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Weed detection in precision agriculture: leveraging encoder-decoder models for semantic segmentation

Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3547-3561页

作者： Thiagarajan, Shreya Vijayalakshmi, A. Grace, G. Hannah Shell India Markets Private Limited Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Division of Mathematics School of Advanced Sciences Vellore Institute of Technology Chennai India

Precision agriculture uses data gathered from various sources to improve agricultural yields and the effectiveness of crop management techniques like fertiliser application, irrigation management, and pesticide application. Reduced usage of agrochemicals is a key step towards more sustainable agriculture. Weed management robots which can perform tasks like selective sprinkling or mechanical weed elimination, contribute to this objective. A trustworthy crop/weed classification system that can accurately recognise and classify crops and weeds is required for these robots to function. In this paper, we explore various deep learning models for achieving reliable segmentation results in less training time. We classify every pixel of the images into different categories using semantic segmentation models. The models are based on an encoder-decoder architecture, where feature maps are extracted during encoding and spatial information is recovered during decoding. We examine the segmentation output on a beans dataset containing different weeds, which were collected under highly distinct environmental conditions, including cloudy, rainy, dawn, evening, full sun, and shadow. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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Designing Adaptive Multiple Dependent State Sampling Plan for Accelerated Life Tests

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computer Systems science & Engineering 2023年第8期46卷 1631-1651页

作者： Pramote Charongrattanasakul Wimonmas Bamrungsetthapong Poom Kumam Division of Mathematics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology KrungthepBangkok10120Thailand Division of Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiPathum Thani12110Thailand Center of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research Laboratory Room SCL 802 Fixed Point LaboratoryScience Laboratory BuildingDepartments of MathematicsFaculty of ScienceKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand

A novel adaptive multiple dependent state sampling plan(AMDSSP)was designed to inspect products from a continuous manufacturing process under the accelerated life test(ALT)using both double sampling plan(DSP)and multiple dependent state sampling plan(MDSSP)*** accelerated conditions,the lifetime of a product follows the Weibull distribution with a known shape parameter,while the scale parameter can be determined using the acceleration factor(AF).The Arrhenius model is used to estimate AF when the damaging process is *** economic design of the proposed sampling plan was also considered for the ALT.A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number(ASN)and total cost of inspection(TC)under both producer’s and consumer’s *** results are presented to support the AMDSSP for the ALT,while performance comparisons between the AMDSSP,the MDSSP and a single sampling plan(SSP)for the ALT are *** indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated *** AMDSSP also had a higher operating characteristic(OC)curve than both the existing sampling *** real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.

关键词： Accelerated life test acceleration factor adaptive of multiple dependent state sampling plan average sample number total cost of inspection weibull distribution

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MULTI-RESOLUTION DIFFUSION MODELS FOR TIME SERIES FORECASTING 12

MULTI-RESOLUTION DIFFUSION MODELS FOR TIME SERIES FORECASTIN...

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12th International Conference on Learning Representations, ICLR 2024

作者： Shen, Lifeng Chen, Weiyu Kwok, James T. Division of Emerging Interdisciplinary Areas Hong Kong University of Science and Technology Hong Kong Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

The diffusion model has been successfully used in many computer vision applications, such as text-guided image generation and image-to-image translation. Recently, there have been attempts on extending the diffusion model for time series data. However, these extensions are fairly straightforward and do not utilize the unique properties of time series data. As different patterns are usually exhibited at multiple scales of a time series, we in this paper leverage this multi-resolution temporal structure and propose the multi-resolution diffusion model (mr-Diff). By using the seasonal-trend decomposition, we sequentially extract fine-to-coarse trends from the time series for forward diffusion. The denoising process then proceeds in an easy-to-hard non-autoregressive manner. The coarsest trend is generated first. Finer details are progressively added, using the predicted coarser trends as condition variables. Experimental results on nine real-world time series datasets demonstrate that mr-Diff outperforms state-of-the-art time series diffusion models. It is also better than or comparable across a wide variety of advanced time series prediction models. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Time series

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A Comparative Study of DenseNet121 and InceptionResNetV2 model for DeepFake Image Detection 3

A Comparative Study of DenseNet121 and InceptionResNetV2 mod...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Manoranjitham, R. Swaroop, Somu Santhi Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

The rise of deep fake technology poses a significant challenge to societal integrity, necessitating robust detection methods. This research study investigates the effectiveness of DenseNet121 and InceptionResNetV2 deep learning models in detecting deep fake images. The study starts with the curation of the dataset and then applies different preprocessing methods to improve the quality of the dataset like scaling, normalization, and data augmentation. The DenseNet121 and InceptionResNetV2 models are trained to differentiate authentic and manipulated images based on distinctive features. DenseNet121's dense connectivity facilitates feature reuse, enabling effective extraction of discriminative features crucial for identifying manipulated content. In contrast, InceptionResNetV2's sophisticated architecture exploits both inception and residual connections, enhancing its ability to capture intricate patterns indicative of deep fake manipulation. The comparative analysis of performance metrics reveals InceptionResNetV2's superiority, achieving an impressive accuracy rate of 99.78% compared to DenseNet121. These findings underscore the efficacy of leveraging existing architectures for identifying deep fake images, thereby contributing to the ongoing efforts to combat misinformation and safeguard digital content integrity. © 2024 IEEE.

关键词： Deep learning

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Social Media Analytics for Disaster Management using BERT Model 1

Social Media Analytics for Disaster Management using BERT Mo...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Varghese, Sherin R Juliet, Sujitha Anitha, J. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359756

Disasters, both natural and human-induced, continue to affect the global population in multiple ways. In the past decade, social media has evolved and has seen a rapid, exponential increase in the number of users and their data. The manner in which communities anticipate, react to, and recover from natural and man-made disasters have been entirely shifted by rapid information transfer, public sentiment analysis, and real-time communication. There is now a high demand for social media based disaster management systems. This research project introduces a sentiment analysis framework tailored to Twitter data, incorporating BERT as a core component. Additionally, we employ a range of NLP methodologies for evaluation and comparative analysis. The results indicate that the employment of BERT yields a noteworthy enhancement in accuracy, surpassing alternative models by 5% to 7%. In summary, our research accentuates the effectiveness of employing BERT-based sentiment analysis to optimize disaster management strategies. © 2023 IEEE.

关键词： Sentiment analysis

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Disease Classification in X-Ray Images using Convolutional Neural Networks 2

Disease Classification in X-Ray Images using Convolutional N...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Allen Matthew, T.J. Shanish Roshan, K. Paul Jerome Immanuel, K. Mithun, S. Jemima Jebaseeli, T. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350340235

Convolutional Neural Networks (CNNs) have emerged as a revolutionary tool in the domain of disease categorization, fundamentally changing the landscape of medical imaging diagnostics. This study deeply investigates the application of CNNs to automatically and with high precision classify diseases using medical imagery, including X-rays, MRIs, and CT scans. The utilization of CNNs in this research takes advantage of their proficiency in recognizing intricate patterns and features within medical images, thereby simplifying the disease diagnosis process. The incorporation of CNNs in disease classification represents a promising advancement in the healthcare sector. Through extensive training on extensive datasets, these neural networks can discern intricate patterns and subtle irregularities in medical images, resulting in improved precision and efficiency in disease diagnosis. By automating the categorization process, healthcare providers can minimize the likelihood of human errors and enhance the speed of diagnosis, ultimately leading to improved patient outcomes. This study emphasizes the potential of CNNs to reshape disease classification, offering a glimpse into a healthcare future where advanced technology plays a pivotal role in ensuring accurate and timely disease identification. It also underscores the ongoing necessity for research and development in the field of medical image analysis, as CNNs continue to reshape the healthcare landscape with their ability to revolutionize disease diagnostics. © 2023 IEEE.

关键词： Automated diagnosis Deep learning Medical imaging Neural Networks Xray

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Knee Osteoarthritis Detection Using X-Rays and DNN 3

Knee Osteoarthritis Detection Using X-Rays and DNN

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sinha, Vaishnawi Veemaraj, Ebenezer Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India Division of Data Science and Cyber Security Tamil Nadu Coimbatore India

ISBN: (纸本)9798350375190

Knee osteoarthritis (OA) is a prevalent degenerative joint disease that affects millions worldwide, leading to pain, disability, and reduced quality of life. Early detection and intervention are crucial for effective management and prevention of disease progression. In this paper, we propose a novel approach for knee OA detection using X-ray images and deep neural networks (DNNs). Specifically, we investigate the performance of two popular DNN architectures: EfficientNet-B5 and DenseNet121. We begin by pre-processing a dataset of knee X-ray images to extract relevant features and mitigate noise. Subsequently, we fine-tune the pre-trained EfficientNet-B5 and DenseNet121 models using transfer learning techniques to adapt them to the task of knee OA detection. We then evaluate and compare the performance of these models in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Our experimental results demonstrate the efficacy of both EfficientNet-B5 and DenseNet121 architectures in knee OA detection, outperforming traditional machine learning approaches. However, we observe variations in their performance metrics, indicating nuanced differences in their ability to capture and discriminate pathological features in knee X-ray images. Furthermore, we conduct an in-depth analysis to understand the factors contributing to the performance disparities between the two models. Overall, our study contributes to the growing body of research aimed at leveraging DNNs for medical image analysis tasks, particularly in the domain of musculoskeletal disorders. The insights gained from this research can potentially aid clinicians in early diagnosis and personalized treatment planning for knee OA patients, ultimately improving patient outcomes and healthcare efficiency. © 2024 IEEE.

关键词： Grading

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Investigation of Red Fox Algorithm for Solving Task Assignment Problem in Heterogeneous Unmanned Aerial Vehicle Swarm 3

Investigation of Red Fox Algorithm for Solving Task Assignme...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Manaswitha, K. Issac, Titus Silas, Salaja Terance, J Sebastian Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350372120

In recent years, there have been significant advancements in Unmanned Aerial Vehicles (UAVs), leading to their integration into everyday life. UAVs are constrained by limited energy, communication, and localization. The various challenges in UAV research include localization, routing, navigation, and task assignment. Recently, UAV swarms have been employed across a wide range of applications. This study focuses on solving task assignment problems within UAV swarms, considering the unique multi-objective resource constraints of UAVs. The work explores the feasibility of adopting the Red Fox Algorithm to address the task assignment problem in UAV swarms. The investigation examines two primary input parameters: Task Energy Demand and distance. The evaluation includes energy utilization and shortest distance as performance metrics. Notably, the proposed approach outperforms the Particle Swarm Optimization Algorithm (PSO), resulting in a 17.47% lower energy utilization while maintaining a 17.83% reduction in the shortest distance travelled. By leveraging the Red Fox Algorithm, the work aims to enhance the efficiency and effectiveness of task allocation in UAV swarms, contributing to their broader adoption across various applications. © 2024 IEEE.

关键词： Energy utilization

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IoT-Based Plant Health Monitor Using NodeMCU and ESP8266 5

IoT-Based Plant Health Monitor Using NodeMCU and ESP8266

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Tandel, Rajat Ebenezer, V. Santhiya, P. Edwin, Bijolin Stewart Kirubakaran, S. Thanka, Roshni Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Data Sciences and Cyber Security Coimbatore India

ISBN: (纸本)9798331518097

Plant Monitor Using NodeMCU(ESP8266)is a technology that is advanced;it uses IoT-Internet of Things to measure and monitor different plant phenomena. This system, which is based on sensors that collect data on different factors such as soil type, temperature, humidity, and other vital metrics vital for plant growth, is also integrated. Bringing IoT into the picture, the system can remotely sense the metrics and having control over other factors which include watering, shading, and even security provision for the plants to make it thrive. Besides that, it will also send notifications to users, hence allowing them to engage in the monitoring process through mobile applications with support from their smartphones. The Smart Plant Monitoring System though creates an impact on the efficiency of plant care also impacts on water conservation, automation of plant care process and better agricultural practices. © 2024 IEEE.

关键词： Efficiency

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