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Optimized VGG16 Model for Advanced Classification of Cotton Leaf Diseases 4

Optimized VGG16 Model for Advanced Classification of Cotton ...

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4th international conference on intelligent Technologies, CONIT 2024

作者： Walia, Nikhil Sharma, Rishabh Kumar, Mukesh Choudhary, Ankur Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India Sharda University Department of Computer Science and Engineering Sharda School of Engineering and Technology U.P Greater Noida India

ISBN: (纸本)9798350349900

the cotton leaf disease infliction anywhere puts the production of agricultural products at risk as well as sustainable farming causing both problems and solutions of the meaningless disease detection. In this research, we propose an optimized Visual Geometry Group (VGG) 16 model for the advanced classification of four major cotton leaf diseases: leaf curl, bacterial blighty, leaf spot, and wilt. Integration of deep learning methods, transfer learning as well and other approaches is what the model focuses on to raise the disease detection accuracy rate and realize the management of agriculture precision. And, there was a complete dataset, which included images of cotton plants from infected leaves with various diseases. this dataset was annotated properly and preprocessed. VGG16 model was pre-trained with a substantial data set of ImageNet image database and then fine-tuned to perform the cotton leaf disease classification task using existing transfer learning techniques. the achieved model efficacy has been evaluated using commonly accepted evaluation metrics. these include accuracy, precision, recall, and F1-score. Results show that the invented technique performs well when the revised VGG 16 model obtains satisfactory accuracy and disease-related metrics when applied to all three disease categories. the level of comparative analysis with current best models justifies done method efficacy and its strength in cotton leaf disease classification. this study's findings have an essential consequence on precision farming. It grants farmers a scalable solution relevant for the quick and automated monitoring, early diagnosis, and possibly treatment of plant diseases. Future investigation may rule in the superiority of applying the modified model to practical agricultural fields for ready accessibility and usability. Ultimately, these findings are moving us into the phase of development of agricultural informatics and offer evidence of the ability of deep learning to radic

关键词： Cotton

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Application of Multi-agent Technologies for Transforming Network engineering Education 7

Application of Multi-agent Technologies for Transforming Net...

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7th international conference on Information Technologies in engineering Education, Inforino 2024

作者： Telnov, Yury Filippovich Diveev, Rustem Ilyasovitch Plekhanov Russian University of Economics Department of Applied Information Technologies and Information Security Moscow Russia

ISBN: (纸本)9798350371369

the article substantiates the possibilities of implementing network programs of higher and additional professional education based on multi-agent technologies that transform all basic educational processes, ensuring their flexibility and adaptability to the dynamic needs of the labor market. the use of multi-agent technologies for engineering education, which is characterized by the use of complex and high-cost software, hardware and engineering systems that require the organization of collective sharing of resources, is becoming especially relevant. For multi-agent technologies, a method is chosen for representing agents in the form of asset administrative shells of fourth-generation industry systems, representing digital twins of information, educational, software, technical and organizational-methodological resources. the article proposes the structure of a multi-agent networked learning system;requirements for information representation of educational processes and the composition of key services in the asset administrative shells are defined. It introduces a multi-agent technology for the interaction of participants in the creation of networked educational programs. the software implementation of a multi-agent networked learning system based on the FIPA (Foundation for intelligent Physical Agents) standards and the specifications of the asset administrative shells of the Platform Industry 4.0 project will enhance the adaptability of networked educational programs. this will be achieved through the individualization of the learning process, with the real-time involvement of resources from various educational institutions and the collective use of software, technical, and engineering complexes. the collection of big data on the execution of educational processes in the asset administrative shells will contribute to both the real-time adjustment of the multi-agent system to the evolving competency model of learners and the improvement of the educational content th

关键词： learning systems

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An Improved Dual Stream DGCNN Method for Fine-Grained Positive Emotion Classification 11

An Improved Dual Stream DGCNN Method for Fine-Grained Positi...

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11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Hu, Shuming Yu, Zhiwen Xu, En Wang, Yao Zhang, Ying Guo, Bin School of Computer Science Northwestern Polytechnical University Xian China Harbin Engineering University Harbin China Hong Kong Baptist University Department of Physics Hong Kong Hong Kong

ISBN: (纸本)9798331531904

Recent advances in EEG-based emotion computation have garnered significant interest from the fields of neuroscience and computer science. Despite this, the bulk of EEG-based emotion classification research has concentrated on the dimensions of arousal and valence, with scant attention to the classification of positive emotions as experienced in daily life. Addressing this gap, our study focuses on the classification of four specific positive emotions using the open-source FACED dataset. We processed EEG data by segmenting it, extracting features, and then designing and training the DualStreamDGCNN deep learning model, which achieved the best accuracy of 60.88%. Furthermore, we visualized the adjacency matrices of the four emotions from our model, offering insights from a neuroscientific perspective. this research is pivotal for advancing our understanding of the types and dynamics of positive emotions in practical applications, such as enhancing educational environments and boosting employee efficiency. © 2024 IEEE.

关键词： Electroencephalography

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A GENERAL FRAMEWORK FOR SAMPLE-EFFICIENT FUNCTION APPROXIMATION IN REINFORCEMENT learning 11

A GENERAL FRAMEWORK FOR SAMPLE-EFFICIENT FUNCTION APPROXIMAT...

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11th international conference on learning Representations, ICLR 2023

作者： Chen, Zixiang Li, Chris Junchi Yuan, Angela Gu, Quanquan Jordan, Michael I. Department of Computer Science University of California Los Angles United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Department of Statistics University of California Berkeley United States

With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL). In this paper, we propose a general framework that unifies model-based and model-free RL, and an Admissible Bellman Characterization (ABC) class that subsumes nearly all Markov Decision Process (MDP) models in the literature for tractable RL. We propose a novel estimation function with decomposable structural properties for optimization-based exploration and the functional eluder dimension as a complexity measure of the ABC class. Under our framework, a new sample-efficient algorithm namely OPtimization-based ExploRation with Approximation (OPERA) is proposed, achieving regret bounds that match or improve over the best-known results for a variety of MDP models. In particular, for MDPs with low Witness rank, under a slightly stronger assumption, OPERA improves the state-of-the-art sample complexity results by a factor of dH. Our framework provides a generic interface to design and analyze new RL models and algorithms. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Reinforcement learning

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T-RAPPI: A Machine learning Model for the Corredor Metropolitano 11

T-RAPPI: A Machine Learning Model for the Corredor Metropoli...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Traverso, Deneb Pacheco, Gonzalo Castañeda, Pedro San Isidro Lima Peru

ISBN: (纸本)9789897587450

the public transportation system in Lima, Peru, faces significant challenges, including bus shortages, long queues, and severe traffic congestion, which diminish service quality. these issues arise from a lack of modern management tools capable of efficiently handling the Metropolitano bus system. this paper introduces T-RAPPI, a predictive model based on Random Forest, developed to estimate bus arrival times at Metropolitano stations. Using historical data on bus arrivals and operational parameters, the model achieved exceptional accuracy, with an R2 score of 0.9998 and a MAPE of 0.0554%, demonstrating its robustness and ability to minimize prediction errors. the implementation of T-RAPPI represents a substantial improvement over existing systems, providing operators with data-driven insights to optimize route planning and bus allocation. Additionally, the model's integration into the mobile application Metropolitano + enhances the commuting experience by offering users real-time bus arrival predictions, reducing uncertainty and wait times. Future extensions of this work could include incorporating real-time traffic and weather data to further enhance prediction accuracy and expanding the model to other transit systems in Lima and beyond. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Mass transportation

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HOW ROBUST IS UNSUPERVISED REPRESENTATION learning TO DISTRIBUTION SHIFT? 11

HOW ROBUST IS UNSUPERVISED REPRESENTATION LEARNING TO DISTRI...

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11th international conference on learning Representations, ICLR 2023

作者： Shi, Yuge Daunhawer, Imant Vogt, Julia E. Torr, Philip H.S. Sanyal, Amartya Department of Engineering Science University of Oxford United Kingdom Department of Computer Science ETH Zurich Switzerland Department of Computer Science ETH AI Center ETH Zurich Switzerland

the robustness of machine learning algorithms to distributions shift is primarily discussed in the context of supervised learning (SL). As such, there is a lack of insight on the robustness of the representations learned from unsupervised methods, such as self-supervised learning (SSL) and auto-encoder based algorithms (AE), to distribution shift. We posit that the input-driven objectives of unsupervised algorithms lead to representations that are more robust to distribution shift than the target-driven objective of SL. We verify this by extensively evaluating the performance of SSL and AE on both synthetic and realistic distribution shift datasets. Following observations that the linear layer used for classification itself can be susceptible to spurious correlations, we evaluate the representations using a linear head trained on a small amount of out-of-distribution (OOD) data, to isolate the robustness of the learned representations from that of the linear head. We also develop "controllable" versions of existing realistic domain generalisation datasets with adjustable degrees of distribution shifts. this allows us to study the robustness of different learning algorithms under versatile yet realistic distribution shift conditions. Our experiments show that representations learned from unsupervised learning algorithms generalise better than SL under a wide variety of extreme as well as realistic distribution shifts. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Supervised learning

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Sugarcane Severity Assessment with Deep learning and Random Forest: A Multi-class Approach 11

Sugarcane Severity Assessment with Deep Learning and Random ...

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11th IEEE international conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Banerjee, Deepak Kukreja, Vinay Rana, Deepak Singh Garg, Ashish Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350308433

Researchers evaluate a classification method based on a thorough table of performance metrics in this abstract. data is categorized using this method into five significant levels (Significance Levels 1 to 5), which stand for various classes. the model's precision regularly oscillates between 96.77% to 97.06%, demonstrating its accuracy along with a low false positive rate. Recall ratings, which range from 96.34% to 97.38%, further demonstrate the model's accuracy in identifying occurrences of each class. High recall with accuracy values is a byproduct of exceptional F1 scores, ranging from 96.59% to 97.13%, achieving a balance between precision and recall. the total amount of instances inside each class is shown by the support values, having class 5 receiving the most support (1025), indicating a possible class imbalance. the support proportion shows that a sizable chunk of the dataset belongs to class 5. Its overall performance is summarized by macro, micro, and overall weighted averages, all of which have a constant precision, recall, or F1-score of 96.88%. It achieves an outstanding overall accuracy of over 96.87% with a data set of 4,800 photos. A micro average accuracy, recall, & F1-score of 96.88% show how well the model categorizes cases across the dataset. Class differences are taken into consideration, and the weighted average accuracy stays at 96.88%. In conclusion, the categorization model routinely outperforms industry standards, demonstrating its ability to precisely categorize data into different categories and efficiently achieve its intended purpose. © 2024 IEEE.

关键词： Deep learning

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Advanced Machine learning Methods for Accurate Prediction of Loss Circulation in Drilling Well Log 11

Advanced Machine Learning Methods for Accurate Prediction of...

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11th IEEE international conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2024

作者： Beheshtian, Saeed Roodbari, Sara Kishan Ghorbani, Hamzeh Azodinia, Mohamadreza Mudabbir, Mohamed Varkonyi-Koczy, Annamaria R. Islamic Azad University Department of Petroleum Engineering Omidiyeh Branch Omidiyeh Iran Petroleum University of Technology Gas Engineering Department Ahvaz Iran University of Traditional Medicine of Armenia Faculty of General Medicine Yerevan Armenia Obuda University John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350350142

Drilling mud (DM) plays an important role for exploration of oil and gas reservoirs. Lost circulation (LC) has highly influenced drilling costs due to the waste and loss of the drilling fluid (DF), which stems from some variables such as mud composition, permeability, and mud pressure. To predict this important parameter, a dataset includes 1003 data points was collected from two oil field wells in southern Iran. For the prediction of LC in this article, three hybrid algorithms were used, combining three optimization algorithms with one network algorithm, including Social Ski-Driver (SSD), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) with the Multilayer Perceptron Network (MLP). Analysis shows the combination of the MLP with SSD give good and higher accuracy than other algorithms. the statical error meteoric for the test dataset, as indicated by an RMSE of 30.85 and an impressive R2 of 0.94, show their results are better and over than other competing algorithms. © 2024 IEEE.

关键词： Drilling fluids

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Diagnosis of Lung Diseases from Chest X-Ray Images Using Different Fusion Techniques 11

Diagnosis of Lung Diseases from Chest X-Ray Images Using Dif...

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11th international conference on Information and Communication Technology, ICoICT 2023

作者： Mostafa, Fatma A. Elrefaei, Lamiaa A. Fouda, Mostafa M. Hossam, Aya Shoubra Benha University Faculty of Engineering Department of Electrical Engineering Cairo Egypt Idaho State University Department of Electrical and Computer Engineering PocatelloID United States

ISBN: (纸本)9798350321982

Lung diseases refer to a group of disorders that affect the lungs and respiratory system. Several factors, such as genetics, environmental pollution, infections, and smoking can these. Lung diseases include coronavirus (COVID-19), pneumonia, chronic obstructive pulmonary disease (COPD), and asthma. Lung diseases cause significant damage to lung function and lead to respiratory failure or even death. the symptoms of lung diseases can range from mild difficulty breathing to severe ones, including chest pain, bloody coughing, and shortness of breath. Early detection can increase the chances of successful treatment and improve the overall outcome for affected individuals. Artificial intelligence (AI) has demonstrated considerable potential for detecting and diagnosing lung diseases through machine learning algorithms and deep learning models. the detection of lung diseases using chest X-rays (CXRs) is demonstrated in this paper by applying feature-level fusion (FLF) and decision-level fusion (DLF) techniques. FLF involves concatenating the features from two models before the classification process. In comparison, DLF is executed after training the two models and then concatenating the results to make a single decision. the two models are DenseNet-169 and Vision Transformer (ViT-L32). On the COVID-19 Radiography database, the proposed models have been tested and trained. the data has been preprocessed using data augmentation and a blurring method. An 'Adam' optimizer is used while compiling the model. the accuracy of the DLF is 93.3%, while the FLF achieved an accuracy of 94.54%, which is better than the accuracy of the models without fusion. © 2023 IEEE.

关键词： COVID-19

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A Method Based on Entity Interaction Graph for Detecting Social Events Using GDELT 11

A Method Based on Entity Interaction Graph for Detecting Soc...

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11th international conference on intelligent Computing and Wireless Optical Communications, ICWOC 2023

作者： Zhao, Dongxu Zhang, Xin Wang, Yinsen Qiao, Fengcai Pan, Yan National University of Defense Technology College of Information Systems and Management Science and Technology on Information Systems Engineering Laboratory Changsha China National University of Defense Technology College of Advanced Interdisciplinary Studies The First Interdisciplinary Center Changsha China

ISBN: (纸本)9798350321791

Social events are group activities gathered at a certain time and location. Timely detecting social events is of great significance to grasp the dynamics of social development. Most of the existing researches on social event detection are oriented to unstructured data, and these methods are difficult to applied for structured data. Considering that structured data focuses on the event level and has strong authority, it is necessary to put efforts into related research on structured data. However, the existing researches on structured data rely heavily on rule-based feature construction, which cannot fully mine the hidden layer features representing events. Fortunately, deep learning methods can solve this problem. In this paper, we design a social event detection method based on entity interaction graph mining to achieve this task. Firstly, we build a feature mining module based on multi-relational graph convolutional network, and update the feature information by the powerful feature mining ability of graph convolutional network. then, a feature selection module is designed based on the graph pooling mechanism to select important nodes and edges. Finally, the feature concatenation module is constructed, the feature mining layer and feature selection layer are reasonably laid out, the embeddings of nodes and edges of corresponding layers are obtained by max pool and average pool, and the embeddings of corresponding layers are concatenated before classification. Finally, a classifier is constructed to obtain the final output. In order to demonstrate effectiveness and performance of our model, we construct datasets and design evaluation experiments on this basis. Experimental results show that our model outperforms the baselines with better generalization. © 2023 IEEE.

关键词： Embeddings

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