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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Li, Xueru Zhang, Yan Liu, Lingjie Tian, Ye Zhang, Yue Harbin Engineering University College of Computer Science and Technology Harbin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Xidian University Hangzhou Institute of Technology Hangzhou China Key Laboratory of Peace-Building Big Data of Zhejiang Province Zhejiang China

ISBN: (纸本)9798350357677

With the continuous growth in demand for meat and frozen products in the cold chain market, illegally smuggled frozen goods have quickly entered the market due to their low prices, leading to a series of food safety issues. This article proposes a method for identifying the risks of smuggled frozen meat for urban public safety awareness using the YOLOv7 model. The YOLOv7 model is used to detect data captured at various traffic checkpoints, obtaining spatiotemporal information of frozen goods vehicles. This information is then combined with map data to generate vehicle trajectory information. The trajectory similarity between the frozen vehicle trajectories and the specialized database of frozen transportation routes is analyzed using the EDwP distance algorithm. The EDwP algorithm employs a threshold-free approach, dynamically interpolating using the concept of projection, and automatically adapting to non-uniform sampling rates to address the issue of inconsistent trajectory data sampling rates. The experimental results of the YOLOv7-EDwP algorithm were verified by verifying the alerts generated in the recent period through on-site verification by a third-party inspection agency, with a recognition accuracy of 90.77%. This holds significant significance in the detection and warning of smuggled frozen meat. © 2023 IEEE.

关键词： Transportation routes

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Overview and Assessment Approaches of Energy Consumption and GHG Emissions Through the Development and Application of Artificial Intelligence - An Example from the Agricultural Sector

Overview and Assessment Approaches of Energy Consumption and...

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IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

作者： Stefan Rilling Jens Henningsen Katharina Milde Daniel Martini Lorenz Wickert Patricia Kelbert Adaptive Reflective Teams Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Sankt Augustin Germany Data Science Fraunhofer Institute for Experimental Software Engineering IESE Kaiserslautern Germany Team Digital Technologies Advisory Board for Technology and Construction in Agriculture KTBL Darmstadt Germany

ISBN: (数字)9798350355444

ISBN: (纸本)9798350355451

This article looks at the sustainability of various artificial intelligence (AI) methods. In recent months, AI methods have received a great deal of public attention and the benefits for sustainability have been emphasised. However, the ecological impacts of AI methods must also be considered in this debate. This article presents an overview of various approaches to sustainability assessment and addresses issues relating to energy consumption and the associated Carbon dioxide (CO2) emissions, and a Life Cycle Assessment (LCA) calculation for a concrete agricultural AI-use case.

关键词： Life cycle assessment Energy consumption Conferences Forestry Carbon dioxide Metrology Agriculture Artificial intelligence Sustainable development Carbon emissions

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A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big data Analytics

A Comparative Analysis of Artificial Intelligence and Busine...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Bhagath Chandra Chowdari Marella Gopi Chand Vegineni Sireesha Addanki Ehsan Ellahi Arun Kumar K Rishabh Mandal Department of Financial Services Insights & Data Capgemini India Enrollment and Eligibility Team India System soft Technologies llc Information Technology India Department of Information Systems Cleveland State University Ohio United States Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system ( $\mathbf{92.6 \%}$ ) and the neural network (NNs) models ( ${92}.7 \%$ ). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans.

关键词： Radio frequency Machine learning algorithms Accuracy Artificial neural networks Big data Market research Classification algorithms Business intelligence Random forests Recruitment

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deepNIR: datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques

arXiv

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arXiv 2022年

作者： Sa, Inkyu Lim, JongYoon Ahn, Ho Seok MacDonald, Bruce CSIRO Data61 Robotics and Autonomous Systems Group Robot Perception Team Switzerland CARES Department of Electrical Computer and Software Engineering University of Auckland New Zealand

This paper presents datasets utilised for synthetic near-infrared (NIR) image generation and bounding-box level fruit detection systems. A high-quality dataset is one of the essential building blocks that can lead to success in model generalisation and the deployment of data-driven deep neural networks. In particular, synthetic data generation tasks often require more training samples than other supervised approaches. Therefore, in this paper, we share the NIR+RGB datasets that are re-processed from two public datasets (i.e., nirscene and SEN12MS), expanded our previous study, deepFruits, and our novel NIR+RGB sweet pepper(capsicum) dataset. We oversampled from the original nirscene dataset at 10, 100, 200, and 400 ratios that yielded a total of 127 k pairs of images. From the SEN12MS satellite multispectral dataset, we selected Summer (45 k) and All seasons (180 k) subsets and applied a simple yet important conversion;digital number (DN) to pixel value conversion followed by image standardisation. Our sweet pepper dataset consists of 1615 pairs of NIR+RGB images that were collected from commercial farms. We quantitatively and qualitatively demonstrate that these NIR+RGB datasets are sufficient to be used for synthetic NIR image generation. We achieved Frechet Inception Distance (FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweet pepper datasets respectively. In addition, we release manual annotations of 11 fruit bounding boxes that can be exported as various formats using cloud service. Four newly added fruits [blueberry, cherry, kiwi, and wheat] compound 11 novel bounding box datasets on top of our previous work presented in the deepFruits project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The total number of bounding box instances of the dataset is 162 k and it is ready to use from cloud service. For the evaluation of the dataset, Yolov5 single stage detector is exploited and reported impressive mean-average-precision, mAP[0.

关键词： Generative adversarial networks

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A Machine Learning Based Risk Identification Method for Illegal Pig Slaughtering

A Machine Learning Based Risk Identification Method for Ille...

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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Miao, Jiajun Shen, Yujun Zhang, Yan Liu, Lingjie Tian, Ye Zhao, Yu Zhou, Chunyan Harbin Engineering University College of Computer Science and Technology Harbin China Hangzhou Hikvision Digital Technology Co. Ltd Hangzhou China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Xidian University Hangzhou Institute of Technology Harbin China Key Laboratory of Peace-Building Big Data of Zhejiang Province Hangzhou China

ISBN: (纸本)9798350357677

With the impact of African swine fever, the survival rate of domestic pigs has sharply decreased, and pork prices continue to rise. Many unscrupulous elements obtain illegal profits by avoiding inspection and quarantine, processing sick and dead pigs, and injecting water, etc. Unquarantined meat products pose serious food safety risks. In this context, we propose a machine learning based risk identification method for illegal slaughter of live pigs. Firstly, we use the RetinaNet algorithm to detect live pig transport vehicles and use a dual residual network to Super-Resolution the vehicle area in the detected image to improve facial clarity. Subsequently, the ArcFace algorithm is used to match the facial information with the ex-convicts, and finally, the Location of vehicles passing through, time, frequency and facial analysis results are combined for calculation and scoring and provide the risk warning for illegal slaughter of live pigs. © 2023 IEEE.

关键词： Risk assessment

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Multi-agent statistical discriminative sub-trajectory mining and an application to NBA basketball

arXiv

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arXiv 2023年

作者： Bunker, Rory Duy, Vo Nguyen Le Tabei, Yasuo Takeuchi, Ichiro Fujii, Keisuke Graduate School of Informatics Nagoya University Japan Data-Driven Biomedical Science Team RIKEN Center for Advanced Intelligence Project Japan RIKEN Center for Advanced Intelligence Project Japan Graduate School of Engineering Mechanical Systems Engineering Nagoya University Japan PRESTO Japan Science and Technology Agency Japan

Improvements in tracking technology through optical and computer vision systems have enabled a greater understanding of the movement-based behaviour of multiple agents, including in team sports. In this study, a Multi-Agent Statistically Discriminative Sub-Trajectory Mining (MA-Stat-DSM) method is proposed that takes a set of binary-labelled agent trajectory matrices as input and incorporates Hausdorff distance to identify sub-matrices that statistically significantly discriminate between the two groups of labelled trajectory matrices. Utilizing 2015/16 SportVU NBA tracking data, agent trajectory matrices representing attacks consisting of the trajectories of five agents (the ball, shooter, last passer, shooter defender, and last passer defender), were truncated to correspond to the time interval following the receipt of the ball by the last passer, and labelled as effective or ineffective based on a definition of attack effectiveness that we devise in the current study. After identifying appropriate parameters for MA-Stat-DSM by iteratively applying it to all matches involving the two top- and two bottom-placed teams from the 2015/16 NBA season, the method was then applied to selected matches and could identify and visualize the portions of plays, e.g., involving passing, on-, and/or off-the-ball movements, which were most relevant in rendering attacks effective or ineffective. © 2023, CC BY.

关键词： Geographic information systems

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A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

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Computer systems Science & engineering 2023年第8期46卷 1789-1809页

作者： Hicham Moujahid Bouchaib Cherradi Oussama El Gannour Wamda Nagmeldin Abdelzahir Abdelmaboud Mohammed Al-Sarem Lhoussain Bahatti Faisal Saeed Mohammed Hadwan EEIS Laboratory ENSET of MohammediaHassan II University of CasablancaMohammedia28820Morocco STIE Team CRMEF Casablanca-SettatThe Provincial Section of El JadidaEl Jadida24000Morocco Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia Department of Computer Science University of Sheba RegionMarib14400Yemen DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Information Technology College of ComputerQassim UniversityBuraydah 51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of ***,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray *** imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging *** this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected *** model is trained on a dataset containing thousands of X-ray images collected from different *** model was tested and evaluated on an independent *** order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning *** experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with *** proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging *** finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

关键词： Artificial intelligence intelligent diagnostic systems decisionmaking COVID-19 convolutional neural network

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Anderson Accelerated Feasible Sequential Linear Programming

arXiv

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arXiv 2022年

作者： Kiessling, David Pas, Pieter Astudillo, Alejandro Patrinos, Panagiotis Swevers, Jan MECO Research Team Dept. of Mechanical Engineering KU Leuven Flanders Make DMMS-M Leuven Belgium STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Belgium

This paper proposes an accelerated version of Feasible Sequential Linear Programming (FSLP): the AA(d)-FSLP algorithm. FSLP preserves feasibility in all intermediate iterates by means of an iterative update strategy which is based on repeated evaluation of zero-order information. This technique was successfully applied to techniques such as Model Predictive Control and Moving Horizon Estimation, but it can exhibit slow convergence. Moreover, keeping all iterates feasible in FSLP entails a large number of additional constraint evaluations. In this paper, Anderson Acceleration (AA(d)) is applied to the zero-order update strategy improving the convergence rate and therefore decreasing the number of constraint evaluations in the inner iterative procedure of the FSLP algorithm. AA(d) achieves an improved contraction rate in the inner iterations, with proven local linear convergence. In addition, it is observed that due to the improved zero-order update strategy, AA(d)-FSLP takes larger steps to find an optimal solution, yielding faster overall convergence. The performance of AA(d)-FSLP is examined for a time-optimal point-to-point motion problem of a parallel SCARA robot. The reduction of the number of constraint evaluations and overall iterations compared to FSLP is successfully demonstrated. Copyright © 2022, The Authors. All rights reserved.

关键词： Numerical methods

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Faster R-CNN assessment for air bubbles detection in the conformal coating application

Faster R-CNN assessment for air bubbles detection in the con...

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International Conference on Microelectronics, ICM

作者： Nizar Zouhri Aimad El Mourabit Alaoui Ismaili Zine El Abidine System & Data Engineering Team Ensa. Abdelmalek Essaâdi University Tangier Morocco Information Communication and Embedded Systems Team University Mohammed V Rabat Morocco

ISBN: (纸本)9781665408400

The detection of defects in Printed Circuit Boards (PCB) during the assembly process is an important quality requirement for the electronic *** present paper emphasizes the assessment of computer vision implying Faster-RCNN object detection architecture, with the aim of air bubbles defects detection in PCB following the conformal coating *** image configurations have been used to increase artificially the training model’s performance, such as air bubbles size, location (on a flat PCB’s surface, between components leads) and illumination, *** reaching this cap, we used a random pattern of choice regarding the images with different configurations to properly evaluate its performance, especially the accuracy, precision and *** results showed that Faster-RCNN delivers the lowest detection performance for the air bubbles located between components leads compared to the ones located on flat PCB *** paper shows the accuracy, precision and sensitivity performance results by applying the Faster-RCNN object detection architecture for air bubbles defect detection in the conformal coating application, with aim of providing reference for future research in the field of air bubbles detection.

关键词： Training Computer vision Sensitivity Printed circuits Lighting Object detection Computer architecture

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Anti-Collision System for Accident Prevention in Underground Mines using Computer Vision 22

Anti-Collision System for Accident Prevention in Underground...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamed Imam Karim Baïna Youness Tabii Intissar Benzakour Youssef Adlaoui El Mostafa Ressami El Hassan Abdelwahed Alqualsadi Research Team Rabat IT Center ENSIAS Mohammed V University in Rabat ENSIAS Morocco and Digitalization & Microelectronic Smart Devices - MAScIR Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Morocco Alqualsadi Research Team Rabat IT Center ENSIAS Mohammed V University in Rabat ENSIAS Morocco Information Retrieval and Data Analytics (IRDA) Research Team Rabat IT Center ENSIAS ENSIAS Morocco Reminex Research and Engineering center MANAGEM Group REMINEX Morocco Digitalization & Microelectronic Smart Devices - MAScIR Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Morocco Laboratory of Computer Systems Engineering (LISI) FSSM Cadi Ayyad University FSSM Morocco

ISBN: (纸本)9781450396943

Underground prospecting operations are often characterized by critical safety issues mainly due to poor visibility and blind spots around large vehicles and equipment. This can result in vehicle-to-vehicle collisions, as well as vehicle-to-pedestrian or structural-element collisions, resulting in accidents. In this article, we discuss an anti-collision system for pedestrian identification in deep mines under the premise that we are looking to prevent collisions with moving machinery. This study presents the findings from testing an image processing module and sensory system based on deep learnig in the context of "smart connected mine" project.

关键词： Yolov5 Pedestrian detection Deep Learning Underground mining

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