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Advanced dairy cow monitoring: enhanced detection with precision 3D tracking

Multimedia Tools and Applications 2025年第18期84卷 19117-19146页

作者： Wang, Ranran Li, Yingxiu Yue, Peng Yuan, Chunhong Tian, Fuyang Lu, Xin College of Mechanical and Electronic Engineering Shandong Agricultural University Shandong Tai’an271018 China Department of Computer Science Faculty of Engineering University of Sheffield SheffieldS1 4DP United Kingdom Faculty of Agriculture Iwate University Iwate Morioka020-8550 Japan Faculty of Science and Engineering Iwate University Iwate Morioka020-8550 Japan

Ensuring the welfare of dairy cows requires precise monitoring of their daily exercise to evaluate their physical health. This necessitates innovative methods beyond traditional motion sensors. We present a novel method that integrates an enhanced YOLOv5s object detection model with the DeepSORT multi-object tracking algorithm to meticulously track dairy cow movements, providing holistic information about their health. Our research started with the establishment of a dedicated dataset tailored for cow detection. We then segmented the detection scope to focus on specific regions of interest. Within the modified YOLOv5s model, we replaced the standard CSPDarknet53 backbone with DenseNet to achieve deep separable convolution and feature reorganization modules, leading to reduced parameters, augmented feature expression, and better information flow. In particular, the SPD-Conv module was incorporated to retain intricate details, essential for detecting smaller and low-resolution targets. The transition from Generalized Intersection over Union (GIoU) Loss to Complete Intersection over Union (CIoU) loss improved detection accuracy and sped up model convergence. Our clustering approach, based on the elbow rule, optimized K-means clustering, enhancing speed and precision. For multi-object tracking, the DeepSORT model was tailored to cater to varying cow sizes, and we chose an algorithm to associate appearance information. We converted pixel data into real-world distances, providing exact 3D cow movement metrics. Experimental validation confirmed the efficacy of our approach. Our enhanced model surpassed the original YOLOv5s in performance by 11.1% for accuracy (97.4%), 9.6% for recall (97.8%), and 11.0% for average accuracy (98.2%). The comprehensive accuracy stood at 92.1% for our model. In conclusion, our innovative methodology offers a non-invasive means to monitor dairy cow exercise, paving the way for advanced health assessment techniques in the dairy sector. © The Aut

关键词： K-means clustering

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Arabic Handwritten Document OCR Solution with Binarization and Adaptive Scale Fusion Detection 6

Arabic Handwritten Document OCR Solution with Binarization a...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Waly, Alhossien Tarek, Bassant Feteha, Ali Yehia, Rewan Amr, Gasser Fares, Ahmed Egypt-Japan University of Science and Technology E-JUST Faculty of Engineering Computer Science and Engineering Departement Alexandria21934 Egypt Benha University Faculty of Engineering Electrical Engineering Department Cairo11629 Egypt

ISBN: (纸本)9798350378511

The problem of converting images of text into plain text is a widely researched topic in both academia and industry. Arabic handwritten Text Recognation (AHTR) poses additional challenges due to diverse handwriting styles and limited labeled data. In this paper we present a complete OCR pipeline that starts with line segmentation using Differentiable Binarization and Adaptive Scale Fusion techniques to ensure accurate detection of text lines. Following segmentation, a CNN-BiLSTM-CTC architecture is applied to recognize characters. Our system, trained on the Arabic Multi-Fonts Dataset (AMFDS), achieves a Character Recognition Rate (CRR) of 99.20% and a Word Recognition Rate (WRR) of 93.75% on single-word samples containing 7 to 10 characters, along with a CRR of 83.76% for sentences. These results demonstrate the system's strong performance in handling Arabic scripts, establishing a new benchmark for AHTR systems. © 2024 IEEE.

关键词： Arabic Optical Character Recognition convolutional neural networks line segmentation recurrent neural networks Scene text detection

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Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction

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Journal of Rock Mechanics and Geotechnical engineering 2024年第11期16卷 4386-4398页

作者： Arsalan Mahmoodzadeh Abed Alanazi Adil Hussein Mohammed Hawkar Hashim Ibrahim Abdullah Alqahtani Shtwai Alsubai Ahmed Babeker Elhag Department of Civil Engineering University of HalabjaHalabjaKurdistan Region46018Iraq Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Communication and Computer Engineering Faculty of EngineeringCihan University-ErbilKurdistan RegionIraq Department of Civil Engineering College of EngineeringSalahaddin University-Erbil44002ErbilKurdistan RegionIraq Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Civil Engineering College of EngineeringKing Khalid UniversityAbha61413Saudi Arabia

In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)*** models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio *** assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM *** results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models ***,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters *** is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering *** this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.

关键词： Rock slope stability Open-pit mines Machine learning(ML) Limit equilibrium method(LEM)

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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Providing and evaluating a comprehensive model for detecting fraudulent electronic payment card transactions with a two-level filter based on flow processing in big data

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International Journal of Information Technology (Singapore) 2023年第8期15卷 4161-4166页

作者： Banirostam, Hamid Banirostam, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

Previous research on fraud detection modeling is often based on a single algorithm, optimizing categories and clusters to find fraudulent patterns that they have provided unsupervised or supervised methods alone and within the framework of Hadoop. The proposed model, a model based on big data analysis extracts important features of user behavior patterns such as time, device type, values, and type of transaction, and their behavioral modeling. By creating different profiles for users, threshold values will be set for each of them. The proposed model for real-time fraud detection of electronic cash payment cards includes two fast and explicit filters. Fast filtering is the combination of the Hidden Markov Model of the first order and Self Organizing Maps (SOM) for fast transaction processing and the Baum-Welch algorithm to find the local Maximum Likelihood. Explicit filter model training is a combination of multilayer Perceptron Neural Network algorithms and Logistic Regression to create a cardholder profile and measure the amount of new transaction deviation from the created profile with the reduction mapping approach or parallel execution of the model. Also, transactions performed for aggregation filters and prediction of the output results of the Schaffer Demister function with the parameter θ in order to detect the degree of deviation of the transaction behavior from the normal state according to the profile of the customer and the maximum difference between two consecutive sequences of observations is used. Finally, the fraudulent or non-fraudulent label on the transaction will be applied and added to the relevant database for storage for later use. According to the model simulation results, the proposed Accuracy, Precision, Recall, and F1-Score criteria are 0.999, 0.9834, 0.7906, and 0.9214, respectively. Simulation results show that the proposed model will perform better in each of the compared criteria against each of the single algorithms. © 2023, The Author

关键词： Accuracy Electronic card Fraud Supervised algorithm Transaction Unsupervised

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Effort Estimation in Agile Software Development - Is AI a Resourceful Addition? 11

Effort Estimation in Agile Software Development - Is AI a Re...

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11th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2024

作者： Saklamaeva, Vasilka Pavlič, Luka University of Maribor Faculty of Electrical Engineering and Computer Science Koroška cesta 46 Maribor2000 Slovenia

Effort estimation in agile software development serves as a helpful step to estimate the progress expected in the future development iteration (sprint). Using different estimation methods, developers can estimate how much they can get done, and their assigned project managers can forward this information to the customer. This, in turn, helps to achieve better customer collaboration, respond to changes and deliver a working software in the determined time. With the rising adoption of Artificial Intelligence (AI) in nearly all steps of the Software Development Life Cycle (SDLC), our research motivation was to examine the addition of AI into the planning process of the SDLC, especially regarding effort estimation. This paper encompasses the difference between widespread and AI-driven estimation methods in agile development, as well as the results of their use in an experiment. Our findings suggest that the chosen AI tool significantly overestimated the time required for task implementation. Additionally, users expressed a preference for utilizing AI as a complement to their own expertise, rather than relying on it exclusively. © 2024 Copyright for this paper by its authors.

关键词： CAI conversational AI convo-AI GAI generative AI sprint planning

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A Cascade of Consequences: Improving an Accident Analysis Method by Learning from a Real Life Telecommunications Accident 13

A Cascade of Consequences: Improving an Accident Analysis Me...

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13th International Conference on Data science, Technology and Applications, DATA 2024

作者： Wienen, Hans C.A. Bukhsh, Faiza A. Vriezekolk, Eelco Pires, Luís Ferreira Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Dutch Authority for Digital Infrastructure Netherlands

ISBN: (纸本)9789897587078

Telecommunications networks are vital enablers of modern society. Large accidents in these networks that cause their unavailability can therefore have a severe impact on the functioning of society. Learning from these accidents can help prevent them and thus make our society more resilient. In this paper, we present an accident analysis method (TRAM) which we have developed by extending the AcciMap method and we report on its application to analyse a severe accident in a telecommunications network. We validate notation for representing and breaking positive feedback loops in a network breakdown, and we suggest a method to enhance the prioritisation of recommendations derived from our analysis. Furthermore, our research reveals that splitting the analysis based on the expertise of the method’s participants negatively impacts the efficiency of the overall process. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Accident Analysis Incident Analysis Telecommunications

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A Cross-Modal Interactive Memory Network Based on Fine-Grained Medical Feature Extraction for Radiology Report Generation

A Cross-Modal Interactive Memory Network Based on Fine-Grain...

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

作者： Ma, Xitong Kuang, Yuansen Yuan, Lin Tian, Cheng Zeng, Yijie Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Radiology report generation is an essential task in the medical field, which aims to automate the generation of medical terminology descriptions of radiology images. However, this task currently suffers from several problems: 1) existing methods need to manually build knowledge graphs or templates (consuming time and effort) to introduce medical or prior knowledge to assist in report generation;2) previous models cannot handle the problem of data bias well (anomaly reports and anomaly descriptions make up only a tiny portion of the dataset), causing the models to ignore the learning of anomaly descriptions easily;3) existing approaches cannot robustly supervise the model, resulting in incomplete and inconsistent reports being generated. To address these issues, we propose a cross-modal interactive memory network based on fine-grained medical feature extraction. In our model, we design a cross-modal interactive memory network to automatically store and remember the required medical text knowledge and use this medical knowledge to help generate reports. Furthermore, we design an abnormal medical knowledge enhancement module to enhance the learning of abnormal fine-grained knowledge through the interaction of disease topics and their states to interact with text features. In addition, we design a cross-modal joint semantic loss unit to reduce semantic differences between different features and improve the visual representation ability of the model. We experimented and evaluated our model on MIMIC-CXR and IU-Xray datasets to compare with other baseline models. © 2024 IEEE.

关键词： Radiology

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A Collaborative Heterogeneous Graph Neural Network for Personalized News Recommendation

A Collaborative Heterogeneous Graph Neural Network for Perso...

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

作者： Shi, Chenglong Geng, Haibin Jiang, Wenfeng Li, Jinbao Wu, Chao Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Personalized news recommendation is the process of predicting the relevance of news to users and recommending news to user to fulfill their information needs. However, existing news recommendation methods extract semantic information from users and candidate news respectively, ignoring semantic interaction information between users and candidate news. Furthermore, previous models only use same node types for message passing, ignoring different characteristics and topology between different node types. In addition, existing methods learn news representations through text representations, ignoring semantic correlation information between entity relationships and texts. To solve these problems, we propose a personalized news recommendation model named CoHG. In our model, we design a collaborative fusion module to obtain semantic interaction information through interacting user history news with candidate news. Furthermore, we design a heterogeneous gated graph neural network that maps different node types into a same space to extract higher-order information in user graphs for message passing. Moreover, we design an enhanced relevant attention module to enhance semantic correlation information of text content by aggregating text representation and entity representation into a unified representation. Finally, we conducted experiments on MIND and Adressa datasets to compare with other baseline models. © 2024 IEEE.

关键词： Graph neural networks

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An Entity Relation Extraction Framework Based on Large Language Model and Multi-Tasks Iterative Prompt engineering

An Entity Relation Extraction Framework Based on Large Langu...

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

作者： Geng, Haibin Shi, Chenglong Jiang, Xuesong Kong, Zan Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Document-level entity relation extraction is an important task in the field of natural language processing, which plays an important role in semantic understanding and knowledge graph construction. However, existing deep neural networks and graph neural networks models are limited by their performance and parameters number, which can not capture global semantics and have poor generalization ability. Furthermore, existing methods employing large language model for entity relation extraction do not establish good relationships among multi-tasks of entity relation extraction task, resulting in more information can not be effectively shared and transmitted between tasks. In addition, previous approaches can not effectively eliminate false entities and relationships. To solve these problems, we propose an entity relation extraction framework based on large language model and multi-tasks iterative prompt engineering. In our model, we design an iterative prompt engineering, which can better establish the relationship among multi-tasks, and ensure every task to obtain the optimal results. Moreover, we design semantic merging, group disambiguation and self-verification modules to eliminate the false entity relations and noise nodes. Additionally, we design summary prompts to provide sufficient global semantics for better text segmentation. Finally, we evaluated our model on wikiann, wikineural, ACE2005, CoNLL2003, CoNLL2004, and SciERC datasets and compared it with other baseline models. © 2024 IEEE.

关键词： Graph neural networks

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