Hybrid Recommendation Scheme Based on Deep Learning

Big data has been developed for nearly a decade, and the information data on the network is exploding. Facing the complex and massive data, it is difficult for people to get the demanded information quickly, and the recommendation algorithm with its characteristics becomes one of the important methods to solve the massive data overload problem at this stage. In particular, the rise of the e-commerce industry has promoted the development of recommendation algorithms. Traditional, single recommendation algorithms often have problems such as cold start, data sparsity, and long-tail items. The hybrid recommendation algorithms at this stage can effectively avoid some of the drawbacks caused by a single algorithm. To address the current problems, this paper makes up for the shortcomings of a single collaborative model by proposing a hybrid recommendation algorithm based on deep learning IA-CN. The algorithm first uses an integrated strategy to fuse user-based and item-based collaborative filtering algorithms to generalize and classify the output results. Then deeper and more abstract nonlinear interactions between users and items are captured by improved deep learning techniques. Finally, we designed experiments to validate the algorithm. The experiments are compared with the benchmark algorithm on (Amazon item rating dataset), and the results show that the IA-CN algorithm proposed in this paper has better performance in rating prediction on the test dataset.

Intermediate-term narrow-range earthquake forecasting: an interdisciplinary tool based on seismological and geodetic observations

<p>A novel forecasting tool, able to fully exploit the information content of the available data, is proposed for the synergic use of seismological and geodetic information, in order to delineate, at the intermediate-term narrow-range, the regions where to concentrate prevention actions and seismic risk mitigation planning. An application of the proposed interdisciplinary procedure, defining a new paradigm for time dependent hazard assessment scenarios, is exemplified illustrating its application to the Italian territory.</p><p>From seismological viewpoint, long-lasting practice and results obtained for the Italian territory in two decades of rigorous prospective testing of fully formalized algorithms (e.g. CN), proved the feasibility of earthquake forecasting based on the analysis of seismicity patterns at the intermediate-term (i.e. several months) middle-range scale (i.e. few hundred kilometers). An improved but not ultimate precision can be achieved reducing as much as possible the space-time volume of the alarms, by jointly considering seismological and geodetic information. In the proposed scheme geodetic information (i.e. GNSS and SAR) are used to reconstruct the velocity and strain pattern along transects properly oriented according to the a priori known tectonic and seismological information. Specifically, considering properly defined transects within the regions monitored by CN algorithm, the possible velocity variations and the related strain accumulation can be highlighted, with due consideration of the errors involved in GNSS data.</p><p>Through a refined retrospective analysis, duly involving the accuracy analysis of the newly available geodetic results, space­time precursory features could be highlighted within ground velocities and seismicity, analyzing the 2016-2017 seismic crisis in Central Italy and the 2012 Emilia sequence. The analysis, including counter examples, evidenced reliable anomalies in the strain rate distribution in space, whereas no time dependence was detected in the long term (more than 10 years) preceding the occurrence of the studied events.</p><p>With these results acquired, a systematic analysis of velocity variations (together with their accuracy) is performed, by defining a set of transects uniformly distributed, as far as possible, along and across major seismotectonic features of the Italian region, with a spacing of about 40-50 km and properly covering the regions monitored by CN algorithm. As a rule most of the transects contain information that appear to be useful for earthquake forecasting purposes. The few exceptions, naturally connected with the local very limited extension of land, are in Calabria and Western Sicily.</p><p>The obtained results show that the combined analysis of the results (time dependent within decadal interval) of intermediate-term middle-range earthquake prediction algorithms, like CN, with those from the processing of adequately dense and permanent GNSS network data (time independent within the same decadal interval), may allow to highlight in advance the localized strain accumulation. Accordingly the extent of the alarmed areas, identified based on seismicity patterns at the intermediate scale can be significantly reduced (from few hundred to few tens kilometres).</p>

Garber Filter Based Hybridized Algorithm For Feature Extraction From A Fuzzy Fingerprint Image

Proper extraction of fingerprint functions is important for matching the fingerprint algorithms. Different pieces of fingerprint information, such as rigid orientation and frequency should be taken into consideration for good results. The quality of a fingerprint image is often required to improve the function extraction process. In this article we introduce a Hybridized Garber Filter Algorithm (HGFA) for Fuzzy Fingerprint Image Feature Extraction for effective fingerprint recognition.This paper describes a fingerprint detection system consisting of image preprocessing, filtration, extraction and recognition matching.Preprocessing of images includes normalization based on median value and variation. In order to prepare the fingerprint image further processing, Gabor filters are extracted. The Poincaré index with a partitioning technique is used for the identification of a particular point. The extraction of the ridge line is shown and also the minute extraction with CN algorithm

Cooperative Multi-UAV Collision Avoidance Based on a Complex Network

The conflict resolution problem in cooperative unmanned aerial vehicle (UAV) clusters sharing a three-dimensional airspace with increasing air traffic density is very important. This paper innovatively solves this problem by employing the complex network (CN) algorithm. The proposed approach allows a UAV to perform only one maneuver—that of the flight level change. The novel UAV conflict resolution is divided into two steps, corresponding to the key node selection (KS) algorithm based on the node contraction method and the sense selection (SS) algorithm based on an objective function. The efficiency of the cooperative multi-UAV collision avoidance (CA) system improved a lot due to the simple two-step collision avoidance logic. The paper compares the difference between random selection and the use of the node contraction method to select key nodes. Experiments showed that using the node contraction method to select key nodes can make the collision avoidance effect of UAVs better. The CA maneuver was validated with quantitative simulation experiments, demonstrating advantages such as minimal cost when considering the robustness of the global traffic situation, as well as significant real-time and high efficiency. The CN algorithm requires a relatively small computing time that renders the approach highly suitable for solving real-life operational situations.

Assessing ‘alarm-based CN’ earthquake predictions in Italy

<p>The quantitative assessment of the performance of earthquake prediction and/or forecast models is essential for evaluating their applicability for risk reduction purposes. Here we assess the earthquake prediction performance of the CN model applied to the Italian territory. This model has been widely publicized in Italian news media, but a careful assessment of its prediction performance is still lacking. In this paper we evaluate the results obtained so far from the CN algorithm applied to the Italian territory, by adopting widely used testing procedures and under development in the Collaboratory for the Study of Earthquake Predictability (CSEP) network. Our results show that the CN prediction performance is comparable to the prediction performance of the stationary Poisson model, that is, CN predictions do not add more to what may be expected from random chance.</p>

CN: a consensus algorithm for inferring gene regulatory networks using the SORDER algorithm and conditional mutual information test

Diagram of the CN algorithm.

An Efficient Hierarchy Algorithm for Community Detection in Complex Networks

Community structure is one of the most fundamental and important topology characteristics of complex networks. The research on community structure has wide applications and is very important for analyzing the topology structure, understanding the functions, finding the hidden properties, and forecasting the time-varying of the networks. This paper analyzes some related algorithms and proposes a new algorithm—CN agglomerative algorithm based on graph theory and the local connectedness of network to find communities in network. We show this algorithm is distributed and polynomial; meanwhile the simulations show it is accurate and fine-grained. Furthermore, we modify this algorithm to get one modified CN algorithm and apply it to dynamic complex networks, and the simulations also verify that the modified CN algorithm has high accuracy too.