IEEE based Final Year Software Projects


Data Science Projects





Block Chain Projects


Title : AN EFFICIENT AND PRIVACY PRESERVING BIOMETRIC IDENTIFICATION SCHEME IN CLOUD COMPUTING WITH BLOCKCHAIN

Details : Biometric identification has become increasingly popular in recent years. With the development of cloud computing, database owners are motivated to outsource the large size of biometric data and identification tasks to the cloud to get rid of the expensive storage and computation costs, which however brings potential threats to users’ privacy. In this paper, we propose an efficient and privacy-preserving biometric identification outsourcing scheme. Specifically, the biometric data is encrypted and outsourced to the cloud server. To execute a biometric identification, the database owner encrypts the query data and submits it to the cloud. The cloud performs identification operations over the encrypted database and returns the result to the database owner. A thorough security analysis indicates the proposed scheme is secure even if attackers can forge identification requests and collude with the cloud. Compared with previous protocols, experimental results show the proposed scheme ach

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Title : BLOCKCHAIN-ENABLED E-VOTING

Details : E-VOTING IS AMONG the key public sectors that can be disrupted by blockchain technology.1 The idea in blockchain-enabled e-voting (BEV) is simple. To use a digital-currency analogy, BEV issues each voter a “wallet” containing a user credential. Each voter gets a single “coin” representing one opportunity to vote. Casting a vote transfers the voter’s coin to a candidate’s wallet. A voter can spend his or her coin only once. However, voters can change their vote before a preset deadline.

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Title : IEEE 2019 PRIVACY PRESERVING SEARCHABLE ENCRYPTION WITH FINE-GRAINED ACCESS CONTROL

Details : Searchable encryption facilitates cloud server to search over encrypted data without decrypting the data. Single keyword based searchable encryption enables a user to access only a subset of documents, which contains the keyword of the user’s interest. In this paper we present a single keyword based searchable encryption scheme for the applications where multiple data owners upload their data and multiple users access the data. We use attribute based encryption scheme that allows user to access the selective subset of data from cloud without revealing his/her access rights to the cloud server. The proposed scheme is proven adaptively secure against chosen-keyword attack in the random oracle model. We have implemented the scheme on Google cloud instance and the performance of the scheme found feasible in real-world applications.

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Artificial Intelligence Projects


Title : Predict the Diagnosis of Heart Disease Patients Using Classification Mining Techniques

Details : The data mining can be referred as discovery of relationships in large databases automatically and in some cases it is used for predicting relationships based on the results discovered. Data mining plays an important role in various applications such as business organizations, e-commerce, health care industry, scientific and engineering. In the health care industry, the data mining is mainly used for Disease Prediction.the objective our works to predict the diagnosis of heart disease with reduced number of attributes. Here fourteen attributes involved in predicting heart disease. But fourteen attributes are reduced to six attributes by using Genetic algorithm. Subsequently three classifiers like Naive Bayes, Classification by Clustering and Decision Tree are used to predict the diagnosis of heart disease after the reduction of number of attributes

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Title : Remote Multimodal Biometric Identification Based on the Fusion of the Iris and the Fingerprint

Details : With the development of various services through the Web and especially with the emergence of electronic commerce, all suppliers of products and services are providing considerable efforts to secure against all possible fraudulent intrusions. It appears that biometrics is the only method that can satisfy the requirements of remote identity in terms of relevance and reliability. In this paper, we propose a client-server network architecture for a remote multimodal biometric identification. As a matter of fact, we use two modalities, namely, the human iris and his fingerprint in order to strengthen the security, since the unimodal biometric systems cannot always be used reliably to perform recognition. However, the association of the information presented by the various modalities may allow a precise recognition of the identity. Concerning the fusion of these two modalities, we used a new approach at the scores level based on a classification method by the decision tree and a combination

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Title : Truth Discovery in Crowdsourced Detection of Spatial Events

Details : The ubiquity of smartphones has led to the emergence of mobile crowdsourcing tasks such as the detection of spatial events when smartphone users move around in their daily lives. However, the credibility of those detected events can be negatively impacted by unreliable participants with lowquality data. Consequently, a major challenge in quality control is to discover true events from diverse and noisy participants’ reports. This truth discovery problem is uniquely distinct from its online counterpart in that it involves uncertainties in both participants’ mobility and reliability. Decouplingthesetwotypesofuncertaintiesthroughlocationtracki ngwill raise severe privacy and energy issues, whereas simply ignoring missing reports or treating them as negative reports will significantly degrade the accuracy of the discovered truth. In this paper, we propose a new method to tackle this truth discovery problem through principled probabilistic modeling.

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Networking Projects


Title : Cost Minimization Algorithms for Data Center Management

Details : Due to the increasing usage of cloud computing applications, it is important to minimize energy cost consumed by a data center, and simultaneously, to improve quality of service via data center management. One promising approach is to switch some servers in a data center to the idle mode for saving energy while to keep a suitable number of servers in the active mode for providing timely service. In this paper, we design both online and offline algorithms for this problem. For the offline algorithm, we formulate data center management as a cost minimization problem by considering energy cost, delay cost (to measure service quality), and switching cost (to change servers’ active/idle mode). Then, we analyze certain properties of an optimal solution which lead to a dynamic programming based algorithm. Moreover, by revising the solution procedure, we successfully eliminate the recursive procedure and achieve an optimal offline algorithm with a polynomial complexi

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Title : Multi-party secret key agreement over statedependent wireless broadcast channels

Details : We consider a group of m trusted and authenticated nodes that aim to create a shared secret key K over a wireless channel in the presence of an eavesdropper Eve. We assume that there exists a state dependent wireless broadcast channel from one of the honest nodes to the rest of them including Eve. All of the trusted nodes can also discuss over a cost-free, noiseless and unlimited rate public channel which is also overheard by Eve. For this setup, we develop an information-theoretically secure secret key agreement protocol. We show the optimality of this protocol for “linear deterministic” wireless broadcast channels. This model generalizes the packet erasure model studied in literature for wireless broadcast channels. Here, the main idea is to convert a deterministic channel to multiple independent erasure channels by using superposition coding. For “state-dependent Gaussian” wireless broadcast channels, by using insights from the deterministic problem, we propose an achievabil

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Title : Optimizing Cloud-Service Performance: Efficient Resource Provisioning via Optimal Workload Allocation

Details : Cloud computing is being widely accepted and utilized in the business world. From the perspective of businesses utilizing the cloud, it is critical to meet their customers’ requirements by achieving service-level-objectives. Hence, the ability to accurately characterize and optimize cloud-service performance is of great importance. In this paper a stochastic multi-tenant framework is proposed to model the service of customer requests in a cloud infrastructure composed of heterogeneous virtual machines. Two cloudservice performance metrics are mathematically characterized, namely the percentile and the mean of the stochastic response time of a customer request, in closed form. Based upon the proposed multi-tenant framework, a workload allocation algorithm, termed maxmin-cloud algorithm, is then devised to optimize the performance of the cloud service. A rigorous optimality proof of the max-min-cloud algorithm is also given. Furthermore, the resource-provisionin

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Android Projects


Title : A Micro-Location Based Dynamic Device Oriented Control System for IOT Applications for cs branch

Details : device-oriented control system is based on the devices to be controlled and the location they use devices, providing a control service system with dynamic operation interface: when detecting the approaching users, the system would automatically notify users the available devices and provide users with the control options for the devices.

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Title : A Provably Secure General Construction for Key Exchange Protocols Using Smart Card and Password

Details : Key exchange protocols using both smart card and password are widely used nowadays since they provide greater convenience and stronger security than protocols using only a password. Most of these protocols are often limited to simple network systems, and they may have security risks. We propose a general construction for key exchange protocols using smart card and password to avoid these flaws. The constructed protocol from the general construction has only one additional communication round than the original public encryption scheme. This construction is proven secure under random oracle model, so it can resist several common types of attacks. It is also adapted well to various networks. Compared with related protocols, the proposed key exchange protocol generated from the general construction has better secure properties and good computational efficiency in storage cost and operation time.

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Title : An NFC featured three level authentication systems for tenable transaction and abridgment of ATM card Blocking intricacies

Details : The flexible use of credit and debit card transactions has become increasingly ubiquitous and so have the associated vulnerabilities that make them a common target for cyber criminals. Furthermore, a prevalent complication associated with blocking of ATM cards involves tedious interactive processes and even possibly long waiting times during interaction with customer care services. Using a three factor authentication scheme employing NFC (Near Field Communication: an emerging technology evolved from a combination of contact-less identification and inter connection providing data exchange), Dash Matrix Algorithm and One-time password, we describe and quantify the potential to overcome common transaction liabilities (brute force attack, Shoulder surfing, skimming of ATM cards, etc.).

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Cloud Computing Projects


Title : A SECURE CLIENT-SIDE FRAMEWORK FOR PROTECTING THE PRIVACY OF HEALTH DATASTORED ON THE CLOUD

Details : Abstract :In the past decade, Cloud-Computing emerged as a new computing concept with a distributed nature using virtual network and systems. Many businesses rely on this technology to keep their systems running but concerns are rising about security breaches in cloud computing. Cloud providers (CPs) are taking significant measures to maintain the security and privacy of the data stored on their premises, in order to preserve the customers’ trust. Nevertheless, in certain applications, such as medical health records for example, the medical facility is responsible for preserving the privacy of the patients’ data. Although the facility can offload the overhead of storing large amounts of data by using cloud storage, relying solely on the security measures taken by the CP might not be sufficient. Any security breach at the CP’s premises does not protect the medical facility from being held accountable. This work aims to solve this problem by presenting a secure appro

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Title : AN ATTRIBUTE-BASED CONTROLLED COLLABORATIVE ACCESS CONTROL SCHEME FOR PUBLIC CLOUD STORAGE

Details : In public cloud storage services, data are outsourced to semi-trusted cloud servers which are outside of data owners’ trusted domain. To prevent untrustworthy service providers from accessing data owners’ sensitive data, outsourced data are often encrypted. In this scenario, conducting access control over these data becomes a challenging issue. Attribute-based encryption (ABE) has been proved to be a powerful cryptographic tool to express access policies over attributes, which can provide a fine-grained, flexible, and secure access control over outsourced data. However, the existing ABE-based access control schemes do not support users to gain access permission by collaboration. In this paper, we explore a special attribute-based access control scenario where multiple users having different attribute sets can collaborate to gain access permission if the data owner allows their collaboration in the access policy. Meanwhile, the collaboration that is not designated in the access poli

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Title : AN EFFICIENT AND PRIVACY-PRESERVING BIO METRIC IDENTIFICATION SCHEME IN CLOUD

Details : Bio-metric identification has become increasingly popular in recent years.With the development of cloud computing, database owners are motivated to outsource the large size of bio metric data and identification tasks to the cloud to get rid of the expensive storage and computation costs, which, however, brings potential threats to users’ privacy. In this paper, we propose an efficient and privacy-preserving bio-metric identification outsourcing scheme. Specially, the bio metric To execute a bio metric identification, the database owner encrypts the query data and submits it to the cloud. The cloud performs identification operations over the encrypted database and returns the result to the database owner. A thorough security analysis indicates that the proposed scheme is secure even if attackers can forge identification requests and collude with the cloud. Compared with previous protocols, experimental results show that the proposed scheme achieves a better performance in both prepara

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Image Processing Projects


Title : Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data

Details : Pattern mining is one of the most important tasks to extract meaningful and useful information from raw data. This task aims to extract item-sets that represent any type of homogeneity and regularity in data. Although many efficient algorithms have been developed in this regard, the growing interest in data has caused the performance of existing pattern mining techniques to be dropped. The goal of this paper is to propose new efficient pattern mining algorithms to work in big data. To this aim, a series of algorithms based on the MapReduce framework and the Hadoop open-source implementation have been proposed. The proposed algorithms can be divided into three main groups. First, two algorithms [Apriori MapReduce (AprioriMR) and iterative AprioriMR] with no pruning strategy are proposed, which extract any existing itemset in data. Second, two algorithms (space pruning AprioriMR and top AprioriMR) that prune the search space by means of the well-known anti-monotone property are proposed.

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Title : Enhanced Password Processing Scheme Based on Visual Cryptography and OCR

Details : Traditional password conversion scheme for user authentication is to transform the passwords into hash values. These hash-based password schemes are comparatively simple and fast because those are based on text and famed cryptography. However, those can be exposed to cyber-attacks utilizing password by cracking tool or hash-cracking online sites. Attackers can thoroughly figure out an original password from hash value when that is relatively simple and plain. As a result, many hacking accidents have been happened predominantly in systems adopting those hash-based schemes. In this work, we suggest enhanced password processing scheme based on image using visual cryptography (VC). Different from the traditional scheme based on hash and text, our scheme transforms a user ID of text type to two images encrypted by VC. The user should make two images consisted of subpixels by random function with SEED which includes personal information. The server only has user’s ID and one of the images

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Title : Estimation of nitrogen and classification of weeds

Details : Estimation of nitrogen content and weed control is essential and critical operation and can affect crop yield. Fertilizers and weedicides play an important role in maintaining nitrogen and weed control but their role is under criticism due to perceived excessive use and they are potentially harmful to the environment. Autonomous estimation of nitrogen content and weed control concepts have recently being extensively researched due to the advantages that they possess. In this proposed work, we systematically choose methods to be used for the estimation of nitrogen and classification of weeds.

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Internet of Things Projects


Title : Design and Realization of the Accelerometer based Transportation System

Details : An accident is a deviation from expected behavior of event that adversely affects the property, living body or persons and the environment. Security in vehicle to vehicle communication or travelling is primary concern for everyone. The work presented in this article documents the designing of an accident detection system. The accident detection system design informs the police control room or any other emergency calling system about the accident. An accelerometer sensor has been used to detect abrupt change in g-forces in the vehicle due to accident. When the range of g- forces comes under the accident severity, then the microcontroller activates the GSM modem to send a prestored SMS to a predefined phone number. Also a buzzer is switched on. The product design was tested in various conditions. The test result confirms the stability and reliability of the system. Note : Call for Final year engineering project ask for detailed synopsis for ECE students

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Title : Design of automatic accident detection and management in vehicular environment using IOT…

Details : Road accidents rates are very high nowadays, especially two wheelers. Timely medical aid can help in saving lives. This system aims to alert the nearby medical center about the accident to provide immediate medical aid. The attached accelerometer in the vehicle senses the tilt of the vehicle and the Heartbeat sensor on the user’s body senses the abnormality of the heartbeat to understand the seriousness of the accident. Thus the systems will make the decision and sends the information to the smart phone, connected to the accelerometer and heartbeat sensor, through Bluetooth. The Android application in the mobile phone will sent text message to the nearest medical center and friends. Application also shares the exact location of the accident that can save the time. Note : Call for Final year best engineering project ask for detailed synopsis for ECE students Call for more details – 9972364704 / 8073744810 ..

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Title : Alcoholic breath analyzer and tilt detector for vehicle secured ignition.

Details : The road accidents are increasing at high rate now a day. Traffic authority also taking several measures to bring down the accidents. Immediate action will be taken if any violates the traffic rules. The drink and drive is one of the case because of which the road accidents may happen. Drinking is not an offense but drinking and driving is an offense. A drunken driver may not only cause harm to his life, he may take lives of others too. So this is one of the serious cases to be controlled. This project implements an automated system which checks for alcohol level of the driver and the engine will be turned ON only if the alcohol level is below some limit. Also the will check weather the driver is feeling sleepy by checking his head position by using tilt sensor. Note : Call for Final year best engineering project ask for detailed synopsis for ECE students Call for more details – 9972364704 / 8073744810

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Data Mining Projects


Title : A WORKFLOW MANAGEMENT SYSTEM FOR SCALABLE DATA MINING ON CLOUD

Details : The extraction of useful information from data is often a complex process that can be conveniently modeled as a data analysis workflow. When very large data sets must be analyzed and/or complex data mining algorithms must be executed, data analysis workflows may take very long times to complete their execution. Therefore, efficient systems are required for the scalable execution of data analysis workflows, by exploiting the computing services of the Cloud platforms where data is increasingly being stored. The objective of the paper is to demonstrate how Cloud software technologies can be integrated to implement an effective environment for designing and executing scalable data analysis workflows. We describe the design and implementation of the Data Mining Cloud Framework (DMCF), a data analysis system that integrates a visual workflow language and a parallel runtime with the Software-as-a-Service (SaaS) model. DMCF was designed taking into account the needs of real data mining applica

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Title : Cyberbullying Detection based on SemanticEnhanced Marginalized Denoising Auto-Encoder

Details : As a side effect of increasingly popular social media, cyber bullying has emerged as a serious problem afflicting children, adolescents and young adults. Machine learning techniques make automatic detection of bullying messages in social media possible, and this could help to construct a healthy and safe social media environment. In this meaningful research area, one critical issue is robust and discriminative numerical representation learning of text messages. In this paper, we propose a new representation learning method to tackle this problem. Our method named Semantic-Enhanced Marginalized Denoising Auto-Encoder (smSDA) is developed via semantic extension of the popular deep learning model stacked denoising auto encoder. The semantic extension consists of semantic dropout noise and sparsity constraints, where the semantic dropout noise is designed based on domain knowledge and the word embedding technique. Our proposed method is able to exploit the hidden feature structure of bully

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Title : Efficient Algorithms for Mining Top-K High Utility Itemsets

Details : High utility itemsets (HUIs) mining is an emerging topic in data mining, which refers to discovering all itemsets having a utility meeting a user-specified minimum utility threshold min_util. However, setting min_util appropriately is a difficult problem for users. Generally speaking, finding an appropriate minimum utility threshold by trial and error is a tedious process for users. If min_util is set too low, too many HUIs will be generated, which may cause the mining process to be very inefficient. On the other hand, if min_util is set too high, it is likely that no HUIs will be found. In this paper, we address the above issues by proposing a new framework for top-k high utility itemset mining, where k is the desired number of HUIs to be mined. Two types of efficient algorithms named TKU (mining Top-K Utility itemsets) and TKO (mining Top-K utility itemsets in One phase) are proposed for mining such itemsets without the need to set min_util. We provide a structural comparison of the

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Big Data Projects


Title : A Scalable Data Chunk Similaritybased Compression Approach for Efficient Big Sensing Data Processing on Cloud

Details : Big sensing data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity. Cloud computing provides a promising platform for big sensing data processing and storage as it provides a flexible stack of massive computing, storage, and software services in a scalable manner. Current big sensing data processing on Cloud have adopted some data compression techniques. However, due to the high volume and velocity of big sensing data, traditional data compression techniques lack sufficient efficiency and scalability for data processing. Based on specific on-Cloud data compression requirements, we propose a novel scalable data compression approach based on calculating similarity among the partitioned data chunks. Instead of compressing basic data units, the compression will be conducted over partitioned data chunks. To restore original data sets, some restoration functions and predictions will SHIELD TECHNOLOGIES, 2232,

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Title : FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters

Details : Traditional parallel algorithms for mining frequent itemsets aim to balance load by equally partitioning data among a group of computing nodes. We start this study by discovering a serious performance problem of the existing parallel Frequent Itemset Mining algorithms. Given a large dataset, data partitioning strategies in the existing solutions suffer high communication and mining overhead induced by redundant transactions transmitted among computing nodes. We address this problem by developing a data partitioning approach called FiDoop-DP using the MapReduce programming model. The overarching goal of FiDoop-DP is to boost the performance of parallel Frequent Itemset Mining on Hadoop clusters. At the heart of FiDoop-DP is the Voronoi diagram-based data partitioning technique, which exploits correlations among transactions. Incorporating the similarity metric and the Locality-Sensitive Hashing technique, FiDoop-DP places highly similar transactions into a data partition to improve loca

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Title : NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media

Details : Nowadays, a big part of people rely on available con-tent in social media in their decisions (e.g. reviews and feedback on a topic or product). The possibility that anybody can leave a review provide a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this study, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review datasets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks. Using the importance of spam features help us to obtain better results in terms of different metrics experimented on real-world

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Machine Learning Projects


Title : An Optimization Approach to Improve Classification Performance in Cancer and Diabetes Prediction

Details : There are many destructive diseases in the world which cause rapid death by taking time to affect such as cancer and diabetes. They take a lot of time to spread, thus they are curable or somewhat scalable to a great extent if they are diagnosed soon after introduced into the human body. Research shows that almost all type of cancer can be cured if they are detected in the early stage. It is also true for diabetes as they can be controlled if they are detected at the right time. So, a prediction technique that takes help from the computer and processes data from affected user to detect possible contamination can be a great tool for assisting both the doctors and patients with these diseases. A challenge in the process is that the detection accuracy has to be acceptable in order to make the system a reliable one. In this study, we have analyzed medical data using several classification algorithms in order to optimize classifier performance for cancer and diabetes prediction.

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Title : Diagnosis of COPD Based on a Knowledge Graph and Integrated Model

Details : Chronic obstructive pulmonary disease (COPD) is a chronic lung disease that causes a progressive decline in respiratory function. Diagnosing COPD in the early curable stages is very important and may even save the life of a patient. In this paper, we present an integrated model for diagnosing COPD based on a knowledge graph. First, we construct a knowledge graph of COPD to analyze the relationship between feature subsets and further discover the knowledge of implied diseases from the data. Second, we propose an algorithm for sorting features and an adaptive feature subset selection algorithm CMFS-η, which selects an optimal subset of features from the original high-dimensional set. Finally, the DSA-SVM integrated model is suggested to build the classifier for the diagnosis and prediction of COPD. We performed extensive experiments on the dataset from the hospital outpatient electronic medical record database. The classification accuracy of our method was 95.1%. It is superior to some

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Title : Disease Influence Measure Based Diabetic Prediction with Medical Data Set Using Data Mining

Details : The problem of diabetic prediction has been well studied in this paper. The disease predictions have been explored using various methods of data mining. The use of medical data set on the prediction of diabetic mellitus has been analyzed. This paper performs a detailed survey on disease prediction using data mining approaches based on diabetic data set. The presence of disease has been identified using the appearance of various symptoms. However, the methods use different features and produces varying accuracy. The result of prediction differs with the methods/measures/ features being used. Towards diabetic prediction, a Disease Influence Measure (DIM) based diabetic prediction has been presented. The method preprocesses the input data set and removes the noisy records. In the second stage, the method estimates disease influence measure (DIM) based on the features of input data point. Based on the DIM value, the method performs diabetic prediction. Different approaches of disease predi

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Web Security Projects


Title : Diagnosis of COPD Based on a Knowledge Graph and Integrated Model Chronic obstructive pulmonary disease (COPD) is a chronic lung disease that causes a progressive decline in respiratory function. Diagnosing COPD in the early curable stages is very

Details : Chronic obstructive pulmonary disease (COPD) is a chronic lung disease that causes a progressive decline in respiratory function. Diagnosing COPD in the early curable stages is very important and may even save the life of a patient. In this paper, we present an integrated model for diagnosing COPD based on a knowledge graph. First, we construct a knowledge graph of COPD to analyze the relationship between feature subsets and further discover the knowledge of implied diseases from the data. Second, we propose an algorithm for sorting features and an adaptive feature subset selection algorithm CMFS-η, which selects an optimal subset of features from the original high-dimensional set. Finally, the DSA-SVM integrated model is suggested to build the classifier for the diagnosis and prediction of COPD. We performed extensive experiments on the dataset from the hospital outpatient electronic medical record database. The classification accuracy of our method was 95.1%. It is superior to some

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Title : A CNN-based Framework for Comparison of Contactless to Contact-based Fingerprint

Details : Accurate comparison of contactless 2D fingerprint images with contact-based fingerprints is critical for the success of emerging contactless 2D fingerprint technologies, which offer more hygienic and deformation-free acquisition of fingerprint features. Convolutional neural networks (CNN) have shown remarkable capabilities in biometrics recognition. However, there has been almost nil attempt to match fingerprint images using CNNbased approaches. This paper develops a CNN-based framework to accurately match contactless and contact-based fingerprint images. Our framework firstly trains a multi-Siamese CNN using fingerprint minutiae, respective ridge map and specific region of ridge map. This network is used to generate deep fingerprint representation using a distance-aware loss function. Deep fingerprint representations generated in such multi-Siamese network are concatenated for more accurate cross comparison. The proposed approach for cross-fingerprint comparison is

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Title : A Leopard Cannot Change Its Spots”: Improving Face Recognition Using 3D- based Caricatures

Details : —Caricatures refer to a representation of a person in which the distinctive features are deliberately exaggerated, with several studies showing that humans perform better at recognizing people from caricatures than using original images. Inspired by this observation, this paper introduces the first fully automated caricature-based face recognition approach capable of working with data acquired in the wild. Our approach leverages the 3D face structure from a single 2D image and compares it to a reference model for obtaining a compact representation of face features deviations. This descriptor is subsequently deformed using a ’measure locally, weight globally’ strategy to resemble the caricature drawing process. The deformed deviations are incorporated in the 3D model using the Laplacian mesh deformation algorithm, and the 2D face caricature image is obtained by projecting the deformed model in the original camera-view. To demonstrate the advantages of caricatur

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