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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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A Machine learning Approach to Forecast the Food Prices for Food Security Issues 11

A Machine Learning Approach to Forecast the Food Prices for ...

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11th international conference on intelligent Systems and Embedded Design, ISED 2023

作者： Kumar, Arvind Singh, Sunil Kumar Kumari, Priya Mahatma Gandhi Central University Dept. of Computer Science and Information Bihar Motihari India

ISBN: (纸本)9798350318159

Food security is one of the major challenges that must be addressed to provide sufficient and nutritious food to everyone. In this work, we have used Ethiopia's monthly food price data from 2000 to 2023. the work aims to effectively predict food prices by applying machine learning models. We have applied the SVM, KNN, LR, DT, and RM models, in which it is found that RF is one of the best models with the highest accuracy 98.98%. Further, to consolidate it, we have applied the Friedman test to measure the model's ranking, and we have observed that RF is the highest ranked among all the applied models. the findings of the work conclude the same. © 2023 IEEE.

关键词： Food supply

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OPTIMAL CONSERVATIVE OFFLINE RL WIth GENERAL FUNCTION APPROXIMATION VIA AUGMENTED LAGRANGIAN 11

OPTIMAL CONSERVATIVE OFFLINE RL WITH GENERAL FUNCTION APPROX...

引用

11th international conference on learning Representations, ICLR 2023

作者： Rashidinejad, Paria Zhu, Hanlin Yang, Kunhe Russell, Stuart Jiao, Jiantao Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Department of Statistics University of California Berkeley United States

Offline reinforcement learning (RL), which aims at learning good policies from historical data, has received significant attention over the past years. Much effort has focused on improving offline RL practicality by addressing the prevalent issue of partial data coverage through various forms of conservative policy learning. While the majority of algorithms do not have finite-sample guarantees, several provable conservative offline RL algorithms are designed and analyzed within the single-policy concentrability framework that handles partial coverage. Yet, in the nonlinear function approximation setting where confidence intervals are difficult to obtain, existing provable algorithms suffer from computational intractability, prohibitively strong assumptions, and suboptimal statistical rates. In this paper, we leverage the marginalized importance sampling (MIS) formulation of RL and present the first set of offline RL algorithms that are statistically optimal and practical under general function approximation and single-policy concentrability, bypassing the need for uncertainty quantification. We identify that the key to successfully solving the sample-based approximation of the MIS problem is ensuring that certain occupancy validity constraints are nearly satisfied. We enforce these constraints by a novel application of the augmented Lagrangian method and prove the following result: with the MIS formulation, augmented Lagrangian is enough for statistically optimal offline RL. In stark contrast to prior algorithms that induce additional conservatism through methods such as behavior regularization, our approach provably eliminates this need and reinterprets regularizers as "enforcers of occupancy validity" than "promoters of conservatism". © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Importance sampling

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Diagnosis of Liver Diseases Using Machine learning Algorithms and their Prediction Using Logistic Regression and ANN 3

Diagnosis of Liver Diseases Using Machine Learning Algorithm...

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3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Harshini, Puduri Sri Naresh, K. Pamulapati, Sai Rohith Lavanya, A. GRIET Electronics and Communication Engineering Telangana Hyderabad India

ISBN: (纸本)9798350338607

Globally, liver diseases affect a large number of population and they are the 11th leading cause of death. It is tedious and time-consuming for trained experts to identify patients with liver disease through liver biopsy and MRI checks. Liver disease can be difficult to identify in the early stages due to mild symptoms. As a result, symptoms emerge when it is too late. A timely diagnosis can potentially save a person's life. Machine learning can detect early signs of these diseases, even when they are not readily apparent to even experienced doctors. therefore, the objective of this work is to assess how well various machine-learning algorithms perform. Support Vector Machine, Logistic Regression, Naïve Bayes, Conventional Neural Network, and Artificial Neural Network were the five algorithms we employed in this study. the performance of these techniques was evaluated using a variety of measuring metrics, such as accuracy, f-1 score, precision, and recall. the accuracy was 65.26%, 73.65%, 70.05%, 97.5%, and 93.75% for SVM, LR, NB, CNN, and ANN. the analysis's findings indicate that CNN achieved the greatest accuracy. Furthermore, the current study focused on the use of clinical data and imaging scans to predict liver disease. © 2023 IEEE.

关键词： Neural networks

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Construction mode of injection molding machine intelligent enterprise based on cloud-edge-terminal framework 11

Construction mode of injection molding machine intelligent e...

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11th international conference on Information Technology - IoT and Smart City (ITIoTSC)

作者： Xie, Guangpeng Yu, Junhe Zhan, Hongfei Wang, Rui Ningbo Univ Mech Engn & Mech Ningbo 315000 Zhejiang Peoples R China

ISBN: (纸本)9798350328356;9798350328349

Currently, the development of the global manufacturing industry is moving towards digitization, intelligence and greenery. However, the injection molding machine industry, which plays a critical role in the entire manufacturing sector, still faces challenges such as slow information communication, low management efficiency, and difficulties in effectively controlling product quality. To this end, the paper proposes a smart enterprise construction model of the injection molding machine industry based on cloud-edge-terminal framework, including data terminal layer, edge services layer, and cloud processing layer. In data terminal layer, production and operation data is collected and standardized to form the data space, providing data support for the cloud processing layer and edge services layer. In cloud processing layer, the data is sent to the cloud platform for training and learning to generate corresponding models and algorithms for the edge service layer. In the edge layer, according to the data and the models, the services, such as workshop management, supply chain management, and remote operation and maintenance, are offered to realize the intelligence of the enterprise.

关键词： smart enterprise cloud-edge-terminal framework collaborative manufacturing

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Blockchain and Deep learning-Based Decentralized Anomaly Detection Framework for VANET 18

Blockchain and Deep Learning-Based Decentralized Anomaly Det...

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18th IEEE international conference on Vehicular Electronics and Safety, ICVES 2024

作者： Jetani, Harshil Patel, Janam Mahida, Nikunjkumar Patel, Man Gupta, Rajesh Tanwar, Sudeep Gupta, Ankur Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat 382481 India Model Institute of Engineering and Technology Department of Computer Science and Engineering J and K India

ISBN: (纸本)9798350387278

the automotive industry is increasing exponentially day by day providing a safer and efficient experience to the consumers. intelligent Transportation Systems (ITS) has become more crucial with the increasing number of vehicles. Moreover, there is a growing demand for intelligent services like efficient traffic monitoring in vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication environments. Vehicular Ad-hoc Network (VANET) represents the demanding and vibrant environment for securing the interaction between the vehicle, RSUs, and pedestrians through the Controller area network (CAN). RF jamming Attacks can be integrated through wireless mediums. the paper presents an improvement in securing VANETs through the combination of Blockchain technology (BT) with Deep learning (DL). In DL-based model, RNN is utilized for detecting and classifying malicious activities. BT ensures secure transactions and automated trust-less interactions using smart contracts. thus, the non-attack data classified by DL layer is communicated using BT. IPFS is used for storing the information to enhance the scalability while mitigating the maintenance cost of BT. the accuracy achieved by the RNN model during evaluation was 97%. Multiple models were evaluated over metrics like accuracy, precision, recall, F1-score, and ROC-AUC curve. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Precision Phytopathology in Agriculture: A Federated learning CNN Framework for Banana Leaf Disease Classification 11

Precision Phytopathology in Agriculture: A Federated Learnin...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Shukla, Ajay Narayan Joshi, Kireet Singh Yadav, Ajay Pratap Kukreja, Vinay Mehta, Shiva Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Uttar Pradesh Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

the outbreak of several panicle and foliage diseases causes a major threat to agricultural production and sustainability. this article introduces the disease categorization method for three types of leaf disease that uses the CNN architecture through a federated learning framework to classify them into four dryness levels. In this, four stakeholders, who are data centers that contribute to this collaborative learning, demonstrate its effectiveness in tackling agriculture domains in a wide range of fields of agribusiness. through profound experiments that we have illustrated and intensive studies of the resulting model, we found that this model is capable of coping with the local variations and transforming them into a highly global learning model. the results of our study shown remarkable accuracy in categorising diseases into four categories of severity: grade, class, grade, and critical. the proof that the macro critical point, micro critical point, and train approaches at the same time was given by the best performance of the model, which had the highest accuracy values. zs_1, zs_2, zs_3, and zs_4 customers mean percentages were 92.74%, 95.79%, 95.9%, and 96.22%, respectively, indicating uniform quality from all categories. the weighted averages, which also consider the distribution structure, broadly resulted in a similar pattern;the scores were 92.74%, 95.81%, 95.88%, and 96.2%. the aggregate individual classes of Micro were almost the same as the Weighted averages, making an indication that Micro's model was accurate in this way. the task shows that federated learning can be successfully applied to solving agriculture problems related to plant disease recognition and thus is closely tied to the broader discussion of sustainable farming strategies. © 2024 IEEE.

关键词： Fruits

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Comparative Analysis of Conventional and Modified Capsule Networks for Sperm Morphology Assessment 10

Comparative Analysis of Conventional and Modified Capsule Ne...

引用

10th international conference on Signal Processing and intelligent Systems, ICSPIS 2024

作者： Mohammadi, Hossein Serbes, Gorkem Ilhan, Hamza Osman Yildiz Technical University Department of Computer Engineering Istanbul Turkey Yildiz Technical University Department of Biomedical Engineering Istanbul Turkey

ISBN: (纸本)9798331532543

the evaluation of sperm morphology is an essential factor in the diagnosis of male-related infertility. Manual sperm morphology assessments are labor intensive and can vary significantly between observers. Although automated sperm morphology analysis approaches have shown significant success in the last two decades, there is still a need for deep learning based solutions due to the challenges faced in new data sets such as the dramatic increase seen in number of sperm samples and the diversity of the labeled abnormality types. As a solution to these challenges, this research performs a comparative analysis of the conventional capsule network (CapsNet) and its modified version (FixCaps), using a recently introduced imbalanced dataset referred to as Hi-LabSpermMorpho which contains a total of 18,456 images labeled into 18 different classes. the classes include variants of abnormalities of the head, neck, and tail of the sperm, as well as the normal class. the evaluation focused on assessing the accuracy, computational efficiency, and the effects of different optimization strategies for both models. the results have shown that FixCaps achieved higher performance than CapsNet, with its improvements such as using large-kernel convolutions and the CBAM attention mechanism, which allow the model to capture more complex features and spatial relationships of the images in the dataset. FixCaps resulted in 52.67% classification accuracy while CapsNet performed only 37.89%. © 2024 IEEE.

关键词： Image classification

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PHASE2VEC: DYNAMICAL SYSTEMS EMBEDDING WIth A PHYSICS-INFORMED CONVOLUTIONAL NETWORK 11

PHASE2VEC: DYNAMICAL SYSTEMS EMBEDDING WITH A PHYSICS-INFORM...

引用

11th international conference on learning Representations, ICLR 2023

作者： Ricci, Matthew Moriel, Noa Piran, Zoe Nitzan, Mor School of Computer Science and Engineering The Hebrew University Jerusalem Israel School of Computer Science and Engineering Racah Institute of Physics Faculty of Medicine The Hebrew University Jerusalem Israel

Dynamical systems are found in innumerable forms across the physical and biological sciences, yet all these systems fall naturally into equivalence classes: conservative or dissipative, stable or unstable, compressible or incompressible. Predicting these classes from data remains an essential open challenge in computational physics on which existing time-series classification methods struggle. Here, we propose, phase2vec, an embedding method that learns high-quality, physically-meaningful representations of low-dimensional dynamical systems without supervision. Our embeddings are produced by a convolutional backbone that extracts geometric features from flow data and minimizes a physically-informed vector field reconstruction loss. the trained architecture can not only predict the equations of unseen data, but also produces embeddings that encode meaningful physical properties of input data (e.g. stability of fixed points, conservation of energy, and the incompressibility of flows) more faithfully than standard blackbox classifiers and state-of-the-art time series classification techniques. We additionally apply our embeddings to the analysis of meteorological data, showing we can detect climatically meaningful features. Collectively, our results demonstrate the viability of embedding approaches for the discovery of dynamical features in physical systems. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Embeddings

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Service Evaluation of Urban Roads Based on Mobile Phone Signaling data 11

Service Evaluation of Urban Roads Based on Mobile Phone Sign...

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11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Yan, Cheng He, Yujia Yu, Yan Yan, Qiqi Gao, Xinxin Zhang, Chenhe Wuhan University of Technology School of Resources and Environmental Engineering WuHan China

ISBN: (纸本)9798350303513

Service evaluation of urban roads is conducive to optimizing the efficiency of road utilization and service capacity. In this paper, based on mobile phone signaling data and intelligent algorithms, a street-scale traffic zone division method is proposed and the traffic zone OD matrix is calculated. After that, based on the calculated OD matrix, the traffic assignment of foot traffic is performed by using the stochastic user equilibrium model and BPR function. Finally, the foot traffic based on mobile phone signaling data are allocated to the roads, enabling precise analysis of the urban road services. the study shows that mobile phone signaling data for urban road service evaluation in Ningbo can avoid traditional questionnaire statistics such as incomplete foot traffic. It offers a new approach to transportation decision-makers for regulated management and development. © 2023 IEEE.

关键词： Stochastic systems

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Applying Machine learning Techniques for Multiple Medical Conditions 28th

Applying Machine Learning Techniques for Multiple Medical Co...

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28th international conference on Knowledge Based and intelligent information and engineering Systems, KES 2024

作者： Costandache, Mihai-Andrei Faculty of Computer Science "Alexandru Ioan Cuza" University Iasi Romania

Health is one of the aspects of life that the emerging technology has changed. automated systems, powered by machine learning, can analyze large volumes of data of many forms, text, image, or audio. the newer automated systems still do not replace the medics, they are just instruments. A mixed approach can be applied where the patient uses an application to get a verdict, but he/she consults a medic to find out more or to get the necessary medication. In our research years, we discovered how machine learning is used in medicine by studying the existing works, and proposed other solutions. Even if they have aspects in common with other types of applications, medical applications require a specific, stricter approach (correctness, usability, etc.). In our projects, we were making particular engineering decisions by considering the patient's interest - do we need to store the recordings?, when should the medic be involved in the medical analysis?, etc. We also noticed that even if the machine learning techniques are general, approaching different diseases requires us to adapt our methods. this paper does not cover our research fully, but we consider it is an interesting work summarizing the good practice principles in medical software and showing our own works as applications. © 2024 the Authors.

关键词： machine learning medicine sound processing

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