检索结果-内蒙古大学图书馆

FinSecure: Utilizing IoT Sensors for Formaldehyde Detection and Fish Freshness Detection for Enhancing Safety in Fish Consumption Using Machine Learning and Deep Learning

FinSecure: Utilizing IoT Sensors for Formaldehyde Detection ...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Harish, S. Somesh, K. Suntharavadivelan Venkatesan, M. Prabhavathy, P. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India Vellore Institute of Technology Department of Computer Science and Engineering and Information Systems Vellore India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

The global food supply heavily relies on fisheries, highlighting the crucial importance of ensuring the safety of fish products. However, the widespread application of antibiotics and the existence of compounds such as formaldehyde present significant hazards to both human consumers and the underwater ecosystem. These risks are compounded by processes like bio-magnification, further emphasizing the need for vigilant monitoring and regulation. This project presents a comprehensive approach to fisheries management and food safety monitoring through the integration of advanced machine learning, deep learning techniques and IoT sensor technology. The overall objective is to develop a model that can determine whether the given sample Fish Species is safe or unsafe for human consumption, by considering parameters: Fish Freshness Detection, using a HCHO Sensor to determine the Formaldehyde levels in the given Fish and finally using these parameters to determine whether the fish can be consumed or not. For the Fish Freshness Detection, utilizing Transfer Learning (pre-trained MobileNetV2 model), the system achieves an impressive accuracy of 99.02%. Thus, integrating all these models, determine whether the supplied fish is safe for consumption or not. By integrating these functionalities, the system contributes to proactive fisheries and food safety monitoring and protection. The holistic approach of this project, combining accurate positions the developed model and IoT application as valuable assets for sustainable aquaculture practices and the preservation of aquatic ecosystems. The project's outcomes contribute to the advancement of several interdisciplinary domains such as machine learning, and fisheries management, laying the groundwork for continued innovation in this interdisciplinary domain. © 2024 IEEE.

关键词： Fish

来源：评论

学校读者我要写书评

暂无评论

Home-based Detection and Prediction of Diabetic Foot Ulcers at Early Stage Using Sensor technology and Supervised Learning

引用

Journal of Harbin institute of technology(New Series) 2024年第1期31卷 26-37页

作者： Kamasamudram Bhavya Sai Rishi Raghu Sai Surya Varshith Nukala Jayashree Jayaraman Vijayashree Jayaraman School of Computer Science and Engineering Vellore Institute of TechnologyVellore 632014India

For years,foot ulcers linked with diabetes mellitus and neuropathy have significantly impacted diabetic patients’ health-related quality of life(HRQoL). Diabetes foot ulcers impact15% of all diabetic patients at some point in their lives. The facilities and resources used for DFU detection and treatment are only available at hospitals and clinics,which results in the unavailability of feasible and timely detection at an early stage. This necessitates the development of an at-home DFU detection system that enables timely predictions and seamless communication with users,thereby preventing amputations due to neglect and severity. This paper proposes a feasible system consisting of three major modules:an IoT device that works to sense foot nodes to send vibrations onto a foot sole,a machine learning model based on supervised learning which predicts the level of severity of the DFU using four different classification techniques including XGBoost,K-SVM,Random Forest,and Decision tree,and a mobile application that acts as an interface between the sensors and the patient. Based on the severity levels,necessary steps for prevention,treatment,and medications are recommended via the application.

关键词： diabetic foot ulcer podiatry diabetes mellitus healthcare footcare internet of things machine learning human⁃computer interaction

来源：评论

学校读者我要写书评

暂无评论

EARA-PSOCAE: an energy aware resource allocation and particle swarm optimisation based cryptographic algorithm in e-healthcare cloud environment

引用

International Journal of Cloud Computing 2023年第5期12卷 497-513页

作者： Palani, S. Babu, K. Ramesh Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

In cloud platforms, large amount of energy is consumed by execution of scientific workflow. So, VMs has to be deployed in energy efficient manner. Throughout the world, wide attention is attracted by cloud platform’s energy consumption. Cooling systems, light systems, network peripherals, monitors, console, processor’s cooling fan, server running consumes large amount of power in a cloud data centre. In order to face these issues, energy-aware resource allocation method is proposed in this work, which is termed as EnReal. For execution of scientific workflow, virtual machines are deployed dynamically which focus on current e-health standards and solutions. In general e-health systems, client platform security is not addressed by this method which is an important factor to be considered. In e-health domain infrastructures, for privacy establishment, new particle swarm optimisation (PSO) with cryptography-based security algorithm (PSOCA) is proposed in this work. Controlled environment can be created by this for data’s privacy easy management as well as for security. For experimentation, CloudSim framework is adapted in cloud simulation environment. Hence the proposed method is evaluated using various parameters like energy consumption and resource utilisation. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

COVINet: A hybrid model for classification of COVID and Non-COVID pneumonia in CT and X-Ray imagery

引用

International Journal of Cognitive Computing in engineering 2023年第1期4卷 149-159页

作者： Mittal, Vasu Kumar, Akhil School of Computer Science & Engineering Vellore Institute of Technology Chennai India

The COVID-19 pandemic has resulted in a significant increase in the number of pneumonia cases, including those caused by the Coronavirus. To detect COVID pneumonia, RT-PCR is used as the primary detection tool for COVID-19 pneumonia but chest imaging, including CT scans and X-Ray imagery, can also be used as a secondary important tool for the diagnosis of pneumonia, including COVID pneumonia. However, the interpretation of chest imaging in COVID-19 pneumonia can be challenging, as the signs of the disease on imaging may be subtle and may overlap with normal pneumonia. In this paper, we propose a hybrid model with the name COVINet which uses ResNet-101 as the feature extractor and classical K-Nearest Neighbors as the classifier that led us to give automated results for detecting COVID pneumonia in X-Rays and CT imagery. The proposed hybrid model achieved a classification accuracy of 98.6%. The model's precision, recall, and F1-Score values were also impressive, ranging from 98-99%. To back and support the proposed model, several CNN-based feature extractors and classical machine learning classifiers have been exploited. The outcome with exploited combinations suggests that our model can significantly enhance the accuracy and precision of detecting COVID-19 pneumonia on chest imaging, and this holds the potential of being a valuable resource for early identification and diagnosis of the illness by radiologists and medical practitioners. © 2023

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Sign Language Recognition Using Convolutional Neural Network Based on VGG-16 Deep Learning Approach 5th

Sign Language Recognition Using Convolutional Neural Network...

引用

5th International Conference on Advances in information Communication technology and Computing, AICTC 2024

作者： Athish, M.J. Sowmya, S. Lakshaya, E. Preetha, S. Rishiya Shankar, S.U. Sendhil Kumar, K.S. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819761050

Communication is a significant part of our lives, as it facilitates interaction among individuals. Language serves as a mode of communication for all individuals for expressing their thoughts and interpretations. However, hearing-impaired individuals often face hurdles in using spoken language. The technology for recognizing sign language made a commendable effort to improve communication levels among hearing-impaired individuals. In the current era of deep learning, which is a subset of machine learning, Convolutional Neural Network models have been developed and are now in use for gesture recognition. This recognition is based on the CNN model and VGG-16 model. It is used to hone the temporal features. The dataset used here is American Sign Language Dataset (A–Z and 0–9). The focus of this work is to develop a sign language recognition system to fulfill the need for communication of impaired people in society. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Stereotactic Needle Path Planning for Brain Biopsy Based on Volumetric Segmentation of MRI and Heuristic Approach 5th

Stereotactic Needle Path Planning for Brain Biopsy Based on ...

引用

5th International Conference on Advances in information Communication technology and Computing, AICTC 2024

作者： Krishnan, Sushil Bhogale, Atharva Gawande, Aarya Jaisankar, N. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819761050

Any abnormal growth in brain cells refers to brain tumor. With large variety of tumors reported, accurate diagnosis is very essential for appropriate intervention. Traditionally, biopsies are preferred diagnostic tool, where a small piece of tissue or some cells are collected from suspected region and tested in a laboratory. Brain being the control and coordination center, biopsy in brain requires utmost precision and accuracy to avoid any damage to healthy tissues. Stereotactic needle biopsies are specialized minimally invasive technique for collecting the required samples. In such cases precise path planning is very essential. Availability of large medical image datasets and enormous computational capability encourages to train deep learning models that can accurately delineate and identify the tumor’s location, shape, size, and borders within the patient's brain. Volumetric segmentation combined with heuristic path planning algorithm can play a synergistic role in increasing the success rates of brain biopsies. The main objective of the work is to propose a framework for precise needle insertion for brain tumor biopsies. The work proceeds by comparing performances of various model for volumetric segmentation of brain MRI scans and identifying shortest path to the tumor, utilizing the segments obtained. This approach minimizes blood loss and the need for extensive medical intervention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biopsy

来源：评论

学校读者我要写书评

暂无评论

Enhancing Multiclass Pneumonia Classification with Machine Learning and Textural Features

引用

Machine Graphics and Vision 2023年第3-4期32卷 83-106页

作者： Godbin, A. Beena Jasmine, S. Graceline School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The highly infectious and mutating COVID-19, known as the novel coronavirus, poses a substantial threat to both human health and the global economy. Detecting COVID-19 early presents a challenge due to its resemblance to pneumonia. However, distinguishing between the two is critical for saving lives. Chest X-rays, empowered by machine learning classifiers and ensembles, prove effective in identifying multiclass pneumonia in the lungs, leveraging textural characteristics such as GLCM and GLRLM. These textural features are instilled into the classifiers and ensembles within the domain of machine learning. This article explores the multiclass categorization of X-ray images across four categories: COVID-19-impacted, bacterial pneumonia-affected, viral pneumonia-affected, and normal lungs. The classification employs Random Forest, Support Vector Machine, K-Nearest Neighbor, LGBM, and XGBoost. Random Forest and LGBM achieve an impressive accuracy of 92.4% in identifying GLCM features. The network’s performance is evaluated based on accuracy, precision, sensitivity and F1-score. © 2023 institute of information technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

IoT-Supported Indoor Navigation System Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

引用

12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

来源：评论

学校读者我要写书评

暂无评论

Renaissance of Fuzzy and Fast Matching Entity with DSHS Algorithm

引用

SN computer science 2024年第6期5卷 734页

作者： Kari, Venkatram Amalanathan, Geetha Mary School of Computer Science and Engineering Vellore Institute of Technology University

Entity matching is a crucial aspect of data management systems, requiring the identification of real-world entities from diverse expressions. Despite the human ability to recognize equivalences among entities, machines struggle due to variations in expression. This challenge underscores the necessity for a robust framework capable of matching entities based on their attributes. While previous studies have explored transformation techniques, NLP, and deep learning, they often fall short in accuracy and scalability or overlook internal constraints. Such shortcomings result in costly rework and compromised business decisions. To address these issues, proposed “Deep Semantic Homophonic Synonymy” (DSHS) algorithm, a hybrid approach integrating rule-based, NLP, and deep learning techniques. By employing semantic matching, rule-based preprocessing, NLP enrichment, phonetic analysis, abbreviation variations, and spelling corrections, the DSHS algorithm effectively overcomes entity matching challenges. This solution, comprising six modules including Preprocessing, Transformation, Translation, Word Embedding, Attribute Similarity, and Classification, outperforms existing EM models in terms of accuracy and scalability. This solution is a comprehensive entity matching, offering significant advancements in the field and providing a reliable framework for resolving complex matching requirements. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data pipeline Data sync up MDDR Message queue Schema registry

来源：评论

学校读者我要写书评

暂无评论

Secure File Sharing System Using Hybrid Authentication and Secured Key Management Algorithm 4th

Secure File Sharing System Using Hybrid Authentication and S...

引用

4th International Conference on Smart IoT systems: Innovations and Computing, SSIC 2023

作者： Dokania, Aryan Bamb, Anshul Dheeba, J. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819751990

When web file sharing is used, users can safely receive and share any files or documents over the web using any of their preferred web browsers. Though the existing cryptographic algorithms perform well in end-to-end encryption, they are not resistant to attacks that extract information from the computer system, such as passwords and encryption/decryption keys. In this paper, a hybrid method is proposed for secure and efficient end-to-end encryption. This paper focuses on secure key exchange, efficient data encryption, and strengthening of passwords, which is achieved by a combination of four cryptographic algorithms. The proposed hybrid cryptographic method is secure against several attacks, including man-in-the-middle attacks and side-channel attacks. The proposed method offers many advantages over traditional end-to-end encryption methods, including security, efficiency, and resistance to side-channel attacks. Extensive analytical and experimental results show that the proposed method is efficient enough to be used in a file sharing system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：