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Indoor object recognition based on YOLOv5 with EIOU loss function 3

Indoor object recognition based on YOLOv5 with EIOU loss fun...

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3rd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Qi, Weimin Chen, Hangong Ye, Yuting Yu, Guoshuai Engineering Training Center Jianghan University China Institute of Artificial Intelligence Jianghan University China

ISBN: (纸本)9781510668522

At present, large-scale object classification and recognition tasks inevitably encounter problems of training efficiency and model accuracy. The solution of this problem highly depends on the definition of loss function. A better loss function can get a more accurate model with fewer epoches, which undoubtedly helps to save computility and time cost and improve the security of the model. Nowadays, the task of indoor object recognition plays an important role in the fields of computer vision aided blind people, home robots and so on. However, the task of indoor object recognition is plagued by the above problems due to its huge category of recognition objects. Starting with the task of indoor object recognition, this paper innovatively uses EIOU loss function and YOLOv5 deep learning convolutional neural network in this field, which improves training efficiency and recognition accuracy. Some original bounding box regression loss functions of YOLO series (such as GIOU, DIOU, CIOU) have defects in that the prediction box coincides with the center point of the truth value box, and the horizontal vertical ratio is the same. The paper uses the EIOU loss function to solve this problem. This paper focuses on the network structure of YOLOv5, the defects of YOLOv5's native loss function, the calculation method and advantages of EIOU loss function, and compares the performance gap between EIOU and CIOU. Finally, the EIOU loss function is used to complete the indoor article identification task. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computer vision

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Enterprise Support Hierarchical Model with Secure Data Protocol 3

Enterprise Support Hierarchical Model with Secure Data Proto...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Vinay Simha Reddy, T. Parvathi, S. Sarada, Ch. Mohmmed, Karrar Hatif Sampath Dakshina Murthy, A. Saikumar, K. Malla Reddy University Dept of Artificial Intelligence and Machine Learning Maisammaguda Hyderabad Telangana Dulapally India Mlr Institute of Technology Department of Computer Science and Information Technology Telangana Hyderabad India Cvr College of Engineering Department of Cse Telangana Hyderabad India Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq Department of Ece Andhra Pradesh Duvvada India Koneru Lakshmaiah Education Foundation Dept of Cse Telangana Hyderabad500075 India

ISBN: (纸本)9798350319125

CHARON is a cloud-upheld capacity framework that can store and share large information in a manner that is protected, solid, and proficient. It does this by utilizing different cloud sources and capacity areas to meet the lawful prerequisites for private individual information. CHARON has three things that make it stick out: It doesn't require trust in any one individual or thing. It needn't bother with a client-oversaw server. It handles large records well across a bunch of putting-away administrations in various regions of the planet. We likewise made another Byzantine-strong information-driven rental plan to stay away from composing issues between clients utilizing shared documents. We test CHARON by displaying test processes from science, a notable huge information region, utilizing miniature and application-based measures. The outcomes show that our novel plan isn't just imaginable, however, it additionally stirs up to multiple times better compared to other cloud-based arrangements from beginning to end. © 2023 IEEE.

关键词： CHARON Cloud computing

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Hybrid Optimization Based BERT Model for Drug Detection in NLP 3

Hybrid Optimization Based BERT Model for Drug Detection in N...

引用

3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Kruthika, C.G. Namitha Devi, N.N. Madlur, Vijayananda V Asha, K. Aruna, T.M. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Vidya Vikas Institute of Engineering and Technology Department of Computer Science and Engineering Mysuru India Mangalore Institute of Technology and Engineering Department of Computer Science and Engineering Moodabidri India Reva University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350340358

When it comes to natural language processing (NLP), sentence embedding is a hot topic because of the knowledge it transfers to subsequent tasks. Recent studies have shown that sequential neural networks, which can be trained on large quantities of data, can be used to encode info through a one-way analysis of text sequences, making them a powerful tool for drug detection. The purpose of this study is to smear cutting-edge deep learning techniques to publicly available data in order to accurately detect and identify adverse medication reactions that have not been recorded by medical professionals (open data). This study presents a deep learning-based strategy employing Enhanced Encoder on a dataset of 10,000 reviews culled from WebMD and *** and labelled by hand (IBERT). The model is created by integrating the BERT with many parallel blocks of a single-layer deep Convolutional Neural Network (CNN) that use varying kernel sizes and filters. To deal with ambiguity, the biggest obstacle to NLP, such a combination is helpful. Cuckoo Search (CS) and Antlion Optimization (ALO) are coupled to create a hybrid optimization strategy for determining the best values for the proposed model's hyper-parameters. Results from a number of different classification methods demonstration that the suggested model meaningfully outdoes the state-of-The-Art models. © 2023 IEEE.

关键词： Embeddings

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Forecasting Airlines Customer Demand: A Comparative Analysis of Time Series Models 3

Forecasting Airlines Customer Demand: A Comparative Analysis...

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3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Abouelwafa, Mariam Mohamed Ibrahim, Ahmed Ehab Saber, Amira Alaa Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

The airline industry is renowned for employing sophisticated strategies, making accurate customer demand fore-casting essential for operational planning and resource allocation. Revenue management systems heavily rely on precise passenger demand forecasts. This research paper consists of two main parts. Firstly, it compares and evaluates the performance of different time series models, namely SARIMA, ARIMA, and auto-regression, for forecasting airline customer demand. These forecasting methods are applied using features such as month, number of passengers, international flights, and total number of flights. Secondly, the models are tested on pattern analysis, including level, seasonality, and trend, and the evaluation metrics are assessed. The outcomes of this study contribute to the existing knowledge on forecasting airline customer demand, offering valuable insights for optimizing operational planning and resource allocation in the airline industry. The comparative analysis of forecasting models provides practical guidance for airlines to select the most suitable model for their specific requirements. The evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared (R), and Mean Absolute Percentage Error (MAPE), clearly indicate the superior performance of the SARIMA methodology in terms of forecast bias. The experimental results demonstrate that SARIMA models incorporating seasonality patterns achieve the best forecasting outcomes. Keywords: Revenue management, accurate forecasts, evaluation matrix, time series algorithms © 2023 IEEE.

关键词： Resource allocation

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Ensemble Nonlinear machine Learning Model for Chronic Kidney Diseases Prediction 3

Ensemble Nonlinear Machine Learning Model for Chronic Kidney...

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3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Sampath, S. Prasad, Mudarakola Lakshmi Hussain, Mohammad Manzoor Parameswari, R. Anil Kumar, D. Reddy, Pundru Chandra Shaker Adichunchanagiri Institute of Technology Department of Information Science and Engineering Karnataka Chikkamagaluru India Institute of Aeronautical Engineering Computer Science and Engineering Hyderabad Telangana Dundigal India B v Raju Institute of Technology Computer Science and Engineering Telangana Narsapur India Sathyabama Institute of Science and Technology Department of Mathematics TamilNadu India PACE Institute of Technology and Sciences Department of Electronics and Communication Engineering AP Ongole India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350340358

Living in a major metropolitan area has been linked to an increased risk of developing multiple forms of chronic-kidney-disease(CKD). In developed nations, predicting CKDs is a top priority. Predictive analytics for the purpose of predicting CKDs are the primary focus of this work. However, it is getting harder and harder to forecast outcomes for massive samples. While doing so, the MapReduce architecture makes it possible to write predictive algorithms by combining map and reduce operations. Problems with the scalability and effectiveness of anticipative learning approaches are alleviated by the comparatively straightforward programming interface. To efficiently handle small subsets of massive datasets, the authors propose using an iterative weighted mapreduce approach. Ensemble-nonlinear support-vector-machines(ENSVM) and random-forests(RF) are used to design a binary classification issue. As a result, the suggested approach generates nonlinear blends of kernel activations in example prototypes, as opposed to the conventional linear combination of activations. In addition, an ensemble of deep-SVM is utilized to integrate the descriptors, with the product rule being employed to merge the classifiers' likelihood estimates. Prediction accuracy and results interpretability are used to gauge performance. © 2023 IEEE.

关键词： MapReduce

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In-depth Analysis of Information Overflows and its Role in Shaping Future Knowledge Structures 3

In-depth Analysis of Information Overflows and its Role in S...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Paranthaman, V. Kamal, M.D. Raj Balaji, V. Sunil, G. Adhi College of Engineering and Technology Dept. of Mechanical Engineering TN Chennai India Velammal Institute of Technology Department of Mechanical Engineering TN Chennai India Loyola Institute of Technology Department of Mechanical Engineering TN Chennai India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350343274

This study provides a thorough evaluation of data deluges and how they will play a significant role in the development of future knowledge architectures. The suggested methodology is adaptable and interdisciplinary since it combines cutting-edge data mining, NLP, and network analysis methods. Data mining, NLP, network analysis, content analysis, big data analytics, cognitive mapping, SNA, MNA, QR, and IV are all compared to the proposed method, as are machine learning algorithms, qualitative research methods, and data visualization. Accuracy, relevance, depth of analysis, innovation, practical consequences, and clarity are only a few of the areas where the suggested technique shines. Its greater analytical skills are shown by its persistent outperformance of conventional approaches. Additionally, the suggested approach better accords with ethical norms and responsible research procedures, thus they are all considered as well. In summary, this study demonstrates the value of looking at information floods from several angles of study. The suggested technique not only provides cutting-edge analytic instruments for negotiating the intricacies of the digital information ecosystem, but also gives a forward-looking viewpoint by anticipating new patterns and developments. It demonstrates how crucial it is to use cutting-edge methods and evolve with the information age. © 2023 IEEE.

关键词： Natural language processing systems

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StylusAI: Stylistic Adaptation for Robust German Handwritten Text Generation 18th

StylusAI: Stylistic Adaptation for Robust German Handwritten...

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18th international conference on Document Analysis and recognition (ICDAR)

作者： Riaz, Nauman Saifullah, Saifullah Agne, Stefan Denge, Andreas Ahmed, Sheraz German Res Ctr Artificial Intelligence GmbH DFKI Smart Data & Knowledge Serv SDS Trippstadter Str 122 D-67663 Kaiserslautern Germany RPTU Kaiserslautern Landau Dept Comp Sci Erwin Schrodinger Str 52 D-67663 Kaiserslautern Germany DeepReader GmbH D-67663 Kaiserslautern Germany

ISBN: (纸本)9783031705359;9783031705366

In this study, we introduce StylusAI, a novel architecture leveraging diffusion models in the domain of handwriting style generation. StylusAI is specifically designed to adapt and integrate the stylistic nuances of one language's handwriting into another, particularly focusing on blending English handwriting styles into the context of the German writing system. This approach enables the generation of German text in English handwriting styles and German handwriting styles into English, enriching machine-generated handwriting diversity while ensuring that the generated text remains legible across both languages. To support the development and evaluation of StylusAI, we present the 'Deutscher Handschriften-Datensatz' (DHSD), a comprehensive dataset encompassing 37 distinct handwriting styles within the German language. This dataset provides a fundamental resource for training and benchmarking in the realm of handwritten text generation. Our results demonstrate that StylusAI not only introduces a new method for style adaptation in handwritten text generation but also surpasses existing models in generating handwriting samples that improve both text quality and stylistic fidelity, evidenced by its performance on the IAM database and our newly proposed DHSD. Thus, StylusAI represents a significant advancement in the field of handwriting style generation, offering promising avenues for future research and applications in cross-linguistic style adaptation for languages with similar scripts.

关键词： Handwriting Generation Diffusion Models Handwriting Text recognition Transformers

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Multiple Roles of Big Data Platform and Artificial intelligence Architecture 3

Multiple Roles of Big Data Platform and Artificial Intellige...

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3rd international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2023

作者： Zhang, Jing Film School Modern College of Northwest University Shanxi Xi'an710130 China

ISBN: (纸本)9781643684581

Multi-role and artificial intelligence have gradually pushed the industry to a new stage of 'human-machine integration' and 'role intelligence'. Methods: Based on the big data platform, the feasibility of artificial intelligence and multiple roles of products was studied, and artificial intelligence and multiple roles provided intelligent interaction opportunities for the social production and visual dissemination of product knowledge system. Content: The construction of products by big data platform can realize the collaborative production of intelligent machines and professional designers, and multi-role interaction can realize the new dynamic level of knowledge of copywriting planning, visual design, structure generation and function reconstruction. As the artificial intelligence matching users and designers, the product big data platform has dynamic connection and interaction, which can further promote the hierarchical transformation of the knowledge system of users and designers from independent thinking to diversified vision, and from broad connection to multi-dimensional connection. The multi-role and artificial intelligence architecture can aggregate the multi-dynamic and interactive freedom of the product's main content economy, assets and culture in the upstream and downstream, as well as the traceable flow information of the big data platform, thus bringing the increment of the product's internal and external stock content and the value increment of the asset. Results: The product big data platform further proposed to build a 'human-machine integration' and 'role intelligence' data system with multi-role communication foreground, artificial intelligence knowledge center and blockchain technology background as the core. © 2023 The Authors.

关键词： Big data

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A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET Images 1

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022

作者： Sun, Xiao An, Chengyang Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

Head and neck (H &N) cancer is one of the most prevalent cancers [1]. In its treatment and prognosis analysis, tumors and metastatic lymph nodes may play an important role but their manual segmentations are time-consuming and laborious. In this paper, we propose a coarse-to-fine ensembling framework to segment the H &N tumor and metastatic lymph nodes automatically from Positron Emission Tomography (PET) and Computed Tomography (CT) images. The framework consists of three steps. The first step is to locate the head region in CT images. The second step is a coarse segmentation, to locate the tumor and lymph region of interest (ROI) from the head region. The last step is a fine segmentation, to get the final precise predictions of tumors and metastatic lymph nodes, where we proposed a ensembling refinement model. This framework is evaluated quantitatively with aggregated Dice Similarity Coefficient (DSC) of 0.77782 in the task 1 of the HECKTOR 2022 challenge[2, 3] as team SJTU426. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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Survey on Artificial intelligence- based Depression Detection using Clinical Interview Data 3

Survey on Artificial Intelligence- based Depression Detectio...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Begum, Shaik Rasheeda Sait, Saad Yunus Srm Institute of Science and Technology Department of Computer Science and Engineering Chengalpattu District Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Chengalpattu District Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Depression is a current and significant mental illness that most people face in their daily lives. Depression affects not just the emotional state of the person but also the physical, psychological and mental state of the individual. In contrast to other diseases, depression cannot be detected by laboratory tests, remains undetected due to a lack of understanding and ignorance, and can worsen, leading to suicide. Currently, the diagnostic procedure for detecting depression adopts face-to-face interactions and self-reported questionnaires by physicians. Recognizing depression necessitates a psychiatric analysis of human behavior and social interactions. People with depression behave differently than normal people, as can be seen in the patients' audio and video recordings. Computer vision and machine learning have been used to detect depression to fight the disease at an earlier stage. This article introduces a survey of research on the identification of depression using clinical interview data through Unimodal and Multimodal approaches. © 2022 IEEE.

关键词： Diseases

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