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An Efficient Deep Learning Approach for Jute Pest Classification Using Transfer Learning 6

An Efficient Deep Learning Approach for Jute Pest Classifica...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Islam, Muhammad Tanvirul Rahman, Md. Sadekur Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Jute is considered as one of the most vital crops in the world. For some countries jute is the principal source of earnings and GDP. One of the primary elements influencing jute yield is jute pests. Accurate pest identification makes it possible to take prompt preventative action to minimize financial losses. Considering the fact, to classify jute pests, the study suggests different jute pest classification models, which are based on transfer learning. The best model offers high performance and resilience. A VCI-validated dataset comprising 7235 images has been utilized in the analysis. The dataset encompasses images classified into 17 distinct jute pest classes. The dataset is already divided into three categories train, test and validation. To increase the dataset size, data augmentation is applied to the training set. To improve performance, all the models were integrated with the transfer learning model. VGG 16, ResNetl0l, DenseNet201, InceptionV3, Xception, and MobileN etV2 were used to train the parameters on the publicly available ImageN et dataset followed by some customized dense layers. The models were assessed using different types of metrics, including confusion matrix, F1 score, precision, and recall. Compared to other models DenseNet201 outclassed other models, acquiring 97% accuracy. The fundamental information and technical support for jute pest classification are provided by this study. © 2024 IEEE.

关键词： Deep learning

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Harmony Forecast: Using Machine Learning to Predict Relationship Quality 1

Harmony Forecast: Using Machine Learning to Predict Relation...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Nallagonda, Harshitha Sai Kondra, Varun Teja Gorla, Srinivas Samantula, Mohankrishna Gitam Deemed to be University Dept. of Computer Science and Engineering Visakhapatnam India

ISBN: (纸本)9798350377002

Several things influence human relations. Many of each other's personality traits can have an impact on a relationship between two people. These characteristics have the power to strengthen their love or spark conflict. Personality traits can be used to evaluate the quality of the relationship between the partners. To determine whether the individual will maintain or end the relationship, the relationship's quality has been evaluated based on personality traits. A thorough analysis has been conducted on the personality traits that impact relationships to determine whether they will have an impact, might have an impact or have no impact at all. A study was carried out to investigate the impact of personality traits on relationships. The relationship quality was assessed based on the personality trait which will make the person stay in the relationship, might or might not stay, or not stay in the relationship. The data collected and the pre-existing data combined was used to calculate the relationship quality. © 2024 IEEE.

关键词： Adversarial machine learning

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Impact of Novel Pre-Processing Techniques on Retinal Images 2

Impact of Novel Pre-Processing Techniques on Retinal Images

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sahu, Deepti Kaur, Mandeep Sharda University Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350371055

Glaucoma, a gradually developing eye condition that can damage the optic nerve, is one of the leading causes of irreversible blindness worldwide. The detection of glaucoma is required in its initial phases for the prevention of the disease. Several machine learning and deep learning models have been developed to detect the disease. These models require a preprocessed structured data set for better efficiency and accuracy of detection. The image datasets used for this purpose have been found with several issues of outliers and noise that limit the accuracy of the results. Hence, proper handling of the dataset in terms of pre-processing is vital. This paper presents a novel pre-processing technique for better glaucoma detection as it is necessary to remove outliers from the retinal images. The proposed 'EMF' model for pre-processing comprises Enhancement, Morphology, and Filtering of the images to get better results on the Retina. Drishti dataset has been used to evaluate the efficiency of the proposed technique. This dataset has several outliers that limit the accuracy, making it difficult to diagnose glaucoma. However, with the usage of the proposed technique, the dataset could be efficiently utilized for glaucoma detection. Compared to current state-of-the-art techniques, the findings from the proposed approach were shown to be better in terms of MSE, PSNR, and entropy. © 2024 IEEE.

关键词： Frequency domain Glaucoma Pre-processing Region of interest Retinal image Spatial domain

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Structured Implementation of ML Algorithms for Cardiovascular Disease Detection 5

Structured Implementation of ML Algorithms for Cardiovascula...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Arora, Drishti Asif, Farhan Amity University Dept. of Computer Science & Engineering Uttar Pradesh India

ISBN: (纸本)9798350383522

Cardiovascular diseases encompass a range of medical conditions that impact the heart and blood vessels The prediction of heart disease pose considerable complexity and present a formidable challenge within the medical field According to the WHO, heart diseases are the leading cause of death worldwide, claiming approximately 17.9 million lives each year, accounting for around 31% of all global deaths To address the need for accurate heart disease prediction, extensive research has employed advanced machine learning models This study conducted a comprehensive evaluation of seven distinct classification models, combining various physiological factors with well-known machine learning algorithms The model employed in this study includes Naïve Bayes, Logistic Regression, Decision Trees, Random Forest, XGBoost, CatBoost, and Voting Classifiers Using these models, a robust heart disease prediction system is designed, facilitating precise evaluation of an individual's risk The observation shows that through meticulous evaluation and comparison, the Random Forest algorithm, which is a bagging technique, outperforms the existing state-of-the-art methods It exhibited remarkable accuracy, yielding prediction results of approximately 90.16% This exceptional accuracy establishes the random forest algorithm as the pre-eminent model for precise and reliable heart disease prediction within the scope of this study. © 2024 IEEE.

关键词： Cardiovascular Disease Machine Learning Classifiers Performance Evaluation Predictive Analysis

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A Review on Deep Learning Based Question Answering with Natural Language Processing in Healthcare

A Review on Deep Learning Based Question Answering with Natu...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Javeed, Abdul Saleem Gopala Krishnan, C. Gitam School of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350359688

Over the Internet, an efficient approach and promising solution to retrieve significant information envisages the beginning of Question Answering Systems (QAS). Because of data sources availability, the deep learning model based on open domain QAS focuses a substantial quantity of research work. Because of the lack of medical datasets, less attention is received in the medical domain. The deep learning (DL) utility increases the development of huge data in the healthcare domain. In the past years, popularity has grown in hardware parallelism and quick data storage availability. The challenge, pipelines, and tasks involved in medical NLP and medical imaging deploy the existing review of research in this work. The medical NLP and imaging fields deploy DL structures with its extensive survey is presented here. Over all 39 research papers are collected for this survey analysis from the years 2017 to 2023. The diagnosis is enhanced with medical imaging, Natural language processing, and DL combination to detect the appropriate combination. Each of the outcomes is shown graphically. The information provided in this survey is quite useful for newcomers carrying out research in medical informatics. © 2024 IEEE.

关键词： and Medical Data Deep Learning Healthcare Natural Language Processing Question Answering

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A New Half-Bridge/Dual-Stacked-Switches Structured Electrolytic Capacitor-less AC/DC Bi-Directional On-Boad Charger for High-Voltage EV Battery

A New Half-Bridge/Dual-Stacked-Switches Structured Electroly...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Derakhshan, Siamak Lam, John York University Dept. Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350376067

This paper introduces a novel half-bridge (HB)/dual-stacked-switches based electrolytic capacitor-less bidirectional AC/DC converter for high voltage (HV) electric vehicle (EV) systems. The proposed two-stage AC/DC converter incorporates a closed-loop Power Factor Correction (PFC) mechanism based on the dq model of the input LCL filter, ensuring high-quality input current with minimized Total Harmonic Distortion (THD) during both conversion and inversion operations of the converter. Moreover, the front-end HB PFC circuit operates in Continuous Conduction Mode (CCM) that results in the elimination of the high peak current in the switches. Additionally, a dedicated closed-loop control system is deployed in the dual-stacked-switches resonant converter to significantly reduce the output voltage ripple, thus eliminating the use of bulky electrolytic-type storage capacitors. Output voltage regulation is realized by using Variable Frequency Modulation (VFM). Soft-switching operation is guaranteed for all the switches and diodes using CLLC resonant circuit for both conversion and inversion modes of operation. The proposed circuit's performance is verified through a 1kW, 120Vrms/800Vdc proof-of-concept prototype. © 2024 IEEE.

关键词： Electrolytic capacitors

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A Narrative Assistant for Traffic Accidents Based on Large Language Models (LLM) 37

A Narrative Assistant for Traffic Accidents Based on Large L...

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37th Annual Conference on Legal Knowledge and Information Systems, JURIX 2024

作者： Kung, Jo-Chi Chang, Chia-Hui Huang, Huai-Hsuan Chien, Kuo-Chun Dept. of Computer Science & Information Engineering National Central University Taiwan

ISBN: (纸本)9781643685625

This study introduces the Collision Clarification Generator (CCG), a Large Language Model-based system designed to assist in documenting traffic accidents. The CCG comprises three modules: Questioning, Information Extraction, and Accident Sequence Generation, which collectively streamline the process of gathering and structuring accident information. The system employs predefined question templates and a standardized Traffic Accident Record Format (TARF) to ensure comprehensive data collection. Evaluation of the CCG involved both human assessment and LLM-based automatic evaluation. Results showed an F1 score of 0.909 in human evaluation, and scores exceeding 7 out of 10 for accuracy and completeness in LLM-based assessment. These findings demonstrate the CCG’s effectiveness in accurately documenting accident information, potentially facilitating subsequent legal and insurance processes. © 2024 The Authors.

关键词： Highway accidents

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Classification of Brain Tumor Images using Segmentation and Transfer Learning 5

Classification of Brain Tumor Images using Segmentation and ...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Bindu, J. Hima Devi, M. Uma Dept. of Computer Science and Engineering Andhra Pradesh Guntur522213 India

ISBN: (纸本)9798350395235

Classifying brain tumors is considered imperative for developing successful treatments. In this regard, binary classification of brain tumor was the major objective of this paper. This work uses a magnetic resonance imaging (MRI) dataset for classification purpose. Due to data paucity, the classifying frameworks in this proposed work tested the transfer learning method for extracting features by utilizing four convolutional neural networks (CNNs) of pre-trained capabilities. This research aimed at decreasing the training time, improving accuracy in classifying images, and preventing overfitting. Instead of considering the same datasets, the study considered segmented images of the original dataset for training. Four segmentation methods were used for creating a new segmented dataset of the original dataset. The authors done segmentation and trained the architecture with few pre-processes for three separate epoch numbers in place for investigating the effect on classification accuracy and time consumption. Additionally, profits were gained from obtaining suitable results within a minimal number of epochs in a constrained time amount. The experimental data proved that the transfer learning method has produced accurate outcomes with a classification accuracy of 96.98%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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ImportChainBD: Blockchain Solutions for Transparent and Accountable Imported Cosmetic Products 3

ImportChainBD: Blockchain Solutions for Transparent and Acco...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Khan, Md. Adib Karmoker, Sagar Rumpa, Ummay Khadiza Rahman, Habibur Hossain, Md. Sabbir Anik, Nur Adnan Chowdhury Hasan, Mahamudul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350354348

To improve the supply-chain management system of Bangladesh for imported cosmetic products this paper proposes a blockchain-based solution. One of the major goals of this project is to increase the tax compliance of the government by stopping counterfeit and illegally imported cosmetic products. Which will enhance the trust of consumers and the brand's reputation. Using this system is quite easy and convenient, as All the information will be stored in the blockchain, so it will become a secure and immutable product tracking system for importers, distributors, and retailers to maintain their ledgers just by printing and scanning a QR code through an IoT device, and consumers can check the authenticity of these cosmetic products just by scanning them. If the product is verified to be suspicious, The Mobile Court Management System has the power to check its origin. Finally, we can say that it will avoid the sale of counterfeit items in the market and increase government revenue on tax. © 2024 IEEE.

关键词： Cosmetics

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Enhancing Indoor Navigation Efficiency with WiFi Signal Mapping 15

Enhancing Indoor Navigation Efficiency with WiFi Signal Mapp...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, N. Sai Pranavdhara Narayanan, Gayathri Singh, Aman Kumar Cheeramvelil, Geo Jolly Sethulekshmi, U.S. Kumar, R. Adithya Tripathi, Akshat Amrita Vishwa Vidyapeetham Dept. Of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

In the contemporary digital landscape, efficient indoor navigation has become imperative for large, intricate spaces like shopping malls, airports, hospitals, and universities. Traditional GPS-based methods often prove inadequate in these indoor settings due to poor reception and a lack of fine-grained location accuracy. To address these challenges, the work employs WiFi signals, pervasive in modern buildings, and harnesses their ubiquity and reliability for sophisticated indoor navigation. The team developed a Python package for collecting WiFi signal details, implementing robust preprocessing to filter out private networks, and storing the data in a database. The core of the system lies in a machine learning ensemble consisting of Gaussian Bayes classifier, decision tree, and K-nearest neighbors (KNN). This ensemble utilizes WiFi signal strengths to predict users' real-time positions within large indoor spaces. The system aims to enhance user experiences and simplify wayfinding in complex indoor environments. © 2024 IEEE.

关键词： Decision Tree Gaussian Bayes Classifier Indoor Navigation K-nearest Neighbors Machine Learning Ensemble WiFi Signals

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