Journal of Kufa for Mathematics and Computer最新文献

Improving Collaborative Filter Using BERT 利用BERT改进协同过滤

Journal of Kufa for Mathematics and Computer Pub Date : 2023-08-31 DOI: 10.31642/jokmc/2018/100204

Riyam Rwedhi, Salam Al-augby

{"title":"Improving Collaborative Filter Using BERT","authors":"Riyam Rwedhi, Salam Al-augby","doi":"10.31642/jokmc/2018/100204","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100204","url":null,"abstract":"With the increasing number of books published and the difficulty of obtaining appropriate research attention, the recommendation systems can increase the affordability and availability of these books. In this work, we expand our work to enhance the accuracy of book collaborative filtering by applying semantic similarity to book summaries, in addition to that addressing major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of three stages: preprocessing, building the system, and evaluation. The technologies used in the pre-processing stage included reduction and normalization. The construction system is divided into two phases: semantic similarity and recommendation. The semantic similarity is done by using BERT for sentence embedding and cosine similarity to calculate the similarity between sentences. During the recommendation phase by using CF based on KNN. In the evaluation stage, classification accuracy metrics had been used. The proposed approach improved the accuracy of the book recommendation system and increased the accuracy to 0.89 compared to previous works on a dataset of 271,000 book summaries. The proposed approach yielded better results due to avoiding problems in previous work, such as scalability and sparsity, by using BERT with CF based KNN. Filtering the data using BERT and the KNN algorithm in the CF added strength to the recommendation, which led to an increase in the accuracy rate.","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123322945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of the Two Rowed Weyl Module in the Case of Partitions (7,5) and Skew-Partition (7, 5) / (1, 0) 双划Weyl模块在分区(7,5)和斜分区(7,5)/(1,0)中的应用

Journal of Kufa for Mathematics and Computer Pub Date : 2023-08-31 DOI: 10.31642/jokmc/2018/100217

H. Hassan, Ghsoon Rabeea Raheem

引用次数: 0

Category Theory and New Classes of Semi Bornological Group 范畴论与半片群的新类

Journal of Kufa for Mathematics and Computer Pub Date : 2023-08-31 DOI: 10.31642/jokmc/2018/100214

N. Jasim, A. Imran, Huda Amer Abdul Ameer, Sh.K.Said Husain

引用次数: 0

A Novel Integral Transform :INEM - Transform 一种新的积分变换:INEM -变换

Journal of Kufa for Mathematics and Computer Pub Date : 2023-08-31 DOI: 10.31642/jokmc/2018/100218

Intidhar Zamil, Emad A. Kuffi

引用次数: 0

Syndetic proximal set in topological transformation group 拓扑变换群中的综合近端集合

Journal of Kufa for Mathematics and Computer Pub Date : 2023-08-31 DOI: 10.31642/jokmc/2018/100206

Enas Abdullah

引用次数: 0

Distributive Semimodules: Theory, Properties, and Extensions 分布半模:理论、性质和扩展

Journal of Kufa for Mathematics and Computer Pub Date : 2023-08-31 DOI: 10.31642/jokmc/2018/100215

A. Abdulkhaliq, Asaad M. A. Alhossaini

引用次数: 0

Survey in Image and Audio Steganography by using the Deep Learning Methods 基于深度学习方法的图像和音频隐写研究综述

Journal of Kufa for Mathematics and Computer Pub Date : 2023-08-31 DOI: 10.31642/jokmc/2018/100222

Zeina Al Hadad, Ibtisam Hassoun Ali

{"title":"Survey in Image and Audio Steganography by using the Deep Learning Methods","authors":"Zeina Al Hadad, Ibtisam Hassoun Ali","doi":"10.31642/jokmc/2018/100222","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100222","url":null,"abstract":"Steganography is the practise of secretly encoding information into another medium (called a cover media) such that its existence cannot be identified. Steganography is the method that is utilised to successfully do this task. Through the use of digital image steganography, one is able to achieve the capacity of secure communication, which is vital in the bulk of the applications that are now being created. The use of steganography is applicable in a wide variety of advantageous settings. Its ascent to the forefront of current security systems may be attributed to a confluence of factors, including a phenomenal rise in processing power and a heightened awareness among individuals of the need of safeguarding their personal information. When it comes to the creation of a steganographic method, the most significant challenge that must be surmounted is finding the optimal balance between quality and quantity. The method is distinguished from linked systems like encryption and watermarking by its ability to embed information, as well as by the fact that it cannot be seen by the human eye. This article presents a thorough examination and analysis of some contemporary steganographic methods that are regarded as state-of-the-art approaches. In addition, we have included an in-depth discussion of the most important steganographic technologies now available. Recent steganographic techniques that are based on deep learning have been enhanced, which has enabled the resolution of issues that were previously present","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121648565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction Model based on Iris Dataset Via Some Machine Learning Algorithms 基于Iris数据集的机器学习预测模型

Journal of Kufa for Mathematics and Computer Pub Date : 2023-08-31 DOI: 10.31642/jokmc/2018/100210

Chya Fatah Aziz, B. Awrahman

引用次数: 0

Instantaneous Frequency and Chirp Rate Estimation for Noisy Quadratic FM Signals by CNN 基于CNN的含噪二次调频信号瞬时频率和啁啾率估计

Journal of Kufa for Mathematics and Computer Pub Date : 2023-08-31 DOI: 10.31642/jokmc/2018/100201

Huda Saleem, Zahir M. Hussain

{"title":"Instantaneous Frequency and Chirp Rate Estimation for Noisy Quadratic FM Signals by CNN","authors":"Huda Saleem, Zahir M. Hussain","doi":"10.31642/jokmc/2018/100201","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100201","url":null,"abstract":"Deep learning and machine learning are widely employed in various domains. In this paper, Artificial Neural Network (ANN) and Convolution Neural Network (CNN) are used to estimate the Instantons Frequency (IF), Linear Chirp Rate (LCR), and Quadratic Chirp Rate (QCR) for Quadratic Frequency Modulated (QFM) signals under Additive White Gaussian (AWG) noise and Additive Symmetric alpha Stable (ASαS) noise. SαS distributions are impulsive noise disturbances except for a few circumstances, lack a closed-form Probability Density Function (PDF), and an infinite second-order statistic. Geometric SNR (GSNR) is used to determine the impulsiveness of mixture noise for Gaussian and SαS noise. ANN is a machine learning classifier with few layers that reduce FE, LCRE, and QCRE complexity and achieve high accuracy. CNN is a deep learning classifier that is built with multiple layers of FE, LCRE, and QCRE. CNN is more accurate than ANN when dealing with large amounts of data and determining optimal features. The results reveal that SαS noise is substantially more damaging to FE, LCRE, and QCRE than Gaussian noise, even when the magnitude is modest, and it is less damaging when alpha is greater than one. After training DCNN for FE, LCRE, and QCRE estimation of QFM signals. The 2D-CNN model accuracy achieved 98.7603 and 1D-CNN is 75.8678 for ten epochs. ANN model accuracy achieved 37.5 for 1000 epochs. The accuracy of TFD (spectrogram & pspectrum) for frequency estimation of QFM signals was 38.4254 by spectrogram and 38.6746 by pspectrum.","PeriodicalId":115908,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127690861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayes estimators with extension of Jeffery prior information for Time censored data and Failure censored data 时间截尾数据和失效截尾数据的扩展杰弗瑞先验信息Bayes估计

Journal of Kufa for Mathematics and Computer Pub Date : 2023-08-31 DOI: 10.31642/jokmc/2018/100208

Haneen Reed Sahib, Hadeel Salim Al-Kutubi

引用次数: 0