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Multi-publisher and Subscriber Lightweight Communication for IoT Using the Cloud 5th

Multi-publisher and Subscriber Lightweight Communication for...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Trivedi, Atindra Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

ISBN: (纸本)9789819719228

The rapid proliferation of IoT devices has spurred the need for efficient and scalable communication protocols that enable seamless data exchange between IoT sensors and cloud-based platforms. This paper delves into the realm of multi-publisher and subscriber lightweight communication for IoT, leveraging the capabilities of the MQTT protocol. In the context of the IoT, sensors play a pivotal role as data collectors, extracting valuable insights from the physical world. This work elucidates the process of connecting these sensors to the cloud, emphasizing the significance of MQTT as the communication bridge. The MQTT protocols publish–subscribe model fosters a decoupled communication paradigm, allowing IoT sensors to act as publishers and cloud-based entities as subscribers. This architecture not only minimizes resource consumption but also accommodates the growing number of IoT sensors while ensuring efficient data transmission and real-time analysis. Through a comprehensive exploration of the interplay between IoT sensors, cloud connectivity, and MQTT communication, this paper sheds light on the potent synergy driving the evolution of IoT ecosystems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Real-Time Periocular Based Face Recognition System 7

Real-Time Periocular Based Face Recognition System

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Ahalya Rani, G. Harshitha, Anthannagari Reddy, Chada Sadwik Yogendra, Antharvedi Sailaja, Annareddy B V Raju Institute of Technology Computer Science and Engineering Telangana Narsapur502313 India

ISBN: (纸本)9798350372816

This paper presents a stable technique for extracting periocular characteristics from photographs, with an emphasis on critical regions such as eye outlines and their environs. Our technique, which employs edge detection, Gabor filters, and Local Binary Patterns (LBP), accurately captures differentiating periocular properties required for security systems. It properly deals with issues including illumination differences, occlusions, and accessories. In addition, the system has a face identification component optimized for congested situations, which can recognize numerous faces and provide a count of those spotted. Experimental evaluations validate the algorithm's efficacy, highlighting its potential for biometric authentication and monitoring. Detecting and counting faces in crowded environments provides vital information about crowd control, safety, and operational efficiency in a variety of scenarios. © 2024 IEEE.

关键词： Gabor filters

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Cotton Leaf Disease Based on Image Processing Using Deep Learning

Cotton Leaf Disease Based on Image Processing Using Deep Lea...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Menaga, D. Shri Roshan, B.T. Shri Vikaas, S.R. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319019

One of the most important occupations in India is agriculture. Out of all the crops, cotton is the best and is crucial to the agricultural economy of the country. In India, 40-50 million people work in the cotton trade and processing, while six million farmers directly depend on the crop. The cotton leaf disease has grown in importance over the last few decades, resulting in losses to crops, farming operations, and financial resources. To achieve this aim, we first need to acquire different images of cotton plants. We can use image processing techniques to analyze dead leaf images and extract features like color, texture, and other characteristics with the Deep CNN model's assistance. In addition to being less expensive and more straightforward, automatic disease detection supports machine vision, which offers image-based automated process control and inspection. To properly train the algorithm, we will be using a dataset of approximately 1752(approximately 440 images in each class) images classified into different categories according to the diseases. This model will be developed using tools present in Anaconda such as Jupyter Notebook, Spyder etc. The results of this project will demonstrate whether using it in real-time applications is feasible and whether traditional or manual disease and pest identification could benefit from the use of IT- based solutions. © 2024 IEEE.

关键词： Cotton

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Application of Species Distribution Modelling in Agriculture: A Review 1

引用

International Conference on Data Analytics and Management, ICDAM 2023

作者： Agbezuge, Eric Yaw Balakrishnan, P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (数字)9789819965472

ISBN: (纸本)9789819965465

Identification of the right abiotic factors for an ambient environment, soil, and water is critical for successful agricultural activities. However, these factors have been influenced by climate change, and other factors as such are no longer in their natural state of equilibrium. This has affected the ecosystem and has made previously suitable environments currently unsuitable for most organisms and crop production. To have production of healthy crops with high yield and quality, precise agricultural inputs are required, including the right environmental abiotic factors. This goal has been achieved to some extent via extensive studies carried out by various scholars to ascertain their impact and possible mitigating action. In this paper, we first investigated the factors that influence the environment adversely and the application of SDM as a solution depicted in other studies and further presented an overview of some of the SDM-based software packages and their applications, with much emphasis on agronomy. This review aims to provide insight into, how effective computer-based SDM has been or otherwise in resolving the challenges in the agriculture sector and the future direction for the application of SDM-based solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Climate change

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GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning 38

GITA: Graph to Visual and Textual Integration for Vision-Lan...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wei, Yanbin Fu, Shuai Jiang, Weisen Zhang, Zejian Zeng, Zhixiong Wu, Qi Kwok, James T. Zhang, Yu Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Australia Institute for Machine Learning University of Adelaide Australia Tencent China

Large Language Models (LLMs) are increasingly used for various tasks with graph structures. Though LLMs can process graph information in the textual format, they overlook the rich vision modality, which is an intuitive way for humans to comprehend structural information and conduct general graph reasoning. The potential benefits and capabilities of representing graph structures as visual images (i.e., visual graph) are still unexplored. To fill the gap, we innovatively propose an end-to-end framework, called Graph to vIsual and Textual IntegrAtion (GITA), which incorporates visual graphs into general graph reasoning. Besides, we construct the Graph-based Vision-Language Question Answering (GVLQA) dataset from existing graph data, which is the first vision-language dataset for general graph reasoning. Extensive experiments on the GVLQA dataset and five real-world datasets show that GITA outperforms mainstream LLMs on general graph reasoning. Moreover, experimental results demonstrate the effectiveness of the layout augmentation on visual graphs and pretraining on the GVLQA dataset. © 2024 Neural information processing systems foundation. All rights reserved.

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Detection of Anomaly in Internet of Things using Gated Recurrent Unit with Artificial Bee Colony 1

Detection of Anomaly in Internet of Things using Gated Recur...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Karthik, M. Ganesh Divya, N. Sree Shnaeen, Amaar Haamid Alagarraja, K. Praveena, S. Gitam University Department of Computer Science and Engineering Bengaluru India Mahatma Gandhi Institute of Technology Department of Information Technology Hyderabad India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electronics and Communication Engineering Mamallapuram India

ISBN: (纸本)9798331529246

Now a days, development in Internet of Things (IoT) networking generates large attack surface for cybercriminals, that impact the industry has resulted with exponential increase in cyber-attacks. To develop a robust anomaly detection system, a Gated Recurrent Unit Anomaly Detection (GRUAD) is proposed in this research. Initially, the data is collected from the Toyama IoT Network Traffic Dataset (TON-IoT) dataset which includes samples of packet capture data (PCAP files) and network logs (text files). Then, the collected data is pre-processed using data cleaning technique which removes noisy and irrelevant data, then label encoding and data normalization assist to transform data into a suitable format while data splitting. Further, features are selected using Mutual Information (MI) in which features with high mutual information with the target variable are selected. Then, Pearson Coefficient Correlation (PCC) identifies features with strong linear correlations. At the end, K-Best selects the top-ranked features. Finally, the Multistep-Prediction is performed using GRUAD by hyperparameter tuning the Artificial Bee Colony (ABC). The proposed model gained better results in terms of precision with value 98.54%, f1 score of 98.74%, Accuracy of 98.62%, recall of 98.77% comparing with existing method Logistic Regression (LR) respectively. © 2025 IEEE.

关键词： Logistic regression

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Optimized Energy Efficient Routing Network using Lévy Flight with Seagull Optimization in Wireless Sensor Networks 9

Optimized Energy Efficient Routing Network using Lévy Fligh...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Buvanesvari, M. Shivaprakash, M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Thandalam Pachama602117 India Manakula Vinayagar Institute of Technology Pondicherry University Department of Computer Science and Engineering Puducherry605107 India

ISBN: (纸本)9798350377972

Wireless Sensor Network (WSN) is comprised of infinite Sensor Nodes (SNs), which are low-priced, low-power, and minute features. These nodes are data-centric and have the responsibility to gather important data in the pretentious area and transfer the data to the sink node (control base or center station) in a single- or multi-hop technique. To commence WSNs to sustain in a healthy and secure atmosphere, safety devices according to identity authentication and cryptography have been proposed to oppose outside threats on WSNs. Routing is predominant in WSNs for optimizing their efficiency, scalability, and longevity. Routing represents a process of describing the optimum routes for the data transmission from SNs to the sink. Dynamic routing guarantees reliable and timely data transmission however balances the network load and decreases energy intake. WSNs can gain improved routing using metaheuristics that outcomes in lengthy operational lifespans and better performance of the network. This study presents an Optimized Energy Efficient Routing Network using Lévy Flight with Seagull Optimization Algorithm (OEERN-LFSOA) technique in WSN. The presented OEERN-LFSOA technique mainly focusing on enhancing the energy efficiency and lifespan of WSNs using routing approaches. To achieve this, the presented OEERN-LFSOA method involves the integration of the Lévy Flight with Seagull Optimization Algorithm (LFSOA) model. Furthermore, the presented OEERN-LFSOA method obtains a fitness function for exploiting the lifetime and diminishing consumption of energy. A comprehensive set of simulations was directed to show the performance of the OEERN-LFSOA method. The experimental outcomes stated that the OEERN-LFSOA method achieves better performance than other recent techniques. © 2024 IEEE.

关键词： Sensor nodes

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Performance Analysis of Multiple HAPS-Based Hybrid FSO/RF Space-Air-Ground Network 16

Performance Analysis of Multiple HAPS-Based Hybrid FSO/RF Sp...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Singh, Deepshikha Swaminathan, R. Marrapu, Aravind Madhukumar, A.S. Indian Institute of Technology Indore Department of Electrical Engineering India School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350383119

Sixth generation (6G) networks are anticipated to support increasingly data-hungry applications by expanding the network capabilities and connectivity. Free space optics (FSO) communication with space-air-ground (SAG) network becomes a potential solution to meet the enormous traffic demands and ubiquitous connectivity requirement in 6G networks. In spite of various advantages offered by FSO communication such as high bandwidth availability, large unlicensed spectrum, better security, etc., the FSO link exhibits a high degree of responsiveness to atmospheric turbulence, attenuation, and pointing errors. Thus to mitigate the severe atmospheric effects, we propose a multiple high-altitude platform station (HAPS)-based SAG network, where HAPS functions as a decode-and-forward (DF) relay node between ground station and satellite. We take into account a hybrid FSO/radio frequency (RF) link for connecting ground station and HAPS nodes and an FSO link between HAPS and satellite. The performance analysis of the proposed SAG network involves evaluating the outage probability and average symbol error rate (SER) considering the presence of pointing errors and assuming Málaga distribution in case of FSO and shadowed k - μ fading distribution in case of RF link. Furthermore, asymptotic analysis is conducted to determine the diversity gain of the proposed system. Numerical results demonstrate that the proposed multiple HAPS-based hybrid FSO/RF SAG network outperforms all other existing HAPS-based SAG network models in the literature. © 2024 IEEE.

关键词： DF relaying free space optics (FSO) high-altitude platform station (HAPS) hybrid FSO/RF performance analysis

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Stock Market Price Prediction Using GRU and XGB 21

Stock Market Price Prediction Using GRU and XGB

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21st OITS International Conference on Information technology, OCIT 2023

作者： Shrestha, Aashmit Kumari, Rina Jena, Ajay Kumar Parida, Ajaya Kumar Parida, Nirupama Parida, Raj Kumar Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India National Institute of Technology Computer Science and Engineering Meghalaya Shillong India Kalinga Institute of Industrial Technology School of Computer Engineering India

ISBN: (纸本)9798350358230

Predicting the future course of the stock market is challenging because of its volatility and multifaceted dependencies. This study introduces a novel approach that harnesses the power of Deep Learning Recurrent Neural Network (RNNs), including established models like LSTM, Bidirectional LSTM, GRU, and XGBOOST, with a unique focus on improving prediction accuracy. By integrating historical data and technical indicators, such as Moving Averages, our framework refines prediction strategies. Our key innovation lies in the strategic combination of GRU, specialized for capturing long-Term trends, and XGBoost, which generates final forecasts with remarkable precision. This novel approach enhances the overall strategy and substantially increases forecast accuracy, making a significant contribution to the field of stock market prediction. © 2023 IEEE.

关键词： Forecasting

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A Novel Hybrid AI Framework for Real-Time Prediction and Risk Assessment of Vector-Borne Epidemics using Spatiotemporal Data 4

A Novel Hybrid AI Framework for Real-Time Prediction and Ris...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Munirathnam, Rajesh Maindola, Meenakshi Bhargav, H.K. Patil, Prachi Ramesh Bhatt, Pooja Natrayan, L. TechnoGen Inc. Data & Analytics Division NJ United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Department of Computer Science and Engineering Tumakuru India Jagdambha College of Engineering and Technology Department of Electronics and Telecommunication Engineering Yavatmal India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Saveetha School of Engineering Simats Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350352931

Vector-borne epidemics constitute major threats to global health that call for improved modeling systems for managing diseases. In order to incorporate such factors into an actual model, a traditional forecasting process may fail to grasp the nature of disease transmission adequately. We present a hybrid AI system based on LSTM and GCN for forest bug outbreak detection in real time. The framework also integrated a spatial-temporal analysis of climate variability, vector population fluctuations, and human mobility with climate variables across 15 regions over a five-year period (2019-2023). The hybrid framework also produced superior results, achieving a 94.2% prediction rate for health risk disease outbreaks, compared to a 17.3% improvement in traditional machine learning. When used to distinguish between high-risk and low-risk zones, the model demonstrated the following metrics: sensitivity of 92.8% and specificity of 95.1%. Real-time risk assessment demonstrated 89.6% effectiveness in indicating early warning and the best prediction lead time average of 21 days. This framework significantly enhances the prediction of vector-borne diseases by offering a dependable, real-time platform for disease surveillance and risk assessment. Its high accuracy and early warning capabilities make the system a useful tool in epidemiological and public health campaigns and control measures. © 2024 IEEE.

关键词： Prediction models

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