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Internet of Things Driven Rule Based Management Model for Heterogeneous Environment

SN Computer Science 2024年第5期5卷 565页

作者： Rambabu, Kalathiripi Saravanan, K. Misra, Siddharth Patil, Sandip Ramesh Rao, M. Srinivasa Mishra, Nilamadhab Chaudhari, Niraj C. Department of Electronics and Communication Engineering B V Raju Institute of Technology Narsapur Telangana Medak India Department of Information Technology R. M. K Engineering College (Autonomous) KavaraipettaiGummidipondi Taluk Thiruvallur Dist Tamilnadu Chennai 601206 India Marketing Birla Global University Odisha Bhubaneswar India Department of Communication Skills Bharati Vidyapeeth College of Engineering Belpada Navi Mumbai 400614 India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal 466114 India Dr. Moonje Institute of Management & amp Computer Studies Maharashtra Nashik India

The development of heterogeneous systems in the environment of smart homes using the Internet of Things (IoT) has been accelerated by recent developments in computer and communication technology. Nevertheless, a large number of these heterogeneous networks are stand-alone and do not adapt to cooperative task execution. As a result, inter-operation can be challenging, especially when it comes to providing services that residents prefer. In this study, we provide a novel rule-based management model (RBM) for a heterogeneous system in an IoT-based smart home environment. The suggested framework uses Event-Condition-Action (E-C-A) and the Simple Object Access Protocol (SOAP) to facilitate communication across different systems. With the help of the E-C-A rule mechanism, we have constructed the framework with several sub-systems to show its efficacy for inter-operation. A Local Area Network (LAN) environment was used to evaluate the framework's performance, and it proved to be trustworthy for an IoT-based smart home environment. The values of the experimental result are throughput (98.5%), execution time (80 s), communication cost (70%), energy management (98%), and latency (73 s). We apply swarm intelligence and grey wolf optimization (SI-GWO) to speed up the RBM and boost the performance of this model. Our experimental result shows that our RBM model is more efficient than other methods. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Event-Condition-Action (E-C-a) Internet of Things (IoT) Simple Object Access Protocol (SOAP) Smart home environment

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ActivityMamba: A CNN-Mamba hybrid neural network for efficient human activity recognition

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IEEE Transactions on Mobile computing 2025年

作者： Luo, Fei Li, Anna Jiang, Bin Khan, Salabat Wu, Kaishun Wang, Lu Great Bay University School of Computing and Information Technology Dongguan China Lancaster University School of Computing & Communications United Kingdom University of Petroleum College of Oceanography and Space Informatics Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Guangzhou China

Current research in human activity recognition primarily emphasizes enhancing accuracy, with limited exploration into computational efficiency and hardware compatibility. Recently, Mamba has sparked substantial interest within the realm of deep learning. Mamba is a hardware-aware algorithm enabling very efficient training and inference. Researchers are applying Mamba to various tasks, demonstrating significant promise in both language and vision tasks. It is worthwhile to investigate the use of Mamba for efficient human activity recognition. In this paper, we proposed a hybrid neural network that integrates CNN and visual Mamba, called ActivityMamba. The SE-Mamba block in ActivityMamba utilizes both CNN's local and Mamba's global context modeling while keeping computation and memory efficiency. We evaluated the ActivityMamba on five public benchmark datasets collected by using three different sensing techniques. ActivityMamba achieved higher performance than vision transformers, vision Mamba, and CNNs with fewer FLOPs and parameters. It sets a new SOTA on all five datasets, which are 91.78% OA and 89.13% F1 on the USC-HAD dataset, 99.19% OA and 98.64% F1 on the UT-HAR dataset, 99.82% OA and F1 on the DIAT dataset, 98.59% OA and 98.65% F1 on the UCI-HAR dataset, and 95.41% OA and 93.14% F1 on the UniMib dataset. Our work is the first to investigate the CNN-Mamba hybrid network for efficient human activity recognition. © 2002-2012 IEEE.

关键词： Active learning

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Sentiment Mining and Visualization of Covid-19-Related Weibo Dataset

Sentiment Mining and Visualization of Covid-19-Related Weibo...

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Software engineering and Computer Science (CSECS), International Conference on

作者： Jinhong Li Yejuan Xie Yushi Li Yunzhe Wang Yushan Pan Chengtao Ji Computing Department Xi'an Jiaotong-Liverpool University Suzhou China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

The COVID-19 pandemic profoundly disrupted daily life worldwide, eliciting diverse emotional responses as individuals faced the uncertainties of the crisis. Social media platforms like Weibo became critical outlets for public expression, capturing a rich spectrum of sentiments that evolved with key events and policy changes. This study utilizes Weibo data to conduct fine-grained sentiment analysis, addressing the limitations of traditional models that classify emotions into broad categories (positive, negative, neutral), which fail to capture the complexity of emotions during a prolonged health crisis. By comparing the performance of three advanced NLP models-BERT-LSTM, XLNet, and RoBERTa-this research identifies BERT-LSTM as a promising candidate for detecting temporal dependencies and nuanced emotions, including fear, anger, sadness, disgust, gratitude, surprise, and optimism. Leveraging BERT-LSTM, we annotated unlabeled data to build a comprehensive dataset, which was then used to develop a dynamic visualization system. This system illustrates sentiment trends over time, offering public health officials and policymakers actionable insights into emotional responses to critical events and policy developments. By revealing emotional responses to critical events, policy changes, and health developments, the system supports data-driven decision-making and enhances understanding of public mood dynamics. The findings highlight the value of fine-grained sentiment analysis and dynamic visualization in navigating public health crises, offering a framework that can be adapted for future crises or real-time sentiment monitoring efforts.

关键词： COVID-19 Sentiment analysis Uncertainty Social networking (online) Blogs Real-time systems Emotional responses Systems support Public healthcare Software engineering

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Simple and Efficient Knowledge Graph Attention Network for Recommendation

Simple and Efficient Knowledge Graph Attention Network for R...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Li, Yangding Fu, Shaobin Feng, Hao Zeng, Yangyang Wang, Jinghao Jiang, Zhihao Zhang, Lvyun Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University School of Information Science and Engineering Changsha China Shenzhen Lazada Software Technology Company Limited Shenzhen China Hechi University School of Big Data and Computer Science Guangxi China

ISBN: (纸本)9798350312492

Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive. © 2023 IEEE.

关键词： Recommender systems

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Enhancing Gastrointestinal Image Classification: A Fusion of CNN and Vision Trasformers

Enhancing Gastrointestinal Image Classification: A Fusion of...

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Contemporary computing and Communications (InC4), IEEE International Conference on

作者： Bachu Ganesh R Annamalai Senthil Jayapal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Collage of Computing and Information Science University of Technology and Applied Sciences Ibra Sultanate of Oman

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

In order to meet the pressing demand for early diagnosis in healthcare, we proposed a unique hybrid architecture in this study that is intended for the categorization of gastrointestinal (GI) illnesses. With fewer trainable parameters, faster training times, and less storage needed for model weights, our suggested architecture prioritizes efficiency achieves an impressive 94% accuracy. To effectively diagnose GI disorders, this design combines convolutional neural networks (CNNs) with Transformers. The creation of this hybrid architecture, which boasts excellent accuracy in illness categorization, decreased storage requirements, and efficiency in training, is one of the research's accomplishments. Our proposed model demonstrates its flexibility for many uses, encouraging simple implementation and expandability. Extensive tests performed on a large dataset of endoscopic images highlight our hybrid architecture's better performance over current techniques. With this study, diagnostics for gastrointestinal diseases have advanced significantly, opening the door to future diagnostic systems that will be more precise and effective. The proposed architecture has outperformed the traditional pre-trained models both in classification accuracy and time to train the model with no weights.

关键词： Training Deep learning Accuracy Computer architecture Transformers Gastrointestinal tract Convolutional neural networks

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A Critical Analysis of Object Tracking Using Radial Function Networks

A Critical Analysis of Object Tracking Using Radial Function...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Mishra, Amit Kumar Pandey, Neeraj Kumar Diwakar, Manoj Bahadure, Nilesh Bhaskarrao Dumka, Ankur Patni, Jagdish Chandra Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science & Engineering Dehradun India Symbiosis Institute of Technology Dept. of CSE Nagpur Campus Pune India Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India Alliance University Alliance School of Advance Computing Dept. of CSE Karnataka Bengaluru India

ISBN: (纸本)9798350394412

A important method in several fields, including driverless cars, medical imaging, human-computer interaction, and investigation systems, is object tracking. Because of their flexibility to changes in object presence and ambient factors, as well as their capacity to estimated complex, non-linear functions, radial networks have become a potent tool for object stalking in recent years. The article conditions for the analysis of the radical working networking where the algorithm is a tracking segment that aims in moving the object. Object recognition is one of the most basic and centralized factor analyses in determining outputs having relevant values and a strong research background. Radial functioning is a phenomenon that has gained rapid training and desirable properties for improving the presentation of networking sites. radial networks, a form of artificial neural network, utilize radial basis functions as initiation functions. Because of this characteristic, RBF networks are especially well-suited for applications like object tracking that call for high accuracy in function approximation and pattern recognition. We explore the design of RBF networks in detail, emphasizing the functions of the input layer, output layer, and hidden layer using radial basis functions. We examine the mathematical foundations that allow RBF networks to represent intricate connections in the data, emphasizing how well they can generalize and interpolate. © 2024 IEEE.

关键词： Radial basis function networks

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3rd Order Coupled-Resonator Bandpass Filter Assisted by Groove Gap-Waveguide technology for 26-GHz Radio Link Diplexer 10

3rd Order Coupled-Resonator Bandpass Filter Assisted by Groo...

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10th IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024

作者： Alibakhshikenari, Mohammad Parand, Peiman Zidour, Ali Virdee, Bal S. Longhi, Patrick See, Chan Hwang Limiti, Ernesto University of Rome "Tor Vergata" Electronics Engineering Department Rome00133 Italy University of M'hamed Bougara Faculty of Technology Departemnt of Electrical Systems Engineering Boumerdes Algeria London Metropolitan University Center for Communications Technology LondonN7 8DB United Kingdom Edinburgh Napier University School of Computing Engineering and the Built Environment EdinburghEH10 5DT United Kingdom

ISBN: (纸本)9798350364163

This paper investigates the design of a bandpass-filter (BPF) intended for 26-GHz radio-link diplexer using Groove-Gap-Waveguide (GGW) technology. Its unit-cells consist of a rectangular metal-pin which is connected to a bottom metal plate and its other side is electromagnetically coupled to a top-plate with an air-gap of approximately λ/4 that creates a bandgap, preventing unwanted electromagnetic-wave propagation. An array of these unit-cells forms a cavity resonator, allowing wave propagation over a specific conductive region. To achieve the desired passband response, a rectangular slot-channel is milled on the bottom metal plate, perturbing the cavity modes and creating the desired filter characteristics. © 2024 IEEE.

关键词： Bandpass filters

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Climate Change Analysis Using Machine Learning 3

Climate Change Analysis Using Machine Learning

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sharma, Dolly Gupta, Pawan Rawat, Gourav Kumar, Sharad Malik, Suraj Bansal, Nidhi School of Computing Department Graphic Era Hill University Haldwani India Biet Jhansi Dr. A. P. J. Abdul Kalam Technical University Faculty of Information Technology Lucknow India School of Engineering and Technology Sharda University Department of Computer Science & Engineering Greater Noida India Biet Jhansi Dr. A. P. J. Abdul Kalam Technical University Faculty of Computer Science & Engineering Lucknow India Iimt University Department of Computer Science and Engineering Meerut India Nagpur Campus Pune India

ISBN: (纸本)9798331518592

More analysis is required day to day due to the frequent changes in the weather. Every second the subject needs accurate analysis. This research paper is suggesting a useful technique for analysis using machine learning classification. Dynamic change occurs frequently in this scenario as the real-time application needs to get the process within the machine for accurate output. Accuracy is also a constraint for the situation. The dataset designed based on the selected location is divided by the number of zones, only then the analysis is done for processing to show the result. Sometimes an area is also restricted due to heavy disasters. Due to this care, the problem is divided into the sample area and classification is applied. The results generated by this research paper can show the optimal solution for the determined scenario. © 2024 IEEE.

关键词： Adversarial machine learning

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Urban Traffic Management for Reduced Emissions: AI-based Adaptive Traffic Signal Control

Urban Traffic Management for Reduced Emissions: AI-based Ada...

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Sustainable computing and Smart Systems (ICSCSS), International Conference on

作者： C. Ashokkumar D. Abitha Kumari S. Gopikumar N. Anuradha R. Santhana Krishnan Sakthidevi. I Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India Computer Science and Engineering RMK Engineering College Thiruvallur India Department of Civil Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Science and Humanities SCAD College of Engineering and Technology Cheranmahadevi India Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Information Technology Adhiyamaan college of Engineering Hosur India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Urban areas face significant challenges in managing traffic to reduce emissions and improve air quality. Traditional traffic management strategies often struggle to adapt to dynamic traffic conditions, leading to increased congestion and pollution. In response, researchers and policymakers are turning to innovative approaches, such as AI-based adaptive traffic signal control, to address these challenges effectively. Urban traffic management is crucial for mitigating emissions and improving air quality in densely populated areas. This research proposes an AI-based approach, namely Deep Flow (DQF), for adaptive traffic signal control to reduce emissions effectively. DQF integrates Deep Q-Learning with flow-based traffic modeling to dynamically adjust traffic signal timings based on real-time traffic flow data. To evaluate the effectiveness of DQF, extensive simulation analyses are conducted, comparing its performance with existing algorithms using appropriate metrics such as average delay, throughput, and emissions level. Results demonstrate that DQF outperforms traditional algorithms by significantly reducing emissions while maintaining efficient traffic flow. This study contributes to the advancement of eco-friendly urban traffic management strategies through innovative AI techniques.

关键词： Adaptation models Heuristic algorithms Simulation Urban areas Air quality Turning Throughput

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Banana Fruit Ripeness Assessment with Transfer Learning Models to Achieve Sustainable Quality

Banana Fruit Ripeness Assessment with Transfer Learning Mode...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Vinay Gautam Raj Gaurang Tiwari Ambuj Kumar Agarwal Himani Maheshwari Naresh Kumar Trivedi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India School of Computing Graphic Era Hill University Dehradun Uttrakhand India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Fruit ripeness is a crucial factor in agriculture as it dictates the quality of the fruit. Manually determining the maturity of fruit has various drawbacks, including the need for a lot of labor, time, and potential for inconsistency. One of the most important economic sectors in the world is agriculture. Still, there are instances when the manual method of judging fruit maturity is utilized. Fruit ripeness may be automatically classified with the emerging technologies of artificial intelligence. A hybrid deep-learning approach is suggested in this study for fruit ripeness identification. In order to determine when bananas are ripe, this study uses deep learning models called Xception and Inception that have already been trained. First, the input photographs were processed using both models to extract primary characteristics. Then, the pictures were categorized according to the degree of ripeness of the bananas. Incorporating Co-Lab and other deep learning software, the models were put into action. The Xception pre-trained network achieved a classification accuracy of 98.99%, significantly outperforming state-of-the- art models in a performance evaluation.

关键词： Training Deep learning Accuracy Biological system modeling Computational modeling Transfer learning Training data Agriculture Software Real-time systems

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