Article
Computer Science, Artificial Intelligence
Ines Feki, Sourour Ammar, Yousri Kessentini, Khan Muhammad
Summary: The COVID-19 pandemic has led to a need for efficient diagnosis methods, with deep learning proving to be valuable in analyzing chest X-ray images. This study introduces a collaborative federated learning framework for medical institutions to screen COVID-19 without sharing patient data, showing competitive results compared to traditional data-sharing models. By addressing privacy concerns and utilizing private data, this framework allows for the rapid development of powerful models for COVID-19 screening.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Tripti Goel, R. Murugan, Seyedali Mirjalili, Deba Kumar Chakrabartty
Summary: Coronavirus Disease 2019 (COVID-19) has spread worldwide, leading to limited healthcare services in many countries. Screening hospitalized individuals efficiently through chest radiography is crucial in the fight against COVID-19. A two-step Deep Learning (DL) architecture, named Multi-COVID-Net, has been proposed for COVID-19 diagnosis using chest X-ray images, which outperforms other state-of-the-art methods in terms of performance results when tested with publicly available datasets.
APPLIED SOFT COMPUTING
(2022)
Article
Multidisciplinary Sciences
Ahmad Mozaffer Karim, Hilal Kaya, Veysel Alcan, Baha Sen, Ismail Alihan Hadimlioglu
Summary: In order to achieve a more accurate diagnosis of COVID-19, complementary practices such as CT and X-ray in combination with RT-PCR are discussed. This study proposes a new computer-aided diagnosis application for COVID-19 detection using deep learning techniques. The results show that the NB classifier with Ant Lion Optimization Algorithm and CNN produced the best results with high accuracy and precision.
Article
Computer Science, Information Systems
Sarra Guefrechi, Marwa Ben Jabra, Adel Ammar, Anis Koubaa, Habib Hamam
Summary: This study designed a deep learning system for extracting features and detecting COVID-19 from chest X-ray images, and fine-tuned three powerful neural networks on an enhanced dataset through transfer learning, achieving efficient and accurate COVID-19 detection methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Haval I. Hussein, Abdulhakeem O. Mohammed, Masoud M. Hassan, Ramadhan J. Mstafa
Summary: Hundreds of millions of people worldwide have been affected by COVID-19, leading to significant damage to health, economy, and welfare. In order to detect infected patients and provide timely care, lightweight CNN-based diagnostic models were developed for automatic and early detection of COVID-19 from chest X-ray images. These models achieved high accuracy rates and reduced computational and memory requirements compared to existing heavyweight models.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biology
Guangyu Jia, Hak-Keung Lam, Yujia Xu
Summary: Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are crucial for COVID-19 diagnosis. This paper proposes modified MobileNet and ResNet architectures to achieve high test accuracy for classifying COVID-19 CXR and CT images. The proposed methods outperform comparative models in classification accuracy, sensitivity, and precision, demonstrating their potential in computer-aided diagnosis for healthcare applications.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Adi Alhudhaif, Kemal Polat, Onur Karaman
Summary: The study developed a reliable convolutional-neural-network (CNN) model for the classification of COVID-19 from chest X-ray views, aiming to address bias issues from the database. The DenseNet-201 architecture outperformed other models with the highest accuracy, precision, recall, and F1-scores.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
R. Karthik, R. Menaka, M. Hariharan
Summary: Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and strengthens coronavirus testing methods. A custom CNN architecture has been proposed in this research to learn unique convolutional filter patterns for each kind of pneumonia, showing significant potential in augmenting current testing methods for COVID-19.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Shadi A. Aljawarneh, Romesaa Al-Quraan
Summary: The aim of this study was to develop models to evaluate large X-ray images of the chest and determine whether the images show signs of pneumonia. The enhanced CNN model showed the highest accuracy for pneumonia detection.
Article
Computer Science, Information Systems
Vedika Gupta, Nikita Jain, Jatin Sachdeva, Mudit Gupta, Senthilkumar Mohan, Mohd Yazid Bajuri, Ali Ahmadian
Summary: This study aims to develop a computer-aided design system that uses chest X-ray images and pre-trained deep neural networks to classify the images into COVID-19, viral pneumonia, and healthy categories. The results show that AlexNet achieves high accuracy and performance in terms of precision, recall, and F1 score.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Automation & Control Systems
Shanjiang Tang, Chunjiang Wang, Jiangtian Nie, Neeraj Kumar, Yang Zhang, Zehui Xiong, Ahmed Barnawi
Summary: Efficient screening of COVID-19 cases is crucial to prevent the rapid spread of the disease, and the EDL-COVID model, combining deep learning and ensemble learning, shows promising results in COVID-19 case detection with a higher accuracy compared to the COVID-Net model.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Rawan Alaufi, Manal Kalkatawi, Felwa Abukhodair
Summary: The COVID-19 pandemic has become a global health threat for over 2 years. The current detection method, RT-PCR, is slow and inaccurate. Deep learning diagnostic studies using medical chest imaging have emerged as a better solution. This paper reviews these studies, analyzes their approaches, and discusses ideas and solutions to address the challenges.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Chemistry, Analytical
Nur-A-Alam, Mominul Ahsan, Md. Abdul Based, Julfikar Haider, Marcin Kowalski
Summary: This study proposed a machine vision approach for detecting COVID-19 by fusing features extracted from HOG and CNN, then training CNN (VGGNet) for classification with high accuracy. Various techniques were used for processing X-ray images, including MADF and watershed segmentation, with overfitting problems addressed through cross-validation analysis.
Article
Engineering, Multidisciplinary
Sobhan Sheykhivand, Zohreh Mousavi, Sina Mojtahedi, Tohid Yousefi Rezaii, Ali Farzamnia, Saeed Meshgini, Ismail Saad
Summary: The study proposed a new method using deep neural networks for automatic identification of pneumonia (including COVID-19), achieving high accuracy and separation capability. The method utilized multiple functional scenarios and advanced technologies, showing better performance compared to other deep transfer learning approaches.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Rachna Jain, Meenu Gupta, Soham Taneja, D. Jude Hemanth
Summary: Covid-19 is a rapidly spreading viral disease that affects both humans and animals. Deep learning techniques can provide useful analysis of chest x-ray images to aid in the screening of Covid-19. The Xception model shows the highest accuracy in detecting chest x-ray images compared to other models.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Harshit Kaushik, Tarun Kumar, Kriti Bhalla
Summary: With the rapid development of AI, IoT, and HCC, the popularity of smart homes has increased significantly. However, ensuring the security of residents in smart homes remains a challenging task. This research proposes a real-time facial emotion-based security framework that uses a CMOS camera to predict security concerns in smart homes. Experimental results show that the framework achieves high accuracy.
APPLIED SOFT COMPUTING
(2022)
Article
Green & Sustainable Science & Technology
Tarun Kumar, Ravi Srinivasan, Monto Mani
Summary: This paper proposes an Emergy-based method to evaluate the effectiveness of integrating IoT-based sensing systems into smart buildings. The method employs three novel Emergy Neutrality Indices (ENIs) and is applied to a solar house retrofitted with an IoT-based sensing system. The study demonstrates the effectiveness of the integration and highlights the significance of reporting these indices. Designers and stakeholders can use these ENIs as useful tools for evaluating the environmental effectiveness of integrating smart sensing systems into buildings.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Unais Sait, Vandana Ravishankar, Tarun Kumar, Rahul Bhaumik, Gokul K. Lal, Kriti Bhalla, Kamble Sanket Sanjay
INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Gokul K. Lal, Unais Sait, Tarun Kumar, Rahul Bhaumik, Sanjana Shivakumar, Kriti Bhalla
INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE
(2020)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Rahul Bhaumik, Sunny Prakash Prajapati, Tarun Kumar, Vishal Mishra, Kriti Bhalla
2019 IEEE GLOBAL HUMANITARIAN TECHNOLOGY CONFERENCE (GHTC)
(2019)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Vishal Mishra, Tarun Kumar, Sanjana Shivakumar, Vandana D. Ravishankar, Kriti Bhalla, Brajesh Dhiman
2019 IEEE GLOBAL HUMANITARIAN TECHNOLOGY CONFERENCE (GHTC)
(2019)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Unais Sait, Vandana D. Ravishankar, Tarun Kumar, Sanjana Shivakumar, Gokul K. Lal, Kriti Bhalla, Manvendra Singh, Rahul Bhaumik
2019 IEEE GLOBAL HUMANITARIAN TECHNOLOGY CONFERENCE (GHTC)
(2019)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)