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A Review of Machine learning and IoT Approaches for the Soil Quality Assessment in Agricultural and Land Management 4

A Review of Machine Learning and IoT Approaches for the Soil...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Rajasathiya, K. Palanikumar, R. P.S.R.R College of Engineering Sivakasi India P.S.R. Engineering College Sivakasi India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

this research paper examines several soil qualities, such as texture, structure, density, porosity, wetness, and pH, and how these affect agricultural productivity and soil management. It discusses how to analyze soil texture using mechanical screening and sedimentation techniques, as well as the link between soil density and pore space. the paper highlights the importance of soil moisture in plant growth and discusses various methods for monitoring soil moisture, including remote sensing. Additionally, it looks into how soil pH affects plant health and offers helpful suggestions for adjusting soil pH to maximize growing conditions. the literature review focuses on advances in machine learning and IoT technologies that improve soil analysis and prediction, emphasizing its potential for precision agriculture and sustainable land management. the conclusion emphasizes the need of understanding soil parameters in order to maximize agricultural outputs and the usefulness of advanced modelling approaches in establishing sustainable agricultural practices. this study finally demonstrates the importance of exact soil data for good agricultural decision-making and environmental management. © 2024 IEEE.

关键词： Soil moisture

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4th International conference on intelligent Engineering and Management, ICIEM 2023

4th International Conference on Intelligent Engineering and ...

引用

4th International conference on intelligent Engineering and Management, ICIEM 2023

ISBN: (纸本)9798350341126

the proceedings contain 68 papers. the topics discussed include: app based implementation of modern agriculture utilities for farmers;application of intelligent computing (IC) in human resources (HR) analytics: towards better tomorrow;artificial intelligence based chatbot for promoting equality in high school advising;assessment of human activity recognition based on impact of feature extraction prediction accuracy;biometric and RFID passive tag based student identification system for secure attendance management;blockchain and federated learning enabled smart traffic management system for smart cities;climatic temperature forecasting with regression approach;covid-19 detection on X-ray image using deep learning;critical review on applications of self-healing concrete in reinforced concrete structures;decipherable for artificial intelligence in Medicare: a review;dementia identification using machine learning algorithms: comparative analysis;and design and implementation of an easy-to-use tracking device for logistic applications.

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Deep learning for Enhanced Detection and Characterization of Pulmonary Nodules 4

Deep Learning for Enhanced Detection and Characterization of...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Lavanya, G. Muthulakshmi, M. Latha, Manohar Keerthinathan, A. Vishal Krishh, P. Sulthana, Sheba Meghana, Meka Kavya Uma Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Amrita School of Computing Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Chennai India Rajalakshmi Engineering College Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350349900

this study aims to develop, an effective customized Convolutional Neural Network model that can accurately identify areas on CT scans that have lung nodules and those that do not. the study utilizes two datasets, the LUNA-16 dataset for training and the IQ-Oth/NCCD dataset for testing. Additionally, we employ a U-Net model for precise lung nodule segmentation. this segmentation approach aimed to achieve a comprehensive characterization of nodules based on their spatial characteristics. the testing phase involves evaluating the performance of our trained models on the IQ-Oth/NCCD dataset, particularly focusing on localizing nodules in malignant cases. Overall, the research highlights the effectiveness of deep learning in early detection and diagnosis of lung cancer. this work contributes to improving the accuracy and reliability of automated pulmonary nodule detection systems, with a focus on identifying nodules in patients with suspected malignancies. © 2024 IEEE.

关键词： Lung cancer

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Person Re-Identification using Deep learning 4

Person Re-Identification using Deep Learning

引用

4th International conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Saini, Taruna Gandhi, Ridhima Roy, Sanu Diwakar, Manoj Singh, Prabhishek Mishra, Amit Kumar Graphic Era Deemed to Be University Department of Cse Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India Graphic Era Hill University Dept. of Cse Dehradun India

ISBN: (纸本)9798350364729

Re-identification of person (re-ID) is a computer vision task, which is trying to find out the same individual's pictures through a single camera scenario. For addressing Re-ID, we have proposed a deep learning architecture i.e. ResNet, and open Visual Inference and Neural optimization (Open VINO) toolkit from intel. In this paper, we have utilized the probable feature of learning in the ResNet model that is instructed on enormous datasets and fine-turned on strenuous market-1501 re-ID standard dataset. this process implicates data preprocessing, model selection, training, and optimization steps that are important for acceptable results. We have used data augmentation and triplet loss function to construct implanting more selective as a final step in this pipeline and our developed ResNet model is supported by OpenVINO for hardware-specific optimizations and model conversion. Our Solution on ResNet delivers real-time processing speeds using GPU. this approach shows that is possible and easy to deploy precise person Re-Id for surveillance, forensic identification, and security monitoring. © 2024 IEEE.

关键词： Deep learning

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Navigating the Battleground: An Analysis Of Adversarial threats And Protections In Deep Neural Networks 4

Navigating The Battleground: An Analysis Of Adversarial Thre...

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4th IEEE International conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sehgal, Vishakha Sharma, Sanjay Pathak, Shivansh Ahuja, Kamlesh Mahakal Institute of Technology Ujjain India

ISBN: (纸本)9798331518981

Deep learning techniques find broad applications in important areas such as malware detection systems, self-driving cars, and health care. However, it is still possible to attack deep learning soft intelligent models using adversarial example approaches. this study investigates the works and findings of recent research, whose central focus has been on adversarial machine learning, its weaknesses, and its application. Some of the focus has been on malware attacks that use deep learning frameworks, such as neural network based Jacobian saliency map attacks and anatomy of the Carlini and Wagner attacks. this has again posed a limitation to the present-day research on the increasing range of the actors and their role within the range of types of attack as well as strategies and development of countermeasures, including making biases and models to mitigate the threats. Furthermore, there is also a deficiency in the evaluation of defence mechanisms, particularly in developing appropriate parameters that would demonstrate the efficacy of the existing models. In conclusion, this paper presents communication and deliberations on areas that offer an interesting promise for future research in which the challenges experienced in applying deep learning systems will be satisfactorily addressed. © 2024 IEEE.

关键词： Adversarial machine learning

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Adversarial Machine learning: Attacking and Safeguarding Image Datasets 4

Adversarial Machine Learning: Attacking and Safeguarding Ima...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Chowdhury, Koushik Saarland University Saarbrucken Germany

ISBN: (纸本)9798331529635

this paper examines the vulnerabilities of convolutional neural networks (CNNs) to adversarial attacks and explores a method for their safeguarding. In this study, CNNs were implemented on four of the most common image datasets, namely CIFAR-10, ImageNet, MNIST, and Fashion-MNIST, and achieved high baseline accuracy. To assess the strength of these models, the Fast Gradient Sign Method was used, which is a type of exploit on the model that is used to bring down the models accuracies by adding a very minimal perturbation to the input image. To counter the FGSM attack, a safeguarding approach went through, which includes retraining the models on clear and pollutant or adversarial images to increase their resistance ability. the next step involves applying FGSM again, but this time to the adversarially trained models, to see how much the accuracy of the models has gone down and evaluate the effectiveness of the defense. It appears that while most level of robustness is achieved against the models after adversarial training, there are still a few losses in the performance of these models against adversarial perturbations. this work emphasizes the need to create better defenses for models deployed in real-world scenarios against adversaries. © 2024 IEEE.

关键词： Adversarial machine learning

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A Deep learning based Two branch model for Automated Recognition and Segmentation of Solar Panels In-flight Imagery 4

A Deep Learning based Two branch model for Automated Recogni...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Manjunath, T.G. Vikramathithan, Ac Pasha, Syed Mathin India Reva University Dept of Cse India

ISBN: (纸本)9798350349900

Due to the repercussions of climate change on the entire planet, reducing greenhouse gas emissions will require teamwork. Since the energy industry is the primary source of climate change emissions, there is a lot of work being done to shift away from power plants that give out carbon towards imperishable energy sources like solar power. To help researchers and policy makers develop plans for the continued growth of solar energy, a thorough database of solar panel locations is essential. the purpose of this research is to identify and build the overall map of solar panel within a geographical area that includes the location and size of the panel. Using aerial photography, deep learning techniques are employed to automatically recognise the placement and surface zone of solar panels. the framework was trained using a dataset of satellite images that were produced specifically for the study. It is a two-section model that combines a semantic segmentation model with an image classifier. With an Intersection over Union(IOU) points of 0.85 for segmentation accomplishment and an accuracy of 0.97 for classification, this work offers a scalable and effective way to detect solar panels. © 2024 IEEE.

关键词： Semantic Segmentation

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AI Lip Reader Detecting Speech Visual Data with Deep learning 4

AI Lip Reader Detecting Speech Visual Data with Deep Learnin...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Geetha, C. Rohan Jai, D. Sandheep Krishna, A. Reddy, Seelam Sai Vara Prasad R.M.K. Engineering College Department of Computer Science and Engineering Kavaraipettai Tamil Nadu Thiruvallur India

ISBN: (纸本)9798350349900

Introducing an AI-driven lipreading system adept at decoding speech across diverse languages, including English, Tamil, and Telugu. Leveraging a deep learning architecture comprising a 3D Convolutional Neural Network (3DCNN) with Bidirectional Long Short-Term Memory (BiLSTM) units, the model achieves remarkable accuracy in transcribing spoken words solely from visual cues provided by lip movements. Addressing critical accessibility needs, this system holds promise for applications in assistive technologies and human-computer interaction systems. through rigorous experimentation and evaluation, the lipreading model demonstrates an impressive overall accuracy of 98.4%, underscoring its efficacy and robustness in recognizing spoken words across multiple languages. Advanced evaluation techniques, including ROC curves, confusion matrices, and classification reports, provide comprehensive insights into the model's performance, enabling targeted refinements and optimizations. this work represents a significant advancement in the field of lipreading, offering a valuable contribution to multimodal communication and fostering inclusivity in diverse linguistic contexts. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of MATLAB and Python Neural Networks for Diabetes Prediction 1

引用

4th International conference on optimization learning Algorithms and Applications

作者： Pimentel, Gabriel Oliveira Dessanti, Augusto Luvisa Teixeira, Joao Paulo Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300253 Braganca Portugal Inst Politecn Braganca Lab Sustentabilidade & Tecnol Regioes Montanha Su P-5300253 Braganca Portugal Fed Inst Rio Grande Farroupilha Brazil Fed Ctr Technol Educ Minas Gerais Leopoldina Brazil

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

In recent years, artificial intelligence (AI) has become an integral part of many everyday applications. the growth of AI is bringing intellectual benefits to humans. It is revolutionizing discovery, learning, communication, and work. Machine learning, especially deep learning, is critical to this progress, providing complex models to process data more effectively. Neural networks, which emulate biological processes, find applications across diverse fields, notably in medicine, where they automate disease diagnosis and prediction tasks, such as diabetes. this paper proposes a comparative analysis between Python and MATLAB for diabetes prediction using a dataset with 100,000 individuals. the study conducts simulations on both platforms and validates the results using metrics such as precision, specificity, accuracy and F-measure. Additionally, the study emphasizes the importance of platform selection based on considerations of functionality and cost, offering insights into optimizing outcomes in healthcare applications.

关键词： Neural Network Feed-forward in Matlab and Python MLP in Matlab and Python MATLAB Python

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Joint optimization of Service Caching and Task Scheduling in Energy-Efficient Mobile Edge Computing 4

Joint Optimization of Service Caching and Task Scheduling in...

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4th International conference on intelligent Technology and Embedded Systems, ICITES 2024

作者： Chen, Bin Huang, Wenxuan Wang, Linhui Zhang, Yi Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798331506292

this paper studies an energy-efficient task scheduling problem that takes into account the cooperation among service caching-enabled mobile edge computing (MEC) servers. We consider a MEC system consisting of multiple sub-networks of MEC nodes and formulate the studied problem as a constrained optimization model that makes decisions on energy-efficient assignment of task requests. To cope with the formulated optimization problem, we propose a metaheuristic approach that is built upon the searching mechanism of the squirrel search algorithm (SSA) but involves improved operators and name the metaheuristic approach ISSA. By using an offloading sequence (i.e., a permutation of all computation tasks) to represent the scheduling solution, ISSA uses a cooperative task assignment heuristic for evaluating the fitness value of each candidate solution. this heuristic is responsible for dispatching each task in the sequence to a certain MEC node within the sub-network or outsourcing the task to the cloud in case of insufficient resources, such that the total energy consumption can be calculated. In addition, when updating the squirrel positions to facilitate the metaheuristic search, we employ a new updating rule to enhance ISSA's capability in exploring the solution space. We perform extensive simulations to demonstrate the advantage of our proposed ISSA over other metaheuristic approaches in reducing the total energy consumption. © 2024 IEEE.

关键词： Constrained optimization

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