Real estate investments have become more popular last few decades. People who are investing in a new house are more conservative with their budget and market strategies. The existing system involves calculation of house prices without the necessary prediction about future market trends and price increase.
The proposed system has two modes of oper...
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The recent incorporation of new Data Mining and Machine Learning services within Cloud Computing providers is empowering users with extremely comprehensive data analysis tools including all the advantages of this type of environment. Providers of Cloud Computing services for Data Mining publish the descriptions and definitions in many formats and o...
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Wireless sensor networks (WSN) are integral part of Industrial Internet of Things (IIOT), the said networks comprise of elements possessing low power processors. WSNs are used for gathering data in the monitoring region, using which vital information about the sensor and the monitoring region can be attained (placement of the sensor node is critica...
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Television group of onlookers rating is a vital pointer as to prevalence of projects and it is likewise a factor to impact the income of communicate stations through promotions. Albeit higher evaluations for a given program are gainful for the two supporters and promoters, little is thought about the components that make programs increasingly allur...
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Recommender system algorithms are widely used in e-commerce to provide personalized and more accurate recommendations to online users and enhance the sales and user stickiness of e-commerce. This paper discusses several recommendation algorithms and the challenge of tradition recommender system in big data situation, and then proposes a framework o...
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Churn prediction is an important topic in the freeonline game industry. Reducing the churn rate of a game signifi-cantly helps with the success of the game. Churn prediction helpsa game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Play...
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Learning with streaming data has received extensiveattention during the past few years. Existing approaches assumethat the feature space is fixed or changes by following explicitregularities, limiting their applicability in real-time applications.For example, in a smart healthcare platform, the feature space ofthe patient data varies when different...
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Engineering changes (ECs) are new product devel-opment activities addressing external or internal challenges, suchas market demand, governmental regulations, and competitivereasons. The corresponding EC processes, although perceived asstandard, can be very complex and inefficient. There seem to besignificant differences between what is the “officia...
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The identification of network attacks which target information and communication systems has been a focus of the research community for years. Network intrusion detection is a complex problem which presents a diverse number of challenges. Many attacks currently remain undetected, while newer ones emerge due to the proliferation of connected devices...
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The recent incorporation of new Data Mining and Machine Learning services within Cloud Computing providers is empowering users with extremely comprehensive data analysis tools including all the advantages of this type of environment. Providers of Cloud Computing services for Data Mining publish the descriptions and definitions in many formats and o...
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The existing models of reversible data hiding in encrypted images (RDH-EI) are based on single data-hider, where the original image cannot be reconstructed when the data-hider is damaged. To address this issue, this paper proposes a novel model with multiple data-hiders for RDH-EI based on secret sharing. It divides the original image into multiple...
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Gene expression programming (GEP) is a data driven evolutionary technique that well suits for correlation mining. Parallel GEPs are proposed to speed up the evolution process using a cluster of computers or a computer with multiple CPU cores. However, the generation structure of chromosomes and the size of input data are two issues that tend to be ...
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The traditional single minimum support data mining algorithm has some problems, such as too much space occupied by data, resulting in insufficient accuracy of the algorithm, which is difficult to meet the needs of the development of the times. Therefore, an intrusion data mining algorithm based on multiple minimum support is proposed. First, the fe...
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Representation learning has been proven to play an important role in the unprecedented success of machine learning models in numerous tasks, such as machine translation, face recognition and recommendation.The majority of existing representation learning approaches often require large amounts of consistent and noise-free labels. However, due to var...
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Periodicity is a frequently happening phenomenon for social interactions in temporal networks. Mining periodic communities are essential to understanding periodic group behaviors in temporal networks. Unfortunately, most previous studies for community mining in temporal networks ignore the periodic patterns of communities.In this paper, we study th...
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The reversible data hiding in encrypted images by MSB prediction of P. Puteaux and W. Puesch not only provides high embedding bit-rates, but also entails a very low mathematical complexity.This correspondence investigates its security and shows flaws in embedding imperceptibility, unauthorized detection/removal of embedded data and unauthorized a...
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The goal of our work is to discover dominant objects in a very general setting where only a single un labeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), whi...
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Frequent-pattern mining is a common means to reveal the hidden trends behind data. However, most frequent-pattern mining algorithms are designed for DRAM, instead of nonvolatile memories (NVMs) which are preferred by energy-limited systems.Due to the huge differences between the characteristics of NVMs and those of DRAM, existing frequent-pattern m...
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Understanding complex biological phenomena involves answering complex biomedical questions on multiple biomolecular information simultaneously, which are expressed through multiple genomic and proteomic semantic annotations scattered in many distributed and heterogeneous data sources; such heterogeneity and dispersion hamper the biologists’ ability...
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For the path coverage testing of a Message-Passing Interface (MPI) program, test data generation based on an evolutionary optimization algorithm (EOA) has been widely known. However, during the use of the above technique, it is necessary to evaluate the fitness of each evolutionary individual by executing the program, which is generally computation...
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ALTEC defined and developed a framework with the main aim to process a big amount of data allowing a seamless connection between the collected information and the analyses performed by end users.This is the ASDP environment, that allows to organize data in the most adapt domain data store in order to have data ready for complex analyses. In particu...
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So far, there is no literature to evaluate the fault level of sensitive equipment(FLSE) caused by voltage sag from the perspective of the power grid. In practice, although the FLSE are dominated by the voltage sag of node, the voltage sag of the node is associated with whole power grid, including the voltage grade and location of the node, the di...
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Online process mining refers to a class of techniques for analyzing in real-time event streams generated by the execution of business processes. These techniques are crucial in the reactive monitoring of business processes, timely resource allocation and detection/prevention of dysfunctional behavior. This paper fills the gap by identifying a set o...
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Cellular vehicle-to-everything (V2X) communication is expected to herald the age of autonomous vehicles in the coming years.With the integration of block chain in such networks, information of all granularity levels, from complete blocks to individual transactions, would be accessible to vehicle sat any time.Specifically, the block chain technology...
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Heterogeneous information networks (HINs) are usually used to model information systems with multi-type objects and relations. In contrast, graphs that have a single type of nodes and edges, are often called homogeneous graphs. Measuring similarities among objects is an important task in data mining applications, such as web search, link prediction...
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With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio -temporal data has become increasingly available nowadays.Mining valuable knowledge from spatio - temporal data is critically important to many real-world applications including human mobility understanding, sm...
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The advancement of several significant technologies, such as artificial intelligence, cyber intelligence, and machine learning, has made big data penetrate not only into the industry and academic field but also our daily life along with a variety of cyber-enabled applications. In this article, we focus on a deep correlation mining method in heterog...
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Matrix factorization (MF), a popular unsupervised learning technique for data representation, has been widely applied in data mining and machine learning.
According to different application scenarios, one can impose different constraintson the factorization to find the desired basis, which captures high-level semantics for the given data, and lea...
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To assess the performance of electrification in an aircraft, multi-physics modeling becomes a good choice for the design of more-electric equipment. The high computational cost and huge design space of this complex model lead to difficulties in the optimal design of the electrical power system, thus, model simplification is mandatory. This paper f...
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The Soil Moisture and Ocean Salinity (SMOS) andSoil Moisture Active Passive (SMAP) missions provide Level-1brightness temperature (Tb) observations that are used for global soil moisture estimation.The nature of these Tb data differs: the SMOS Tb observations contain atmospheric and select reflected extraterrestrial (“Sky”) radiation, whereas the S...
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Experience mining is considered a substantial extension of opinion mining. Experience mining covers the description of all events that are related to the user's perception in the interaction with the object.
There is information about the user`s experience that cannot be obtained with polarity analysis or sentiment analysis. The information obtai...
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Bitcoin and its decentralized computing paradigm for digital currency trading are one of the most disruptive technology in the 21st century.
This paper presents a novel approach to developing a Bitcoin transaction forecast model, DL Forecast, by leveraging deep neural networks for learning Bitcoin transaction network representations. DL Forecast ...
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The city’s district attraction ranking plays an essential role in the city’s government because it can be used to reveal the city’s district attraction and thus help government make decisions for urban planning in terms of the smart city .
The traditional methods for urban planning mainly rely on the district’s GDP, employment rate, population de...
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In recent decades, mobile or the Internet of Thing(IoT) devices are dramatically increasing in many domains and applications. Thus, a massive amount of data is generated and produced.
Those collected data contain a large amount of in-teresting information (i.e., interestingness, weight, frequency, or uncertainty), and most of the existing and gen...
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User identification across multiple datasets has a wide range of applications and there has been an increasing set of research works on this topic during recent years.However, most of existing works focus on user identification with a single input data type, e.g., (I) identifying a user across multiple social networks with online data and (II) dete...
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? Time series are generated at an unprecedented rate in domains ranging from finance, medicine to education.
? Collections composed of heterogeneous, variable-length and misaligned times series are best explored using a plethora of dynamic time warping distances. However, the computational costs of using such elastic distances result in unaccepta...
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The discovery and exploitation of hidden informa-tion in collected data has gained attention in many areas, particularly in the energy field due to their economic and environmental impact.Data mining techniques have then emerged as a suitable toolbox for analysing the data collected in modern network management systems in order to obtain a meaning...
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The Vehicle-to-Grid (V2G) network is, where the battery-powered vehicles provide energy to the power grid, is highly emerging. A robust, scalable, and cost-optimal mechanism that can support the increasing number of transactions in a V2Gnetwork is required.
Existing studies use traditional block chain as to achieve this requirement. Block chain-en...
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This paper aims to evaluate the economic performance of a grid-connected system with storage under a dynamic electricity pricing environment with time-of-use (TOU) and feed-in electricity tariffs. The storage can assist in the demand side management as well as to reduce the electricity bill of the consumers. In this case, consumers would purchase e...
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The rapid development of e-learning and the use of LMS (moodle) have triggered some universities and schools in Indonesia to develop e-learning. However, most of their e-learning materials or contents still underuse the powerful features available in the LMS. The powerful features of the LMS are described and discussed along with their educational ...
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Ridesharing is a mobility concept in which a trip is shared by a vehicle’s driver and one or more passengers called riders. Ridesharing is considered as a more environmentally friendly alternative to single driver commutes in pollution-creating vehicles on overcrowded streets. In this paper, we present the core of a new strategy of the ridesharing ...
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Hostel Management is an application developed to manage the various activities in hostel. The particular project is deal with the problems on managing the a hostel and avoids the problem occurs when carried out manually. Identification of the drawbacks of the existing system leads to the designing of computerized system that will be compatible t...
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With the advancementofweb technology and its growth, there is huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideasandsharing opinions. Social networkingsiteslike Twitter, Face book, Google are rapidly gaining popularity as they allow people...
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