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2020 International Conference on Intelligent Engineering and Management, ICIEM 2020

作者： Hsu, Hao-Hsuan Huang, Nen-Fu Han, Chuan-Hsiang Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan National Tsing Hua University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9781728140971

In traditional insurance policies, the insured drivers have to go through a complicated procedure after an accident then wait a long time for human appraisers' evaluation of insurance claims. In this scenario, the insured drivers can't receive immediate financial assistance, while the financial agencies put much effort into evaluations. To address these problems, we propose a novel approach to evaluating insurance claims of accidents automatically by artificial intelligence (AI). This paper applied noted deep learning models of YOLO and Mask R-CNN to detect collisions in motor dashcam videos. This paper incorporates both of their strengths into our system. YOLO is applied to quickly detect if an accident happened. Mask R-CNN is applied to examine if a collision happened and which object was hit by the driver's motorcycle. In this paper, collision analysis was conduct with motor dashcam videos. The analysis results show our approach can effectively detect different types of collisions for further auto insurance. © 2020 IEEE.

关键词： computer vision

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Trauma Emergencies at University Hospital of Brazzaville, Congo

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Surgical science 2022年第3期13卷 144-154页

作者： Massamba Miabaou Didace Boukassa Léon Note Madzele Murielle Monka Marius Ondima Irène Moyikoua Armand Massamba Alphonse Digestive Surgery Department University Hospital Brazzaville Congo Polyvalent Surgery Department University Hospital Brazzaville Congo Traumatology-Orthopaedic Department University Hospital Brazzaville Congo Department of Pediatric Surgery University Hospital Brazzaville Congo Laboratory of Numerical Analysis Computer Science and Applications Faculty of Sciences and Technology Marien Ngouabi University Brazzaville Congo

Background: Trauma is the main reason for consultation in the emergency unit. They deserve particular attention in the Congo where road accidents and violence of all kinds are increasingly observed. Methods: This study who carried out in the surgical emergencies at the University Hospital of Brazzaville, aims to analyze the epidemiological and etiological aspects, the anatomical lesions encountered;to describe their management, as well as to evaluate the induced mortality while emphasizing their place in all surgical emergencies. The prospective and analytical study included 2127 patients admitted to a traumatic emergency unit from January 1 to June 30, 2018. Medical records served as data sources. The measure of trauma severity was determined using Champion’s Trauma Score. The epidemiological, clinical and therapeutic parameters were studied. Results: The population most affected was adolescents and young adults (mean age, 27.3 years) with a male predominance: 1318 men (i.e. 61.9%) for 809 women (i.e. 38.1%). Road accidents (n = 819, or 38.5%) were the main cause of injuries, followed by violence (n = 702, or 33%) and falls (27.3%). The skull, limbs, thorax and abdomen are the main targets. The most observed lesions were limb fractures (n = 344, i.e. 16.1%), intracranial hematomas and contusions (n = 315, i.e. 14.8%), thoracic and abdominal wounds (n = 28, i.e. 1.3%). Mortality was evaluated at 0.6%, and was correlated with the severity of the lesions and the type of trauma. Conclusion: Trauma is very common in emergency consultations in Congo, with a hospital frequency of 75.1% of all emergencies. Preventive actions are essential to reduce their frequency.

关键词： Emergencies Trauma Accidents Public Road Violence

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Empirical Analysis of Rainfall Prediction System using an DGRNN-SAM Approach

Empirical Analysis of Rainfall Prediction System using an DG...

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International Conference on Inventive Research in Computing applications (ICIRCA)

作者： P HEMA Uppara Raghu Babu E. Manigandan A Rajesh Kumar Nagamalleswari Devireddy Ravi Rastogi Department of Mathematics R.M.K. College of Engineering and Technology Thiruvallur Tamilnadu India Civil Engineering Department Srinivasa Ramanujan Institute of Technology Ananthapuramu Andhra Pradesh India Department of Computer Applications Saveetha College of Liberal Arts and Sciences Thandalam Chennai Tamilnadu India Computer Science and Engineering N.S.N. College of Engineering and Technology Karur Tamilnadu India Department of Freshman Mohan Babu University Tirupathi Andhra Pradesh India Electronics Department NIELIT Gorakhpur Uttar Pradesh India

Precipitation forecasting, especially for shorter timescales, is a critical issue in meteorological service. Most current research aims to improve prediction methods by using radar or satellite imagery. On the other hand, there is a situation in which numerous sensors at different observation sites acquire the same set of weather features. There is room for improvement in existing work due to the fact that imperfect site observations might still yield useful information for weather prediction at neighboring locations. This study proposes a multitask neural network model to automatically extract characteristics from time series gathered at observation sites and to utilize the correlation between different sites for weather forecasting, thus resolving the aforementioned problem. As far as currently aware, this is the first attempt to use multi-task learning and deep learning approaches to predict the amount of short-term rainfall based on characteristics from many places. In particular, it recast the task as an end-to-end multi-site neural network model, which allows to mimic inter-site correlations and transfer learning from one site to another. The method that has been suggested begins with the preprocessing step of cleaning and normalizing the data. When training the model with DGRNN-SAM, the process of feature selection and evaluation is carried out utilizing differential evaluation. When contrasted with other two models, such as LSTM and GRU, the suggested method fares quite well in terms of its performance.

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Three-dimensional Chaotic Fractional Maps without Fixed Points: Dynamics, Coexisting Hidden Attractors and Hardware Implementation

Three-dimensional Chaotic Fractional Maps without Fixed Poin...

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International Conference on Fractional Differentiation and Its applications (ICFDA)

作者： Giuseppe Grassi Amina Aicha Khennaoui Adel Ouannas Viet-Thanh Pham Dipartimento Ingegneria Innovazione Universita del Salento Lecce Italy TLSI Department NTIC Faculty University of Constantine 2 Constantine Algeria Department of Mathematics and Computer Science University of Oum El-Bouaghi Algeria Nonlinear Systems and Applications Faculty of Electrical and Electronics Engineering Ton DucThang University Ho Chi Minh City Vietnam

In recent years there has been a great interest in investigating the dynamic properties of fractional systems, described by differential or difference equations of non-integer order. A number of manuscripts have been written regarding the presence of chaos in fractional maps characterized by the existence of both “self-excited attractors” and “hidden attractors However, most of the papers have dealt with the underlying theory in chaotic dynamics, rather than the hardware implementation of the maps. Specifically, referring to the realization of fractional maps with “hidden attractors no paper has been published so far. The paper aims to bridge the gap between theory and implementation by presenting the first example of hardware realization of a three-dimensional (3D) fractional map characterized by coexisting chaotic hidden attractors. The manuscript exploits first the Grunwald-Letnikov fractional difference operator to introduce a new class of chaotic discrete systems without fixed points. Then, the coexistence of several chaotic hidden attractors is shown, along with the coexistence of a number of bifurcations, depending on the values of the initial conditions. Finally, the conceived 3D fractional map is implemented in hardware via a microcontroller. The motivation is to probe the presence of coexisting chaotic hidden attractors in real working systems described by non-integer order difference equations.

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Fine-Tuning Optimization of Small Language Models: A Novel Graph-Theoretical Approach for Efficient Prompt Engineering

Fine-Tuning Optimization of Small Language Models: A Novel G...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Venkata Gadiraju Hao-Yu Tsai Hsiao-Chun Wu Manali Singha C.-H. Scott Huang Guannan Liu Shih Yu Chang Yiyan Wu School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge USA Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan Innovation Center for AI Applications Yuan Ze University Chungli Taiwan Institute for Genomic Medicine (IGM) Nationwide Children’s Hospital Columbus USA Department of Applied Data Science San Jose State University San Jose USA Communications Research Centre Ottawa Canada

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

In the realm of fine-tuning pre-trained language models in modern prompt engineering, we introduce a novel graph-theoretical approach to address the resource-intensive challenges to the conventional data fine-tuning methods for prompt engineering. Leveraging semantic and contextual prompt relationships, we propose to form a novel prompt graph, which facilitates a new comprehensive representation of prompt similarities. Building upon this new graph structure, our proposed approach can minimize the training time during the fine-tuning process for small language models by identifying and utilizing cliques corresponding to condensed subsets of highly similar prompts. This new strategic reduction in training data can greatly reduce the training time, particularly for resource-constrained applications in practice. Our proposed new approach leads to a significant reduction in the original prompt-graph order and a more focused and streamlined fine-tuning process. This data-reduction strategy demonstrates the potential to enable finetuning language models for prompt engineering with smaller datasets subject to less computational resource. The real run-time analysis for the training process of a small language model GPT2 have been undertaken to show the advantage of our proposed new approach.

关键词： Training Computational modeling Simulation Semantics Training data Data models Prompt engineering

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Optimizing Advertising Campaigns through Advanced Deep Learning-Driven Audience Segmentation for Enhanced Targeting and Engagement

Optimizing Advertising Campaigns through Advanced Deep Learn...

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Automation and Computation (AUTOCOM), International Conference on

作者： Gunda Nikhil Anil P S Saif Obaid Archana Sehgal T. Kuppuraj Aanandha Saravanan K CEO Digital Connect Hyderabad India Department of Management Gnanam School of Business Mary's junction Sengipatti Thanjavur Tamilnadu Department of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq Lovely Professional University Phagwara Punjab India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore India Department of ECE Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The present work discusses the utilization of deep learning techniques for precise dividing the audience for advertising in order to increase efficiency and effectiveness of campaigns. Hence, the paper employs a diverse dataset of consumers' demographic data, purchase history, social media activity, and reviews and design a hybrid deep learning model using CNN, RNN-LSTM, and attention mechanisms. In all the assessment measurements of accuracy, precision, recall, and Fl-score, this model offers better results compared with the old-shoot segmentation methods. The performance analysis depicts great enhancements in CTR, conversion rate, as well as ROAS all of which highlight a superior engagement of the ads. Indeed, the outcome points to the effectiveness of deep learning in digital marketing that can provide clients with more efficient advertising tools. Nonetheless, the issues regarding computational capabilities and ethical concerns pose viable and useful innovations and solutions, thus contributing to the progression of deep learning theories in audience segmentation and advertising.

关键词： Deep learning Ethics Technological innovation Accuracy Attention mechanisms Social networking (online) Reviews History Advertising Long short term memory

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Combining and comparing regional epidemic dynamics in Italy: Bayesian meta-analysis of compartmental models and model assessment via global sensitivity analysis

Research Square

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Research Square 2021年

作者： Cereda, Giulia Viscardi, Cecilia Baccini, Michela Department of Statistics Computer Science Applications University of Florence Florence50134 Italy Florence Center for Data Science University of Florence Florence50134 Italy

During autumn 2020, Italy faced a second important SARS-CoV-2 epidemic wave. We explored the time pattern of the instantaneous reproductive number, R0(t), and estimated the prevalence of infections by region from August to December calibrating SIRD models on COVID19-related deaths, fixing at values from literature Infection Fatality Rate (IFR) and infection duration. A Global Sensitivity Analysis (GSA) was performed on the regional SIRD models. Then, we used Bayesian meta-analysis and meta-regression to combine and compare the regional results and investigate their heterogeneity. The meta-analytic R0(t) curves were similar in the Northern and Central regions, while a less peaked curve was estimated for the South. The maximum R0(t) ranged from 2.61 (North) to 2.15 (South) with an increase following school reopening and a decline at the end of October. Average temperature, urbanization, characteristics of family medicine and health care system, economic dynamism, and use of public transport could partly explain the regional heterogeneity. The GSA indicated the robustness of the regional R0(t) curves to different assumptions on IFR. The infectious period turned out to have a key role in determining the model results, but without compromising between-region comparisons. © 2021, CC BY.

关键词： Coronavirus

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Development of an Artificial Intelligence-Based Machine Tool to Predict Outcome in Intracerebral Hemorrhage

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Journal of Stroke Medicine 2022年第2期5卷 126 - 132页

作者： Girish Menon Hareesha KS Karthik Gajula Sampath Kumar Ajay Hegde Department of Neurosurgery Kasturba Medical College Manipal Academy of Higher Education Manipal Karnataka India Department of Data Science & Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Electronics & Communications Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

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Notice of Violation of IEEE Publication Principles: HopNet based Associative Memory as FC layer in CNN for Odia Character Classification

Notice of Violation of IEEE Publication Principles: HopNet b...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Ramesh Chandra Sahoo Sateesh Kumar Pradhan Poonam Tanwar Research Scholar Computer science & Applications Utkal University Bhubaneswar India Computer science & Applications Utkal University Bhubaneswar India Department of Computer Science MRCE Faridabad

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127927

Notice of Violation of IEEE Publication Principles “HopNet based Associative Memory as FC Layer in CNN for Odia Character Classification” by Ramesh Chandra Sahoo, Sateesh Kumar Pradhan, Poonam Tanwar in the Proceedings of the 10th International Conference on Cloud Computing, Data science & Engineering (Confluence), January 2020 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. This paper contains copied content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. “Unsupervised Learning using Pretrained CNN and Associative Memory Bank” by Qun Liu and Supratik Mukhopadhyay in the Proceedings of the International Joint Conference on Neural Networks (IJCNN), July 2018 A deep neural network such as convolutional neural network is a popular and most commonly applied technique in image processing for classification for the last few years. The overhead of the feature extraction step will be avoided due to the implicit feature extraction nature of convolutional neural network (CNN) and these extracted features contain substantial information that could be sufficient for an image classification problem. Fully connected (FC) layers in CNN take the results of the last convolution and/or pooling layer and then use them to recognize or classifying images into labels. In this paper, we present an associative memory-based model named Hopfield network as a fully connected layer to store patterns for classification in CNN architecture like LeNet-5. The main purpose of using Hopfield network is to avoid backpropagation as it is a fully connected recurrent network as the state-of-art results which we have obtained are comparable with other models. To measure the performance of the new architecture, we used NIT,

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A Geometric Understanding of Deep Learning

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Engineering 2020年第3期6卷 361-374页

作者： Na Lei Dongsheng An Yang Guo Kehua Su Shixia Liu Zhongxuan Luo Shing-Tung Yau Xianfeng Gu DUT-RU Co-Research Center of Advanced ICT for Active Life Dalian University of TechnologyDalian 116620China Department of Computer Science Stony Brook UniversityStony BrookNY 11794-2424USA School of Computer Science Wuhan UniversityWuhan 430072China School of Software Tsinghua UniversityBeijing 100084China Center of Mathematical Sciences and Applications Harvard UniversityCambridgeMA 02138USA

This work introduces an optimal transportation(OT)view of generative adversarial networks(GANs).Natural datasets have intrinsic patterns,which can be summarized as the manifold distribution principle:the distribution of a class of data is close to a low-dimensional *** mainly accomplish two tasks:manifold learning and probability distribution *** latter can be carried out using the classical OT *** the OT perspective,the generator computes the OT map,while the discriminator computes the Wasserstein distance between the generated data distribution and the real data distribution;both can be reduced to a convex geometric optimization ***,OT theory discovers the intrinsic collaborative-instead of competitive-relation between the generator and the discriminator,and the fundamental reason for mode *** also propose a novel generative model,which uses an autoencoder(AE)for manifold learning and OT map for probability distribution *** AE–OT model improves the theoretical rigor and transparency,as well as the computational stability and efficiency;in particular,it eliminates the mode *** experimental results validate our hypothesis,and demonstrate the advantages of our proposed model.

关键词： Generative Adversarial Deep learning Optimal transportation Mode collapse

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