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Fruit Freshness Detection Using CNN 1

Fruit Freshness Detection Using CNN

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1st international conference on Futuristic Technologies, INCOFT 2022

作者： Kulkarni, Mukund Chaudhari, Archana Shegokar, Shekhar Rudrawar, Krushna Joshi, Shaunak Tibile, Shruti Tayade, Isha Vishwakarma Institute of Technology Department of Instrumentation Engineering Pune India

ISBN: (纸本)9781665450461

Quality of fruits and vegetables are most important as these are the main ingredients for our health. Freshness of fruits and vegetables are most important but segregation of rotten and fresh is time consuming, when done manually and degrades quality of fruits. Application based on Computer vision and image processing reduces this tedious and focuses on the quality of fruit and vegetable detection for a long period of time. The proposed method uses CNN to analyze and detect the variation and pattern for accurate *** work recommends a semantic segmentation of fresh or the rotten portion observed in any fruit. It is entirely based on the RGB image of the fruit. © 2022 IEEE.

关键词： image recognition

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PergaNet: A Deep learning Framework for Automatic Appearance-Based Analysis of Ancient Parchment Collections 21st

PergaNet: A Deep Learning Framework for Automatic Appearance...

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21st international conference on image Analysis and processing (ICIAP)

作者： Paolanti, Marina Pietrini, Rocco Della Sciucca, Laura Balloni, Emanuele Compagnoni, Benedetto Luigi Cesarini, Antonella Fois, Luca Feliciati, Pierluigi Frontoni, Emanuele Univ Politecn Marche VRAI Vis Robot & Artificial Intelligence Lab Dipartimento Ingn Informaz DII Via Brecce Bianche 12 I-60131 Ancona Italy Univ Macerata Dept Polit Sci Commun & Int Relat I-62100 Macerata Italy Univ Macerata Dept Humanities I-62100 Macerata Italy Archivio Stato Milano Via Senato 10 I-20121 Milan Italy

ISBN: (纸本)9783031133244;9783031133237

Archival institutions and program worldwide work to ensure that the records of governments, organizations, communities and individuals be preserved for the next generations as cultural heritage, as sources of rights, and to hold the past accountable. The digitalization of ancient written documents made of parchment were an important communication mean to humankind and have an invaluable historical value to our culture heritage (CH). Automatic analysis of parchments has become an important research topic in fields of image and pattern recognition. Moreover, Artificial Intelligence (AI) and its subset Deep learning (DL) have been receiving increasing attention in pattern representation. Interest in applying AI to ancient image data analysis is becoming mandatory, and scientists are increasingly using it as a powerful, complex, tool for statistical inference. In this paper it is proposed PergaNet a lightweight DL-based system for historical reconstructions of ancient parchments based on appearance-based approaches. The aim of PergaNet is the automatic analysis and processing of huge amount of scanned parchments. This problem has not been properly investigated by the computer vision community yet due to the parchment scanning technology novelty, and it is extremely important for effective data recovery from historical documents whose content is inaccessible due to the deterioration of the parchment. The proposed approach aims at reducing hand-operated analysis and at the same time at using manual annotation as a form of continuous learning. PergaNet comprises three important phases: classification of parchments recto/verso, the detection of text, then the detection and recognition of the "signum tabellionis". PergaNet concerns not only the recognition and classification of the objects present in the images, but also the location of each of them. The analysis is based on data from the ordinary use and does not involve altering or manipulating techniques in order to gener

关键词： Deterioration

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Determination of Gout Disease using machine learning 1

Determination of Gout Disease using Machine Learning

引用

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

作者： Sanjay Kumar, M. Hudson, B. Priya, Vetri Ambeth Kumar, V.D. Tharun, C. Sri Saran, R.G. Panimalar Engineering College Department of Ai&ds Tamil Nadu Chennai India Panimalar Engineering College Department of Cse Tamil Nadu Chennai India

ISBN: (纸本)9781665476553

Aim of this study is to determine whether the subject is diagnosed with the gout disease or not. The saliva is an organic body fluid that has numerous components present in it. Uric acid is one of the sub components present in the saliva. Saliva is one of the body fluid that can be easily taken from the subject. By using it, the concentration of the uric can be found. Maintaining the Uric acid level in the human body is very important. Any change in the level of the uric acid leads to various diseases. Gout is one of the most common disease that is caused due to the increase of the uric acid level in the body. The analyzation of the saliva through HPLC method helps in determining the concentration of the uric acid. Since the output of the HPLC chromatography is graph, the graph image obtained from the subject is compared with the existing datasets using the machine learning algorithm. machine learning algorithm compares the Output of the subject with the dataset. Based on the result the subject is given a suggestion whether they should consult a doctor or not. © 2022 IEEE.

关键词： pattern matching

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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 ...

引用

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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