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Automated Weed Detection and Removal using Deep Learning 4

Automated Weed Detection and Removal using Deep Learning

引用

4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Thenmozhi, T. Subashini, S. Pradeepa, K. Pooja, E. Sri Ramakrishna Engineering College Dept of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354218

Precision agriculture uses cutting-edge technology to improve farming operations, increase crop yields, and reduce resource use. This study focuses on developing an autonomous weed and crop detection system that leverages the cutting-edge You Only Look Once (YOLO) *** purpose is to equip farmers with a real-time solution to identify and distinguish crops fromweeds in agricultural fields. The process entails creating a well-annotated dataset with photos of both crops and weeds. A pre- trained YOLO model, such as YOLOv3 or YOLOv4, is chosen and fine-tuned with the annotated dataset. The YOLO configuration file is changed to meet the unique crop and weed detection needs. The training procedure continues until adequate convergence is obtained. The trained YOLO model is then connectedwith agricultural equipment, such as drone-mounted cameras or other sensing devices, allowing for real- time detection in the field. Post-processing techniques are used to improve detection results while minimizing false positives. Decision-making algorithms are developed to direct activities based on the detected crops and weeds, allowing for focused interventions such as precision herbicide administration. © 2024 IEEE.

关键词： Precision agriculture

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Forecasting Stock Price by LSTM-CNN Hybrid Model and Compares Deep Learning Models 1

Forecasting Stock Price by LSTM-CNN Hybrid Model and Compare...

引用

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

作者： Arora, Aditi Upadhyay, Yashaswi Shukla, Satvik Saket Abes Engineering College Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350377002

The The accurate forecasting of stock prices holds significant importance in devising effective trading strategies and making informed decisions about stock transactions. The inherent volatility in stock prices, driven by factors like geopolitical tensions, corporate earnings, and commodity costs, underscores the need for precise predictions. Domestic factors such as central bank policies, governmental decisions, inflation, and global market uncertainties also trigger stock price fluctuations. Estimating this volatility is a complex challenge for traders. A novel approach is presented in this research, proposing a hybrid model that integrates two distinct algorithms - Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN). This model harnesses diverse data representations from stock time series to enhance price prediction accuracy. Comparative analyses are performed with alternative models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Networks) and Naïve Bayes to ascertain the most effective prediction paradigm with minimal error. The study demonstrates that combining temporal and image features extracted from the same dataset diminishes prediction errors compared to using these features independently. The hybrid model outperforms singular prediction models, highlighting a high enhancement. Among the individual models, the LSTM model exhibits superior performance in terms of prediction accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Face Generation and Recognition in Forensic science 11

Face Generation and Recognition in Forensic Science

引用

11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Ravi, Gayathri Joy, Heynes Jitto, Jeffin Joshy, Jocelyn Jose, Jisha Mary Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350379136

Suspect identification can be challenging for forensic investigations since standard procedures are time-consuming and prone to mistakes. This calls for the creation of novel approaches utilizing developments in machine learning (ML) and artificial intelligence (AI). In order to overcome these obstacles, the proposed Face Generation and Recognition in Forensic science will make use of sophisticated recognition algorithms and AI-based face generation models. Fully trained Stable Diffusion model is applied to generate high-quality face images from textual descriptions. Image Generation, Text Guided Image Manipulation using Denoising Diffusion Probabilistic Models (DDPMs), and Dataset Matching are the three primary components of the process. Using a stable diffusion model, Image Generation quickly creates high-resolution images from word prompts by combining an autoencoder (VAE), U-Net, and text encoder. With the introduction of an alternate noise space for DDPMs, Text Guided picture Manipulation makes it possible to do meaningful picture altering tasks in response to text prompts. VGG-16 , a convolutional neural network architecture is used in dataset matching to extract features and calculate similarity, which makes dataset alignment and comparison easier. The suggested methodology gives law enforcement authorities effective tools for identifying suspects, which represents a substantial development in forensic investigations. The project intends to increase the efficiency of criminal investigations, accelerate the matching process with large datasets, and enhance the accuracy of facial sketches by utilizing AI and ML approaches. The approach's ability to produce coherent and contextually relevant face images is validated by experimental results, which also show the approach's potential for speeding up the conclusion of criminal cases, particularly unsolved cold cases. All things considered, Face Generation and Recognition in Forensic science is a promising step in st

关键词： Convolutional neural networks

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An Approach for XAI Visualizations for Explainability of Alzheimer's Detection 2

An Approach for XAI Visualizations for Explainability of Alz...

引用

2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Khanapur, Shraddha Bharadwaj, Chirag B. Bhardwaj, Raghav Nayak, Jyothi S. BMS College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350367461

In the field of medical image analysis, the integration of machine learning (ML) models has significantly enhanced the process of identifying and diagnosing Alzheimer's disease. However, the opacity of these models, often termed as "black boxes,"raises concerns regarding their interpretability and reliability in clinical settings. Explainable artificial intelligence (XAI) techniques have emerged as crucial tools to address these challenges by clarifying the reasoning behind model outputs. This paper offers an extensive review of literature concerning the employment of Explainable Artificial Intelligence (XAI) methodologies, such as SHAP, LIME, GradCAM, DeepLIFT, Saliency Maps, and LRP to augment the comprehensibility of ML models applied in the detection of Alzheimer's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Alzheimers Disease Detection through LASSO and RFE based Feature Selection from EEG Data

Alzheimers Disease Detection through LASSO and RFE based Fea...

引用

2024 IEEE International Conference on Biomedical engineering, computer and Information Technology for Health, BECITHCON 2024

作者： Peya, Zahrul Jannat Joly, Nurzahan Akter Mostafa, Md. Sharzul Al-Azad, Sharmina Islam, Md. Shymon Alam, Sk. Ahadul Dept. of Computer Science and Engineering North Western University Khulna9100 Bangladesh Dept. of Computer Science and Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh

ISBN: (纸本)9798331534356

Alzheimer’s disease is a progressive neurological condition that affects memory, cognition, and basic daily functions, with symptoms typically appearing later in life. This study aims to develop a reliable, non-invasive method for early detection of Alzheimer’s using electroencephalogram (EEG) data. Given the absence of a cure, precise diagnostic tools are crucial. EEG is an affordable and non-invasive alternative, particularly beneficial for disadvantaged populations. Our approach focuses on improving early diagnosis, monitoring disease progression, and providing individualized care. Using Recursive Feature Elimination and Lasso for feature selection, we classified EEG data with machine learning algorithms (Random Forest, Support Vector Machine, and Decision Tree), with Random Forest achieving the highest accuracy of 95.45%. © 2024 IEEE.

关键词： Neurodegenerative diseases

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VisualBlend - A Mixed Approach on Forensic Image Generation and Recognition 6

VisualBlend - A Mixed Approach on Forensic Image Generation ...

引用

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

作者： Unnikrishna, Pillai Midhun Pulkuzhiyil, Vinayak Krishna, Jyothika Wilson, Naveen Kumar, Veena A. Saintgits College Of Engineering Dept. of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350381689

An extensive use case of Generative Adversarial Networks (GANs) that not only combines artistic creativity with technological innovation but also significantly improves identification precision, filling in critical gaps in the smooth integration of textual descriptions with visual representations. The motivation is rooted on the difficulties that currently exist in accurately identifying human face sketches by directly transforming them into images, an area where prior approaches were inadequate. Our special dual-function technology is designed to get beyond these obstacles and provides a fresh approach for forensic investigators, artists, and sculptors. It has the ability to convert text to image as well as sketch to image. This easy transformation of textual and drawing data represents an integrated combination of creativity and technology that is set to have an impact on a number of disciplines, such as suspect identification and crime scene reconstruction. © 2024 IEEE.

关键词： Generative adversarial networks

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Streamline Pro Next-Gen Recruitment Solution

Streamline Pro Next-Gen Recruitment Solution

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kumar, Vinod Sangeetha, G. Gopal, K. Kumar, P. Rajalakshmi Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331543617

An innovative full-stack project called 'StreamlinePro Next-Gen Recruitment Solution' aimsto transform the hiring process. With its modern technologies, this fully inclusive platform enhances the hiring process to satisfy the demands of administrators, HR professionals, and job seekers. The Job Board Application is developed around a powerful system for posting jobs and accepting applications. Experts in human resources can readily post job openings, andneed to offer corporate papers to validate their skills. This ensures that reliable sources are used to publish job openings. Conversely, candidates can use the website to look for jobs that fit their qualifications and apply immediately. Posting resumes is required, and an applicant tracking system (ATS) reviews them after are uploaded. After reviewing the entries, the ATS rates the resumes according to theirquality and applicability. One of the primary aspects of the StreamlinePro Next-Gen Recruitment Solution is its exclusive referral mechanism. Employees in a company may indicate that are willing to suggest others. Job searchers can contact these references, and if accepted, both parties can communicate through an integrated chat system. This recommendation system encourages networking and enhances job-seeking opportunities by leveraging existing staff networks. The application also offers the capability of real-time job status tracking. Applicants can monitor the progress of their applications and receive alerts regarding their receipt, review, and acceptance by employers. This tool helps clarity and informs job seekers during the hiring process. © 2024 IEEE.

关键词： Apprentices

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Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features

Sparse Oblique Decision Trees: A Tool to Understand and Mani...

引用

KDD Workshop on Human-Interpretable AI 2024, HI-AI 2024

作者： Hada, Suryabhan Singh Carreira-Perpiñán, Miguel Á. Zharmagambetov, Arman LinkedIn Dept. of Computer Science & Engineering University of California Merced United States Dept. of Computer Science & Engineering University of California Merced United States

The widespread deployment of deep nets in practical applications has lead to a growing desire to understand how and why such black-box methods perform prediction. Much work has focused on understanding what part of the input pattern (an image, say) is responsible for a particular class being predicted, and how the input may be manipulated to predict a different class. We focus instead on understanding what internal features computed by the neural net are responsible for a particular class. We achieve this by mimicking part of the net with a decision tree having sparse weight vectors at the nodes. We are able to learn trees that are both highly accurate and interpretable, so they can provide insights into the deep net black box. Further, we show we can easily manipulate the neural net features in order to make the net predict, or not predict, a given class, thus showing that it is possible to carry out adversarial attacks at the level of the features. We demonstrate this robustly in MNIST and ImageNet with LeNet5 and VGG networks. © 2024 Copyright for this paper by its authors.

关键词： Deep neural networks

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Enhanced Text-Summarization and Language Simplification:End-to-End Approach for Content Comprehension 1

Enhanced Text-Summarization and Language Simplification:End-...

引用

1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Riza Ayisha, P. Mathew, George Government Engineering College Dept. of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350307795

Text summarization is the process of shortening a large body of text in a document to form a shorter while preserving its most important information. With the exponential growth of digital data, there is a need for automated text summarization to help users quickly comprehend the content. The use of machine learning techniques, particularly deep learning models, has shown great promise in generating high-quality summaries. Among these models, BERT (Bidirectional Encoder Representations from Transformers) has emerged as a state-of-the-art method for various natural language processing (NLP) tasks, including text summarization. In this paper, we propose to implement a text summarization system using the BERT model. Apart from the normal text summarization, we will be implementing a Language Simplification model that simplifies complex sentence structures or use simpler vocabulary while preserving the original meaning. This model transforms convoluted or verbose sentences into shorter and more straight-forward constructions The paper aims to develop an end-to- end summarization system that takes a long document as input and generates a summary of the most important information contained in the document. For example, like taking the entire news from an online portal and generates it summary. © 2024 IEEE.

关键词： Signal encoding

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Optimizing Federated Learning Efficiency: Exploring Learning Rate Scheduling in Sparsified Ternary Compression

Optimizing Federated Learning Efficiency: Exploring Learning...

引用

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

作者： Nithyaniranjana Murthy, C. Manjula, Sh. University of Visvesvaraya College of Engineering Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350359688

Federated Learning facilitates the collaborative training of a deep learning model by leveraging the combined data of multiple entities, with a focus on maintaining the privacy of individual datasets. The Sparse Ternary Compression (STC) method improves gradient sparsification by integrating downstream compression, ternarization, and optimal Golomb encoding of weight updates. Although STC enhances compression techniques in federated learning, the broader scope of Federated Learning lies within the domain of machine learning. Researchers investigating improvements in STC compression techniques must consider neural network concepts and the impact of hyperparameters, such as learning rate, on convergence speed and accuracy. Increased communication across multiple devices poses challenges in achieving desired accuracy levels. Our research underscores the importance of selecting appropriate learning rate schedules when implementing algorithms like STC. Incorrect choice of learning rate schedules can hinder convergence, even if the algorithm functions effectively, emphasizing the crucial role of hyperparameters in reducing communication costs and achieving desired accuracy levels © 2024 IEEE.

关键词： Federated learning

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