Article
Computer Science, Artificial Intelligence
Seyed Mohammad Jafar Jalali, Milad Ahmadian, Sajad Ahmadian, Abbas Khosravi, Mamoun Alazab, Saeid Nahavandi
Summary: This study proposes an automated X-ray image analysis framework based on deep optimized CNNs to aid healthcare professionals in distinguishing COVID-19 patients from non-patients. The framework utilizes a modified version of the GSK optimization algorithm along with a selective ensemble approach combining reinforcement learning with optimized CNNs to improve classification effectiveness and reduce ensemble size. The experimental results demonstrate the excellent performance of the proposed method in accurately identifying COVID-19 patients.
APPLIED SOFT COMPUTING
(2021)
Article
Chemistry, Analytical
Sara Lombardi, Piergiorgio Francia, Rossella Deodati, Italo Calamai, Marco Luchini, Rosario Spina, Leonardo Bocchi
Summary: This study proposes a method for identifying COVID-19 patients using deep learning approaches to analyze the raw PPG signal from a pulse oximeter. The results indicate that photoplethysmography may be a useful tool for assessing microcirculation and early recognition of SARS-CoV-2-induced microvascular changes.
Article
Medicine, General & Internal
Xu-Jing Yao, Zi-Quan Zhu, Shui-Hua Wang, Yu-Dong Zhang
Summary: The COVID-19 virus has caused significant impact globally since late 2019, prompting the need for more accurate detection methods. This research developed an efficient deep learning framework named CSGBBNet to improve COVID-19 detection using lung CT scans. Results showed high accuracy and outperformance of previous methods, merging biomedical research and AI in the field of COVID-19 detection.
Article
Medicine, General & Internal
Gouri Shankar Chakraborty, Salil Batra, Aman Singh, Ghulam Muhammad, Vanessa Yelamos Torres, Makul Mahajan
Summary: COVID-19 is a respiratory disease caused by SARS-CoV-2. This study proposes an ensemble deep learning-based technique for accurate and reliable detection of the disease. By combining three CNN models (Xception, VGG19, and ResNet50V2) using a weighted average ensemble, high accuracy rates of 97.25% and 94.10% are achieved for binary and multiclass classification, respectively.
Article
Computer Science, Artificial Intelligence
Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger
Summary: Recent work has shown that adding shorter connections in convolutional networks can make the network deeper, more accurate, and more efficient in training. This paper introduces Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward manner. DenseNets alleviate the vanishing-gradient problem, encourage feature reuse, and improve parameter efficiency, leading to significant improvements in object recognition tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Seyed Mohammad Jafar Jalali, Milad Ahmadian, Sajad Ahmadian, Rachid Hedjam, Abbas Khosravi, Saeid Nahavandi
Summary: This paper proposes a method based on convolutional neural networks and K-nearest neighbors classifier to detect COVID-19 disease. To improve the accuracy, an improved competitive swarm optimizer is used for hyperparameter tuning. Experimental results show that the proposed method outperforms other models in the literature in terms of performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Omar M. Elzeki, Mahmoud Shams, Shahenda Sarhan, Mohamed Abd Elfattah, Aboul Ella Hassanien
Summary: CXR imaging is a vital tool for early detection of COVID-19, and the proposed CXRVN model, based on three different COVID-19 X-Ray datasets, uses a lightweight architecture with a single fully connected layer to reduce memory usage and processing time effectively. Evaluation results show that CXRVN achieves an average accuracy of 94.5% after training with different datasets and comparing with pre-trained models using fine-tuning and transfer learning technologies.
PEERJ COMPUTER SCIENCE
(2021)
Article
Chemistry, Multidisciplinary
Andrea Loddo, Fabio Pili, Cecilia Di Ruberto
Summary: This study aims to explore an automated COVID-19 detection system from CT images to assist clinicians in obtaining valuable information. Experimenting with ten convolutional neural networks on a public dataset, the results show that the VGG19 architecture performs best in network comparison and patient status classification, but exhibits limitations in cross-dataset experiments.
APPLIED SCIENCES-BASEL
(2021)
Article
Biology
Farhan Sadik, Ankan Ghosh Dastider, Mohseu Rashid Subah, Tanvir Mahmud, Shaikh Anowarul Fattah
Summary: In this paper, a deep convolutional neural network (CNN) based scheme is proposed for the automated accurate diagnosis of COVID-19 from lung CT scan images. The proposed scheme includes automated lung region segmentation, elimination of irrelevant slices, and effective feature extraction. Experimental results demonstrate that the scheme achieves satisfactory performance in diagnosing COVID-19.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Chemistry, Analytical
Ghada Atteia, Amel A. Alhussan, Nagwan Abdel Samee
Summary: This study introduces a Bayesian-optimized convolutional neural network (CNN) for the detection of acute lymphoblastic leukemia (ALL). The optimized CNN model demonstrates superior performance in classifying leukemia images compared to other deep learning models.
Article
Multidisciplinary Sciences
Lingzhi Kong, Jinyong Cheng
Summary: This research combines deep learning technology with Xception neural networks and LSTM to achieve automatic diagnosis of pneumonia patients in X-ray images, with an accuracy rate of 96% and significant implications for improving diagnostic accuracy.
Article
Computer Science, Artificial Intelligence
Junding Sun, Xiang Li, Chaosheng Tang, Shui-Hua Wang, Yu-Dong Zhang
Summary: An improved intelligent global optimization algorithm was proposed by the research team to optimize the hyperparameters of models for better detection of COVID-19 and pneumonia. Experimental results showed that the momentum factor biogeography-based optimization outperformed biogeography-based optimization in optimizing convolutional neural networks, enhancing detection performance.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Ronghua Shang, Jiaming Wang, Licheng Jiao, Xiaohui Yang, Yangyang Li
Summary: This paper proposes a spatial feature-based convolutional neural network (SF-CNN) for solving PolSAR classification problems. The special structure of SF-CNN can expand the training set by combining different samples and enhance the network's ability to extract discriminative features in low-dimensional feature space. Experimental results show that SF-CNN outperforms standard CNN in PolSAR image classification tasks.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Ruize Zhang, Liejun Wang, Shuli Cheng, Shiji Song
Summary: In recent years, there has been a growing interest in neural network-based medical image classification methods, which have shown remarkable performance. While convolutional neural network (CNN) architectures have been commonly used for extracting local features, the newly emerged transformer architecture has gained popularity due to its ability to explore the relevance of remote elements in images through self-attention. This paper proposes a network based on multilayer perceptrons (MLPs) that can simultaneously learn local features and capture overall feature information in medical images using spatial and channel dimensions, providing novel ideas for future medical image classification tasks.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Biomedical
Ali Deeb, Ahmad Debow, Saleem Mansour, Viacheslav Shkodyrev
Summary: This study investigated the effectiveness of using ResNet and AdjCNet, deep convolutional neural networks with attention mechanisms, to detect COVID-19 in CT scan images. The proposed method achieved an outstanding classification accuracy of 99.23% for identifying COVID-19, Normal, or Community Acquired Pneumonia (CAP) in CT images. Four-folds cross-validation demonstrated a mean accuracy and precision of 98.98% and 99.01%, respectively, for COVID-19 detection using CT-scan images.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Pradeep Jangir, Hitarth Buch, Seyedali Mirjalili, Premkumar Manoharan
Summary: This paper proposes a new multi-objective algorithm called MOMPA, which is based on the Marine-Predator Algorithm and can handle multiple conflicting objectives in optimization problems. Experimental results demonstrate the merits of the proposed method.
EVOLUTIONARY INTELLIGENCE
(2023)
Review
Computer Science, Artificial Intelligence
Chnoor M. Rahman, Tarik A. Rashid, Abeer Alsadoon, Nebojsa Bacanin, Polla Fattah, Seyedali Mirjalili
Summary: This paper provides a comprehensive investigation of the dragonfly algorithm in the engineering field. It discusses the overview and modifications of the algorithm, surveys its applications in engineering, and compares its performance with other algorithms. The results show that the dragonfly algorithm performs excellently in small to intermediate applications. The purpose of this research is to assist other researchers in studying and utilizing the algorithm to optimize engineering problems.
EVOLUTIONARY INTELLIGENCE
(2023)
Correction
Computer Science, Interdisciplinary Applications
Bestan B. Maaroof, Tarik A. Rashid, Jaza M. Abdulla, Bryar A. Hassan, Abeer Alsadoon, Mokhtar Mohammadi, Mohammad Khishe, Seyedali Mirjalili
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Correction
Computer Science, Interdisciplinary Applications
Ali Mohammadi, Farid Sheikholeslam, Seyedali Mirjalili
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Mohammed Qaraad, Souad Amjad, Nazar K. Hussein, Seyedali Mirjalili, Mostafa A. Elhosseini
Summary: This study proposes a time-based leadership particle swarm-based Salp (TPSOSA) algorithm to address the limitations of Particle swarm optimization (PSO). TPSOSA is a novel search technique that solves the issues of population diversity, exploitation and exploration imbalance, and premature convergence in the PSO algorithm. The experimental data and statistical tests show that TPSOSA is competitive and often superior to other algorithms.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Ali Safaa Sadiq, Amin Abdollahi Dehkordi, Seyedali Mirjalili, Jingwei Too, Prashant Pillai
Summary: This article focuses on developing a new trustworthy and efficient routing mechanism for routing data traffic over IoT-FinTech mobile networks. A new nonlinear Levy Brownian generalized normal distribution optimization (NLBGNDO) algorithm is proposed to solve the problem of finding an optimal path from source to destination sensor nodes. The proposed mechanism maintains wise and efficient decisions over the selection period in comparison with other methods.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Asmaa M. M. Khalid, Hanaa M. M. Hamza, Seyedali Mirjalili, Khaid M. M. Hosny
Summary: A new multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) is proposed for solving global optimization problems with up to three objective functions. The algorithm uses an archive to store non-dominated POSs during the optimization process. A roulette wheel selection mechanism is utilized to select effective archived solutions by simulating the frameshifting technique Coronavirus particles use for replication. The efficiency is evaluated by solving twenty-seven multi-objective problems and comparing the results with five common multi-objective metaheuristics using six evaluation metrics. The obtained results and the Wilcoxon rank-sum test demonstrate the superiority of this novel algorithm and its applicability in solving multi-objective problems.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Nima Khodadadi, Laith Abualigah, Qasem Al-Tashi, Seyedali Mirjalili
Summary: The Chaos Game Optimization (CGO) is effective for single-objective optimization, but cannot handle multiple objectives. This study proposes a multi-objective CGO (MOCGO) algorithm that stores Pareto-optimal solutions and utilizes multi-objective optimization. MOCGO is evaluated using seventeen case studies and outperforms existing methods.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Davut Izci, Serdar Ekinci, Seyedali Mirjalili, Laith Abualigah
Summary: In this work, a new master/slave model driven and optimization algorithm-based PID plus PIDD2 controller is proposed for the stable and efficient operation of an AVR system. The optimization algorithm optimally tunes the PIDD2 controller with the aid of a cost function, leading to significant improvements in efficiency and stability.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Education & Educational Research
Sourajit Ghosh, Md. Sarwar Kamal, Linkon Chowdhury, Biswarup Neogi, Nilanjan Dey, Robert Simon Sherratt
Summary: Students are crucial for a nation's future. Tailoring higher education courses to students' interests is a major challenge. AI and ML approaches have been employed to study student behavior, but concerns about interpretability and understandability remain due to the black-box nature of most algorithms.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Article
Engineering, Industrial
Reza Shahabi-Shahmiri, Thomas S. Kyriakidis, Mohammad Ghasemi, Seyed-Ali Mirnezami, Seyedali Mirjalili
Summary: This study proposes a bi-objective mixed integer linear programming framework for the multi-mode resource-constrained project scheduling problem under uncertain conditions. The framework considers minimizing project makespan and resource costs as objectives, and takes into account renewable and non-renewable resources and different modes for activities implementation. It efficiently addresses model uncertainty by using a fuzzy chance constrained programming method and extending two robust possibilistic programming models. The capability of the framework is validated using problem instances from PSPLIB and MMLIB, and a computational comparison is presented to assess the performance of the possibilistic programming models.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Mohammad Tubishat, Feras Al-Obeidat, Ali Safaa Sadiq, Seyedali Mirjalili
Summary: This paper introduces a novel approach to spam email detection by enhancing the Dandelion Optimizer algorithm. The authors propose a new local search algorithm and a reduction equation to improve the algorithm's performance in high-dimensional problems. They evaluate the improved algorithm using the Spam base dataset and compare it to other popular algorithms, showing superior performance in various metrics. The paper highlights the significant advancement made in the Improved DO algorithm and its potential for solving high-dimensional optimization problems.
Article
Computer Science, Information Systems
Nima Khodadadi, Ehsan Khodadadi, Qasem Al-Tashi, El-Sayed M. El-Kenawy, Laith Abualigah, Said Jadid Abdulkadir, Alawi Alqushaibi, Seyedali Mirjalili
Summary: This paper proposes a binary version of the Arithmetic Optimization Algorithm (BAOA) to tackle the feature selection problem in classification. The BAOA uses the distribution behavior of main arithmetic operators and outperforms other binary algorithms in terms of classification accuracy, selected features, and optimum fitness values.
Article
Computer Science, Information Systems
Sunday O. Oladejo, Stephen O. Ekwe, Lateef A. Akinyemi, Seyedali A. Mirjalili
Summary: Due to the limitations of single optimisation algorithms, new optimisation techniques are required. This paper proposes a novel metaheuristic called the deep sleep optimiser (DSO), which mimics human sleeping patterns to solve optimisation problems. The DSO is modelled on the rise and fall of homeostatic pressure during the deep sleep stage of human sleep. Its performance is demonstrated and compared with other metaheuristics using various functions and problems, showing that the DSO performs well and often outperforms others.
Article
Computer Science, Artificial Intelligence
Ahmad Taheri, Keyvan RahimiZadeh, Amin Beheshti, Jan Baumbach, Ravipudi Venkata Rao, Seyedali Mirjalili, Amir H. Gandomi
Summary: In this paper, a novel evolutionary optimization algorithm called Partial Reinforcement Optimizer (PRO) is introduced. The PRO algorithm is based on the psychological theory of partial reinforcement effect (PRE) and is mathematically modeled to solve global optimization problems. Experimental results demonstrate that the PRO algorithm outperforms existing meta-heuristic algorithms in terms of accuracy and robustness.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
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)