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Efficient Image Retrieval Technique with Local Edge Binary pattern Using Combined Color and Texture Features 1st

Efficient Image Retrieval Technique with Local Edge Binary P...

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1st international conference on Computational intelligence for Engineering and Management Applications, CIEMA 2022

作者： Sucharitha, G. Kalyani, B.J.D. Sekhar, Chandra G Srividya, Ch. Computer Science Engineering Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9789811984921

Image proceeding is foundation for pattern recognition and machine learning. The recent research focuses on image retrieval techniques with machine and deep learning methodologies. This paper recommends an other approach for extracting images based on combined characteristics of color and texture with edge binary pattern technique. The suggested descriptor first translates an RGB image into HSV color space. In this approach, the HSV color model makes use of the fundamental properties of color image as color, intensity, and brightness of an image. The hue (H) and saturation (S) components support to extract the color features, and the value component is more feasible to obtain the texture features;upon the value constituent of each image, local maximum edge binary patterns (LMEBPs) are used to find the relations among the pixels for every 3 × 3 matrix in order to extract texture features. Finally, all three histograms were used to extract the feature vector. The presented algorithm is experimented on two well-known color databases, Corel-10k and MIT-Visex. The comparative analysis of the proposed approach in terms of retrieval performance, with existing methods like CS-LBP, LEPSEG, LEPINV, has shown a substantial perfection in terms of precision and recall. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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A Review on EMG-based pattern Identification Methods for Effective Controlling of Hand Prostheses

A Review on EMG-based Pattern Identification Methods for Eff...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： S. Ramkumar Dhanusha Rema T. Archana Devi K Elavarasi T Selvaganapathi S. Gokila Department of Computer Science Christ (Deemed to be University) Bangalore India Department of Food Technology Karpagam Academy of Higher Education Coimbatore India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Department of CSE Karpagam Institute of Technology Coimbatore India

The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper’s conclusion.

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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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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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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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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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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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Intelligent Routing for IoT-Driven MANETs Using Computational intelligence 3

Intelligent Routing for IoT-Driven MANETs Using Computationa...

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3rd international conference on machine Learning and Data Engineering, ICMLDE 2024

作者： Kaur, Ramanpreet Taneja, Kavita Taneja, Harmunish Dept. of Computer Science and Applications Panjab University Chandigarh India Dept. of Computer Science and Applications DAV College Chandigarh India

The interlinkage of the Internet of Things (IoT) with Mobile Ad Hoc Networks (MANETs) creates an autonomous communication ecosystem, imperative for applications ranging from disaster recovery to smart agriculture. On the other hand, this coalescing gives rise to crucial challenges, especially in energy efficiency, higher transmission rates, and network connectivity. As the devices in IoT-driven MANETs have restricted battery life, the optimum use of energy is vital for extending network lifespan. The global explosion of IoT devices demands rapid data transfer and constant connectivity. Moreover, frequent topological changes brought on by the high mobility devices in IoT-driven MANETs elevate the chances of link failure. These disruptive actions undermine network productivity resulting in reduced throughput and higher latency. Computational intelligence approaches offer credible solutions for resolving these concerns. This study presents a Unified Energy-aware Link Auto-configuration Routing Model (UELARM) for IoT-driven MANETs that favors energy-aware paths and the auto-configuration of broken links. UELARM exploits the flower pollination algorithm by incorporating three vital parameters such as energy cost, average community time, and packet loss to explore the optimal route. Additionally, for automatically configuring broken links in IoT-driven MANETs, UELARM presents a route repair approach based on mobility prediction, drawing inspiration from the termite colony optimization algorithm. The simulation results indicate that the UELARM outpaces other state-of-the-art routing algorithms regarding energy consumption, throughput, delay and network lifetime. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Vehicular ad hoc networks

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Improved YOLOv8 Algorithm for Detection of Small and Complex Morphological Defects in Transmission Lines 3

Improved YOLOv8 Algorithm for Detection of Small and Complex...

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3rd international conference on Image Processing, Computer Vision and machine Learning, ICICML 2024

作者： Wang, Yong Zhang, Linghao Xiong, Xingzhong Kuang, Junwei Xiang, Siyu Gou, Xiang Sichuan University of Science and Engineering School of Automation and Information Engineering Yibin China State Grid Sichuan Electric Power Research Institute Internet of Things Key Laboratory of Sichuan Province Chengdu China Sichuan University of Science and Engineering Artificial Intelligence Key Laboratory of Sichuan Province Yibin China Sichuan Shuneng Dianke Energy Technology Co. Ltd. Chengdu China

ISBN: (纸本)9798350355413

In recent years, power grid companies have used artificial intelligence image recognition technology to conduct inspections of transmission lines. Although some results have been achieved, defects of small sizes and complex shapes are still easily missed or misdetected. In response to this problem, the DRM-YOLOv8 algorithm is proposed: in the feature extraction part, a C2f_DCNv3_DLKA module that combines DLKA (Deformable Large Kernel Attention) and DCNv3 (Deformable Convolution Network v3) is designed to improve the identification and extraction capabilities of important features;a heavy-parameterized cross-over algorithm is proposed The stage feature fusion local network optimizes and fuses high-level semantic features and low-level spatial features;finally, a multi-head adaptive spatial feature fusion detection head is designed. By adding an additional small target detection layer and improving cross-scale feature fusion, the detection capabilities of multiple small targets with significant size differences are improved. Experimental results show that on a data set containing twenty types of real transmission line defects, compared with the original YOLOv8n model, the overall average detection accuracy (mAP@0.5) reached 84.1%, an increase of 5.2%, for small size and complex shape defects The detection accuracy is increased by 8% and 2% respectively, which is significantly better than the existing algorithm, showing excellent robustness and generalization, and effectively improving the detection ability of multiple types of defects in transmission lines in complex scenarios. © 2024 IEEE.

关键词： defect detection deformable convolution transmission line YOLOv8

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Natural Language Processing for Conversational AI: Chatbots and Virtual Assistants 3

Natural Language Processing for Conversational AI: Chatbots ...

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3rd IEEE international conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Shrivastava, Neeraj Tewari, Pushpa Sujatha, S. Bogireddy, Srinivasa Rao Varshney, Neeraj Sharma, Vinod Medicaps University Dept. of Engineering Indore India Guru Nanak College Dept. of Vocational Studies Dhanbad India K. Ramakrishnan College of Technology Dept. of Civil Engineering Trichy India Horizon Systems Inc Dept. of Software Engineer Phoenix AZ United States Gla University Dept. of Cse Mathura India Jiwaji University Dept. of Computer Science Gwalior India

ISBN: (纸本)9798331521691

The frontline in developing the capabilities of chatbots and virtual assistants involves the use of NLP, enabling these machines to evolve into truly advanced, interactive machines that are able to comprehend and respond to human language with an almost unimaginable degree of accuracy. The paper will analyze some of the major NLP techniques and methodologies used for such systems: machine learning algorithms, neural networks, and semantic analysis. This would also mean that, with the help of big data and continuous learning, the work of chatbots and virtual assistants will range from answering queries to personalized recommendations. Language understanding, generation, and context management are discussed in detail, underlining how NLP improves user experience by making interactions more natural and intuitive. The challenges of ambiguous language processing, conversational context, and ensuring data privacy and security are discussed as well. Case studies and applications in real life show the impact of NLP on different sectors: customer service, healthcare, and e-commerce. The findings bring out the potential of NLP to change human-computer interaction and allow for more responsive and intelligent digital assistants. © 2025 IEEE.

关键词： Artificial intelligence Chatbots Natural Language Processing NLP Sentiment Analysis User Experience Virtual Assistants Voice recognition

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