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Content Based Image Retrieval of Remote Sensing Images Based on Deep Features INR 5000 INR 5000
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5c9e3825e996640001ecccca
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Content Based Image Retrieval of Remote Sensing Images Based on Deep Features

This paper presents the results of applying deep features to the problem of content based image retrieval of remote sensing images. Extraction of deep features from the last layers of a trained convolutional neural network from deep learning approaches demonstrates a higher performance than feature extraction using shallow methods. In this paper we used deep features obtained from a fine tuned convolutional neural network and we also focused on experiments of dimension reduction methods of these deep features. We test these methods using UCM Merced and RSSCN7 datasets. Despite their shorter length deep features obtained as a result of dimension reduction methods, are shown to achieve higher performance of content based retrieval.

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Co-Detect: Financial Fraud Detection With Anomaly Feature Detection INR 5000 INR 5000
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5c9e37d028faf10001b620a6
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Co-Detect: Financial Fraud Detection With Anomaly Feature Detection

Financial fraud, such as money laundering, is known to be a serious process of crime that makes illegitimately obtained funds go to terrorism or other criminal activity. This kind of illegal activities involve complex networks of trade and financial transactions, which makes it difficult to detect the fraud entities and discover the features of fraud. Fortunately, trading/ transaction network and features of entities in the network can be constructed from the complex networks of the trade and financial transactions. The trading/transaction network reveals the interaction between entities, and thus anomaly detection on trading networks can reveal the entities involved in the fraud activity; while features of entities are the description of entities, and anomaly detection on features can reflect details of the fraud activities. Thus, network and features provide complementary information for fraud detection, which has potential to improve fraud detection performance. However, the majority of existing methods focus on networks or features information separately, which does not utilize both information. In this paper, we propose a novel fraud detection framework, CoDetect, which can leverage both network information and feature information for financial fraud detection. In addition, the CoDetect can simultaneously detecting financial fraud activities and the feature patterns associated with the fraud activities. Extensive experiments on both synthetic data and real-world data demonstrate the efficiency and the effectiveness of the proposed framework in combating financial fraud, especially for money laundering.

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Child depression finder by using machine learning INR 5000 INR 5000
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5c9e376293c0cc0001d3e610
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Child depression finder by using machine learning

Suicide is the second leading cause of death among young adults but the challenges of preventing suicide are significant because the signs often seem invisible. Research has shown that clinicians are not able to reliably predict when someone is at greatest risk.. Machine learning and data extracted from one 20-second phase of the task are used to predict diagnosis in a large sample of children with and without an internalizing diagnosis. Nevertheless, the proposed approach provides a rapid, objective, and accurate means for diagnosing internalizing disorders in young children. This new approach reduces the time required for diagnosis while also limiting the need for highly trained personnel – each of which can help to reduce the length of waitlists for child mental health services. While these results can likely be improved and extended, this is an important first step in reducing the barriers associated with assessing young children for internalizing disorders.

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Body Structure Aware Deep Crowd Counting INR 5000 INR 5000
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5c9e36cb497c1c00014121e4
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Body Structure Aware Deep Crowd Counting

Crowd counting is a challenging task, mainly due to the severe occlusions among dense crowds. This paper aims to take a broader view to address crowd counting from the perspective of semantic modeling. In essence, crowd counting is a task of pedestrian semantic analysis involving three key factors: pedestrians, heads, and their context structure. The information of different body parts is an important cue to help us judge whether there exists a person at a certain position. Existing methods usually perform crowd counting from the perspective of directly modeling the visual properties of either the whole body or the heads only, without explicitly capturing the composite body-part semantic structure information that is crucial for crowd counting. In our approach, we first formulate the key factors of crowd counting as semantic scene models. Then, we convert the crowd counting problem into a multi-task learning problem, such that the semantic scene models are turned into different sub-tasks. Finally, the deep convolutional neural networks are used to learn the sub-tasks in a unified scheme. Our approach encodes the semantic nature of crowd counting and provides a novel solution in terms of pedestrian semantic analysis. In experiments, our approach outperforms the state-of the-art methods on four benchmark crowd counting data sets. The semantic structure information is demonstrated to be an effective cue in scene of crowd counting.

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AI-assisted Prediction on Potential Health Risks with Regular Physical Examination Records INR 5000 INR 5000
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5c9e3341e996640001ecccbe
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AI-assisted Prediction on Potential Health Risks with Regular Physical Examination Records

Abstract: With the development of society and economy, people pay more attention to their own health. The demand of more personalized health service is gradually rising. However, due to the lack of experienced doctors and physicians, most healthcare organizations cannot meet the medical demand of public. With the widespread use of hospital information system, there is huge amount of generated data which can be used to improve healthcare service. Thus, more and more data mining applications are developed to provide people more customized healthcare service. In this paper, we propose an AI-assisted prediction system, which leverages data mining methods to reveal the relationship between the regular physical examination records and the potential health risk. It can predict examinees’ risk of physical status next year based on the physical examination records this year. The system provides a user-friendly interface for examinees and doctors. Examinees can know their potential health risks while doctors can get a set of examinees with potential risk. It is a good solution for the mismatch of insufficient medical resources and rising medical demands.

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A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining INR 5000 INR 5000
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5c9a0811376f0900011aad49
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A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining

Abstract: The explosive growth in popularity of social networking leads to the problematic usage. An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model(STM)to improve the accuracy. To increase the scalability of STM, we further improve the efficiency with performance guarantee. INTRODUCTION With the explosive growth in popularity of social networking and messaging apps, online social networks (OSNs) have become a part of many people’s daily lives. Most research on social network mining focuses on discovering the knowledge behind the data for improving people’s life. While OSNs seemingly expand their users’ capability in increasing social contacts, they may actually decrease the face-to-face interpersonal interactions in the real world. Due to the epidemic scale of these phenomena, new terms such as Phubbing (Phone Snubbing) and Nomophobia (No Mobile Phone Phobia) have been created to describe those who cannot stop using mobile social networking apps. In fact, some social network mental disorders (SNMDs), such as Information Overload and Net Compulsion, have been recently noted.1 For example, studies point out that 1 in 8 Americans suffer from problematic Internet use2. Moreover, leading journals in mental health, such as the American Journal of Psychiatry, have reported that the SNMDs may incur excessive use, depression, social withdrawal, and a range of other negative repercussions. Indeed, these symptoms are important components of diagnostic criteria for SNMDs e.g., excessive use of social networking apps – usually associated with a loss of the sense of time or a neglect of basic drives, and withdrawal – including feelings of anger, tension, and/or depression when the computer/apps are inaccessible. SNMDs are social-oriented and tend to happen to users who usually interact with others via online social media. Those with SNMDs usually lack offline interactions, and as a result seek cyber-relationships to compensate. Today, identification of potential mental disorders often falls on the shoulders of supervisors (such as teachers or parents) passively. However, since there are very few notable physical risk factors, the patients usually do not actively seek medical or psychological services. Therefore, patients would only seek clinical interventions when their conditions become very severe. Existing System: Psychology has identified several crucial mental factors related to SNMDs, they are mostly examined as standard diagnostic criteria in survey questionnaires. To automatically detect potential SNMD cases of OSN users, extracting these factors to assess users’ online mental states is very challenging. For example, the extent of loneliness and the effect of disinhibition of OSN users are not easily observable.3 Therefore, there is a need to develop new approaches for detecting SNMD cases of OSN users. We argue that mining the social network data of individuals as a complementary alternative to the conventional psychological approaches provides an excellent opportunity to actively identify those cases at an early stage. Proposed System: Specifically, we formulate the task as a semi-supervised classification problem to detect three types of SNMDs : i) Cyber-Relationship Addiction, which shows addictive behavior for building online relationships; ii)Net Compulsion, which shows compulsive behavior for online social gaming or gambling; and iii) Information Overload, which is related to uncontrollable surfing. By exploiting machine learning techniques with the ground truth obtained via the current diagnostic practice in Psychology, we extract and analyze the following crucial categories of features from OSNs: 1) social comparison, 2) social structure, 3) social diversity, 4) para social relationships, 5) online and offline interaction ratio, 6) social capital, 7) disinhibition, 8) self-disclosure, and 9) bursting temporal behavior. These features capture important factors or serve as proxies for SNMD detection. For example, studies manifest that users exposed to positive posts from others on Facebook with similar background are inclined to feel malicious envy and depressed due to the social comparison. The depression leads users to disorder behaviors, such as information overload or net compulsion. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS:  System : Pentium Dual Core.  Hard Disk : 120 GB.  Monitor : 15’’ LED  Input Devices : Keyboard, Mouse  Ram : 1 GB SOFTWARE REQUIREMENTS:  Operating system : Windows 7.  Coding Language : JAVA/J2EE  Tool : Netbeans 7.2.1  Database : MYSQL Hong-HanShuai, Chih-Ya Shen, De-NianYang, Senior Member, IEEE, Yi-Feng Lan, Wang-Chien Lee, Philip S. Yu, Fellow, IEEE andMing-Syan Chen, Fellow, IEEE “A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining”, IEEE Transactions Knowledge and data Engineering, 2018.

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Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy INR 5000 INR 5000
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5c98d98a376f0900011aa68c
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Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy

Abstract: Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system (FDS). This lack of realism concerns two main aspects: 1) the way and timing with which supervised information is provided and 2) the measures used to assess fraud-detection performance. This paper has three major contributions. First, we propose, with the help of our industrial partner, a formalization of the fraud-detection problem that realistically describes the operating conditions of FDSs that everyday analyze massive streams of credit card transactions. We also illustrate the most appropriate performance measures to be used for fraud-detection purposes. Second, we design and assess a novel learning strategy that effectively addresses class imbalance, concept drift, and verification latency. Third, in our experiments, we demonstrate the impact of class unbalance and concept drift in a real-world data stream containing more than 75 million transactions, authorized over a time window of three years.

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