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Air quality and dust level monitoring systems in hospitals using IoT

Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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Image Caption Generation using CNN and Audio Conversion 2

Image Caption Generation using CNN and Audio Conversion

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Priya, K. Babu, R.Vijay Reddy, M.Muralidhar Mohan Reddy, T.H. Maanesh, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Artificial Intelligence & Data Science Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Arizona state university Department of Data Science Tempe United States

ISBN: (纸本)9798350368413

This research focuses on developing a system that can generate descriptive captions for images and convert them into audio output. The system employs a Convolutional Neural Network (CNN) to extract visual features from images, which are then fed into a Gated Recurrent Unit (GRU) to generate textual descriptions. The generated captions are subsequently converted into audio using text-to-speech techniques. By training the model on a large dataset of image-caption pairs, the system learns to associate visual information with textual descriptions, enabling accurate and coherent caption generation. This research contributes to the advancement of image understanding and generation, with potential applications in various fields such as image search, accessibility, and content creation. © 2024 IEEE.

关键词： Convolutional neural networks

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Prediction of Word Game Trends and Human Behavior Based on ARIMA and BP Neural Networks

Prediction of Word Game Trends and Human Behavior Based on A...

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2023 International Conference on Artificial Intelligence, Systems and Network Security, AISNS 2023

作者： Wu, Rongbang Ye, Zhijie Ni, Mo Fuzhou University College of Computer and Data Science College of Software Fujian Fuzhou350108 China

ISBN: (纸本)9798400716966

As an emerging word puzzle game provided by New York Times daily, Wordle attracts everyone worldwide because the rules are simple, and the game is specifically designed for relaxing in fragmented time. This study aims to enhance the Wordle game experience by analyzing historical data, predicting future gamer numbers using double ARIMA that improves upon traditional ARIMA, and exploring word attributes. We investigated the impact of word frequency and the number of repeated letters on puzzle difficulty, revealing a correlation through Pearson's coefficient test. Utilizing a BP neural network, we predicted word difficulty and optimized K-means clustering for a comprehensive analysis. Additionally, we discovered a relationship between gamer numbers and special days, conducting sensitivity analysis to refine our model. Our study contributes by pioneering the double ARIMA approach, refining clustering methods, inferring word difficulty mathematically, and uncovering hidden insights in human behavior through multidimensional data analysis. © 2023 ACM.

关键词： K-means clustering

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LGMD-based Neural Network for Detecting Abnormal Velocity Targets in Moving Crowd 9

LGMD-based Neural Network for Detecting Abnormal Velocity Ta...

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9th International Symposium on computer and Information Processing Technology, ISCIPT 2024

作者： Zhao, Zhongxiang Hu, Bin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Guizhou University College of Computer Science & Technology Department of Computer Science Guiyang550025 China Artificial Intelligence Research Institute of Guizhou University Guizhou University Guiyang550025 China

ISBN: (纸本)9798350388404

Detecting abnormal velocity targets is critical especially in public surveillance for crowd activity monitoring. Although there are some explorations on one such issue, traditional methods still perform poorly in complex scenes. In this paper, a novel visual neural network is investigated to perceive abnormal velocity targets in moving crowd, based on the latest neurophysiological achievements revealed in locusts' vision systems. The proposed neural network contains two neural counterparts, i.e., the presynaptic and the postsynaptic networks. The former one receives visual signals and processes them to capture the motion cues of different targets, and the latter one filters the salience energies to perceive abnormal velocity targets in the field of view. Numerical experiments carried out show that the proposed neural network can effectively detect abnormal velocity targets in moving crowd. This study is an important step towards dynamic visual information processing in crowd behavior analysis. © 2024 IEEE.

关键词： Visual servoing

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Spatio-Temporal Graph Neural Networks for Water Temperature Modeling 1

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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition, S+SSPR 2024

作者： Fankhauser, Benjamin Bigler, Vidushi Riesen, Kaspar Institute of Computer Science University of Bern Bern Switzerland Institute for Optimisation and Data Analysis Bern University of Applied Sciences Biel Switzerland

ISBN: (数字)9783031805073

ISBN: (纸本)9783031805066

River water temperature modeling – a time series problem where spatial relations matter – is important to understand our environment. Currently two research directions are present to tackle this problem, viz. Recurrent Neural Networks, namely long short-term memory (LSTM), and Graph-based approaches, which exploit the natural tree structure of rivers. In the present paper, we extend a state-of-the-art LSTM method for water temperature modeling with a Graph Convolutional Network and a Graph Isomorphism Network. This novel combination results in a spatio-temporal neural network which can be applied for node predictions in any graph having nodes with unique identifiers. In the present paper, we apply the novel procedure on the Swiss River Network (a data set with decades of measurements of river temperature and atmospheric variables in Switzerland). In an experimental evaluation we show that the proposed method is robust in convergence and improves the state-of-the-art result by several percentage points in terms of Root Mean Squared Error. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Rivers

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Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model 31

Enhancing Rumor Detection Methods with Propagation Structure...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Cui, Chaoqun Li, Siyuan Ma, Kunkun Jia, Caiyan School of Computer Science and Technology & Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798891761964

Pretrained Language Models (PLMs) have excelled in various Natural Language Processing tasks, benefiting from large-scale pretraining and self-attention mechanism's ability to capture long-range dependencies. However, their performance on social media application tasks like rumor detection remains suboptimal. We attribute this to mismatches between pretraining corpora and social texts, inadequate handling of unique social symbols, and pretraining tasks ill-suited for modeling user engagements implicit in propagation structures. To address these issues, we propose a continue pretraining strategy called Post Engagement Prediction (PEP) to infuse information from propagation structures into PLMs. PEP makes models to predict root, branch, and parent relations between posts, capturing interactions of stance and sentiment crucial for rumor detection. We also curate and release large-scale Twitter corpus: TwitterCorpus (269GB text), and two unlabeled claim conversation datasets with propagation structures (UTwitter and UWeibo). Utilizing these resources and PEP strategy, we train a Twitter-tailored PLM called SoLM. Extensive experiments demonstrate PEP significantly boosts rumor detection performance across universal and social media PLMs, even in few-shot scenarios. On benchmark datasets, PEP enhances baseline models by 1.0-3.7% accuracy, even enabling it to outperform current state-of-the-art methods on multiple datasets. SoLM alone, without high-level modules, also achieves competitive results, highlighting the strategy's effectiveness in learning discriminative post interaction features. © 2025 Association for Computational Linguistics.

关键词： Tweets

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Threshold Huffman-Based Covert Timing Channel Detection

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International Journal of Network Security 2025年第1期27卷 61-71页

作者： Lin, Yuwei Chen, Yonghong Zhuang, Xiaolong Huang, Xuwen School of Computer Science and Technology Huaqiao University Fujian Xiamen361000 China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Fujian Xiamen361000 China

Network covert timing channels in computer networks evade effective detection by traditional security mechanisms such as firewalls, posing a significant threat to network security. However, most existing methods struggle to detect such channels when the window size is small. Additionally, the emergence of the new ϵ-κlibur channel has resulted in a decline in performance for detection methods such as GAS, SnapCatch, and DNN when detecting this particular channel. In this paper, we investigate a Huffman tree-based approach for detecting anomalous nodes in traffic and achieving the classification of channels through the design of weight intervals. Our method achieves an average detection rate of over 95% for each channel. Additionally, compared to GAS, SnapCatch, and DNN, our method improves the detection rates for the ϵ-κlibur channel by 11.75%, 8.46%, and 4.81%, respectively. The experimental results demonstrate that our proposed approach successfully detects ϵ-κlibur and achieves detection in a small window. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： computer system firewalls

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A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective 38

A2PO: Towards Effective Offline Reinforcement Learning from ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qing, Yunpeng Liu, Shunyu Cong, Jingyuan Chen, Kaixuan Zhou, Yihe Song, Mingli College of Computer Science and Technology Zhejiang University China State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Offline reinforcement learning endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with the support of behavior policies to tackle the out-of-distribution problem. However, existing works often suffer from the constraint conflict issue when offline datasets are collected from multiple behavior policies, i.e., different behavior policies may exhibit inconsistent actions with distinct returns across the state space. To remedy this issue, recent advantage-weighted methods prioritize samples with high advantage values for agent training while inevitably ignoring the diversity of behavior policy. In this paper, we introduce a novel Advantage-Aware Policy Optimization (A2PO) method to explicitly construct advantage-aware policy constraints for offline learning under mixed-quality datasets. Specifically, A2PO employs a conditional variational auto-encoder to disentangle the action distributions of intertwined behavior policies by modeling the advantage values of all training data as conditional variables. Then the agent can follow such disentangled action distribution constraints to optimize the advantage-aware policy towards high advantage values. Extensive experiments conducted on both the single-quality and mixed-quality datasets of the D4RL benchmark demonstrate that A2PO yields results superior to the counterparts. Our code is available at https://***/Plankson/A2PO. © 2024 Neural information processing systems foundation. All rights reserved.

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Wireless Sensor Network-Driven IoT Solution for Advanced Borewell Rescue Robotics 4

Wireless Sensor Network-Driven IoT Solution for Advanced Bor...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Meera, V. Mercia, Sharon Nesa PadmaPriya, J. Murugavalli, S. Panimalar Engineering College Artificial Intelligence And Data Science Chennai City Campus Chennai India Panimalar Engineering College Computer Science Engineering Chennai City Campus Chennai India Panimalar Engineering College Department of Computer Science Engineering Chennai India Panimalar Engineering College Department of Computer Science Engineering Chennai City Campus Chennai India

ISBN: (纸本)9798350387063

The fact that deaths from bore wells persist in India is highly alarming, particularly when young people are involved. Since 2009, there have been more than 40 documented child deaths, and the National Disaster Response Force (NDRF) reported a worrisome 70% failure rate for rescue efforts. The solution must be found quickly and effectively. In response to this humanitarian problem, this paper has proposed a novel design for a robotics device. This proposed system is a creative illustration of how human interaction and technology may be used in tandem to swiftly evaluate the victims and guarantee their safe release. The main element of this solution is a skillfully designed robotic arm that smoothly integrates Internet of Things (IoT) devices (Rasp Pi or Arduino) and is powered by high-precision servo motors. Among the many features it offers are an advanced APR (Auto Playback Recorder) system, an infrared-based CCTV camera, a strong LED lighting system, advanced sensors (Passive Infrared, Ultrasonic Distance Measurement, Heartbeat, Temperature, and Gas), and a dual-functional mask. Artificial intelligence (AI) is another tactic that will be used in the rescue operation. This paper discusses the pressing need for safe, efficient bore well rescues and offers a comprehensive analysis of the methodology. This project uses cutting edge technology and human collaboration to stop more catastrophes and increase the speed and effectiveness of life-saving procedures in emergency situations. In an effort to contribute to the creation of a more secure and safe future, this research is presented as a critical first step in lowering the death toll related to Indian bore wells. © 2024 IEEE.

关键词： Wireless sensor networks

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Movable Antenna-Aided Hybrid Beamforming for Multi-User Communications

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9899-9903页

作者： Zhang, Yichi Zhang, Yuchen Zhu, Lipeng Xiao, Sa Tang, Wanbin Eldar, Yonina C. Zhang, Rui University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications Chengdu611731 China Kash Institute of Electronics and Information Industry Kash844000 China Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Chinese University of Hon g Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China

In this correspondence, we propose a movable antenna (MA)-aided multi-user hybrid beamforming scheme with a sub-connected structure, where multiple movable sub-arrays can independently change their positions within different local regions. To maximize the system sum rate, we jointly optimize the digital beamformer, analog beamformer, and positions of sub-arrays, under the constraints of unit modulus, finite movable regions, and power budget. Due to the non-concave/non-convex objective function/constraints, as well as the highly coupled variables, the formulated problem is challenging to solve. By employing fractional programming, we develop an alternating optimization framework to solve the problem via a combination of Lagrange multipliers, penalty method, and gradient descent. Numerical results reveal that the proposed MA-aided hybrid beamforming scheme significantly improves the sum rate compared to its fixed-position antenna (FPA) counterpart. Moreover, with sufficiently large movable regions, the proposed scheme with sub-connected MA arrays even outperforms the fully-connected FPA array. © 1967-2012 IEEE.

关键词： Budget control

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