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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Ayub Baig Bura Vijay Kumar K. Arun Maitrey Bhookya Ramesh Karunakara Rai B IT Department Malla Reddy Engineering College for Women Hyderabad India School of Computer Science and Artificial Intelligence SR University Warangal India Computer Science and Engineering (Data Science) Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The High-Spatial-Resolution (HSR) remote sensing is categorized through its ability to generate fine-grained spatial data. The image resolution is typically calculated through Ground Sample Distance (GSD) that presents the smallest ground object size that can be solved in an images. This paper presents a remote sensing image semantic segmentation for resolving issues through Hierarchical Atrous Pyramid (HASP) and Spatial Adaptive Network (SPANet) based framework. The HASP is utilized to resolve the issues of difficult background and high-scale variances through expanding the receptive domain of network. The SPANet has a capability to adjust convolutional operations adaptively that reduces the computational load. It can lead to quicker inference time and make it is feasible for real-time applications. The loss function is calculated which assists network to enhance the segmentation result. The parameters like accuracy, Intersection over Union (IoU), mean IoU (mIoU) and mean F1 (mF1) are utilized to estimating model performance on iSAID dataset. The SPANet obtains high accuracy of 98.74%, IoU of 84.68%, mIoU of 72.26% and mF1 of 81.59% which is comparatively higher than existing methods like Foreground-Aware Relation Network (FarSeg++) and Cascade Class-Aware Enhanced Network (CCENet).

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Design of a Mobile data Collection Robot for Learning-based Localization and Autonomous Driving

Design of a Mobile Data Collection Robot for Learning-based ...

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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Baykar, Ali Omer Lambrecht, Jens Kural, Ayhan Uygur, Selcuk Eray Yildiz, Ahmet Istanbul Technical University Department of Mechanical Engineering Istanbul Turkey Turkish-German University Department of Mechanical Engineering Istanbul Turkey Berlin Institute of Technology Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Germany Turkish-German University Istanbul Turkey Macromedia University of Applied Sciences Hamburg Germany

ISBN: (纸本)9798350306590

This study introduces a mobile robot capable of collecting position and corresponding visual data seamlessly from both indoor and outdoor settings within the same sequence. The mobile robot has been specifically designed to navigate obstacles such as stairs and steps during transitions between indoor and outdoor environments. To accomplish this, the robot incorporates differential driving dynamics and is equipped with essential sensors including two stereo cameras, LIDAR, IMU, and GNSS. The entire system operates on the Robot Operating System (ROS). Consequently, it becomes possible to create a comprehensive dataset that encompasses not only the routes traversed by mobile vehicles but also includes all vehicle and pedestrian roads, as well as indoor spaces, found within a campus environment. © 2023 IEEE.

关键词： Mobile robots

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Improve Accuracy in Healthcare data Analysis using Competitive Ensemble Deep Learning Model

Improve Accuracy in Healthcare Data Analysis using Competiti...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Jakkula Sravanthi Chada Sampath Reddy A. Mahendar V. Ravi Kumar Swathi Buragadda G S Pradeep Ghantasala Gaurav Gupta Department of Information Technology VNR Vignana Jyoti Institute of Engineering and Technology Telangana India Department of Computer Science Engineering Sree Chaitanya Institute of Technological Sciences Karimnagar Telangana India Department of Computer Science Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Department of Computer Science Engineering ACE Engineering College Ghatkesar India Department of Computer Science Engineering Lakireddy Balireddy College of Engineering Kakinada India Department of Computer Science and Engineering Alliance University Bengaluru India Yogananda School of Artificial Intelligence Computers and Data Sciences Shoolini University Solan India

This paper discusses the significance of Machine Learning (ML) and Deep Learning (DL) techniques for structured and unstructured healthcare data. As healthcare data is increasing tremendously, it is difficult to identify hidden patterns in huge amounts of data. DL handles a massive amount of clinical data and provides better outcomes. A novel competitive ensemble deep learning model has been proposed to improve the classification performance of structured data. However, dealing with unstructured data, the proposed work highlights a competitive DL model for Twitter sentiment analysis. In addition, this paper discusses the proposed Competitive Ensemble Deep Learning (CEPL) algorithm for text data. The proposed model is compared with a traditional model to evaluate the model’s performance in the range of 0.2%-0.5%.

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Are defenses for graph neural networks robust? 22

Are defenses for graph neural networks robust?

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Felix Mujkanovic Simon Geisler Stephan Günnemann Aleksandar Bojchevski Dept. of Computer Science & Munich Data Science Institute Technical University of Munich CISPA Helmholtz Center for Information Security

ISBN: (纸本)9781713871088

A cursory reading of the literature suggests that we have made a lot of progress in designing effective adversarial defenses for Graph Neural networks (GNNs). Yet, the standard methodology has a serious flaw - virtually all of the defenses are evaluated against non-adaptive attacks leading to overly optimistic robustness estimates. We perform a thorough robustness analysis of 7 of the most popular defenses spanning the entire spectrum of strategies, i.e., aimed at improving the graph, the architecture, or the training. The results are sobering - most defenses show no or only marginal improvement compared to an undefended baseline. We advocate using custom adaptive attacks as a gold standard and we outline the lessons we learned from successfully designing such attacks. Moreover, our diverse collection of perturbed graphs forms a (black-box) unit test offering a first glance at a model's robustness.

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hao, Fengqi Zhu, Shulei Ma, Dexin Dong, Xiangjun Kong, Hoiio Liu, Xia Mu, Chunhua Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China City University of Macau Faculty of Data Science 999078 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China Communication College Qingdao Agricultural University Qingdao266109 China Maize Research Institute Shandong Academy of Agricultural Sciences National Engineering Laboratory of Wheat and Maize Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Jinan250100 China

ISBN: (纸本)9781665410205

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction - receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings. © 2024 IEEE.

关键词： Deep learning Maize seedling detection Object detection UAV

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Randomized message-interception smoothing: gray-box certificates for graph neural networks 22

Randomized message-interception smoothing: gray-box certific...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Yan Scholten Jan Schuchardt Simon Geisler Aleksandar Bojchevski Stephan Günnemann Dept. of Computer Science & Munich Data Science Institute Technical University of Munich CISPA Helmholtz Center for Information Security

ISBN: (纸本)9781713871088

Randomized smoothing is one of the most promising frameworks for certifying the adversarial robustness of machine learning models, including Graph Neural networks (GNNs). Yet, existing randomized smoothing certificates for GNNs are overly pessimistic since they treat the model as a black box, ignoring the underlying architecture. To remedy this, we propose novel gray-box certificates that exploit the message-passing principle of GNNs: We randomly intercept messages and carefully analyze the probability that messages from adversarially controlled nodes reach their target nodes. Compared to existing certificates, we certify robustness to much stronger adversaries that control entire nodes in the graph and can arbitrarily manipulate node features. Our certificates provide stronger guarantees for attacks at larger distances, as messages from farther-away nodes are more likely to get intercepted. We demonstrate the effectiveness of our method on various models and datasets. Since our gray-box certificates consider the underlying graph structure, we can significantly improve certifiable robustness by applying graph sparsification.

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning

Machine Learning for Climate Change Impact Assessment and Ad...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： T. Suresh Balakrishnan Prabhakar Krishnan U. Samson Ebenezar M. Mohammed Nizarudeen N. Kamal Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri-Campus India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu Dept. of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level.

关键词： Climate change Machine learning Planning data analysis Quality assessment Meteorology Support vector machines Convolutional neural networks Statistical analysis Random forests Long short term memory Feedback loop Evidence theory

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Correction: Web service location‑allocation using discrete NSGA‑II with matrix based genetic operations and a repair mechanism

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Journal of Ambient Intelligence and Humanized Computing 2023年第1期15卷 797-798页

作者： Verma, Shanu Pant, Millie Snasel, Vaclav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee India Centre for Artificial Intelligence and Data Science Indian Institute of Technology Roorkee Roorkee India Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic

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Semantic Feature Extraction-Based Twitter Sentiment Analysis Using Atom Search Optimizer and Ensemble Classifier

Semantic Feature Extraction-Based Twitter Sentiment Analysis...

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： R Archana Reddy Jukanti Rekha M Shirisha Abbas Hameed Abdul Hussein Kassem AL-Attabi School of Computer Science and Artificial Intelligence SR University Warangal India Department of CSE (Data Science) CMR Technical Campus Hyderabad India Department of CSE (Networks) Kakatiya Institute of Technology & Science Warangal India College of Pharmacy Ahl Al Bayt University Karbala Iraq The Islamic university Najaf Iraq

In modern days, a computer-based technique called natural language processing (NLP) is used to process spoken and written natural language data that is specific to human culture. Numerous NLP research directions and technologies have steadily arisen in the process of mining large amounts of textual data. Furthermore, prior models ignored the combined effect of relative distance and grammatical distance on the aspect words, instead only including the relative or grammatical distance of words into the model. This study examines the use of learning algorithms for Twitter sentiment analysis that is based on emotions like fear, anger, joy, and sadness. Sentiment-140 dataset is used in this research to conduct the analysis. First, a pre-processing step using tokenization, stemming, adding part of speech, and punctuation removal is carried out using the collected datasets. Then, using Bag of Words (BoW), Latent Dirichlet Analysis (LDA), Term Frequency, and Inverse Document Frequency (TF-IDF), the processed data is employed for semantic feature extraction. The next step is to choose the best features for accurate classification using the Atom Search Optimizer (ASO). Finally, the chosen features are divided into various classes using a Voting based Ensemble Classifier (VEC). From the experimental results, it is observed that the proposed VEC has outperformed existing methods by obtaining 98.69% of accuracy on and Sentiment-140 dataset.

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Towards Scalable Kernel-Based Regularized System Identification

Towards Scalable Kernel-Based Regularized System Identificat...

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IEEE Conference on Decision and Control

作者： Lujing Chen Tianshi Chen Utkarsh Detha Martin S. Andersen Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China MOSEK ApS Copenhagen Denmark

This paper proposes a methodology for scalable kernel-based regularized system identification based on indirect methods. It leverages stochastic trace estimation methods and an iterative solver such as LSQR for the efficient evaluation of hyperparameter selection criteria. It also uses a derivative-free optimization approach to hyperparameter estimation, which avoids the need for computing gradients or Hessians of the objective function. Moreover, the method is matrix-free, which means it only relies on a matrix-vector oracle and exploits fast routines for various structured matrix-vector products. Our preliminary numerical experiments indicate that the methodologygy scales significantly better than direct methods, especially when dealing with large datasets and slowly decaying impulse responses.

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