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Deep Learning Based Segmentation of Weed Images in crop fields using U-Net 3

Deep Learning Based Segmentation of Weed Images in crop fiel...

引用

3rd International Conference for Advancement in technology, ICONAT 2024

作者： Naik, K. Jairam National Institute of Technology Raipur Dept. of Computer Science & Engineering Raipur India

ISBN: (纸本)9798350354171

Weeds are problems for crop yields because they exert harmful effects on the yields by competing with cultivated crops for necessary resources like nutrients, sunlight, and soil. As a result, continual attempts are being made to create safer and more effective herbicide formulations. For precision weeding and to reduce pesticide pollution, segmenting weeds in photos is extremely important. However, weeds and crops are comparable in the field setting, making it challenging to precisely segregate weed from intricate field photos. A deep learning-based system for segmenting weeds from photos was proposed in this paper. This algorithm can distinguish between weeds and crops and soil in photos. Utilizing a condensed U-net, this semantic segmentation method was created. An image augmentation strategy was suggested because image tagging for the semantic segmentation of weeds is challenging. The classification algorithms used in the modified U-Net are Support Vector Machine (SVM), Random Forrest (RF) and Decision Tree (DT). These were also used for predicting the weed part in a sample image before their segmentation. The threshold accuracy for segmenting the image using to be around 51.85% with a segmentation time of approximately 01.02 ms whereas our modified U-Netis obtained 91.03% accuracy and the segmentation time of 50.35ms. The results showed the modified U-Net successfully differentiated weeds from images with numerous other plants. This study seeks to demonstrate that the weed-associated image segmentation technique is effectively identifying and separating weeds in intricate field conditions. © 2024 IEEE.

关键词： Semantic Segmentation

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Automated Skin Cancer Classification and Detection Using Convolutional Neural Networks and Dermoscopy Images 4

Automated Skin Cancer Classification and Detection Using Con...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sara, Umme Das, Utpol Kanti Sikder, Juel Chowdhury, Sudipta Roy Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar Rangamati4500 Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%. © 2025 The Authors. Published by Elsevier B.V.

关键词： Lung cancer

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Applications and Approaches of Sentiment Analysis: A Current Review 6

Applications and Approaches of Sentiment Analysis: A Current...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sandra, G. Silas, Salaja Rajsingh, Elijah Blessing Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350381689

Limitless amount of data is loaded in every moment in social media platforms in the forms of audio, videos, texts, and photos. They are rich sources of information, which has immense value. Hence extraction of information from them is inevitable. Many methods are employed for this and the popular among them to mine the information is sentiment analysis. To materialize the objectives of this study, a systematic review of articles published from 2013 to 2023 was done. Our study reviewed the relevance of sentiment analysis in social media by probing the different approaches followed, the platforms used and its applications, as its main objectives. Its results show that majority of studies applied machine learning approach or hybrid method for executing sentiment analysis. © 2024 IEEE.

关键词： applications approaches categories dataset sentiment analysis

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Transformer-Based Self-Supervised Imputation for Medical Data 4

Transformer-Based Self-Supervised Imputation for Medical Dat...

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4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Singh, Aryan Kumar Saikia, Arpit Baro, Pranita Borah, Malaya Dutta National Institute of Technology Silchar Dept. of Computer Science and Engineering Cachar India

ISBN: (纸本)9798350391183

Dealing with missing data is necessary to develop a solid predictive medical research model. This work presents a hybrid dynamic imputation method devoted to addressing the challenge of missing data, specifically focusing on medical datasets. In this work, we investigate the incompleteness of the dataset using a sophisticated measure called the Incompleteness Factor. This metric is used to guide a hybrid dynamic approach. A novel Transformer-based Self-Supervised imputation method is employed to learn complex data dependencies to impute missing data. This paper introduces one potential solution to obtain model-level accuracy increase for tasks like disease prediction, patient prognosis, etc., over medical datasets. The proposed method aims to remove the measurement biases caused by incompleteness and preprocess the data to enhance the credibility and clinical relevance of the experimental results. © 2024 IEEE.

关键词： Self-supervised learning

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Notes Application using Android Studio and Cloud-based Data Management 7

Notes Application using Android Studio and Cloud-based Data ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Dodda, Ratnam Yanala, Maneesh Reddy Pavishetti, Srija Cvr College of Engineering Dept. of Computer Science And Information Technology Hyderabad India

ISBN: (纸本)9798350359299

In today's rapidly advancing technological land-scape, the widespread adoption of mobile applications has become a defining feature of our digital age. This research study presents a on the development of a mobile notes application using Android Studio, with the primary goal of offering users a versatile platform for creating and managing notes. The proposed notes application is designed with a simple and user-friendly interface, facilitating effortless note creation, editing, and deletion. Moreover, it has features like voice-to-text for making notes, text-to-speech for reading the notes aloud, note sharing for easily sharing ideas with others and image insertion for storing the photocopy of the written notes or diagrams based on user interest. This research work uses Android Studio as the integrated development environment (IDE) and utilizes the Java programming language for the app's core functionalities. Furthermore, it incorporates Firebase Authentication to ensure secure user account management and the Firestore for Database for seamless storage and retrieval of notes. This research study provides an essential tool for students, professionals, and anyone seeking a convenient means to keep track of vital information. By combining mobile application development tools and cloud-based data management like Firebase, the application simplifies how information is managed, aligning with the way we live in the digital age. © 2024 IEEE.

关键词： Authentication

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Detection of Specific Language Impairment Using XGBoost Model 15

Detection of Specific Language Impairment Using XGBoost Mode...

引用

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

作者： Rai, Rajnish Thenkanidiyoor, Veena Dept. of Computer Science and Engineering National Institute of Technology Goa Goa India

ISBN: (纸本)9798350370249

Specific Language Impairment (SLI) in children, a neurodevelopmental disorder presenting significant hurdles in language acquisition. Motivated by the prevalence and complexity of SLI diagnosis, this article introduces an methodology using Mel-Frequency Cepstral Coefficients (MFCC) and chroma features combined with the XGBoost algorithm for classification. By analyzing a diverse dataset encompassing both SLI-diagnosed and non SLI children, the proposed model achieves efficiency of 99.3% accuracy on unseen data. Notably, the model’s strategic feature selection and utilization of XGBoost demonstrate superior performance compared to existing methods, highlighting its importance in SLI diagnosis and intervention planning. This work significantly advances machine learning-based diagnostic tools and holds promise for improving the quality of life for individuals impacted by SLI. ©2024 IEEE.

关键词： Adversarial machine learning

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A Deep Learning Approach to Enhance Air Quality Prediction: Comparative Analysis of LSTM, LSTM with Attention Mechanism and BiLSTM

A Deep Learning Approach to Enhance Air Quality Prediction: ...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Bhardwaj, Deepanshu Ragiri, Prakash Rao Netaji Subhas University of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350364866

In this study, we address the challenge of predicting air quality indices (AQIs) by leveraging Long Short-Term Memory (LSTM) networks. Recognizing the dynamic nature of air quality data, we explore the efficacy of three LSTM variants - standard LSTM, LSTM with an attention mechanism, and Bidirectional LSTM (BiLSTM) - in capturing temporal dependencies and improving forecast accuracy. Our methodology involved preprocessing a comprehensive dataset of air quality measures, followed by an experimental design that tested each model variant under different sequence lengths (30 and 365 days). The models were evaluated based on their mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) across three response variables: Nitrogen Oxides (NO), Particulate Matter up to 10 micrometers in size (PM10), and Particulate Matter up to 2.5 micrometers in size (PM2.5). Notably, when comparing the RMSE values across the three models, the LSTM with the attention mechanism consistently outperformed the standard LSTM and BiLSTM models, particularly with a sequence length of 30 days, indicating a superior ability to capture the intricacies of temporal dependencies in environmental data. Moreover, the shorter sequence length generally yielded better predictive performance, suggesting that longer sequences might introduce unnecessary complexity or overfitting. These indications of our study suggest a promising direction for the deployment of attention-based models in real-time AQI prediction systems, which could enhance the accuracy of environmental forecasts and inform public health decisions more effectively. Future work will explore the integration of additional environmental factors, the refinement of model frameworks, and the expansion of the dataset to include multi-city comparisons. © 2024 IEEE.

关键词： Mean square error

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Improving Time Series Forecasting Accuracy with Transformers: A Comprehensive Analysis with Explainability 3

Improving Time Series Forecasting Accuracy with Transformers...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Joseph, Susmera Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science Engineering Kottayam India

ISBN: (纸本)9798350369083

Time series forecasting is crucial in numerous sectors, including healthcare, energy, and finance. Transformer models, initially designed for natural language processing applications, demonstrate potential in capturing temporal dependencies within sequential data. In this paper, the application of transformer models in time-series forecasting is explored, and their performance and interpretability are investigated. Transparent models build trust and facilitate decision-making, especially in industries with ethical guidelines. While inherently interpretable time-series Transformers exist, there is a gap in research regarding post hoc methods, which is an area ripe for exploration. Understanding and comparing complex Transformer models for time series forecasting can benefit from advancements in explainability, ensuring accurate and comprehensible predictions. In comparison to five other transformer-based models and classical models, the Temporal Fusion Transformer (TFT) model outperformed them in short-term forecasting. Further enhancement of the model performance was achieved through a meticulous analysis of the Encoder-Decoder's importance, which contributed significantly to refining the forecasting accuracy. The vanilla transformer was explained using permutation feature importance. The model stability was analyzed using perturbation methods. © 2024 IEEE.

关键词： Spatio-temporal data

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Lip Reading Into Text Using Deep Learning 14

Lip Reading Into Text Using Deep Learning

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Manglani, Krishna Bahuguna, Utkarsh Gupta, Subhash Chand Amity School of Engineering and Technology Dept. of Computer Science Uttar Pradesh India

ISBN: (纸本)9798350344837

An essential part of computer vision, lip reading, has grown significantly and is now used in autonomous driving, public safety, and hearing-impaired communication. This work provides an in-dept. analysis of both conventional and deep learning-based lip reading systems, examining their development over time, difficulties encountered, and critical role in human communication. The suggested LipNet model, which leverages the complementary abilities of Long Short-Term Memory networks (LSTMs) for temporal modelling and Convolutional Neural Networks (CNNs) for spatial feature extraction, is a central focus of this research. When LipNet is tested using the GRID Corpus dataset, it outperforms baseline models in terms of performance. Saliency maps and viseme analysis highlight LipNet's ability to capture phonologically relevant areas in lip motions, providing insight into the network's attention processes. Apart from examining the importance of datasets such as OuluVs2and GRID Corpus, this study presents GhostNet, a lightweight model that tackles the requirement for effective network designs. The testing results highlight LipNet's competitive Character Error Rate (CER) and Word Error Rate (WER) in comparison to baseline models, proving that it is the best method for real-world use. The research ends with a summary of potential future developments in lip reading technology, such as modal integration, realtime applications, personalisation, ethical issues, and the goal of creating a communication environment that is more inclusive. This study highlights the effectiveness of LipNet and paves the way for future developments in the ever-evolving area of lip reading by examining a variety of methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of Blockchain-based Secured Election Voting System

Design of Blockchain-based Secured Election Voting System

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Chinchamalatpure, Sumedh V. Kapse, Smita R. Balki, Aditya K. Rahangdale, Vaishnavi A. Fulzele, Vidisha K. Telrandhe, Shital Yeshwantrao Chavan College of Engineering Computer Technology Dept. Nagpur India India

ISBN: (纸本)9798350348460

The modern electoral process faces numerous challenges concerning security, transparency, and inclusivity. Traditional voting systems have struggled to keep pace with the demands of a rapidly evolving digital landscape. In response to these challenges, this paper proposes a novel approach to develop and implement an IoT and blockchain-based voting system (machine), aimed at providing unparalleled security and transparency to the electoral process. The proposed voting machine leverages the power of the Internet of Things (IoT) to enable secure and efficient voter authentication. Utilizing biometric data and unique identifiers associated with IoT devices, the system ensures accurate voter identification, mitigating the risk of fraudulent activities. By incorporating IoT technology, the voting process becomes more accessible, accommodating a broader range of citizens. The implementation of such a voting machine promises to transform the electoral landscape, ushering in a new era of secure and transparent elections. With real-time vote counting and results, citizens can have immediate access to election outcomes, bolstering public confidence in the integrity of the democratic process. This paper outlines the design, architecture, and security measures that underpin IoT and blockchain-based voting machines. © 2024 IEEE.

关键词： blockchain Electoral Voting System IoT IoT and Blockchain-based electronic voting Transparency

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