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An Efficient Diagnostic Approach for Multi-Class Classification of Wheat Leaf Disease Using Deep Transfer and Ensemble learning

An Efficient Diagnostic Approach for Multi-Class Classificat...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sudhir Saraswat Salil Batra Protyush P Neog Emani Likith Sharma P. Pavan Kumar Ankit Kumar Pandey Department of Computer Science and Engineering Lovely Professional University Phagwara Punjab

Wheat (Triticum aestivum L.) is a critical component of global food security, feeding billions of people worldwide. However, the high occurrence of diseases in wheat crops poses a significant threat to agricultural production. For efficient management and the protection of food supply, prompt and accurate detection of these diseases is essential. Deep learning has been a powerful method for automating the diagnosis of illnesses affecting wheat leaves in recent years. There are over dozens of wheat diseases that can harm the crop. As a result, manual diagnosis of these diseases can be very challenging. automated disease classification in wheat can help improve both crop yield quantity and quality. It can also be a helpful tool for crop quality assessment and pricing. The proposed research provides a comprehensive analysis of recent advances in deep learning-based methods for wheat leaf disease diagnosis and a state-of-the-art methodology has been proposed to accurately detect 8 different classes of wheat leaf disease. The proposed framework performs significantly better than popular deep learning models such as CNN, SimpleNet, EfficientNet, VGG16, ResNet50, and VGG-FCN-VD16 by harnessing the power of transfer learning and ensemble learning. Experimental results show that the proposed method also outperforms all existing models with a classification accuracy of 98.08%.

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Advanced Deep learning Method for Thyroid Nodule Detection and Evaluation in Ultrasound Images

Advanced Deep Learning Method for Thyroid Nodule Detection a...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sesha Vidhya S Swagata Sarkar Rashmi B R Suseendra R Prakash Kumar P S Srimathi S Department of ECE RMK College of Engineering and Technology Department of Artificial intelligence and Data Science Sri Sairam Engineering College Sai Leo Nagar West Tambaram Chennai 44 Department of Computer Science and Engineering Raja Rajeswari College of Engineering Kumbalgodu Karnataka Department of Information Technology Panimalar Engineering College Department of Information Technology K S R Institute for Engineering and Technology Tiruchengode Namakkal Department of Bio Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

Thyroid nodule detection in its earliest stages is a difficult endeavor. Thyroid nodule diagnosis requires a lot of time and effort if done manually. The original ultrasound imaging of thyroid nodules is very variable due to differences in equipment and technicians. The issue with nodules is determining whether or not they are cancerous. Thyroid nodule evaluation through ultrasonography is presently advised as the first line of treatment. Ultrasound imaging can be helpful in making a diagnosis; But this relies heavily on the proficiency of the radiologist. There is a great need for faster and more efficient automated techniques of screening ultrasound pictures. This research study presents a deep learning method to detect thyroid nodules quickly and accurately. Some image processing techniques are required for optimal utilization of ultrasound imaging data of thyroid nodules. In this study, authors used deep neural networks InceptionResnet V2 and image augmentation to create a system for autonomous thyroid nodule categorization. For testing, neural networks with four-layer network nodes and the Local Laplacian Filtering technique are used to improve the image quality. The proposed method's reliability is evaluated by using the Thyroid Digital Image database (TDID), a freely available dataset. The comparative findings demonstrate that the proposed method is superior to the current state of advancement in machine learning. Based on experiments, it is evident that the proposed technique has an Accuracy 0.99, Precision 0.98, Recall 0.96, Fl-Score 0.98, AVC-ROC 0.99.

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Mutual Information based Efficient Spike Encoding on FPGA

Mutual Information based Efficient Spike Encoding on FPGA

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international conference on VLSI Design

作者： Omkareshwar Gundoji Dighanchal Banerjee Sounak Dey Arpan Pal Embedded Devices & Intelligent Systems TCS Research Kolkata India

Spiking Neural Networks (SNN) coupled with Neuromorphic Computing is considered as a bio-plausible, power and data-efficient AI/ML technique especially suitable for resource-constrained edge devices such as robots, satellites, automated cars etc. While the performance of an SNN often suffers due to lossy spike encoding of real valued data, its implementation on actual hardware is also limited due to commercial unavailability of neuromorphic chips. While on one hand, better encoding techniques are being improvised, on the other hand, FPGAs (instead of ASICs) are nowadays considered as a plausible alternative for hardware implementation. In this paper, we have shown how a Mutual Information based information maximization technique for spike encoding can be successfully implemented on a Xilinx Zynq ZedBoard. The implemented system is found to perform at par with software simulation w a minimal error of 0.014 when tested on four different univariate time series. When running at a frequency of 200MHz, the system is observed to consume 0.018 watts (dynamic power), have a latency of 10.35 ms and a throughput of 96 samples/sec. Moreover, the FPGA-based system has the advantage of being used as a plug-and-play encoder device for any neuromorphic computing platform.

关键词： Performance evaluation Neuromorphic engineering Time series analysis Computer architecture Encoding Hardware Software

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Compromise Privacy in Large-Batch Federated learning via Malicious Model Parameters 1

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22nd international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zhang, Shuaishuai Huang, Jie Zhang, Zeping Qi, Chunyang School of Cyber Science and Engineering Southeast University Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

Federated learning (FL) is a distributed learning paradigm, in which users share their gradients instead of local data to preserve privacy. Previous works have shown that the server in FL can reveal user’s local data by inverting shared gradients. However, a large batchsize can defense against these attacks effectively by obfuscating gradients calculated on each data. In this paper, we propose a novel Gradient Inversion Attack which can compromise privacy in large-batch FL. Firstly, the server constructs malicious model parameters which can mitigate the confusion of gradients. Then users’ model parameters are tampered purposely by the server. From users’ shared gradients computed on malicious model parameters, the server can recover private local trainsets perfectly in large-batch FL. Experiments on CIFAR100 show that our method can recover 92%, 77% and 54% of the data points in a batch with batchsize 128, 256 and 512, respectively. Compared with previous works, our method has a higher performance and versatility. © 2023, Springer Nature Switzerland AG.

关键词： learning systems

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Collision Detection in LoRaWAN Using Machine learning

Collision Detection in LoRaWAN Using Machine Learning

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international Symposium on intelligent Systems and Informatics (SISY)

作者： Nabil Abdoun Samira Abboud Haya Altaleb Zoltán Rajnai Department of Liberal Education School of Arts and Sciences Lebanese American University (LAU) Beirut Lebanon Department of Computer Science École Supe´rieure d’Électronique de l'Ouest (ESEO) Angers France Bánki Donát Faculty of Mechanical and Safety Engineering O´ buda University Budapest Hungary

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

In this paper, we investigate the application of machine learning techniques as a productive solution for carrying out LoRaWAN packet collision detection. Our focus is on a common channel that is shared by several LoRa end-devices via the asynchronous ALOHA protocol, with the aim of delivering data packets in IoT scenarios. We evaluate the effectiveness of two algorithms in this setup: Random Forest and Decision Tree. Higher efficiency is exhibited for identification and likelihood of False Positive Rate, even when packets transmissions collide.

关键词： Accuracy Machine learning algorithms Neural networks LoRaWAN Robustness Decision trees Collision avoidance intelligent systems Informatics Random forests

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A Cognitive Solver with Autonomously Knowledge learning for Reasoning Mathematical Answers 22

A Cognitive Solver with Autonomously Knowledge Learning for ...

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22nd IEEE international conference on data Mining (ICDM)

作者： Liu, Jiayu Huang, Zhenya Lin, Xin Liu, Qi Ma, Jianhui Chen, Enhong Univ Sci & Technol China Sch Data Sci Anhui Prov Key Lab Big Data Anal & Applicat Langfang Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Langfang Peoples R China

ISBN: (纸本)9781665450997

Reasoning answers to mathematical problems requires machines to think and operate like a human to learn knowledge from mathematical data, which is one of the fundamental tasks for exploring general artificial intelligence. Most solutions focus on mimicking how humans understand problems, which generate the necessary expressions for answers. However, they are still far from enough since they ignore the core ability of humans to acquire knowledge from experience. In this paper, we propose a Cognitive Solver (CogSolver) that is capable of autonomously learning knowledge from scratch to solve mathematical problems, inspired by two cognitive science theories. Specifically, we draw one insight from the dual process theory to establish an intelligent BRAIN-ARM framework, and refer to another information processing theory to summarize the knowledge learning process into Store-Apply-Update steps. In CogSolver, the BRAIN system stores three types of mathematical knowledge, including semantics knowledge, relation knowledge, and mathematic rule knowledge. Then, the ARM system applies the knowledge in BRAIN to answer the problems. Specifically, we design a knowledge-aware module and a commutative module in ARM to improve its reasoning ability, where the knowledge is organically integrated into answer reasoning process. After solving the problems, BRAIN updates the stored knowledge according to the feedback of ARM, where we develop knowledge filters to eliminate the redundant ones and further form a more reasonable knowledge base. Our CogSolver carries out the above three steps iteratively, which behaves more like a human. We conduct extensive experiments on real-world math word problem datasets. The experimental results demonstrate the improvement in answer reasoning and clearly show how CogSolver gains knowledge from the problems, leading to superior interpretability. Our codes are available at https://***/bigdata-ustc/CogSolver.

关键词： knowledge learning mathematical reasoning

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PLSR: Unstructured Pruning with Layer-Wise Sparsity Ratio 22

PLSR: Unstructured Pruning with Layer-Wise Sparsity Ratio

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Zhao, Haocheng Yu, Limin Guan, Runwei Jia, Liye Zhang, Junqing Yue, Yutao Institute of Deep Perception Technology Jitri Wuxi China XJTLU-JITRI Academy of Industrial Technology Xi'an Jiaotong-Liverpool University Suzhou China Xi'an Jiaotong-Liverpool University Department of School of Advanced Technology Suzhou China University of Liverpool Department of Electrical Engineering and Electronics Liverpool United Kingdom University of Liverpool Department of Mathematical Sciences Liverpool United Kingdom

ISBN: (纸本)9798350345346

In the current era of multi-modal and large models gradually revealing their potential, neural network pruning has emerged as a crucial means of model compression. It is widely recognized that models tend to be over-parameterized, and pruning enables the removal of unimportant weights, leading to improved inference speed while preserving accuracy. From early methods such as gradient-based, and magnitude-based pruning to modern algorithms like iterative magnitude pruning, lottery ticket hypothesis, and pruning at initialization, researchers have strived to increase the compression ratio of model parameters while maintaining high accuracy. Currently, mainstream algorithms focus on the global pruning of neural networks using various scoring functions, followed by different pruning strategies to enhance the accuracy of sparse model. Recent studies have shown that random pruning with varying layer-wise sparsity ratio has achieved robust results for large models and out-of-distribution data. Based on this discovery, we propose a new score called FeatIO, which is based on module input and output feature map sizes. As a score function used in PaI, FeatIO surpasses the performance of other PaI score functions. Additionally, we propose a novel pruning strategy called Pruning with Layer-wise Sparsity Ratio (PLSR), which conbines the layer-wise sparsity ratios and magnitude-based score function, resulting in optimal evaluation performance. Almost all algorithms exhibit improved performance when using our novel pruning strategy. The combination of PLSR and FeatIO consistently outperforms other algorithms in testing, demonstrating the significant potential of our proposed approach. Our code will be available here. © 2023 IEEE.

关键词： Layer-wise Sparsity Model Com-pression Pruning Unstructured Pruning

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Automation of PGAA Spectra Analysis with Deep learning

Automation of PGAA Spectra Analysis with Deep Learning

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IEEE international conference on Industrial Informatics (IndIN)

作者： Daniel Boschmann Christian Stieghorst David Knezevic Loubna Kadri Oliver Niggemann Institute of Automation Technology Helmut Schmidt University Hamburg Germany Research Neutron Source FRM II Technical University of Munich Munich Germany Engineering Materials Science Centre (GEMS) at MLZ Helmholtz-Zentrum Hereon GmbH Munich Germany

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Analyzing Prompt Gamma Activation Analysis (PG AA) spectra poses significant challenges, particularly in accurately identifying and quantifying the elements present. Traditional expert analysis, while effective, is time-consuming. This paper addresses the need for an efficient, automated solution to enhance the analysis process. The research investigates the use of machine learning (ML) and deep learning (DL) algorithms in the automated analysis of PGAA spectra. We aim to establish a new metric for comparing automated analysis with expert analysis, providing a baseline using Linear Regression, Random Forest, 1D Convolutional Neural Network, Feed Forward Neural Network, and Autoencoder algorithms. Established metrics like Mean Square Error (MSE) and Mean Absolute Error (MAE) are utilized to compare the performance of these automated approaches against traditional expert analysis. Using actual spectra from various research projects and semi-real augmented data, the study demonstrates that the Feed Forward Neural Network (FFNN) and Autoencoder algorithms can effectively predict the magnitude of the present elements. These findings suggest especially D L algorithms could signifi-cantly assist researchers and industry personnel by providing a rough overview of the material and saving valuable time. However, the automated approach requires further refinement, particularly in handling noisy data, predicting additional crucial information, and integrating more prior knowledge into the anal-ysis. This research offers valuable insights into the application of ML algorithms in spectral analysis and lays a foundation for further advancements in the field.

关键词： Deep learning Machine learning algorithms Neural networks Mean square error methods Prediction algorithms Feeds Personnel Noise measurement Spectral analysis Random forests

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The IoT Service Agent Model based on Federated learning to Improve Service Quality

The IoT Service Agent Model based on Federated Learning to I...

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IEEE international conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Tse-Chuan Hsu William Cheng-Chung Chu Shyh-wei Chen Dept. of Computer Science & Information Management Soochow University Taipei Taiwan Dept. of Computer Science Tunghai University Tiachung Taiwan

The combination of learning analysis technology and terminal equipment can provide a rapidly imitating learning method between devices, and cognitive intelligence affects the learning effect. To enhance automated learning services, we can use federated learning methods to enable the training of different devices and enhance each other through devices. Using the training database to improve the quality of automated learning services. In this study, a novel agent-assisted active detection and data collection framework is designed. Monitoring agents can learn from each other to establish intelligent models, and through mutual communication between devices. Can check if established data can be applied to machine data model to get data. It can be used for intelligent manufacturing in the future. The agent may learn methods of learning and managing between devices having different properties. Obtaining experimental simulation and control data, and using machine learning to analyze growth progress and results allow for a deeper analysis of associated adjustments and anticipated changes.

关键词： Training Analytical models Federated learning Computational modeling Software quality data collection data models

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EEG-Eye Movements Cross-Modal Decision Confidence Measurement with Generative Adversarial Networks 11

EEG-Eye Movements Cross-Modal Decision Confidence Measuremen...

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Fei, Cheng Li, Rui Zhao, Li-Ming Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong Univ Key Lab Shanghai Educ Commiss Intelligent Interac Dept Comp Sci & Engn Ctr Brain Like Comp & Machine Intelligence 800 Dongchuan Rd Shanghai Peoples R China Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr 800 Dongchuan Rd Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Med RuiJin Hosp RuiJin Mihoyo LabClin Neurosci Ctr 197 Ruijin 2nd Rd Shanghai 200020 Peoples R China

ISBN: (纸本)9781665462921

Decision confidence is an individual's feeling of correctness or optimization when making a decision. Various physiological signals, including electroencephalography (EEG) and eye movements have been studied extensively in measuring levels of decision confidence in humans. While multimodal fusion generally performs better than single-modal approaches, it requires data from different modalities at a greater cost. In particular, collection of EEG data is more complicated and time consuming while eye movement signals are much easier to acquire. To tackle this problem, we propose a crossmodal method based on generative adversarial learning. In our method, the intrinsic relationship between eye movement and EEG features in a high-level feature space can be learned in the training phase, and then we can obtain multimodal information during the test phase when only eye movements are available as inputs. Experimental results on the SEED-VPDC dataset demonstrate that our proposed method outperforms singlemodal methods trained and tested only on eye movement signals with an improvement of approximately 5.43% in accuracy, and maintains competitive performance in comparison with multimodal methods. Our cross-modal approach requires only eye movements as inputs and reduces reliance on EEG data, making the decision confidence measurement more applicable and practicable.

关键词： Eye movements

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