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Intelligent waste classification approach based on improved multi-layered convolutional neural network

Multimedia Tools and Applications 2024年第36期83卷 84095-84120页

作者： Chhabra, Megha Sharan, Bhagwati Elbarachi, May Kumar, Manoj Department of Computer Science & amp Engineering School of Engineering & ampTechnology Sharda University UP Gr. Noida India School of Engineering and Sciences Department of Computer Science and Engineering SRM University-AP Andhra Pradesh Amaravati India Head of School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates MEU Research Unit Middle East University Amman11831 Jordan

This study aims to improve the performance of organic to recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating to the poor waste segregation schemes. Separating organic waste from recyclable waste can lead to a faster and more effective recycling process. Manual waste classification is a time-consuming, costly, and less accurate recycling process. Automated segregation in the proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset of 2 class category with 25077 images is divided into 70% training and 30% testing images. The performance metrics used are classification Accuracy, Missed Detection Rate (MDR), and False Detection Rate (FDR). The results of Improved DCNN are compared with VGG16, VGG19, MobileNetV2, DenseNet121, and EfficientNetB0 after transfer learning. Experimental results show that the image classification accuracy of the proposed model reaches 93.28%. © The Author(s) 2024.

关键词： Convolutional neural networks

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WBC Subtype Detection using Deep Learning with Optimizing Hyperparameters by Genetic Algorithm

WBC Subtype Detection using Deep Learning with Optimizing Hy...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Shuvo, Mehedi Hasan Rahman, Md. Tahamidur Ahsan, Sk. Md. Masudul Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350316001

The human body's defense mechanism, the immune system, heavily relies on white blood cells (WBCs). These cells come in five subtypes: Eosinophil, Basophil, Neutrophils, Monocyte, and Lymphocyte. Each subtype plays a vital role in the diagnosis of hematological diseases, such as leukemia, as their presence or absence can indicate the presence of a disease. In the past, hematologists have manually examined blood smears under a microscope to detect the subtype of WBCs. However, this method is both time-consuming and prone to errors. This study proposes a new approach, which uses a deep learning-based convolutional neural network (CNN) architecture in combination with an evolution-based algorithm (genetic algorithm) to detect WBC subtypes with greater accuracy. A large dataset of 12,000 images was used for training and analysis. The proposed model, which utilizes the You Only Look Once version 5 (YOLOv5) for image localization and segmentation, was able to accurately localize WBCs with an IoU score of 94% for more than 80% of images and detect the subtypes with an accuracy of 98%. This study aims to overcome the limitations of previous research by providing a more efficient and accurate method for detecting WBC subtypes. © 2023 IEEE.

关键词： Genetic algorithms

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Hybrid Vision Transformer and CNN for Detection of Overripe Coffee Berry Disease (OCBD) in Coffee Plantation 2

Hybrid Vision Transformer and CNN for Detection of Overripe ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Selvanarayanan, Raveena Surendran, R. Gomathi, T. Kartheesan, L. Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

The article proposes an innovative Hybrid Vision Transformer and CNN architecture for the precise identification of Overripe Coffee Berry Disease (OCBD) in coffee farms. The suggested model adeptly integrates the advantages of Vision Transformers and CNNs, allowing it to capture both the global and local characteristics of coffee berries. Experimental findings indicate that the Hybrid model outperforms conventional CNN-based methods, with an impressive accuracy of 96.7% on a varied dataset. The model's resilience to fluctuating illumination conditions and obstructions makes it a viable alternative for practical applications in coffee plantation management. © 2024 IEEE.

关键词： Image segmentation

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Obesity Risk Prediction Using Machine Learning Approach 1

Obesity Risk Prediction Using Machine Learning Approach

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Maria, A.S. Sunder, R. Kumar, R.Satheesh Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350336009

Approximately about two billion peoples are affected by obesity that has drawn significant attention on social media. As the sedentary lifestyle which includes consumption of junk foods, no physical activities,spending more on screen,etc are one of the causes of *** generally refers to that a person's body possessing an excessive amount of *** is a huge increase in obesity cases which resulting cardiac problems,stroke,insomnia, breathing problems,***-2 diabetes has been detected in the patients suffering from obesity recently. The studies showing that there are lot of young individuals and children's who has been suffering from overweight and obesity issues in Bangladesh. Here, a strategy for predicting the risk of obesity is proposed that makes use of various machine learning methods. The dataset Obesity and Lifestyle taken from Kaggle site which is collection of different data based on the eating habits and physical conditions,such as height, weight,calorie intake,physical activities are just a few of the 17 different categories in the dataset that reflect the elements that cause obesity. Several machine learning methods include Gradient Boosting Classifier, Adaptive Boosting (ADA boosting), K-nearest Neighbor (K-NN), Support Vector Machine (SVM), Random Forest, and Decision Tree. A few important performance factors are used to group the models. Predicting the levels of high, medium, and low obesity in this case using the experimental results. The gradient boosting techniques have the highest accuracy 97.08% in comparison to other classifiers © 2023 IEEE.

关键词： Support vector machines

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GLCM Based Feature Extraction and Medical X-ray Image Classification Using Machine Learning Techniques 1st

GLCM Based Feature Extraction and Medical X-ray Image Classi...

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1st EAI International Conference on Intelligent Systems and Machine Learning, ICISML 2022

作者： Rout, Jyotiranjan Das, Swagat Kumar Mohalik, Priyabrata Mohanty, Subhashree Mohanty, Chandan Kumar Behera, Susil Kumar Department of Computer Science and Engineering Balasore College of Engineering and Technology Balasore India

ISBN: (纸本)9783031350771

The automated system is now created with excellent accuracy to detect abnormalities in X-ray images. To enhance the appearance of medical photographs, image pre-processing methods are applied, so that high accuracy can be achieved with constrained means. Images are often classified based on their textural properties, which are measured using the Gray Level Co-occurrence Matrix (GLCM). The grey level correlation matrix provides statistical information of the second order on the grey levels of neighboring pixels in a picture (GLCM). In this proposed paper, medical X-ray images are classified and their features are extracted using an ensemble learning model. By extracting image features using the GLCM feature extraction method, this proposed model is able to distinguish between healthy and sick images (Gray level co-occurrence matrix).to improve the efficiency of the Ensemble learning classification method, it is compared against various algorithms using performance indicators, including Logistic regression, Gaussian Naive Bayes, as well as Random Forest. When this approach is compared to existing methods, the proposed ensemble model has an accuracy rate of 97% in classifying normal and diseased images. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Image enhancement

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Classification of Rice Variant using Deep Learning

Classification of Rice Variant using Deep Learning

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Kumar, K Susheel Verma, Prem Kumari Chaurasia, Subham Singh, Nagendra Pratap Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Karnataka Manipal India National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India Axis Institute of Technology and Management Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798331533311

Rice is a popular crop in the world. This paper has constructed a model using deep Learning for classification. Classification is a critical process in identifying the class of an image. Here, the Rice dataset has been taken to classify the class of rice Breed. Image Classification is a complex process that is affected by various factors. The emphasis is placed on summarizing essential superior class procedures and the strategies used for improving classification accuracy. This paper uses the deep learning approach for classification, such as CNN, and some activation functions, such as ReLu Softmax with Dense layers. The classification process used various domains, such as Machine Learning and deep learning. In this model, five classes of rice breed have been taken and classified with an accuracy of 9 6%. © 2024 IEEE.

关键词： Adversarial machine learning

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An Effective Machine Learning Approach for Music Genre Classification with Mel Spectrograms and KNN

An Effective Machine Learning Approach for Music Genre Class...

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2023 International Conference on Communication, Circuits, and Systems, IC3S 2023

作者： Alam Siddiquee, Md. Nura Hossain, Md. Ali Wahida, Farah Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350325904

The automatic classification of musical genre is extremely useful in the indexing of music. In the area of music information retrieval (MIR), categorizing music files based on their genre is commonly a subjective task performed by humans. Our study develops a classifier to predict the genre of audio files. GTZAN genre classification dataset was used to extract important features from audio files. We implemented a K-Nearest Neighbor (KNN) and have chosen a value of k as 2, 5, and 10. With KNN, the best performance was achieved with the value of k=10. The proposed feature extraction and classification models end up with better accuracy in musical genre classification. © 2023 IEEE.

关键词： Music

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Diagnosis and Classification of Fetal Health Based on CTG Data Using Machine Learning Techniques 1st

Diagnosis and Classification of Fetal Health Based on CTG...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Islam, Md. Monirul Rokunojjaman, Md. Amin, Al Akhtar, Md. Nasim Sarker, Iqbal H. Dhaka1212 Bangladesh Department of Computer Science and Mathematics Bangladesh Agricultural University Mymensingh2202 Bangladesh Department of Computer Science and Technology Chongqing University of Technology Banan District Chongqing China Department of Computer Science and Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh

ISBN: (纸本)9783031346217

Cardiotocograms (CTGs) is a simple and inexpensive way for healthcare providers to monitor fetal health, allowing them to take step to lessen infant as well as mother died. The technology operates by emitting ultrasound pulses and monitoring the response, revealing information such as fetal heart rate (FHR), fetal movements, uterine contractions, and more. Knowing the state of fetal, doctors and patients can take necessary steps in a time. Machine learning can play a vital role in this field. In this paper, we classified the state of fetal including normal state, suspect state, pathological state on the fetal disease dataset using seven machine learning model named AdaBoost (AdB), Random Forest (RF), K- nearest Neighbors (K-NN), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Decision Tree Classifier (DTC), and Logistic Regression (LR). To validate the experimental task, we used several performance metrics containing accuracy, precision, recall, and F1-score. We also used a scaling technique named standard scalar for doing an unbiased dataset. Among the classification models, GCB outperforms the best by achieving the accuracy 95 %, precision (for normal 96 %, suspect 85 %, pathological 97 % ), recall (for normal 98 %, suspect 78 %, pathological 94 % ), and F1-score (for normal 97 %, suspect 81 %, pathological 96 % ). Although, RF, SVM, and K-NN perform better precision (100 % ) in the class of pathological state only. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Classification (of information)

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Evaluation of Logistic Regression and Decision Tree Models for Lung Cancer Prediction Using Survey Data

Evaluation of Logistic Regression and Decision Tree Models f...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Narayana, K.E. Jayashree, K. ThavaVinu, A. Sindhuja, M. Jaeyalakshmi, M. Rajalakshmi Engineering College Department of Information Technology Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Prathyusha Engineering College Department of Information Technology Chennai India Acharya University Department of Computer Science and Engineering Karakul Uzbekistan Rajalakshmi Engineering College Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798331522988

Lung cancer is one of the most deadly and ubiquitous forms of cancer globally. Early detection can make a significant difference in survival rates, prognosis, etc. Background The present study compares the performance of logistic regression and decision trees using a survey-based dataset for predicting lung cancer. This dataset has a total of 284 cases with 16 features;behavioral patterns, symptomatic data, and demographic details. Logistic regression is a binary classification algorithm that uses input features to model the probability of lung cancer presence given these input features using for example a sigmoid function. Using Decision Trees can identify significant fields in a dataset while reducing Gini Impurity for models that categorize patients based on feature values. The performance of both models is evaluated according to accuracy, F1-score, precision, and recall measures. From the preliminary results, it looks like the Decision Tree works a little better than the logistic regression model which gets an accuracy of 94%, but this one does with more accuracy (97%). This result emphasizes the inclusion of logistic regression within this context, as it may be able to predict lung cancer with greater accuracy. Additionally, to ensure the generalization and robustness of the model, further investigation and trials will focus on optimizing performance through ensemble approaches, hyperparameter tuning, and cross-validation. This comparative analysis demonstrates the effectiveness of machine learning techniques in developing prediction models for early lung cancer detection, an important step toward a quick and appropriate intervention. © 2024 IEEE.

关键词： Lung cancer

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Enhanced Fake News Detection through the Fusion of Deep Learning and Repeat Vector Representations 2

Enhanced Fake News Detection through the Fusion of Deep Lear...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Mahmud, Tanjim Hasan, Imran Aziz, Mohammad Tarek Rahman, Taohidur Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350327533

The rapid dissemination of information across social networks, online news portals, and digital platforms has given rise to a pervasive issue - fake news. This misinformation, deliberately propagated by groups with ulterior motives, has the potential to divert societies in alarming directions. In a digital landscape inundated with millions of daily stories, distinguishing fact from fabrication has become a formidable challenge, casting a dark cloud of uncertainty over the information age. In response to this critical issue, this paper presents an innovative approach for enhanced fake news detection through the fusion of deep learning and Repeat Vector representations. In an era where individuals of all ages increasingly rely on online news sources, the dissemination of misinformation has become an alarming norm. It is a challenge to discern deceptive narratives that aim to sow discord and create undue *** confront this challenge head-on, we propose a deep neural network model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architectures using the Repeat Vector method. This hybrid model excels in deciphering complex patterns within 1D data, effectively tackling the menace of fake news. Our rigorous evaluation demonstrates that our proposed model outperforms contemporary methods, achieving an impressive validation accuracy of up to 98.94% on our *** a world increasingly plagued by misinformation, our approach represents a significant stride toward safeguarding the integrity of information in the digital age. © 2024 IEEE.

关键词： Autoencoder Technique Convolutional Neural Network Deep Learning Long Short-Term Memory

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