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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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application of Deep learning Model for Analysis of Forward Kinematics of a 6-Axis Robotic Hand for a Humanoid

Application of Deep Learning Model for Analysis of Forward K...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Ranbir Singh Ankur Anubhav Agrawal Atul Mishra Pradeep Kumar Arya Aditi Sharma Department of Mechanical Engineering School of Engineering & Technology BML Munjal University Gurugram Haryana India Department of Electronics & Communication Engineering School of Engineering & Technology BML Munjal University Gurugram Haryana India Department of Computer Science Engineering School of Engineering & Technology BML Munjal University Gurugram Haryana India School of Computer Science Engineering and Technology Bennett University Grater Noida UP India Department of CSE Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharastra India

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

Rigid body kinematics can be generically categorized as forward and inverse kinematics. Forward kinematics calculates the precise location and orientation of the tool at the end of a robotic arm based on the specific angles of its joints. In contrast, inverse kinematics calculates the necessary joint movements to reach a specific location of the end effector (tool). These kinematics formulations are evaluated using homogeneous transformations. Computation of the solutions for the homogenous formulations can be computationally expensive for forward kinematics. Further, the computational requirements are significantly larger for inverse kinematics. The precision, resolution and accuracy of the robot kinematics depends on complex mathematical formulations. In today's dynamic world, the application complex mathematics is sometimes considered as exhausted. The increasing deployment of artificial intelligence tools and machine learning technologies is heading towards a paradigm shift towards advanced frameworks with less complexities. This research article presents an innovative artificial intelligence (AI) method that deploys an advanced deep learning techniques to precisely forecast the forward kinematics for a specifically developed 6-axis robotic hand for humanoids.

关键词： Deep learning Analytical models Robot kinematics Humanoid robots Kinematics Arms Mathematics End effectors Complexity theory Autonomous robots

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Quantum machine learning Techniques based on Nurturing Agri-Ontology Framework in Agricultural Science

Quantum Machine Learning Techniques based on Nurturing Agri-...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： R Deepa V Jayalakshmi P Thilakavathy A Bennet Prabhu R Surendran Department of Computing Technologies School of Computing SRM institute of science and technology Chennai India Department of Computer Applications DRBCCC Hindu College Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India KONE Elevator India Technology & Engineering Centre Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The absence of a consistent ontological framework often impedes efficient knowledge organization and sharing within the agricultural community, despite the quantity of data created in agricultural research. The lack of defined vocabulary and conceptual frameworks hinders data integration, agricultural research, and innovation by preventing interoperability between databases and systems. To address these issues, this research work proposes the Nurturing Agri-Ontology Framework (NAOF), which offers a systematic approach to creating and updating agricultural ontologies. Ontologies that faithfully portray agricultural ideas and connections can be more easily created with the help of NAOF, which combines ideas from knowledge management, agricultural science, and ontology engineering. NAOF aims to ensure that the ontologies produced reflect the wide range of viewpoints and terminology used in agricultural research by encouraging cooperation among stakeholders, ontology engineers, and domain specialists. In this study, the improvement of knowledge organization, sharing and integration in the agricultural area is shown with the application of NAOF. Using this research study, scientists can build ontologies that improve agricultural decision-making, data interoperability, and the discovery of new information. Using quantum machine learning techniques, a strong foundation of knowledge can be laid for several agricultural applications, such as precision agriculture, crop management, and the formulation of agricultural policies. Ontology coverage, interoperability, scalability, and usability are established evaluation parameters that are used to assess the efficacy of NAOF in this research work. The analysis shows that NAOF effectively solves the problems and creates a better agricultural knowledge ecosystem by thoroughly evaluating it using these standards.

关键词： Technological innovation Systematics Standards organizations machine learning Organizations Ontologies Stakeholders

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News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep learning Models

News-Driven Stock Price Forecasting in Indian Markets: A Com...

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Advancement in Technology (ICONAT), international conference for

作者： Kaushal Attaluri Mukesh Kumar Tripathi Srinithi Reddy Shivendra Software Engineer-Machine Learning and AI Pegasystems India Worldwide Pvt Ltd Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Information Technology Vasavi College of Engineering Hyderabad India Department of Computer Application D. K. College Dumaraon Bihar India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (AI) and natural language processing (NLP) has significantly advanced stock market forecasting. AI’s ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where AI has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements.

关键词： Deep learning Social networking (online) Predictive models Real-time systems Natural language processing Reliability Forecasting Stock markets Artificial intelligence Long short term memory

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Pituitary Tumor Classification using machine learning

Pituitary Tumor Classification using Machine Learning

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： S P Sreeja V Asha Binju Saju Preethi T S Prajwal Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (数字)9781665474979

ISBN: (纸本)9781665474986

One of the more extreme problems that influences the two, kids and grown-ups is a cerebrum cancer. 85 to 90 percent of all essential Focal Sensory system malignancies are mind cancers. Around 11,700 patients are given a mind cancer finding every year. For the individuals who have a harmful cerebrum or Central Nervous System growth, the 5-year endurance rate is generally 34% for guys and 36% for ladies or women. In this paper, it is used with the classifiers to detect the tumor in human they are CNN, KNN, Logistic regression and SVM algorithms. Benign, malignant, pituitary, and other types of brain tumors are all classified. KNN-Classifier is used to categories tumors, and cross-validating accuracy score and hyperparameter tuning are used. The cross validation includes the experimental simulation with the best average score for K. Hyper parameters of the KNN Classifier state the best score when approaching the prediction with the highest accuracy score. Convolutional neural networks (CNNs) are used in our method to investigate discriminative information while integrating multi-layer dictionary learning. From the comparisons it is been analyzed that CNN Classifier has the best performance and achieved with 85.24 % accuracy.

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Multi-site External Sets Harmonization with M-ComBat: An application to Functional Connectivity in a Normative Framework 3

Multi-site External Sets Harmonization with M-ComBat: An App...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Sampaio, Inês Won Tassi, Emma Bianchi, Anna M. Borgwardt, Stefan Kambeitz, Joseph Kambeitz-Ilankovic, Lana Meisenzahl, Eva Salokangas, Raimo K. R. Upthegrove, Rachel Wood, Stephen J. Koutsouleris, Nikolaos Brambilla, Paolo Maggioni, Eleonora Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Department of Neurosciences and Mental Health Fondazione Irccs Ca' Granda Maggiore Policlinico Milan Italy Department of Psychiatry and Psychotherapy Center for Brain Behaviour and Metabolism University of Luebeck Luebeck Germany Department of Psychiatry Psychiatric University Hospital University of Basel Basel Switzerland Department of Psychiatry and Psychotherapy Faculty of Medicine University Hospital of Cologne Cologne Germany Faculty of Psychology and Educational Sciences Department of Psychology Ludwig-Maximilian University Germany Department of Psychiatry and Psychotherapy Medical Faculty Heinrich-Heine University Duesseldorf Germany Department of Psychiatry University of Turku Turku Finland Institute of Mental Health Centre for Human Brain Health University of Birmingham Birmingham United Kingdom School of Psychology University of Birmingham Birmingham United Kingdom Orygen Melbourne Australia Centre for Youth Mental Health University of Melbourne Melbourne Australia Department of Psychiatry and Psychotherapy Ludwig-Maximilian University Munich Munich Germany Max Planck Institute of Psychiatry Munich Germany Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Department of Pathophysiology and Transplantation University of Milan Milan Italy

ISBN: (纸本)9798350378009

Multi-site neuroimaging datasets are difficult to integrate due to confounding site effects. ComBat has been a widely used statistical-based model for this type of harmonization. Nevertheless, it suffers some drawbacks which prevent its application in external validation frameworks in machine learning (ML) analyses. First, ComBat relies on all current sites to estimate model parameters, leading to the necessity of re-fitting the model when data from new unseen sites is added. Then, it requires the inclusion of biological information of interest (e.g. diagnosis) in the model fitting process, which is incompatible with the harmonization of samples with unknown outcomes, a necessary condition to develop AI applications based on predictive models to be employed in clinical settings. In this work, we propose to solve the former issues by employing modified ComBat (M-ComBat) in a normative framework (NM-ComBat). To assess its harmonization efficacy, we compared four different ComBat variations, including its standard application (S-ComBat), M-ComBat, and the normative variation of the latter, NS-ComBat and NM-ComBat, in harmonizing a multi-site functional connectivity (FC) dataset. Our results show that NM-ComBat enabled the successful harmonization of external datasets, successfully eliminating site effects from data, while preserving biological covariates of interest, such as age, sex, and diagnosis. These results paved the way for the application of ComBat in ML analysis and external validation frameworks, contributing to the generalizability of developed models and their potential clinical applicability. © 2024 IEEE.

关键词： Nuclear weapons

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Integrated Colorization and Styling Approach for Enhancing Image Clarity

Integrated Colorization and Styling Approach for Enhancing I...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Gollapalli Madhu Shalini Rakam Anand Thalloju Sankeerth H. Venkateswara Reddy Shanmugasundaram Hariharan Vinay Kukreja Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Image exploration adopts several dimensionalities with web-based tasks, integrating Streamlit and OpenCV to create an interactive platform for advanced image enhancement. Alongside the transformative tool enabling conversion of black and white images into vibrant compositions, an innovative styling suite offers diverse artistic filters. Notably, the addition of a camera option allows users to capture photos directly within the interface. Through Streamlit’s intuitive interface and OpenCV’s robust image processing, users experience real-time previews for seamless colorization and filtering. The proposed methodology involves designing a responsive web interface, optimizing Streamlit widgets for file uploads and style selection, and leveraging OpenCV for image processing. Results demonstrate the application’s effectiveness, providing immediate insights into style impact. Challenges include processing speed optimization, with future research directions exploring advanced machine learning (ML) models for dynamic style transfer. In summary, this research advances interactive image processing, offering a refined platform for creative visual content enhancement. The results obtained is promising leading to effective image colorization and styling, thereby improving the quality of the image.

关键词： Visualization Filters machine learning Streaming media Cameras User experience Real-time systems

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Color image segmentation based on learning from spatial and temporal discontinuities

Color image segmentation based on learning from spatial and ...

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2010 The 3rd international conference on Computational Intelligence and Industrial application (PACIIA2010)

作者： Chen Pan,Feng Cui College of Information Engineering,China Jiliang University,Hangzhou,Zhejiang,China,

This paper presents a coarse-to-fine learning method based on Extreme learning machine (ELM) for color image segmentation. Firstly,we locate a part of the object and background as candidate regions for sampling. By sampling from high gradient pixels (spatial discontinuity) and learning by ELM,we can extract object roughly. Due to ELM could produce different models by training with same data,the difference of their segmentation results shows some flicker in temporal feature (temporal discontinuity). So we can resampling from spatial and temporal discontinuities,and then produce a new classification model. The new model could extract object more accurately. Experimental results in natural image segmentation demonstrate the proposed scheme can reliably extract the object from the complex scenes.

关键词： Image segmentation, ELM, learning by sampling

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Information Retrieval Using Domain Adapted Language Models: application to Resume Documents for HR Recruitment Assistance 23rd

Information Retrieval Using Domain Adapted Language Models: ...

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23rd international conference on Computational Science and its applications (ICCSA)

作者： Bouhoun, Zakaria Guerrois, Theo Li, Xianli Baker, Mouna Gado, Nassara Elhadji Ille Roumili, Emir Vitillo, Francesco Bechet, Lies Benmiloud Plana, Robert Assyst EOS Courbevoie France

ISBN: (纸本)9783031371042;9783031371059

To attract and hire the most talented candidate who fit a company's requirements and have the potential to drive its growth, HR professionals spend a significant amount of time sourcing candidates, reviewing their resumes, and assessing their experiences against the needs of open positions. However, with the rise of online recruitment, this process has become increasingly challenging to manage manually. Therefore, job-resume matching has emerged as a crucial technique to automate and streamline the recruitment process. By leveraging machine learning algorithms, job-resume matching allows recruiters to quickly identify the most qualified candidates by comparing their skills and experiences with the requirements of vacant positions. The challenge today is to develop an approach that can effectively encode the semantic meaning of textual information in job descriptions and candidate resumes. In this paper, we propose a novel approach that combines TSDAE and BERT models to create more specialized sentence embeddings for HR data. This approach is powered by clean data extracted using a dedicated CV parsing tool. We also present two methods to automatically create labelled data for training the model. The benchmark of our method against real data shows an improvement of up to 11 points compared to the baseline model without TSDAE, highlighting the effectiveness of our proposed approach.

关键词： text embedding job-resume matching model domain adaptation

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Big Data and Deep learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and computer Vision Systems

Big Data and Deep Learning in Smart Cities: A Comprehensive ...

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Computing and machine Intelligence (ICMI), international conference on

作者： Victor Adewopo Nelly Elsayed Zag Elsayed Murat Ozer Constantinos L. Zekios Ahmed Abdelgawad Magdy Bayoumi School of IT University of Cincinnati Cincinnati USA Dept. of Elec. & Comp. Eng. Florida International University Florida United States School of Eng. & Tech. Central Michigan University Michigan USA Dept. of Elec. & Comp. Eng. University of Louisiana Lafayette Louisiana

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

In the dynamic urban landscape, where interplay of vehicles and pedestrians defines the rhythm of life, integrating advanced technology for safety and efficiency is increasingly crucial. This study delves into the application of cutting-edge technological methods in smart cities, focusing on enhancing public safety through improved traffic accident detection. Action recognition plays a pivotal role in interpreting visual data and tracking object motion such as human pose estimation in video sequences. The challenges of action recognition include variability in rapid actions, limited dataset, and environmental factors such as (Weather, Illumination, and Occlusions). In this paper, we present a novel comprehensive dataset for traffic accident detection. This dataset is specifically designed to bolster computer vision and action recognition systems in predicting and detecting road traffic accidents. We integrated datasets from wide variety of data sources, road networks, weather conditions, and regions across the globe. This approach is underpinned by empirical studies, aiming to contribute to the discourse on how technology can enhance the quality of life in densely populated areas. This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities. These dataset is expected to advance academic research and also enhance real-time accident detection applications, contributing significantly to the evolution of smart urban environments. Our study marks a pivotal step towards safer, more efficient smart cities, harnessing the power of AI and machine learning to transform urban living.

关键词： computer vision Visualization Smart cities Tracking Video sequences Transportation Transforms

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A Two-Stage Speed up Model for Case-Based learning in Real Time Strategy Games

A Two-Stage Speed up Model for Case-Based Learning in Real T...

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2010 The 3rd international conference on Computational Intelligence and Industrial application (PACIIA2010)

作者： Yan Li College of Mathematics and computer Hebei University Baoding, Hebei Province, China Xiaolei Tong machine learning Center Hebei University Baoding, Hebei Province, China Jiayue Dai machine learning Center Hebei University Baoding, Hebei Province, China

To meet the requirement of real time strategy (RTS) games, this paper proposes a two-stage speed up model using the combination of genetic algorithm and ant algorithm (GA-AA) and then artificial neural network (ANN) in a RTS game. The task is to tackle the optimization and adaptive defensive positioning game problems. In the first stage, we use GA to perform the initial optimization, and then AA is incorporated to speed up GA. In the second stage, the results of GA-AA can be used as cases to train an ANN, which obtains the optimal solutions very fast and then completes the whole off-line learning process. These optimal solutions stored in the trained ANN are considered to be still useful to provide good solutions in even random generated environments. Thus the two-stage speed up model not only needs less off-line training time, but also can recommend good on-line solutions very quickly. Experimental results are demonstrated to support our idea.

关键词： Real time strategy games (RTS), Case-based learning, Genetic algorithm, Ant algorithm, Artificial neural network.

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