Article
Engineering, Multidisciplinary
Marwah Sattar Hanoon, Ali Najah Ahmed, Arif Razzaq, Atheer Y. Oudah, Ahmed Alkhayyat, Yuk Feng Huang, Pavitra Kumar, Ahmed El-Shafie
Summary: This study investigates the capability of various machine learning algorithms in predicting the power production of a reservoir located in China. The proposed models can efficiently predict the hydropower generation and provide valuable insights for energy decision-makers.
AIN SHAMS ENGINEERING JOURNAL
(2023)
Article
Computer Science, Information Systems
R. Geetha, K. Ramyadevi, M. Balasubramanian
Summary: The prediction of electricity consumption is crucial for smart energy management, as it plays an important role in planning power generation and distribution systems and understanding customer lifestyles. However, existing forecasting models have shown subpar accuracy and require improvement.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Yun Zheng, Yisu Ge, Sami Muhsen, Shifeng Wang, Dalia H. Elkamchouchi, Elimam Ali, H. Elhosiny Ali
Summary: Forecasting wind speed is crucial for wind energy conversion systems (WECS) to meet customer demands and efficiently manage electricity production. This research proposes a kernel ridge regression (RR) model to predict wind speed for planning wind farms. The model's effectiveness is validated by comparing it with two reference prediction models. The study's relevance lies in its ability to accurately forecast wind speeds and its potential benefits for cost and risk management in wind power planning.
ADVANCES IN ENGINEERING SOFTWARE
(2023)
Article
Energy & Fuels
Daniel Asante Otchere, Tarek Omar Arbi Ganat, Raoof Gholami, Syahrir Ridha
Summary: The use of Artificial Intelligence (AI) in the petroleum industry has increased to accelerate decision making, reduce cost and time. Supervised machine learning has become popular in establishing relationships between complex non-linear datasets. Support Vector Machine (SVM) and Relevant Vector Machine (RVM) have emerged as competitive algorithms, often outperforming Artificial Neural Network (ANN).
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2021)
Article
Mathematics, Interdisciplinary Applications
Oznur Oztunc Kaymak, Yigit Kayamank
Summary: This paper discusses the impact of global economic recession and the COVID-19 pandemic on oil prices, and aims to improve a model for more accurate predictions. The study finds that this model outperforms other models and artificial neural networks in forecasting crude oil prices.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Materials Science, Characterization & Testing
S. Bandara, P. Rajeev, E. Gad, B. Sriskantharajah, I. Flatley
Summary: This paper introduces a stress wave propagation technique combined with artificial neural network algorithm for assessing the condition of timber poles, with the success rates of ANN model, SVM classifier, and k-means clustering algorithm being 92%, 87%, and 81% respectively. Through signal classification, intact and defective poles can be accurately identified.
JOURNAL OF NONDESTRUCTIVE EVALUATION
(2021)
Article
Chemistry, Physical
Yuan Tian, Xinxin Wang, Yanrong Liu, Wenping Hu
Summary: This study establishes two databases to predict the solubility of CO2 and N2 in various kinds of ILs under different temperature and pressure conditions. By dividing ILs into multiple ionic fragments and combining with support vector machine and artificial neural network, a quantitative structure-property relationship model is built to establish the relationship between gas solubility and ILs structure. The results show that both IFC-SVM and IFC-ANN models can accurately and reliably predict the solubility of CO2 and N2 in ILs, guiding the screening of ILs.
JOURNAL OF MOLECULAR LIQUIDS
(2023)
Article
Water Resources
Sarmad Dashti Latif, K. L. Chong, Ali Najah Ahmed, Y. F. Huang, Mohsen Sherif, Ahmed El-Shafie
Summary: Sediment transport is crucial for predicting flood events, tracking coastal erosion, planning for water supplies, and managing irrigation. AI-based models, such as LSTM, ANN, and SVM, were investigated in predicting sediment transport in the Johor river, with LSTM outperforming other models.
APPLIED WATER SCIENCE
(2023)
Review
Chemistry, Analytical
Yingying Liao, Lei Han, Haoyu Wang, Hougui Zhang
Summary: This paper reviews the existing methods for predicting railway track degradation, including traditional methods and machine learning-based methods. By using machine learning methods, it is possible to identify the degradation pattern and develop accurate prediction models.
Article
Computer Science, Information Systems
Ahmed M. Abd M. El-Haleem, Mohab Mohammed Eid, Mahmoud M. Elmesalawy, Hadeer A. Hassan Hosny
Summary: Due to the COVID-19 pandemic and the development of educational technology, e-learning has become essential in the educational process. However, sectors such as engineering, science, and technology face challenges in adopting e-learning as it requires a special Laboratory Learning Management System (LLMS) to support online lab activities. This paper proposes a generic technique based on Artificial Intelligence (AI) to assess student performance in online labs and implements it as a performance evaluation module in the LLMS. The technique analyzes the student's mouse dynamics and can work with any simulation or control software used by virtual or remote controlled laboratories, supporting automatic detection of student performance and providing appropriate assistance. The study confirms that the proposed technique achieves a precision of up to 91% in automatically evaluating student performance.
Article
Engineering, Multidisciplinary
Xinpo Sun, Yuzhang Bi, Hojat Karami, Shayan Naini, Shahab S. Band, Amir Mosavi
Summary: This study presents a hybrid approach using SVR tool and Fruitfly Optimization Algorithm to improve accuracy of predicting scour hole geometry below ski-jump spillways. The results show that the proposed hybrid method outperforms the simple SVR model and provides reliable predictions.
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
(2021)
Article
Computer Science, Information Systems
Saurabh Pal
Summary: Chronic kidney disease (CKD) is a common disease that is difficult to diagnose early. The main goal is to diagnose kidney failure, and this study develops a model for the early detection of CKD. Machine learning algorithms are used to predict and manage the disease. The proposed model shows a 3% increase in accuracy compared to existing models, providing support for improved classification of CKD.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Srikrishna Iyer, T. Velmurugan, A. H. Gandomi, V. Noor Mohammed, K. Saravanan, S. Nandakumar
Summary: The proposed system presents a multi-robot-based fault detection system for railway tracks, utilizing a hardware prototype that implements a master-slave robot mechanism, combining ultrasonic sensor inputs and image processing to classify surface defects, with the CNN model performing well. Fault location and status can be relayed to a central location using GSM, GPS, and cloud storage technologies. The system is extended to optimize energy utilization and increase the overall network throughput by simulating the LEACH protocol with 100 robot nodes.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Telecommunications
Kalyan Das, Satyabrata Das, Sibarama Panigrahi
Summary: In a sensor cloud system, an efficient hybrid forecasting method is developed to reduce data transmission between sensors and the cloud while maintaining the availability of data to end-users. The method models sensor data using support vector machine and neural network to provide accurate forecasts to the users.
WIRELESS PERSONAL COMMUNICATIONS
(2023)
Review
Computer Science, Information Systems
Arijit Chakraborty, Sajal Mitra, Debashis De, Anindya Jyoti Pal, Ferial Ghaemi, Ali Ahmadian, Massimiliano Ferrara
Summary: Protein-Protein Interaction (PPI) is a crucial network in biology that requires fast, accurate, and critical analysis, with Support Vector Machine (SVM) being an effective tool for solving complex classification problems.
Article
Energy & Fuels
Ahmad A. Adewunmi, Muhammad Shahzad Kamal, Sunday O. Olatunji
Summary: An SVR-based model was proposed in this study to predict the performance of ionic liquid demulsifiers in water/oil separation. The model demonstrated high accuracy and can be used for smart screening of ionic liquids to enhance demulsification efficiency in industries such as petroleum.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2022)
Article
Chemistry, Analytical
Maha M. Alshammari, Afnan Almuhanna, Jamal Alhiyafi
Summary: This study utilized machine learning techniques to assist radiologists in reading mammograms and classifying breast tumors. Features were extracted from the region of interest that radiologists manually annotated, and these features were incorporated into a classification engine to build models. The study found that optimized Support Vector Machine or Naive Bayes algorithms achieved 100% accuracy.
Review
Computer Science, Artificial Intelligence
Hind Baaqeel, Sunday Olatunji
Summary: Authentication using biometrics has gained attention with the rise of computing and mobile devices, but faces challenges such as intra-class variability and high false rejection rates. Adaptive solutions have been introduced to address these issues, but may also be vulnerable to exploitation by attackers. Research aims to encourage the development of more robust adaptive solutions to overcome identified gaps in the field.
Article
Chemistry, Multidisciplinary
Ilham A. Elaalami, Sunday O. Olatunji, Rachid M. Zagrouba
Summary: Object recognition is crucial in computer vision, especially in applications such as real-time surveillance and self-driving cars. However, object detection models are susceptible to adversarial attacks, and existing methods lack generalization to various detectors. This paper proposes a new adversarial attack called AT-BOD, which successfully fools both single-stage and multi-stage detectors.
APPLIED SCIENCES-BASEL
(2022)
Article
Crystallography
Sunday Olusanya Olatunji, Taoreed Owolabi
Summary: This study develops empirical models based on three extreme learning machines to determine the superconducting critical temperature of MgB2 superconductor, using structural distortion, room temperature resistivity, and residual resistivity ratio as descriptors. The developed models outperform existing models in the literature and can provide quick estimation of the influence of dopants on the superconducting transition temperature of MgB2 superconductor.
Article
Engineering, Multidisciplinary
Khameel Mustapha, Jamal Alhiyafi, Aamir Shafi, Sunday Olusanya Olatunji
Summary: This study investigates the non-linear response of 3D-printed polymeric lattice structures with and without structural defects. A support vector machine surrogate model is developed based on physical compression tests and geometric models. The model shows high accuracy in predicting the compressive stress and deflection response. The developed model provides a cost-saving method for predicting the response of polymeric structures without the need for repeated physical experiments.
JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY
(2023)
Article
Mathematical & Computational Biology
Sunday O. Olatunji, Aisha Alansari, Heba Alkhorasani, Meelaf Alsubaii, Rasha Sakloua, Reem Alzahrani, Yasmeen Alsaleem, Mona Almutairi, Nada Alhamad, Albandari Alyami, Zainab Alshobbar, Reem Alassaf, Mehwash Farooqui, Mohammed Imran Basheer Ahmed
Summary: This study focuses on early diagnosis of rheumatoid arthritis and investigates the possibility of diagnosing the disease using clinical data before the symptoms appear. By utilizing machine learning algorithms and clinical laboratory tests, a novel ensemble technique for preemptive prediction of RA is proposed, achieving a high level of accuracy.
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
(2022)
Article
Mathematical & Computational Biology
Sunday O. Olatunji, Aisha Alansari, Heba Alkhorasani, Meelaf Alsubaii, Rasha Sakloua, Reem Alzahrani, Yasmeen Alsaleem, Reem Alassaf, Mehwash Farooqui, Mohammed Imran Basheer Ahmed, Jamal Alhiyafi
Summary: Alzheimer's Disease is a silent disease that exponentially increases with age. The number of AD patients in Saudi Arabia is expected to triple by 2060, and current medical capabilities cannot confirm AD with certainty. This study aims to predict AD in Saudi Arabia using machine learning techniques and found that support vector machine performs well in preemptive diagnosis.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2022)
Article
Engineering, Multidisciplinary
Nuhu Dalhat Mu'azu, Sunday Olusanya Olatunji
Summary: Computational intelligence (CI) predictive models based on k-Nearest Neighbor (KNN) algorithms were developed for Cd ions removal from contaminated soil using environmentally friendly chelating-agent polyaspartate. The performances of the KNN models were compared with response surface methodology (RSM) models using whole data set (KNN1) and split data (KNN2) scenarios. The KNN1 demonstrated better performances than the RSM models, while the KNN2 models showed enhanced performance in soil remediation efficiency.
AIN SHAMS ENGINEERING JOURNAL
(2023)
Article
Computer Science, Theory & Methods
Ismail. B. Mustapha, Shafaatunnur Hasan, Hatem S. Y. Nabbus, Mohamed Mostafa Ali Montaser, Sunday Olusanya Olatunji, Siti Maryam Shamsuddin
Summary: One of the most studied challenges in machine learning is the class imbalance problem, and recent studies have shown that deep neural networks are susceptible to it. Traditional learning objective, empirical risk minimization (ERM), is biased towards the majority class, thus unable to achieve optimal imbalance learning in deep neural networks. This study investigates an alternative learning objective called group distributionally robust optimization (gDRO) for imbalance learning on tabular imbalanced data. Experimental findings reveal that gDRO outperforms ERM and other classical imbalance methods in terms of g-mean and roc-auc, across various benchmark imbalance binary tabular data.
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Sunday O. Olatunji, Aisha Alansari, Heba Alkhorasani, Meelaf Alsubaii, Rasha Sakloua, Reem Alzah-Rani, Yasmeen Alsaleem, Reem Alassaf, Mehwash Farooqui, Mohammed Imran Basheer Ahmed
Summary: Lung cancer is a serious malignant disease that affects patients' daily life and survival. Preemptive diagnosis and treatment can improve the success rate of lung cancer prevention and treatment. This study predicts lung cancer by using clinical and demographic features, and employs various machine learning algorithms to enhance the accuracy of the prediction.
2022 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND MACHINE LEARNING APPLICATIONS (CDMA 2022)
(2022)
Article
Computer Science, Theory & Methods
Bador Al Sari, Rawan Alkhaldi, Dalia Alsaffar, Tahani Alkhaldi, Hanan Almaymuni, Norah Alnaim, Najwa Alghamdi, Sunday O. Olatunji
Summary: This research studied the quality of sentiment analysis of impressions about Saudi cruises from three selected social media platforms. The highest classification accuracy was achieved by the RF algorithm with over-sampled data from Snapchat, reaching 100% accuracy. The results showed that 80% of sentiments were positive while 20% were negative.
JOURNAL OF BIG DATA
(2022)
Article
Management
Muizz O. Sanni-Anibire, Rosli Mohamad Zin, Sunday Olusanya Olatunji
Summary: This study develops a machine learning model for delay risk assessment in tall building projects, identifying the most relevant risk factors and achieving a classification accuracy of 93.75% using ANN. The model can support construction professionals in project risk management.
INTERNATIONAL JOURNAL OF CONSTRUCTION MANAGEMENT
(2022)
Review
Management
Muizz O. Sanni-Anibire, Rosli Mohamad Zin, Sunday Olusanya Olatunji
Summary: The construction industry is plagued by inefficiency and productivity losses, including delays. The impact of delays can result in time and cost overruns, legal disputes, and project abandonment. Despite the increasing trend of construction globalization, there is a lack of systematic review on research about construction delays. This study provides an overview of the causes of construction delays and conducts a meta-data analysis based on Relative Importance Index (RII) values from influential studies in the past 15 years. Based on this analysis, the top five causes of delays globally are identified as contractor's financial difficulties, delay in approval of completed work, slow delivery of materials, poor site organization and coordination, and poor planning of resources and duration estimation/scheduling.
INTERNATIONAL JOURNAL OF CONSTRUCTION MANAGEMENT
(2022)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)