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A Transformer Based Indian Signed Language recognition 1

A Transformer Based Indian Signed Language Recognition

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1st international conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Saproo, Varun Aggarwal, R.K. National Institute of Technology Kurukshetra Dept. of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350365016

Effective communication is essential to human interaction. The people with hearing and speaking disability primarily rely on Signed Language for communication. Each Sign Language has its own grammar and vocabulary. Individuals who can speak and hear proficiently often struggles to understand and respond to Sign Language gestures, creating a barrier to meaningful communication. This paper attempts to bridge the communication gap by using sophisticated deep learning models like transformers. INCLUDE is a dataset tailored for the Indian population, focusing specifically on Indian Sign Language. Our work involves breaking down videos into frames and employing image processing techniques. Furthermore, we utilize pre-trained models, such as VGG16, EfficientNet, and MobileNet, to extract latent embedding, which are then processed through Transformer network for classification. Hyperparameter tuning is used to optimize model performance. This allowed us to reach an accuracy of 92 % which is better than existing approaches. The proposed method was also tested on LSA64 dataset giving an impressive accuracy of 99.34%. © 2024 IEEE.

关键词： Deep learning

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Enhanced Camouflage Detection Using Advanced image processing Techniques for Real-Time Object recognition and pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy image processing Surveillance image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Design of a Multisensor System for a Smart Cooking Assistant 1

Design of a Multisensor System for a Smart Cooking Assistant

引用

1st IEEE international conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Ruiz, Gustavo George, Kiran Kilambi, Sai Chaitanya Soni, Pratishtha Panangadan, Anand Electrical and Computer Engineering Department California State University Fullerton FullertonCA92831 United States Computer Science Department California State University Fullerton FullertonCA92831 United States

ISBN: (纸本)9798350371987

Homeless people and people who have only recently escaped homelessness are at risk of being malnourished due to several factors. Personalized cooking recipe recommendation systems are one technology-based approach to provide information to such populations that can assist them with making more informed choices about their food. Current recipe recommendation systems do not provide any guidance on how to cook for a person who has limited experience with cooking. This work describes a system that acts as a smart cooking assistant. The main idea is for the system to observe the user perform the steps of cooking based on a recipe, and then provide automatic reminders on when to move to the next step. The system consists of both hardware and machine learning-based software components. The hardware consists of a camera, infrared thermal camera, and temperature sensor. These are integrated around a Raspberry Pi mini-computer. This suite of sensors is mounted over a stovetop and constantly monitors the cooking area, specifically the area where a cooking pot is over the stovetop. Convolutional Neural Network-based image processing algorithms are used to analyze the sequence of images from the cameras to identify the stage of cooking that the user is performing. ©2024 IEEE.

关键词： machine learning

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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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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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