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Fish Detection and Classification Based on Improved ViT 2

Fish Detection and Classification Based on Improved ViT

引用

2nd international conference on Automation, Robotics and Computer engineering, ICARCE 2023

作者： Ji, Daxiong Hussain, Ahmad Faraz Hussain, Sheharyar Ogbonnaya, Somadina Godwin Zhu, Songming Wang, Xinwei Institute of Marine Electronics and Intelligent Systems Ocean Collage Zhejiang University Zhoushan China Donghai Lab Zhoushan China College of Biosystems Engineering and Food Sciencez Zhejiang University Hangzhou China Ocean Academy Zhejiang University Zhoushan China

ISBN: (纸本)9798350308341

It is crucial for marine researchers and preservationists to routinely evaluate the relative abundance of fish species in their environments and monitor changes in their populations. Different automated computer-based fish sampling systems in underwater videos have been offered instead of arduous hand sampling. However, no ideal solution exists for automated fish identification and species classification. This is mainly because of the challenges involved in filming underwater, such as varying light levels, fish concealment, changing backgrounds, murky water, limited resolution, intricate backgrounds, fish shape distortions, obstruction from other water bodies, and subtle differences among specific fish species. The proposed method uses an improved vision transformer (IMViT) for detection and classification. To capture the inductive bias information from images, the traditional ViT model incorporates the convolutional and residual units of ResNet to improve the architecture of the ViT model when working with a small-scale dataset. Our results reveal that deep learning (DL) models can detect and classify species using underwater fish images and obtain considerable results. 'Fish-dataset' is used for evaluations. This method corroborates significantly more discrimination accuracy ameliorations than the previously proposed techniques. The proposed model may be used for various underwater objects in light of the experimental findings. © 2023 IEEE.

关键词： Fish

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Battery-Less Environmental Sensor Platform for Enclosures in a Zoo 22

Battery-Less Environmental Sensor Platform for Enclosures in...

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22nd international conference on Electronics Packaging, ICEP 2023

作者： Kanaya, Haruichi Kawano, Masafumi Shinohara, Shun-Ichi Takiguchi, Osamu Nogami, Hirofumi Kyushu University Department of Electronics Fukouka819-0395 Japan Omuta City Zoo Fukuoka836-0871 Japan ALSENS Inc. Kanagawa252-0131 Japan Kyushu University Department of Mechanical Engineering Fukouka819-0395 Japan

ISBN: (纸本)9784991191152

In this paper, in order to realize the animal welfare in the zoo, battery-less environmental sensing system was designed and tested. This system has a small size and low power consumption with small solar cell. Some sensors and receivers are placed on the giraffes' enclosure. The 3-dimensional real time temperature and humidity data are obtained in the high-rise enclosures by using battery-less sensors. © 2023 Japan Institute of Electronics Packaging.

关键词： Animals

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An Empirical Study of the Impact of Field Features in learning-to-rank Method 22nd

An Empirical Study of the Impact of Field Features in Learni...

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22nd international conference on intelligent data engineering and automated learning

作者： Yang, Hua Goncalves, Teresa Univ Evora Dept Informat Evora Portugal Zhongyuan Univ Technol Sch Comp Sci Zhengzhou Peoples R China

ISBN: (纸本)9783030916077;9783030916084

In learning-to-rank for information retrieval, a ranking model is usually learned using features that are extracted from the different fields of the documents and naively combined. However, such a conventional way to learn a ranking model does not accurately reflect the utility and contribution of the fields and may also risk joining highly correlated features from different fields. It lacks an empirical analysis of how field-grouped features determine or influence the performance of the ranking models learned. In this paper, we classify features by the fields they are extracted from and investigate the role of using field-grouped features in the learning-to-rank method, particularly to see whether using field-grouped features leads to a different and better performance than using the naively combined feature list. Our experiments, on two large scale publicly available learning-to-rank benchmark datasets, show that ranking models learned using field-grouped features have competitive advantages over the models learned using a naively combined feature list, and that aggregation results of different fields present a better performance. These results suggest that learning ranking models using field-grouped features can be useful to obtain more effective performances in learning-to-rank methods.

关键词： learning-to-rank Field features Aggregation

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Analysing agricultural information through machine learning and artificial intelligence for SMART IRRIGATION 2

Analysing agricultural information through machine learning ...

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2nd IEEE international conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Lakshmi, C. Pagadala, Abhitha Bandam, Sanjana Mythri Penugonda, Abitha Gangaraju, Bhavana Ganapavarapu, Thanuja Amirtharajan, Rengarajan Thanikaiselvan, V. Mahalingam, Hemalatha School of EEE SASTRA Deemed University Thanjavur India School of Electronics Engineering Vellore Institute of Technology Vellore India King Abdulaziz University Department of Electrical and Computer Engineering Jeddah22254 Saudi Arabia

ISBN: (纸本)9798350347982

Traditional agriculture has been the global foundation for development for centuries. However, to meet this demand and the exponential growth of the population, farmers need water to irrigate their property. Farmers require a fix that modifies their business practices because of the scarcity of using this resource. To keep up with and satisfy Demand, Agriculture 4.0 has become a reality thanks to new technologies. automated Irrigation: Smart irrigation systems can automatically adjust schedules based on real-time data, such as weather conditions, soil moisture levels, and evapotranspiration rates. This helps farmers apply water more efficiently, reducing water waste and improving crop health. By collecting and analysing agricultural information through a combination of the Internet of Things and artificial intelligence, decisions have become increasingly precise to make decision-making easier. A cost-effective, intelligent, and adaptable irrigation strategy that can be used in a variety of settings is presented in this paper. For smart agriculture, machine learning algorithms are the foundation for this strategy. MongoDB and the Node-RED platform were used. We created an acquisition map from a collection of sensors (soil moisture, temperature, and rain) in a setting that guaranteed improved plant development for months. Based on our data, we used many different models: SVM, Simple Bayes, KNN, and Regression using logit. The outcomes showed that K-Nearest Neighbours outperformed other models (LR, SVM, NB) with a 98.6% identification rate and a 0.12 root mean square error (RMSE). In addition, we finally made available the online tool that combines the predictions made by our models with the different data released by the sensors for improved environmental visualisation and control. © 2023 IEEE.

关键词： Soil moisture

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BK-MSSNN: BI-Kernel Based Multi-Scale Subgraph Neural Networks 22

BK-MSSNN: BI-Kernel Based Multi-Scale Subgraph Neural Networ...

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22nd international conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2024

作者： Jiang, Kang Peng, Jia-Wang Yuan, Xin Xu, Wei Tong, He Chen, Sheng-Bing Xu, Li-Xiang College of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia College of Aeronautical Engineering Beijing Polytechnic Beijing100176 China College of Artificial Intelligence and Big Data Hefei University Hefei230601 China School of Artificial Intelligence Hefei Institute of Technology Hefei238076 China

ISBN: (纸本)9798331528072

Graph Neural Networks (GNNs) have achieved remarkable success in handling graph-related tasks. Most GNN frameworks are based on Message Passing Neural Networks (MPNNs). However, subgraph structures are often complex and multi-scale, and the performance of MPNN in graph isomorphism discrimination tasks may have certain limitations. To address this issue, this paper investigates the semantic associations between subgraphs of different scales and proposes a novel Bi-Kernel Multi-Scale Subgraph Neural Network (BK-MSSNN) to represent more finegrained subgraph semantic information. Specifically, we sample global subgraphs and local subgraphs of different scales, and integrate two graph kernel methods, namely the RW kernel and the Weisfeiler-Lehman kernel, to construct multiple associative relationships between subgraphs for updating node embeddings. Experimental results on five public datasets demonstrate that our proposed model outperforms other benchmark models in graph classification tasks. © 2024 IEEE.

关键词： Graph neural networks

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From Classification to Visualization: A Two Way Trip 22nd

From Classification to Visualization: A Two Way Trip

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22nd international conference on intelligent data engineering and automated learning

作者： Cuesta, Marina Martin de Diego, Isaac Lanchas, Carmen Acena, Victor Moguerza, Javier M. Rey Juan Carlos Univ Data Sci Lab C Tulipan S-N Mostoles 28933 Spain Madox Viajes C Cantabria 10 Arroyomolinos 28939 Spain

ISBN: (纸本)9783030916077;9783030916084

High Dimensional data (HDD) is one of the biggest challenges in data Science arising from Big data. The application of dimensionality reduction techniques over HDD allows visualization and, thus, a better problem understanding. In addition, these techniques also can enhance the performance of Machine learning (ML) algorithms while increasing the explanatory power. This paper presents an automatic method capable of obtaining an adequate representation of the data, given a previously trained ML model. Likewise, an automatic method is introduced to bring a Support Vector Machine (SVM) model based on an adequate representation of the data. Both methods provide an Explanaible Machine learning procedure. The proposal is tested on several data sets providing promising results. It significantly eases the visualization and understanding task to the data scientist when a ML model has already been trained, as well as the ML selection parameters when a reduced representation of data has been achieved.

关键词： High dimensional data Visualization Classification Explanaible machine learning

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Prioritizing Management Action of Stricto Sensu Course: data Analysis Supported by the k-means Algorithm 22nd

Prioritizing Management Action of Stricto Sensu Course: Data...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： de Souza, Luciano Azevedo do Canto Souza, Wesley da Silva, Welesson Flávio de Souza, Hudson Hübner de Mello, João Carlos Correia Baptista Soares Costa, Helder Gomes Universidade Federal Fluminense RJ Niterói24210-240 Brazil Universidade Federal de Viçosa MG Viçosa36570-900 Brazil

ISBN: (纸本)9783031274084

The challenge of balancing the benefits and pitfalls of adopting general or customized management strategies is always present. However, the planning effort must be sufficiently flexible because making judgments before complete and in-depth evaluation could mean making a decision too late. The management of a stricto sensu specialization course relies heavily on academic publication rates and citation counts, as is well known. In this study, we present and test a novel idea for cluster analysis using the k-means method and the creation of generic responses for the groups found. The SCOPUS platform of the 17 researchers who make up the faculty of a production engineering course in Brazil was examined for indicators over the previous ten years. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cluster analysis

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A NOVEL APPROACH FOR SECURE STEGANOGRAPHY: INTER-FORMAT CONVERSION COMPENSATION FOR ENHANCED data SECURITY 20

A NOVEL APPROACH FOR SECURE STEGANOGRAPHY: INTER-FORMAT CONV...

引用

20th international conference Applied Computing, AC 2023 and the 22nd international conference WWW/Internet, ICWI 2023

作者： Azooz, Hasan Salah, Khawla Ben Kherallah, Monji Naceur, Mohamed Saber Almuthanna University Iraq University of Gafsa Tunisia Faculty of Sciences of Sfax University of Sfax Tunisia National Engineering School of Tunis Tunisia

Steganography research and development are becoming more common in the information society, due to the widespread use of digital media formats and the challenges of managing digital resources and controlling the ownership and use of computer files. At thesame time, addressing steganography challenges is an important issue in the context of the evolving network communication infrastructure for Internet users - participants in open and uncontrolled interaction in the media space. The research concludes that it is necessaryto develop a new qualitative concealing algorithm that enables the hiding of large data. Amounts of data (MB) in still images in commongraphic formats The JPEG format was chosen as the most popular in everyday use scenarios for digital graphics, especially digital images.A qualitative analysis of classes of steganography algorithms focused on embedding data in graphic images for the purpose of covertly transmitting large amounts of data in digital images in JPEG format was carried out, resulting in the development of algorithms for working with digital image structures in JPEG and BMP graphic formats. A mechanism has been developed to compensate for the loss of data bits during inter-format conversions. Additionally, programmatic functions have been established for data insertion and extraction. Machine learning can be used to fine-tune the embedding process, making the resulting stego images harder to decipher for attackers. By usinga machine learning model that has been trained to distinguish between cover and stego images, the embedding process can be fine-tuned tomake the stego image less distinguishable from the original cover image. © ICWI *** rights reserved.

关键词： Steganography

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An Implementation of the "Guess Who?" Game Using CLIP 22nd

An Implementation of the "Guess Who?" Game Using CLIP

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22nd international conference on intelligent data engineering and automated learning

作者： Marti Sarri, Arnau Rodriguez-Fernandez, Victor Valencian Int Univ Calle Pintor Sorolla 21 Valencia 46002 Spain Dimai SL Cam & Font Calda 10 Navarcles 08270 Spain Univ Policn Madrid Sch Comp Syst Engn Calle Alan Turing Madrid 28038 Spain

ISBN: (纸本)9783030916077;9783030916084

CLIP (Contrastive Language-Image Pretraining) is an efficient method for learning computer vision tasks from natural language supervision that has powered a recent breakthrough in deep learning due to its zero-shot transfer capabilities. By training from image-text pairs available on the internet, the CLIP model transfers non-trivially to most tasks without the need for any data set specific training. In this work, we use CLIP to implement the engine of the popular game "Guess who?", so that the player interacts with the game using natural language prompts and CLIP automatically decides whether an image in the game board fulfills that prompt or not. We study the performance of this approach by benchmarking on different ways of prompting the questions to CLIP, and show the limitations of its zero-shot capabilites.

关键词： CLIP Guess who Zero-shot learning Language-image models

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A Two-Stream Light Graph Convolution Network-based Latent Factor Model for Accurate Cloud Service QoS Estimation 22

A Two-Stream Light Graph Convolution Network-based Latent Fa...

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22nd IEEE international conference on data Mining (ICDM)

作者： Bi, Fanghui He, Tiantian Luo, Xin Univ Chinese Acad Sci Chinese Acad Sci Chongqing Sch Chongqing Inst Green & Intelligent Technol Chongqing Peoples R China ASTAR Inst High Performance Comp Ctr Frontier AI Res Singapore Singapore Southwest Univ Coll Comp & Informat Sci Chongqing Peoples R China

ISBN: (纸本)9781665450997

Historical Quality-of-Service (QoS) data regarding past user-service invocations are vital to understand the user behaviors and cloud service conditions. A Matrix Factorization (MF)-based Collaborative Filtering (CF) model has proven to be highly effective in performing representation learning to such QoS data. However, its performance is hindered by its linear interaction and implicit encoding of collaborative QoS signal. To address this critical issue, this paper presents a Two-stream Light Graph Convolution Network-based latent factor (TLGCN) model with the three-fold ideas: 1) constructing a multilayered and fully-connected network to represent services nonlinear latent features;2) integrating the user-service interactions, i.e., the bipartite graph structure into the representation learning process with a light graph convolution network for illustrating the high-order connectivity information in QoS data;and 3) incorporating the data density-oriented modeling mechanism into the input and output of TLGCN for high computational efficiency. Experimental results on two real QoS datasets demonstrate that the proposed TLGCN model significantly outperforms its state-of-the-art peers in both estimation accuracy for missing QoS data and computational efficiency.

关键词： Quality-of-Service Representation learning data Science Cloud Service Missing data Estimation Graph Neural Network Non-Euclidean data

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