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A Deep Learning Approach for Classification and Segmentation of Leafy Vegetables and Diseases

A Deep Learning Approach for Classification and Segmentation...

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

作者： Islam, Ashraful Sama Raisa, Syeda Rafiatus Khan, Nahiyan Habib Rifat, Ariful Islam Sylhet Engineering College Dept. of Computer Science and Engineering Sylhet Bangladesh American International University-Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Bangladesh's economy is heavily dependent on agriculture, making the region an exceedingly crucial sector that requires great vigilance. In comparison to other vegetables, leafy greens have yet to garner as much attention as other crops that this study's large-scale analysis comes up with, particularly when categorizing readily available leafy vegetables and identifying the affected area and whether they are affected or not. Plant diseases can cause substantial losses in agricultural productivity, and many researchers are working to address the difficulties farmers encounter in identifying and treating these conditions while diagnosing their diseases. The purpose of this study is to develop a dependable and effective approach for classifying seven varieties of leafy vegetables and detecting seven types of plant diseases precisely in Bangladesh using machine learning and deep learning CNN models. To this end, this paper analyzed and provided solutions using a total of eight models: five classification models (VGG16, VGG19, ResNet50, YOLOv5, and YOLOv8) and three instance segmentation models (YOLOv5, YOLOv7, and YOLOv8). Two distinct datasets, 'LeafyVclassify7BD' with 3306 images and 'LeafyVdisease7BD' with 4493 images, were developed to classify leaves, segment the diseased area, and classify and detect damaged leaves. The research outputs will help strengthen the region's ability to control plant diseases more effectively while also cutting diagnostic costs and enhancing disease detection accuracy in the country's agricultural surroundings. © 2023 IEEE.

关键词： Classification (of information)

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An ensemble learning-based machine learning with voting mechanism for chronic disease prediction

An ensemble learning-based machine learning with voting mech...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Soualihou, Soualihou Ngnamsie Kim, Jeong-Dong Zonyfar, Candra Lee, Hyun Lee, Taek Lee, Jung-Been Sun Moon University Dept. of Computer Science and Electronic Convergence Asan Korea Republic of Sun Moon University Dept. of Computer Science and Engineering Asan Korea Republic of Sun Moon University Division of Computer Science and Engineering Asan Korea Republic of

ISBN: (纸本)9798350394528

Chronic diseases present a significant challenge in healthcare, often requiring ongoing medical attention and posing limitations on patients' daily activities. Diagnosis of such diseases is hindered by the absence of specific symptoms making them hard to detect and or prevent. Addressing this issue, researchers have turned to computational approaches, analyzing patients' medical records to predict the presence or absence of chronic diseases with promising results. However, there is potential for further improvement. This paper introduces an ensemble classifier, ELVot-CroDiP, designed to enhance chronic disease prediction. ELVot-CroDiP harnesses the collective strengths of various machine learning algorithms through a majority voting mechanism, thereby boosting predictive accuracy. The model's performance was evaluated using precision, recall, f1-score, and accuracy across five medical datasets, including heart disease, chronic kidney disease, diabetes, and heart stroke. Comparative analysis shows that ELVot-CroDiP offers superior performance in predicting chronic diseases compared to existing models, as evidenced by its higher scores in the mentioned evaluation metrics for each dataset tested. © 2024 IEEE.

关键词： Forecasting

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A BrainNet (BrN) based New Approach to Classify Brain Stroke from CT Scan Images

A BrainNet (BrN) based New Approach to Classify Brain Stroke...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Tripura, Dhonita Haque, Imdadul Dutta, Mithun Dev, Shaikat Jahan, Tanjila Ghosh, Shomitro Kumar Islam, Md. Ashiqul Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350396485

Worldwide, brain stroke is known as the 2nd leading cause of death, and based on Indian history, three people have suffered every minute. There are mainly two different types of brain stroke: ischemic stroke and Hemorrhagic stroke used to train the proposed models. Ischemic stroke is the most common and it contributes mostly to 80% of the brain stroke and Hemorrhagic stroke contributes mostly to 20% of the brain stroke. In the proposed model, there has been used a hybrid model called BrainNet (BrN) as CNN(Convolutional Neural Network) and SVM(Support Vector Machine)to classify brain stroke disease. After applying the required proposed model, it has produced a smart score of 91.91% accuracy, and compared to the existing model it performs pretty well. The BrainNet (BrN) model is mainly designed based on a deep neural network with dataset collection, preprocessing, and feature extraction with the desired model and make the classification concerning SVM. With compare to the existing model, it is an acceptable performance that belongs to the collected dataset designed with Ischemic stroke and Hemorrhagic stroke disease within the total number of 2515 data. © 2023 IEEE.

关键词： Support vector machines

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Identification of Genomic Associations Between Parkinson's and Neurodegenerative Diseases Using Bioinformatics Models 3

Identification of Genomic Associations Between Parkinson's a...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Rana, Md. Shohel Podder, Nitun Kumar Rana, Humayan Kabir Hasan, Md. Imam Azam, Md. Shafiul Rahim, Md Abdur Hasan Sazzad Iqbal, S.M. Saha, Subir Pabna University of Science and Technology Dept. of Computer Science and Engineering Pabna Bangladesh Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350345360

Neurodegenerative diseases (NDGs) can slowly damage the brain as well as the central and peripheral nervous systems. Parkinson's disease (PD) is a sensitive sensory disease that causes a malfunction of dynamic equilibrium. PD causes certain brain neuronal cells to gradually split or die. There are certain significant characteristics of PD that may increase the risk of acquiring deferent NDGs including Alzheimer's disease (AD), Amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Multiple sclerosis disease (MSL). We conducted a transcriptomic analysis to find connections between PD and NDGs. To explore how PD may affect the advancement of NDGs, we employed a quantitative framework to analyze the gene expression microarray dataset. To obtain genomic associations between PD and NDGs, we performed gene expression profiling by identifying dysregulated pathways, gene ontologies, PPIs, and phylogenetic analysis. We found 15, 11, 28, and 24 significant genes are shared between PD and AD, ALS, HD, and MSL respectively. We also identified a significant number of functional and ontological pathways, hub proteins and phylogenetic associations that indicate PD may have a link to develop NDGs (AD, ALS, HD, and MSL). We validate our findings through gold benchmark databases. This systematic approach is highly beneficial in understanding the causes and progression of NDGs owing to the effect of PD. © 2023 IEEE.

关键词： Neurodegenerative diseases

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The Effects of Selected Preferred Music on Perceived Emotions through Audiovisual Stimuli 9

The Effects of Selected Preferred Music on Perceived Emotion...

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9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data science, BCD 2024

作者： Thaiwong, Thunrada Fukumoto, Makoto Graduate School of Computer Science and Engineering Fukuoka Institute of Technology Fukuoka Japan Fukuoka Institute of Technology Dept. of Computer Science and Engineering Fukuoka Japan

ISBN: (纸本)9798350394191

Music surrounds us, and there is no denying that music in visual media can shape and evoke emotions. Yet, understanding how musical preference influences emotions through audio and visual stimuli remains an important task. To address this task, we investigated the role of musical preference effect on perceived emotions induced through music and visual stimuli (i.e., animation), using a 7-point scale for valence-arousal ratings and physiological responses in electroencephalogram (EEG) band power. The perceived emotions are categorized into four states: happiness, calmness, fear, and sadness. One emotional state contains 4 sessions: (1) Preferred Music, (2) Unfamiliar Music, (3) Preferred Music+Animation, and (4) Unfamiliar Music+ Animation. Behavior-wise, the rating results showed that the presence of preferred music resulted in higher perceived valence ratings, particularly in happiness. However, no stimulus had a significant effect on perceived arousal ratings and satisfaction ratings. Physiologically, the EEG indexes showed that the presence of preferred music appears to affect an increase in alpha power across various emotions except sadness, whereas unfamiliar music seems to affect beta power, particularly in happiness and calmness. Overall, these findings supported that musical preference is an affective factor reflecting the levels of valence and alpha power in EEG. Nevertheless, our findings did not confirm a significant difference between only preferred music and combining it with animation. Interestingly, we also found that participants were more likely to be attracted to and perceive positive (i.e., happiness and calmness) emotions easily through preferred music and/or visual stimuli than negative (i.e., fear) emotions. © 2024 IEEE.

关键词： Animation

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TSPvis: A Temperature Sensitive Paint Formulation Visual Design System 6

TSPvis: A Temperature Sensitive Paint Formulation Visual Des...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Liao, Jing Wu, Yadong Wang, Guijuan Wang, Fupan Chen, Huarong Long, Li Southwest University of Science and Technology Dept. Computer Science and Technology Mianyang China Sichuan University of Science and Engineering Dept. Computer Science and Engineering Yibin China

ISBN: (纸本)9798350325485

The composition and proportion parameters of temperature sensitive paint formulation are complex. The current methods have a long development cycle and low efficiency. We design a visual formulation design system for temperature sensitive paint, helps desingers use historical data to improve the efficiency of formulation design. TSPvis provides a visual formulation design function, implements the proportion decision of formulation based on decision tree, and recommends reference for ingredients proportion to the formulation designer. We use the DPC clustering algorithm to quickly obtain the radial basis center, and use the Adam algorithm to improve the weight calculation from the hidden layer to the output layer, to improve RBF neural network. TSPvis use this RBF as a model to predict performance of the formulation which designed before, and feeds back to the designer to improve the formulation designing of temperature sensitive paint. © 2023 IEEE.

关键词： Decision trees

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A MobileUnet++-based Abnormality Segmentation and Multi-scale Network Approach for Brain Tumor Classification

A MobileUnet++-based Abnormality Segmentation and Multi-scal...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Kumar, Veesam Pavan Pattanaik, Satya Ranjan Kumar, V Sunil Dept of Computer Science and Engineering Rourkela India Dept of Computer Science and Engineering Bhubaneswar India Pbr Visvodaya Institute of Technology & Science Dept of Computer Science and Engineering Kavali India

ISBN: (纸本)9798350360660

Tumors are a pervasive concern in modern life, driven by cellular irregularities that disrupt the orderly division necessary for healthy cell growth. However, brain tumors present unique challenges compared to tumors in other regions due to the intricacies of brain structure and function. A brain tumor occurs when cells in the cerebral cortex undergo uncontrolled proliferation. Diagnosing brain tumors, especially in their early stages is a formidable task that often relies on manual examination of medical images by radiologists or healthcare professionals. Despite various approaches to brain tumor classification, precision remains paramount while ensuring practical utility in segmentation and classification tasks. Traditional methods are often manual, time-consuming and lacking in automated classification capabilities, hampering efficient decision-making. The advent of deep learning technology has revolutionized medical image processing, particularly in the realm of brain tumor analysis. Deep learning algorithms, powered by vast datasets and computational prowess, offer remarkable capabilities in automating segmentation and classification tasks, thereby enhancing accuracy and efficiency in diagnosing brain tumors. Consequently, this study introduces a method for smart brain tumor segmentation and classification leveraging deep learning techniques. The process begins with acquiring essential images through established benchmarking techniques. These images are then processed in the abnormality segmentation phase, utilizing Mobile-Unet++ (MUnet++) for segmentation. Following segmentation, the images undergo a classification phase, where brain tumors are classified using Multi-scale Mobilenet (MMNet). To validate effectiveness of proposed technique, an efficacy study is conducted, comparing it against traditional methodologies for segmentation and classification of brain. © 2024 IEEE.

关键词： Deep learning

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An Ensemble ML Model to Predict the Wastage of Food: Towards Achieving the Food Sustainability

An Ensemble ML Model to Predict the Wastage of Food: Towards...

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

作者： Masuk Shopnil, Md Masrur Husna, Asmaul Sultana, Shaheena Islam, Muhammad Nazrul Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh NotreDame University Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Food waste is a major issue in students' hostel or dormitories, where uneaten food often ends up in the garbage, resulting in economic losses as well as reducing the food sus-tainability. Although Machine Leaning (ML) has emerged as a promising tool for predicting future events in several files, but a very few has been focused to generate accurate predictions and reducing food waste by analyzing read data for a particular context or a country. Therefore, the objective of this research is to explore the factors of affecting food waste in students' hostel or dormitories and to develop an ensemble ML-based food waste prediction system for the student hostel or dormitories. In order to attain these objectives, this study firstly find out the 18 key features that ensure a comprehensive understanding of food waste patterns;then collected the data against these features over the last six months from various mess records of a students' hostel of an engineering university situated in Dhaka, Bangladesh. Secondly, six classification models were developed and analyzed their results and found that Random Forest and Decision Tree performed best accurate prediction (75.41%). Thirdly, the all possible combinations of the studies ML models were explored to find out the best possible ensemble combination;as such, the proposed ensemble ML model showed the highest F1-score of 86.88% that considers the Random Forest and AdaBoost;while all other possible combinations showed around 80-82% F1-score. This research thus contributes to the broader goal of reducing food waste and promoting sustainability in students' mess and other food service settings;as well as may assist the management team in making data-driven decisions about food production, purchasing, and resource allocation. © 2023 IEEE.

关键词： Forecasting

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Exploring the Impact of Univariate Feature Selection Method on Machine Learning Algorithms for Heart Disease Prediction

Exploring the Impact of Univariate Feature Selection Method ...

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

作者： Islam, Mainul Islam, Rafiqul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Dept. of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

This study compares various machine learning techniques such as logistic regression, support vector machine (SVM), random forest, Naïve Bayes, and decision tree. Later, a feature selection approach that considers the Univariate Feature Selection (UFS) method was applied to select the best features based on statistical testing. In this research, we employed the ANOVA F-test to pick the most significant features, and we used a cutting-edge dataset for experimental analysis. The ANOVA F-test was used to exploit the target variable's dependency on the continuous variables. The results show that employing the UFS approach for picking features improves the performance of the algorithms. According to the experimental results, using UFS with the logistic regression technique yields 93.3% accuracy, whereas the standalone Naïve Bayes algorithm achieved the best accuracy. © 2023 IEEE.

关键词： Feature Selection

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Classification of Depression, Anxiety and Stress from Handwriting and Drawing with Stacking Models 8

Classification of Depression, Anxiety and Stress from Handwr...

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Bayrak, Semra Golgiyaz, Sedat Aykut, Murat Karadeniz Technical University Graduate School of Natural and Applied Science Dept. of Computer Engineering Trabzon61080 Turkey Bingöl University Dept. of Computer Engineering Bingöl Turkey Karadeniz Technical University Dept. of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798331540104

Recently, emotion analysis from online handwriting and drawings has become an important research topic. This study aims to determine the optimal model combination using dynamic and statistical features obtained from handwriting and drawings for detecting emotional states. The feature vectors are derived from physical (kinematic), statistical and signal processing (spectral, cepstral and frequency domain) analyses. To address the class imbalance problem, ADASYN (Adaptive Synthetic Sampling) and Tomek Links methods were applied together. In this way, the number of samples of the minority class has been increased, while over-sampled classes have been reduced. Dimensionality reduction was realized using PCA, and Gradient Boosting Classifier (GBC) feature selection. After determining the fewest and most effective features, the samples were classified using the two-level stacking ensemble method. eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), GBC, Random Forest (RF) methods are selected for base and meta models. Model and hyperparameter optimization was carried out by Optuna frameork. The performance of the models is evaluated through experiments in publicly available EMOTion recognition from HAndWriting and draWing (EMOTHAW) database. The aim was to determine the emotional states of depression, anxiety and stress with the data obtained from 7 different drawing/writing tasks of 129 people. The results obtained from accuracy, f1-score, precision and recall metrics show that the model provides remarkable performance. © 2024 IEEE.

关键词： Dimensionality reduction

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