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A Comprehensive Data Driven Approach on Crop Yield and Fertilizer Efficiency 4

A Comprehensive Data Driven Approach on Crop Yield and Ferti...

引用

4th International Conference for Emerging Technology, INCET 2023

作者： Kesarapu, Kiran Kiran, Nelluru Sai Dhara, Erothi Manju Rupa, Reddymasu Chhabra, Gurpreet Singh Dept of Computer Science and Engineering Visakhapatnam India

ISBN: (纸本)9798350335750

India's economy, which is mostly dependent on agricultural production growth and agroindustry goods, is an agricultural nation. A significant field of research for agricultural production analysis is data mining. Every farmer wants to know how much harvest he may anticipate. Examine a number of relevant factors, such as the location and the pH level used to calculate the soil's alkalinity. Moreover, the proportion of nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). Location is utilized in conjunction with the usage of third-party apps like APIs to identify factors such as weather and temperature, soil type, nutrient value, the quantity of rainfall, and soil composition. All of these parameters will be reviewed, and the data will be trained to develop a model using several efficient machine-learning techniques. The system incorporates a model to give the user precise recommendations regarding the right fertilizer ratio based on field atmospheric and soil data, which improves crop output and increases farmer revenue. © 2023 IEEE.

关键词： Decision trees

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Smart Plant Watering System for Advanced Irrigation 2

Smart Plant Watering System for Advanced Irrigation

引用

2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Pujitha, Pasupuleti Sai Bhoomika, H.R. Saideep, Konduri Rahul Reddy, T. Banshal, Sumit Kumar Karmakar, Mousumi Shekhar, R. Dept. of Computer Science & Engineering Alliance University Bangalore India

ISBN: (纸本)9798350383867

The Smart Plant Watering System is an Internet of Things (IoT) solution for automated plant irrigation. It combines an ESP8266 Wi-Fi module for real-time monitoring with soil moisture and DHT11 sensors for data collection. It makes use of tools such as Blynk and Thing Speak to facilitate data visualization and remote control. An online crop recommendation systems using ML approaches and written in Python makes recommendations for the best crops to plant depending on the analysis of sensor data. With its ability to maximize irrigation, reduce water use, and improve crop management, this system represents a major breakthrough in precision agriculture for increased productivity and environmental sustainability. © 2024 IEEE.

关键词： Soil moisture

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Improving Multi-Domain Task-Oriented Dialogue System with Offline Reinforcement Learning

Improving Multi-Domain Task-Oriented Dialogue System with Of...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Prajapat, Dharmendra Toshniwal, Durga Indian Institute of Technology Dept of Computer Science and Engineering Roorkee India

ISBN: (纸本)9798350362480

Task-oriented dialogue (TOD) system is designed to accomplish user-defined tasks through dialogues. The TOD system has progressed towards end-to-end modeling by leveraging pre-trained large language models. Fine-tuning the pre-trained language models using only supervised learning leads to the exposure bias and token loss problem and it deviates the models from completing the user's task. To address these issues, we propose a TOD system that leverages a unified pre-trained language model, GPT-2, as a base model. It is optimized using supervised learning and offline reinforcement learning (RL). The issues in the TOD system are mitigated using a non-differentiable reward function. The reward is calculated using the weighted sum of the success rate and BLEU evaluation metrics. The success rate and BLEU metrics in reward calculation guide the language model for user task completion while ensuring a coherent and fluent response. Our model is acquired by fine-tuning a pre-trained model on the dialogue-session level which comprises user utterance, belief state, system act, and system response. Experimental results on MultiWOZ2.1 demonstrate that our model increases the inform rate by 1.60% and the success rate by 3.17% compared to the baseline. © 2024 IEEE.

关键词： Supervised learning

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SmartBlend: Smart Video Recommendations on YouTube 3

SmartBlend: Smart Video Recommendations on YouTube

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Singh, Kulvinder Maurya, Aditya Kumar Solanki, Drishti Chandigarh University Dept of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350375190

In the dynamic era of online education, the pursuit of a personalized and effective learning experience is paramount. A transformative approach in online education by integrating Multimodal Data Mining and Data Synthesis to develop a Smart System for Learning Video Recommendations. With a focus on Personalized Learning and Adaptability, the methodology employs advanced techniques like Multimodal Learning Analytics and Machine Learning for a comprehensive analysis of learning materials. SmartBlend introduces a novel approach to personalized learning video recommendations on YouTube, leveraging Multimodal Data Mining and Data Synthesis. The SmartBlendEdu framework employs advanced techniques like Multimodal Learning Analytics and Machine Learning for a comprehensive analysis of learning materials. © 2024 IEEE.

关键词： Data mining

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Multiple Objects Identification for Autonomous Car using YOLO and CNN 7

Multiple Objects Identification for Autonomous Car using YOL...

引用

7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Prakash, M. Janarthanan, M. Devi, D. Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

One of the most anticipated 21st-century technologies and one of the subjects of the current study that is gaining significant research attention is autonomous driving. By detecting and responding to the vehicle's immediate environment, autonomous driving tries to navigate roads without human assistance. It presents significant difficulties for computer vision and machine learning. Processing several candidate object locations, which are frequently referred to as 'proposals,' is one difficulty. These choices only offer basic localization, which needs to be refined in order to obtain precise localization. However, solutions to these issues frequently come at the expense of efficiency, precision, or simplicity. R-CNN first introduced in 2013, and their upgraded versions Fast R-CNN and Faster R-CNN are recent examples of cutting-edge deep learning models that tackle the issue of object detection. YOLO, a paradigm for object detection created for speed and real-time use, was released in 2015. © 2023 IEEE.

关键词： Autonomous vehicles

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A Comparative Study of Feature Extraction Methods in Image Classification Using Convolution Neural Network Model 1

A Comparative Study of Feature Extraction Methods in Image C...

引用

1st International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2023

作者： Dsouza, Divya Jennifer Rodrigues, Anisha P. Dept of Computer Science Engineering Udupi India

ISBN: (纸本)9798350306637

Image classification is the supervised classification in which one assigns labels to images based on its characteristics or features. The term Outlier which is also referred as anomaly are points, patterns or sequences of data that do not fall into the normal behaviour of the system under consideration. Outlier detection refers to the study of identifying the genuine outlier within a given data set. This paper aims to present a deep learning convolution neural network which is based on outlier detection technique with applied gabor, LBP and SIFT filters for feature extraction that can detect outliers by assigning weights to selected features such as to prioritise its significance. A comparison is made with the traditional CNN and applied filters on two image data-sets. The first dataset is the publicly available malaria parasites blood smear sample dataset and the the second dataset is the publicly available facial expressions dataset. Classification related evaluation metrics were applied to the results for analysis. Our experimental performance indicate that the applied filters combined with CNN performs considerable better in detecting existing anomalies with good accuracy but the training time to create features exceeds the traditional approach. © 2023 IEEE.

关键词： Gabor filters

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A Multistage Cascaded Neural Network Model to Identify the Severity of Epileptic Attack

A Multistage Cascaded Neural Network Model to Identify the S...

引用

2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Simon, Martha Thampi, Lidiya Lilly Dept. of Computer Science College of Engineering Trivandrum India Indian Institute of Information Technology Kottayam Dept. of Computer Science Kottayam India

ISBN: (纸本)9798350376135

Epilepsy is a neurological disorder characterized by recurrent seizures, impacting the quality of life for millions of individuals worldwide. Timely and accurate detection of epileptic seizures is crucial for proper diagnosis and effective treatment. Electroencephalogram (EEG) signals provide vital information about brain activity and serve as a valuable tool for epilepsy detection. The methodology opted for this study was an enhanced multilayer feed forward neural network for automatic detection of epilepsy from EEG signals and hence predicting the severity. The authors build a multistage cascaded neural network model for achieving the same. Initially a Simple Artificial Neural Network (SimpleANN) is designed to classify EEG segments into seizure, healthy and interictal. Later a Multilayer Feed Forward Neural Network (MFFNN) is employed for detecting the severity of epileptic patients and hence can be classified into primary and secondary stages. The experimental results showed that the proposed SimpleANN classifier had provided noticeable results, and outperforms all tested classifiers. The study involves the collection of EEG data from a diverse group of patients, including those with epilepsy and healthy individuals. This automated epilepsy identifying system passes through different stages including decomposition, feature extraction for achieving a better performance model. The proposed system was tested and compared with SVM, KNN, MLP and Decision Tree under different measurement metrics. ANN has managed to give an accuracy of 80 - 90% which is significantly higher as compared to other machine learning models. The proposed work can automatically learn complex patterns from EEG data provides an advantage in identifying subtle seizure related features that may not be evident through manual analysis. Furthermore, the model offers real-time capabilities, making it suitable for continuous monitoring of patients at risk of seizures. © 2024 IEEE.

关键词： Electroencephalography

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CNN-Based Image Classification for Face-Spoof Detection Using AI and ML

CNN-Based Image Classification for Face-Spoof Detection Usin...

引用

2023 International Conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Pushpanjali, Km Srivastav, Shashank Dept of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350302820

With the rapid development of digital content identification in recent years, automatic image classification has become the most difficult task in computer vision. Compared to human vision, it is challenging for systems to comprehend and analyze images automatically. A few examinations have been finished to conquer issues in the existing grouping framework;however, the result was restricted exclusively to low even-out picture natives. However, these methods fail to classify images accurately. Our system employs a machine learning algorithm in this paper to produce the anticipated outcomes in computer visions. We achieved an experimental accuracy of 97.5% for grayscale image classification, demonstrating that our model can accurately classify images. © 2023 IEEE.

关键词： Image classification

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A Rubric-Centric Approach for Automated Test Correction Utilizing RAG and Fine Tuning 4

A Rubric-Centric Approach for Automated Test Correction Util...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Harshavardhan, G. Singh, Kulvinder Chandigarh University Dept. of Computer Science & Engineering Punjab India

ISBN: (纸本)9798350387490

Assessments are a popular method to quantify a person's knowledge, skill, or cognitive ability. However, to grade such assessments, particularly subjective questions requires a significant amount of time and effort for the examiners. There is also a degree of variance due to inconsistency from different examiners. Thus institutions often resort to taking objective-type questions, which may not completely capture a person's understanding. Unfortunately, many other proposed solutions, tend to be difficult to implement and scale. To address these issues, this paper proposes an innovative method to automate the grading of subjective assessments by leveraging recent advances in artificial intelligence. This approach aims to replicate the grading style of human experts, offering significant benefits for educational institutions, faculty, and students by enhancing the efficiency and quality of grading. Widespread adoption can help increase the quality of education overall, resulting in more qualified graduates. This method involves using a rubric to fine- tune pre-trained language models, providing them with clear guidelines to ensure consistent and accurate grading. Inspired by how rubrics guide human examiners to minimize variation, this approach will use rubrics to instruct the model on the most relevant grading criteria. Utilizing such clearly defined and precise grading criteria, users can obtain reliable results, comparable to human experts. © 2024 IEEE.

关键词： Natural language processing systems

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BeTS: Abstractive Text Summarization with Transfer Learning 2

BeTS: Abstractive Text Summarization with Transfer Learning

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Bhardwaj, Meher Ethari, Hrishikesh Charan, Annepu Sai Singh Moirangthem, Dennis Iiit Manipur Dept. of Computer Science and Engineering Imphal India

ISBN: (纸本)9798350360523

We introduce BeTS, which stands for Bert Transformer Summarizer, an abstractive summarization model that is built using transfer learning. Our model consists of a pre-trained BERT encoder and a generative transformer decoder to produce abstract summaries. The model is trained by a simple two step process, where the decoder is first trained and then the entire model is fine-tuned end-to-end. We train the model with CNN/Daily Mail summarization dataset and use the trained weights for transfer learning on the AMI dialog summarization dataset. We successfully demonstrate that our model outperforms the baseline models by at least 7.65 ROUGE points on the AMI data. We also demonstrate that our model shows good generalization performance by testing on different unseen input data and compare the generated results. We are able to generate significantly better summaries on inputs such as dialog compared to the current state-of-the-art model. © 2024 IEEE.

关键词： Decoding

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