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SMART FIT- Elevating Fashion with image processing 1st

SMART FIT- Elevating Fashion with Image Processing

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Nayak, Pavana Pratham, R.U. Gupta, Sunny Department of CSE New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9783031823824

Say goodbye to the guesswork of finding the perfect cut! Introducing Smart Fit, revolutionizing the fashion industry with its cutting-edge technology. With just a few taps, select the garment you wish to purchase and within seconds, witness how they seamlessly drape over your unique body shape. No more disappointment or hassle with returning ill-fitting clothes. Smart Fit empowers an individual to take the informed decisions by effortlessly comparing different sizes and styles in real time. Experience the future of online shopping with Smart Fit - where finding the perfect fit is both effortless and enjoyable. Tired of the frustration of returning items purchased online due to sizing issues? Put an end to your sizing woes with Smart Fit. By leveraging image processing, shopping for clothes becomes an entirely new experience. Simply upload the outfit of your choice, and watch as Smart Fit skillfully adjusts it to your body contours. With Smart Fit, selecting the ideal size every time becomes a breeze - no more guesswork or unpleasant surprises. Start making smarter and more confident purchases now with Smart Fit at your fingertips. Join the fashion revolution and embrace a new era of personalized online shopping tailored to your unique style and body shape. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Purchasing

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25th International conference on Internet Computing and IoT, ICOMP 2024, and 22nd International conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

25th International Conference on Internet Computing and IoT,...

引用

25th International conference on Internet Computing and IoT, ICOMP 2024, and 22nd International conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859229

The proceedings contain 33 papers. The special focus in this conference is on Internet Computing and IoT. The topics include: Malware Detection in the IoT Home Network;IoT-Based Analysis of Environmental and Motion Data for Comfort and Energy Conservation in Optimizing HVAC Systems;improving Critical Controls Using IoT and Computer Vision;A Data-Driven Driving Under the Influence (DUI) Detection, Notification and Prevention System Using Artificial Intelligence and Internet-Of-Things (IoT);understanding User Interactions with IoT Process Models: A Demographic Perspective;Advancing IoT Process Modeling: A Comparative Evaluation of BPMNE4IoT and Traditional BPMN on User-Friendliness, Effectiveness, and Workload;Threat Detection Using MLP for IoT Network;harnessing Social Robotics and the Internet of Things to Reduce the Risk of Older Adults Developing Hypothermia and Dehydration;re/Imagining Smart Home Automation Framework in the Era of 6G-Enabled Smart Cities;smart Roadway Monitoring: Pothole Detection and Mapping via Google Street View;Energy-Efficiency Modeling for AI Applications on Edge Computing;optimizing Wireless Sensor Network Node Placement Using Bacterial Foraging Optimization;The Vital Role of Small and Marginal Farmer in Future of Our Climate: Democratization of Machine Learning, Artificial Intelligence, and Dairy Cow Necklace Sensors in Achieving the UN Climate Change Goals (COP21) and the Paris Agreement;autonomous Driving Prototype with Raspberry Pi by Using image processing Technology;towards Implementation of Privacy-Preserving Federated Learning Aggregation Using Multi-key Homomorphic Encryption;advancing Nursing Education Through Virtual Reality Training: A Revolutionary Approach to Ensuring Patient Safety;multiDrone Simulator An Open Source Multi-plataform Tool to Use in Tests of Optimized Flight of Group of Drones;Revolutionizing Multiplayer Gaming: A Deep Dive into VisionXO, a 3D Multiplayer Tic-Tac-Toe Game.

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DeepFake image, Video and Audio Detection 1st

DeepFake Image, Video and Audio Detection

引用

1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Mahima, A.H. Monica, M. Neha, S. Raykar, Deepti Balaji B N M Institute of Technology Bengaluru560 070 India

ISBN: (纸本)9783031823824

In the realm of digital media, the proliferation of deepfake technology has brought forth significant concerns regarding its potential for misuse, particularly in areas such as political disinformation and privacy infringement. To counter these threats, we propose a groundbreaking approach to deepfake analysis that encompasses both image and audio detection. This method integrates advanced techniques such as Res-Next CNN for precise frame-level feature extraction from images and MFCC feature extraction for audio, coupled with LSTM-based RNN for comprehensive temporal analysis across both modalities. This model exhibits a remarkable ability to accurately discern between authentic content and deepfakes in both images and audio recordings. Additionally, our methodology emphasizes adaptability and scalability, ensuring its effectiveness across various digital platforms and evolving deepfake techniques in both visual and auditory domains. By continuously refining our model with ongoing advancements in AI and deep learning, we remain steadfast in our commitment to staying ahead of emerging threats posed by malicious actors. In addition to its utility in detecting deepfakes across multiple modalities, our system features a user-friendly interface for swift mitigation of AI-generated manipulations. A key feature of our system is the use of probability scores to indicate the confidence level of the classification. The resultant probability score obtained for various input data ranged between 50% and 90%. This probabilistic approach provides a nuanced measure of the system's confidence in its predictions, offering more detailed insights compared to the binary accuracy metrics commonly used in other studies. By leveraging the power of AI to combat AI, our methodology prioritizes simplicity and reliability, validated through rigorous evaluations on diverse datasets encompassing both image and audio samples. Through these efforts, we safeguard against the harmful impact of deepf

关键词： Audio recordings

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Autonomous Driving Prototype with Raspberry Pi by Using image processing Technology

Autonomous Driving Prototype with Raspberry Pi by Using Imag...

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作者： Zhang, Chris Huang, Jerry Yinran Chen, Eric Shen, Jinhui Liu, Mark Yining Wang, Yanxiao Ni, Haowei Qin, Lang Cansight Technology Corporation VancouverBC Canada Nanjing Foreign Language School Jiangsu Nanjing China R.C. Palmer Secondary School RichmondBC Canada Communication University of China Jiangsu Nanjing China University of Toronto TorontoON Canada Saint Patrick Regional Secondary School VancouverBC Canada High School Affiliated to Nanjing Normal University Jiangning Campus Jiangsu Nanjing China

ISBN: (纸本)9783031859229

With the development of the internet, the concept of self-driving cars is no longer unfamiliar with us. From military use to commercial use, autopilot technology is playing a bigger and bigger role in society nowadays. In order to make this kind of invention easier for us to use in daily life, a self-driving car prototype is designed. It comes with basic functions like detecting and moving ability which can help people do a few household necessities. This kind of robot may be connected to the household WIFI in the future to realize more complex functions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： OpenCV Raspberry Pi self-driving

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SafeARUnity: Real-Time image processing to Enhance Privacy Protection in LBARGs 14th

SafeARUnity: Real-Time Image Processing to Enhance Privacy ...

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14th International conference on Videogame Sciences and Arts, VJ 2024

作者： Ribeiro, Tiago Marto, Anabela Gonçalves, Alexandrino Santos, Leonel Rabadão, Carlos de C. Costa, Rogério Luís CIIC ESTG Polytechnic University of Leiria Leiria Portugal

ISBN: (纸本)9783031817120

Augmented Reality applications overlay our physical world with digital components in an interactive 3D space. These applications generally capture information about the physical world around the user through cameras and sensors, which can identify user movements and interactions with objects in the real world. In recent years, Location-Based Augmented Reality Games (LBARGs) have been used in several contexts, such as entertainment, tourism, and education. However, by capturing information about the environment, AR applications can lead to failures in maintaining user and bystander privacy. This paper addresses the identification and protection of sensitive data in LBARGs. We introduce LootAR, a location-based mobile AR game, and the SafeARUnity library, a real-time image processing middleware that acts as a layer between the AR application and the device’s camera, identifying and sanitizing sensitive data prior to rendering. Implementation aspects are discussed, involving Unity Sentis, a toolkit for running machine learning models in Unity, and YOLO, a fast single-stage object detector optimized for real-time applications. We also demonstrate the integration of SafeARUnity in mobile games, using LootAR as a case study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Middleware

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Harnessing Elastic Adaptive Learning Network for Low-Light image Enhancement 2nd

Harnessing Elastic Adaptive Learning Network for Low-Light I...

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2nd International conference on Applied Intelligence, ICAI 2024

作者： Xing, Meng Li, Xiaomei Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology NJ Ningbo315201 China Medical Innovation Center and State Key Laboratory of Cardiology Shanghai East Hospital School of Medicine Institute for Regenerative Medicine Tongji University Shanghai200123 China Department of Artificial Intelligence Institute Tianjin Normal University Tianjin300387 China

ISBN: (纸本)9789819619030

In recent years, curve estimation-based low-light image enhancement methods have gained increasing attention due to they do not need reference images for training. However, existing methods often encounter issues such as overexposure, color distortion, and artifacts when processing images with uneven luminance distribution. To address this issue, this paper proposes an approximate global convergence curve estimation based unsupervised low-light image enhancement method, called Harnessing Elastic Adaptive Learning Network (HEAL-Net). Specifically, we design an Approximate Global Convergence Curve (AGC-Curve) to reduce the model's sensitivity to input variations. Additionally, we introduce a Dynamic Deep Feature Mapping (DDFM) branch, which further smooths the luminance distribution by capturing the correlations between color channels. Experimental results demonstrate that our method achieves advanced performance across multiple benchmarks and exhibits strong robustness to inputs with uneven luminance distribution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mapping

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Intrusion Detection from Surveillance Video Using Frame Based image processing Techniques 1st

Intrusion Detection from Surveillance Video Using Frame Base...

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Mohamed Yaseen, M.S. Radeesh, P. Chitra, P. Ramesh, Kishore SRM Institute of Science and Technology Vadapalani Tamil Nadu Chennai India

ISBN: (纸本)9783031823824

In India, the number of surveillance cameras has increased rapidly, making it challenging for law enforcement to go through the vast amount of recorded footage. We suggest a technique for quickly reviewing large amounts of video feed in order to address this issue. By utilizing sophisticated algorithms such as facial recognition, lightweight YuNet model face detection, and motion detection through background subtraction, our system accelerates the analysis of videos from various camera systems. Our model is a great tool for any agency looking to improve its operational efficiency when handling large amounts of monitoring data because it can quickly identify important events and produce concise summaries with timestamps. This piece of research contributes towards development within this field since such new methods are combined into one single solution thereby advancing surveillance technology while at the same time providing concrete benefits for everyday challenges faced by law enforcement agencies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Intrusion detection

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12th CCF conference on BigData, BigData 2024

12th CCF Conference on BigData, BigData 2024

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12th CCF conference on BigData, BigData 2024

ISBN: (纸本)9789819610235

The proceedings contain 26 papers. The special focus in this conference is on BigData. The topics include: A Multi-dimensional Early Warning Mechanism for Biological Invasions: A Case Study of Vespa Mandarinia;DLP-FR: Learning Predictable Degradation for Robust Blind Face Restoration;A Joint Relation Extraction Model Utilizing Message Passing Mechanism for Feature Fusion to Alleviate FN Problem from Distant Supervision;urban Traffic Management: A Predictive Approach Using Mobile Phone Data;FedPSED: Federated Public Safety Emergency Detection Based on Adaptive Aggregation Strategy;evaluation of Retrieval-Augmented Generation: A Survey;CMNet: Fast Time Series Forecasting Based on Hybrid Convolution-MLP Architecture;a Domain Adaptive Based Reinforcement Learning Algorithm for Job Shop Scheduling Problems;Large-Scale Data Generation Using SWLBM on the Sunway TaihuLight Supercomputer and Subsequent Data Mining with Physics-Informed Neural Networks;Enhancing Interaction Graph of Data Schema and Syntactic Structure with Pre-trained Language Model for Text-to-SQL;spatial Evolution Analysis of the Level of Digital Economy Development in China;predicting Calibrated Conversion Rate of Online Advertising Using a Multi-task Mixture-of-Experts Calibration Model;a Transformer-Based Spatio-Temporal Graph Neural Network for Anomaly Detection on Dynamic Graphs;morphological Semantic Ensemble Filtering of Massive Sentence Pairs for Neural Machine Translation;improving Event-Level Financial Sentiment Analysis with Retrieval-Augmented Multipath Chain-of-Thought Prompting;POSRho: Efficient Spearman’s Rho Calculation for Big Data;a Fusion Tuning Method for Named Entity Recognition;Design of AXI Bus-Based IP Core for image processing;automated Clinical Summary Generation via Integrating Structured and Unstructured Data;a Review on Deep Learning for Sequential Recommender Systems: Key Technologies and Directions;TrajUT: Intruder Trajectory Recovery on Utility Tunnel via Video Surveillance S

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XcUNet for Skin Lesion Segmentation Followed by Clustering Based Post-processing 4th

XcUNet for Skin Lesion Segmentation Followed by Clustering B...

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4th International conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

作者： Verma, Neetu Ranvijay Yadav, Dharmendra Kumar Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad Prayagraj Uttar Pradesh Prayagraj India

ISBN: (纸本)9783031837951

Human cancer is one of the worst illnesses and is mainly caused by inherited instability and a variety of molecular disruptions. Worldwide, skin cancer is one of the most common types of cancer, and patient survival depends on an early and precise diagnosis. Although it is crucial, clinical evaluation of skin lesions is fraught with difficulties, including significant wait times and individual interpretations. Dermatologists have difficulties in early skin cancer detection, and deep learning has recently been widely used in supervised and unsupervised learning tasks. Defining the lesion region is crucial to the effectiveness of these techniques because it allows for accurate segmentation of skin cancer lesions at various stages, which helps in early diagnosis and treatment. In this study, we have proposed an Xception model-based U_Net (XcUNet) model for skin lesion image segmentation. The proposed model’s lower computational load from separable convolutions and residuals usually results in better detection times and more sophisticated comprehension of spatial hierarchies and patterns. This attribute is extremely important in clinical fields where quick outcomes are required, such as skin lesion segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Early Diagnosis of Glaucoma and Diabetic Retinopathy Using Fundus images Based on Ensemble Approach 1st

Early Diagnosis of Glaucoma and Diabetic Retinopathy Using F...

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Parthasarathy, Arjun Vasu, Harini Palani, Durgadevi Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9783031823886

Detecting eye diseases like Glaucoma and Diabetic Retinopathy early is crucial to prevent irreversible vision loss;these conditions, characterized by optic nerve damage are recognized as leading causes of blindness. Fundus photography involves capturing images of the back of the eye and it plays a crucial role in diagnosing and monitoring various eye conditions, aiding in the early detection and management of these diseases and their subtypes, such as the different stages of glaucoma and diabetic retinopathy. Existing methods for analysing fundus images rely on traditional image processing techniques like filtering and transformation, and classification methods like CNN or SVMs. These methods do not reliably capture data, leading to lower accuracy in identification. In this study, we utilized fundus images from the Fundusimage1000 dataset obtained from Kaggle, which contains a comprehensive collection of images capturing various stages of glaucoma and diabetic retinopathy. This study proposes an ensemble method for glaucoma and diabetic retinopathy detection using fundus images. By utilizing Grey-Level Co-occurrence Matrix for texture analysis, this method integrates classification models XGBoost and Light GBM to make accurate predictions for early identification of the subtypes of these two diseases, including the various stages such as Mild, Moderate, Advanced, Proliferative or Nonproliferative. This combination of texture analysis and ensemble learning yields effective results in automated disease identification. The proposed approach also achieves a high accuracy of 99%, offering a valuable tool for medical professionals to improve diagnostic outcomes and patient care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image texture

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