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| 1 | 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 |
| 2 | 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 |
| 3 | 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 |
| 4 | 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 |
| 5 | Emotional learning based position control of pneumatic actuators显示文摘 | GARMSIRI N SEPEHRI N | 2014 | Intelligent automation&soft computing2014，20，3： | 1 |
| 6 | Generalization of rough sets using modal logic显示文摘 | Yao Y Y Lin T Y | 1996 | Intelligent Automation and Soft Computing1996，2，： | 1 |
| 7 | L2- Stability analysis of proportional Takagi-Sugeno fuzzy controller based control systems 显示文摘 | BAN Xiaojun GAO Xiaozhi HUANG XianLin | 2010 | Intelligent Automation and Soft Computing2010，6，1： | 1 |
| 8 | Mobile robot path planning using hybrid genetic algorithm and traversahility vectors method 显示文摘 | LOOC K RAJESWARI M WONG E K | 2004 | Intelligent Automation and Soft Computing2004，10，1： | 1 |
| 9 | Haptic feedback in teleoperation of multifingered robot hands显示文摘 | SHEN Y T LO W T LIU Y H | 2003 | Int J on Intelligent Automation and Soft Computing2003，，： | 1 |
| 10 | Generalization of rough sets using modal logics显示文摘 | Yao Y Y Lin T Y | 1996 | Intelligent Automation and Soft Computing1996，2，： | 1 |
| 11 | Reversible visible watermarking with lossless data embedding based on difference value shift 显示文摘 | Zhang X P Wang S Z Feng G R | 2011 | Intelligent Automation and Soft Computing2011，17，2： | 1 |
| 12 | Context-aware woriflow management for intelligent navigation applications in pervasive environments显示文摘 | Tang F You I Guo M | 2010 | Intelligent Automation and Soft Computing2010，16，4： | 1 |
| 13 | Generalization of rough sets using modal logic 显示文摘 | YAO Yiyu LIN T Y | 1996 | International Journal of Intelligent Automation and Soft Computing1996，，2： | 1 |
| 14 | Application of electro-mechanical impedance sensing technique for online aging monitoring of rubber显示文摘 | ZHANG Yu-xiang XU Fu-hou | 2012 | Intelligent Automation and Soft Computing2012，18，8： | 1 |
| 15 | Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy显示文摘Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment analysis.One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy.Unfortunately,existing work always considers feature-level fusion or decision-level fusion,and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion.To improve the performance of multimodal sentiment analysis,we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy(BAHFS).Firstly,we apply BiGRU to learn the unimodal features of text,audio and video.Then we fuse the unimodal features into bimodal features using the bimodal attention fusion module.Next,BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal features.Finally,BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level fusion.Based on the CMU-MOSI and CMU-MOSEI datasets,extensive experiments have been carried out to verify BAHFS’s superiority. | Zhizhong Liu Bin Zhou Lingqiang Meng Guangyu Huang | 2023 | Intelligent Automation & Soft Computing2023，37，8： | 1 |
| 16 | Generalization of rough sets using modal logic显示文摘 | Yao Y Y Lin T Y | 1996 | Intelligent Automation and Soft Computing1996，2，： | 1 |
| 17 | A crack monitoring method and system for concrete structure显示文摘 | Zhang Benniu Zhou Zhixiang Li Xingxing | | AutoSoft-Intelligent Automation and Soft Computing Special Issue:Bridge Health Monitoring and Environmental Protection of Roads Intelligent Automation and Soft Computing0，，： | 1 |
| 18 | Voltage stability enhancement via model predictive control of load显示文摘 | Hiskens I A Gong B | 2006 | Intelligent Automation Soft Computation2006，12，1： | 1 |
| 19 | Predictive control of greenhouse temperature based on mixed logical dynamical systems显示文摘 | QIN L L SHI C LING Q | 2010 | Intelligent Automation and Soft Computing2010，16，6： | 1 |
| 20 | Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification显示文摘Internet of Things(IoT)is the most widespread and fastest growing technology today.Due to the increasing of IoT devices connected to the Internet,the IoT is the most technology under security attacks.The IoT devices are not designed with security because they are resource constrained devices.Therefore,having an accurate IoT security system to detect security attacks is challenging.Intrusion Detection Systems(IDSs)using machine learning and deep learning techniques can detect security attacks accurately.This paper develops an IDS architecture based on Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)deep learning algorithms.We implement our model on the UNSW-NB15 dataset which is a new network intrusion dataset that cate-gorizes the network traffic into normal and attacks traffic.In this work,interpolation data preprocessing is used to compute the missing values.Also,the imbalanced data problem is solved using a synthetic data generation method.Extensive experiments have been implemented to compare the performance results of the proposed model(CNN+LSTM)with a basic model(CNN only)using both balanced and imbalanced dataset.Also,with some state-of-the-art machine learning classifiers(Decision Tree(DT)and Random Forest(RF))using both balanced and imbalanced dataset.The results proved the impact of the balancing technique.The proposed hybrid model with the balance technique can classify the traffic into normal class and attack class with reasonable accuracy(92.10%)compared with the basic CNN model(89.90%)and the machine learning(DT 88.57%and RF 90.85%)models.Moreover,comparing the proposed model results with the most related works shows that the proposed model gives good results compared with the related works that used the balance techniques. | Rasha Almarshdi Laila Nassef Etimad Fadel Nahed Alowidi | 2023 | Intelligent Automation & Soft Computing2023，，1： | 1 |
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