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共 12985 条记录，以下是11-20 订阅

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Distance Based Single-Channel Target Speech Extraction

Distance Based Single-Channel Target Speech Extraction

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shi, Runwu Yen, Benjamin Nakadai, Kazuhiro Dept. of Systems & Control Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9798350368741

This paper aims to achieve single-channel target speech extraction (TSE) in enclosures by solely utilizing distance information. This is the first work that utilizes only distance cues without using speaker physiological information for single-channel TSE. Inspired by recent single-channel Distance-based separation and extraction methods, we introduce a novel model that efficiently fuses distance information with time-frequency (TF) bins for TSE. Experimental results in both single-room and multi-room scenarios demonstrate the feasibility and effectiveness of our approach. This method can also be employed to estimate the distances of different speakers in mixed speech. Online demos are available at https://***/distance-demo-page/. © 2025 IEEE.

关键词： distance based sound separation single-channel Target speech extraction

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Blockchain Application for Sustainable Supply Chain Management in Indian Agriculture

Blockchain Application for Sustainable Supply Chain Manageme...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Manvizhi, N. Pugazhendi, A. Gilmary, Rosario Ifet College of Engineering Dept. of Information Technology Tamilnadu Villupuram India

ISBN: (纸本)9798331509828

Increasing demand for transparency in agri-food through customer expectation and regulatory imposition has fostered a swift adoption of blockchain technology. Demand for transparency is rising in the agri-food industry driven by consumer expectations and regulatory requirements, which has fast-tracked the adoption of blockchain technology. It creates innovative solutions to augment traceability and combat food deception, while also informing consumers about their origin. Blockchain is by nature trusted and immutable;thus, it is well suited for agri-food stock chains. Considering that the transactions on such supply chains have to be safe and transparent, the existing Smart contract technology based on Ethereum is being improved. Even if updating is difficult and disruptive, the topic of data migration and its users is growing more difficult. This has led most of the agri-food sector migration to Binance Smart Chain (BSC). BSC offers the benefits of confirmation times for transactions, a more efficient consensus mechanism, and thus better scalability. On the latter aspect, use of BSC will eventually ease the updation of smart contracts, thereby upgrading the entire speed and reliability of blockchain solutions for ensuring transparency and traceability in a supply chain. © 2025 IEEE.

关键词： Smart contract

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AgroConnect: A Flutter-Based E-Commerce Platform for Enhancing Agricultural Supply Chain Efficiency and Transparency

AgroConnect: A Flutter-Based E-Commerce Platform for Enhanci...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Thendral, R. Selva Ganapathy, J. Ifet College of Engineering Dept. of Information Technology Tamilnadu Villupuram India

ISBN: (纸本)9798331509828

AgroConnect is a completely Flutter-based mobile app that acts as the much-needed 'connection' between farmers and retailers, providing an e-commerce platform streamlined specifically for the agricultural sector. In this way, it empowers the farmer to manage and showcase his products effectively to thrive in a competitive marketplace while providing retailers with the ease of smooth purchasing. These include real-time inventory tracking that prevents wastage and maximizes sales together with secure payment processing that earns the parties maximum trust. Effective communication tools ensure interaction at the point. Some of the communication tools that ensure on-spot discussions about the available products, negotiations over prices, and inquiries are the ones that enhance accessibility to the markets and optimize the agricultural chain of supply. Available on Android and iOS, AgroConnect strives to revolutionize the farming industry by fostering greater transparency and efficiency throughout the agricultural ecosystem, ultimately paving the way for a more sustainable, interconnected marketplace for all farmers, retailers, and consumers. © 2025 IEEE.

关键词： Marketplaces

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AI Driven Fall Detection: A Pose Estimation Approach 2

AI Driven Fall Detection: A Pose Estimation Approach

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Bandodkar, Priyanka Fernandes, Aisha Goa College of Engineering Goa College of Engineering Dept. of Information Technology Goa India

ISBN: (纸本)9798331505745

Current human collapse detection methods are plagued by several issues. These methods often rely on manual monitoring or simplistic threshold-based sensor systems, which can be slow to respond and prone to human errors. Traditional systems frequently struggle with high false alarm rates, where non-critical events are mistaken for collapses. Conversely, they might miss actual collapses due to their limited detection capabilities. The proposed method aims to detect falls in video footage by using pose estimation techniques. The project utilizes the OpenPifPaf library to extract keypoints of a person and Shufflenetv2k30, which is a pre-trained model that can be used to extract relevant features from input images, essential for accurate human pose estimation. It focuses on calculating the velocity change at the neck keypoints to identify falls. The velocity threshold is fine-tuned for the current dataset. When a fall is detected, the system continuously monitors the person to determine whether they remain motionless. If the person remains still for a predefined period, an alarm is triggered. The alarm service can be customized, allowing the system to connect with different types of alert mechanisms. Here, a mobile computing system and application are utilized for the same. The method is evaluated with the Le2i public dataset. © 2025 IEEE.

关键词： Error correction

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Improvising Early Detection of Diabetes through Deep Learning on Retinal Fundus images 3

Improvising Early Detection of Diabetes through Deep Learnin...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Balaji, Krishna Ganesh, Maniprabha Shankar Murali, Daneesh Pachiappan, Kalaivani Iyyanar, P. Jayakumar, Nithya Kongu Engineering College Dept. of Computer Science Erode India Sona College of Technology Dept. of Information Technology Salem India K.S Rangasamy College of Technology Dept. of Information Technology Tiruchengode India

ISBN: (纸本)9798331509675

Diabetes is a chronic disease whose timely and accurate diagnosis will prevent serious complications from health. This paper explores using iridology principles in a deep learning method to detect diabetes from retinal images in order to streamline the process and automate the procedure. The objective here is to create a non-invasive procedure for early and reliable detection of the disease. Deep learning in medical imaging is the new way to interfere with conventional modes of diagnostics. The retinal imaging is non-invasive and has, over the years, been put to use in medical examinations, and its potential is now absolutely being utilized in the early diagnosis of diabetes. A retinal analysis that is based on principles, integrating them into iridology, concentrating on the patterns and signs portrayed in the iris for possible reflections of a general health condition, is aimed at achieving earlier indications of diabetes in a more effortless and rapid way. Models such as ResNet50, ResNet101, and EfficientNetB7 were precious but actually, it was InceptionV3 that delivered a great accuracy of 97.14%. This result proves the feasibility application of InceptionV3 for diagnosing diabetes in the early stages, thus eventually providing an easy and non-invasive way of moving forward to better health. © 2025 IEEE.

关键词： Ophthalmology

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Pathology Image Classification for Colorectal Cancer Prediction Using Experience Replay

Pathology Image Classification for Colorectal Cancer Predict...

引用

2025 IEEE International Conference on Computational, Communication and information technology, ICCCIT 2025

作者： Kumar, Penumacha Naveen Krishna, Ch. Nanda Lukka, Sai Lasya Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Kanuru India

ISBN: (纸本)9798331512965

Pathology image classification helps diagnosis and monitoring as well as treatment of a disease. Thus, it can help the decision-making of a health worker. Still, conventional machine learning models struggle with medical data that often changes, as they typically need retraining on the full dataset to learn new information on a frequent basis. This retraining requires a lot of computations and sometimes loses what it 'knew' previously. This is called catastrophic forgetting. To solve this problem, we propose a continual learning framework, using Experience Replay to gradually add new pathology image data, while retaining knowledge of older tasks. Using Experience Replay, the model replays a sample of past data while training so that it does not forget any new information. The proposed framework is validated on the PathMNIST dataset, where it shows better classification accuracy and robustness than the classical retraining. This indicates that continual learning has the potential to be used for pathology image classification, and as a result, it can be scalable and adaptive to the needs of large-scale medical data. Through such advancements diagnostic accuracy will increase, resource use will become more efficient, and patient outcomes will improve in medicine. © 2025 IEEE.

关键词： Contrastive Learning

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Machine Learning-based Soil Analysis for Crop Forecasting

Machine Learning-based Soil Analysis for Crop Forecasting

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Gupta, Raji Singh, Shiv Prakash Gupta, Rishabh Saurabh Agarwal, Shikha Ajay Kumar Garg Engineering College Dept. of Information Technology Ghaziabad India

ISBN: (纸本)9798331508685

Agriculture is a crucial part of India's economy, contributing over 17% to the nation's GDP (Gross Domestic Product), engaging over 60% of the workforce but still the effort-to-yield ratio of the sector remains significantly low. Analyzing soil properties, such as temperature, pH, humidity, water retention capacity, etc., helps determine soil quality, which is crucial for optimal agricultural productivity. This assessment later recommended crops that align with soil conditions and external environments, maximizing yield potential, and sustainable land management thereby preventing degradation. Similar to most studies aimed at advancing agriculture, the main objective of this research is to drive development by implementing ML-based models for crop classification. This paper presents a comparative analysis of various machine learning including KNN, SVC, Random Forest, etc on a single dataset. The comparative analysis highlights the advantages of each algorithm over others, helping to select the best-suited model to be implemented for real life applications. Our findings prove that the Random Forest algorithm outperforms the others, achieving the highest accuracy of 99.24%. © 2025 IEEE.

关键词： agriculture crop nutrient prediction soil analysis

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Bird Repeller Using Opencv 4

Bird Repeller Using Opencv

引用

4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Upendra, M. Anuradha, T. Prakash, L. Dept. of Information Technology Siddhartha Engineering College Velagapudi Ramakrishna Vijayawada India

This research addresses one of the major problems in agriculture that farmers are currently facing: birds causing significant crop damage in agricultural fields. To reduce this damage, a person's presence is often required in the farm field to repel the birds. To eliminate this need and automate the repellent system, the project aims to develop a bird repellent device using computer vision techniques to detect and repel birds from fields. Existing work lags behind in using effective algorithms and developing hardware. Object detection algorithms like the Haar Cascade Classifier and YOLOv8 are used;YOLOv8 is a powerful object detection algorithm, and it is chosen as the primary algorithm for its accuracy and speed in detecting birds. A hardware device is developed using a Raspberry Pi with a Pi camera and a Bluetooth speaker. Upon detecting birds in the farm fields through the Pi camera, distress calls are produced through the Bluetooth speakers to repel the birds. © 2025 The Authors. Published by Elsevier B.V.

关键词： Bird Repellent Cascade Classifier Computer Vision Distress Call image detection Open CV pi camera Raspberry pi YOLO

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Wrist Fracture Detection Using Deep Learning

Wrist Fracture Detection Using Deep Learning

引用

2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Rafi, Shaik Mahmamad Anuradha, T. Srinivas, Kothapalli S. V. V. R. Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Kanuru India

ISBN: (纸本)9798331529833

Effective treatment of wrist bone fractures depends on early and precise identification. The finding shows that disparities in accessing timely emergency services translate into adverse patient outcomes, especially in regions with fewer healthcare facilities and extended distances, mostly rural areas. Distal radial fractures or wrist fractures are among the commonly encountered musculoskeletal injuries and the treatment and subsequent management depend on timely and precise identification of the fracture pattern. This paper discusses a wrist fracture detection scheme using a deep learning approach for diagnosing wrist bone fractures from X-ray images which can close the health disparities due to the lack of adequate early diagnosis. and his team developed the system using DenseNet-201, MobileNetV2, ResNet-50, EfficientNetV2, U-net and Inception V3 architecture because each was selected for a certain use effect patient outcome. Wrist fractures, among the most common musculoskeletal injuries, require early and accurate diagnosis to optimize treatment and rehabilitation. This project presents a deep learning-based system to detect wrist fractures in X-ray images, designed to bridge healthcare inequalities by facilitating early and automated diagnosis. The system integrates DenseNet-201, MobileNetV2, ResNet-50, EfficientNetV2, U-Net, and Inception V3 models, each chosen for specific roles. DenseNet-201, MobileNetV2, and ResNet-50 are classification networks for fracture detection while U-Net is segmentation network to segment fractured areas. Furthermore, the results using EfficientNetV2 and Inception V3 improve diagnostic capability and image *** work compares the pros and cons of each model in terms of accuracy, computational time, and loss and presents recommendations for the most appropriate models for clinical applications in practice. © 2025 IEEE.

关键词： Deep learning

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Exploring the Potential of MANETs: A Study on Raspberry Pi and UAV Integration 3

Exploring the Potential of MANETs: A Study on Raspberry Pi a...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Archana, B. Mahalakshmi, A. Revathi, K. Ashok Kumar, K. Hema, A.B. Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Dept. of Information Technology SNS College of Engineering Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This research investigates the feasibility of utilizing Mobile Ad Hoc Networks (MANETs) in conjunction with Raspberry Pi-equipped Unmanned Aerial Vehicles (UAV) swarms. The primary objective is to overcome the limitations of traditional communication infrastructures in dynamic and decentralized environments. Specifically, we address the challenge of establishing reliable and efficient communication links within a swarm of UAVs engaged in various applications, including disaster response, military reconnaissance, and environmental monitoring. By integrating Raspberry Pi devices with UAVs, we aim to create a self-organizing communication network that enables seamless data transmission, coordinated swarm operations, and enhanced mission effectiveness. The research explores different synchronization strategies, including master-slave and decentralized approaches, to optimize swarm coordination. Furthermore, we investigate the potential of incorporating advanced technologies such as machine learning, computer vision, and swarm intelligence to further enhance the autonomy, adaptability, and overall performance of the UAV swarm. © 2025 IEEE.

关键词： Mobile ad hoc networks

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