Mardan A. Pirdawood, Shadman Kareem, Omar Al-Rassam
The significance of image encryption has risen due to the widespread use of images as a key means of sharing data across different applications. Encryption methods are crucial in defending the confidentiality and integrity of valuable image data. This work proposes a novel method of image encryption technique based on the Elzaki transformation and substitution process, which is made possible by the extension of the Maclaurin series coefficients. The image is encrypted using an infinite series of hyperbolic functions and the Elzaki transform; the inverse Elzaki transform is then used to decrypt the image. Using modular arithmetic, the coefficients that result from the transformation are keyed.
{"title":"Encryption of Color Images with a New Framework","authors":"Mardan A. Pirdawood, Shadman Kareem, Omar Al-Rassam","doi":"10.14500/aro.11618","DOIUrl":"https://doi.org/10.14500/aro.11618","url":null,"abstract":"The significance of image encryption has risen due to the widespread use of images as a key means of sharing data across different applications. Encryption methods are crucial in defending the confidentiality and integrity of valuable image data. This work proposes a novel method of image encryption technique based on the Elzaki transformation and substitution process, which is made possible by the extension of the Maclaurin series coefficients. The image is encrypted using an infinite series of hyperbolic functions and the Elzaki transform; the inverse Elzaki transform is then used to decrypt the image. Using modular arithmetic, the coefficients that result from the transformation are keyed.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141343885","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}
Leila Nouri, Salah I. Yahya, Abbas Rezaei, S. Majidifar
This review paper provides a comprehensive overview of microstrip passive components for energy harvesting and 5G applications. The paper covers the structure, fabrication and performance of various microstrip passive components such as filters, couplers, diplexers and triplexers. The size and performance of several 5G and energy harvester microstrip passive devices are compared and discussed. The review highlights the importance of these components in enabling efficient energy harvesting and high-speed communication in 5G networks. Additionally, the paper discusses the latest advancements in microstrip technology and identifies key research challenges and future directions in this field. Overall, this review serves as a valuable resource for researchers and engineers working on microstrip passive components for energy harvesting and 5G applications.
{"title":"Microstrip Passive Components for Energy Harvesting and 5G Applications","authors":"Leila Nouri, Salah I. Yahya, Abbas Rezaei, S. Majidifar","doi":"10.14500/aro.11620","DOIUrl":"https://doi.org/10.14500/aro.11620","url":null,"abstract":"This review paper provides a comprehensive overview of microstrip passive components for energy harvesting and 5G applications. The paper covers the structure, fabrication and performance of various microstrip passive components such as filters, couplers, diplexers and triplexers. The size and performance of several 5G and energy harvester microstrip passive devices are compared and discussed. The review highlights the importance of these components in enabling efficient energy harvesting and high-speed communication in 5G networks. Additionally, the paper discusses the latest advancements in microstrip technology and identifies key research challenges and future directions in this field. Overall, this review serves as a valuable resource for researchers and engineers working on microstrip passive components for energy harvesting and 5G applications.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141361538","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}
Hemin Ibrahim, C. K. Loo, Shreeyash Y. Geda, Abdulbasit K. Al-Talabani
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.
{"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}
Ahmed Adil Nafea, Manar AL-Mahdawi, Mohammed M. AL-Ani, Nazlia Omar
The detection of adverse drug reactions (ADRs) is an important piece of information for determining a patient’s view of a single drug. This study attempts to consider and discuss this feature of drug reviews in medical opinion-mining systems. This paper discusses the literature that summarizes the background of this work. To achieve this aim, the first discusses a survey on detecting ADRs and side effects, followed by an examination of biomedical text mining that focuses on identifying the specific relationships involving ADRs. Finally, we will provide a general overview of sentiment analysis, particularly from a medical perspective. This study presents a survey on ADRs extracted from drug review sentences on social media, utilizing and comparing different techniques.
{"title":"A Review on Adverse Drug Reaction Detection Techniques","authors":"Ahmed Adil Nafea, Manar AL-Mahdawi, Mohammed M. AL-Ani, Nazlia Omar","doi":"10.14500/aro.11388","DOIUrl":"https://doi.org/10.14500/aro.11388","url":null,"abstract":"The detection of adverse drug reactions (ADRs) is an important piece of information for determining a patient’s view of a single drug. This study attempts to consider and discuss this feature of drug reviews in medical opinion-mining systems. This paper discusses the literature that summarizes the background of this work. To achieve this aim, the first discusses a survey on detecting ADRs and side effects, followed by an examination of biomedical text mining that focuses on identifying the specific relationships involving ADRs. Finally, we will provide a general overview of sentiment analysis, particularly from a medical perspective. This study presents a survey on ADRs extracted from drug review sentences on social media, utilizing and comparing different techniques.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372616","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}
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.
在科技时代，教会计算机解读音乐符号的过程被称为光学音乐识别（OMR）。其目的是将图像中的乐谱转换成计算机可读的格式。最近，序列到序列模型和注意力机制（用于文本和手写识别）被用于音符识别。然而，由于过长的乐谱序列逐渐消失，上述由长短时记忆组成的 OMR 模型在学习音符之间的关系时面临困难。因此，我们提出了一个新的框架，利用图像分割技术将这一过程分成几个步骤。此外，本研究还解决了 OMR 中的重叠问题。重叠会导致对音乐符号的误读，产生不准确的结果。因此，我们提出了一种新颖的算法来检测和分割彼此极为接近的符号。我们的实验基于使用卷积神经网络块作为乐谱图像的特征提取器，以及序列到序列模型来检索相应的语义表示。我们在 "乐谱印刷图像 "数据集上对所提出的方法进行了评估。结果证实，我们建议的框架成功地解决了长序列乐谱的问题，在最佳情况下，非重叠符号的 SER 为 0%。此外，我们的方法在解决重叠问题方面也取得了可喜的成果：重叠符号的 SER 为 23.12%。
{"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}
Colorectal cancer (CRC) ranks as the third leading cause of cancer-related deaths globally. It is characterized as a genomic disorder marked by diverse genomic anomalies, including point mutations, genomic rearrangements, gene fusions, and alterations in chromosomal copy numbers. This research aims to identify previously undisclosed genetic variants associated with an increased risk of CRC by employing next-generation sequencing technology. Genomic DNA was extracted from blood specimens of five CRC patients. The sequencing data of the samples are utilized for variant identification. In addition, the Integrative Genomic Viewer software (IGV) is used to visualize the identified variants. Furthermore, various in silico tools, including Mutation Taster and Align GVGD, are used to predict the potential impact of mutations on structural features and protein function. Based on the findings of this research, 12 different genetic variations are detected among individuals with CRC. Inherited variations are located within the following genes: MSH6, MSH2, PTPRJ, PMS2, TP53, BRAF, APC, and PIK3CA.
{"title":"Analyzing Colorectal Cancer at the Molecular Level through Next-generation Sequencing in Erbil City","authors":"Vyan A. Qadir, Kamaran K. Abdoulrahman","doi":"10.14500/aro.11495","DOIUrl":"https://doi.org/10.14500/aro.11495","url":null,"abstract":"Colorectal cancer (CRC) ranks as the third leading cause of cancer-related deaths globally. It is characterized as a genomic disorder marked by diverse genomic anomalies, including point mutations, genomic rearrangements, gene fusions, and alterations in chromosomal copy numbers. This research aims to identify previously undisclosed genetic variants associated with an increased risk of CRC by employing next-generation sequencing technology. Genomic DNA was extracted from blood specimens of five CRC patients. The sequencing data of the samples are utilized for variant identification. In addition, the Integrative Genomic Viewer software (IGV) is used to visualize the identified variants. Furthermore, various in silico tools, including Mutation Taster and Align GVGD, are used to predict the potential impact of mutations on structural features and protein function. Based on the findings of this research, 12 different genetic variations are detected among individuals with CRC. Inherited variations are located within the following genes: MSH6, MSH2, PTPRJ, PMS2, TP53, BRAF, APC, and PIK3CA.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140266794","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}
Zrar Khald Abdul, Abdulbasit K. Al-Talabani, Chnoor M. Rahman, S. M. Asaad
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.
{"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}
Kosrat N. Kaka, R. A. Omer, Dyari M. Mamada, Aryan F. Qader
In this research work, a new compound, namely 2,6-dibromo-2,6-bis(bromo(phenyl)methyl)cyclohexanone (1), is synthesized and characterized for possible applications in organic electronic devices. The formation of the compound was confirmed by Fourier-transform infrared spectroscopy, 1H-, and 13C-NMR spectroscopy measurements. Furthermore, the spectroscopic and optoelectronic properties of the chemical compound were theoretically investigated using density-functional theory (DFT). Herein, the B3LYP/cc-pVDZ level was used to discover the compound electrostatic potentials and frontier molecular orbitals. The theoretical investigations predicted by DFT were compared with the experimentally obtained results from the ultraviolet visible spectra of the compound after being dissolved in various solvents. Results showed that the experimental band-gap energy of the compound is 3.17 eV, whereas its theoretical value was calculated to be 3.33 eV. The outcome of the achieved results suggests the viability of 2,6-dibromo-2,6-bis(bromo(phenyl)methyl)cyclohexanone for possible applications in organic electronic devices
{"title":"Bromination of Chalcone","authors":"Kosrat N. Kaka, R. A. Omer, Dyari M. Mamada, Aryan F. Qader","doi":"10.14500/aro.11431","DOIUrl":"https://doi.org/10.14500/aro.11431","url":null,"abstract":"In this research work, a new compound, namely 2,6-dibromo-2,6-bis(bromo(phenyl)methyl)cyclohexanone (1), is synthesized and characterized for possible applications in organic electronic devices. The formation of the compound was confirmed by Fourier-transform infrared spectroscopy, 1H-, and 13C-NMR spectroscopy measurements. Furthermore, the spectroscopic and optoelectronic properties of the chemical compound were theoretically investigated using density-functional theory (DFT). Herein, the B3LYP/cc-pVDZ level was used to discover the compound electrostatic potentials and frontier molecular orbitals. The theoretical investigations predicted by DFT were compared with the experimentally obtained results from the ultraviolet visible spectra of the compound after being dissolved in various solvents. Results showed that the experimental band-gap energy of the compound is 3.17 eV, whereas its theoretical value was calculated to be 3.33 eV. The outcome of the achieved results suggests the viability of 2,6-dibromo-2,6-bis(bromo(phenyl)methyl)cyclohexanone for possible applications in organic electronic devices","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":"140423811","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}
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.
{"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}
Ahmed Adil Nafea, Mustafa S. Ibrahim, Abdulrahman A. Mukhlif, Mohammed M. AL-Ani, Nazlia Omar
The detection of adverse drug reactions (ADRs) plays a necessary role in comprehending the safety and benefit profiles of medicines. Although spontaneous reporting stays the standard approach for ADR documents, it suffers from significant under reporting rates and limitations in terms of treatment inspection. This study proposes an ensemble model that combines decision trees, support vector machines, random forests, and adaptive boosting (ADA-boost) to improve ADR detection. The experimental evaluation applied the benchmark data set and many preprocessing techniques such as tokenization, stop-word removal, stemming, and utilization of Point-wise Mutual Information. In addition, two term representations, namely, term frequency-inverse document frequency and term frequency, are utilized. The proposed ensemble model achieves an F-measure of 89% on the dataset. The proposed ensemble model shows its ability in detecting ADR to be a favored option in achieving both accuracy and clarity.
{"title":"An Ensemble Model for Detection of Adverse Drug Reactions","authors":"Ahmed Adil Nafea, Mustafa S. Ibrahim, Abdulrahman A. Mukhlif, Mohammed M. AL-Ani, Nazlia Omar","doi":"10.14500/aro.11403","DOIUrl":"https://doi.org/10.14500/aro.11403","url":null,"abstract":"The detection of adverse drug reactions (ADRs) plays a necessary role in comprehending the safety and benefit profiles of medicines. Although spontaneous reporting stays the standard approach for ADR documents, it suffers from significant under reporting rates and limitations in terms of treatment inspection. This study proposes an ensemble model that combines decision trees, support vector machines, random forests, and adaptive boosting (ADA-boost) to improve ADR detection. The experimental evaluation applied the benchmark data set and many preprocessing techniques such as tokenization, stop-word removal, stemming, and utilization of Point-wise Mutual Information. In addition, two term representations, namely, term frequency-inverse document frequency and term frequency, are utilized. The proposed ensemble model achieves an F-measure of 89% on the dataset. The proposed ensemble model shows its ability in detecting ADR to be a favored option in achieving both accuracy and clarity.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447237","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：