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Revolutionizing wildlife protection: a novel approach combining deep learning and night-time surveillance

Multimedia Tools and Applications 2024年 1-35页

作者： Madhasu, Nithya Pande, Sagar Dhanraj VIT-AP University Andhra Pradesh India Department of Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Maharashtra Pune India

The increasing instances of animals encroaching on human settlements, as well as the illicit trafficking of wildlife, have prompted immediate actions to protect the natural heritage. In addition to this, the difficulties of night-time animal surveillance are also being faced. This paper highlights the urgent necessity for comprehensive animal welfare monitoring and effective anti-illegal trafficking prevention. The expensive cost of installing night vision cameras heightens the necessity of locating an affordable and effective solution. To overcome these issues, a unique strategy, combining colorization utilizing Customised Conditional GAN (Generative Adversarial Net) for night-time applications and YOLO-CNAS (You Only Look Once-Customised Neural Architecture Search) classification for intelligent wildlife detection is proposed. Colorization during the night enables the discovery of critical nocturnal behaviors, encouraging a greater knowledge and relationship with nature. Using these powerful deep learning algorithms helps to recognize the wildlife throughout both daylight and night-time hours, as well as smugglers trespassing into the forest. The suggested method obtains an amazing testing accuracy rate that has improved from 55.73% to 72.54% and then finally to 94.67%, demonstrating the revolutionary approach's potential for animal protection. The extensive dataset used for training and assessment was meticulously sourced from various websites including iNaturalist, Unsplash, and Pexels. These platforms provided a rich array of diverse images. During the pressing need to protect the nation's irreplaceable heritage, the intelligent model provides a ray of hope. Conservationists and policymakers may work together to safeguard the natural heritage by enabling effective wildlife surveillance and maintaining a happy coexistence between humans and animals for future generations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of S

关键词： Animals

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EAMR-Net: A multiscale effective spatial and cross-channel attention network for retinal vessel segmentation

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Mathematical Biosciences and engineering 2024年第3期21卷 4742-4761页

作者： Prethija, G. Katiravan, Jeevaa School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Information Technology Velammal Engineering College Chennai600066 India

Delineation of retinal vessels in fundus images is essential for detecting a range of eye disorders. An automated technique for vessel segmentation can assist clinicians and enhance the efficiency of the diagnostic process. Traditional methods fail to extract multiscale information, discard unnecessary information, and delineate thin vessels. In this paper, a novel residual U-Net architecture that incorporates multi-scale feature learning and effective attention is proposed to delineate the retinal vessels precisely. Since drop block regularization performs better than drop out in preventing overfitting, drop block was used in this study. A multi-scale feature learning module was added instead of a skip connection to learn multi-scale features. A novel effective attention block was proposed and integrated with the decoder block to obtain precise spatial and channel information. Experimental findings indicated that the proposed model exhibited outstanding performance in retinal vessel delineation. The sensitivities achieved for DRIVE, STARE, and CHASE_DB datasets were 0.8293, 0.8151 and 0.8084, respectively. © 2024the Author(s).

关键词： Drops

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Foundation models for topic modeling: a case study

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Frontiers of computer science 2025年第2期19卷 129-131页

作者： Han ZENG Jia-Ming SUN Chun-Shu LI Zhuying LI Tong WEI School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration Southeast UniversityNanjing 210096China

1 Introduction In Natural Language Processing(NLP),topic modeling is a class of methods used to analyze and explore textual corpora,i.e.,to discover the underlying topic structures from text and assign text pieces to different *** NLP,a topic means a set of relevant words appearing together in a particular pattern,representing some specific *** is beneficial for tracking social media trends,constructing knowledge graphs,and analyzing writing *** modeling has always been an area of extensive research in *** methods like Latent Semantic Analysis(LSA)and Latent Dirichlet Allocation(LDA),based on the“bag of words”(BoW)model,often fail to grasp the semantic nuances of the text,making them less effective in contexts involving polysemy or data noise,especially when the amount of data is small.

关键词： words semantic textual

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WSETO: wild stock exchange trading optimization algorithm enabled routing for NB-IoT tracking system

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International Journal of Information Technology (Singapore) 2024年第8期16卷 4895-4905页

作者： Sivadevuni, Sreeparnesh Sharma Naveen, J. Department of Computer Science and Engineering School of Engineering and Technology Christ University Karnataka Bangalore 560001 India

The Narrowband Internet of Things (NB-IoT) communication plays a significant role in the IoT due to the capability of generating broad exploration with the usage of limited power. Over the past few years, the Low Power Wide Area Networks (LPWAN) have been efficient in the data acquisition and remote monitoring area however they failed to generate high data rates, low latency, and the consumption of low power. To solve these problems, NB-IoT technology has developed in long-term asset tracking and it replaces the Global Positioning System (GPS) with its ubiquitous coverage. In this research, the Wild Stock Exchange Trading Optimization technique (WSETO) is proposed for a routing-based NB-IoT tracking system. The WSETO is the combination of the Wild Geese Algorithm (WGA) and SETO. By employing WSETO, the routing to the relevant target location is established effectively. The existing techniques like Low Power Asset Tracking of NB-IoT (LoPATraN), Monitoring system based on NB-IoT and BeiDou System/GPS (BDS/GPS), and Narrowband Physical Uplink Shared Channel (NPUSCH) are used to compare the WSETO approach. In rounds with a value of 2000, the WSETO demonstrates a superior location error of 0.001 in comparison to existing methods such as LoPATraN, a monitoring system utilizing NB-IoT and BDS/GPS, as well as NPUSCH. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Global positioning system Low power wide area networks Narrowband internet of things Stock exchange trading optimization Wild geese algorithm

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Comprehensive Survey of the Landscape of Digital Twin Technologies and Their Diverse Applications

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computer Modeling in engineering & sciences 2024年第1期138卷 125-165页

作者： Haiyu Chen Haijian Shao Xing Deng Lijuan Wang Xia Wang School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of Nevada Las VegasLas VegasNV89154USA Department of Electrical and Computer Engineering National University of Singapore117583Singapore

The concept of the digital twin,also known colloquially as the DT,is a fundamental principle within Industry 4.0 *** recent years,the concept of digital siblings has generated considerable academic and practical ***,academia and industry have used a variety of interpretations,and the scientific literature lacks a unified and consistent definition of this *** purpose of this study is to systematically examine the definitional landscape of the digital twin concept as outlined in scholarly literature,beginning with its origins in the aerospace domain and extending to its contemporary interpretations in the manufacturing ***,this investigationwill focus on the research conducted on Industry 4.0 and smartmanufacturing,elucidating the diverse applications of digital twins in fields including aerospace,intelligentmanufacturing,intelligent transportation,and intelligent cities,among others.

关键词： Digital twins Industry 4.0 smart manufacturing digital thread modeling simulation

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Cost-effective Alternatives for Squarer in Pan-Tompkins Algorithm 24

Cost-effective Alternatives for Squarer in Pan-Tompkins Algo...

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6th International Electronics Communication Conference, IECC 2024

作者： Nagatomo, Taiki Sato, Toshinori Department of Electronics Engineering and Computer Science Fukuoka University Graduate School of Engineering Japan Department of Electronics Engineering and Computer Science Fukuoka University Faculty of Engineering Japan

ISBN: (纸本)9798400717598

Reducing power consumption in battery-powered medical edge devices is highly required. This study aims to enhancing power efficiency in the Pan-Tompkins algorithm used for electrocardiogram (ECG) feature detection by adopting approximate computing. This paper specifically focuses on the squaring step within the algorithm, investigating eight alternative approaches to replace the multiplier with a dedicated squarer. We evaluate power and area reduction rates, as well as the feature detection accuracy of the proposed approximate squarers. The best candidate achieves a 99.40% power reduction and a 99.47% area reduction while maintaining a 99.67% detection accuracy. © 2024 ACM.

关键词： Electrocardiograms

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FeBT: A Feature Balancing Transformer for Corporate ESG Forecasting

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4063-4074页

作者： Li, Yawen Zhuang, Mengyu Ye, Guanhua Li, Yan Wang, Junheng Zhou, Jinyi Zhang, Pengfei Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Beijing University of Posts and Telecommunications School of Computer Science National Pilot School of Software Engineering Beijing China University of Electronic Science and Technology of China School of Management and Economics Chengdu China University of Science and Technology Beijing School of Economics and Management Beijing China The University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia

Environmental, social, and governance (ESG) serves as a crucial indicator for evaluating firms in terms of sustainable development. However, the existing ESG evaluation systems suffer from limitations, such as narrow coverage, subjective bias, and lack of timeliness. Therefore, there is a pressing need to leverage machine learning methods to predict the ESG performance of firms using their publicly available data. Traditional machine learning models encounter the feature imbalance problem due to the heterogeneity in ESG-related features. Common approaches typically involve unfolding all features, thereby granting highdimensional folding features greater exposure and accessibility to downstream models, which results in the neglect of lowdimensional features. To fill the research gap regarding fully using the heterogeneous features of enterprises to enhance AIbased ESG prediction performance, we propose the Feature Balancing Transformer (FeBT), a model based on autoencoders and Transformer blocks. FeBT incorporates a novel feature balancing technique that compresses and enhances high-dimensional features from imbalanced data into low-dimensional representations, thereby ensuring a more balanced impact of high-dimensional and low-dimensional features on the model's performance in the downstream ESG forecasting module. Extensive experiments verified the superior performance of FeBT compared with stateof-the-art methods in real-world ESG-related datasets and evidenced that our feature balancing module provides significant insights from high-dimensional folding features. © 1989-2012 IEEE.

关键词： Predictive Models Transformers Data Models Forecasting Accuracy Biological System Modeling Training Time Series Analysis Support Vector Machines Vectors ESG Forecasting Data Masking Time Series Forecasting Machine Learning Models Autoencoder High Dimensional Feature Transformer Block Time Series Support Vector Machine Time Series Data Linear Discriminant Analysis Air Quality Long Short Term Memory Recurrent Neural Network Attention Mechanism Final Prediction Transformer Model Word Embedding XG Boost Gated Recurrent Unit Linear Probe Air Quality Index Positional Encoding Multi Head Attention Mechanism Light GBM Traditional Transformation Compressed Representation Time Series Prediction Attention Heads Prediction Model K Nearest Neighbor Imbalance In Levels Latent Representation

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VivID: A Visually Improved GIF Encoding Network Design

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 6101-6113页

作者： Wang, Yifei Liu, Gaozhi Zhu, Zhiying Zhang, Xinpeng Qian, Zhenxing Fudan University Department of School of Computer Science Shanghai200433 China East China University of Science and Technology Department of China School of Information Science and Engineering Shanghai200237 China

Graphics Interchange Format (GIF) encoding is the art of reproducing an image with limited colors. Existing GIF encoding schemes often introduce unpleasant visual artifacts such as banding artifact, dotted-pattern noise and color shift, especially when the palette size is small. To address the issues above, we propose VivID, a Visually Improved GIF Encoding Network Design, which is compatible with exiting GIF decoders. VivID consists of three modules and two of them provide the functionality within the GIF encoding pipeline. Firstly, in order to reduce the color shift introduced by color quantization, we design the multi-palette extractor to create a GIF image with minimal distortion by extracting a near-optimal palette. This module can significantly improve the image fidelity and gains adaptability to multiple palette sizes after only one-time training. Furthermore, to reduce banding artifact and the dotted-pattern noise caused by dithering process, we propose banding remover which can randomize quantization error to neighbourhood by utilizing a learnable dithering pattern. Moreover, to further eliminate the banding artifacts, we design the banding scorer module, which is a novel metric for evaluating banding artifact and it correlates well with subjective perception. We adopt it as a customized loss for training dithering module. Extensive experiments across various aspects demonstrate that VivID produces visually pleasing results even when the palette size is extremely small, outperforming both traditional and existing learning based GIF encoding methods. © 1991-2012 IEEE.

关键词： Encoding (symbols)

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Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia Detection

Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia De...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Dhivyaa, C.R. Nithya, K. Shanmugapriya, P. Deebika, R. Kumar, K. Sathis Sudhakar, R. School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Srm Institute Of Science And Technology Computer Science And Engineering Trichy India Computer Science And Engineering Erode India K.S.R College Of Engineering Computer Science And Engineering Tiruchengode India Bannari Amman Institute Of Technology Computer Science And Engineering Sathyamangalam India Nandha College Of Technology Computer Science And Engineering Erode India

ISBN: (纸本)9798350364828

Pneumonia is an infection often caused by several viral infections and prediction of pneumonia requires expertise from radiotherapists, posing challenges, especially in remote areas. Developing an automatic pneumonia detection system is crucial to detect and treat pneumonia disease. Hence, an innovative approach is proposed to detect pneumonia by utilizing the ensemble of deep learning models, specifically VGG16, ResNet50, and InceptionV3. The ensemble method integrates predictions from these various architectures to enhance overall classification performance. Rather than starting the training of each model from the beginning, transfer learning techniques are utilized to fine-tune Convolutional Neural Network (CNN) models using a pre-existing knowledge base. This involves leveraging pre-trained models and adapting them specifically for the disease diagnose on Chest X-ray image dataset. The features are extracted from VGG16, ResNet50, and InceptionV3 models and the classes of disease are predicted by using softmax layers. Finally, ensemble learning is employed to fuse the predictions of multiple neural network architectures within Tri-UnityNet, enhancing its accuracy and robustness in pneumonia detection. By leveraging the complementary strengths of diverse models, the ensemble approach achieves superior performance, contributing to more reliable diagnostic outcomes. © 2024 IEEE.

关键词： Forecasting

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A hyperledger fabric-based EMR sharing mechanisms with proxy re-encryption and IPFS

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Cluster Computing 2024年第5期27卷 6953-6983页

作者： Huang, Der-Chen Liu, Ling-Chun Deng, Yong-Yuan Chen, Chin-Ling Zeng, Kuang-Wei Department of Computer Science and Engineering National Chung-Hsing University Taichung402 Taiwan School of Information Engineering Changchun Sci-Tech University Jilin Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

With the advent of the information age, data storage has not only developed from paper information systems to electronic information system storage, but has also extended to cloud database storage methods. To date, we can see the application of big data and cloud in various fields and levels, making induction, arrangement, transmission, and exchange of data easier and faster. The electronic and systematisation of patient medical records in the medical environment is an example. The use of electronic medical records can help improve the quality of medical care, reduce duplication of treatment, and reduce the waste of examination resources. It has many advantages, but also many risks. Medical institutions may abuse electronic medical data without the consent of patients, leading to the leakage of patient privacy data and causing serious social impact. Therefore, this study proposes a medical record exchange and sharing mechanism based on the Hyperledger blockchain architecture combined with proxy reencryption. When pharmacists need to obtain medical records for related research, patients can decide whether to share medical records, and hospitals can also revoke pharmacist access rights at any time to achieve more private, secure, and convenient sharing of medical records. The proposed scheme meets various blockchain security requirements and the BAN (Burrows–Abadi–Needham) logic proof model (Burrows–Abadi–Needham) is applied to assess the correctness of the proposed scheme. The proposed scheme performs well in terms of computational and communication costs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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