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3rd international conference on machine intelligence and Signal Processing, MISP 2021

3rd International Conference on Machine Intelligence and Sig...

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3rd international conference on machine intelligence and Signal Processing, MISP 2021

ISBN: (纸本)9789811908392

The proceedings contain 67 papers. The special focus in this conference is on machine intelligence and Signal Processing. The topics include: Internet of Things: A Survey on Fused machine Learning-Based Intrusion Detection Approaches;Image Fusion-Based Watermarking in IWT-SVD Domain;on Twin Bounded Support Vector machine with Pinball Loss;traffic Rule Violation Detection System: Deep Learning Approach;template-Based Thinning Method for Handwritten Gujarati Character’s Strokes and its Classification for Writer-Dependent Gujarati Font Synthesis;a Model on Intrusion Detection Using Firefly Algorithm and Classical machine Learning;deep Learning Framework Based on Audio–Visual Features for Video Summarization;a Comparative Study of Deep Learning Models for Word-Sense Disambiguation;MultiYOLO: Learning New YOLO Categories Without Full Retraining;Analysis of Fifteen Approaches to Automated COVID-19 Detection Using Radiography Images;vehicle Routing Problem Using Reinforcement Learning: Recent Advancements;disease Detection of Plant Leaves with the Aid of Region Growing and Neural Network: A Comparative Analysis;a Web Application for Early Prediction of Diabetes Using Artificial Neural Network: A Comparative Study;machine Learning Equipped Web-Based Disease Prediction and Recommender System;comparing the Predictive Accuracy of machine Learning Algorithms for Neonatal Mortality Risk Classification;Extraction of Waterbody Using Object-Based Image Analysis and XGBoost;Deep Learning-Based Image Retrieval in the JPEG Compressed Domain;fake News Detection Using Genetic Algorithm-Based Feature Selection and Ensemble Learning;Application of SPSS for Forecasting of Renewable Energy as Future Energy in India;robust Multi-task Least Squares Twin Support Vector machines for Classification;human Emotion Classification Based on Speech Enhancement Using Neural Networks.

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Temporal Consistent Automatic Video Colorization via Semantic Correspondence

Temporal Consistent Automatic Video Colorization via Semanti...

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2023 IEEE/CVF conference on Computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Zhang, Yu Chen, Siqi Wang, Mingdao Zhang, Xianlin Zhu, Chuang Zhang, Yue Li, Xueming Beijing University of Posts and Telecommunications School of Artificial Intelligence China Beijing University of Posts and Telecommunications School of Digital Media and Design Arts Beijing China

ISBN: (纸本)9798350302493

Video colorization task has recently attracted wide attention. Recent methods mainly work on the temporal consistency in adjacent frames or frames with small interval. However, it still faces severe challenge of the inconsistency between frames with large interval. To address this issue, we propose a novel video colorization framework, which combines semantic correspondence into automatic video colorization to keep long-range consistency. Firstly, a reference colorization network is designed to automatically colorize the first frame of each video, obtaining a reference image to supervise the following whole colorization process. Such automatically colorized reference image can not only avoid labor-intensive and time-consuming manual selection, but also enhance the similarity between reference and grayscale images. Afterwards, a semantic correspondence network and an image colorization network are introduced to colorize a series of the remaining frames with the help of the reference. Each frame is supervised by both the reference image and the immediately colorized preceding frame to improve both short-range and long-range temporal consistency. Extensive experiments demonstrate that our method outperforms other methods in maintaining temporal consistency both qualitatively and quantitatively. In the NTIRE 2023 Video Colorization Challenge, our method ranks at the 3rd place in Color Distribution Consistency (CDC) Optimization track. Code will be available online at https://***/bupt-ai-cz/TCVC © 2023 IEEE.

关键词： Image enhancement

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Enhancing Crop Yield Prediction in India: A Comparative Analysis of machine Learning Models 3

Enhancing Crop Yield Prediction in India: A Comparative Anal...

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

作者： Chandhar, Kothakonda Ramineni, Kamalakar Ramakrishna, Erroju Ramana, T. Venkata Sandeep, Achi Kalyan, Karmakonda School of Computer Science & Artificial Intelligence Sr University Telangana Warangal India Department of AIML CVR Engineering College Telangana India Computer Science & Engineering Sri Indu College of Engineering & Technology Rangareddy Ibrahimpatnam India Computer Science & Engineering Jyothismathi Institute of Technology and Science Telangana Karimnagar India

ISBN: (纸本)9798350319125

Farming is very important for India's economy. Lots of people in India depend on farming to make a living. But farming has become harder because the weather, climate, and environment keep changing. These changes make it tough to grow crops well. We proposed a mechanism called crop yield prediction. It helps us predict how much crops will grow and helps farmers decide what to plant. We use things like where the farm is, how much rain it gets, how hot or cold it is, and what kind of soil is there to make these predictions. These things help us figure out how much crop we can expect. In this proposal we tried out some ma-chine learning models like the Gradient Boosting Regressor, Random Forest Regressor, Support Vector Regressor, and Decision Tree Regressor. We wanted to see which one is the best at predicting crop yields. It turns out the Decision Tree Regressor did the best job and was really accurate with the da-ta we had. This method helps farmers because it means we can use this model to make better predictions about how much food we can grow, which helps the farming community in India grow more food in a good way. © 2023 IEEE.

关键词： Decision Tree Regressor Gradient Boosting Regressor Random Forest Regressor Support Vector Regressor

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A Systematic Review of Artificial intelligence and Mental Health in the Context of Social Media 3rd

A Systematic Review of Artificial Intelligence and Mental He...

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3rd international conference on Artificial intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-Computer Interaction (HCII)

作者： Chen, Xing Genc, Yegin Pace Univ New York NY 10038 USA

ISBN: (纸本)9783031056437;9783031056420

Objective: We conducted a systematic review on the use of Artificial intelligence (AI) in the psychology domain within the context of social media's role on mental health. We identified the types of mental health studies that AI has supported, reviewed the machine learning methods in these studies, and reported on the different approaches for data collection. We provided a critical review of the applicability of these methods in real-world settings. Finally, we discussed the challenges faced in this area of study and provided advice for other researchers interested in solving these issues in the future. Methods: We collected our data from three outlets: ACM, JMIR, and CLPsych. We focused on the studies on the application of AI in clinical psychology in the past six years (2016 to 2021). Results: A total of 37 articles were included in our study for further review. The number of publications increased over time. While CLPsych has the highest number of articles, Reddit was the commonly used social media site for data collection. Suicide was the most mentioned mental disorder mentioned in the studies. SVM was the most frequently used approach when applying AI in mental health studies. Conclusion: Our review of the existing literature identified three issues on the topic. First, social media is underutilized for mental health care. Second, there is a lack of collaboration between seemingly disconnected research communities: i.e., machine learning experts and clinicians. Third, little attention is paid to humans when conducting the research.

关键词： Artificial intelligence Social media Mental health Psychology machine learning Clinical

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A Quantum-inspired Approach to Estimate Optimum-Path Forest Prototypes based on the Traveling Salesman Problem 27th

A Quantum-inspired Approach to Estimate Optimum-Path Forest ...

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27th international conference on pattern recognition, ICPR 2024

作者： Miranda, Maria Angélica Krüger Fanchini, Felipe Fernandes Passos, Leandro Aparecido Rodrigues, Douglas Costa, Kelton Augusto Pontara da Sherer, Rafal Papa, João Paulo Institute of Computing Campinas State University - UNICAMP Campinas Brazil School of Sciences Bauru Brazil Institute of Computational Intelligence Czestochowa University of Technology Czestochowa Poland

ISBN: (纸本)9783031781827

Quantum mechanics emerge as a promise for the future of computing, broadening the horizons for solutions concerning complex tasks, e.g., NP-hard problems. Alongside quantum computing, machine learning has become indispensable. This paper explores the potential integration of quantum computing principles into the Optimum-Path Forest (OPF), a graph-based framework comprised of solutions for machine learning, optimization, and image processing. We are particularly interested in the supervised OPF approach, which elects the most representative samples for each class, aka prototypes, as the connected samples from different classes in a minimum spanning tree (MST) computed over the training set. By harnessing quantum parallelism and superposition, this paper introduces a new approach to identifying prototypes employing a quantum-based Traveler Salesman Problem (TSP) algorithm, which provides an alternative to computing MSTs and yields a hybrid version of the OPF classifier. The experiments on established datasets demonstrated the promising potential of this approach while also underscoring the necessity for further research in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Traveling salesman problem

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Comparison of Algorithms for Classification of Financial intelligence Reports 3rd

Comparison of Algorithms for Classification of Financial Int...

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3rd EAI international conference on Data and Information in Online Environments (DIONE)

作者： Zaina, Roberto Jeronimo de Macedo, Douglas Dyllon Dutra, Moises Lima Culmant Ramos, Vinicius Faria de Araujo, Gustavo Medeiros Univ Fed Santa Catarina Florianopolis Brazil

ISBN: (纸本)9783031223235;9783031223242

This work shows the application of machine learning algorithms to decide whether Financial intelligence Reports analyzed by the Brazilian Federal Police should be investigated or archived. We explain how the suspicious financial operations found in the Financial intelligence Reports are a crucial piece of information to combat money laundering crimes. We depict the processing of such reports, which are often used to initiate a police investigation. When that is not the case, the reports are archived. In this work, we propose using machine learning to analyze these reports. We trained and used three classification algorithms: Decision Tree, Random Forest, and KNN. The results show that most reports should be archived. While Decision Tree and Random Forest indicated that about 2/3 of the reports should be archived, KNN indicated that about 4/5 of them should be archived. In the end, this work shows the feasibility of automating the analysis of the Financial intelligence Reports by the Brazilian Federal Police, despite the need for more adjustments and tests to improve the accuracy and precision of the models developed.

关键词： Financial intelligence report Artificial intelligence machine learning

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3D Facial Reconstruction Based on a Single Image Using CNN 3rd

3D Facial Reconstruction Based on a Single Image Using CNN

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Agaba, Ramzi Malah, Mehdi Abbas, Fayçal Babahenini, Mohamed Chaouki ReLaCS2 Laboratory Computer Science Department University Larbi Ben Mhidi Oum El Bouaghi Algeria ICOSI Laboratory Computer Science Department University of Abbes Laghrour Khenchela Khenchela Algeria LESIA Laboratory Computer Science Department University of Abbes Laghrour Khenchela Khenchela Algeria LESIA Laboratory Computer Science Department University of Mohamed Khider Biskra Biskra Algeria

ISBN: (纸本)9783031463341

In recent years, 3D facial reconstruction marked its presence in several areas, such as biometric security, computer vision, image synthesis, and video games. Many facial recognition applications require accurate 3D reconstruction;however, this task is complex and requires many calculations. This paper presents a new method based on CNN, which offers an automatic solution for the 3D reconstruction of the face depending on a single 2D image. Our method operates in three steps: the first step is to train a convolutional neural network with a self-made dataset to predict and detect the landmarks of the face and estimate the positions accurately from a single facial image in the image space. The second step involves producing the face’s geometric shape (mesh). Finally, the third step is to do an automatic translation between the 3D space of the object and the 2D image space to determine the texture referrals that correspond to each face polygon;thus, our method produces superior results in terms of accuracy and visual quality, considering all the parameters of the model (shape, expression, reflectance, and lighting) as inputs. Our method is simple, easy to implement, and offers a real-time 3D reconstruction of the face. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Research on Target recognition and Location Algorithm Based on Camera and Lidar

Research on Target Recognition and Location Algorithm Based ...

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Frontiers of Electronics, Information and Computation Technologies (ICFEICT), international conference on

作者： Zhaodong Li Tingting Yang Yiquan Wang Yu Yang Xuyang An Xijun Zhao China North Artificial Intelligence & Innovation Research Institute Collective Intelligence & Collaboration Laboratory Beijing China China North Artificial Intelligence & Innovation Research Institute Beijing China

The method of Target recognition and location the key technology for team-accompanied robots to realize personnel-following navigation. In this paper, the target recognition algorithm based on YOLOv3 is combined with the target positioning algorithm based on European point cloud clustering to realize the recognition and location of following targets. The YOLOv3 algorithm using the camera as a sensor can accurately and real-time recognize the target person; the algorithm based on lidar point cloud clustering can effectively obtain the location information of the target person. By fusing the two algorithms, target location can be performed simultaneously with target recognition, and the reliability and accuracy of the algorithm are improved at the same time.

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Role of Artificial intelligence and machine Learning in scalable Internet of Things Applications

Role of Artificial Intelligence and Machine Learning in scal...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Neeraj Singla Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Anything non-living that can take decisions on its own is called artificially intelligent. It is a technique designed to mimic human behaviour. On the other hand machine learning if a subset of artificial intelligence. It is a statistic method used to enable machine to improve with experience. They both go hand in hand. Now IoT has been there in existence for an enormous amount of time but during recent times due to large scale good connectivity for efficient data transfer, machine learning and analytics and Conversional Artificial intelligence has made IoT possible.

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Framework for the Deployment of Intelligent Smart Cities (ISC) using Artificial intelligence and Software Networking Technologies 3

Framework for the Deployment of Intelligent Smart Cities (IS...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Mohamed, Nachaat Upadhyay, Ramakant Jakka, Geethamanikanta Rambabu, P.V. Alfurhood, Badria Sulaiman Singh, Devesh Pratap Universiti Sains Malaysia Rabdan Academy UAE Homeland Security Abu Dhabi United Arab Emirates Sanskriti University Department of CSE Mathura Uttar Pradesh India University of the Cumberlands Data Analytics Information Technology United States Civil Engineering Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia

ISBN: (纸本)9798350399264

This article's goals include providing a framework for interpersonal and communication to safeguard welfare services and assess how well the innovation ecosystem is now understood. An essay starts with an overview or description of "urban area"This viewpoint includes neighborhood services, modern amenities, or smart infrastructure. Information and technology, digital operations use, monitoring, and interconnection, amongst other things. The section that follows examines various urban planning concerns, either administration or expansion. Utilizing diverse technologies effectively is made possible by innovative information about towns or an answer to a question concerning high-quality public infrastructure. An innovative method of managing networks is the Software Defined Network (SDN) architecture. Unlike switches in a conventional network computing environment, changes in SDN do not process inbound sachets in the similar means. If no inbound packets are identified after searching the linking tables, they are passed to the control system, the SDN's operating system, for processing. A Spread Denial of Service (DoS) outbreak poses the biggest risk to the cyber security of an SDN *** system or application layers of the disease-ridden devices connected to the system will be the target of the attack. The approach for determining whether incoming packets are contaminated or not is clever machine learning-based, and it is proposed in this paper. In the context of smart cities, the deployment of artificial intelligence (AI) has newly drawn more and more attention. The purpose of this study is to perform a works assessment to examine the role of AI in the various parts of smart towns given the relevance of the topic. This study's technique combines a thorough literature evaluation to find the most pertinent papers with reference system investigation to reveal the subtleties of the subject. The findings of this study highlight the significance of this technology, whic

关键词： Smart city

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