Applied Computational Intelligence and Soft Computing最新文献

An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic 基于深度学习的智能框架，用于大流行病期间的在线古兰经学习

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2023-12-22 DOI: 10.1155/2023/5541699

Natasha Nigar, Amna Wajid, S. A. Ajagbe, Matthew O. Adigun

{"title":"An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic","authors":"Natasha Nigar, Amna Wajid, S. A. Ajagbe, Matthew O. Adigun","doi":"10.1155/2023/5541699","DOIUrl":"https://doi.org/10.1155/2023/5541699","url":null,"abstract":"The COVID-19 pandemic influenced the whole world and changed social life globally. Social distancing is an effective strategy adopted by all countries to prevent humans from being infected. Al-Quran is the holy book of Muslims and its listening and reading is one of the obligatory activities. Close contact is essential in traditional learning system; however, most of the Al-Quran learning schools were locked down to minimize the spread of COVID-19 infection. To address this limitation, in this paper, we propose a novel system using deep learning to identify the correct recitation of individual alphabets, words from a recited verse and a complete verse of Al-Quran to assist the reciter. Moreover, in the proposed approach, if the user recites correctly, his/her voice is also added to the existing dataset to leverage proposed approach effectiveness. We employ mel-frequency cepstral coefficients (MFCC) to extract voice features and long short-term memory (LSTM), a recurrent neural network (RNN) for classification. The said approach is validated using the Al-Quran dataset. The results demonstrate that the proposed system outperforms the state-of-the-art approaches with an accuracy rate of 97.7%. This system will help the Muslim community all over the world to recite the Al-Quran in the right way in the absence of human help due to similar future pandemics.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":"9 22","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138947355","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

Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets 针对医学数据集使用自适应提升框架增强基础分类器的性能

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2023-12-22 DOI: 10.1155/2023/5542049

Durr e Nayab, Rehan Ullah Khan, A. M. Qamar

{"title":"Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets","authors":"Durr e Nayab, Rehan Ullah Khan, A. M. Qamar","doi":"10.1155/2023/5542049","DOIUrl":"https://doi.org/10.1155/2023/5542049","url":null,"abstract":"This paper investigates the performance enhancement of base classifiers within the AdaBoost framework applied to medical datasets. Adaptive boosting (AdaBoost), being an instance of boosting, combines other classifiers to enhance their performance. We conducted a comprehensive experiment to assess the efficacy of twelve base classifiers with the AdaBoost framework, namely, Bayes network, decision stump, ZeroR, decision tree, Naïve Bayes, J-48, voted perceptron, random forest, bagging, random tree, stacking, and AdaBoost itself. The experiments are carried out on five datasets from the medical domain based on various types of cancers, i.e., global cancer map (GCM), lymphoma-I, lymphoma-II, leukaemia, and embryonal tumours. The evaluation focuses on the accuracy, precision, and efficiency of the base classifiers in the AdaBoost framework. The results show that the performance of Naïve Bayes, Bayes network, and voted perceptron is highly improved compared to the rest of the base classifiers, attaining accuracies as high as 94.74%, 97.78%, and 97.78%, respectively. The results also show that in most cases, the base classifiers perform better with AdaBoost compared to their performance, i.e., for voted perceptron, the accuracy is improved up to 13.34%.For bagging, it is improved by up to 7%. This research aims to identify such base classifiers with optimal boosting capabilities within the AdaBoost framework for medical datasets. The significance of these results is that they provide insight into the performance of the base classifiers when used in the boosting framework to enhance the classification performance of classifiers in scenarios where individual classifiers do not perform up to the mark.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":"50 48","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138946363","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

Corrigendum to “An Efficient Blind Image Deblurring Using a Smoothing Function” 对 "使用平滑函数的高效盲图像去模糊 "的更正

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2023-12-09 DOI: 10.1155/2023/9812479

Kittiya Khongkraphan, Aniruth Phonon, Sainuddeen Nuiphom

引用次数: 0

Aspect-Based Sentiment Analysis for Afaan Oromoo Movie Reviews Using Machine Learning Techniques 利用机器学习技术对阿凡-奥罗莫语电影评论进行基于方面的情感分析

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2023-12-07 DOI: 10.1155/2023/3462691

Obsa Gelchu Horsa, K. K. Tune

{"title":"Aspect-Based Sentiment Analysis for Afaan Oromoo Movie Reviews Using Machine Learning Techniques","authors":"Obsa Gelchu Horsa, K. K. Tune","doi":"10.1155/2023/3462691","DOIUrl":"https://doi.org/10.1155/2023/3462691","url":null,"abstract":"Aspect-based sentiment analysis (ABSA) is the subfield of natural language processing that deals with essentially splitting data into aspects and finally extracting the sentiment polarity as positive, negative, or neutral. ABSA has been widely investigated and developed for many resource-rich languages such as English and French. However, little work has been done on indigenous African languages like Afaan Oromoo both at the document and sentence levels. In this paper, ABSA for Afaan Oromoo movie reviews was investigated and developed. To achieve the proposed objective, 2800 Afaan Oromoo movie reviews were collected from YouTube using YouTube Data API. Following the data preprocessing, predetermined aspects of the Afaan Oromoo movie were extracted and labeled into positive or negative aspects by domain experts. For implementation, different machine learning algorithms including random forest, logistic regression, SVM, and multinomial naïve Bayes in combination with BoW and TF-IDF were applied. To test and measure the proposed system, accuracy, precision, recall, and f1-score were used. In the case of random forest, the accuracy obtained in combination with both BoW and TF-IDF was 88%. Using the SVM, the accuracy generated with BoW and TF-IDF was 88% and 87%, respectively. Applying logistic regression, the accuracy generated with both BoW and TF-IDF was 87%. Using multinomial naïve Bayes, the accuracy generated in combination with both BoW and TF-IDF was 88%. To improve the optimal performance evaluation parameters, different hyperparameter tuning settings were applied. The implementation result shows that the optimal values of models’ performance evaluation parameters were generated using different hyperparameter tuning settings.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":"22 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138591336","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

Applications of Quantum Probability Amplitude in Decision Support Systems 量子概率振幅在决策支持系统中的应用

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2023-12-07 DOI: 10.1155/2023/5532174

S. Payandeh

{"title":"Applications of Quantum Probability Amplitude in Decision Support Systems","authors":"S. Payandeh","doi":"10.1155/2023/5532174","DOIUrl":"https://doi.org/10.1155/2023/5532174","url":null,"abstract":"Establishing various frameworks for managing uncertainties in decision-making systems have been posing many fundamental challenges to the system design engineers. Quantum paradigm has been introduced to the area of decision and control communities as a possible supporting platform in such uncertainty management. This paper presents an overview of how a quantum framework and, in particular, probability amplitude has been proposed and utilized in the literature to complement two classical probabilistic decision-making approaches. The first such framework is based in the Bayesian network, and the second is based on an element of Dempster–Shafer (DS) theory using the definition of mass function. The paper first presents a summary of these classical approaches, followed by a review of their preliminary enhancements using the quantum model framework. Particular attention was given on how the notion of probability amplitude is utilized in such extensions to the quantum-like framework. Numerical walk-through examples are combined with the presentation of each method in order to better demonstrate the extensions of the proposed frameworks. The main objective is to better define and develop a common platform in order to further explore and experiment with this alternative framework as a part of a decision support system.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":"45 16","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138593592","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

Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models 使用迁移深度学习模型的基于图像的阿拉伯手语识别系统

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2023-12-06 DOI: 10.1155/2023/5195007

Qanita Bani Baker, Nour Alqudah, Tibra Alsmadi, Rasha Awawdeh

{"title":"Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models","authors":"Qanita Bani Baker, Nour Alqudah, Tibra Alsmadi, Rasha Awawdeh","doi":"10.1155/2023/5195007","DOIUrl":"https://doi.org/10.1155/2023/5195007","url":null,"abstract":"Sign language is a unique communication tool helping to bridge the gap between people with hearing impairments and the general public. It holds paramount importance for various communities, as it allows individuals with hearing difficulties to communicate effectively. In sign languages, there are numerous signs, each characterized by differences in hand shapes, hand positions, motions, facial expressions, and body parts used to convey specific meanings. The complexity of visual sign language recognition poses a significant challenge in the computer vision research area. This study presents an Arabic Sign Language recognition (ArSL) system that utilizes convolutional neural networks (CNNs) and several transfer learning models to automatically and accurately identify Arabic Sign Language characters. The dataset used for this study comprises 54,049 images of ArSL letters. The results of this research indicate that InceptionV3 outperformed other pretrained models, achieving a remarkable 100% accuracy score and a 0.00 loss score without overfitting. These impressive performance measures highlight the distinct capabilities of InceptionV3 in recognizing Arabic characters and underscore its robustness against overfitting. This enhances its potential for future research in the field of Arabic Sign Language recognition.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":"45 5","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138596745","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

Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis 用于增强败血症诊断中的集合分类器的条件表生成对抗网

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2023-11-25 DOI: 10.1155/2023/8819052

A. Alfakeeh, M. S. Sharif, A. Zorto, Thiago Pillonetto

{"title":"Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis","authors":"A. Alfakeeh, M. S. Sharif, A. Zorto, Thiago Pillonetto","doi":"10.1155/2023/8819052","DOIUrl":"https://doi.org/10.1155/2023/8819052","url":null,"abstract":"Antibiotic-resistant bacteria have proliferated at an alarming rate as a result of the extensive use of antibiotics and the paucity of new medication research. The possibility that an antibiotic-resistant bacterial infection would progress to sepsis is one of the major collateral problems affecting people with this condition. 31,000 lives were lost due to sepsis in England with costs about two billion pounds annually. This research aims to develop and evaluate several classification approaches to improve predicting sepsis and reduce the tendency of underdiagnosis in computer-aided predictive tools. This research employs medical datasets for patients diagnosed with sepsis, and it analyses the efficacy of ensemble machine learning techniques compared to nonensemble machine learning techniques and the significance of data balancing and conditional tabular generative adversarial nets for data augmentation in producing reliable diagnosis. The average F Score obtained by the nonensemble models trained in this paper is 0.83 compared to the ensemble techniques average of 0.94. Nonensemble techniques, such as Decision Tree, achieved an F score of 0.90, an AUC of 0.90, and an accuracy of 90%. Histogram-basedgradient boosting classification tree achieved an F score of 0.96, an AUC of 0.96, and an accuracy of 95%, surpassing the other models tested. Additionally, when compared to the current state-of-the-art sepsis prediction models, the models developed in this study demonstrated higher average performance in all metrics, indicating reduced bias and improved robustness through data balancing and conditional tabular generative adversarial nets for data augmentation. The study revealed that data balancing and augmentation on the ensemble machine learning algorithms boost the efficacy of clinical predictive models and can help clinics decide which data types are most important when examining patients and diagnosing sepsis early through intelligent human-machine interface.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":"35 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139238146","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

Three-Axes Mems Calibration Using Kalman Filter and Delaunay Triangulation Algorithm 使用卡尔曼滤波器和 Delaunay 三角测量算法进行三轴微系统校准

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2023-11-22 DOI: 10.1155/2023/7658064

Anwer Sabah Ahmed, Qais Al-Gayem

引用次数: 0

An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application 分析早期慢性肾病的智能诊断系统在临床中的应用

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2023-11-22 DOI: 10.1155/2023/3140270

N. I. Md. Ashafuddula, Bayezid Islam, Rafiqul Islam

{"title":"An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application","authors":"N. I. Md. Ashafuddula, Bayezid Islam, Rafiqul Islam","doi":"10.1155/2023/3140270","DOIUrl":"https://doi.org/10.1155/2023/3140270","url":null,"abstract":"Chronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare, clinical data pose challenges: missing values, noisy inputs, and redundant features, affecting early-stage CKD prediction. Thus, this study presents a novel, fully automated machine learning approach to tackle these complexities by incorporating feature selection (FS) and feature space reduction (FSR) techniques, leading to a substantial enhancement of the model’s performance. A data balancing technique is also employed during preprocessing to address data imbalance issue that is commonly encountered in clinical contexts. Finally, for reliable CKD classification, an ensemble characteristics-based classifier is encouraged. The effectiveness of our approach is rigorously validated and assessed on multiple datasets, and the clinical relevancy of the strategy is evaluated on the real-world therapeutic data collected from Bangladeshi patients. The study establishes the dominance of adaptive boosting, logistic regression, and passive aggressive ML classifiers with 96.48% accuracy in forecasting unseen therapeutic CKD data, particularly in early-stage cases. Furthermore, the effectiveness of the FSR technique in reducing the prediction time significantly is revealed. The outstanding performance of the proposed model demonstrates its effectiveness in addressing the complexity of healthcare CKD data by incorporating the FS and FSR techniques. This highlights its potential as a promising computer-aided diagnosis tool for doctors, enabling early interventions and improving patient outcomes.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139247537","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 Improved Hashing Approach for Biological Sequence to Solve Exact Pattern Matching Problems 解决精确模式匹配问题的生物序列改进哈希算法

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2023-11-20 DOI: 10.1155/2023/3278505

Prince Mahmud, Anisur Rahman, Kamrul Hasan Talukder

{"title":"An Improved Hashing Approach for Biological Sequence to Solve Exact Pattern Matching Problems","authors":"Prince Mahmud, Anisur Rahman, Kamrul Hasan Talukder","doi":"10.1155/2023/3278505","DOIUrl":"https://doi.org/10.1155/2023/3278505","url":null,"abstract":"Pattern matching algorithms have gained a lot of importance in computer science, primarily because they are used in various domains such as computational biology, video retrieval, intrusion detection systems, and fraud detection. Finding one or more patterns in a given text is known as pattern matching. Two important things that are used to judge how well exact pattern matching algorithms work are the total number of attempts and the character comparisons that are made during the matching process. The primary focus of our proposed method is reducing the size of both components wherever possible. Despite sprinting, hash-based pattern matching algorithms may have hash collisions. The Efficient Hashing Method (EHM) algorithm is improved in this research. Despite the EHM algorithm’s effectiveness, it takes a lot of time in the preprocessing phase, and some hash collisions are generated. A novel hashing method has been proposed, which has reduced the preprocessing time and hash collision of the EHM algorithm. We devised the Hashing Approach for Pattern Matching (HAPM) algorithm by taking the best parts of the EHM and Quick Search (QS) algorithms and adding a way to avoid hash collisions. The preprocessing step of this algorithm combines the bad character table from the QS algorithm, the hashing strategy from the EHM algorithm, and the collision-reducing mechanism. To analyze the performance of our HAPM algorithm, we have used three types of datasets: E. coli, DNA sequences, and protein sequences. We looked at six algorithms discussed in the literature and compared our proposed method. The Hash-q with Unique FNG (HqUF) algorithm was only compared with E. coli and DNA datasets because it creates unique bits for DNA sequences. Our proposed HAPM algorithm also overcomes the problems of the HqUF algorithm. The new method beats older ones regarding average runtime, number of attempts, and character comparisons for long and short text patterns, though it did worse on some short patterns.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":"287 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139257629","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