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共 34591 条记录，以下是4281-4290 订阅

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An Adaptive Nature-Inspired Paticle Bee Colony Deep Neural Network Technique for Event Detection in Video Sequences

An Adaptive Nature-Inspired Paticle Bee Colony Deep Neural N...

引用

Cognitive, Green and Ubiquitous Computing (IC-CGU), International Conference on

作者： Kumud Sachdeva Jasminder Kaur Sandhu Rakesh Sahu Dept. of Computer Science Engineering Chandigarh University Gharuan Punjab India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

Classifying and detecting difficult video events based on visual modalities remains an uncertain problem. Conventional video presentation approaches ineffectively extract each modality, hindering video event detection (VED) rates. The research methodology involves pre-processing steps: uploading video data samples, extracting video frames, converting to grayscale (rgb2gray), enhancing, and calculating smooth frames from the UCF -101 dataset. This pre-processing phase aims to deliver high-quality frames without data loss. Next, the feature extraction process employs the HoG method to extract feature vectors from refined video frames, facilitating the training process and efficiently reducing dimensions. An adaptive, nature-inspired PBC method is then implemented to select reliable and optimized feature sets from the extracted ones. These selected optimized feature vectors are input into different events of the deep neural network (DNN) classifier. Finally, reliable feature sets are identified through feature matching, and experimental outcomes demonstrate a significant 21.3% enhancement in VED and classification, assessed through accuracy rate, specificity (SP), sensitivity (SN), etc. Compared with traditional approaches using manually designed feature sets, the proposed approach proves more effective. Simulation outcomes on publicly available VED databases consistently outperform state-of-the-art video representation methods such as EFS-linear multi-support vector machine (MSVM), convolutional neural network (CNN), etc.

关键词： Training Visualization Adaptive systems Sensitivity Event detection Video sequences Feature extraction

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Hybrid Multimodal AI Framework for Real-Time Employee Activity Recognition and Workplace Motion Analytics

Hybrid Multimodal AI Framework for Real-Time Employee Activi...

引用

Smart Technologies, Communication and Robotics (STCR)

作者： Arun Kumar C A. Kethsy Prabavathy Dept. Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

In modern workplaces, finding a balance between employee productivity and privacy is a significant challenge. The Hybrid Multimodal AI Framework for Real-Time Employee Activity Recognition and Workplace Motion Analytics (HMAI-ERA) is a novel system that employs computer vision, motion tracking, and behavioral analysis to monitor employee activity in real time. The framework incorporates face identification (FaceNet & MTCNN), screen usage monitoring, mouse and keyboard activity tracking, and movement tracking to tag employees as "working" or "not working." Logs of activities with timestamps are saved in MongoDB for advanced analytics and visualization with an admin dashboard implemented using Flask. The framework also detects recurring mouse movement and idle screen usage to mark for potential anomalies. By combining several modalities, HMAI-ERA offers a non-intrusive yet effective way of monitoring workplace productivity. Experimental results indicate that the system can successfully monitor employee engagement with low false positives. The proposed framework offers a scalable, real-time, and privacy-aware solution for modern businesses seeking data-driven workplace insights.

关键词： Productivity computer vision Visualization Tracking Face recognition Employment Activity recognition Real-time systems Artificial intelligence Monitoring

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“RNN-Driven Prognosis of Heart Disease Based on Health Parameters”

“RNN-Driven Prognosis of Heart Disease Based on Health Para...

引用

Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Parvez Rahi Inderjeet Singh Ajay Pal Singh Alok Kumar Dept. of Computer Science & Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Cardiovascular diseases (CVD) are the leading cause of death globally and are estimated to affect 17. 9 million deaths annually. Early diagnosis is very important if one is to prevent adverse consequences. However, traditional diagnostic methods can take time to complete and miss subtle trends in patient information. This research presents an innovative approach utilizing a Recurrent Neural Network (RNN) model, optimized through Stochastic Gradient Descent (SGD), for the prediction of seven distinct types of heart disease: CAD, Heart Failure, AFib, Hypertensive Heart Disease, Congenital Heart Disease, Cardiomyopathy, and PAD. Their architecture is specifically suited for direct modeling of sequential patterns of the health related data that in turn helps to detect temporal patterns and changes in them. This model use ReLU activation function in their hidden layers, dropout in preventing overfitting and batch normalization for stable learning. Moreover, the feature augmentation is used in the model making it use various psychological and physiological parameters that help in making comprehensive predictions on the risks of heart disease. After training the RNN with a Kaggle dataset and properly tuning it, the model obtained an accuracy level of 91%, which means that is highly useful in identifying risk factors of cardiovascular diseases and could be used for early diagnosis and risk factors identification.

关键词： Heart Training Solid modeling Recurrent neural networks Stochastic processes Psychology Predictive models Cardiovascular diseases Batch normalization Tuning

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Real Estate Price Prediction Using PySpark MLlib

Real Estate Price Prediction Using PySpark MLlib

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Kadam Prajwal Dharmaraj Pradeep Kumar Gupta Keerthana Ajith Mahi Kolli Sangita Khare Niharika Panda Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

Prices of real estates in metropolitan landscapes harbour immense importance in steering the complexities of dynamic city environments. In India, the real estate sector contributes around 6–7% to India's GDP. Therefore, accurate forecasting of real estate prices is a factor in making informed decisions, affecting various stakeholders. Many techniques were used for this in past several years, like Hedonic models, Repeat-Sales models, Rule-Based Systems and Heuristics, etc. This study leverages the power of machine learning and big data to develop a robust framework for predicting housing prices in metropolitan landscapes. We employ PySpark, a powerful big data processing framework with built-in MLlib library (machine learning library), to analyse large-scale housing data encompassing various cities in India & to predict the prices for the same. By implementing a comparative analysis of prominent regression models - Random Forest, Linear Regression, Decision Tree, and Gradient-Boosted Tree - our approach identifies the most effective algorithms for real estate price prediction using MLlib library. Also, this study highlights the need for scalable solutions to manage an increasing number of data sources and emphasizes the PySpark library which will simplify big data handling and enable parallel computing. This study paves the way for utilizing advanced machine learning techniques and big data platforms to gain valuable insights into real estate markets. Our findings emphasize the critical role of combining these powerful tools to navigate the complex dynamics of urban housing and predict prices with greater accuracy.

关键词： Adaptation models Accuracy Machine learning algorithms Urban areas Predictive models Big Data Libraries

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Privacy-Preserving Techniques in AI-Based Healthcare: A Comprehensive Review

Privacy-Preserving Techniques in AI-Based Healthcare: A Comp...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Nandini Sharma Pritaj Yadav Dept. of Computer Science & Engineering Rabindranath Tagore University Bhopal India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

The incorporation of Artificial Intelligence (AI) in the healthcare has transformed patient care, diagnostics, and personalized Medicare in the today's era. As a result, it also increases significant privacy concerns due to sensitive healthcare data. This paper provides the comprehensive review of privacy-preserving methods, including federated learning, homomorphic encryption, differential privacy, blockchain, and secure multiparty computation that analyzing them in terms of privacy guarantees, computational complexity, scalability, and real-time applicability. Future work focuses on developing hybrid frameworks, lightweight privacy solutions for edge computing, and quantum-resistant cryptography to address evolving security threats. These advancements will play the pivotal role in ensuring secure, scalable, and ethically sound AI-driven healthcare systems.

关键词： Privacy Ethics Differential privacy Quantum computing Reviews Scalability Medical services Real-time systems Artificial intelligence Edge computing

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Detecting Near-Surface Melt-Water and Basal Ice-Water Interfaces by VHF Radar Sounder Data

Detecting Near-Surface Melt-Water and Basal Ice-Water Interf...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sanchari Thakur Lorenzo Bruzzone Dept. of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Very high-frequency (VHF) radar sounding from an orbital platform is a promising mission concept for subsurface observations of the Earth’s polar caps for mapping the ice sheets and ice shelves from the surface to the base. Previous feasibility studies have analyzed the detectability of the basal interface, the internal layers and the subglacial lakes in simulated VHF radargrams. However, there are few studies on the detection of near-surface melt-water and the ice-ocean interface, which are important for predicting the stability of the polar ice. This paper both presents a novel semi-supervised basal ice-water detection algorithm and exploits a state-of-the-art dielectric inversion technique to detect surface melt for the analysis of simulated VHF radargrams. Results show that about 90% of the ice-ocean interfaces are correctly detected with the proposed detection algorithm and that the dielectric inversion revealed a pool of melt-water in the Antarctic peninsula.

关键词： Radar remote sensing Radar detection Radar Lakes Ice Stability analysis Dielectrics

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A Real-Time Detection of Patient's Aggressive Behaviors on a Smart Sensor

A Real-Time Detection of Patient's Aggressive Behaviors on a...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Yudai Inoue Vasily G. Moshnyaga Dept. Electronics Engineering and Computer Science Fukuoka University Fukuoka Japan

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

Patient aggression in older people with dementia is one of the most challenging problems of medical care in today's nursing homes. Existing wireless autography-based technologies help caregivers to collect data related to behavioral and psychological symptoms of dementia. However, their inability to process the data in real-time reduces the efficiency of patient assessment and affects the quality of care. In this paper we present a computationally inexpensive technique that identifies aggressive behaviors on a sensor that measures the wrist acceleration of the dominant patient hand. Experiments show that the technique effectively detects aggressive behaviors in real-time and significantly reduces the wireless data transmissions.

关键词： Wireless communication Wrist Wireless sensor networks Accuracy Medical services Real-time systems Frequency measurement

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Exploring the Efficacy of Deep Learning and Statistical Approaches in Temperature Forecasting

Exploring the Efficacy of Deep Learning and Statistical Appr...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Tarunkumar K Aditya Umesh Maithri Humbarwadi Sohan B S M S Bhargavi Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

Temperature forecasting has become increasingly critical due to the rapidly changing climate, especially in urban environments where temperature fluctuations impact public health, infrastructure, and overall quality of life. Accurately predicting temperature patterns is difficult owing to the complexity of climate systems and the increasing variability caused by global climate change. This study addresses the issue by incorporating key weather features to forecast temperatures. The study investigates conventional statistical models, such as ARIMA and SARIMA, and cutting-edge methodologies for deep learning, such as Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Transformers. Among these, the Transformer model stands out because it uses its attention mechanism to detect precise trends in longitudinal data, making it ideal for high-precision temperature forecasting. By enhancing the accuracy of these predictions, the system provides an optimal tool for mitigating the impacts of climate change, helping urban areas better prepare for and adapt to temperature fluctuations. The study highlights the importance of advanced forecasting models in addressing the growing challenge of climate variability, contributing to more effective climate adaptation strategies.

关键词： Deep learning Temperature distribution Climate change Accuracy Urban areas Bidirectional long short term memory Predictive models Transformers Forecasting Meteorology

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AuthiFi: An Advanced Blockchain e-KYC Solution with Enhanced Wallet Security Using Shamir’s Secret Sharing

AuthiFi: An Advanced Blockchain e-KYC Solution with Enhanced...

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International Conference on computer and Information Technology (ICCIT)

作者： Sagar Karmoker Md. Adib Khan Mohammad Rifat Ahmmad Rashid Rashedul Amin Tuhin Nur Adnan Chowdhury Anik Mobinul Ahasan Tanjid Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Implemented conventional KYC methods in Bangladesh have several drawbacks such as data leakage, unproductiveness, and information opacity that affect the identity confirmation process of users across the organizations. Blockchain technology provides an efficient and secure means of developing supply chain transparency. The research outlines the design of an e-KYC system using blockchain, with wallet sharing service provided through sharding and enhanced identity validation through machine learning. Some questions answered by the system include private key management, transaction, and gas fees for user sovereignty, security, and accurate data in transactions. All users benefit from ease in sharding of wallets, while machine learning ensures that the chances of fraud are minimized, and accuracy enhanced. Specifically, this approach improves security, transparency, and operation, which can dramatically transform the process of identity verification in the financial sector of Bangladesh making the system more reliable.

关键词： Sharding Accuracy Supply chains Refining Redundancy Machine learning Transforms Blockchains Reliability Research and development

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A Review on Deep Learning Based Question Answering with Natural Language Processing in Healthcare

A Review on Deep Learning Based Question Answering with Natu...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： Abdul Saleem Javeed C Gopala Krishnan Dept. of Computer Science & Engineering GITAM School of Technology Bengaluru INDIA

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Over the Internet, an efficient approach and promising solution to retrieve significant information envisages the beginning of Question Answering Systems (QAS). Because of data sources availability, the deep learning model based on open domain QAS focuses a substantial quantity of research work. Because of the lack of medical datasets, less attention is received in the medical domain. The deep learning (DL) utility increases the development of huge data in the healthcare domain. In the past years, popularity has grown in hardware parallelism and quick data storage availability. The challenge, pipelines, and tasks involved in medical NLP and medical imaging deploy the existing review of research in this work. The medical NLP and imaging fields deploy DL structures with its extensive survey is presented here. Over all 39 research papers are collected for this survey analysis from the years 2017 to 2023. The diagnosis is enhanced with medical imaging, Natural language processing, and DL combination to detect the appropriate combination. Each of the outcomes is shown graphically. The information provided in this survey is quite useful for newcomers carrying out research in medical informatics.

关键词： Deep learning Surveys Reviews Soft sensors Pipelines Medical services Question answering (information retrieval)

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