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A Smart Agriculture Friendly Intelligent Flying Robot Design to Identify Plant Leaf Diseases by using Digital image processing with learning Approach

A Smart Agriculture Friendly Intelligent Flying Robot Design...

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

作者： M. S. Kavitha R. Harikrishnan S. Vijay Shankar S. Krishnakumari S. Irin Sherly Department ECE R.M.K.Engineering College Chennai Department of ECE S.A. Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of ECE Loyola ICAM College of Engineering and Technology Chennai Department of Information Technology Panimalar Engineering College Chennai

Smart agriculture practices have become crucial in meeting the growing global food demand while minimizing resource use. In this context, the development of intelligent systems and automation plays a pivotal role. This paper introduces a novel solution: a smart agriculture-friendly intelligent flying robot equipped with cutting-edge digital image processing techniques and a learning approach to identify plant leaf diseases. The suggested system merges state-of-the-art robotics and aerial technology with advanced machine learning techniques, specifically the VGG-SVM model. The flying robot is equipped with a high-resolution camera to capture images of plant leaves in real-time, and the VGG-SVM model processes these images for disease detection. VGG offers deep feature extraction capabilities, while SVM provides accurate classification, making the model adept at discerning healthy from diseased leaves. Through digital image processing, the robot captures high-resolution images of plant leaves, which are subsequently subjected to advanced machine learning algorithms. These algorithms analyze and classify the images, providing real-time insights into the health of crops. The combination of precision aerial data collection and machine learning ensures early disease detection and targeted intervention, optimizing crop yield, reducing resource consumption, and ultimately contributing to sustainable agriculture. Proposed model excels with the highest accuracy of 0.97, indicating an overall high rate of correct predictions.

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AI-based image processing Technique for Dielectrophoresis (DEP) in BioMEMs Applications

AI-based Image Processing Technique for Dielectrophoresis (D...

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IEEE International Conference on Semiconductor Electronics (ICSE)

作者： Clarence Augustine TH Tee Burhanuddin Yeop Majlis Sheng Li En Hao Yu Muhamad Ramdzan Buyong Le Song W P Yeo Ahmad Rifqi Md Zain Yelong Zheng College of Physics & Electrical Information Engineering Zhejiang Normal University People’s Republic of China Institute of Microengineering and Nanoelectronics (IMEN) Universiti Kebangsaan Malaysia Selangor Malaysia Zhejiang Institute of Optoelectronics People’s Republic of China Photonics & Sensors Key Research Laboratory Zhejiang Normal University People’s Republic of China State Key Laboratory of Precision Measurement Technology and Instruments Tianjin University Tianjin People’s Republic of China

ISBN: (数字)9798350378313

ISBN: (纸本)9798350378320

The integration of artificial intelligence (AI) methodology in the flexible electronics, BioMEMs and Dielectrophoresis (DEP) system provides an accurate, comprehensive, simple and cost-effective approach especially in the real time cell sorting, target identification, monitoring, biological entities separation, analysis and postulation. Here, a novel approach integrating AI algorithms with DEP technology for BioMEMs system has been demonstrated in real-time bacteria identification via detecting, identifying simultaneously, lock-in monitoring bacterial movement trajectories, analyzing, measuring and calculating bacteria’s velocities of different frequencies. The AI-based bacterial recognition system creates real-time, fast, accurate, unlimited bacteria lock-in monitoring with high accuracy. Furthermore, the bacteria can be tracked, monitored, and analyzed using big data analysis and deep learning (DL) in real-time via a visual control interface system, as part of the Internet of Things (IoT) and intelligent information sharing between BioMEMS sensors and IoT networks.

关键词： Semiconductor device measurement Accuracy Bacteria real-time systems Frequency measurement Trajectory Internet of Things

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IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification

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INTERNATIONAL JOURNAL OF MACHINE learning AND CYBERNETICS 2021年第11期12卷 3235-3248页

作者： Le, Dac-Nhuong Parvathy, Velmurugan Subbiah Gupta, deepak Khanna, Ashish Rodrigues, Joel J. P. C. Shankar, K. Duy Tan Univ Inst Res & Dev Danang 550000 Vietnam Duy Tan Univ Fac Informat Technol Danang 550000 Vietnam Kalasalingam Acad Res & Educ Dept Elect & Commun Engn Krishnankovil India Maharaja Agrasen Inst Technol Dept Comp Sci & Engn Delhi India Univ Fed Piaui BR-64049550 Teresina Brazil Inst Telecomunicacoes P-1049001 Lisbon Portugal Alagappa Univ Dept Comp Applicat Karaikkudi Tamil Nadu India

At present times, the drastic advancements in the 5G cellular and internet of things (IoT) technologies find useful in different applications of the healthcare sector. At the same time, COVID-19 is commonly spread from animals to persons, but today it is transmitting among persons by adapting the structure. It is a severe virus and inappropriately resulted in a global pandemic. Radiologists utilize X-ray or computed tomography (CT) images to diagnose COVID-19 disease. It is essential to identify and classify the disease through the use of image processing techniques. So, a new intelligent disease diagnosis model is in need to identify the COVID-19. In this view, this paper presents a novel IoT enabled Depthwise separable convolution neural network (DWS-CNN) with deep support vector machine (DSVM) for COVID-19 diagnosis and classification. The proposed DWS-CNN model aims to detect both binary and multiple classes of COVID-19 by incorporating a set of processes namely data acquisition, Gaussian filtering (GF) based preprocessing, feature extraction, and classification. Initially, patient data will be collected in the data acquisition stage using IoT devices and sent to the cloud server. Besides, the GF technique is applied to remove the existence of noise that exists in the image. Then, the DWS-CNN model is employed for replacing default convolution for automatic feature extraction. Finally, the DSVM model is applied to determine the binary and multiple class labels of COVID-19. The diagnostic outcome of the DWS-CNN model is tested against Chest X-ray (CXR) image dataset and the results are investigated interms of distinct performance measures. The experimental results ensured the superior results of the DWS-CNN model by attaining maximum classification performance with the accuracy of 98.54% and 99.06% on binary and multiclass respectively.

关键词： COVID-19 deep learning Convolutional neural network Feature extraction Multilabel classification

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An Efficient Motor imagery Classification Framework using Sparse Brain Connectivity and Class-consistent Dictionary learning from Electroencephalogram Signals 23

An Efficient Motor Imagery Classification Framework using Sp...

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Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and image processing

作者： Subrata Pain Debasis Samanta Monalisa Sarma Advanced Technology Development Center Indian Institute of Technology Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Subir Chowdhury School of Quality and Reliability Indian Institute of Technology Kharagpur India

ISBN: (纸本)9798400716256

Identifying patterns in high-dimensional and noisy neuro-imaging data such as Electroencephalogram (EEG) has always been challenging. This fact hampers the performance of the real-time Brain-Computer Interfaces (BCI) controlled by EEG recordings of brain signals, such as imagined motor activity (MI-EEG). Traditionally, the MI-EEG signals are classified by mainly two approaches, firstly, using the Common Spatial Patterns (CSPs) extracted from MI-EEG as features for Machine learning (ML) classifiers, and secondly, using various deep learning (DL)-based approaches. But these methods are computationally heavy and ignore the dynamic topological interactions between the brain regions. Recently, due to high performance and low testing time, sparse representation-based classifiers have been utilized for MI-EEG classifications with higher real-life applicability. But these methods consider a fixed training dictionary consisting of inherently noisy training features. This study proposes an efficient MI-EEG classification framework utilizing a sparse representation of brain connectivity features and a dictionary learned from the training features. Here, the dynamic synchrony of co-activating brain regions is captured by suitable connectivity measures, which are further used as features. Further, sparse representations of the connectivity features are obtained through sparsity and dictionary learning. As this dictionary learning method preserves the class information during dictionary updation, this method is best suited for classification tasks. The dictionary learning method simultaneously learns a reduced dictionary and a linear classifier further used for classification. The proposed framework is validated using a publicly available MI dataset, and corresponding classification performances are observed. The proposed framework outperforms the other sparsity-based classifiers in mixed-, intra-, and cross-subject performances, making it suitable for real-life BCI applications.

关键词： Brain Connectivity Dictionary learning Electroencephalogram Patterns Motor Imagination Sparse Representations

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IDTrust: deep Identity Document Quality Detection with Bandpass Filtering

IDTrust: Deep Identity Document Quality Detection with Bandp...

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IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Musab Al-Ghadi Joris Voerman Mickaël Coustaty Olivier Lessard Nicolas Sidere Computer Science L3i Laboratory La Rochelle University France Information Technology Department Hashemite University Jordan IMDS SAS France

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

The increasing use of digital technologies and mobile-based registration procedures highlights the vital role of personal identity documents (IDs) in verifying users and safeguarding sensitive information. However, the rise in counterfeit ID production poses a significant challenge, necessitating the development of reliable and efficient automated verification methods. This paper introduces IDTrust, a deep-learning framework for assessing the quality of IDs. IDTrust is a system that detects the quality of identity documents using a deep learning-based approach. This method eliminates relying on original document patterns for quality checks and pre-processing steps for alignment. As a result, it offers significant improvements in terms of dataset applicability. Using a bandpass filtering-based method, the system aims to detect and differentiate ID quality effectively. Comprehensive experiments on the MIDV-2020 and L3i-ID datasets identify optimal parameters, significantly improving discrimination performance, and effectively distinguishing between original and scanned ID documents.

关键词： Band-pass filters Computer vision Accuracy Filtering Conferences Computational modeling Production real-time systems Computational efficiency Reliability

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A decision-making mechanism for task offloading using learning automata and deep learning in mobile edge networks

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HELIYON 2024年第1期10卷 e23651页

作者： Tan, Xin Zhao, DongYan Wang, MingWei Wang, Xin Wang, XiangHui Liu, WenYuan Ghobaei-Arani, Mostafa Shaanxi Univ Sci & Technol Sch Elect Informat & Artificial Intelligence Xian 710021 Shaanxi Peoples R China Shaanxi Univ Sci & Technol Shaanxi Artificial Intelligence Joint Lab Xian 710021 Shaanxi Peoples R China Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran Islamic Azad Univ Prod & Recycling Mat & Energy Res Ctr Qom Branch Qom Iran

The development of mobile networks has led to the emergence of challenges such as high delays in storage, computing and traffic management. To deal with these challenges, fifth-generation networks emphasize the use of technologies such as mobile cloud computing and mobile edge computing. Mobile Edge Cloud Computing (MECC) is an emerging distributed computing model that provides access to cloud computing services at the edge of the network and near mobile users. With offloading tasks at the edge of the network instead of transferring them to a remote cloud, MECC can realize flexibility and real-time processing. During computation offloading, the requirements of Internet of Things (IoT) applications may change at different stages, which is ignored in existing works. With this motivation, we propose a task offloading method under dynamic resource requirements during the use of IoT applications, which focuses on the problem of workload fluctuations. The proposed method uses a learning automata-based offload decisionmaker to offload requests to the edge layer. An auto-scaling strategy is then developed using a long short-term memory network which can estimate the expected number of future requests. Finally, an Asynchronous Advantage Actor-Critic algorithm as a deep reinforcement learningbased approach decides to scale down or scale up. The effectiveness of the proposed method has been confirmed through extensive experiments using the iFogSim simulator. The numerical results show that the proposed method has better scalability and performance in terms of delay and energy consumption than the existing state-of-the-art methods.

关键词： Mobile edge cloud computing Computation offloading learning automata deep reinforcement learning

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Construction of urban agricultural health informatics safety supervision system based on imaging and deep learning

Construction of urban agricultural health informatics safety...

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作者： Liu, Zhanyu School of Business Administration Henan Polytechnic University Henan China Energy Economics Research Center Henan Polytechnic University Henan China Taihang Research Institute Henan Polytechnic University Henan China

Construction of urban agricultural health informatics safety supervision (UAHISS) system based on medical imaging and deep learning (DL) is provided in this research. The designed UAHISS is based on computerized medical image information processing, and reduces the daily reporting and writing work as well as providing timely image information to the clinicians. In the designed UAHISS, following novelties are presented. (1) We develop the novel medical imaging processing model for the efficient recognition and classification of the big medical visual data. (2) We design the novel DL system for the health informatics application scenarios. (3) The comprehensive health informatics safety supervision system is development and validated through complex parallel data. (4) The urban agricultural information system with the human health safety prior is designed and the further suggestions are provided. Through the verification, the real-time performance of the proposed model is tested. It is reflected from the results, the proposed model can detect the health informatics safety supervision information accurately. © 2020 John Wiley & Sons, Ltd.

关键词： deep learning

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RAD-U-Net: Residual Attention-Based Dual-Path U-Net for Segmentation of Multiple Myeloma Cells in Microscopic images for Clinical Diagnosis

RAD-U-Net: Residual Attention-Based Dual-Path U-Net for Segm...

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Power, Electrical, and Electronics and Industrial Applications (PEEIACON), IEEE International Conference on

作者： Sudipta Progga Islam Nahin Ul Sadad Nazrul Islam Mondal Dept.of CSE RUET Rajshahi Bangladesh

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

Multiple myeloma, a malignancy originating in the bone marrow, poses significant health risks and can lead to severe systemic dysfunction and potentially fatal outcomes. Manual microscopic analysis of multiple myeloma cells remains a cornerstone in the diagnosis of this disease. However, this process is labor-intensive, time-consuming, and prone to human error. To address these challenges, we propose a novel approach leveraging advanced image processing and deep learning techniques for automated segmentation of multiple myeloma cells from microscopic images. We build a novel Residual Attention-based Dual-path U-Net architecture tailored specifically for this task. Our approach leverages deep learning techniques, including dual pathways for feature extraction with residual and attention mechanisms, to precisely detect multiple myeloma cells from complex microscopic images. Through rigorous experimentation, our model achieves outstanding segmentation performance, with a mean Intersection over Union (mIoU) of 91.50%, a dice score of 95.44%, mean average precision (mAP) of 96.46%. Despite its lightweight design, with only 5.7 million parameters, our model demonstrates remarkable accuracy as mentioned above, showcasing its potential for real-world clinical applications. Additionally, our proposed architecture maintains a balance between segmentation accuracy and computational efficiency because of reduced parameter, making it a more practical and effective solution for cell segmentation tasks, especially in scenarios where computational resources are limited or efficiency is a priority. However, there remains potential for further improvement in certain metrics, such as the mIoU and mAP, suggesting opportunities for refining the model to achieve even better segmentation accuracy.

关键词： Measurement deep learning image segmentation Accuracy Microscopy Computational modeling Refining Computer architecture Feature extraction Computational efficiency

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AI based Analysis of Hyperspectral images with Spectacular Efficacy in Video Analytics

AI based Analysis of Hyperspectral Images with Spectacular E...

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Intelligent & Innovative Practices in Engineering & Management (IIPEM), International Conference on

作者： Samarth Yogesh Jadhav Rutuja Rajatam More Bhagyashree Ashok Tingare G. Prasanna Lakshmi Shivam Kolse N.K. Darwante Department of Information Technology Sanjivani College of Engineering Kopargaon MH India Department of Artificial Intelligence and Data Science D Y Patil College of Engineering Akurdi Pune School of Computer Science and Engineering Sandip University Nashik Maharashtra Department of Electronics and Computer Engineering Sanjivani College of Engineering Kopargaon MH India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

Hyperspectral imaging and artificial intelligence (AI) have transformed imaging and data processing through their ability to capture and analyze detailed spectral information. This paper explores the integration of hyperspectral imaging with AI, focusing on its impact on video analytics. Hyperspectral imaging provides comprehensive, multi-dimensional data by capturing a wide range of spectral wavelengths, enabling precise material identification and environmental monitoring beyond traditional RGB imaging. The synergy between hyperspectral imaging and AI enhances real-time analysis and decision-making by leveraging deep learning algorithms for pattern recognition and anomaly detection. This study introduces a novel AI-based hyperspectral image analysis approach for video analytics, utilizing convolutional neural networks (CNNs) and hybrid CNN-attention models to improve object recognition and classification. The methodology is validated through experiments that measure classification accuracy, processing speed, and resilience to varying conditions. Results demonstrate significant improvements in accuracy and efficiency over traditional methods, highlighting the potential for advanced applications in surveillance, environmental monitoring, and industrial quality control.

关键词： Accuracy image analysis Visual analytics Surveillance Quality control real-time systems Environmental monitoring Object recognition Artificial intelligence Hyperspectral imaging

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Automate extraction of braille text to speech from an image

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ADVANCES IN ENGINEERING SOFTWARE 2022年第0期172卷

作者： Revelli, Vishnu Preetham Sharma, Gauri Devi, S. Kiruthika SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

Our project falls under the domain of Natural Language processing and Machine learning. With the advent of digitization, it became necessary to preserve data produced by humans. Voice and text are how we communicate. Visual impairment can be defined as partial or complete loss of vision. The primary goal of this project is to extract text from braille text images, so as to store it in a drive for later use. Although there are various ways to implement this, we'll use deep learning. Visually impaired people face numerous difficulties like interacting with the surroundings, navigation, etc. Different machine learning models like CNN, etc. have been used to solve this problem but usually have a high cost of detection in terms of computational time and hence they are not suitable for real-time applications [1]. To meet the project objective, we studied about Convolutional Neural Networks. The plan for picking this field is that it empowers machines to see the world as people do, see it likewise and even utilize the information for a huge number of undertakings like image Recognition, image Analysis and Classification, Media Recreation, Recommendation Systems, and so on [2]. This paper proposes a customized CNN model based on Braille Character dataset which has 26 different classes of all alphabets. This model uses a feature extractor that recognizes relevant features corresponding to each image, and then uses a classifier network to classify images into a set of classes. This model will take an image input, then pass those individual images to our classifier and then detect the braille language characters present in that image and give a translated English text and audio output. The final accuracy we obtained was 0.9615. These results show the model's robustness against factors like low light illumination.

关键词： deep learning Convolutional neural networks Machine learning

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