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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on image Analysis and processing, ICIAP 2019

2nd International Workshop on Recent Advances in Digital Sec...

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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on image Analysis and processing, ICIAP 2019

ISBN: (纸本)9783030307530

The proceedings contain 39 papers. The special focus in this conference is on Recent Advances in Digital Security. The topics include: Modern vs Diplomatic Transcripts for Historical Handwritten Text recognition;improving Ancient Cham Glyph recognition from Cham Inscription images Using Data Augmentation and Transfer learning;Oracle Bone Inscription Detector Based on SSD;shot Boundary Detection for Automatic Video Analysis of Historical Films;the Epistle to Cangrande Through the Lens of Computational Authorship Verification;a Cockpit of Measures for image Quality Assessment in Digital Film Restoration;augmented Reality for the Valorization and Communication of Ruined Architecture;classification of Arabic Poems: from the 5th to the 15th Century;a Page-Based Reject Option for Writer Identification in Medieval Books;on the Cross-Finger Similarity of Vein patterns;minimizing Training Data for Reliable Writer Identification in Medieval Manuscripts;fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep learning;slide Screening of Metastases in Lymph Nodes via Conditional, Fully Convolutional Segmentation;A learning Approach for Informative-Frame Selection in US Rheumatology images;a Serious Game to Support Decision Making in Medical Education;nerve Contour Tracking for Ultrasound-Guided Regional Anesthesia;Skin Lesions Classification: A Radiomics Approach with Deep CNN;semantic 3D Object Maps for Everyday Robotic Retail Inspection;collecting Retail Data Using a Deep learning Identification Experience;a Large Scale Trajectory Dataset for Shopper Behaviour Understanding;Improving Multi-scale Face recognition Using VGGFace2;An IOT Edge-Fog-Cloud Architecture for Vision Based Pallet Integrity;the Vending Shopper Science Lab: Deep learning for Consumer Research.

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Vehicle Registration Plate recognition Using YoloV8 and IOT 1

Vehicle Registration Plate Recognition Using YoloV8 and IOT

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1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Nadaf, Juber Kadam, Amol Jadhav, Pramod Kadam, Prasad Patil, Vinod Gaikwad, Milind College of Engineering Department of Computer Pune India College of Engineering Department of CSBS Pune India College of Engineering Department of E & TC Pune India College of Engineering Department of Information Technology Pune India

ISBN: (纸本)9798350355611

Number Plate recognition or the vehicle License Plate recognition is an interesting topic of research in smart cities which utilizes image processing techniques. Since the number of cars is rising at an exponential rate, automated systems are supposed to store vehicle data for a variety of applications. In this intended algorithm, an efficient technique has been identified for recognition of different vehicle number registration plates. Through this technique, we can easily deal with problems like low light, irregular font number plates, wrong angles. We use the method of Optical Character recognition to identify characters that are extracted from the registration plate. Even though this project works efficiently for common use cases, it has its own drawbacks and limitations even though we are not using deep learning or any other machine learning algorithms. One of the important steps in vehicle plate recognition is the character recognition which is a technique to obtain individual text characters. YOLOv8 (You Only Look Once), and Darknet- 53 are the methods that is used to extract the features of the data. In this model, the input data is the image of the vehicle that is captured from the camera sensors or cell phones and later cropped to display only the registration plate. We undergo data-pre-processing to improve the quality or the pixels of image on the number plate. Testing is done using two different models one is the model which is obtained with more pre-processing data and the other obtained without any pre-processing data. We evaluate two datasets of number plate images: one with no interference and one where the colour intensity of the image has been decreased to test, the accuracy of the model © 2025 IEEE.

关键词： Deep learning

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A blind assistant system utilizing object detection for improved spatial awareness 1st

A blind assistant system utilizing object detection for impr...

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1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Eqbal, Shahid Bharadwaj, Prashant Singh, Pratistha Tiwari, Rashmi Department of ECE Galgotias College of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032874036

A prototype of a blind assisting system that utilizes machine learning for real-time object detection and classification to help visually impaired people to navigate independently without relying on external assistance. The program employs image processing and machine learning techniques to accurately detect objects in real-time via the device camera. It subsequently notifies the users about the obstacle and its location through voice output. The prototype is based on SSD-Mobilnet-v2 and provides a viable solution for those with visual impairment, to enhance their quality of life. © 2025 the Author(s).

关键词： image processing Object detection Real time SSD-Mobilenet-v2

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Neuromorphic devices assisted by machine learning algorithms

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international JOURNAL OF EXTREME MANUFACTURING 2025年第4期7卷 042007-042007页

作者： Huo, Ziwei Sun, Qijun Yu, Jinran Wei, Yichen Wang, Yifei Cho, Jeong Ho Wang, Zhong Lin Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 101400 Peoples R China Univ Chinese Acad Sci Sch Nanosci & Engn Beijing 100049 Peoples R China Guangxi Univ Ctr Nanoenergy Res Sch Phys Sci & Technol Nanning 530004 Peoples R China Shandong Zhongke Naneng Energy Technol Co Ltd Dongying Peoples R China Yonsei Univ Dept Chem & Biomol Engn Seoul 03722 South Korea Georgia Inst Technol Atlanta GA 30332 USA

Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. It features parallel interconnected neural networks, high fault tolerance, robustness, autonomous learning capability, and ultralow energy dissipation. The algorithms of artificial neural network (ANN) have also been widely used because of their facile self-organization and self-learning capabilities, which mimic those of the human brain. To some extent, ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations. This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms. First, the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed. Second, the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures. Furthermore, the fabrication of neuromorphic devices, including stand-alone neuromorphic devices, neuromorphic device arrays, and integrated neuromorphic systems, is discussed and demonstrated with reference to some respective studies. The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated. Finally, perspectives, suggestions, and potential solutions to the current challenges of neuromorphic devices are provided. The review discusses the basic structure of simple neuron models inspired by biological neurons and how they process information in simple neural networks, laying the foundation for neuromorphic device *** progress in the fabrication of neuromorphic devices is highlighted, focusing on advancements in materials, structures, and the development of st

关键词： neuromorphic devices machine learning algorithms artificial synapses memristors field-effect transistors

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Vision-Based Autonomous Car Brake and steering Assistance Using Deep learning 1

Vision-Based Autonomous Car Brake and Steering Assistance Us...

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1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Anees, H. Agrawal, Pooja School of Robotics Defence Institute of advanced technology Pune India

ISBN: (纸本)9798350355611

This paper presents a novel approach for enhancing vehicle safety and navigation through an integrated system for lane detection, vehicle alignment, and automatic braking using visual feedback. Our proposed system employs advanced deep learning and computer vision techniques with real-time processing to detect the exact boundaries of lane and ensures precise vehicle movement within the lane. The system continuously analyses lane markings and modifies the vehicle's position to ensure optimal lane adherence by utilizing a combination of machine learning algorithms and camera-based image processing. Additionally, the system incorporates an adaptive braking mechanism that identifies vehicles ahead using visual inputs. Furthermore, the jerks experienced during steering alignment can be greatly reduced by the suggested steering control system. The system's efficiency in various driving conditions is evidenced by its experimental simulation results, which also show improvements in collision avoidance and lane-keeping accuracy. This approach contributes to improved driving convenience and road safety by marking a substantial advancement in autonomous driving technologies. © 2025 IEEE.

关键词： steering

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A Comprehensive study of Deep learning and machine learning Methods for Land Application and Cover Segmentation 1

A Comprehensive Study of Deep Learning and Machine Learning ...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Gupta, Kimmi Raghav, Abhinav Khera, Heena Srinivasan, R. School of Computer Science Engineering & Technology Bennett University Greater Noida India IILM University Greater Noida India IILM University Greater Noida India Galgotias University Greater Noida India

ISBN: (纸本)9798331518578

Research in the Delhi NCR area from 2000 to 2024 is compared in this study. Firstly, a flash flood occurred due to the heavy rain in July 2013. Grasslands are among the vegetative types in L ULC, which cannot be univocally classified as either natural in-land or artificial urban. But some areas of grassland, when it came time to classify L ULC, three different machine learning techniques were considered: random forest, support vector machine, and feedforward neural networks. Optimization The model best for random forest performed better than those two others. The study used statistical analysis tools, the 'sustainable development prism' and 'patch-corridor matrix' models, a landscape index, and time series data to explore how BGS changed in new urban areas. The research made use of Landsat datasets and optimized machine learning algorithms to classify L ULC, extract built-up areas, analyse landscape fragmentation and investigate built-up expansion in Delhi NCR by means of frequency analysis. In the EFPCNNM ensemble deep learning model, convolutional neural networks, feature pyramids, and ensemble learning are combined to increase the accuracy of remote sensing picture classification. The EFPCNNM process includes everything necessary for image analysis: pre-processing, feature extraction, feature pyramid creation, and ensemble learning. Exciting new possibilities for environmental monitoring, urban planning and property mapping emerge from this study. Sustainable urban development and resource management require effective monitoring and interpretation of the changes in L ULC, as well as employing remote sensing data alongside machine learning and deep learning technology. © 2025 IEEE.

关键词： Convolutional neural networks

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Hyperbilirubinemia or jaundice detection using CNN in machine learning 1st

Hyperbilirubinemia or jaundice detection using CNN in machin...

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1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Gupta, Priyanka Upasna Rai, Vishal Department of Computer Science and Engineering ABES Institute of Technology Ghaziabad India

ISBN: (纸本)9781032874036

Jaundice, also called congenital hyperbilirubinemia, is a common condition caused by alterations in an infant’s red blood cell metabolism within the initial week of life. There are many symptoms associated with this multifactor disorder, and we have systems available both invasive and non-invasive. A practical and precise method for monitoring neonatal jaundice using the CNN algorithm has been described in this paper. The method’s accuracy and viability are predicated on the different detection systems examined. The methodologies and detection techniques discussed offer practical insights. To find a linear regression function, the SVR algorithm was used to find linear relationships within an elevated feature space. An approaching nonlinear function was used to map the input data to a scope. The color card is an additional method of detection that allows for the rapid identification of disease fatalities based on skin color and eyes. The old invasive methods of diagnosing jaundice have several drawbacks, including the inability to monitor simultaneously, the length of time required, the trauma to the patient, and the thousands of dollars involved. To reduce the discomfort associated with these techniques, a non-intrusive approach has been devised to identify jaundice in the early months of life, utilizing the image processing technologies and CNN algorithm. © 2025 the Author(s).

关键词： Neonatal monitoring

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A Multi Model Deep learning Framework for Sarcasm Detection in Regional Social Media: An Overview 1

A Multi Model Deep Learning Framework for Sarcasm Detection ...

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1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Bhadgale, Anilkumar M. Maheswari, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350355611

Sarcasm detection in social media is a challenging task due to its inherent reliance on contextual cues, tone, and cultural nuances. In recent years, multi-model deep learning frameworks have emerged as a powerful approach for addressing these challenges, particularly in regional social media, where language variations and local idiomatic expressions complicate the detection process. This survey explores the latest developments in multi-model deep learning frameworks for sarcasm detection, focusing on their application in regional social media. The survey begins by reviewing foundational techniques in sarcasm detection, including traditional machine learning approaches that rely on handcrafted features. These methods, although effective in certain contexts, often fail to capture the subtleties of sarcasm in informal, region-specific languages. The advent of deep learning has led to significant advancements, particularly through models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. These architectures, combined with Natural Language processing (NLP) techniques, have enhanced the ability to identify sarcasm through text analysis. However, single-modal approaches focusing solely on text fail to fully capture sarcasm's multimodal nature, especially on platforms where users often express themselves through a combination of text, images, emojis, and video. This has led to the development of multi-model frameworks that integrate various data modalities, such as text, image, and user behaviour, to better understand the context of sarcastic expressions. In regional social media, where local language and cultural symbols play a crucial role, these multi-model approaches prove even more valuable. This survey highlights key multi-model frameworks, emphasizing their use in regional settings. By examining datasets, model architectures, and evaluation metrics, the survey underscores the importance of combining textual and non-textual

关键词： Convolutional neural networks

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems Engineering, WISE 2024

PhD Symposium, Posters, Demos, and A Web for more inclusive,...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems Engineering, WISE 2024

ISBN: (纸本)9789819614820

The proceedings contain 36 papers. The special focus in this conference is on Web Information Systems Engineering. The topics include: Effective Transparent Monitoring of Personal Data;iCNN-LstM: An Incremental CNN-LstM Based Ransomware Detection System;a Mini Review on Purchaser Security in the Metaverse: Challenges and Solutions;low-Resource Dataset Synthetic Generation for Hate Speech Detection;Web Open Data to SDG Indicators: Towards an LLM-Augmented Knowledge Graph Solution;leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval;time Distance Aware for Multi-component Graph Collaborative Filtering;scientific Documents Recommendation Based on Graph Convolutional Network;semantic Communication of images Using image Generation and image Captioning Models;leveraging Optimization Techniques for Effective Arabic Query Expansion;DURLLCON: Deep Reinforcement learning for URLLC Optimization in Multi-edge Networks;FMM-RNS: A Fast HMM Map Matching Method Based on Road Network Simplification;Context-Aware Selection of machine learning as a Service (MLaaS) in IoT Environments;GraphTFD: A Fraud Detection System Based on Graph Transformer;MLGE-AC-UFD: Multi-level Graph Embedding and Approximate Computation for Unsupervised Fraud Detection;SeCORE: Quantitative Security Assurance and Evaluation Platform;developing Geospatial Web Applications Using Question Answering Engines, Knowledge Graphs and Linked Data Tools;A Visual Query Builder for DBpedia;FL-PPELA: Partial Parameter Enhancement and Local Adaptive Aggregation for Personalized Federated learning;ORCPM: An Online Regional Core pattern Mining System;MTRM: A Web-Miner Multi-Threshold Mining Co-location patterns to Mitigate Redundancy;constrained Path Optimization on Time-Dependent Road Networks;prompt strategies for Sarcastic Meme Detection: A Comparative Analysis;Deepfake Detection in Cancer Medical Imaging Using CNN Architectures;strengthening Cybersecurity: The Influence o

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Efficient Algorithm Analysis Of Kidney images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

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2025 IEEE international students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Thanuja, G.V. Ramya, S. Manipal Institute of Technology Manipal Academy of Higher Education Dept. Electronics and Communication Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798331529833

The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic's flexibility, efficient timing, and adaptability in algorithm modification, coupled with the processing power provided by the embedded processor. Integrating image processing into this sector is an ideal addition, especially in the growing field of edge detection, which is crucial in areas like image pattern recognition, machine learning, and data processing. This paper presents a case study focused on kidney CT scan images for edge detection, utilizing the Sobel and Canny edge detection techniques on the target FPGA device, xc7z020clg484-1. An IP core was designed and generated using VIVADO software, converting image pixels into binary form for pre-processing through these edge detection algorithms. The algorithms were simulated and synthesized on the xc7z020clg484-1 FPGA, with the entire design implemented in Verilog code to create the IP, which connects to the DMA controller throughout the system. This IP core can process multiple CT scan images simultaneously, making it valuable for various biomedical applications. The primary aim of this research is to compare the Sobel and Canny edge detection algorithms to identify the best approach for developing an IP core, evaluated by metrics such as total power consumption, CPU time, execution time, LUT count, and FF usage. The resulting IP core is efficient and conserves resources, making it suitable for other embedded applications. © 2025 IEEE.

关键词： image analysis

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