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检索条件"机构=Department Of Computer-Aided Design And Engineering Design"

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Harnessing Key Phrases in Constructing a Concept-Based Semantic Representation of Text Using Clustering Techniques 8th

Harnessing Key Phrases in Constructing a Concept-Based Seman...

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8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023

作者： Mansour, Ali Mohammad, Juman Kravchenko, Yury Kravchenko, Daniil Silega, Nemury Department of Computer Aided Design Southern Federal University Taganrog347900 Russia Department of System Analysis and Telecommunications Southern Federal University Taganrog347900 Russia

ISBN: (纸本)9783031495519

This paper introduces a modified approach for representing text as semantic vectors, building upon the Bag of Weighted Concepts (BoWC) method developed in previous research. The limitations of the BoWC method are addressed, and a proposed solution is presented. Instead of using unigrams, the authors propose extracting key phrases that best represent each document to generate high-quality concepts and reduce concept overlap. These unique key phrases are then used to construct the concept dictionary. Document vectors are created by mapping document key phrases to the concept dictionary using a modified concept weighting function that considers the weight of the key phrase within the document. To evaluate the effectiveness of the resulting vectors, they were employed in a clustering task and compared against robust baselines. Experimental studies demonstrate that the proposed modifications enhance the quality of document vector representation, as evidenced by a minimum 4% increase in clustering accuracy based on the V1 metric. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： CF-IDF concept extraction document vectorization key phrase extraction keyword extraction text clustering text mining

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Comprehensive Overview of Gas and Bio-Sensors Enabling Artificial Intelligence in Healthcare 2

Comprehensive Overview of Gas and Bio-Sensors Enabling Artif...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Raut, Yashasvi Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra India Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra India

ISBN: (纸本)9798331528713

Gas and biosensors are crucial in the modern healthcare system, enabling non-invasive monitoring and diagnosis of various medical conditions. These sensors are used in various applications, including smart home health monitoring devices and critical care units. Common non-imaging sensor types include gas or VOC sensors, diagnostic ECG sensors, blood glucose sensors, pulse oximeters, blood pressure sensors, body temperature sensors, EEG sensors, accelerometers, and imaging sensors like X-rays, MRIs, CT scans, and ultrasounds. Healthcare-related sensors offer numerous advantages, including diagnosing and treating medical conditions, monitoring vital signs, enabling remote patient monitoring, personalized healthcare, and early detection and prevention. However, issues like data accessibility, privacy, comfort, security, and accuracy must be considered when installing sensors in homes. Efficient data sharing and analysis require devices, sensors, and platforms to communicate effectively. Real-time data processing is essential for prompt diagnosis and intervention. In this article, we have put it briefly: medical sensors are transforming the way we track, identify, and manage adverse health conditions. © 2024 IEEE.

关键词： Glucose sensors

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ResNet50-Based Deep Learning Model for Lung Disease Detection

ResNet50-Based Deep Learning Model for Lung Disease Detectio...

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

作者： Hajbe, Janhavi R. Patle, Sneha K. Talvekar, Shreya V. Khonde, Tanvi W. Yesankar, Prajyot R. Faculty of Engineering and Technology Department of Computer Science & Design Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331509828

By enabling a highly accurate examination of the chest x-ray, deep learning, for example, is changing the methods of recognizing lung disorders. In order to classify lung diseases, such as bacterial pneumonia, viral pneumonia, TB, and COVID-19, this work describes the use of ResNet50, a kind of CNN and a deep learning technique. We prove the potential of deep learning to discern these diseases from the two conventional approaches and most importantly, the patterns one could implicitly observe. The article also discusses the challenges that deep learning may pose on to the healthcare industry including data privacy, availability of data and the issue of overfitting. The paper links with this part by looking at the future of this area of specialization, which includes;multimodal fusion, operational AI, real-time identification, personalized care, data issues, and concerns, and ethics. But if deep learning is successful in addressing all the above-discussed challenges then it can bring the situation to the real world where the identification of lung diseases happens in a previous phase which in turn results in better treatment procedures and higher patient outcomes. © 2025 IEEE.

关键词： Lung cancer

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Disease Prediction and Specialist Recommendation Using Machine Learning 1

Disease Prediction and Specialist Recommendation Using Machi...

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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

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

Multimodal Approach for depression detection: Integrating sp...

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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

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Effect of Pesticides on Crop Health Monitoring Based on NDVI Using Machine Learning 3

Effect of Pesticides on Crop Health Monitoring Based on NDVI...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Pawar, Suvarna Badure, Aman Bade, Adikrishna Kolhe, Sai Takwane, Prasad Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331518592

The aim of this research paper is to evaluate the crucial impact of pesticides. This control measure is an important agricultural input for improving crop health. However, due to the nature of agriculture, we need developments in machine learning which are urgently required to analyze it. The utilization of pesticides is important in preventing severe damage from pest and disease outbreaks. This work suggests the application of remote sensing finding devices like drones with multispectral imaging, which can be used to obtain Normalized Difference Vegetation Index (NDVI), with the aim of measuring the status of the crops' health as a function of the chlorophyll content of the plants. The model being presented is based on the use of IoT sensors, drone images, and machine learning algorithms to supervise the energy status of the plants in the field. Preliminary imaging data based on NDVI will highlight the images of drones conditions. However, in this research offers solutions to the problem of diverse resolution images that need to be registered in order to create a mosaic. In the end, the results will illustrate how pesticides impact the efficiency of crops, which will help in developing sustainable agriculture solutions that are environmentally friendly. © 2024 IEEE.

关键词： crop health machine learning NDVI pesticides Precision agriculture

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

E-Authentication System using Eye-Blinking and Deep Learning

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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

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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...

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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

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Prediction of Student Performance in Online Classes During COVID-19 Pandemic 5th

Prediction of Student Performance in Online Classes During C...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Sudharshana, N. Naren, J. Sumathi, D. Chennai India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (纸本)9789819747108

Despite global disruptions caused by the COVID-19 pandemic, education sector adapted and continued to function online. Undergraduate courses happened online. Variations in students’ performance emerged due to differing approaches in incorporating online education into routine life amidst challenging conditions at home and in society during the pandemic. It was necessary to find out whether online education aided academics. A solution based on a machine learning is proposed and implemented in the paper to forecast an individual’s performance using data gathered from a user survey among university students regarding their perceptions of online education. The user survey comprised questions about factors that could affect academic performance. A diverse range of algorithms are meticulously evaluated to identify one that delivers precise predictions of student performance, leading to an effective restructure of educational landscapes to replicate the immersive and engaging experience of in-person classes, fostering a conducive environment for students to wholeheartedly focus on academic pursuits. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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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...

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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

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