Article
Engineering, Electrical & Electronic
Ali Mohd Ali, Mohammad R. Hassan, Faisal Aburub, Mohammad Alauthman, Amjad Aldweesh, Ahmad Al-Qerem, Issam Jebreen, Ahmad Nabot
Summary: Hepatitis C is a significant public health concern, and machine learning algorithms have been used to improve the diagnostic process. However, there is a concern about their interpretability.
Article
Computer Science, Artificial Intelligence
Pradip Dhal, Chandrashekhar Azad
Summary: Feature selection is crucial in machine learning, but faces challenges in real-world applications. This paper investigates its framework, models, and methods, classifying and discussing algorithms in different data types and applications.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Mariana Daniel, Rui Guerra, Antonio Brazio, Daniela Rodrigues, Ana Margarida Cavaco, Maria Dulce Antunes, Jose Valente de Oliveira
Summary: This study explores the use of feature engineering for preprocessing in fruit classification, as well as the division and selection of wavelength domain spectra. These methods can improve classification accuracy and reduce over-training. Experimental results show that the proposed method outperforms traditional approaches in accuracy and can identify features with physical chemistry significance.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Mathematics
Sang-Ha Sung, Sangjin Kim, Byung-Kwon Park, Do-Young Kang, Sunhae Sul, Jaehyun Jeong, Sung-Phil Kim
Summary: Research shows that feature selection using EEG data in BCI technology can effectively predict whether individuals correctly detect facial expression changes, with specific EEG features largely influencing the detection of expression changes. Various feature selection methods and machine learning techniques were used to achieve high classification accuracy.
Article
Computer Science, Information Systems
Harun Olcay Sonkurt, Ali Ercan Altinoz, Emre Cimen, Ferdi Kosger, Gurkan Ozturk
Summary: This study achieved high accuracy in differentiating bipolar disorder patients from healthy controls by utilizing a broader neurocognitive evaluation and a novel machine-learning algorithm.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2021)
Article
Computer Science, Theory & Methods
Lu Zhou, Ye Zhu, Tianrui Zong, Yong Xiang
Summary: The paper proposes a DDoS attack flow classification system called SAFE, which accurately and quickly identifies attack flows in the network layer. The proposed method achieves better classification performance in terms of accuracy and efficiency compared to existing methods.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Toxicology
Jian Jiang, Jonas van Ertvelde, Goekhan Ertaylan, Ralf Peeters, Danyel Jennen, Theo M. de Kok, Mathieu Vinken
Summary: Drug-induced intrahepatic cholestasis (DIC) is a challenging hepatic toxicity that is difficult to predict in early drug development stages. In vitro toxicogenomics assays using human liver cells have proved to be a practical approach for predicting DIC. This study applied machine learning algorithms to identify transcriptomic signatures of DIC and developed a prediction model with high accuracy and sensitivity. The identified genes provide insights into the mechanisms of DIC and enhance the predictive accuracy of DIC, contributing to the advancement of hazard identification methodologies.
ARCHIVES OF TOXICOLOGY
(2023)
Article
Ecology
Dimitrios Effrosynidis, Avi Arampatzis
Summary: The study found that the wrapper methods SHAP and Permutation Importance are the most effective, while filter methods perform poorly and embedded methods are intermediate. LightGBM performed better among the two machine learning algorithms used. The ensemble method Reciprocal Ranking outperformed all other methods and showed high stability.
ECOLOGICAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Usman Ahmed, Jerry Chun-Wei Lin, Gautam Srivastava, Muhammad Aleem
Summary: This paper presents an approach to map OpenCL applications to heterogeneous multi-core architecture by using a machine learning-based classifier. By selecting features and comparing performance metrics, it achieved efficient classification results. The optimized method was compared with traditional algorithms and applied on AMD and Polybench benchmarks.
Article
Computer Science, Artificial Intelligence
Victor Hugo da Silva Muniz, Joao Baptista de Oliveira e Souza Filho
Summary: This paper discusses the importance of music genre in music recommendations and presents a method to improve system performance through the generation of new handcrafted features and feature selection.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Microbiology
Songbo Liu, Chengmin Cui, Huipeng Chen, Tong Liu
Summary: This study introduces an ensemble learning-based method for identifying important features in phage protein, aiming to understand its relationship with host bacteria and develop antimicrobial agents. The selected features are found to have significant biological significance based on the analysis conducted.
FRONTIERS IN MICROBIOLOGY
(2022)
Article
Computer Science, Information Systems
Vladimir Kurbalija, Zoltan Geler, Tijana Vujanic Stankov, Igor Petrusic, Mirjana Ivanovic, Igor Kononenko, Marija Semnic, Marko Dakovic, Robert Semnic, Zoran Bosnic
Summary: The paper discusses the importance of early diagnosis of Alzheimer's disease (AD) due to the increasing prevalence of age-related neurodegenerative diseases. Machine learning techniques are used to analyze a dataset and evaluate the performance of different classifiers and feature selection algorithms. The results suggest that neuropsychological attributes have high predictive power for AD diagnosis.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2023)
Article
Environmental Sciences
Zahra Jafari, Ebrahim Karami, Rocky Taylor, Pradeep Bobby
Summary: Drifting icebergs pose significant risks in remote offshore areas. Traditional monitoring methods are often impractical, making satellite-based monitoring using deep learning techniques a viable solution for accurate and efficient iceberg classification.
Article
Environmental Sciences
Shan He, Peng Peng, Yiyun Chen, Xiaomi Wang
Summary: This paper investigates the optimal combination of feature selection methods and classifiers for crop classification. It constructs 18 multi-crop classification models and evaluates their performance. The results show that different feature selection methods have different effects on different models, and the classification strategy combining spectral, textual, and environmental indexes can improve crop recognition ability.
Article
Chemistry, Multidisciplinary
Mohammed Hadwan, Mohammed Al-Sarem, Faisal Saeed, Mohammed A. Al-Hagery
Summary: Analyzing the sentiment of Arabic texts is a significant research challenge. Existing studies on Arabic sentiment analysis have focused on Twitter data while neglecting the reviews on Google Play or the App Store. This paper aims to analyze user opinions of six healthcare applications and proposes an improved sentiment classification approach using machine learning models.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Esraa Hassan, Mahmoud Y. Shams, Noha A. Hikal, Samir Elmougy
Summary: This article discusses the application of optimization algorithms in machine learning to improve model accuracy, and provides a detailed introduction to various optimization strategies and their complexities. The results of the study demonstrate that using the appropriate optimizer can significantly improve the performance and accuracy of machine learning models.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Green & Sustainable Science & Technology
Ahmed M. Elshewey, Mahmoud Y. Shams, Abdelghafar M. Elhady, Samaa M. Shohieb, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Zahraa Tarek
Summary: This study uses the Daily Delhi Climate Dataset and time series forecasting techniques to predict the temperature in Delhi. A hybrid forecasting model, combining Wavelet Decomposition (WD) and Seasonal Auto-Regressive Integrated Moving Average with Exogenous Variables (SARIMAX), is created to accurately forecast the temperature. Experimental results show that the WD-SARIMAX model performs better than other recent models for temperature forecasting in Delhi.
Article
Chemistry, Analytical
Ahmed M. Elshewey, Mahmoud Y. Shams, Nora El-Rashidy, Abdelghafar M. Elhady, Samaa M. Shohieb, Zahraa Tarek
Summary: This paper presents an advanced model called BO-SVM to classify individuals with Parkinson's disease. Bayesian Optimization is used to optimize the hyperparameters of six machine learning models, and the performance of these models is evaluated using various metrics. Experimental results show that the SVM model achieves the highest accuracy of 92.3% after hyperparameter tuning using BO.
Article
Computer Science, Artificial Intelligence
Heba Mamdouh Farghaly, Tarek Abd El-Hafeez
Summary: The feature selection problem is a significant challenge in pattern recognition, especially for classification tasks. This work explores the use of association analysis in data mining to select meaningful features and proposes a novel feature selection technique for text classification. The technique effectively reduces redundant information while achieving high accuracy using only 6% of the features.
Article
Green & Sustainable Science & Technology
Zahraa Tarek, Ahmed M. M. Elshewey, Samaa M. M. Shohieb, Abdelghafar M. M. Elhady, Noha E. E. El-Attar, Sherif Elseuofi, Mahmoud Y. Y. Shams
Summary: Soil erosion, the removal of soil particles from the earth's surface, occurs in three phases: dislocation, transport, and deposition. Various factors influence the velocity of soil erosion, including soil type, assembly, infiltration, and land cover. This paper proposes a model, RS-RF, which combines random search optimization with the Random Forest algorithm, for predicting soil erosion. The RS-RF model achieved the best outcomes compared to other machine learning techniques, with an accuracy rate of 97.4% using a dataset of 236 instances and 11 features.
Article
Computer Science, Interdisciplinary Applications
Marwa Khairy, Tarek M. M. Mahmoud, Ahmed Omar, Tarek Abd El-Hafeez
Summary: Research on abusive language and its detection has gained attention due to the impact of cyberbullying on individuals and society. The widespread accessibility of social media sites has led to a substantial increase in hate speech, bullying, and other forms of abuse. This study aimed to automate the detection of offensive language and cyberbullying, using a new Arabic balanced data set and comparing single and ensemble machine learning algorithms. The results showed that ensemble machine learning outperformed single learner classifiers, with the voting ensemble model achieving the best performance. Further improvement was achieved through hyperparameter tuning on an Arabic cyberbullying data set.
LANGUAGE RESOURCES AND EVALUATION
(2023)
Article
Computer Science, Information Systems
Mahmoud Y. Shams, Ahmed M. Elshewey, El-Sayed M. El-kenawy, Abdelhameed Ibrahim, Fatma M. Talaat, Zahraa Tarek
Summary: This study utilizes machine learning models to predict water quality index and water quality classification, and improves the accuracy through parameter optimization and tuning. The experimental results show that the GB model performs the best in classification with an accuracy of 99.50%. In regression, the MLP regressor model outperforms other models with a determination coefficient of 99.8%.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Computer Science, Artificial Intelligence
Marwa Khairy, Tarek M. Mahmoud, Tarek Abd-El-Hafeez
Summary: This paper investigates the effectiveness of techniques for addressing class imbalance in cyberbullying datasets. Through experimental study, it is found that the performance of resampling techniques depends on the dataset size, imbalance ratio, and classifier used.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Chemistry, Analytical
Mohammed Eman, Tarek M. Mahmoud, Mostafa M. Ibrahim, Tarek Abd El-Hafeez
Summary: This paper proposes a novel method for masked face recognition, which combines deep-learning-based mask detection, landmark and oval face detection, and robust principal component analysis (RPCA). Experimental results show that our proposed method outperforms existing methods in terms of accuracy and robustness to occlusion. The proposed method achieves a recognition rate of 97%, which is significantly higher than the state-of-the-art methods. Our proposed method represents a significant improvement over existing methods for masked face recognition, providing high accuracy and robustness to occlusion.
Article
Multidisciplinary Sciences
Ahmed Omar, Tarek Abd El-Hafeez
Summary: This paper presents a comparative study of quantum computing and machine learning for Arabic language document classification. The results show that quantum computing slightly outperforms classic machine learning in sentiment analysis of Arabic tweets and has faster processing times for larger datasets. Additionally, classic machine learning achieves higher accuracy when dealing with smaller datasets.
SCIENTIFIC REPORTS
(2023)
Article
Multidisciplinary Sciences
Doaa A. Abdel Hady, Tarek Abd El-Hafeez
Summary: Urinary Incontinence (UI) is defined as uncontrolled urine leakage and is an indication of pelvic floor dysfunction. This study aimed to predict core muscle activity in multiparous women with FSD using machine learning models instead of relying on ultrasound imaging. The developed model showed high accuracy in predicting pelvic tilt and lumbar angle, potentially revolutionizing the assessment and management of this condition.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Theory & Methods
Belal A. Hamed, Osman Ali Sadek Ibrahim, Tarek Abd El-Hafeez
Summary: The study proposes a novel model that combines machine learning methods and pattern-matching algorithm for DNA sequence classification. The model aims to improve the accuracy and efficiency of DNA sequence classification by effectively categorizing DNA sequences based on their features. The results show that the SVM linear classifier achieves the highest accuracy and F1 score among the tested algorithms, suggesting that the proposed model outperforms other algorithms in DNA sequence classification. The study also explores the impact of pattern length on the accuracy and time complexity, finding that the execution time of each algorithm varies with the pattern length.
JOURNAL OF BIG DATA
(2023)
Article
Computer Science, Information Systems
Abdelaziz A. Abdelhamid, El-Sayed M. El-Kenawy, Abdelhameed Ibrahim, Marwa Metwally Eid, Doaa Sami Khafaga, Amel Ali Alhussan, Seyedali Mirjalili, Nima Khodadadi, Wei Hong Lim, Mahmoud Y. Shams
Summary: Feature selection is a crucial task in pattern recognition and data mining. This study proposes a novel hybrid binary meta-heuristic algorithm, bSCWDTO, to solve the feature selection problem. The algorithm outperforms ten state-of-the-art optimization methods and is statistically different from alternative feature selection methods.