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Sustainable Expert Systems (ICSES), International Conference on

作者： Arshadh Ariff Mohamed Abuthahir Riazulhameed Chitra Sabapathy Ranganathan Pramod Pandey Sakthisaravanan B K Sasikala S. Murugan Architect - Data Engineer Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Mphasis Corporation Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Sri Venkateshwara College of Engineering Bengaluru Karnataka India Department of Electrical and Electronics Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Modern agricultural operations have significant challenges due to organic waste management since conventional approaches often prove ineffective and unsustainable. To overcome these challenges, this research suggests a new method for smart composting that combines IoT with gradient-boosting algorithms. This system accurately manages and optimizes the composting process by employing Internet of Things (IoT) sensors to monitor important factors, including temperature, moisture, pH, and oxygen content. Real-time data analysis is then performed using Gradient Boosting algorithms. By combining the IoT with gradient boosting may preventatively measures to maximize decomposition and microbial activity, which speeds up the composting process and reduces resource waste. The produced compost has higher nutritional content and more diverse microbes, making it useful as a soil supplement in farming. In addition to improving organic waste management, this novel method helps with soil enrichment and sustainable agriculture, promoting efficiency and resource conservation. Accurate sensor readings, data integration, real-time tracking, and quickly achieving optimal composting conditions are all challenges.

关键词： Waste management Temperature sensors Temperature measurement Composting Soil Boosting Real-time systems Internet of Things Intelligent sensors Farming

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Fashionable Four Wheeler Parking to Reduce the Human Intervention and to Enhance the Flexibility in Parking System Using IoT

Fashionable Four Wheeler Parking to Reduce the Human Interve...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Manoj P Varun C M Keerthi Kumar N Pradip Padhye K. Aruna Kumari Puja Das Swagata Sarkar Department of Civil Engineering Vidyavardhaka College of Engineering Mysore India Department of Computer Science and business system R.M.K. Engineering College Kavaraipettai India Department of Mechanical Engineering BMS Institute of Technology and Management Bengaluru India School of Management Dr. Vishwanath Karad MIT World Peace University Pune India Department of Mathematics Gitam University Visakhpatanam India Department of Computer science Hiralal Mazumder Memorial college for Women Kolkata India Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India

Due to the lack of a mechanism in place to check online for parking availability, it is now quite difficult to find a parking spot in congested regions. Imagine being able to access information about parking slot availability on your phone and not having to move around to do so. The cutting-edge parking system powered by IoT can overcome this issue. The IoT-based parking system makes it simple to check the availability of parking spaces online. With the aid of this device, the parking system might be fully automated. You might have automatic admission, payment, and exit processes. A NodeMCU-based IOT-based car parking system is being created using five IR sensors, two servo motors, and NodeMCU. Three IR sensors are utilised to determine whether parking spaces are available, and two IR sensors are used at the entry and exit gates to identify vehicles. Based on the sensor value, servo motors control the gates' opening and closing. In this part, we'll show you how to upload data to the cloud that is reachable from anywhere using the Adafruit IO platform.

关键词： Space vehicles Technological innovation Motor drives Cloud computing Costs Logic gates Sensor systems

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An Efficient Ensemble Model for Forecasting Time Series Analysis of Crypto Currency data Using Machine Learning

An Efficient Ensemble Model for Forecasting Time Series Anal...

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2021 IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2021

作者： Saravanakumar, C. Lakshmi, S. Kanaga Prakash, M. Vijayakumar, M. Arun, C. St. Joseph's Institute of Technology Department of Information Technology Chennai India Sri Muthukumaran Institute of Technology Department of Management Studies Chennai India Data Science and Analytics Center Karpagam College of Engineering Coimbatore India St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India R.M.K College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665435215

Time series data are analyzed for predicting the future based on the consistent time period using statistical methods. These analyses are based on the data which are extracted from different sources. Existing method for performing the forecasting process does not fit into the crypto currency data. GARCH and ARIMA models with intelligent mechanisms provide the solution but it is not at the proper level. RNN model takes the data from preexisting intervals and decides the next time series data which is represented as a directed graph. Two types of behavior are considered namely spatial and temporal. The linear model uses the homogeneous data and heterogeneous data analysis is based on the nonlinear model. The nodes and layers are connected based on the input and corresponding activation unit. The memory model is attached to the RNN model for making persistent storage for keeping the processed data for a longer time. The proposed model uses the ensemble method for integrating various RNN models for achieving a high level of accuracy. © 2021 IEEE.

关键词： Time series analysis

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Federated learning for crop yield prediction: A comprehensive review of techniques and applications

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MethodsX 2025年 14卷 103408页

作者： Hiremani, Vani Devadas, Raghavendra M. Preethi Sapna, R. Sowmya, T. Gujjar, Praveen Rani, N. Shobha Bhavya, K.R. Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India MURTI Research Center Smart Agriculture Labs Department of Artificial Intelligence and Data Science GITAM School of Technology GITAM (Deemed to be) University Bengaluru India Department of Computer Science and Engineering GITAM School of Technology GITAM (Deemed to be) University Bengaluru India

The demand for food all over the world requires the implementation of advanced technologies to improve agricultural productivity. Federated Learning (FL) as a decentralized approach to machine learning facilitates collaborative model training on different data sources while maintaining privacy—making it highly applicable technology for sensitive agricultural data. This paper offers a systematic overview of the recent knowledge on the application of FL towards the prediction of crop yield and other agricultural uses. We discussed the mathematical basis of FL, the variety of machine learning models used, the types of used agricultural data, and the major performance metrics. The paper presents real-world applications and lists the current limitations, including communication overhead, data heterogeneity, and interpretability issues. Lastly, we introduce open research directions to inform the development of FL in precision agriculture. © 2025

关键词： Crop yield prediction data privacy Federated learning Machine learning Precision agriculture Rice cultivation

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An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series

arXiv

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arXiv 2024年

作者： Huang, Qiang Meng, Chuizheng Cao, Defu Huang, Biwei Chang, Yi Liu, Yan School of Artificial Intelligence Jilin University Jilin Changchun China International Center of Future Science Jilin University Jilin Changchun China Department of Computer Science University of Southern California California Los Angeles United States Halicioğlu Data Science Institute University of California San Diego San DiegoCA United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Jilin Changchun China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series analysis

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Joint inference of multiple graphs from matrix polynomials

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The Journal of Machine Learning Research 2022年第1期23卷 3302-3336页

作者： Madeline Navarro Yuhao Wang Antonio G. Marques Caroline Uhler Santiago Segarra Department of Electrical and Computer Engineering Rice University Houston TX Institute for Interdisciplinary Information Sciences Tsinghua University and Shanghai Qi Zhi Institute Haidian District Beijing China Department of Signal Theory and Communications King Juan Carlos University Madrid Spain Department of Electrical Engineering & Computer Science Department of Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA

Inferring graph structure from observations on the nodes is an important and popular network science task. Departing from the more common inference of a single graph, we study the problem of jointly inferring multiple graphs from the observation of signals at their nodes (graph signals), which are assumed to be stationary in the sought graphs. Graph stationarity implies that the mapping between the covariance of the signals and the sparse matrix representing the underlying graph is given by a matrix polynomial. A prominent example is that of Markov random fields, where the inverse of the covariance yields the sparse matrix of interest. From a modeling perspective, stationary graph signals can be used to model linear network processes evolving on a set of (not necessarily known) networks. Leveraging that matrix polynomials commute, a convex optimization method along with sufficient conditions that guarantee the recovery of the true graphs are provided when perfect covariance information is available. Particularly important from an empirical viewpoint, we provide high-probability bounds on the recovery error as a function of the number of signals observed and other key problem parameters. Numerical experiments demonstrate the effectiveness of the proposed method with perfect covariance information as well as its robustness in the noisy regime.

关键词： network topology inference graph signal processing spectral graph theory multi-layer graphs network diffusion processes

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

Deep and handcrafted feature supported diabetic retinopathy ...

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2022 International Conference on Machine Learning and data engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes;(i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database. © 2022 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

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2022 International Conference on Machine Learning and data engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： computerized tomography

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EDM: Synthetic data from Exemplar Diffusion Model Improves Non-Communicable Diseases Detection

EDM: Synthetic Data from Exemplar Diffusion Model Improves N...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xing Wu Zhi Li Junfeng Yao Quan Qian Jian Zhang Qun Sun Yike Guo Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China CSSC Seago System Technology Co. Ltd Shanghai China Shanghai Universal Medical Imaging Diagnostic Center Shanghai Chin Shanghai Sixth People’s Hospital Shanghai JiaoTong University Shanghai China Hong Kong University of Science and Technology Hong Kong SAR China

There have been researches revealing obvious associations between facial phenotypes and non-communicable diseases (NCDs), which enables effective health assessment with the integration of model-based learning methods. However, the paucity and poor quality of available datasets hinder the development of potent algorithms to detect NCDs. To meet this challenge, we propose a method called Exemplar Diffusion Model (EDM), the objective of proposed EDM is to generate facial images that illustrate simulated non-communicable diseases, utilizing a normal facial image as input. Extensive experimental results show that the proposed EDM method outperforms the state-of-the-art methods in terms of Frechet Inception Distance (FID) and Quality Score (QS), with improvements of 0.11 and 0.74, respectively. Furthermore, comprehensive ablation studies and comparative experiments prove the value of proposed EDM method in large-scale facial image dataset generation and non-communicable disease detection.

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Optimization of Image Transmission in a Cooperative Semantic Communication Networks

arXiv

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arXiv 2023年

作者： Zhang, Wenjing Wang, Yining Chen, Mingzhe Luo, Tao Niyato, Dusit The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States NTU Singapore

In this paper, a semantic communication framework for image data transmission is developed. In the investigated framework, a set of servers cooperatively transmit image data to a set of users utilizing semantic communication techniques, which enable servers to transmit only the semantic information that accurately captures the meaning of images. To evaluate the performance of studied semantic communication system, a multimodal metric called image-to-graph semantic similarity (ISS) is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. Due to the co-channel interference among users associated with different servers, each server must cooperate with other servers to find a globally optimal semantic oriented RB allocation. We formulate this problem as an optimization problem whose goal is to minimize the sum of the average transmission latency of each server while reaching the ISS requirement. To solve this problem, we propose a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) algorithm. The proposed algorithm enables each server to coordinate with other servers in training stage and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL algorithms, the proposed RL framework improves the exploration of valuable action of servers and the probability of finding a globally optimal RB allocation policy based on local observation of wireless and semantic communication environments. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% and improve the convergence speed by up to 100% compared to the traditional multi-agent RL algorithms. Copyright © 2

关键词： Cooperative communication

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