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An effective object detection and tracking using automated image annotation with inception based faster R-CNN model

International Journal of Cognitive Computing in engineering 2024年第1期5卷 343-356页

作者： Vijiyakumar, K. Govindasamy, V. Akila, V. Department of Computing Technologies School of computing SRM institute of Science and Technology Kattankulathur campus Tamilnadu Chengalpattu district India Department of Information Technology Pondicherry Engineering College Pondicherry India Department of Computer Science and Engineering Pondicherry Engineering College Pondicherry India

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking. © 2024

关键词： Image annotation

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Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samples

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Neural Computing and Applications 2024年第32期36卷 20067-20087页

作者： Saha, Mousumi Chakraborty, Mainak Maiti, Suchismita Das, Deepanwita Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India School of Innovation Design and Engineering Division of Intelligent Future Technologies Malardalen University Västerås Sweden Department of Information Technology Narula Institute of Technology West Bengal Kolkata700109 India Department of Computer Science and Engineering Narula Institute of Technology West Bengal Kolkata700109 India

Breast cancer is a prevalent and highly lethal cancer affecting women globally. While non-invasive techniques like ultrasound and mammogram are used for diagnosis, histological examination after biopsy is considered the gold standard. However, manual examination of tissues for abnormality is labor-intensive, expensive, and requires prior domain knowledge. Early detection, awareness, and access to specialized medical infrastructure in resource-constrained and remote areas are significant challenges but crucial for saving lives. In recent years, deep learning-based approaches have shown promising results in breast cancer detection, facilitated by advancements in GPU memory, computation power, and the availability of digital data. Motivated by these observations, we propose the Breast-NET deep convolutional neural network model for breast cancer detection and grading using histological images. Our model’s performance is evaluated on the BreakHis dataset, and we demonstrate its generalization ability on the Invasive Ductal Carcinoma (IDC) grading and IDC datasets. Extensive experimental and statistical performance analysis, along with an ablation study, validates the efficiency of our proposed model. Furthermore, we demonstrate the effectiveness of transfer learning with seven pre-trained convolutional neural networks for breast cancer detection and grading. Experimental results show that our framework outperforms state-of-the-art approaches in terms of accuracy, space, and computational complexity for the BreakHis, IDC grading, and IDC datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Grading

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Scheduling Tool for Deterministic Communication in Distributed Real-Time Systems 10

Scheduling Tool for Deterministic Communication in Distribut...

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10th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Mach, Jan Tašková, Valéria Kohútka, Lukáš Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

A network with critical data streams, where the timing of incoming and outgoing data is a necessity, is called a deterministic network. These networks are mostly used in association with real-time systems that use periodic hard-real-time data which need to be scheduled. The algorithm proposed in this paper is focused on achieving the lowest possible latency within the specified network topology depending on the physical parameters of the network and the periodic requirements of the communicating nodes. The algorithm allows to add priorities to the data flow between nodes and also the option to specify if preemption is allowed within the network, since not all devices support it. The result is a schedule of hard-real-time data communication within a specified network topology where all requirements of communicating nodes are met if such a solution is possible. © 2023 Copyright for this paper by its authors.

关键词： deadline deterministic scheduling Ethernet scheduling algorithm time-sensitive networking

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Algorithm for Finding a Descriptive Path for Delivering Cargo to Hard-to-Reach Areas 16

Algorithm for Finding a Descriptive Path for Delivering Carg...

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16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Brovkina, Valeria R. Ermakov, Alexander S. Shevketova, Eleonora S. Chernetskaya, Natalia N. Basan, Elena S. Institute of Computer Science and Engineering Southern Federal University Taganrog Russia Institute of Computer Technologies and Information Security Southern Federal University Taganrog Russia

ISBN: (纸本)9798350330885

At the moment, due to the growing interest in unmanned aerial vehicles (UAV), as well as due to the rapid and constant development of the UAV market and high technologies in the field of artificial intelligence, this branch of robotics has begun to occupy more and more sectors of the economy. With the increasing popularity of fast and light vehicles such as UAVs, the demand for efficient flight path planning algorithms has also increased to achieve a variety of goals: overflight, obstacle detection and collision avoidance. One of these goals is achieved by our algorithm for flying around static obstacles in 3D space in a sparse weighted graph. A hybrid method has been developed for finding the safest and fastest route based on Dijkstra's algorithm, which works with satellite images of various terrain classifications. The novelty of the proposed algorithm lies in the combination of artificial neural networks for categorizing terrain classes and using these data when constructing a flight task. The idea is to build a route on top of the safest terrain so that in the event of an emergency fall or landing, the UAV will not be lost. It is more memory efficient because it does not require storing all the vertices in an open list, as do algorithms that perform a similar task. In addition to the software architecture, the most suitable hardware architecture for the intended purpose - delivering cargo to difficult terrain - is presented. © 2023 IEEE.

关键词： Neural networks

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Microsphere-assisted super-resolution polarized light microscopy for polarization-sensitive materials

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Optics Letters 2025年第3期50卷 908-911页

作者： Ye, Ran Zhang, Xiangyu Zhi, Lingjie Cao, Yurong Melinte, Sorin Wang, Zengbo Yang, Songlin School of Computer and Electronic Information Nanjing Normal University Nanjing210046 China Institute of Information and Communication Technologies Electronics and Applied Mathematics Université Catholique de Louvain Louvain-la-Neuve1348 Belgium School of Computer Science and Engineering Bangor University BangorLL57 1UT United Kingdom

Polarization sensitivity is a fundamental phenomenon observed in nature, and its application is vital for advancing scientific discoveries. Here, we present a microsphere-assisted polarized light microscopy method that enables high-quality, label-free super-resolution imaging of polarization-sensitive materials. We investigated the imaging performance of this method using various samples, including colloidal crystals and grating structures. Both simulations and experiments were performed to explore the underlying physical mechanisms driving the enhancement in imaging quality. The results of this work have potential values for the fields of polarized light microscopy and super-resolution imaging. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Microspheres

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Enhancing Change Impact Prediction by Integrating Evolutionary Coupling with Software Change Relationships 24

Enhancing Change Impact Prediction by Integrating Evolutiona...

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18th ACM/IEEE International Symposium on Empirical Software engineering and Measurement, ESEM 2024

作者： Zhou, Daihong Zhang, Jiyue Yu, Ping Guo, Wunan School of Computer Science and Information Engineering Shanghai Institute of Technology China Huawei Technologies Co. Ltd China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology China

ISBN: (纸本)9798400710476

Background: Changes on source code may propagate to distant code entities through various relationships, making related changes obligatory. Identifying change impacts is challenging due to the complexity of how changes spread. Although association rules are widely used for change impact prediction, they rely solely on historical co-changes, which limits their accuracy when entities rarely or never co-change. Aims: This study explores the integration of evolutionary coupling with software change relationships among changed code entities to enhance the state-of-the-art association rule mining technique, TARMAQ. Method: We integrate evolutionary coupling with 12 types of software change relationships, such as structural dependencies and code clones, to better capture associated changes. Results: Analyzing thousands of commits from six open-source systems, we observed: (1) Incorporating software change relationship analysis significantly improves TARMAQ's prediction recall and mean average precision (MAP), (2) The top-5 predictions exhibit notable increasing in precision, recall, F1-score, and MAP, and (3) Based on our implementation, the integrated method is practically applicable. Conclusions: Combining evolutionary coupling and software change relationships can improve the recall and prioritization of impact predictions in association rule-based techniques. © 2024 ACM.

关键词： Prediction models

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Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification

Neural Network-Based Analysis of Forest Fire Aftermath in Cl...

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2024 Symposium on Beyond the Canopy: technologies and Applications of Remote Sensing

作者： Hnatushenko, Viktoriia Hnatushenko, Volodymyr Soldatenko, Dmytro Dept. Information Technologies and Systems Ukrainian State University of Science and Technologies Dnipro Ukraine Dept. Information Technologies and Computer Engineering Dnipro University of Technology Dnipro Ukraine Institute of Photogrammetry and GeoInformation Leibniz Universität Hannover Germany

Today's agricultural sector is characterized by an important role of accurate mapping and monitoring of agriculture with the help of satellite imagery, which allows to optimize the use of resources, to plan crop areas and to forecast productivity. Classification of satellite images with unbalanced sample distribution is a critical problem in this regard. Traditional machine learning algorithms in particular have limitations in dealing with sample imbalance. In this paper, we proposed convolution neural networks for semantic segmentation, where sample imbalance is considered based on a particular loss function coupled with data augmentation. To illustrate our method, we use Sentinel-2 remote sensing (RS) images covering a number of regions in Ukraine, and then we create an image dataset of the region and for training and testing make data augmentation. The models with different architectural features were investigated. The results demonstrate that the proposed CNN has a higher classification accuracy than the ones discussed in the paper: the classification accuracy on the test dataset reached 96.7% with intersection-over-union values of up to 89.7%. This opens the way for further research in the direction of refining algorithms for classify satellite data with an imbalanced class structure. © Author(s) 2024.

关键词： Semantic Segmentation

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Effect of individual atmospheric parameters on beam spreading of higher-order Gaussian beam

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Optical and Quantum Electronics 2024年第12期56卷 1-18页

作者： kumar, Mukesh Khandelwal, Arpit Azeemuddin, Syed Center for VLSI and Embedded System Technologies International Institute of Information Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Jodhpur India Department of Electrical and Computer Engineering Behrend College Penn State University EriePA United States

The beam spreading is essential for evaluating the higher-order Gaussian beam when it propagates through atmospheric turbulence. In this paper, we investigate the impact of individual atmospheric parameters such as jitter, turbulence, wind speed, and thermal blooming on beam spreading of Hermite Gaussian (HG), and Laguerre Gaussian (LG) beams. We also examine spot size variations due to beam quality on various HG and LG modes. It is seen that the impact of spreading caused by beam quality and turbulence is higher than that of jitter, wind speed, and thermal blooming. As the mode order increases, the effect of turbulence strength increases, leading to more spread for HG and LG beams. Spreading due to diffraction and beam quality on higher mode LG beam is higher than the HG beam in different modes. Beam spreading due to thermal blooming for the lower mode is higher compared to the higher mode for LG and HG beams. The sensitivity of higher mode LG beams to turbulence distortions is greater than HG beams as they experience more significant fractional increases in their spot size due to turbulence. The combined beam spread for the Laguerre Gaussian beam is larger than the Hermite Gaussian beam. This paper aims to understand better individual beam spreading in the atmosphere and its impact on the overall performance of higher-order laser propagation to develop optimized laser systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Gaussian beams

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DNA Transcription and Translation Inspired Deep Features for Classification-Based CBIR 23th

DNA Transcription and Translation Inspired Deep Features fo...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Pradhan, Jitesh Pal, Arup Kumar Islam, SK Hafizul Samanta, Debabrata Department of Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur831014 India Jharkhand Dhanbad826004 India Department of Computer Science and Engineering Indian Institute of Information Technology West Bengal Kalyani741235 India Department of Computing and Information Technologies Rochester Institute of Technology Rochester Kosovo

ISBN: (纸本)9783031648465

DNA is a macromolecule that carries the genetic information of nearly all living things on the planet. They not only determine the characteristics and behavior of an organism but also pass the essential features to the next generation, ensuring that "like begets like." Because of their same genetic structure, organisms of the same species appear identical. Inspired by this property, a novel DNA-based scheme for class-based image retrieval has been proposed. The algorithm imitates the flow of genetic information, which is initially stored in the DNA and is transcripted and translated to RNA and amino-acid sequences, respectively, using genetic coding. Since similar images would generate a similar sequence, ensuring the preservation of salient features of the images required for retrieval. Thus, these amino-acid sequences are then deployed on a DNA-inspired ResNet-50 CNN architecture for performing image classification-based image retrieval. The proposed scheme has been extensively tested on six different datasets to demonstrate its performance. Comparative results reveal that the proposed scheme outperforms competing state-of-the-art Content-based Image Retrieval techniques in terms of retrieval performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： DNA

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Geometric Modelling of Fractal Structured Objects based on Integration of R-Function And L-System Methods

Geometric Modelling of Fractal Structured Objects based on I...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Anarova, Shahzoda Azimbayevna, Shakhlo Sadullaeva Berdiev, Golib Ogli, Adhamjon Toxtasinov Ilhomjon Ilxom Kizi, Maftuna Ismoilova Department of Convergence of digital technologies Computer Engineering Faculty Tashkent University of Information Technologies Tashkent Uzbekistan Joint Belarusian-Uzbek Intersectoral Institute of Applied Technical Qualifications Uzbekistan Department of Computer Systems Karshi branch of the Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Uzbekistan

ISBN: (纸本)9798400716928

The article offers suggestions about mathematical modeling of architectural objects based on the functional image using the constructive operations of the theory of R-functions. Given in 2D format 3D surface from the data equations work exit according to approach offer done. Work developed mathematician from the model using construction - architecture L-systems in objects based on three measured geometric to the object processing to give through fractal 3D computer design of an architectural building model work developed. In the article work developed samples of the method opportunities reflection made. Fractal geometry terminology and concepts using natural surfaces, forms enough level right, good quality and quantitative way described. In architecture forms structure spatial organize to be done fractal geometry using natural structures like enough level description possible has been repetitive to himself similar from structures use, fractal construction the rules mathematician models and computer graphics using in architecture application this direction possibilities efficiency increases. © 2024 ACM.

关键词： Fractals

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