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3rd IEEE International Conference on Mobile networks and Wireless Communications, ICMNWC 2023

作者： Aluvala, Srinivas Issa, Ali Ashoor Saranya, K. Almoussawi, Zainab Abed Shakir, Aboothar Mahmood School of Computer Science & Artificial Intelligence SR University Telangana Warangal India National University of Science and Technology Dhi Qar Iraq Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India College of Islamic Science Ahl Al Bayt University Karbala Iraq The Islamic University Najaf Iraq

ISBN: (纸本)9798350317022

The expedition of the classification of the remote sensing images based on their freights have a wide range of applications in the number of fields. The manual yield interpretation for the land cover monitoring is the challenging task, as well as it has consumed much time to classify the Land Cover (LC) image. To address these challenges, this research proposed a pre-trained DenseNet201 architecture of the Deep Learning (DL) technique-based feature extraction for the land cover monitoring. This research utilized the UCM dataset which contains 2100 aerial images clipped from aerial ortho imagery is used for evaluated the performance of the proposed DenseNet201 framework. In pre-processing stage, the noise removal technique is used for removing the unwanted noise in the collected dataset. After pre-processing, the DenseNet201is used for the feature extraction and Convolutional neural Network (CNN) is used for the classification of the LC aerial images. The proposed DenseNet201 framework achieves better results and attains the values of accuracy of 93.67%, precision of 96.28%, recall of 94.32% as well as F1-score of 94.88% when compared to the existing methods like Prototype based Memory Network (PM-Net), Pass Over (POEP) and Recurrent neural Network-Random Forest (RNN-RF). © 2023 IEEE.

关键词： Convolutional neural Network Deep Learning DenseNet201 Land Cover Monitoring Noise removal

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Hollow n-grams Vectorizer for Natural Language processing Problems 13th

Hollow n-grams Vectorizer for Natural Language Processing P...

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13th International Conference on image processing and Communications, IP and C 2023, 13th International Conference on Computer Recognition Systems, CORES 2023

作者： Borek-Marciniec, Weronika Ksieniewicz, Pawel Department of Systems and Computer Networks Wroclaw University of Science and Technology Wyb. Wyspiańskiego 27 Wroclaw50370 Poland

ISBN: (纸本)9783031416293

Words don’t come easy, which fosters the use of generative artificial intelligence models in ongoing popularity of widely available applications such as ChatGPT. The result is an even greater flood of online content that takes time to process. It is where Natural Language processing tools for classification come in handy. Distinguishing fake news, event types, and other tasks can help process everyday information. In practice, such systems must work on data streams where fast prediction is needed. To achieve it, methods not based on neural networks can be used. Instead, they require feature extractions from the text to convert it to the model input. The primary methods used for this purpose are bag-of-words and n-grams, which allow converting the corpus of texts into a numerical format. This paper proposes a new strategy for creating n-grams – Hollow n-grams – which can be used to create classifiers ensembles with a higher generalization ability than models based on regular n-grams only. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Research on the Comparison of FCN and U-Net in Remote Sensing image Change Detection 14

Research on the Comparison of FCN and U-Net in Remote Sensin...

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14th International Conference on Graphics and image processing (ICGIP)

作者： Zhou, Xing Xue, Xiaorong Qu, Guangna Liaoning Univ Technol Sch Elect & Informat Engn Jinzhou 121000 Peoples R China

ISBN: (纸本)9781510666313;9781510666320

Remote sensing image change detection has a wide range of applications in urban planning, disaster monitoring, environmental protection and other fields. Since fully convolutional neural network has a good performance in image processing, it is widely used in remote sensing image change detection, among which U-Net and FCN are two important fully convolutional neural networks. After a comparative analysis of the two neural network structures, it is proposed that the FCN structure has a better ability to extract changed informations. At the same time, a skip connection method CSC is proposed which can enhance the feature extraction ability of FCN. The computational complexity of FCN is almost unchanged after CSC is applied. The change detection capability of CSC-FCN exceeds that of U-Net when the computational complexity is much lower than that of U-Net. It is concluded that the FCN structure has better change detection ability in dealing with multi-channel data containing complex timing information.

关键词： Fully convolutional neural network FCN u-net change detection remote sensing image

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Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish

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artificial INTELLIGENCE REVIEW 2022年第5期55卷 4077-4116页

作者： Li, Daoliang Du, Ling China Agr Univ Natl Innovat Ctr Digital Fishery Beijing Peoples R China China Agr Univ Beijing Engn & Technol Res Ctr Internet Things Ag Beijing 100083 Peoples R China China Agr Univ China EU Ctr Informat & Commun Technol Agr Beijing 100083 Peoples R China China Agr Univ Key Lab Agr Informat Acquisit Technol Minist Agr Beijing 100083 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China

Monitoring the growth conditions and behavior of fish will enable scientific management, reduce the threat of losses caused by disease and stress. Traditional monitoring methods are time-consuming, laborious, and untimely monitoring readily leads to aquaculture accidents. As a non-invasive, objective, and repeatable tool, machine vision systems have been widely used in various aspects of aquaculture monitoring. Nevertheless, the complex underwater environment makes it difficult to obtain ideal data processing results only using traditional image processing methods. Due to their powerful feature extraction capabilities, deep learning (DL) algorithms have been widely used in underwater image processing. Hence, the combination of DL algorithms and machine vision for the automated monitoring of aquaculture is of great importance. As evidence for the multidisciplinary aspects of DL applications, attention is focused on the latest DL methods applied to five fields of research: classification, detection, counting, behavior recognition, and biomass estimation. Meanwhile, due to the low training efficiency of DL models caused by insufficient dataset, transfer learning and GAN have also put into spotlight of this filed to pursue high performance of DL models. We also present the challenges and benchmarks in terms of the advantages and disadvantages of the selected method in each field. In addition, we review the sources of image acquisition and pre-processing methods in aquaculture. Finally, the challenges and prospects of DL in aquaculture machine vision systems are discussed. The literature review shows that the deep neural networks such as AlexNet, LSTM, VGG, and GoogLeNet, have been used for aquaculture machine vision systems.

关键词： Deep learning Machine vision Aquaculture image acquisition image preprocessing

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Split Computing based Quantized PoseNet Model for Distributed AI Architecture in IoT-Edge 15

Split Computing based Quantized PoseNet Model for Distribute...

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15th International Conference on Communication Systems and networks (COMSNETS)

作者： Karjee, Jyotirmoy Naik, Praveen S. Anand, Kartik Byadgi, Chandrashekhar S. Dabbiru, Ramesh Babu Venkat Srinidhi, N. Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9781665477062

In recent times, Internet of Things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep neural Network (DNN) models on them. To address this, we introduce Split Computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called Distributed artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. The QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. The result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.

关键词： Performance evaluation Computational modeling image edge detection Computer architecture Bandwidth Inference algorithms Partitioning algorithms

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artificial neural networks: a practical review of applications involving fractional calculus

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EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS 2022年第10期231卷 2059-2095页

作者： Viera-Martin, E. Gomez-Aguilar, J. F. Solis-Perez, J. E. Hernandez-Perez, J. A. Escobar-Jimenez, R. F. Tecnol Nacl Mexico CENIDET Interior Internado Palmira S-N Cuernavaca 62490 Morelos Mexico CONACyT Tecnol Nacl Mexico CENIDET Interior Internado Palmira S-N Cuernavaca 62490 Morelos Mexico Univ Nacl Autonoma Mexico Escuela Nacl Estudios Super Unidad Juriquilla Blvd Juriquilla 3001 Juriquilla 76230 Queretaro Mexico Univ Autonoma Estado Morelos Ctr Invest Ingn & Ciencias Aplicadas Av Univ 1001 Cuernavaca 62209 Morelos Mexico

In this work, a bibliographic analysis on artificial neural networks (ANNs) using fractional calculus (FC) theory has been developed to summarize the main features and applications of the ANNs. ANN is a mathematical modeling tool used in several sciences and engineering fields. FC has been mainly applied on ANNs with three different objectives, such as systems stabilization, systems synchronization, and parameters training, using optimization algorithms. FC and some control strategies have been satisfactorily employed to attain the synchronization and stabilization of ANNs. To show this fact, in this manuscript are summarized, the architecture of the systems, the control strategies, and the fractional derivatives used in each research work, also, the achieved goals are presented. Regarding the parameters training using optimization algorithms issue, in this manuscript, the systems types, the fractional derivatives involved, and the optimization algorithm employed to train the ANN parameters are also presented. In most of the works found in the literature where ANNs and FC are involved, the authors focused on controlling the systems using synchronization and stabilization. Furthermore, recent applications of ANNs with FC in several fields such as medicine, cryptographic, image processing, robotic are reviewed in detail in this manuscript. Works with applications, such as chaos analysis, functions approximation, heat transfer process, periodicity, and dissipativity, also were included. Almost to the end of the paper, several future research topics arising on ANNs involved with FC are recommended to the researchers community. From the bibliographic review, we concluded that the Caputo derivative is the most utilized derivative for solving problems with ANNs because its initial values take the same form as the differential equations of integer-order.

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An Instrument Tone Recognition System Based on artificial Intelligence and Feature Recognition

An Instrument Tone Recognition System Based on Artificial In...

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image processing and Computer applications (ICIPCA), IEEE International Conference on

作者： Zheng Hu School of Music Zhaoqing University Zhaoqing City Guangdong China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

Instrument tone recognition systems have over time had the highest application value and significance in information retrieval. Notably, the traditional systems and methods often rely on convolutional neural networks (CNN) that serve as fundamental through reportedly, have a poor performance. To this effect, this work seeks to examine a resent artificial intelligence (AI) and a feature recognition system in instrument tone recognition. A proposed model in this case is integrated in an interactive system of humanoid robot. The findings of this study reveal that the proposed resent artificial intelligence (AI) and a feature recognition system in the design of an instrument tone recognition system outperforms the traditional model in terms of recognition accuracy and tonal loss rate. The findings of the research therefore providing methods and ideas that may be used in instrument tonal recognition.

关键词： Emotion recognition image recognition Accuracy Instruments Interactive systems Information retrieval Rhythm Convolutional neural networks Timbre artificial intelligence

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CropViT: A light-weight Transformer Model for Crop Disease Detection 3

CropViT: A light-weight Transformer Model for Crop Disease D...

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3rd International Conference on artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chemmalar Selvi, G. Charan, H.J. Kumar, Dinesh Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Agricultural industry has grown significantly bring sustainable farming practices in improving the food quality, enhancing agricultural productivity and global food security. However, the crop yield and its quality are impacted when the timely identification and management of the crop diseases are not promptly solved. Thus, the crop disease detection becomes the critical aspect in the smart agriculture. Several researchers addressed this crop disease detection by developing optimized solutions through machine learning, deep learning and image processing techniques. However, these techniques face serious challenges when deployed in real-time crop disease identification problems like annotated training images, capturing long-range dependencies, adaption to real-time changes evolving over time and so on. This leads to performance degradation seeking for more powerful applications like some advanced deep learning techniques, in particular, the transformer models. In this paper, a novel crop disease detection model known as CropViT was proposed based on Vision Transformer. This CropViT was developed by fine-tuning the architecture of existing Vision Transformer and PlantVillage dataset was used in the experimental study. Nine crop species were selected and trained using CropViT whose performance was then compared with traditional Convolutional neural Network model. The experimental study clearly showed the highest mean accuracy of 98.64% for CropViT when compared to 95.52% mean accuracy for traditional CNN. The experimental results also outperformed other existing state-of-the-art models. Finally, the work was concluded with the discussion of experimental results and its future scope of work. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimized Deep neural Network for Accurate Detection of Malignant and Benign Brain Tumors 3

Optimized Deep Neural Network for Accurate Detection of Mali...

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3rd International Conference on artificial Intelligence For Internet of Things, AIIoT 2024

作者： Visumathi, J. Kalaivani, N. Hemanth, S.V. Balasubramanian, K. Karthikeyan, S. Amirthayogam, G. Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai600062 India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Mathematics Tamilnadu Chennai600062 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India Kalasalingam Academy of Research and Education Deemed to be University Department of Computer Applications Tamil Nadu Krishnankoil626126 India Arunai Engineering College Department of Computer Science and Engineering Tamilnadu Tiruvannamalai606603 India Hindustan Institute of Technology and Science Deemed To Be University Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

The rapid development of medical technology has ushered in a new age of crucial medical records. A brain tumor is an aberrant mass of rapidly expanding brain cells, some of which have the potential to metastasize. Brain tumors are often diagnosed in clinical practice using magnetic resonance imaging (MRI) scans. These tumors appear as clusters of irregular cells that could endanger patients by invading nearby tissues. However, manually spotting and segmenting brain tumors via MRI scans present complex challenges and are prone to mistakes. Thus, analysis of such data has influenced the prognosis, monitoring, diagnosis, and treatment of tumor-related conditions. To overcome these issues, we propose an Optimized Deep neural Network (ODNN) method to accurately predict benign or malignant brain tumor diseases. Moreover, pre-processing uses an Adaptive Histogram Contrast Normalization (AHCN) technique to enhance the distribution and contrast of MRI image density. Afterward, features can be extracted using a Texture-Based Gray Level Co-Occurrence Matrix (TGLCM) method to separate the dark and bright parts of the image. Finally, a Machine Learning (ML) based ODNN method is employed to classify benign or malignant diseases. Utilizing sensitivity, specificity, recall, and accuracy metrics, the simulation results of the recommended ODNN approach are contrasted with those of preexisting approaches. © 2024 IEEE.

关键词： Deep neural networks

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High Speed Multiplier using Embedded Approximate 4-2 Compressor for image Multiplication 1

High Speed Multiplier using Embedded Approximate 4-2 Compres...

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1st International Conference on artificial Intelligence Trends and Pattern Recognition, ICAITPR 2022

作者： Teja, Thogiti Sai Aditya Sai Teja, G. Ravindra, Jvr Maddisetti, Lavanya Vardhaman College of Engineering Department of Ece India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Multiplication is the most widely used operation in a variety of applications such as digital filters and neural networks. In certain applications (image processing), precise computation is not required and, even low-accuracy computing can yield meaningful results. Since approximate computing is less complex and consumes less resources, it can be used in image processing and multimedia applications. Regular adders have a longer critical delay than compressor adders, making them suitable for use in multiplier architectures. Compressors can be employed in multiplication operations to reduce number of partial products, delay and power dissipation. We presented a 8×8 Dadda multiplier with an approximated 4:2 compressor in this research. On a pixel-by-pixel basis, this multiplier is used to multiply two images. © 2022 IEEE.

关键词： Adders

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