ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY最新文献

Encryption of Color Images with a New Framework 利用新框架加密彩色图像

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ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2024-06-14 DOI: 10.14500/aro.11618

Mardan A. Pirdawood, Shadman Kareem, Omar Al-Rassam

引用次数: 0

Microstrip Passive Components for Energy Harvesting and 5G Applications 用于能量收集和 5G 应用的微带无源元件

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ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2024-06-10 DOI: 10.14500/aro.11620

Leila Nouri, Salah I. Yahya, Abbas Rezaei, S. Majidifar

引用次数: 0

Optimizing Emotional Insight through Unimodal and Multimodal Long Short-term Memory Models 通过单模态和多模态长短期记忆模型优化情感洞察力

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ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2024-06-09 DOI: 10.14500/aro.11477

Hemin Ibrahim, C. K. Loo, Shreeyash Y. Geda, Abdulbasit K. Al-Talabani

{"title":"Optimizing Emotional Insight through Unimodal and Multimodal Long Short-term Memory Models","authors":"Hemin Ibrahim, C. K. Loo, Shreeyash Y. Geda, Abdulbasit K. Al-Talabani","doi":"10.14500/aro.11477","DOIUrl":"https://doi.org/10.14500/aro.11477","url":null,"abstract":"The field of multimodal emotion recognition is increasingly gaining popularity as a research area. It involves analyzing human emotions across multiple modalities, such as acoustic, visual, and language. Emotion recognition is more effective as a multimodal learning task than relying on a single modality. In this paper, we present an unimodal and multimodal long short-term memory model with a class weight parameter technique for emotion recognition on the CMU-Multimodal Opinion Sentiment and Emotion Intensity dataset. In addition, a critical challenge lies in selecting the most effective fusion method for integrating multiple modalities. To address this, we applied four different fusion techniques: Early fusion, late fusion, deep fusion, and tensor fusion. These fusion methods improved the performance of multimodal emotion recognition compared to unimodal approaches. With the highly imbalanced number of samples per emotion class in the MOSEI dataset, adding a class weight parameter technique leads our model to outperform the state of the art on all three modalities — acoustic, visual, and language — as well as on all the fusion models. The challenges of class imbalance, which can lead to biased model performance, and using an effective fusion method for integrating multiple modalities often result in decreased accuracy in recognizing less frequent emotion classes. Our proposed model shows 2–3% performance improvement in the unimodal and 2% in the multimodal over the state-of-the-art achieved results.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141367265","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

A Review on Adverse Drug Reaction Detection Techniques 药物不良反应检测技术综述

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ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2024-06-07 DOI: 10.14500/aro.11388

Ahmed Adil Nafea, Manar AL-Mahdawi, Mohammed M. AL-Ani, Nazlia Omar

引用次数: 0

Deep Learning-Based Optical Music Recognition for Semantic Representation of Non-overlap and Overlap Music Notes 基于深度学习的光学音乐识别，用于非重叠和重叠音符的语义表征

IF 0.6

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2024-03-11 DOI: 10.14500/aro.11402

Rana L. Abdulazeez, Fattah Alizadeh

{"title":"Deep Learning-Based Optical Music Recognition for Semantic Representation of Non-overlap and Overlap Music Notes","authors":"Rana L. Abdulazeez, Fattah Alizadeh","doi":"10.14500/aro.11402","DOIUrl":"https://doi.org/10.14500/aro.11402","url":null,"abstract":"In the technology era, the process of teaching a computer to interpret musical notation is termed optical music recognition (OMR). It aims to convert musical note sheets presented in an image into a computer-readable format. Recently, the sequence-to-sequence model along with the attention mechanism (which is used in text and handwritten recognition) has been used in music notes recognition. However, due to the gradual disappearance of excessively long sequences of musical sheets, the mentioned OMR models which consist of long short-term memory are facing difficulties in learning the relationships among the musical notations. Consequently, a new framework has been proposed, leveraging the image segmentation technique to break up the procedure into several steps. In addition, an overlap problem in OMR has been addressed in this study. Overlapping can result in misinterpretation of music notations, producing inaccurate findings. Thus, a novel algorithm is being suggested to detect and segment the notations that are extremely close to each other. Our experiments are based on the usage of the Convolutional Neural Network block as a feature extractor from the image of the musical sheet and the sequence-to-sequence model to retrieve the corresponding semantic representation. The proposed approach is evaluated on The Printed Images of Music Staves dataset. The achieved results confirm that our suggested framework successfully solves the problem of long sequence music sheets, obtaining SER 0% for the non-overlap symbols in the best scenario. Furthermore, our approach has shown promising results in addressing the overlapping problem: 23.12 % SER for overlapping symbols.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395817","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

Analyzing Colorectal Cancer at the Molecular Level through Next-generation Sequencing in Erbil City 在埃尔比勒市通过新一代测序从分子水平分析结直肠癌

IF 0.6

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2024-03-04 DOI: 10.14500/aro.11495

Vyan A. Qadir, Kamaran K. Abdoulrahman

引用次数: 0

Electrocardiogram Heartbeat Classification using Convolutional Neural Network-k Nearest Neighbor 利用卷积神经网络-最近邻进行心电图心跳分类

IF 0.6

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2024-02-29 DOI: 10.14500/aro.11444

Zrar Khald Abdul, Abdulbasit K. Al-Talabani, Chnoor M. Rahman, S. M. Asaad

{"title":"Electrocardiogram Heartbeat Classification using Convolutional Neural Network-k Nearest Neighbor","authors":"Zrar Khald Abdul, Abdulbasit K. Al-Talabani, Chnoor M. Rahman, S. M. Asaad","doi":"10.14500/aro.11444","DOIUrl":"https://doi.org/10.14500/aro.11444","url":null,"abstract":"Electrocardiogram (ECG) analysis is widely used by cardiologists and medical practitioners for monitoring cardiac health. A high-performance automatic ECG classification system is challenging because there is difficulty in detecting and categorizing different waveforms in the signal, especially in manual analysis of ECG signals, which means, a better classification system is needed in terms of performance and accuracy. Hence, in this paper, the authors propose an accurate ECG classification and monitoring system called convolutional neural network-k nearest neighbor (CNN-kNN). The proposed method utilizes 1D-CNN and kNN. Unlike the existing techniques, the examined technique does not need training during classifying the ECG signals. The CNN-kNN is evaluated against the PhysioNet’s MIT-BIH and PTB diagnostics datasets. The CNN is fed using the ECG beat raw signal directly. In addition, the learned features are extracted from the 1D-CNN model and its dimensions are reduced using two fully connected layers and then fed to the k-NN classifier. The CNN-kNN model achieved average accuracies of 98% and 97.4% on arrhythmia and myocardial infarction classifications, respectively. These results are evidence of the great ability of the proposed model compared to the mentioned models in this article.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140414266","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

Bromination of Chalcone 查耳酮的溴化作用

IF 0.6

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2024-02-28 DOI: 10.14500/aro.11431

Kosrat N. Kaka, R. A. Omer, Dyari M. Mamada, Aryan F. Qader

引用次数: 0

Permeability Prediction for Carbonate Rocks using a Modified Flow Zone Indicator Method 使用改良流区指示器法预测碳酸盐岩的渗透性

IF 0.6

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2024-02-28 DOI: 10.14500/aro.11314

Ahmed J. Mahmood, M. A. Jubair

{"title":"Permeability Prediction for Carbonate Rocks using a Modified Flow Zone Indicator Method","authors":"Ahmed J. Mahmood, M. A. Jubair","doi":"10.14500/aro.11314","DOIUrl":"https://doi.org/10.14500/aro.11314","url":null,"abstract":"Carbonate reservoir rocks are usually heterogeneous, so it is not an easy task to establish a relation between porosity and permeability in these types of reservoir rocks. First, Kozney and Kozney-carmen formulas were used to establish these relations. Later, the flow zone indicator (FZI) method was introduced, which was widely used to find such a relation since it shows better results than the two former methods. In this work, the classical FZI method and a modified form of the FZI method are utilized to identify the hydraulic flow units and rock quality index to predict permeability. In this FZI method, the cementation factor (m) was introduced in calculating the value of FZI. The data collected from core analysis of the cored intervals in the Tanuma and Khasib formations were used as a database for this work. The classical and the modified FZI methods were applied using the database to predict core permeability. The value of the cementation factor was tuned to get a better match between the predicted permeability resulting from applying the modified method and the measured permeability values. Results show that the correlation coefficients resulting from applying the modified FZI method are closer to unity compared with that resulting from the classical FZI method. Cementation factor (m) of m = 3 for Tanuma formation and m = 3 for Khasib formation are the best values used with the modified FZI method. The modified FZI method shows a regression factor of 0.9986 for Tanuma and 0.9942 for Khasib formation.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140420892","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

An Ensemble Model for Detection of Adverse Drug Reactions 检测药物不良反应的集合模型

IF 0.6

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2024-02-20 DOI: 10.14500/aro.11403

Ahmed Adil Nafea, Mustafa S. Ibrahim, Abdulrahman A. Mukhlif, Mohammed M. AL-Ani, Nazlia Omar

引用次数: 0