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Simulation of unmanned survey path planning in debris flow gully based on GRE-Bat algorithm

Journal of Mountain Science 2024年第12期21卷 4062-4082页

作者： LIU Dunlong FENG Duanguo SANG Xuejia ZHANG Shaojie YANG Hongjuan College of Software Engineering Chengdu University of Information TechnologyChengdu 610225China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China

Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data *** planning of UAV advancing along river valleys in wild environments is one of the first and most difficult problems faced by unmanned surveys of debris flow *** study proposes a new hybrid bat optimization algorithm,GRE-Bat(Good point set,Reverse learning,Elite Pool-Bat algorithm),for unmanned exploration path planning of debris flow sources in outdoor *** the GRE-Bat algorithm,the good point set strategy is adopted to evenly distribute the population,ensure sufficient coverage of the search space,and improve the stability of the convergence accuracy of the ***,a reverse learning strategy is introduced to increase the diversity of the population and improve the local stagnation problem of the *** addition,an Elite pool strategy is added to balance the replacement and learning behaviors of particles within the population based on elimination and local perturbation *** demonstrate the effectiveness of the GRE-Bat algorithm,we conducted multiple simulation experiments using benchmark test functions and digital terrain *** to commonly used path planning algorithms such as the Bat Algorithm(BA)and the Improved Sparrow Search Algorithm(ISSA),the GRE-Bat algorithm can converge to the optimal value in different types of test functions and obtains a near-optimal solution after an average of 60 *** GRE-Bat algorithm can obtain higher quality flight routes in the designated environment of unmanned investigation in the debris flow gully basin,demonstrating its potential for practical application.

关键词： Bat algorithm Unmanned surveys Debris flow gully Path planning Unmanned aerial vehicle Reverse learning

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A Comparative Study on Deep Learning based Segmentation Techniques for Tamil Inscriptions 8

A Comparative Study on Deep Learning based Segmentation Tech...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Poornimathi, K. Muralibhaskaran, V. Priya, L. Rajalakshmi Engineering College Department of Computer Science and Business Systems India Rajalakshmi Engineering College Department of Computer Science and Engineering India Rajalakshmi Engineering College Department of Information Technology India

ISBN: (纸本)9798350376425

The digitization and preservation of Tamil inscriptions are crucial for safeguarding the rich cultural heritage they represent. This study presents an in-depth evaluation of deep learning-based segmentation methods specifically designed for the complex and ancient Tamil script. Evaluate several cutting-edge deep learning models, including Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, and attention-based mechanisms, using a dataset of Tamil inscriptions. The evaluation criteria encompass segmentation accuracy, computational efficiency, and the ability to handle the noise and degradation common in historical artifacts. Our findings indicate that traditional methods often falter when faced with the complexities of Tamil script, whereas advanced deep learning techniques markedly enhance segmentation quality. This improvement facilitates more precise transcription and analysis. The comparison emphasizes the benefits and challenges of each method, delivering important insights into their effectiveness for cultural heritage preservation and paving the way for future research in the digitization of ancient scripts. © 2024 IEEE.

关键词： Convolutional neural networks

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COVID-19 Pandemic: A Worldwide Critical Review with the Machine Learning Model-Based Prediction

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 339-349页

作者： Misra, Neeraj Kumar Das, Sanchali Satpathy, Sambit Addula, Santosh Reddy Trivedi, Munesh Chandra School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravathi522237 India School of Computer Science Engineering and Technology Bennett University Greater Noida India Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Information Technology University of the Cumberlands Williamsburg United States Computer Science and Engineering PSSCIVE Bhopal Bhopal India

Machine learning (ML) with data analysis has many successful applications and is widely employed daily. Additionally, they have played a significant role in combating the global coronavirus (COVID-19) outbreak. International occurrences of the COVID-19 pandemic have resulted from the rapid global spread of the SARS-CoV-2 virus. In this study, we examine the ML and data analysis techniques for diagnosis, treatment, and impact of COVID-19, which are based on artificial intelligence (AI). Moreover, we provide an overview of the current state of the art in AI-based machine learning and deep learning techniques as well as the datasets, tools, and performance of the tools that can be used to combat COVID-19. In this survey, we have attempted to provide a comprehensive overview of the most recent state-of-the-art approaches available to researchers in Mathematical and Statistical analysis and the larger medical community as a whole. It also includes information on how data and ML can be used to improve COVID-19 status and encourage further research to prevent COVID-19 outbreaks. Future directions and problems are also described in detail. © The Institution of Engineers (India) 2024.

关键词： COVID-19

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Unsupervised Multi Focus Image Fusion based on Deep Image Prior 4

Unsupervised Multi Focus Image Fusion based on Deep Image Pr...

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4th IEEE International Conference on information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Gao, Yaru Hu, Yanxiang Zhang, Bo Hao, Caixia Chen, Xinran Tianjin Normal University College of Computer and Information Engineering China

ISBN: (纸本)9798350363586

Multi-focus image fusion is an effective method to eliminate the defocusing blur in the imaging process. At present, due to the lack of real supervised data, most multi-focus image fusion algorithms can only synthesize datasets through artificial blur, which may lead to the loss of detailed features. According to Deep Image Prior, utilizing the implicit prior information of neural networks can solve many standard inverse problems. Motivated by this idea, this paper proposes a new unsupervised MFIF method, which uses two deep prior networks to model the fused image and the focus map respectively, then use the edge reconstruction loss function to constrain the training process. The edge reconstruction loss can compensate for the shortcomings in generating deep image prior details. In order to verify the feasibility of the algorithm, four widely used objective evaluation metrics were selected for performance evaluation in the experimental verification stage. © 2024 IEEE.

关键词： Image fusion

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Parameter Identification of Vortex-Induced Vibration Models Using an Improved Fireworks Algorithm 24

Parameter Identification of Vortex-Induced Vibration Models ...

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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Xiao, Chi Lu, Hongyu Tian, Xiaoxia Computer and Information Engineering College Hanshan Normal University China

ISBN: (纸本)9798400716416

The nonlinear model of vortex-induced vibration describes the complex fluid-structure coupling phenomenon to a certain extent. Due to the existence of many local minima, the parameters of nonlinear models are difficult to be identified effectively. One of the researchers' goals is to find a suitable parameter identification algorithm. In this work, an improved fireworks algorithm (ASLPFWA) based on an adaptive selection operator and a Logistic chaotic operator of the initial population is proposed. The algorithm takes the L2 norm as the fitness function to solve the parameter identification problem. In ASLPFWA, firstly, a Logistic chaotic operator is used to initialize the population to solve the non-uniformity problem of the initial population. Then, to enhance the diversity of the next-generation population, an adaptive selection operator based on fitness is used to select a certain proportion of elite individuals and non-elite individuals from the contemporary population. The elite individuals are excellent, while non-elite individuals are randomly selected from the contemporary population, except for the elite individuals. Finally, taking the nonlinear model of vortex-induced vibration as an example, the simulation experiment mainly verifies the performance of ASLPFWA from three aspects: the features of ASLPFWA, the comparison of ASLPFWA and other improved fireworks algorithms, and the validity of parameters identified by ASLPFWA. Experiment results show that ASLPFWA has good stability and a high fitting coefficient (R2=0.8328), and the identified parameters can well describe the impacts of linear stiffness, linear damping, and nonlinear damping on the vortex-induced vibration of long-span bridges. © 2024 ACM.

关键词： Vortex flow

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An Intelligent Approach for Detrimental Emotion Classification and Healthcare Management

An Intelligent Approach for Detrimental Emotion Classificati...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Das, Sumit Nath, Ira Dey, Debonil Bhattacharyya, Ratnadeep Kar, Purba Guha, Aditya Ghosh, Somhrita JIS College of Engineering Information Technology Kalyani India JIS College of Engineering Computer Sc. & Engineering Kalyani India

ISBN: (纸本)9798350381887

The proposed study is based upon facial expression recognition and emotion analysis using deep learning techniques. The main objective is to develop a system that accurately detects and classifies facial expressions, allowing the assessment of depression, stress, and anxiety levels. The model is loaded and used to predict the emotion category of each captured facial image in real-time. The emotions considered in this study include Angry, Sad, Neutral, Happy, Surprise, Fear, and Disgust. The model captures facial images through a camera or webcam, preprocessing the images by converting them to grayscale and resizing them to a standard size. The images are then normalized and reshaped to match the input requirements of the CNN model. It predicts the dominant emotion based on the processed image, and provides insights into the emotional state of the user. Specific rules and algorithms were defined to assign weight-age to each emotion in relation to these psychological factors. Furthermore, SMS alerts are sent to specified phone numbers. The experiments demonstrate the effectiveness of the proposed system in accurately recognizing facial expressions and assessing emotional states. The system achieved high accuracy in emotion classification, allowing for reliable analysis of depression, stress, and anxiety levels. The uniqueness of our research lies in the integration of deep learning techniques, real-time processing, and the analysis of psychological factors. The combination of facial expression recognition and emotion analysis in a practical system contributes to the field of affective computing and has promising applications in mental health monitoring, human-computer interaction, and emotion-driven technologies. © 2024 IEEE.

关键词： Human computer interaction

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Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

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computer Systems Science & engineering 2023年第3期44卷 2129-2145页

作者： V.Thanikachalam M.G.Kavitha V.Sivamurugan Department of Information Technology Sri Sivasubramaniya Nadar College of EngineeringChennai603110India Department of Computer Science and Engineering University College of Engineering PtukottaiRajamadam614701India

In recent times,Internet of Things(IoT)and Deep Learning(DL)mod-els have revolutionized the diagnostic procedures of Diabetic Retinopathy(DR)in its early stages that can save the patient from vision *** the same time,the recent advancements made in Machine Learning(ML)and DL models help in developing computer Aided Diagnosis(CAD)models for DR recognition and *** this background,the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network(ODBN)model i.e.,NS-ODBN model for diagnosis of *** presented model involves Interval Neutrosophic Set(INS)technique to dis-tinguish the diseased areas in fundus *** addition,three feature extraction techniques such as histogram features,texture features,and wavelet features are used in this ***,Optimal Deep Belief Network(ODBN)model is utilized as a classification model for *** model involves Shuffled Shepherd Optimization(SSO)algorithm to regulate the hyperparameters of DBN technique in an optimal *** utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model *** presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation *** resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.

关键词： Diabetic retinopathy machine learning internet of things deep belief network image segmentation

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Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

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Going the distance: Application of Machine Learning Algorithms in predicting Housing Price Dynamics

Going the distance: Application of Machine Learning Algorith...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Sonare, Babita Thepade, Sudeep Gangane, Pratiksha Govardhan, Esha Nawani, Tisha Gadkar, Pragati Information Technology Pimpri Chinchwad College of Engineering Pune India Computer Engineering Pimpri Chinchwad College of Engineering Pune India

ISBN: (纸本)9798350381887

In this paper, we have used various algorithms such as Gradient Boost Regression, Catboost Regression, Decision Tree Regression and K-neighbors Regression by categorizing predictions based on threshold. To address the problems of data quality and other complexities, we performed validation on all the algorithms mentioned above. Predicting house prices often involves many barriers such as taking all the important factors into account, being aware of the demand of a particular area, government policies, spatial locations and houses with different sizes. We overcame these problems with the ML models we built. Among all the algorithms, Catboost Regression gave great results with mean absolute percentage error of 0.018 and r-squared value of 0.904. This paper provides insights in more detail about the strong positive correlation between area and price. © 2024 IEEE.

关键词： Adversarial machine learning

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Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

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computer Systems Science & engineering 2023年第9期46卷 2667-2685页

作者： I.Mettildha Mary K.Karuppasamy Department of Information Technology Sri Ramakrishna Engineering CollegeCoimbatoreTamilnaduIndia Department of Computer Science&Engineering RVS College of Engineering and TechnologyCoimbatoreTamilnaduIndia

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose ***(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance ***,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution ***(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between *** problem can be overcome by the use of Wrappers as they select better features by accounting for test and train *** aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between *** proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)*** methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

关键词： Cloud analytics machine learning ensemble learning distributed learning clustering classification auto selection auto tuning decision feedback cloud DevOps feature selection wrapper feature selection Adaptive Kernel Firefly Algorithm(AKFA) Q learning

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