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检索条件"任意字段=International Conference on Pattern Analysis and Intelligence Robotics"

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Leveraging Machine Learning Algorithms to Optimize Diet Maintenance Based on User Food Intake and Fitness Data

Leveraging Machine Learning Algorithms to Optimize Diet Main...

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Sustainable Communication Networks and Application (ICSCNA), international conference on

作者： K. Clarinda N. Deepa R. Dhamotharan R. Santhana Krishnan J. Relin Fransis Raj J. V. Anchitaalagammai Department of Management Studies SCAD College of Engineering and Technology Cheranmahadevi India Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The increasing prevalence of lifestyle-related health issues has necessitated the development of innovative approaches to diet management. This research introduces the Adaptive Eating pattern Predictor (AEPP), a novel machine learning algorithm designed to optimize diet maintenance by analyzing user food intake and fitness data. The AEPP combines the predictive capabilities of Artificial Neural Networks (ANN) with the probabilistic modeling of Markov Chains, enabling accurate forecasting of future eating patterns based on historical dietary and activity data. A comprehensive simulation analysis was conducted to evaluate the performance of the AEPP against existing algorithms, including traditional recommendation systems and basic machine learning models. Various simulation metrics, such as accuracy, precision, recall, and F1 score, were employed to compare the effectiveness of the AEPP in providing personalized dietary recommendations. The results demonstrate that the AEPP significantly outperforms existing algorithms, highlighting its potential as a valuable tool for individuals seeking to maintain a balanced diet in alignment with their fitness goals. This study contributes to the field of nutritional science by showcasing the advantages of integrating advanced machine learning techniques in personal diet management.

关键词： Adaptation models Machine learning algorithms Accuracy Adaptive systems Collaboration Artificial neural networks Predictive models Prediction algorithms Maintenance Wearable devices

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Performance analysis of Convolutional Neural Network Architecture in Batik Design Classification for Mobile Application

Performance Analysis of Convolutional Neural Network Archite...

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Industrial Electrical and Electronics (ICIEE), international conference on

作者： Alimuddin Ria Arafiyah Irfansyah Nur Aviatna Mukhtar Heri Haryanto Masjudin Department of Electrical Engineering Universitas Sultan Ageng Tirtayasa Cilegon Banten Indonesia Computer Science Faculty of Natural Science and Mathematics Universitas Negeri Jakarta Jakarta Indonesia Department of Statistics Universitas Sultan Ageng Tirtayasa Cilegon Banten Indonesia

ISBN: (数字)9798331519216

ISBN: (纸本)9798331519223

Indonesia, an archipelago rich in cultural heritage, is home to batik, a traditional textile art recognized by UN-Esco. Batik is known for its distinctive and intricate patterns, which have evolved over time and remain an important part of Indonesian cultural identity. Batik's unique pattern characteristics and thousands of patterns make it an interesting subject to study. The purpose of this study is to analyze the performance of different artificial neural network architectures, such as MobileNet, MobileNetV2, MobileNetV3, EfficientNetV2, and ShuffieNetV2, with classifying batik designs for mobile applications. This study evaluated and compared how well each architecture does in classifying batik designs. Testing involves implementing and training artificial neural networks with a dataset of batik designs. The research compared five pre-trained CNN architectures: MobileNet, MobileNetV2, MobileNetV3, EfficientN et V2, and ShuffleNetV2 for classifying batik patterns. The EfficientNetV2 architecture outperformed others, achieving the highest validation accuracy of 85.93% and an F1-score of 86%. MobileNet architecture followed closely with 83.32% validation accuracy and an F1-score of 83%. The results underline the effectiveness of CNN-based transfer learning in cultural heritage preservation, offering a robust method for accurately classifying intricate batik patterns. It is hoped that this research will help in the development of effective and appropriate technologies for using artificial intelligence in the context of art and culture.

关键词： Training Performance evaluation Accuracy Art Transfer learning Mobile handsets Image augmentation Mobile applications Convolutional neural networks Cultural differences

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A System to Identify Walking pattern Using Machine Learning for a Load Reduction Exoskeleton Robot 18

A System to Identify Walking Pattern Using Machine Learning ...

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IEEE international conference on Mechatronics and Automation (IEEE ICMA)

作者： Zhao, Hanqing Nambo, Hidetaka Kanazawa Univ Grad Sch Nat Sci & Technol Artificial Intelligence Lab Elect Engn & Comp Sci Kanazawa Ishikawa Japan

ISBN: (纸本)9781665441018

In this study, to realize the gait recognition and gait prediction of Load reduction exoskeleton robot in the walking process. Firstly, we designed a plantar pressure measurement device. The information about walking posture of human legs, feet, and the whole body can be reflected by the magnitude and distribution of plantar pressure. The plantar pressure measurement device is used to collect data and generate the required training datasets. Gait recognition and gait prediction analysis are obtained by using deep neural networks and recurrent neural networks for gait recognition and gait prediction. Eventually, it can be applied to the control system of exoskeleton walking and abnormal gait prediction.

关键词： Robotic exoskeleton systems Deep Learning IoT man-machine intelligent system Walk Assist

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Big Data and Business intelligence - A Data Driven Strategy for Business in Bosnia Herzegovina

Big Data and Business Intelligence - A Data Driven Strategy ...

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IEEE conference on Systems, Process & Control (ICSPC)

作者： Anes Kelić Ali Almisreb Nooritawati Md Tahir Jamil Bakri Faculty of Engineering and Natural Sciences International University of Sarajevo Sarajevo Bosnia and Herzegovina College of Engineering Universiti Teknologi MARA Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Malaysia Integrative Pharmacogenomics Institute (iPROMISE) Universiti Teknologi MARA Selangor Malaysia Worldwide Green Berhad Selangor Malaysia

ISBN: (纸本)9781665470995

Management accounting is the central piece that controls organisations’ decision-making based on management accounting, yet little research focuses on this area. Hence this study, object classification, descriptive analysis and pattern recognition are implemented to investigate the correlation between decision-making and business intelligence. The data acquisition is from organisations in Bosnia and Herzegovina, specifically from a Structured Query Language Database and quantitative approach. Further, the statistical data generated patterns of sales and expenses that can be recognised, marking down the organisation might have to take a different approach which is based on 55000 transactions accumulated over the past six years. These transactions are then normalised into a standardised format and imported into the Atoti BI with Python. Initial findings showed that based on numerical analysis, the revenue and total revenue increased by 10% in the product price.

关键词： Management accounting Structured Query Language Numerical analysis Filtering Databases Decision making Turning

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Using AI to Detect Android Malware Families

Using AI to Detect Android Malware Families

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international Workshop on Design of Reliable Communication Networks (DRCN)

作者： Saed Alrabaee Mousa Al-kfairy Mohammad Bany Taha Omar Alfandi Fatma Taher Ahmed Hashem El Fiky College of IT UAE University Al Ain UAE College of Technological Innovation) Zayed University Abu Dhabi UAE Data Science and Artificial Intelligence American University of Madaba Madaba Jordan Business Administration VERN’ University of Applied Sciences Zagreb Croatia

ISBN: (数字)9798350348972

ISBN: (纸本)9798350348989

In today's digital era, many smartphone users often overlook security measures when installing apps, leaving Android devices particularly vulnerable to mal ware threats. Addressing this critical issue, there is a significant interest in leveraging Machine Learning (ML) and Deep Learning (DL) as proactive approaches for detecting and classifying Android mal ware, thus aiming to safeguard mobile and loT sectors. This study evaluates the effectiveness of data-driven methods in identifying and cate-gorizing Android malware families, specifically focusing on two advanced models: the 2-D Convolutional Neural Network (CNN) and Random Forest, which are essential for pattern recognition and decision-making. By utilizing a comprehensive dataset of Android malware, our research contrasts these models' performances and unexpectedly finds that Random Forest outperforms CNN, challenging the latter's reputed superiority in complex classification scenarios. This surprising result highlights Random Forest's efficacy in cybersecurity and underscores the potential of ensemble learning methods in this domain, suggesting new directions for future research in malware detection strategies. Our findings contribute to the cybersecurity field by enhancing understanding of ML and DL applications in malware detection and underscore the necessity for continuous exploration into more intricate scenarios and advanced learning methodologies to stay ahead of evolving cyber threats, especially within the Android ecosystem. This research not only opens new avenues for developing sophisticated and tailored MLIDL models but also significantly contributes to bolstering the security of mobile and loT devices, marking a significant step forward in the ongoing battle against malware.

关键词： Deep learning Biological system modeling Static analysis Forestry Reliability engineering Malware pattern recognition

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Crop Classification on Hyperspectral Images Using Residual Network and Multi-Head Self-Attention Feature Extraction

Crop Classification on Hyperspectral Images Using Residual N...

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Data Science and Network Security (ICDSNS), international conference on

作者： N. Naga Saranya V. Vivekanandhan Mohammed Al-Farouni A V Shreyas Gotte Ranjith kumar Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India Department of Computer Science and Engineering Malla Reddy College of Engineering Secunderabad Hyderabad India Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Nowadays, hyperspectral imaging has empowered classification and prediction in many fields such as agriculture, land usage, tourism, etc. due to advancements in deep learning. This research focuses on obtaining efficient crop classification using Residual Network, Multi-Head Self- Attention, Convolutional Neural Network (ResNet-MHSA- CNN) approach. The Adaptive Wiener Filtering (AWF) is applied in preprocessing to eliminate noise, handles blurred parts of images effectively and decreases error rate in inverse filtering. The Residual Network (ResNet) method is employed by implement residual learning to eliminate degradation issues of deep neural networks. The high-resolution depth features of hyperspectral images are extracted by residual learning ResNet network. The Multi-Head Self-Attention (MHSA) utilized to capture long-range dependencies and global context information and helps the model in learning diverse patterns. Finally, the extracted features are given to Convolutional Neural Network (CNN) with complex pattern learning ability that excels in image classification with multiple layers of filters. The proposed ResNet-MHSA-CNN approach is evaluated with Indian Pines dataset and achieved 99.74%, 99% accuracy and kappa value respectively. The proposed method outperformed state-of-art methods such as Cross-Mixing Residual Network of Convolutional Neural Network (CMR-CNN), Deep Stacked Denoising Autoencoder (DSDA).

关键词： Degradation Accuracy Filters Error analysis Noise Crops Feature extraction Convolutional neural networks Residual neural networks Hyperspectral imaging

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Rescaled Hinge Loss Function based Convolutional Neural Network for Identification of Art Forgery by Image analysis

Rescaled Hinge Loss Function based Convolutional Neural Netw...

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Software, Systems and Information Technology (SSITCON), international conference on

作者： K. Sundravadivelu S Karthika Ramy Riad Al-Fatlawy Jothi. R Monisha Mandapati Venkata Rama Sundari Department of Computer Science School of Information Technology Madurai Kamaraj University Madurai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering (DS) Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The rapid growth of Information Technology (IT) as well as Computer Science becomes an important evolution in marketable painting applications. The systematic authentication of paintings embraces the implication into the monarchy of art gathering. Performing the Convolutional Neural Network (CNN) approach for the identification of the reliable as well as forged painting arts according to the colour images is a feasible but trivial selection. However, this is challenging due to variations in artistic styles, textures, and subtle details which ambiguous forgery detection. Hence, this research proposes the Rescaled Hinge Loss Function based CNN (RHLF-CNN) approach for the identification of art forgery. The RHLF improves the model’s capability to discriminate between genuine and forged artworks, leads to more precise classification. The high-definition and hyperspectral image dataset of Chinese characters is utilized to estimate the effectiveness of the RHLF-CNN approach. The Local Binary pattern (LBP) approach is used for the feature extraction process. The Chi-square approach is utilized for extracting the relevant features from the extraction process. The experimental results demonstrates that the proposed RHLF-CNN approach attains the better accuracy of 93.16% as compared to the existing approaches like Pulse-Coupled Neural Network (PCNN).

关键词： Adaptation models Art Authentication Fasteners Feature extraction Forgery Convolutional neural networks Information technology Painting Hyperspectral imaging

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DC Side Voltage Monitoring Model of Transformer based on pattern Recognition

DC Side Voltage Monitoring Model of Transformer based on Pat...

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2020 IEEE international conference on Industrial Application of Artificial intelligence, IAAI 2020

作者： Liu, Xian-Zhong Shao, Wen-Mian School of Robotics Engineering Harbin Huade University Harbin China Engineering Training and Basic Experiment Center Heilongjiang University of Science and Technology Harbin China

ISBN: (数字)9781665404716

ISBN: (纸本)9781665404716

Aiming at the problem that the transformer DC side voltage is affected by the voltage quality during the monitoring process, its monitoring performance is greatly reduced. In order to improve the monitoring effect of the transformer DC side voltage, a transformer DC side voltage monitoring model based on pattern recognition is designed. pattern recognition based on the RMS method using DC voltage transformer discrete signal processing using a pure sinusoidal AC signal with the average RMS mathematical relationship, by calculating the average of the AC voltage, indirectly, of the DC voltage transformer Valid values. From the OPP-CI model that ignores the influence of the ZIB node and the OPP-CI model that considers the influence of the ZIB node, pattern recognition is used to establish the transformer DC side voltage OPP-CI model, and the transformer DC side voltage monitoring process design is combined to realize the transformer DC Monitoring of side voltage. The experimental results show that the error of the monitoring results of the proposed model is relatively low, indicating that the method can improve the monitoring effect of the transformer DC side voltage(Abstract). © 2020 IEEE.

关键词： pattern recognition

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16th international conference on Hybrid Artificial Intelligent Systems, HAIS 2021

16th International Conference on Hybrid Artificial Intellige...

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16th international conference on Hybrid Artificial Intelligent Systems, HAIS 2021

ISBN: (纸本)9783030862701

The proceedings contain 55 papers. The special focus in this conference is on Hybrid Artificial Intelligent Systems. The topics include: Waste Collection Vehicle Routing Problem Using Filling Predictions;optimization of Warehouse Layout for the Minimization of Operation Times;Simple Meta-optimization of the Feature MFCC for Public Emotional Datasets Classification;open-Ended Learning of Reactive Knowledge in Cognitive robotics Based on Neuroevolution;managing Gene Expression in Evolutionary Algorithms with Gene Regulatory Networks;Evolutionary Optimization of Neuro-Symbolic Integration for Phishing URL Detection;interurban Electric Vehicle Charging Stations Through Genetic Algorithms;preface;document Similarity by Word Clustering with Semantic Distance;an Effective Hybrid Genetic Algorithm for Solving the Generalized Traveling Salesman Problem;a Simple Genetic Algorithm for the Critical Node Detection Problem;window Size Optimization for Gaussian Processes in Large Time Series Forecasting;a Binary Classification Model for Toxicity Prediction in Drug Design;structure Learning of High-Order Dynamic Bayesian Networks via Particle Swarm Optimization with Order Invariant Encoding;prototype Generation for Multi-label Nearest Neighbours Classification;learning Bipedal Walking Through Morphological Development;ethical Challenges from Artificial intelligence to Legal Practice;classification of Burrs Using Contour Features of Image in Milling Workpieces;Adaptive Graph Laplacian for Convex Multi-Task Learning SVM;PSO-PARSIMONY: A New Methodology for Searching for Accurate and Parsimonious Models with Particle Swarm Optimization. Application for Predicting the Force-Displacement Curve in T-stub Steel Connections;fight Detection in Images Using Postural analysis;applying Vector Symbolic Architecture and Semiotic Approach to Visual Dialog;transfer Learning Study for Horses Breeds Images Datasets Using Pre-trained ResNet Networks.

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High Resolution Soft Tactile Interface for Physical Human-Robot Interaction

High Resolution Soft Tactile Interface for Physical Human-Ro...

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IEEE international conference on robotics and Automation (ICRA)

作者： Huang, Isabella Bajcsy, Ruzena Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94270 USA

ISBN: (纸本)9781728173955

If robots and humans are to coexist and cooperate in society, it would be useful for robots to be able to engage in tactile interactions. Touch is an intuitive communication tool as well as a fundamental method by which we assist each other physically. Tactile abilities are challenging to engineer in robots, since both mechanical safety and sensory intelligence are imperative. Existing work reveals a trade-off between these principles- tactile interfaces that are high in resolution are not easily adapted to human-sized geometries, nor are they generally compliant enough to guarantee safety. On the other hand, soft tactile interfaces deliver intrinsically safe mechanical properties, but their non-linear characteristics render them difficult for use in timely sensing and control. We propose a robotic system that is equipped with a completely soft and therefore safe tactile interface that is large enough to interact with human upper limbs, while producing high resolution tactile sensory readings via depth camera imaging of the soft interface. We present and validate a data-driven model that maps point cloud data to contact forces, and verify its efficacy by demonstrating two real-world applications. In particular, the robot is able to react to a human finger's pokes and change its pose based on the tactile input. In addition, we also demonstrate that the robot can act as an assistive device that dynamically supports and follows a human forearm from underneath.

关键词： Robot sensing systems Cameras Safety Task analysis Visualization

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