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Prediction of Explicit Features for Recommendation System Using User Reviews

    With the explosive growth of data, it has become very difficult for a person to process the data and find the right information from it. So, to discover the right information from the colossal amount of data that is available online, we need information filtering systems. Recommendation systems (RS) help users find the most interesting information among the options that are available. Ratings given by the users play a vital role in determining the purposes of recommendations. Earlier, researchers used a user’s rating history to predict unknown ratings, but recently a user’s review has gained a lot of attention as it contains a lot of relevant information about a user’s decision. The proposed system makes an attempt to deal with the problem of uncertainty in the rating histories by using textual reviews. Two datasets are used to experimentally analyze the proposed framework. In this approach, clustering techniques are used with natural language processing (NLP) for prediction. It also compares how different algorithms, such as K-mean, spectral, and hierarchical clustering algorithms, produce a varied outcome and concludes which method is appropriate for the given recommendation scenarios. We also validate how the proposed method outperforms the non-clustering-based methods.

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Publication Date
Mon Feb 01 2021
Journal Name
International Journal Of Electrical And Computer Engineering (ijece)
Features of genetic algorithm for plain text encryption

The data communication has been growing in present day. Therefore, the data encryption became very essential in secured data transmission and storage and protecting data contents from intruder and unauthorized persons. In this paper, a fast technique for text encryption depending on genetic algorithm is presented. The encryption approach is achieved by the genetic operators Crossover and mutation. The encryption proposal technique based on dividing the plain text characters into pairs, and applying the crossover operation between them, followed by the mutation operation to get the encrypted text. The experimental results show that the proposal provides an important improvement in encryption rate with comparatively high-speed Process

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Publication Date
Mon Oct 29 2018
Journal Name
International Journal Of Women's Health And Reproduction Sciences
Prediction of Placenta Accreta Using Hyperglycosylated Human Chorionic Gonadotropin

Objectives: Hyperglycosylated human chorionic gonadotropin (hCG) is a variant of hCG. In addition, it has a different oligosaccharide structure compared to the regular hCG and promotes the invasion and differentiation of peripheral cytotrophoblast. This study aimed to measure hyperglycosylated hCG as a predictor in the diagnosis of placenta accreta. Materials and Methods: In general, 90 pregnant women were involved in this case-control study among which, 30 ladies (control group) were pregnant within the gestational age of ≥36 weeks with at least one previous caesarean section and a normal sited placenta in transabdominal ultrasound (TAU). The other 60 pregnant women (case

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Publication Date
Fri Apr 01 2022
Journal Name
Civil Engineering Journal
Prediction of Urban Spatial Changes Pattern Using Markov Chain

Urban land uses of all kinds are the constituent elements of the urban spatial structure. Because of the influence of economic and social factors, cities in general are characterized by the dynamic state of their elements over time. Urban functions occur in a certain way with different spatial patterns. Hence, urban planners and the relevant urban management teams should understand the future spatial pattern of these changes by resorting to quantitative models in spatial planning. This is to ensure that future predictions are made with a high level of accuracy so that appropriate strategies can be used to address the problems arising from such changes. The Markov chain method is one of the quantitative models used in spatial planning to ana

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Publication Date
Tue Sep 03 2019
Journal Name
Eastern-european Journal Of Enterprise Technologies
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Publication Date
Sun Jan 27 2019
Journal Name
Civil Engineering Journal
Prediction of Sediment Accumulation Model for Trunk Sewer Using Multiple Linear Regression and Neural Network Techniques

Sewer sediment deposition is an important aspect as it relates to several operational and environmental problems. It concerns municipalities as it affects the sewer system and contributes to sewer failure which has a catastrophic effect if happened in trunks or interceptors. Sewer rehabilitation is a costly process and complex in terms of choosing the method of rehabilitation and individual sewers to be rehabilitated.  For such a complex process, inspection techniques assist in the decision-making process; though, it may add to the total expenditure of the project as it requires special tools and trained personnel. For developing countries, Inspection could prohibit the rehabilitation proceeds. In this study, the researchers propos

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Publication Date
Tue Jun 30 2015
Journal Name
International Journal Of Computer Techniques
Multifractal-Based Features for Medical Images Classification

This paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4

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Publication Date
Sat Oct 30 2021
Journal Name
Iraqi Journal Of Science
Recursive Prediction for Lossless Image Compression

     This paper introduced an algorithm for lossless image compression to compress natural and medical images. It is based on utilizing various casual fixed predictors of one or two dimension to get rid of the correlation or spatial redundancy embedded between image pixel values then a recursive polynomial model of a linear base is used.

The experimental results of the proposed compression method are promising in terms of preserving the details and the quality of the reconstructed images as well improving the compression ratio as compared with the extracted results of a traditional linear predicting coding system.

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Publication Date
Wed Mar 24 2021
Journal Name
Ieee Access
Smart IoT Network Based Convolutional Recurrent Neural Network With Element-Wise Prediction System

An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to

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Publication Date
Wed Nov 25 2015
Journal Name
Research Journal Of Applied Sciences, Engineering And Technology
Subject Independent Facial Emotion Classification Using Geometric Based Features

Accurate emotion categorization is an important and challenging task in computer vision and image processing fields. Facial emotion recognition system implies three important stages: Prep-processing and face area allocation, feature extraction and classification. In this study a new system based on geometric features (distances and angles) set derived from the basic facial components such as eyes, eyebrows and mouth using analytical geometry calculations. For classification stage feed forward neural network classifier is used. For evaluation purpose the Standard database "JAFFE" have been used as test material; it holds face samples for seven basic emotions. The results of conducted tests indicate that the use of suggested distances, angles

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Publication Date
Sat Jan 02 2010
Journal Name
Journal Of Al-nahrain University
HIDDEN FEATURES DETECTION USING HISTOGRAM MODIFICATION IN MRI IMAGES

Magnetic Resonance Imaging (MRI) uses magnetization and radio waves, rather than x-rays to make very detailed, cross- sectional pictures of the brain. In this work we are going to explain some procedures belongs contrast and brightness improvement which is very important in the improvement the image quality such as the manipulation with the image histogram. Its has been explained in this worked the histogram shrink i.e. reducing the size of the gray level gives a dim low contrast picture is produced, where, the histogram stretching of the gray level was distributed on a wide scale but there is no increase in the number of pixels in the bright region. The histogram equalization has also been discuss together with its effects of the improveme

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