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Enhanced Activity recognition Through Joint Utilization of Decimal Descriptors and Temporal Binary Motions 16th

Enhanced Activity Recognition Through Joint Utilization of D...

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16th international conference on Computational Collective Intelligence (ICCCI)

作者： Gnouma, Mariem Yahia, Samah Ejbali, Ridha Zaied, Mourad Res Team Intelligent Machines Gabes Tunisia Modelisat Anal & Control Syst Gabes Tunisia Univ Gabes Natl Engn Sch Gabes St Omar Ibn El Khattab Gabes 6029 Zrig Tunisia

ISBN: (纸本)9783031708183;9783031708190

Human Action recognition (HAR) has been a prominent area of research within machine learning over the last few decades. Its applications span domains such as visual surveillance, robotics, and pedestrian detection. Despite the numerous techniques introduced by computer vision researchers to address HAR, persistent challenges include dealing with redundant features and computational cost. This paper specifically addresses the challenge of silhouette-based human activity recognition. While previous research on silhouette-based HAR has predominantly focused on recognition from a singular perspective, the aspect of view invariance has often been overlooked. This paper presents a novel framework that aims to achieve view-invariant Human Action recognition. The proposed approach integrates a pre-processing stage based on the extraction of multiple 2D Differential History Binary Motions (DHBMs) from spatio-temporal frames capturing human motion. These multi-batch DHBMs are then used to capture and analyse human behaviour using the Decimal Descriptor pattern (DDP) approach. This strategy enhances the extraction of intricate details from image data, contributing to a more robust HAR methodology. The selected features are processed by the Sparse stacked Auto-encoder (SSAE), a representative of deep learning methods, to provide effective detection of human activity. The subsequent classification is performed using Softmax. The experiments are conducted on publicly available datasets, namely IXMAS and KTH. The results of the study demonstrate the superior performance of our methodology compared to previous approaches, achieving higher levels of accuracy.

关键词： Human activities recognition Differential History of Binary Motion Decimal Descriptor pattern Deep learning

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A Review on Coconut Disease Detector Using Various Deep learning Techniques 1

A Review on Coconut Disease Detector Using Various Deep Lear...

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1st international conference on Computing, Sciences and Communications, ICCSC 2024

作者： Joshi, Kapil Singh, Vineeta Chamoli, Sushant Kiruthigha, R. student, Rohak Tiwari Suman, Shreya Uttaranchal Institute of Technology Uttaranchal University Department of CSE Dehradun India Chhatrapati Shahu Ji Maharaj University Department of Computer Science and Engineering Uttar Pradesh U.P. Kalyanpur Kanpur208024 India Graphic Era Hill University Graphic Era Deemed to be University Department of CSE Uttarakhand Dehradun248007 India SRM Institute of Science and Technology Ramapuram Tamilnadu Chennai India Delhi Public School RK Puram New Delhi India

ISBN: (纸本)9798350353648

The most significant and primary source of income in the economy of the southern region of India is the production of coconuts. Recent observations have shown that the majority of coconut trees are afflicted with diseases, which progressively weaken and reduce the amount of coconut produced. Diseases caused by pests and deficiencies in nutrients affect the majority of tree leaves. Our primary focus is on improving the livelihood of coconut leaves and early disease detection to maximize the benefits of coconut production for farmers. Monitoring of coconut leaves has been done with the assistance of pesticides and fertilizers - the best methods for image processing and machine learning. Automatic recognition will prove advantageous and the quickest method for accurately identifying diseases in coconut leaves, as opposed to relying on human experts. The most suitable classifiers were determined to be SVM and CNN, with accuracy rates of 93.54% and 93.72%, respectively. The project's results will undoubtedly revolutionize the agriculture industry and assist farmers in increasing their output of coconuts. © 2024 IEEE.

关键词： Plant diseases

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Efficiency of machine learning (ML) Models and Medical Imaging in Disease Detection 1

Efficiency of Machine Learning (ML) Models and Medical Imagi...

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1st international conference on Healthcare Innovations, Software and Engineering Technologies, HISET 2024

作者： Bhoite, Amol Patil, Virendra C. Bhinge, Somanath D. Babu, P Ashok Bharat, Penta Kota Laxmi Agrawal, Raghav Department of Radio-Diagnosis Karad India Department of Medicine Karad India Rajarambapu College of Pharmacy Department of Pharmaceutical Chemistry Maharashtra India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350360707

The analysis of illness pictures generated by using high-tech digital instruments forms the basis of a diverse array of medical diagnoses. The use of artificial intelligence (AI) in the evaluation of medical images has enabled precise assessments to be carried out automatically, which has reduced the workload of doctors, cut down on errors and the time it takes to diagnose patients, and enhanced prediction, diagnosis, and treatment planning using AI methods based on medical image processing has a significant influence on decision-making processes. The healthcare system uses M) and deep learning (DL) as two key subfields of advanced AI approaches to diagnose diseases, find treatments, and detect patient risk factors. In this study, we examine challenges encountered while creating and evaluating procedures. © 2024 IEEE.

关键词： Adversarial machine learning

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Navigating the Edge: A Comprehensive Comparison of machine learning Algorithms on Prominent Edge Platforms 4th

Navigating the Edge: A Comprehensive Comparison of Machine L...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Sayed, Aya Nabil Al-Hasan, Tamim Mahmud Bensaali, Faycal Nhlabatsi, Armstrong Qatar Univ Dept Elect Engn Coll Engn Doha Qatar Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar

ISBN: (纸本)9783031821493;9783031821509

As the demand for deploying machine learning (ML) and Deep learning (DL) models at the network's edge continues to grow, it becomes imperative to evaluate and compare the performance of various edge platforms. This study introduces an innovative edge-based pipeline for analyzing home energy and environmental data and addressing challenges in managing imbalanced label distributions. The comparison between random under-sampling and over-sampling strategies reveals the superiority of over-sampling, significantly improving accuracy and f1-score. More specifically, over-sampling using the Decision Tree (DT) technique enhanced accuracy and f1-score by 4.97% and 9.24%, respectively, when compared to the original results. The paper expands its contribution by conducting a thorough performance evaluation of various edge platforms, emphasizing critical metrics. The evaluated edge platforms include a Raspberry Pi 4b+, Jetson Nano Developer Kit, Odroid XU4, and Coral Dev Board. The study conducted an exhaustive comparison of the runtime of these prominent edge platforms when running ML/DL algorithms for tabular and image datasets during the testing phase. The Jetson Nano, particularly with its built-in GPU, demonstrates superior performance in ML/DL tasks, showcasing accelerated processing times for Convolutional Neural Network (CNN)-based algorithms. These findings emphasize the efficacy of the Jetson Nano in handling computationally intensive tasks for edge-based applications.

关键词： Edge Computing machine learning Deep learning Energy Efficiency

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machine learning Method for Microplastic Identification Using a Combination of machine learning and Raman Spectroscopy 1

Machine Learning Method for Microplastic Identification Usin...

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1st IEEE international Symposium on Consumer Technology, ISCT 2024

作者： Ula, R.K. Laila, Nafisah Nur Risnandar De Wibowo Muhammad Sidik, Aryo Junfithrana, Anggy Pradiftha Alzahra, Tasqia Nusa Putra University Research Center for Electronics Nat. Research and Innovation Agency Dept. of Electrical Engineering Sukabumi Indonesia State University of Surabaya Departement of Physics Surabaya Indonesia Nusa Putra University R. C. for Artf. Intell. & Cybersecurity Nat. Research and Innovation Agency Dept. of Electrical Engineering Sukabumi Indonesia Nusa Putra University Dept of Elecrical Engineering Sukabumi Indonesia

ISBN: (纸本)9798350365191

Plastic fragments that contain microplastic can have an impact on ecosystems and human health. Since the 1970s, the complexity of microplastic analysis has been a big challenge. This study introduces a machine learning-based approach, which has demonstrated a faster and safer microplastic analysis. In the evaluation, we compare human annotation, machine learning, and image processing methods by using the structural similarity index (SSIM), which shows overall alignment results, except for the Grogol River sample (2). The performance of machine learning is satisfactory, but there are discrepancies in detecting microplastic types in this sample. Notably, the study identifies the potential PP and LDPE microplastics and diverges from human annotation. Our proposed methods are highlighted in the results, which demonstrate 78 © 2024 IEEE.

关键词： Adversarial machine learning

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Advancements in Facial image processing for Human Age and Gender Identification 1

Advancements in Facial Image Processing for Human Age and Ge...

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1st international conference on Computing, Sciences and Communications, ICCSC 2024

作者： Gupta, Shivangi Adhikari, Sachin Zaid, Md Shams Verma, Ankit Bhatia, Suman Dr. Akhilesh Das Gupta Institute of Professional Studies Department of Artificial Intelligence and Machine Learning Delhi India

ISBN: (纸本)9798350353648

With the increased usage of social media and associated cyber frauds, current scenario demands for authenticated information. This in turn necessitates for the valid and verified information of individuals from various resources which are available online. Various examples of cyber fraud include: fake banking transactions, unauthorized access to various organizations' resources, fake online proxy for absentees through biometric system etc. To counter such issues, in this paper we have targeted facial image processing for Precise age and correct gender recognition which are the most crucial factors in the digital era. Thus, in this study, we have constructed two distinct approaches to figure out human age and gender from facial with a satisfactory runtime and efficiency. Our method uses image processing to examine specific features from facial images of people in different age groups that have been adjusted. It then creates binary masks, performs edge detection, and measures wrinkle densities. To make assessing easier, we developed the ADIENCE facial database, containing images of males and females spanning various age ranges. The database holds twenty-six thousand plus photos featuring more than two thousand individuals sorted by gender and grouped into eight age categories. This special database has been used to train computer program for identification of individual's face based on their age and gender. This has been done by using Convolutional Neural Network (CNN) which makes it easier for program to recognize age and gender from pictures of people's faces. This is definitely going to help in various domains where security is top concern. This can also be helpful in healthcare domain for medical diagnoses. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep learning Algorithm and Video image processing-based Basketball Training System 1

Deep Learning Algorithm and Video Image Processing-based Bas...

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1st IEEE international conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Meng, Haili Chongqing Vocational Institute of Engineering Chongqing China

ISBN: (纸本)9798350323795

The training of basketball players is a complex and hard work. In the game, how to finish the ball quickly and accurately, and how to improve the goal rate and score through correct and effective methods. This paper first introduces the deep learning algorithm and analyzes its basic idea, then expounds the data mining based on the pixel method and the preprocessing of the original data from the two aspects of feature extraction and modeling, and finally uses the experimental results to prove that MATLAB can be applied to the video retrieval, motion recognition and other fields in the basketball training system to achieve efficient basketball game score statistics and teaching guidance. © 2023 IEEE.

关键词： learning algorithms

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Investigation of machine learning Algorithms for pattern recognition in image processing 5

Investigation of Machine Learning Algorithms for Pattern Rec...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Kate, Chennaiah Kalpana, C. Sharma, Arvind Yadav, Ajay Singh Kumar, Ashok Kumar, S. Sandeep St. Peter's Engineering College Department of Information Technology Telangana Hyderabad500100 India Npr College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Dindigul624401 India Government Women Engineering College Department of Electronics and Communication Engineering Rajasthan Ajmer305002 India Srm Institute of Science and Technology Department of Mathematics Uttar Pradesh Ghaziabad201204 India BanasthaliVidyapith Department of Computer Science Rajasthan 304022 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh 522502 India

ISBN: (纸本)9798350321425

In order to recognize patterns in images, this study tests the performance of many 'machine learning algorithms' and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods ('deep learning, support vector machines, decision trees, and random forests') and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its 'accuracy, precision, recall, and F1 score', among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms. © 2023 IEEE.

关键词： Feature extraction

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Application of Band Combination in Landslide Identification of Remote Sensing images Driven by Deep learning 2

Application of Band Combination in Landslide Identification ...

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2nd international conference on image processing, Computer Vision and machine learning, ICICML 2023

作者： Yang, Tianyu Xu, Weijie Hua, Yu Chengdu University of Technology College of Computer Science and Cyber Security Oxford Brookes College Chengdu China

ISBN: (纸本)9798350331417

With the frequent occurrence of geological disasters, landslide identification has become an important research problem. In recent years, with the application and research of deep learning in the fields of computer vision and remote sensing image analysis, deep learning has become a popular method for landslide identification research because of its excellent feature extraction and pattern recognition capabilities. In this paper, we add new bands reflecting vegetation and water of landslide factors to the original bands of remote sensing image samples by linear combination of bands, and conduct two sets of comparison experiments on Unet and Swin-Unet to verify that adding band features can help improve landslide identification accuracy. The experimental results show that the experimental group with additional band features has a numerical improvement in landslide identification accuracy, with F1-score as the evaluation index, the Unet experimental group has a 2.29% improvement and the Swin-Unet experimental group has a 1.78% improvement. The results of this paper have implications for the subsequent application of bands combination of landslide factor features in remote sensing landslide identification driven by deep learning methods. © 2023 IEEE.

关键词： deep learning landslide identification linear combination of bands remote sensing

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Comparative study of image processing Neural Networks using Face Mask Dataset 1

Comparative Study of Image Processing Neural Networks using ...

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1st IEEE international conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Sunny Jindal, Rajni Delhi Technological University Department of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350348057

Over the years there has been huge improvements in the performance of image processing Algorithms due to increase in computation power of Devices as well as use of Neural Networks. This paper focuses on comparison of size and performance of different image Classification networks used widely. Each one of the machine learning models studied presents a new technique to process the input images that might be useful in different scenarios. Our main focus is to identify the framework with low latency, high accuracy as they can be used practically in surveillance equipment's that need to be fast and accurate. © 2023 IEEE.

关键词： Deep neural networks

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