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| 1 | On the performance of artificial bee colony (ABC) algorithm显示文摘 | D. Karaboga B. Basturk | 2007 | Applied Soft Computing Journal2007，，1： | 6 |
| 2 | Design of adaptive Takagi-Sugeno-Kang fuzzy models 显示文摘 | Dragan Kukol | 2002 | Applied Soft Computing2002，，2： | 2 |
| 3 | Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites显示文摘The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker. | Kisaezehra Muhammad Umer Farooq Muhammad Aslam Bhutto Abdul Karim Kazi | 2023 | Intelligent Automation & Soft Computing2023，，4： | 2 |
| 4 | Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making显示文摘 | Guiwu Wei | 2009 | Applied Soft Computing Journal2009，，2： | 2 |
| 5 | Deep Learning Prediction Model for Heart Disease for Elderly Patients显示文摘The detection of heart disease is a problematic task in medical research.This diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical history.The massive advances in deep learning models pursue the devel-opment of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things support.Therefore,in this paper,we propose a deep learning model for elderly patients to aid and enhance the diag-nosis of heart disease.The proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning techniques.The mod-el performance is verified with a full and minimum set of features.Fewer features enhance the processing time of the classification process while the accuracy is compromised.The performance of classifiers with less features has been analyzed with experimental results.The proposed system is built on the Internet of Things Platform for medical data for the classification process which aids medical profes-sionals to detect heart diseases through cloud platforms.The results accuracy is matched to classical learning models such as Convolutional Neural Network(CNN),Deep CNN,and neural ensemble models.The analysis of the proposed diagnostic system can determine the heart disease risks efficiently.Experimental results demonstrate thatflexible modeling and tuning of the hyperparameters can attain an accuracy of up to 97.11%. | Abeer Abdulaziz AlArfaj Hanan Ahmed Hosni Mahmoud | 2023 | Intelligent Automation & Soft Computing2023，，2： | 2 |
| 6 | Hyperparameter Tuning for Deep Neural Networks Based Optimization Algorithm显示文摘For training the present Neural Network(NN)models,the standard technique is to utilize decaying Learning Rates(LR).While the majority of these techniques commence with a large LR,they will decay multiple times over time.Decaying has been proved to enhance generalization as well as optimization.Other parameters,such as the network’s size,the number of hidden layers,drop-outs to avoid overfitting,batch size,and so on,are solely based on heuristics.This work has proposed Adaptive Teaching Learning Based(ATLB)Heuristic to identify the optimal hyperparameters for diverse networks.Here we consider three architec-tures Recurrent Neural Networks(RNN),Long Short Term Memory(LSTM),Bidirectional Long Short Term Memory(BiLSTM)of Deep Neural Networks for classification.The evaluation of the proposed ATLB is done through the various learning rate schedulers Cyclical Learning Rate(CLR),Hyperbolic Tangent Decay(HTD),and Toggle between Hyperbolic Tangent Decay and Triangular mode with Restarts(T-HTR)techniques.Experimental results have shown the performance improvement on the 20Newsgroup,Reuters Newswire and IMDB dataset. | D.Vidyabharathi V.Mohanraj | 2023 | Intelligent Automation & Soft Computing2023，，6： | 2 |
| 7 | Focus: Rainfall prediction model using soft computing technique显示文摘 | Wong K W Wong P M C_ edeon T D | 2003 | Soft Computing2003，7，6： | 1 |
| 8 | A permutation-based dual genetic algorithm for dynamic optimization problems显示文摘 | Liu L L Wang D W Ip W H | 2008 | Soft Computing2008，13，7： | 1 |
| 9 | The reliability of general vague fault-tree analysis on weapon systems fault diagnosis 显示文摘 | CHANG J R CHANG K H LIAO S H | 2006 | Soft Computing2006，10，8： | 1 |
| 10 | A note on functions associated with Godel formulas显示文摘 | Gerla B | 2000 | Soft Computing2000，，4： | 1 |
| 11 | The Equivalence Between Fuzzy Mealy and Moore Machines显示文摘 | Li Yongming Pedrycz W | 2006 | Soft Computing2006，10，10： | 1 |
| 12 | Water allocation improvement in river basin using adaptive neural fuzzy reinforcement learning approach 显示文摘 | Abolpour B Javan M Karamouz M | 2007 | Applied Soft Computing2007，7，1： | 1 |
| 13 | Application of neural networks in forecasting engine systems reliability显示文摘 | Xu K | 2003 | Applied Soft Computing2003，2003，2： | 1 |
| 14 | Type-2 fuzzy logic-based classifier fusion for support vector machines显示文摘 | Chen X J Li Y Harrison R | 2008 | Applied Soft Computing2008，8，3： | 1 |
| 15 | An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem显示文摘 | YANG Jingan ZHUANG Yanbin | 2010 | Applied Soft Computing2010，10，： | 1 |
| 16 | Solving shortest path problem using particle swarm optimization显示文摘 | Mohemmed A W Sahoo N C Geok T K | 2008 | Applied Soft Computing2008，8，4： | 1 |
| 17 | Applying evolutionary algorithm's to materialized view selection in a data warehouse 显示文摘 | HOMG J T CHANG Y J LIU B J | 2003 | Soft Computing2003，7，8： | 1 |
| 18 | Facing Classification Problems with Particle Swarm Optimization显示文摘 | Faleo I D Cioppa A D Tarantino E | 2007 | Applied Soft Computing2007，7，3： | 1 |
| 19 | Cooperative Robotics for Multi-Target Observation 显示文摘 | LynneEParker | 1999 | Intelligent Automation and Soft Computing special issue on Robotics Research at OakRidge National Laboratory1999，5，1： | 1 |
| 20 | Recognizing environment from action sequences using self-organizing maps显示文摘 | Yamada S | 2004 | Applied Soft Computing2004，4，1： | 1 |
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