.Since our main objective is to evaluate the overall effectiveness of the GES program on productivity and welfare outcomes, we used registration to the GES program as our main treatment variable. In particular, participation in the GES is measured by a dummy variable which takes a value of one if the household is registered to the GES and zero otherwise. Note that, in the robustness section, we used receiving a mobile alert as well as actual collection of subsidized fertilizer and improved seed as our treatment variables. In addition to household characteristics, we also included state dummies to control for state-level fixed effects. We further have controls for general conditions such as access to electricity. Finally, the number of years of residence in the village serves as an instrument for participation in the GES. 4In this section, we present the results of our econometric analysis. Firstly, we report the effect of the GES program on productivity, maize income, and welfare outcomes using matching techniques in (a)p-value = 0.543) while the joint significance of covariates was not rejected before matching . Moreover, due to matching, the pseudo-R2 declined from 15.2% to 1.1%.The reliability of the PSM and IPWRA results depends on the quality of our matching. We therefore provide some details on the overall covariate balancing and common support. R2, and significant reduction in the mean standardized bias are indicative of successful balancing of the distribution of covariates between participants and non-participants of the GES. The common support region is presented in The high total bias reduction, the insignificant p-values of the likelihood ratio test after matching, low pseudo-(b)(i)As mentioned in the methodology section, we used a sequential probit model to examine the determinants of participation in the GES due to the sequential steps involved in the GES program. In our setting, first we estimate the determinants of awareness to the GES. We then estimate the determinants of registration to the GES conditional on awareness. Finally, we estimate the determinants of receiving a mobile alert conditional on registration. We followed this step as registration is a strict subset of awareness. Similarly, receiving a mobile alert is a strict subset of registration. We did not include the decision to collect allocated quantities of fertilizer and improved seed in our sequential probit model as it is not a strict subset of receiving a mobile alert.10Number of years of residence in the village) affects the probability of becoming aware of the GES and the conditional probability of registration to the GES conditional on awareness but not the conditional probability of receiving a mobile alert given registration in the GES No control” present a parsimonious specification for maize yield and income that includes only the treatment variable (registration for the GES) along with state-level fixed effects. Estimates of “With control”, present results where standard controls for maize yield and income are included. Estimates of “With IV” present the results of the IV specification in which participation in the GES program is treated as endogenous. We specify a log-linear functional form for maize yield and income from maize production is estimated at levels.The result showing the effects of the GES program on maize yields and maize income is presented in The results presented in land category” which takes on a value of one if a household owns less than 3 hectares of farm land and zero otherwise.In our subsequent discussion, we base all our interpretations based on our preferred specification (IV specifications). The results in (iii)This section presents and discusses the welfare effects of the GES program. We used per-capita total expenditure, food expenditure as well as poverty headcount ratio as indicator for welfare. In our estimation, total expenditure and food expenditure are measured on per capita basis and estimated using a log-linear functional form. Results are reported in On average, per-capita total expenditure increased by 30.7%. Similarly, as a result of participation in the GES program, per-capita food consumption expenditure increased by 39.4%. These results are consistent with previous studies in Nigeria. For instance, The final welfare indicator, headcount poverty ratio, is a binary variable and hence the effect size can be estimated using a probit model specification. However, the parameter estimates for GES will only represent changes in the probability of poverty instead of actual poverty reduction rates. Estimating the effect of GES on poverty reduction instead of changes in the probability of poverty reduction requires examining the distribution of observed poverty of GES participants and the distribution of the counterfactual poverty of GES participants had they not participated in the GES program. In other words, we need to examine the poverty reduction effects of the 30.7% per-capita total expenditure growth reported in (c)In this section we present robustness cheeks for alternative measures of participation in the GES program.(i)In our main analysis, we measure participation in the GES by registration status. Our data show that only registered farmers have received mobile alerts. However, of the registered farmers, only 76% have received mobile alerts. As a robustness check, we define treatment based on receiving a mobile alert. In particular, we created a dummy variable which takes a value of one if the household has received a mobile alert and zero otherwise.Results are reported in (ii)Our second robustness check is based on actual collection of subsidized fertilizer and improved seed by farmers. Our data show that only registered farmers have managed to collect subsidized fertilizer and improved seed. However, only 69% of the registered farmers have collected subsidized fertilizer and improved seed. Similarly, 3.7% of the registered farmers have managed to collect subsidized fertilizer and improved seed without receiving a mobile alert. The second case may arise due to network coverage or mobile phone ownership issues. The program clearly stated that when the use of mobile phone is not tenable, farmers can claim benefits using scratch cards or GES identification cards. As such the second group of the beneficiaries may have collected their share of fertilizer and improved seed through scratch cards or GES identification cards. However, the first group of households may have failed to collect their share due to liquidity constraint as farmers are expected to pay the subsidized price to private agro-dealers or the redemption centers may have been far away which makes the final price expensive due to high transaction costs. Nonetheless, these two issues will introduce measurement errors. Herein, we probe the robustness of our results by measuring treatment by a dummy variable which takes on a value of one if the household has collected subsidized inputs and zero otherwise as a measure of program participation. We focus on maize yield and poverty outcomes. Results are reported in (iii)Herein, we focus on registration without receiving a mobile alert or collecting subsidized fertilizer and improved seeds from redemption centers as a falsification test to examine if our reported results , Table 8(iv)Herein, we provide benefit–cost ratios albeit without considering indirect benefits and costs. For the purpose of calculating the effectiveness of the program, we collected data on the quantity of seed and fertilizer allocated to farmers as well as the final quantity collected and the price paid by farmers. We calculated the cost of subsidy per kg of fertilizer and improved seed as the difference between the market price and the subsidized price of fertilizer and improved seeds. However, data on registration, administrative, and logistic costs of the GES program are not available despite our best efforts to obtain it. Since costs for registration, transportation, storage, procurement, and administration are likely to be significant; we took some of the cost components of the Malawi input subsidy program from 13It is worth noting, however, that our calculation is based on the so called “production-based” approach and ignores indirect benefits . Using t5In Nigeria, improving agricultural productivity through the use of fertilizer and improved seeds is imperative to improve food security and reduce the pervasive nature of rural poverty. However, the use of such key agricultural inputs is rather low even by SSA standard as a result of pervasive input and credit market imperfections. With the premise of improving the use of improved seed and inorganic fertilizer, the government of Nigeria embarked on a mobile phone-based input subsidy program in 2012. Using a unique household-level data from rural Nigeria, this paper examined the productivity and welfare effects of the GES program. In particular, the study provides insights into how effective the program was in enhancing productivity and improving welfare outcomes. The study employed instrumental variable regression approach to control for the potential endogeneity of participation in the GES program.Our main empirical findings are as follows. First, farmers who participated in the GES increased their maize yield by 26.3%. Similarly, maize income of GES participants increased by ₦19,730. These results suggest that the GES enabled farmers to improve their productivity and income from maize production, which is a case for justifying the intervention. Second, in terms of the welfare outcomes, the GES has a positive and statistically significant effect on per-capita total, food, and non-food consumption expenditures. The size of the estimated effects suggests a large improvement in welfare as a result of participation in the GES program. Specifically, GES participants increased their per-capita total consumption expenditure by 30.7%. As a result of this consumption growth, poverty headcount ratio has declined by 17.7% points among participants of the GES. Third, the effect of the interaction term between participation in the GES and farm land size as well as GES and gender is insignificant suggesting the absence of heterogeneity effects based on gender and farm land size. Fourth, the benefit–cost ratio of the program, valued at market prices and without accounting for economy-wide effects, is about 1.11. Each unit of dollar that the government spends as a subsidy yields a return of 1.11 dollars.The results presented in this paper should be interpreted with caution. First, our identification strategy relies on cross-sectional data which limits the generalizability of the results beyond one agricultural production year. The use of panel data and fixed-effects would be an important extension in the future. Second, our benefit–cost ratio calculation does not take into account indirect benefits and costs. Moreover, in estimating impacts on productivity and welfare, we did not consider the possibility of farmers re-selling their fertilizer and improved seeds due to lack of data. Although the GES strictly prohibits re-selling of the received inputs, we cannot rule out the possibility of some recipients selling fertilizer and improved seeds in the local market or at the redemption centers. When subsidized inputs are re-sold at the local market, it will definitely introduce a crowding-out effect. Examining the crowding-out effect of the program will therefore be an important extension.


Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications. Automated Information and Communications Technology (ICT)-based biometric technologies that recognise humans through physiological or behavioural characteristics enable a convenient, accurate and repeatable method for identity assessment . Using iFor automated biometrics, meta-data or soft-biometrics refers to additional characteristics of the subject that can be used to support or sharpen an identification or verification decision . ConsideThe human hand offers a rich seam of anatomical characteristics that are easily obtainable and measurable . FurtherBuilding upon  we wish We explore in this work the use of height, weight and foot size as categorical outputs. In order to obtain the benefits of soft-biometrics  it is not mandatory to use a precise prediction of these traits—as such it is suitable to have a binning classification such as low/medium/high. Taking this into account, machine learning classification algorithms are analysed for the prediction of the aforementioned soft-biometric traits.In previous works, sex prediction from hand lengths has been investigated using logistic regression while the relationship between hand length and height/foot size have been commonly analysed through multilinear regressions. The authors are unaware of any studies analysing the relationship between hand lengths and weight. This current work represents, to the best of the authors’ knowledge, the first attempt to jointly analyse the prediction of these four soft-biometrics from hand features. Furthermore, it also represents the first attempt to use machine learning classification algorithms for the prediction of height, weight and foot size.The main research question here is thus whether machine learning classification algorithms can improve the soft-biometric predictions obtained over-and-above the more commonly used linear/logistic regression approach. As a secondary issue, we wish to explore which classifier algorithm provides the most optimal outcome. In this regard, it is known that no single classifier outperforms others across a range of data. Acknowledging this issue, several different classifiers have been tested here in order to identify an optimal fit when predicting a specific demographic trait. Furthermore, our experiments will explore which, of a range of hand features, are most valuable when predicting each demographic trait.This exploratory analysis is made possible due to the existence of the SuperIdentity Stimulus Database (SSD) collected as part of the SuperIdentity project . This inIn performing this investigation our aim is to provide evidence to a wide range of communities as to the relationships (and importantly the strength of these relationships) that can be discovered between hand measurements and demographic characteristics. These relationships can be utilised in a practical manner to assist with integration within a biometric implementation and provides evidentiary value of forensic relationships. Furthermore, the public availability of the dataset used in this work ensures repeatability and encourages further research in this area.Whilst the use of hand features to predict demographic traits has been widely applied in fields such as forensic science, psychology and anthropology, it currently does not have wide application in the biometric field. Across these domains the prediction of demographic traits can have several practical applications: it can improve the performance of biometric algorithms, and can reduce the search of large biometric databases decreasing the computational time for identification. It can also support re-identification in surveillance scenarios and disaster victim identification. In this work, we analyse the prediction of four key soft-biometric traits . Each have been coded as categorical values using machine learning classification algorithms, in order to assess whether this approach outperforms the more common approach of continuous value prediction by regression techniques.In the following subsections, we provide an overview of the state of the art in the prediction of demographic traits from hand features.Sex prediction is one of the most studied soft-biometric traits with predictive values explored across a range of different biometric modalities such as fingerprints , face 1, iris 1 and handCase and Ross  performeth metacarpal. From left hand features, the best accuracy rate was 89.8% based on three measurements of the 1st metacarpal.In  the authIn  the authMore recently, Jee et al.  analysedA study by Amayeh et al.  represenSimilar to sex prediction, there is a large literature on height prediction from hand features. However, this research comes mostly from the forensic and anthropological fields. Height prediction is of special interest within these fields due to its use in forensic examination, anthropological studies and disaster victim identification scenarios. In the biometric field, height has been proposed as a soft-biometric feature to improve gait-based systems , 23, howIn 1978 Musgrave and Harneja  producedMore recent studies have used the hand length and/or hand breadth features to create linear regression models to predict height. In  left andIn  AgnihotrHabib et al.  presenteJee et al.  used the2) and the RMSE provide a statistical measure of the goodness of fit of the model and the error of the predictions. The subscript indicates whether the model was created for males (M), females (F) or for the whole sample (W).The results detailed above are summarised in The prediction of foot size from hand features has not attracted much attention from the research community. However, as aforementioned, every piece of information that can be added to an identification task has the potential to improve the degree of certainty. Moreover, foot size is common evidence found in forensic investigations. The most relevant work  was condDespite the fact that in 2004 the utilisation of weight was proposed as a soft-biometric , we wereThe literature review for the prediction of sex and height from hand measurements indicates the interest of the research community in these areas. These two soft-biometric attributes have also been demonstrated to be of interest for the biometric community. Nevertheless, the literature review also showed that the use of machine learning classification algorithms has not yet been explored. More generally, the relationships between hand measurements and other soft-biometric measures such as foot size and weight predictions have not been a focus for many previous studies. However, as indicated in , the useIt is also important to highlight the variation of these demographic traits across different ethnicities. Studies have revealed that the relationships between body measurements/features vary between populations and ethnicity . The facThe dataset used in this work has been made publicly available at , includiOur study used a subcorpus of the the SuperIdentity Stimulus Database (SSD)  which isThe SSD hand subcorpus comprises 112 participants  restricted to Caucasians who spoke English as a first language and were aged between 18 and 35 years. Hand geometry images were captured using a Nikon D200 SLR camera, with both the palm of the hand and camera facing downwards. Participants placed each hand on an acetate sheet with a series of positioning pegs. Once the hand was correctly placed, three consecutive photographs were taken of each hand resulting in six images per subject. A series of length measurements (based on the underlying skeleton of the hand) were manually extracted from the first of the three images captured from both left and right hands .The weight distributions show positive skewness , where lOur experiments divided the sample into a series of discrete bins based on subject height, weight and shoe size. Obviously the more bins used, the finer the resolution of data range contained in each bin.The categorisation of demographic traits has been performed using simple discretization . This process divides the full range of the demographic traits into N intervals of equal width. These N intervals are used as categories for the specific demographic trait. The greater the number of categories used, the better resolution for the predictions. However, a high number of categories can affect the performance of the machine learning classifiers. As the number of categories increases, so does the difficulty of the classification problem. Equally, there are fewer observations for each particular category on which to train the classifier. Hence, normally a higher number of categories leads to lower classifier accuracies.In order to analyse the impact of the different number of bins on the performance of the machine learning classifiers, three options have been used: 3, 5 and 7 bins, by means of simple discretisation. This discretisation has been performed independently for each population dataset .As a result of the use of simple discretisation using equal bin width, bins show different frequencies  and was used to perform the feature selection.The feature selection method applied was the “best-first search” algorithm . This feUsing the selected features by the “best-first search” algorithm, machine learning models were created for the three different participant groups being evaluated and also distinguishing between right and left hand images. The evaluation was performed using the testing dataset with the remaining 40% of participants (45 out of 112) and has been undertaken by means of 10-fold cross validation, where 9 folds are used for estimating the classifier model, and the remaining fold for obtaining the performance. This procedure has been carried out 25 times using different random seed numbers  in order to obtain a statistically significant average of the model performance. These results were compared against each other and also with the performance of the linear regression approach.The classifiers used in this study were also part of the WEKA environment  .Decision tree learning is one of the most commonly used algorithms for automatic learning. The decision trees are composed of nodes , branches  and leaves (which provide the classification of the instance). The decision tree analysed in this work is the C4.5 implementation developed by Quinlan , implemeSupport vector machines were introduced by Cortes & Vapnik  in 1995 Multilinear logistic regressions  is one oThe Naïve Bayes classifier  is basedWith the aim of comparing the performance of machine learning classification algorithms with the commonly used linear regression approach, an evaluation of this approach has also been conducted with the SSD data. Linear regression models were created using the three datasets analysed  and both left and right hand data from the training dataset. Their performance was evaluated on the testing dataset using the following methodology:feature selection was performed for linear regression models and the three search directions provided by the “best-first search” algorithm , using all the samples in the dataset,with the selected features, a linear model was created for the prediction of height, weight and foot size,the predictions were categorized into the same 3, 5 and 7 bins detailed in Section 3.3,the accuracy of the binned predictions were calculated.In this section, the results of our experimentation are detailed. The large number of evaluations performed in this work  requires the summarisation of results.Section 4.1 provides the accuracy rates obtained for sex classification, presenting the results by classifier, search direction and right/left hand. Following the sex prediction results, in Section 4.2 the analysis from the prediction of height, weight and foot size will be discussed. In presenting these results they will be summarised for the different elements of the machine learning classification approach: feature selection direction, classifiers and number of bins. The machine learning classifier results will, furthermore, be compared with the linear regression approach.As sex is a categorical variable with two classes, the binning of variables as applied to other demographical traits is not required. Furthermore, in sex classification only the combined sample dataset is considered. The absence of these two factors (number of bins and sample group) considerably reduced the number of evaluations required. The performance obtained for the sex prediction using the four machine learning classifiers, the three feature selection search directions and both right and left hand is presented in The sex classification results show good performance overall with the Support Vector Machine (SVM) classifier producing the best results using forward  feature selection and right hand images (88.7% success ratio). It can also be seen from the results in The best two classifiers (from right and left hand features) used the following lengths as predictors .Regarding the feature selection search directions, forward  direction generally obtained better results. Our analysis showed that a bidirectional search direction generally obtains the same subset of predictors as a forward direction search. The rationale behind this is because both search directions were set to start from an empty feature set.The results obtained are similar to those identified in other studies, with the exception of  where haThe prediction results for the demographic traits of height, weight and foot size have been analysed in terms of the feature selection search direction, the machine learning classifiers, and the number of bins, and performance has been compared with that of the linear regression approach. Considering the possibility of predicting sex from hand features (success rate around 90%), we have also evaluated both machine learning and linear regression approaches for male and female subject subsets.The “best-first search” feature selection algorithm has three different options for the search direction: backward, forward or bi-directional. These three directions were used in our experiments in combination with: the four different classifiers, the 2 hand feature sets (right and left hand features), the three sample groups , the three binning options  and the three demographic traits. The combinations of all these factors resulted in 648 performances values.For each combination of these five factors , As can be seen, the bi-directional search direction obtained a higher frequency than the forward or backward directions. The bi-directional and forward search directions shared a common starting empty feature subset, however bi-directional has the advantage of allowing both single-attribute additions and deletions, whilst forward direction only allows single additions.Analysis of the number of times in which a particular classifier obtained the highest performance for each combination of number of bins, group samples and left and right hand feature sets allowed us to assess the relative performance across the techniques. Assessing the impact of the number of bins for categorisation of demographic traits, the larger the number of bins, the more granularity within the demographic traits categories. Furthermore, a larger number of bins can also imply a better distribution of the sample within those categories. In In general terms, the demographic trait predictions for 3 bins shows a success rate around 70%, with similar performance for the left and right hand. In some group samples and demographic traits the success ratios are 80%, such as foot size prediction in male and female subjects.For 5 bins, the accuracy rate reduces to around to 50% for height and weight predictions and around 60% for foot size, showing again similar results between both hands for the three sample groups, although there is a significant decrease on accuracy for the prediction of female foot size. The accuracy rate is around 40% (50% for foot size prediction) when 7 bins are considered, showing equivalent performance within right and left hand assessment and sample groups with the exception of female log-weight prediction which, in this case, shows higher performance than men and whole population.It is worth noting that the range of these bins is different across the three sample groups. In the case of the whole sample, the range of the values is greater as is the width of each resultant category.Using the same hand features extracted from the SSD dataset and the associated demographic information, linear regression models have been created for height, weight and foot size. The models used the 21 hand measurements detailed in 2) and root mean squared error (RMSE), was selected from the three search directions, which led to nine linear regression models for each right and left hand features. These linear models are summarized in Using the same feature selection algorithm, (BestFirst) and its three search direction options, hand feature subsets have been found for the three demographics, the three sample groups and both right and left hands. The best feature subsets, in terms of adjusted R-squared (Adj R2). However, the hand features selected from right and left hand models are significantly different. This could be explained by the feature selection algorithm and the high correlation between hand features. In order to compare the accuracy across demographic traits, RMSE values have been normalized by the demographic mean values, obtaining adimensional values suitable for comparison. Height can be predicted within 3.8% of accuracy, the log-weight 2.6% and the foot size 15.5%. The poor prediction of foot size may be explained due to the intrinsic shoe size discretisation.From the linear model statistic provided in In Using the linear regression models described in Section 4.2.4, the model predictions for height, log-weight and foot size were calculated. These predictions were categorised within the same bins system used for the machine learning approach (Section 3.3). Using these categorised predictions, the binning accuracy rates were calculated for the different combination of demographic traits, number of bins and group samples. The performance of the machine learning classifiers are generally better than the linear regression models, especially when the number of bins is lower.The performances of log-weight models for machine learning classification and linear regression are presented in For foot size, As previously mentioned, direct comparisons with previous work is difficult due to the use of different population groups, different dataset sizes and different evaluation methodologies. Furthermore, the datasets used in previous studies are either not publicly or easily available, which leads to an inability to reproduce and compare with published results.However, it is possible to implement previous approaches and obtain their accuracy using our publicly available dataset, and compare the performance of our new machine learning methods. These results will allow us to better understand the challenges of soft-biometric prediction from hand images.In the following sections, the results of implementing previous approaches for sex and height predictions based on hand features are presented. The implementation of the foot length prediction has not been possible due to the differences in the data collected. In  both theIn order to implement these previous approaches, some approximation had been used. For those studies based on metacarpals and phalanges lengths , 25, bonThe results achieved in this paper improve both the published and the implemented accuracy rates from , 18. TheThe significant performance improvement between our results and the implementations of previous works have to be moderated by the fact that our implementation is based on feature selection using this specific dataset. These results show the dependency of these analyses on the dataset used and highlight the need for public datasets in order to be able to fairly compare different approaches. The public availability of the hand subcorpus dataset is one of the aims of this study in order to encourage further research in this field and allow direct comparisons.Tables As with sex prediction, the multilinear regression models obtained using previously published models are not as accurate a fit as reported in , 10, 25.The proposed height prediction models for the male and whole populations groups present a better adjusted R-squared and lower RMSE than the models implemented from previous work, except for female height prediction. A disadvantage of our implementations is that these better fit values are obtained with more complex models in terms of the features included as predictors.Similar precautions as in sex results comparison have to be taken when comparing our results for height prediction with previous studies. The feature selection step and the model estimation are highly dependent on the dataset used. Again, the presence of public datasets from different populations will allow an exploration of height (and other soft-biometric traits) prediction models, and will eventually bring more robustness against population-variation.This study aimed to explore the relationship between hand measurements and a range of demographics features. In particular we have investigated how height, weight, sex and foot size can be predicted from 21 hand measurements using common implementations of machine learning classifiers and, for comparison, linear/logistic regression techniques. Within our machine learning classification analysis we have examined the division of demographic data using a series of ‘bins’ thereby allowing the prediction of a data range from the hand measurements. This range of predictions has value in biometric deployments by enabling the significant advantages that accompany the use of soft-biometrics.Our experiments have shown that machine learning classification typically out-performs linear (logistic) regression for the prediction of these four demographic traits based on bin assessment. Although the linear regression approach has the added advantage of being able to predict a continuous range of demographic outcome, the binned soft-biometric prediction of the machine learning classification algorithms may be an interesting option to enhanced biometric identification/verification systems by narrowing search space or improving the certainty (or the time required) to reach a decision.Our results are in line with previous experimentation . Furthermore, we have contributed a novel relationship between subject weight and hand dimension. We have also shown that, if the sex of a subject is used as internal meta-data, we can use this to enhance the prediction of weight, height and foot-size even further by using a model specific to male or female subjects.It should be noted that we have employed our machine learning classifiers without optimisation, leading to the real possibility of being able to improve on our results using these methodologies. Future work could involve enhanced feature selection algorithms, fine-tuning of algorithm parameters, analysis of the dependency of performance with respect feature subsets and utilisation of an ensemble of machine learning algorithms in order to further enhance classification success rates. The public availability of the SSD hand dataset will allow further research in these areas. Moreover, the authors consider that making this dataset publicly available will encourage other researchers to do so and enable the creation of multi-ethnicity datasets. This could boost the creation of more accurate and comprehensive soft-biometric prediction models. Another advantage of having larger datasets is that researchers will be able to use larger machine learning training sets that could further improve the accuracy rate of this approach.Our results are directly applicable to both the biometric and forensic communities. In the former, relationships between these four demographic traits can be used as soft-biometrics to reduce search-space or otherwise provide additional inferred information about the subject undergoing authentication. Results prove that the accuracy of the predictions, specifically in the case of 3 bins , make them all suitable for use as soft-biometric features. Furthermore, their combined use as soft-biometrics can lead to much greater improvements than their individual use. Within the forensics community, a deeper understanding of the physical characteristics of a subject in relation to their demographics adds evidence when establishing identity. As part of the SuperIdentity project , these rS1 Fig(EPS)Click here for additional data file.S2 Fig(EPS)Click here for additional data file.S3 Fig(EPS)Click here for additional data file.S4 Fig(EPS)Click here for additional data file.