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

作者： Santosh Kumar Smriti Kumari Gupta Yogesh Kumar School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This research introduces an innovative real-time human emotion recognition system that seamlessly integrates computer vision techniques to address the challenges faced by individuals in understanding and recognizing human emotions. By focusing on facial expression recognition, which encompasses emotions such as fear, anger, surprise, sadness, and happiness, the proposed system aims to provide a comprehensive understanding of human emotions. The application of facial expression recognition extends beyond individual interactions to various domains, including security, privacy, and marketing. Businesses use Artificial intelligence (AI) to analyze customer satisfaction by interpreting emotional responses to images or videos. These insights play a crucial role in tailoring products and services to meet customer preferences, thereby enhancing marketing strategies and improving overall customer experience.

关键词： Training Emotion recognition Face recognition Computational modeling Training data Convolutional neural networks Artificial intelligence

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AI-Driven Prediction of Medicinal Property Presence in Plants Cultivated with Nutrient-Enriched Manure 3rd

AI-Driven Prediction of Medicinal Property Presence in Plant...

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3rd international conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Khanna, Abhirup Jain, Sapna Choudhury, Tanupriya Kotecha, Ketan Sar, Ayan Uttarakhand Dehradun248007 India University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Maharashtra Pune412115 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International University Pune India

ISBN: (纸本)9789819739905

For thousands of years, medicinal plants have been used for treating a variety of ailments. Medicinal plants have the potential to provide natural and effective remedies for common health problems and are often cost-effective and less toxic as compared to synthetic drugs. Despite the multiple advantages of medicinal plants, their cultivation is still a result of inefficient rudimentary practices. The lack of standardisation in cultivation practices, harvesting and processing of medicinal plants has led to consistency in the final product. Moreover, many farmers need more knowledge and technical expertise to grow medicinal plants, resulting in unsuccessful cultivation operations. The quality of soil and the right amount of macronutrients an important factors determining the growth and quality of medicinal plants. Extensive use of chemical fertilisers and inconsistent levels of micronutrients may lead to loss or diminishing medicinal properties. The works present a hypothesis that emphasises the use of machine learning algorithms for the prediction of medicinal properties. The study presents ways in which machine learning and AI can be used to analyse data from various sources to identify patterns and make predictions regarding the cultivation of medicinal plants. The hypothesis proposed in this research framework was tested with prior tests and also validated against three different concept cases, which ultimately catered to the cultivation and production of different medicinal plants. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Medicinal chemistry

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Utilization of machine Learning Algorithms to Monitor the Growth Path of Fishes in Marine Aquaculture 1

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Ajibade, Samuel-Soma M. Essah, Richard Tapales, Catherine P. Antecristo, Charmaine P. Cababat, Feliciana G. Oyebode, Oluwadare Joshua Akintoye, Kayode A. Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer and Information Sciences Sunway University Selangor Malaysia Department of Computer Science and Engineering Chandigarh University Punjab India College of Arts and Sciences Cebu Technological University Cebu Moalboal Philippines Cebu Technological University Cebu Moalboal Philippines College of Education Cebu Technological University Cebu Moalboal Philippines Department of Civil and Environmental Engineering ABUAD Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria

ISBN: (数字)9783031810862

ISBN: (纸本)9783031810855

Under a realistic cellular metabolic economic model of Nile tilapia, this work investigates Q-learning fish growth trajectory analysis. we offer two Q-learning data mining algorithms the best management policy using simulated data of optimum growth projections starting at the early growth stage and ending at appropriate market share. The early or primitive Q-learning technique learns optimum feed supplying regulatory regime for fish cultivation rate for the fished present the cages, whereas other one continuously adjusts appropriate nutrition controls the law for agricultural fish rate of increase in tanks together within ideal temperature pattern. The cumulative percentage weight of fishes through both vessels on land and afloat cages was tracked with relative dynamic model errors of 1.7% and 6.6%, respectively, using Q-learning control rules. Furthermore, contrasted to drifting cages where its water temp is controlled at 29.7 ℃, the eating and heat management policies minimise the proportional rate of feeding along with wastage of food in by 11%. Data, algorithms, and performance of DL approaches used in adaptive aquaculture farms are also examined. In a nutshell, our goal is to give scholars and clinicians a greater grasp of the latest advancements of deep learning in aquaculture, which will help them develop smart aquacultural applications. With improved machine - learning technologies, a video trying to track biological early warning has made significant progress. Artificial intelligence has helped researchers better understand behavioural reactions to pharmacological and environmental stress. We discuss pioneer efforts in exact monitoring of a gathering of individuals in 2d/3d space in this paper, which introduces the fundamentals of activity recognition. Video tracking's professional and operational difficulties are discussed. The toxicological study based on fish behavioral data is then summarized. © The Author(s), under exclusive license to Springer Nature

关键词： Deep learning

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Toward Autonomous Detection of Anomalous GNSS Data Via Applied Unsupervised Artificial intelligence

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international JOURNAL OF SEMANTIC COMPUTING 2022年第1期16卷 29-45页

作者： Dye, Mike Stamps, D. Sarah Mason, Myles Saria, Elifuraha Ronin Inst 127 Haddon Pl Montclair NJ 07043 USA POB 56 Nederland CO 80466 USA Virginia Tech Dept Geosci 926 Campus Dr Blacksburg VA 24061 USA Virginia Tech Acad Integrated Sci 800 Campus Dr Blacksburg VA 24061 USA Ardhi Univ Dept Geospatial Sci & Technol POB 35176Observation Hill Plot 3Block LUni Salaam Tanzania

Artificial intelligence applications within the geosciences are becoming increasingly common, yet there are still many challenges involved in adapting established techniques to geoscience data sets. Applications in the realm of volcanic hazards assessment show great promise for addressing such challenges. Here, we describe a Jupyter Notebook we developed that ingests real-time Global Navigation Satellite System (GNSS) data streams from the EarthCube CHOrdS (Cloud-Hosted Real-time Data Services for the geosciences) portal TZVOLCANO, applies unsupervised learning algorithms to perform automated data quality control ("noise reduction"), and explores autonomous detection of unusual volcanic activity using a neural network. The TZVOLCANO CHOrdS portal streams real-time GNSS positioning data in 1s intervals from the TZVOLCANO network, which monitors the active volcano Ol Doinyo Lengai in Tanzania, through UNAVCO's real-time GNSS data services. UNAVCO's real-time data services provide near-real-time positions processed by the Trimble Pivot system. The positioning data (latitude, longitude and height) are imported into the Jupyter Notebook presented in this paper in user-defined time spans. The positioning data are then collected in sets by the Jupyter Notebook and processed to extract a useful calculated variable in preparation for the machine learning algorithms, of which we choose the vector magnitude for further processing. Unsupervised K-means and Gaussian Mixture machine learning algorithms are then utilized to locate and remove data points ("filter") that are likely caused by noise and unrelated to volcanic signals. We find that both the K-means and Gaussian Mixture machine learning algorithms perform well at identifying regions of high noise within tested GNSS data sets. The filtered data are then used to train an artificial intelligence neural network that predicts volcanic deformation. Our Jupyter Notebook has promise to be used for detecting potentially hazardous

关键词： TZVOLCANO CHOrdS UNAVCO artificial intelligence machine learning noise reduction unsupervised learning volcanic hazards assessment

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

Third International Conference on Advanced Algorithms and Si...

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

ISBN: (纸本)9781510668522

The proceedings contain 210 papers. The topics discussed include: research progress and prospects of intelligent planning algorithms in navigation collision avoidance;research on determining the end point of carbon kneading based on LSTM algorithm;pavement grade recognition algorithm based on sprung mass acceleration;research on distributed network information security diagnosis method based on matrix algorithm;two-dimensional array antenna directional pattern optimization method based on genetic simulated annealing algorithm;air defense target allocation based on improved genetic algorithm;research on visible and infrared binocular image fusion algorithm based on improved NSCT;study and application of migration technology in ground penetrating radar (GPR) data processing;load forecasting analysis based on the residential electricity consumption data;performance of BOK-MPSK chirp modulation for data transmission;and anomaly detection method of power purchase material data based on B IRCH clustering algorithm and time series.

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Artificial intelligence and its Road to Altering the World Dynamics

Artificial Intelligence and its Road to Altering the World D...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Sehaj Devyansh Batra Ashish Sharma Computer Science and Engineering Chandigarh University Mohali India Department of Electrical Engineering Chandigarh University Mohali India

Humans have longed for computers to take over and make the monotonous and tedious tasks obsolete. Artificial intelligence(AI) is just the right answers to this problem. There are not one but many sectors that are being manipulated by this ever- growing technology. It is safe to say that each day machines are getting better at understanding humans which is due to the evolution and innovation in this field. In this paper, we have uncovered various sectors in which artificial intelligence along with its various branches like Natural Language Processing (NLP), machine Learning(ML) and Computer Vision have helped the world curate a better place by personalizing every daily use item. This has enhanced the way we live. However, there is a lot of data that is being used in order to bring these technologies to life. We have highlighted the constraints as well as the pleasures that this technology can bring us.

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A New Deep Fuzzy Based MSER Model for Multiple Document Images Classification 3rd

A New Deep Fuzzy Based MSER Model for Multiple Document Imag...

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3rd international conference on pattern recognition and Artificial intelligence, ICPRAI 2022

作者： Biswas, Kunal Shivakumara, Palaiahnakote Sivanthi, Sittravell Pal, Umapada Lu, Yue Liu, Cheng-Lin Ayub, Mohamad Nizam Bin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy Beijing China

ISBN: (纸本)9783031090363

Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate. © 2022, Springer Nature Switzerland AG.

关键词： Image classification

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Bio-inspired Sky Polarized Orientation based on the atmospheric polarization symmetry mode and Inertial Sensors 3

Bio-inspired Sky Polarized Orientation based on the atmosphe...

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2023 3rd international conference on Computer, Remote Sensing and Aerospace, CRSA 2023

作者： Chen, Zhihe Teng, Lianwei Zhou, Wenzhou He, Xiaofeng Fan, Chen College of Intelligence Science and Technology National University of Defense Technology Changsha China

This paper presents a sky polarized orientation method based on the symmetry of the atmospheric polarization mode and the inertial sensors. Firstly, a method of extracting solar meridians based on canny operator and Hough Transform is proposed by using the symmetry of atmospheric polarization pattern. Then using the horizontal Angle information of the inertial sensor and the ephemeris information of the sun, a method for solving the yaw Angle of the polarized light is designed. Finally, a single axis rotation experiment and a vehicle-mounted dynamic experiment are carried out to verify and compare the proposed method. Compared with the least square method, the positioning accuracy of the single-axis turntable experiment is improved by 22.61%∼73.86%, and that of the vehicle-mounted dynamic experiment is improved by 47.71%, which shows that the method can provide a new orientation means for unmanned vehicles and unmanned aerial vehicles. © Published under licence by IOP Publishing Ltd.

关键词： Hough transforms

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Interval Type-2 Fuzzy-Support Vector Regression in Representation of Uncertainty in a Non-linear System 15th

Interval Type-2 Fuzzy-Support Vector Regression in Represent...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Umoh, Uduak Eyoh, Imo Asuquo, Daniel Murugesan, Vadivel Alimot, Olanrewaju Department of Computer Science University of Uyo PMB 1017 Akwa Ibom Uyo Nigeria TEFTFund Centre of Excellence in Computational Intelligence Research University of Uyo Uyo Nigeria School of Management National Institute of Technology Karnataka Surathkal India VIT Business School Vellore Institute of Technology Vandalur - Kelambakkam Road Chennai6000127 India

ISBN: (纸本)9783031810824

machine learning algorithms such as Support Vector machine (SVM and Support Vector Regression (SVR) are faced with challenges when confronted with imprecise and noisy data, which can lead to less meaningful outcomes. This paper introduces Interval Type-2 Fuzzy Support Vector Regression (IT2FSVR) as a solution to address uncertainty in non-linear systems. By combining Interval Type-2 Fuzzy Sets (IT2FS) and SVR, the proposed method enhances performance in systems with high levels of noise and non-linearity. The integration of IT2F membership in SVR directly tackles uncertainty in prediction problems, enabling adaptive learning to varying inputs and improving generalization performance. To demonstrate the effectiveness of this approach, the authors tested the performance of IT2F-SVR using a dataset of cardiovascular disease patients. Experimental results demonstrate that IT2F-SVR effectively eliminates uncertainty and significantly improves the learning process, outperforming individual approaches when applied to the same dataset and achieving faster execution times compared to some alternatives, albeit taking more time than SVR. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Membership functions

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Atrial Fibrillation Dynamic Features recognition using 1-D Convolutional Neural Networks 3

Atrial Fibrillation Dynamic Features Recognition using 1-D C...

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3rd IEEE international Biomedical Instrumentation and Technology conference, IBITeC 2023

作者： Ong, Yik Hern Abdul-Kadir, Nurul Ashikin Mahmood, Nasrul Humaimi Wahab, Muhammad Amin Abd Heng, Wei Wei Chan, Weng Howe Embong, Abdul Mutalib Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Bahru Malaysia University of Southampton Malaysia Connected Intelligence Research Group Iskandar Puteri Malaysia Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia Universiti Malaysia Terengganu Pusat Pendidikan Asas Dan Lanjutan Terengganu Malaysia

ISBN: (纸本)9798350302424

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia that requires accurate detection for effective management and treatment. This study aims to propose a simple yet effective one-dimensional convolutional neural network (1D-CNN) for the AF dynamic features recognition which are extracted using a second-order dynamic system (SODS) concept. The SODS concept utilizes a dynamic system approach to capture the short-term behavior of ECG signals. It employs a second-order differential equation to model the ECG signals based on parameters such as the natural frequency (ω), damping coefficient (ξ), and forcing input (u) to enable the characterization and differentiation of ECG signal. The 1D-CNN model was trained and validated with preprocessed ECG signals from the MIT-BIH Atrial Fibrillation and MIT-BIH Normal Sinus Rhythm database. The results demonstrate the efficacy of the SODS and 1D CNN method for AF recognition. The proposed approach achieves high accuracy, with an accuracy of 98.35% using natural frequency, ω feature. © 2023 IEEE.

关键词： Electrocardiograms

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