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Detection and Providing Suggestion for Removal of Weeds Using machine learning Techniques 1

Detection and Providing Suggestion for Removal of Weeds Usin...

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1st international conference on Computer, Power and Communications, ICCPC 2022

作者： Sundaram, S. Ganesh Ponmalar, A. Vijay Priya, V. Deeba, S. Harish Anantha Krishnan, R. Vishwath, J.H. Geomanage Corporation Higashi-nihonbashi Japan Sri Sai Ram Institute of Technology Department of Information Technology Tambaram India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sri Manakula Vinayagar Engineering College Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350397840

Weed generally refers to any plant growing where it is not wanted. These unwanted plants are undesirable as they are aggressive in growth, consuming light, water, nutrients, and space that desirable crops utilize. It would affect production efficiency both in quality and quantity. For decades, weeds have been removed using chemicals that are not very effective. With the advancement in technology, this paper proposes an image processing-based framework and machine learning model for weed detection using Convolutional Neural Network (CNN) and the Xception model for classification. With increased layers, the efficiency and accuracy of the system are improved compared to existing methods. © 2022 IEEE.

关键词： Production efficiency

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Deep learning based Dual image and EMG Signal Low Back Pain Detection Methodology 1

Deep Learning based Dual Image and EMG Signal Low Back Pain ...

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1st IEEE international conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Batri, Krishnan Homavazir, Zulieka Department of Computer Science Engineering Bangalore India ATLAS Skill Tech University ISME - School of Management & Entrepreneurship Mumbai India

ISBN: (纸本)9798350323795

Non-specific low back pain (nLBP) has steadily increased in prevalence with the rise of sedentary and inactive lifestyles among the general population. It is important to identify the affected muscles so that therapy may be tailored to the individual. Despite this, current approaches for recognizing affected muscles rely heavily on clinicians' expertise and lack objective criteria. Most biomedical signal-based diagnostic tools, including surface electromyography (sEMG), can only distinguish between patients and healthy controls. Since it relies on human-created characteristics, EasiSMR is only one work capable of identifying symptomatic muscles;nonetheless, its accuracy is limited. In this article, we offer Proposed work, a system that uses deep learning to identify the muscles that are causing non-specific low back pain. Its raw sEMG signal is converted into both frequency and time domain sEMG first. The data is then sent into a heterogeneous two-stream multitasking deep learning system that analyses each input independently based on its unique properties. In addition, we include the muscles' compensation information into our multitask neural network architecture and propose Spanning CNN to enhance recognition accuracy. Finally, we verify our system's performance by designing and implementing a waist-belt-shaped wirelessly sEMG monitoring and processing system. © 2023 IEEE.

关键词： Muscle

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An Advanced Deep learning Approach Combining image Analysis for Precise Retinal Disease Detection 1

An Advanced Deep Learning Approach Combining Image Analysis ...

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1st international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kumar, Ankit Singh, Kamred Udham Singh, Teekam Kumar, Gaurav Pandey, Saroj Kumar Dwivedi, Pushpendra Dhar Gla University Department of Computer Engineering & Apllications UP Mathura India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Raipur India

ISBN: (纸本)9798350330915

The purpose of this research is to showcase a unique hybrid image-centric deep learning system that was developed for the purpose of accurately identifying and diagnosing retinal diseases. Our method overcomes the limits that are connected with conventional imaging and computational analytics, specifically with regard to the accuracy of diagnostics and the level of processing speed. Our technique incorporates optimised models for retinal pictures in a seamless manner by combining the capabilities of convolutional neural networks (CNNs) with the flexibility of transfer learning. These models are then supplemented by advanced feature extraction algorithms. This new development makes it easier to accurately recognise small visual differences in retinal pictures, which are often missed by traditional approaches. Our study, which is based on a large dataset that includes a wide range of retinal anomalies, guarantees not only the correctness of our solution but also its flexibility to a variety of different situations. By demonstrating considerable gains in diagnostic accuracy, sensitivity, and specificity, as well as a notable decrease in the amount of manual feature engineering efforts, rigorous testing speeds up the diagnostic process without sacrificing its dependability. In addition to its use in retinal diagnosis, this model shows the potential of combining deep learning with conventional image analysis. It suggests that this combination might have a transformational influence on digital healthcare, with applications that could be extended to greater issues in medical imaging. In the rapidly developing field of digital healthcare, our results shed light on a potentially fruitful avenue. This avenue involves the use of advanced machine learning methods for the purpose of improving medical diagnosis. © 2023 IEEE.

关键词： Accuracy CNN Data Optimization Deep learning Feature Extraction Hybrid image Ophthalmological Retinal Disease SVM

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A Novel Framework for Detection of Objects from Video Using Deep learning 8th

A Novel Framework for Detection of Objects from Video Using ...

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8th international conference on Computers, Management and Mathematical Sciences, ICCM 2022

作者： Mulla, Yasmeen Kasam Mane, Kishor CSE Department D. Y. Patil College of Engineering and Technology Maharashtra Kolhapur India CSE Department. D. Y. Patil College of Engineering and Technology Maharashtra Kolhapur India

ISBN: (纸本)9783031511660

Detection of objects in the videos is a most challenging task in surveillance, autonomous driving, robotics, etc. Traditional approaches for detection of objects in videos rely on extraction of features and image processing, which requires a significant amount of manual intervention and having complex and cluttered environments. These algorithms in surveillance systems lack accuracy and most work with labeled samples. The algorithms are time consuming and take significant time for object detection. Nowadays, machine learning is being used mostly for detection of moving objects in videos. Objects detection techniques based on deep learning are promising in the surveillance systems, but they are facing some limitations and drawbacks that attract the attention of the researchers. These deep learning-based algorithms require large amounts of computation, delay detection and tracking, and may not work in novel environments or object categories where training data is not available. This paper demonstrates a novel deep learning method for detection of various objects in videos, for e.g., CCTV surveillance systems. The proposed framework is highly effective in surveillance systems because it can detect and track objects accurately and quickly in real time. The recognition of the moving object has been detected using LBPH (Local Binary pattern Histogram), eigenface, and Fisherface algorithms. All these algorithms are implemented and compared using different parameters such as accuracy, precision, recall, and F1 score. The results show that the LBPH algorithm enhances accuracy than the other two algorithms. The proposed system achieves up to 89% of accuracy for detection of objects using LBPH, which is 8–12% more than the eigenface and Fisherface algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Face recognition

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Various Algorithms and Techniques for Traffic Density Estimation 1

Various Algorithms and Techniques for Traffic Density Estima...

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1st international conference on Computational Science and Technology, ICCst 2022

作者： Venkat, Sanjay Sarkar, Swagata Shreemadhi, B. Siri, Karanam Poorna Bavanika, M. Sri Sairam Engineering College Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9781665476553

Traffic has been a major problem in recent times. Traffic management is a must for safer and faster transportation. Automatic smart signal controlling systems respond to day-to-day world traffic densities to provide precedence and reduction in cross-road delays for travelers. According to the traffic density, the time between signals is adjusted so that the waiting time in traffic reduces. The ML model is capable of detecting accidents and immediately sends notifications or alert messages to nearby hospitals. The model tracks the GPS location of the ambulance and ensures no red signal in its pathway. Zebra crossing signals are also included, the signal timings are switched based on the crossing crowd. Automatic Smart Signal Control is a complete machine learning based project with some applications of IoT and Video / image processing. Implementation of the model all over the major signals and junctions provides faster and safer transportation. © 2022 IEEE.

关键词： image processing

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Qualitative Analysis of Dusting and Soiling in solar PV System using learning Algorithms 1

Qualitative Analysis of Dusting and Soiling in solar PV Syst...

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1st international conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Bavithra, K. Latha, R. Charles Raja, S. Priya K. R, Shanmuga Psg Institute of Technology and Applied Research Department of Eee Tamilnadu Coimbatore India Psg College of Technology Department of Eee Tamilnadu Coimbatore India Thiagarajar College of Engineering Department of Eee Tamilnadu Madurai India Coimbatore Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350371185

Nowadays the demand for renewable energy is being increased, solar plays a major role in supplying the energy demand. However, there are several environmental parameters like humidity, rainfall, high temperature, dust, air pollution, wind speed, snow, aging, and so on block the demonstration and efficiency of solar PV schemes. But particularly the dust buildup on the PV modules has an impact that is negative in various ways. Because it reduces the spectral transmittance which leads to reduced power output and efficiency. Sometimes, it damages the solar panels and the lifespan gets minimized. But there are solutions to prevent dust deposition by periodic cleaning which allows the panels to regain their function and perform to produce better outputs. This paper aims to classify panels as clean and light dusted, medium dusted, and heavy dusted. Using image processing the percentage of the dust that is deposited can be found by differentiating the dusted area in the panel and finding the pattern of dust accumulation related to locality is also analyzed. Convolution Neural Networks and Binarization can determine the accuracy of dust deposited in the panels. Though this paper has used certain algorithms, Otsu's thresholding concepts resulted in more accuracy of about 89%. It automatically finds the optimal threshold intensity by classifying the image into two classes using a class variance. Thus, the system preserves the economic value of the asset. © 2023 IEEE.

关键词： Solar panels

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Deep learning (CNN) and Transfer learning: A Review

Deep Learning (CNN) and Transfer Learning: A Review

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2022 international conference on Applications of Intelligent Computing in Engineering and Science, AICES 2022

作者： Gupta, Jaya Pathak, Sunil Kumar, Gireesh Amity School of Engineering and Technology Department of Computer Science & Engineering Amity University Rajasthan Jaipur India Institute of Engineering & Technology Department of Computer Science & Engineering JK Lakshmipat University Jaipur Rajasthan India

Deep learning is a machine learning area that has recently been used in a variety of industries. Unsupervised, semi-supervised, and supervised-learning are only a few of the strategies that have been developed to accommodate different types of learning. A number of experiments showed that deep learning systems fared better than traditional ones when it came to image processing, computer vision, and pattern recognition. Several real-world applications and hierarchical systems have utilised transfer learning and deep learning algorithms for pattern recognition and classification tasks. Real-world machine learning settings, on the other hand, often do not support this assumption since training data can be difficult or expensive to get, and there is a constant need to generate high-performance beginners who can work with data from a variety of sources. The objective of this paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current solutions and evaluate applications in diverse areas of transfer learning as well as deep learning. © Published under licence by IOP Publishing Ltd.

关键词： Hierarchical systems

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Remote Sensing image Captioning (RSIC): A Technical Review 1st

Remote Sensing Image Captioning (RSIC): A Technical Review

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1st international conference on Data Engineering and machine Intelligence, ICDEMI 2023

作者： Dhinesh, A. Sumathy, P. Department of Computer Science Bharathidasan University Tamilnadu Tiruchirappalli620023 India

ISBN: (纸本)9789819776153

Remote Sensing image Captioning (RSIC) is crucial for many researchers since it has many applications in environmental monitoring, disaster management, urban planning, image retrieval, performance of building planes, military intelligence, and autonomous vehicles. The effective procedure to generate the captions from remote sensing images complements the above-mentioned application domains. Various baseline data sets have been created by the researchers to enhance the quality of captioning by processing the diverse features of the geospatial information. In this paper, we have technically reviewed important literature that follow different algorithms for generating the captions. For example, we have presented the technical review on Vision-Language Aligning Paradigm (VLCA) under the bi-lingual caption generation model, Joint-Training Two-stage (JTTS) technique under multimodel fusion category, Multilevel and Contextual Attention Network (MLCA-Net) under context-aware captioning, LEVIR-CC belongs to transfer learning model, BERT and GPT-3 models belong to transfer-based model, Multiscale Attention (MSA) and Multifeat Attention (MFA) of Multiscale captioning model and Summarization Driven (SD)-RSIC of fine-grained captioning model. We have also presented the performance of each of these methods on various benchmark datasets. For evaluation, different well-known performance metrics are considered. The result is critically evaluated and commented on. In the future, a more rigorous review of these methods along with other relevant methods will be presented along with implementation data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Urban planning

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Cardiac Arrhythmia Diagnosis via Multichannel Independent Component Analysis: An Approach Towards a Better Health Care System 1st

Cardiac Arrhythmia Diagnosis via Multichannel Independent Co...

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1st international conference on Emerging Technology Trends in Internet of Things and Computing (TIOTC)

作者： Sarfraz, Mohammad Khan, Mudassir Hasan Mohammed, Duraid Yahya Hussain, Mays Dheya Zidan, Khamis A. Aligarh Muslim Univ Ctr Interdisciplinary Biomed & Human Factors Engn Dept Elect Engn Aligarh 202002 Uttar Pradesh India AL Iraqia Univ Coll Engn Comp Engn Dept Baghdad Iraq

ISBN: (纸本)9783030972554;9783030972547

With the evolving lifestyle, many cardiac ailments are becoming more frequent, and it has become necessary to provide detailed surveillance of the heart's functioning to ensure healthy living. ECG signals provide details regarding the various forms of arrhythmias. However, owing to the complexities and non-linearity of ECG signals, it is impossible to examine these signals manually. Conventional approaches for specialist inspection of ECGs on paper or television are inadequate for ambulatory, long-term monitoring, and sports ECGs. Automated applications that use signal processing and pattern recognition would be extremely beneficial. Identification of arrhythmias from ECGs is an essential branch of biomedical signal processing and pattern recognition. Motion-induced artifacts are well-known to be a major source of misrecognition and misdiagnosis. On the other hand, the feature extraction method has a significant effect on the reliability and performance of ECG pattern recognition. This paper proposes new approaches and algorithms for pre-processing multi-channel ECG signals and neural networks for arrhythmia classification using independent component analysis (ICA) with two distinct goals: (1) to eliminate motion-induced or associated artifacts, and (2) to better select the features and allow more effective pattern recognition. When processing noisy ECG data with the MIT dataset, cross-validation reveals a major improvement. For noisy signals, classification sensitivity of 97.9% and positive predictivity of 98.1% was achieved in this work. A tenfold neural validation rule was used to achieve 99.3% accuracy in arrhythmia classification. The lower the signal-to-noise level, the more significant is the improvement. This proposed algorithm would be a valuable method for physicians in justifying their diagnosis. With its quick reaction time, the proposed algorithm can be easily integrated into an automated healthcare management system.

关键词： Health care Independent component analysis machine learning Arrhythmia computer-aided diagnosis Feature extraction Neural network

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Feature Descriptors based on Circular Forms of Local patterns for Texture Classification 1

Feature Descriptors based on Circular Forms of Local Pattern...

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1st international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2022

作者： Jagirdar, Srinivas Reddy, K. Venkata Subba Sr University Warangal India Kallam Haranadha Reddy Institute of Technology Guntur India

ISBN: (纸本)9781665436472

Five novel Local Binary pattern (LBP) based descriptors are presented in this paper for performing texture analysis of an image. The performance of any texture classification method depends upon its dimensionality. As existing lo-cal based extractor methods generate descriptors with huge dimensions, the classifiers using them will suffer. In order to deal with this problem Uniform Weighted LBP (UWLBP), strong and Uniform Weighted -LBP (SUWLBP), Uni-form Circular and Elliptical Weighted Texture Matrix (UCEWTM), strong and Uniform CEWTM (SUCEWTM) and strong and Uniform Twofold Spatial Weighted Complex patterns (SUTSWCP) are proposed in this paper. The out-puts of these descriptors are fed into machine learning algorithms like NavieBayes (NB), Multilayer-perceptron (MLP), Ibk and J48. image datasets like Brodtaz, UIUC, Outex-TC-12, KTH-TIPS, ALOT were used to train and test the models. The five descriptors were combined with MLP and compared. The CEWstM coupled with MLP attained has attained a classification rate of 93.11 % which is the best out of the proposed five descriptors. © 2022 IEEE.

关键词： Local binary pattern

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