检索结果-内蒙古大学图书馆

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Hemalatha, S. Thangarajan, R. Selvaraj, S. Dhanya Sree, P. Malathi, P. Vignesh, V. Kongu Engineering College Department of Computer Science and Design Tamil Nadu Erode India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356816

Over recent years, the integration of machine learning (ML) techniques within healthcare has surged, facilitating the development of predictive models to assist in disease diagnosis and treatment. This paper presents an extensive examination of disease prediction utilizing ML algorithms, specifically Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN). The principal aim of this study is to construct a robust predictive model capable of accurately diagnosing diseases based on input symptoms and subsequently offering specialist treatment recommendations. The dataset utilized encompasses a wide array of symptoms associated with diverse diseases, meticulously sourced from reliable medical repositories. Preprocessing techniques, including normalization and feature scaling, are applied to ensure data uniformity and enhance model performance. SVM is selected as a primary classifier due to its efficacy in handling high-dimensional data and complex decision boundaries. Additionally, Random Forest, recognized for its resilience to overfitting and scalability, is incorporated to explore ensemble learning strategies. Moreover, KNN is employed as a proximity-based classifier to exploit similarities between instances in the feature space. Model performance is rigorously assessed using diverse metrics such as accuracy, precision, recall, and F1-score, employing cross-validation techniques to ensure robust and unbiased results. However, Random Forest emerges as the top performer, demonstrating superior accuracy and robustness in managing complex feature interactions. Furthermore, specialist treatment recommendations are generated based on predicted diseases, leveraging domain knowledge and expert rules to provide tailored suggestions. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Multimodal Approach for depression detection: Integrating speech and eye ball movement data 1

Multimodal Approach for depression detection: Integrating sp...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Hemalatha, S. Jothimani, K. Swathi, K. Shibinta, S. Jason Selvakumar, W. Sathish, D. Kongu Engineering College Department of Computer Science and Design Tamil Nadu Erode India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356816

Depression is a common mental health condition that impacts millions of people globally, its diagnosis often relies on subjective assessments. This project introduces a Multimodal Convolutional Neural Network (MCNN) framework tailored for depression detection, leveraging speech and eye movement data as input modalities. The project's foundation lies in combining advanced deep learning techniques with multimodal data analysis. Through the MCNN architecture, the system learns to extract discriminative features from speech recordings and eye movement patterns, enabling holistic assessments of individuals' mental health status. Data acquisition involves the collection and preprocessing of multimodal data samples from individuals both with and without depression. The MCNN model is trained on this curated dataset, exploiting the convolutional layers to process spatial features from eye movement images and temporal features from speech spectrograms concurrently. The data set is preprocessed using different algorithms of MCNN. We use MCFF's algorithm, which represents the spectral envelope of the speech signal. Eyeball movement is processed by cluster-based algorithms that Convert raw eye movement data into a suitable format for analysis. This may involve segmenting the data into fixations and saccades, and representing them as sequences of gaze positions or heatmaps. The anticipated outcome of this project is a scalable, efficient, and accurate system for depression detection, capable of outperforming conventional diagnostic methods. Future directions include extending the MCNN framework to incorporate additional modalities, such as physiological data, and deploying the system in real-world clinical settings to facilitate early intervention and personalized treatment strategies. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

OFDM-structure based waveform designs for integrated sensing and communication

引用

science China(Information sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

来源：评论

学校读者我要写书评

暂无评论

AI based Security in computer vision and machine learning algorithms for hand sign recognition 3

AI based Security in computer vision and machine learning al...

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Thorat, Nilesh N. Kulal, Nilesh M. Baburao, Khedekar Vilas Hirve, Sumit Arun Kolekar, Sachin Sangore, Rohidas Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350354348

In order to facilitate intuitive control and communication across a variety of areas, hand gesture recognition is an essential component of human-computer interaction systems. In this paper, a real-time hand gesture recognition system developed with the MediaPipe library, and convolutional neural networks (CNNs) is presented. The system utilizes a CNN model provided by MediaPipe for accurate detection of hand landmarks in camera feed images. Through extensive preprocessing and feature extraction, hand gestures, including finger gestures and palm orientations, are classified in real-time. The system offers dynamic selection between two modes: logging key points and logging point history, facilitating data collection for training or analysis *** features of the system include bounding rectangle calculation, landmark visualization, and gesture classification, providing users with immediate feedback on detected hand gestures. The modular design and parameter configurability make the system versatile for a wide range of applications, from interactive interfaces to sign language interpretation. The outcomes of the experiments showcase the practicality and efficacy of the suggested method, indicating that it has the potential to improve the experiences of people interacting with computers. Incorporating AI security measures, the system can significantly enhance security protocols in various applications. For instance, it can be used for gesture-based authentication, where specific hand gestures act as passwords to grant access to secure systems. This method can be further secured by combining it with traditional authentication techniques, creating a robust multi-factor authentication framework. Additionally, the real-time detection capability allows for immediate response to unauthorized access attempts, triggering alerts or lockdown procedures when unfamiliar gestures are detected. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Dual modality prompt learning for visual question-grounded answering in robotic surgery

引用

Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 316-328页

作者： Yue Zhang Wanshu Fan Peixi Peng Xin Yang Dongsheng Zhou Xiaopeng Wei National and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian 116622LiaoningChina School of Computer Science and Technology Dalian University of TechnologyDalian 116081LiaoningChina

With recent advancements in robotic surgery,notable strides have been made in visual question answering(VQA).Existing VQA systems typically generate textual answers to questions but fail to indicate the location of the relevant content within the *** limitation restricts the interpretative capacity of the VQA models and their abil-ity to explore specific image *** address this issue,this study proposes a grounded VQA model for robotic surgery,capable of localizing a specific region during answer *** inspiration from prompt learning in language models,a dual-modality prompt model was developed to enhance precise multimodal information ***,two complementary prompters were introduced to effectively integrate visual and textual prompts into the encoding process of the model.A visual complementary prompter merges visual prompt knowl-edge with visual information features to guide accurate *** textual complementary prompter aligns vis-ual information with textual prompt knowledge and textual information,guiding textual information towards a more accurate inference of the ***,a multiple iterative fusion strategy was adopted for comprehensive answer reasoning,to ensure high-quality generation of textual and grounded *** experimental results vali-date the effectiveness of the model,demonstrating its superiority over existing methods on the EndoVis-18 and End-oVis-17 datasets.

关键词： Prompt learning Visual prompt Textual prompt Grounding-answering Visual question answering

来源：评论

学校读者我要写书评

暂无评论

E-Authentication System using Eye-Blinking and Deep Learning 2

E-Authentication System using Eye-Blinking and Deep Learning

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Achary, Rathnakar Shelke, Chetan J Alliance University Alliance College of Engineering and Design Department of Computer Science Bangalore India

ISBN: (纸本)9798350372892

The Personal Identification Number (PIN) are commonly employed as a security measure for authentication purposes. When utilizing a PIN for verification of authentication, the users are required to input a physical PIN. However, this method may be susceptive to security vulnerabilities through methods such as observing someone's shoulder or tracking thermal signatures. In this research, we adopted Eye-Blinking for authentication of the system, with the advantage that it leaves no bodily footprints in the system and provides a more robust alternative for advanced security. The eye-blinking pattern is a highly secured and unique biometric identifier that cannot be easy replicates, making it difficult to hack or bypass. An E-Authentication System using Eye-Blinking and its analysis using deep learning explains an individual's eye-blinking patterns as a unique identifier to verify their identities. The systems capture their eye-blinking patterns and compare them to the stored patterns in its databases. If the patterns match, the systems grant access to the user. The systems aim at achieving high accuracy, reliability, and integration with existing security infrastructure and authentication protocols. © 2024 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

A Review on the Role of IoT in Smart Agriculture with Reference to Efficiency, Sustainability and Precision Farming 5

A Review on the Role of IoT in Smart Agriculture with Refere...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Yesankar, Prajyot R. Bandre, Prajwal Verma, Prateek Gudadhe, Amit G. Gourshettiwar, Palash Puri, Chetan Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India

ISBN: (纸本)9798350379945

Agriculture is changing into a more sustainable and productive sector as a result of the fusion of modern technologies, especially the Internet of Things (Io'T), with traditional farming methods. This shift, dubbed 'Smart Agriculture,' rethinks how farmers conduct cultivation, managing resources, and sustainability by using the power of online access, data analytics, and automation. A network of linked systems and gadgets that gather, transfer, and evaluate real-time data forms the basis of smart agriculture. With previously unheard-of precision and efficiency, farmers may now remotely track and supervise a variety of aspects of their farming activities thanks to this connectivity. The transformative influence of IoT on productivity, sustainability, or precision farming highlights the need for it in agriculture. A crucial element of smart agriculture is precision farming, which entails exact management of resources like pesticides, fertilizers, and water as well as real-time data processing. This study examines the ways in which integrated geospatial technologies, weather forecasting, and IoT -driven automation and actuation support productive and sustainable farming methods. The emphasis on aquaculture monitoring also draws attention to the wide range of uses for IoT in agriculture that go beyond conventional crop growing. The crucial part that IoT plays in meeting the growing need for food production on a worldwide scale while using scarce resources. Precision farming, management of water, and crop monitoring are made possible by Internet of Things technologies including sensors, drones, or automated machinery. These apps support environmental sustainability in agriculture, improve resource efficiency, and enable data-driven decision-making. This research work goes into more detail on the fundamental components of smart agriculture, such as sensor technologies, infrastructure connectivity, automation, data transfer, acquisition, and integration with geospatial tec

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

ER-Net:Efficient Recalibration Network for Multi-ViewMulti-Person 3D Pose Estimation

引用

computer Modeling in engineering & sciences 2023年第8期136卷 2093-2109页

作者： Mi Zhou Rui Liu Pengfei Yi Dongsheng Zhou National and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian116622China School of Computer Science and Technology Dalian University of TechnologyDalian116024China

Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application *** the introduction of end-to-end direct regression methods,the field has entered a new stage of ***,the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal *** this paper,we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy,which is applied to themulti-viewmulti-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external ***,it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy,which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding *** call this method as the Efficient Recalibration Network(ER-Net).Finally,experiments were conducted on two benchmark datasets for this task,Campus and Shelf,in which the PCP reached 97.3% and 98.3%,respectively.

关键词： Multi-view multi-person pose estimation attention mechanism computer vision

来源：评论

学校读者我要写书评

暂无评论

Application of AI and ML in Wound Healing and Skin Regeneration 4

Application of AI and ML in Wound Healing and Skin Regenerat...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Petkar, Taniya G. Kumar, Praveen Sarate, Kirtiksha U. Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331523923

By enabling precise, individualized, and effective treatments, the integration of artificial intelligence (AI) and machine learning (ML) into wound and skin healing is revolutionizing healthcare. Artificial intelligence-controlled tools improve wound assessment, anticipate healing, and collaborate with regular examinations using cutting-edge dressings and imaging technologies. AI computations work on the management of express data to enhance treatment systems, while advancements in nanomaterials like marine collagen and therapies employing young microorganisms further accelerate skin recovery. Despite encouraging findings, there are still problems, such as potential overfitting and dataset limitations. In general, machine learning is changing the way that injuries are treated by providing proactive, tailored solutions that aim to understand outcomes and lower the cost associated with clinical outcomes. © 2025 IEEE.

关键词： Personalized medicine

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Based System to Predict the Plant Disease Using Streamlit 4

A Deep Learning Based System to Predict the Plant Disease Us...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Warbhe, Mohan K. Bore, Joy Jordan Chaudari, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering MaharashtraSawangi Wardha442001 India

ISBN: (纸本)9798331523923

The proposed web application for tomato leaf disease detection exemplifies the transformative power of Artificial Intelligence and computer Vision in modern agriculture. Addressing the critical issue of early and accurate disease detection, this solution bridges the gap between advanced technological innovations and the practical needs of farming. The application is designed using Streamlit and Python to combine the usability of a friendly interface with the power of robust analytical capabilities in CNN, offering real-time reliable diagnosis and actionable insights to farmers. This application provides the highest possible accuracy and adaptability through the thorough training and evaluation of models and is a good asset for disease management. Deployment on scalable cloud platforms ensures access, reliability, and global reach. Future improvements, including extending disease detection to other crops, multilingual support, real-time recommendations, and integration with IoT devices, promise to expand its reach and utility. This initiative is a shining example of how AI-based solutions can transform agricultural practices by enhancing productivity, reducing losses, and ensuring sustainability. The application empowers farmers and contributes to a resilient agricultural ecosystem, addressing the pressing need for innovative and sustainable solutions in a rapidly evolving world. © 2025 IEEE.

关键词： Smart agriculture

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：