Today, forecasting the stock market has been one of the most challenging issues for the ‘‘artificial intelligence’’ AI research community. Stock market investment methods are sophisticated and rely on analyzing massive volumes of data. In recent years, machine-learning techniques have come under increasing scrutiny to assess and improve market pred...
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Background: Use of encrypted messaging/social media apps like Telegram, WhatsApp and Instagram for drug trafficking are on the rise. Channels operating on Telegram and WhatsApp and Instagram handles are blatantly being misused by drug traffickers for offering various narcotic drugs and Psychotropic substances for sale. Description: WhatsApp and Tel...
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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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Day by day the cases of heart diseases are increasing at a rapid rate and it’s very Important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently.
The research paper mainly focuses on which patient is more likely to have a heart disease based on various m...
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Online transactions have become a significant and crucial aspect of our lives in recent years. It's critical for credit card firms to be able to spot fraudulent credit card transactions so that customers aren't charged for things they didn't buy.
The number of fraudulent transactions is rapidly increasing as the frequency of transactions increa...
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: In this paper, we are implementing a credit card fraud detection system, by using big data technologies. Credit card is one of the most divisive products among all the financial tools available.
The usage of credit cards has become common in today’s world and huge volume of transaction happens online. The increase in these transactions has al...
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predicting stock market is one of the challenging tasks in the field of computation. Physical vs. physiological elements, rational vs. illogical conduct, investor emotions, market rumors, and other factors all play a role in the prediction.
All of these factors combine to make stock values very fluctuating and difficult to forecast accurately. ...
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In India, there are continuously more criminal cases filed, which results in an increase in the number of cases still outstanding. These ongoing increases in criminal cases make them challenging to categorise and resolve.
Therefore, it's crucial to identify a location's patterns of criminal activity in order to stop it from happening in order to ...
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In the age of cloud computing, cloud users with limited storage can outsource their data to remote servers. These servers, in lieu of monetary benefits, offer retrievability of their clients’ data at any point of time.
Secure cloud storage protocols enable a client to check integrity of outsourced data. In this article, we explore the possibili...
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The availability of digital technology in the hands of every citizenry worldwide makes an availableunprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the highnumber of free BDA tools, plat...
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The availability of digital technology in the hands of every citizenry worldwide makes an availableunprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the highnumber of free BDA tools, plat...
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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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Deep neural Network (DNN) is becoming a focal point in Machine Learning research. Its application is penetrating into different fields and solving intricate and complex problems. DNN is now been applied in health image processing to detect various ailment such as cancer and diabetes. Another disease that is causing threat to our health is the kidne...
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Traffic big data has brought many opportunities for traffic management applications. However several challenges like heterogeneity, storage, management, processing and analysis of traffic big data may hinder their efficient and real-time applications. All these challenges call for well-adapted distributed framework for smart traffic management that...
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Polycystic Ovary Syndrome (PCOS) is a medical condition which causes hormonal disorder in women in their childbearing years. The hormonal imbalance leads to a delayed or even absent menstrual cycle. Women with PCOS majorly suffer from excessive weight gain, facial hair growth, acne, hair loss, skin darkening and irregular periods leading to inferti...
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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 densi...
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Privacy-preserving distributed data fusion is a pretreatment process in data mining involving security models. In this paper, we present a method of implementing multiparty data fusion, wherein redundant attributes of a same set of individuals are stored by multiple parties. In particular, the merged data does not suffer from background attacks or ...
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Internet is speeding up and modifying the manner in which daily tasks such as online shopping, paying utility bills, watching new movies, communicating, etc., are accomplished. As an example, in older shopping methods, products were mass produced for a single market and audience but that approach is no longer viable. Markets based on long product a...
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This paper deals with a novel sensor network system designed for gathering disaster information including physical environmental information and potential signals of survivers. The system consists of numerous sensor probes and a central database server. The sensor probes organize their own ZigBee network, which is managed by the central database se...
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Traditional top-k algorithms, e.g., TA and NRA, have been successfully applied in many areas such as information retrieval, data mining and databases. They are designed to discover k objects, e.g., top-k restaurants, with highest overall scores aggregated from different attributes, e.g., price and location. However, new emerging applications like q...
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Data mining plays the key role in crime analysis. There are various number of different algorithm in previous research papers like virtual identifier, pruning strategy, support vector machines and apriori algorithms. “VID” (Virtual ID) is to find relationship between record. Then the “Apriori algorithm is used to around six hundred seconds to detec...
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Data mining is an important technique used in many fields with the purpose of acquiring valuable information from big data. This study aims to reveal the relations between the attributes of independent criminal records. The NIBRS database, which includes criminal records in USA that are recorded in 2013, is used in this study. The association rules...
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Opinion mining is very much essential in commerce websites, furthermore, advantages with individuals. An ever-increasing amount of results are stored on the web as well as the amount of people acquiring items from the web are increasing, as a result, the users reviews, or posts are increasing day by day. The reviews towards shipper sites express th...
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Machine learning is a powerful technique which can increase the efficiency and accuracy in disease prediction. In current scenario there is a need for efficient machine learning models that can be used in healthcare system to predict the specific diseases by monitoring the patient’s symptoms over a period. But there exist very few studies pertainin...
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The web application “Expense Tracker” is developed to manage the daily expenses in a more efficient and manageable way. By using this application we can reduce the manual calculations of the daily expenses and keep track of the expenditure. In this application, user can provide his income to calculate his total expenses per day and these results wi...
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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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Privacy-preserving distributed data fusion is a pretreatment process in data mining involving security models. In this paper, we present a method of implementing multiparty data fusion, wherein redundant attributes of a same set of individuals are stored by multiple parties. In particular, the merged data does not suffer from background attacks...
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This paper describes experiences with online quizzes in an operations management course. Online quizzes were introduced to offset larger class sizes. During several quarters, experimentation with online quizzes took place including the number of attempts, the amount of time allowed and the topical coverage in the quizzes. Three research questions a...
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Bio-features are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summaries the factors that hinder biometrics models’ development and deployment on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., s...
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Cross-manifold clustering is an extreme challenge learning problem. Since the low-density hypothesis is not satisfied in cross-manifold problems, many traditional clustering methods failed to discover the cross-manifold structures. In this article, we propose multiple flat projections clustering (MFPC) for cross-manifold clustering. In our MFPC, th...
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In recent years, the appearance of the broad learning system (BLS) is poised to revolutionize conventional artificial intelligence methods. It represents a step toward building more efficient and effective machine-learning methods that can be extended to a broader range of necessary research fields. In this survey, we provide a comprehensive overvi...
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Predictive analytics has a significant potential to support different decision processes. We aimed to compare various machine learning algorithms for the selected task, which predicts credit card clients' default based on the free available data. We chose Random Forest, AdaBoost, XGBoost, and Gradient Boosting algorithm and applied them to a prepar...
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Passwords are pervasively used to authenticate users' identities in mobile apps. To secure passwords against attacks, protection is applied to the password authentication protocol (PAP). The implementation of the protection scheme becomes an important factor in protecting PAP against attacks. We focus on two basic protection in Android, i.e., SSL/T...
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Artificial Intelligence (AI) technology has been widely applied to Internet of Thing (IoT) and one of key applications is intelligence data collection from billions of IoT devices. However, many AI based data collection approaches lack security considerations leading to availability restricted. In this paper, an Intelligent Trust Collaboration Netw...
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Feature selection is of great importance to make prediction for process variables in industrial production. An embedded feature selection method, based on relevance vector machines with an approximated marginal likelihood function, is proposed in this study. By setting hierarchical prior distributions over the model weights and the parameters o...
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Machine learning is widely used in developing computer-aided diagnosis (CAD) schemes of medical images. However, CAD usually computes large number of image features from the targeted regions, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models. In this study, we investigate feasibi...
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This paper presents a recursive feature elimination (RFE) mechanism to select the most informative genes with a least square kernel extreme learning machine (LSKELM) classifier.Describing the generalization ability of LSKELM in a way that is related to small norm of weights, we proposed a ranking criterion to evaluate the importance of genes by the...
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Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to understand the effect of concept drift on the performance of AutoML methods, and which adaptation strategies can ...
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Digital media archives are increasing to colossal proportions in the world today, which includes audio, video and images An Image refers as a picture produced on an electronic display .A digital image is a numeric representation of a two-dimensional image. Digital image processing refers to processing of digital images by using digital computers. N...
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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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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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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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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 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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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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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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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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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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Injuries due to road accidents are one of the most prevalent causes of death apart from health related issues. The World Health Organization states that road traffic injuries caused an estimated 1.35 million deaths worldwide in the year 2016. That is, a person is killed every 25 seconds. This calls for the need to analyse road accidents and the fac...
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