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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Karthigaiveni Madhubala. C G. Avinash E. Saranya P. Rohini G. Yogapriya Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamilnadu India School of Environment and Architecture Design SRM Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science Engineering Sri Eshwar College of Engineering Chennai India Department of Computer Science and Engineering Saranathan College of Engineering Trichy Tamilnadu Chettinad School of Architecture Chettinad Academy of Research and Education Chennai Tamilnadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Agriculture is a fundamental component of human civilization. It contributes to the economy while also providing sustenance. Plant foliage or crops are susceptible to many illnesses during agricultural agriculture. The illnesses impede the development of their respective species. Timely and accurate identification and categorization of illnesses may mitigate the risk of further harm to the plants. The identification and categorization of these disorders have emerged as significant challenges. The conventional methods used by farmers to forecast and identify plant leaf diseases may be tedious and inaccurate. Challenges may occur while endeavoring to manually forecast illness kinds. The failure to promptly identify and categorize plant diseases may lead to the devastation of crops, causing a substantial reduction in yield. Farmers using automated image processing techniques in their fields may mitigate losses and enhance productivity. This study proposes using a deep learning framework utilizing the latest convolutional neural network model EfficientNet to identify tomato illnesses based on 18,161 plain and segmented photos of tomato leaves. The efficacy of two segmentation theories, namely U-net and Modified U-net, for leaf segmentation is shown. The proposed methodology for plant disease identification is straightforward and computationally efficient, necessitating less time for predictions compared to previous deep learning methodologies. This document presents the computed accuracies for several plant and leaf diseases.

关键词： Deep learning Productivity Plant diseases Image segmentation Accuracy Computational modeling Transfer learning Crops Agriculture Robustness

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Adaptive Fuzzy Weighted Broad Network for Imbalanced Medical Diagnosis

Adaptive Fuzzy Weighted Broad Network for Imbalanced Medical...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Leqi Tang Wuxing Chen Zhiwen Yu Huan Wang Jianming Lv Kaixiang Yang South China University of Technology School of Future Technology Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China Guangdong Institute of Scientific & Technical Information Industrial Technology Research Center Guangzhou China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

Medical diagnosis is crucial for providing quality healthcare. Machine learning techniques have received much attention in the medical field for their ability to provide efficient handling of diverse diagnostic problems. Nonetheless, machine learning often faces performance degradation due to imbalanced data in medical diagnosis, and also requires models that can quickly diagnose problems to facilitate doctors' medical decisions. To address this problem, we introduce Adaptive Fuzzy Weighted Broad Learning System (AFWBLS), which aims to use a variety of technologies to improve medical diagnostic accuracy. The adaptive weight enables BLS to take Intra-class density, inter-class density, the distribution of classes and frontier into consideration, which can reduce the impact of imbalanced data. The fuzzy weight gives BLS the ability to reduce the impact of noise and outliers. The Experiment shows that AFWBLS has good performance on imbalanced medical datasets and AFWBLS can maintain stable in datasets with large imbalanced rates.

关键词： Learning systems Degradation Adaptive systems Noise Machine learning Learning (artificial intelligence) Data models Medical diagnosis Medical diagnostic imaging Faces

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FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition

arXiv

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arXiv 2023年

作者： Elbatel, Marawan Martí, Robert Li, Xiaomeng The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Computer Vision and Robotics Institute University of Girona Spain The Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Representational transfer from publicly available models is a promising technique for improving medical image classification, especially in long-tailed datasets with rare diseases. However, existing methods often overlook the frequency-dependent behavior of these models, thereby limiting their effectiveness in transferring representations and generalizations to rare diseases. In this paper, we propose FoPro-KD, a novel framework that leverages the power of frequency patterns learned from frozen pretrained models to enhance their transferability and compression, presenting a few unique insights: 1) We demonstrate that leveraging representations from publicly available pre-trained models can substantially improve performance, specifically for rare classes, even when utilizing representations from a smaller pre-trained model. 2) We observe that pre-trained models exhibit frequency preferences, which we explore using our proposed Fourier Prompt Generator (FPG), allowing us to manipulate specific frequencies in the input image, enhancing the discriminative representational transfer. 3) By amplifying or diminishing these frequencies in the input image, we enable Effective Knowledge Distillation (EKD). EKD facilitates the transfer of knowledge from pre-trained models to smaller models. Through extensive experiments in long-tailed gastrointestinal image recognition and skin lesion classification, where rare diseases are prevalent, our FoPro-KD framework outperforms existing methods, enabling more accessible medical models for rare disease classification. Code is available at https://***/xmed-lab/FoPro-KD. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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A Way of Safe Wireless Networks using IMU/UWB/Vision through Sensor Networks

A Way of Safe Wireless Networks using IMU/UWB/Vision through...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： P Sharmila M Maheswaran T Mohanraj Rajan Verma Bhupendra Malviya S P V SubbaRao S & H Dept Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Mechatronics Engineering Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Computer Science and Engineering Karpagam Academy of Higher Education TN Chitkara Center for Research and Development Chitkara University Himachal Pradesh India Department of Computer Science & Engineering (CSE) IES College of Technology Bhopal M.P India Electronics and Communication Engineering Department Sreenidhi Institute of Science and Technology Hyderabad

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In current military and private spheres, the demand for exact localization within the home of mobile agents, especially small unmanned aircraft (UAVs), has attracted significant attention. This paper shows a new method to indoor UAV localization using Ultra-WideBand technology, combined with a cost-effective Motion Measurement Unit (IMU) while vision-based devices. A vital improvement to the positioning process is brought through the inclusion of an additional Kalman filter (EKF). The utilization of an inexpensive and low-cost IMU with the modeling phase, paired with the inclusion of vision odometer measurements for identifying marks in the closeness of the landing area, forms a key aspect of this method. The addition of range data plays a key role in reducing mistakes affiliated with the limited capabilities of gyroscopes and accelerometers, which are inherent in inertial devices. By deploying these multi-modal technologies that the suggested approach achieves a surprising level of precision, with data showing an amazing precision equal to 10 centimeters. This development holds potential for improving the operating effectiveness of small UAVs in indoor settings, offering a robust solution that joins Ultra-WideBand technology, IMU, while vision-based sensors throughout the framework of an Extension Kalman Filter.

关键词： Location awareness Wireless sensor networks Phase measurement Wireless networks Military aircraft Odometers Sensors

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Deep Learning for Predictive Forecasts and Brain Imaging in Memory Loss Diseases

Deep Learning for Predictive Forecasts and Brain Imaging in ...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： P Arunachalam Vaibhav C. Gandhi Jalpa Bhatt Tushar Desai S. Surendran Nandhini T J Department of Biomedical Engineering VelTech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Engineering Madhuben and Bhanubhai Patel Institute of Technology (MBIT) Constitute College of CVM University Anand Gujarat India Krishna School of Emerging Technology and Applied Research Drs. Kiran & Pallavi Patel Global University Vadodara Gujarat India Department of Computer Science and Engineering Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Enginnering Saveetha Institute of Medical and Technical Science – SIMATS Saveetha University Chennai India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Because deep learning is so adept at finding patterns in complex, high-dimensional data, it has revolutionized computer vision, beating traditional machine learning models. The development of brain imaging technology led to extensive multim…ORIZATIONS: Deep learning algorithms, Alzheimer's illness, and classification This chapter presents a review of recent neuroimaging studies that used deep learning techniques to enhance AD classification and evaluation. Articles Published between January 2013 and July 2018 (searched on Pubmed and Google Scholar) Alzheimer's Disease Deep Learning Following a review of the articles, the findings were rated based on the classification technique and the kind of neuroimaging data utilized in each research. Twelve of the sixteen included research exclusively used deep learning models (CNN for eleven [5] – [14] and Bidirectional Long Short-Term Memory networks—Bi-LSTM for one study); the other four studies mixed classical machine learning with deep learning techniques [1] - [4]. The best accurate diagnosis result was obtained when these multimodal techniques were combined with biomarkers found in cerebrospinal fluid (used for further investigations). Deep learning algorithms have a lot of promise for the diagnostic criteria used to classify AD since they are getting more efficient. Understanding AD is still in its early stages, but deep learning and explainable technologies like these may help shed more light on the symptoms of the illness.

关键词： Deep learning Neuroimaging Training Machine learning algorithms Reviews Prediction algorithms Brain modeling Classification algorithms Alzheimer's disease Unsupervised learning

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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Expression of Concern for: A Parkinson Disease Classification Using Stacking Ensemble Machine Learning Methodology

Expression of Concern for: A Parkinson Disease Classificatio...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Dhyey D. Joshi Hirva H. Joshi Brijeshkumar Y. Panchal Parth Goel Amit Ganatra Department of Computer Science and Engineering Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Anand India Department of Computer Engineering Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Anand India

ISBN: (数字)9781665437899

ISBN: (纸本)9781665437905

Many researchers have recently been attracted to using vocal analysis techniques in the construction of predictive telediagnosis & telemonitoring frameworks for identifying Parkinson's (PD). Numerous data sources of voice signal are readily available; this same UCI-Kaggle dataset labeled “Parkinson's Disease (PD) Vocal Feature-set” was gathered from a collection of conversing workouts for good health person & Parkinson's patients. In this paper, the proposed model has 12 such machine learning-based models including Naive-Bayes, k-closest neighbor, Logistic Regression, multi-Iayer-perceptron, Decision Trees, support vector machines (Linear, Poly, and RBF), and Random Forest Classifier work effectively as they can deduce even minute frequency signals of varying range for the detecting Parkinson Diseased Person & Healthy Person. The proposed model is a stack of Random-Forest-Classifier, Support- Vector-Machine & K-nearest-neighbor with Extra- Tree-Classifier as well as Extreme Gradient Boost and model is implemented by Mean Squared Error, Mean Absolute Error Loss as a loss function. Overall, the proposed model achieved a validation/testing accuracy of 90 - 91 % and a training accuracy of 98 -100 % & proposed model was also trained using Principal Component Analysis (PCA) & Linear Discriminant Analysis (LDA) for getting better accuracy, also to overcome the curse of dimensionality. The proposed model will be beneficial to the Indian healthcare sector for early diagnosis of chronic illness i.e., Parkinson's disease and can at least inform patients at an early stage if they are suffering from Parkinson's disease or not as no medication or drug can cure Parkinson's disease.

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Adaptive Block-Level Quality Parameter Adjustment Towards Low Video Bit-Rate Fluctuation

Adaptive Block-Level Quality Parameter Adjustment Towards Lo...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Longtao Feng Qian Yin Huiwen Ren Zhao Wang Siwei Ma Yuwen He National Engineering Research Center of Visual Technology School of Computer Science Peking University China Advanced Institute of Information Technology Peking University China Bytedance Inc. San Diego CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Existing quantization parameter (QP) adjustment methods in video coding often focus solely on coding efficiency and ignore the impact of bit-rate fluctuations on video transmission and bandwidth waste. This is mainly because intra pictures, in a hierarchical coding structure, are allocated smaller QP and thus consume more bits. To address this issue, we propose an adaptive block-level QP adjustment method. Specifically, intra picture importance (IPI) is first introduced to evaluate the adjustability of intra picture QP. For intra pictures whose QP can be adjusted, we further propose block importance (BI) to determine their optimal block-level QP adjustment. Experimental results show that our proposed method reduce the bit-rate fluctuations while basically maintaining the coding performance. Notably, significant improvements can be observed in high-resolution videos, with a reduction of approximately 11% in bit-rate fluctuations.

关键词： Video coding Fluctuations Image coding Quantization (signal) Visual communication Bandwidth Encoding Motion compensation

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Brain Connectivity Using Deep Learning Algorithms

Brain Connectivity Using Deep Learning Algorithms

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Moses Makuei Jiet Aahash Kamble Chetan Gajanan Puri Prateek Verma Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India

The integration of deep learning into the examination of brain connectivity marks a transformative era in understanding neural networks. This article provides a concise overview of the burgeoning field of deep learning in brain connectivity analysis. It defines the concept, explores functional and structural connectivity, outlines practical applications, delves into the algorithms involved, compares traditional methods vs advanced deep learning approaches for brain connectivity and highlights its revolutionary impact in clinical practice. In deep learning, brain connectivity investigates neural links using advanced artificial intelligence. Functional connectivity, powered by deep neural networks, reveals patterns in brain activity linked to cognition, emotion, and decision-making. Structural connectivity, often utilizing graph neural networks, reconstructs intricate neural pathways. This technology shows significant progress in diagnosing conditions like Alzheimer's, schizophrenia, and autism. As it evolves, its clinical importance becomes evident, offering early disease detection, tailored treatments, and a deeper understanding of neurological disorders. Beyond clinical applications, deep learning in brain connectivity opens avenues for advancements in brain-computer interfaces, enhanced rehabilitation therapies, and precise interventions.

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An Exploration of the Effectiveness of Machine Learning Algorithms for Text Classification

An Exploration of the Effectiveness of Machine Learning Algo...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Ankita Agarwal Aravindan M K Manish Shrivastava S. Sathya Devika A. Verma Jagmeet Sohal Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai - 127 Department of Computer Engineering Centre of Vishwakarma Institute of Information Technology Pune India Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

Text type is an important project in natural language processing (NLP), with programs in facts retrieval, sentiment evaluation, and report categorization. With the exponential increase of virtual information, the call for automatic text category has multiplied. Conventional text category techniques along with rule-based totally structures and statistical methods have obstacles in managing complex and massive datasets. System studying (ML) has emerged as a promising technique to text type, leveraging algorithms which can automatically learn from information and make correct predictions. This paper gives an exploration of the effectiveness of different machine getting to know algorithms for text category responsibilities. We evaluate and compare the performance of famous algorithms consisting of decision bushes, aid vector machines (SVM), ok-nearest pals (KNN), and deep learning fashions which include convolutional neural networks (CNN) and recurrent neural networks (RNN).

关键词： Support vector machines Machine learning algorithms Recurrent neural networks Statistical analysis Text categorization Prediction algorithms Natural language processing

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