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Optimization Ensemble Learning Techniques for Reliable Crop Yield Prediction using ML

Optimization Ensemble Learning Techniques for Reliable Crop ...

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Technological Innovations and Advance Computing (TIACOMP), International Conference on

作者： Kamal Upreti Nagulapalli Lingareddy Sheri Deepika Nishant Kumar Jyoti Parashar Prakash Divakaran Dept. of Computer Science CHRIST (Deemed to Be University) Delhi NCR India Department of CSE Vignan Institute of Technology and Science Telangana India Department of CSE Sreyas Institute of Engineering and Technology Hyderabad Telangana India School of Business and Management CHRIST (Deemed to Be University) Bangalore India Dept. of Computer Science & Engg ADGITM India Himalayan University Itanagar Arunachal Pradesh

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392128

The agricultural sector's increasing reliance on technology has paved the way for advanced data-driven methodologies, with crop yield prediction emerging as a critical focus. This study dives into the complex landscape of crop yield prediction, employing a comprehensive approach that involves data preprocessing, model development, and performance evaluation. This research goes into enhancing crop yield prediction through a thorough data-driven approach. Beginning with comprehensive data preprocessing, including outlier analysis and feature scaling, the study ensures dataset integrity. Ensemble learning, employing Gradient Boosting Regressor, Random Forest Regressor and Decision Tree Regressor, captures intricate relationships within the dataset. Model performance, assessed through R-squared scores, demonstrates promising predictive capabilities. Subsequent outlier analysis and hyperparameter tuning yield substantial improvements, contributing valuable insights for agricultural decision-making. The research not only advances crop yield prediction but also offers practical guidance for integrating machine learning into agriculture, promising transformative outcomes for sustainable practices. The research also highlights how significant interpretability is to machine learning models so that stakeholders can comprehend and embrace them. This allows for a smooth integration of the models into current agricultural practices and encourages openness and reliability in decision-making.

关键词： Computational modeling Decision making Data preprocessing Predictive models Boosting Reliability Ensemble learning Regression tree analysis Crop yield Random forests

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Exploring the Use of Artificial Intelligence for Automated Compliant Transaction Processing

Exploring the Use of Artificial Intelligence for Automated C...

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Disruptive Technologies (ICDT), International Conference on

作者： Sunil Rajaram Landge Srinivasa Rao Gunturu Hari Prasad Josyula T. Kiruthiga Eram Fatima New York NY USA Houston Texas USA Princeton NJ USA Dept. of ECE Vetri Vinayaha College of Engineering and Technology Trichy INDIA Department of Computer Science and Engineering G.L. Bajaj Institute of Management Greater Noida INDIA

This paper explores the capability of the use of synthetic Intelligence (AI) for automatic compliant transaction processing. AI-primarily based structures can examine large volumes of transaction records, detect inconsistencies and compliance troubles, and offer guidelines for development. AI can also be used to create models to pick out fraudulent transactions, conduct patron segmentation and analysis, expect customer behavior, and alert establishments of capability issues. Further, AI-based algorithms can be used to automate a huge variety of responsibilities, inclusive of approval tactics and automatic record storage and retrieval. The implementation of AI-based total technology is expected to lessen guide processing efforts and time, resulting in green, streamlined compliance tactics. By leveraging AI generation, companies could be capable of improving consumer revel, lessening dangers, and improving their normal performance. This paper examines the contemporary country of the artwork in AI-based technologies and discusses their capacity packages within the computerized compliant transaction processing area.

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Real-Time Face Mask Detector

Real-Time Face Mask Detector

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Technology, engineering, management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE International Conference on

作者： Vishwajeet Patel M S Guru Prasad H Aditya Pai A Suresh Kumar J Praveen Gujjar H N Naveen Kumar Dept. of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed-to-be University Bengaluru India Dept. of Electronics and Communication Engg. Vidyavardhaka College of Engineering Mysuru Karnataka India

When the COVID-19 pandemic was present we all have faced various challenges in our everyday lives. Pandemic created worldwide emergency which led to the lockdown in most of the countries resulting into disruption in world trade and movement. To reduce the covid cases WHO recommended to wear mask and maintain social distancing and then these steps became mandatory for some places, such as restaurants, hospitals, grocery stores etc., The purpose of limiting interaction with individuals as much as possible in such situations is simply defeated by having a staff personally check each person to make sure their mask is on. To avoid this dilemma, it became necessary to build a project named ‘Real-time face mask detector’, This reveals whether a mask-wearing individual is there. This project needed the use of some python libraries like TensorFlow, Keras, OpenCV. Using this project, we will be able to Detect mask-on faces in live video clip with accuracy of 97-98 %.

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Design of a Collaborative Energy-Aware Routing Protocol for UAV-VANETs

Design of a Collaborative Energy-Aware Routing Protocol for ...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Tz-Heng Hsu Zhi-Hao Wang Gwo-Jiun Horng Dept. of Computer Science and Information Engineering Southern Taiwan University of Science and Technology Tainan Taiwan R.O.C Dept. of Information Management Southern Taiwan University of Science and Technology Tainan Taiwan R.O.C

Due to the mobility of drones and their limited power supply, the network topology frequently changes, making routing more complex. To address this issue, an energy-aware routing protocol is proposed to extend the lifespan of the unmanned aerial vehicle (UAV). Because vehicles have less power constraints, the proposed algorithm prioritizes vehicles for packet transmission when multiple paths exist, and both UAV networks and vehicular communication networks can aid in packet forwarding to improve the lifespan of UAV networks.

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Grey Wolf Optimizer and Deep Neural Network based Feature Selection and Classification in Medical Image Analysis

Grey Wolf Optimizer and Deep Neural Network based Feature Se...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Katha Chandrashekhar P. Leela K. Reddy Madhavi J. Avanija Naresh Tangudu Computer Science and Engineering Indur Institute of Engineering and Technology Siddipet India Dept. of Data Science School of Computing Mohan Babu University Tirupati India School of Computing Mohan Babu University Tirupati India Aditya Institute of Technology and Management Tekkali India

Accurate and efficient medical image analysis is required for modern healthcare applications, facilitating timely disease diagnosis and treatment. Considering the importance of optimization algorithms and deep learning methodologies, this work presents a novel approach, Grey Wolf Optimizer-Assisted Deep Neural Networks (GWO-DNN), for feature selection and classification in medical image analysis. The proposed framework utilizes the power of the Grey Wolf Optimizer to intelligently select the most relevant features from complex medical images. The selected features are then integrated into Deep Neural Networks, to create accurate models for medical image classification tasks. This integrated approach is designed to improve diagnostic accuracy and enhance patient care. The experimentation and evaluation, reveal that the proposed GWO-DNN based model for image analysis is more efficient than the existing methods.

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Recent Scientific Achievements and Developments in Software Defined Networking: A Survey 1

Recent Scientific Achievements and Developments in Software ...

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1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Bale, Ajay Sudhir Hamsalekha, R. Prakash, Bishwajeet Babu, Pidikiti Surendra Gupta, Rashmi Malik, Suraj New Hoizon College of Engineering Dept. of Ece Bengaluru India Sharda School of Law Sharda University Uttar Pradesh Greater Noida India R&d Engineer Tlc Industrial Equipments Pvt. Ltd Hyderabad India Dr. Ambedkar Institute of Management Studies and Research Maharashtra Nagpur India Iimt University Department of Computer Science and Engineering U.P Meerut India

ISBN: (纸本)9798350315769

Application devices or application programming interfaces (APIs) that are connected with the channel's core embedded system and regulate congestion is known as software-defined networking (SDN). Architecture is the primary distinction among SDN and conventional connectivity. Traditional networking is hardware-based, whereas SDN is software-based. Compared to conventional networks, SDN is far more versatile since the routing protocol is software-based. Without adding extra equipment, it enables managers to handle the connection, alter customization options, supply resources, and boost internet bandwidth from a single user interface. There are a lot of developments in SDN which have enabled networking managers to get more freedom in selecting networking hardware, Adaptable network architecture and robust protection of the network. These new trends are reviewed in this article. We are hopeful that this chapter provides future insights in the research of SDN. © 2023 IEEE.

关键词： Software defined networking

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Multi-label transfer learning for identifying lung diseases using chest X-rays 2

Multi-label transfer learning for identifying lung diseases ...

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： El-Fiky, Azza Shouman, Marwa Ahmed Hamada, Salwa El-Sayed, Ayman Karar, Mohamed Esmail Electronics Research Institute Department of Informatics Cairo Egypt Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Minuf Egypt Menoufia University Faculty of Electronic Engineering Dept. Industrial Electronics and Control Engineering Minuf Egypt

ISBN: (纸本)9781665418423

Chest radiography presents one of the main medical imaging modalities for diagnosing lung diseases. To assist radiologists during interventional procedures, this paper aims at proposing a transfer learning-based classifier to automatically identify 14 different thoracic diseases in Chest X-ray (CXR) images. The proposed method is relied on deep residual neural networks with 50 layers (ResNet-50) to accomplish the diagnostic task of many chest diseases. In this study, a public dataset of 112, 120 frontal radiograph images for Chest X-ray has been used for validating the proposed deep learning classifier. It achieved the best performance of multi-label classification of normal and 14 different lung diseases with an average area under curve (AUC) of 0.911 and F1-score of 0.66. This study demonstrated that the proposed ResNet-50 classifier as a transfer learning model outperforms other relevant methods in the previous studies for automatic multi-label classification of chest X-rays. © 2021 IEEE.

关键词： computer aided diagnosis

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Constrained Reinforcement Learning via Dissipative Saddle Flow Dynamics

Constrained Reinforcement Learning via Dissipative Saddle Fl...

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Asilomar Conference on Signals, Systems & computers

作者： Tianqi Zheng Pengcheng You Enrique Mallada Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Dept. of Industrial Engineering and Management Peking University Beijing China

In constrained reinforcement learning (C-RL), an agent seeks to learn from the environment a policy that maximizes the expected cumulative reward while satisfying minimum requirements in secondary cumulative reward constraints. Several algorithms rooted in sampled-based primal-dual methods have been recently proposed to solve this problem in policy space. However, such methods are based on stochastic gradient descent-ascent algorithms whose trajectories are connected to the optimal policy only after a mixing output stage that depends on the algorithm's history. As a result, there is a mismatch between the behavioral policy and the optimal one. In this work, we propose a novel algorithm for constrained RL that does not suffer from these limitations. Leveraging recent results on regularized saddle-flow dynamics, we develop a novel stochastic gradient descent-ascent algorithm whose trajectories converge to the optimal policy almost surely.

关键词： computers Heuristic algorithms Reinforcement learning Approximation algorithms Extraterrestrial measurements Trajectory Behavioral sciences

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Using Pyramid Vision Transformer for Assisting Glaucoma Diagnosis with Small Datasets

Using Pyramid Vision Transformer for Assisting Glaucoma Diag...

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IEEE International Conference on Networking, Sensing and control

作者： Qinhao Wu Shuaiyu Yao Wendi Ouyang Jingzhe Huang Qi Kang Dept. of Mathematics and Computer Science Eindhoven University of Technology Eindhoven The Netherlands Coll. of Electronic and Information Engineering Tongji University Shanghai China Coll. of Economics and Management Nanjing University of Aeronautics and Astronautics Nanjing China Dept. of Mathematics King's College London London United Kingdom

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Glaucoma is one of the leading causes of blindness worldwide. Patients with open-angle glaucoma have difficulty realizing the graduate vision loss. However, the optic nerve head's morphological change can manifest in the retinal image. Therefore, many studies offer computer-aided glaucoma diagnosis based on retinal image analysis. With the trend of deep learning, there have been several attempts to utilise deep-learning-based glaucoma classification in practice. However, the limited size of the dataset is a problem when adapting deep-learning models to glaucoma classification. Therefore, we test three vision transformer models to handle this problem and demonstrate their performance in glaucoma classification. Also, based on their performance, we give suggestions for their usage in clinical practice. In this paper, we fine-tuned three transformer models for glaucoma classification using two datasets, RIM-ONE_DL and Drishti-GS. Based on our experiments, the pyramid vision transformer (PVT) model achieves the best performance, with good scores in terms of accuracy and sensitivity. This suggests that the PVT can catch the missing detail and can be used to support the glaucoma diagnosis.

关键词： Glaucoma Training Adaptation models computer vision Sensitivity Computational modeling Transformers Retina Reliability Optical sensors

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An Adaptive Neuro Fuzzy to Predict Cryptocurrency Based on the Crisp Method: Case of COVID-19

An Adaptive Neuro Fuzzy to Predict Cryptocurrency Based on t...

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Advances in Data-Driven Analytics And Intelligent Systems (ADACIS), IEEE International Conference on

作者： Hajer Chlif Dalel Kanzari Yosra Ben Said Dept. Prince Higher Institute of Computer Science and Communication Techniques of Hammam Sousse (ISITCom or Infcom) Sousse Tunisia Research Decision and Process Control Laboratory (LARODEC) Higher Institute of Management Tunisia Tunisia Tunisia Faculty of Economic Sciences and Management SFAX Tunisia

Over the past few years, there has been a notable surge in interest towards cryptocurrency, especially in the context of the crisis, where researchers have been diligently examining the influence of the coronavirus on cryptocurrency *** studies have utilized econometric and machine learning techniques to forecast cryptocurrency prices, but most of them have focused solely on the financial domain. This paper introduces a novel approach called ANFPC (Adaptive Neuro Fuzzy Prediction Cryptocurrency), which combines insights from both the financial and health domains to predict the price fluctuations of various cryptocurrencies such as bitcoin, ethereum, cardano, xpr, and dogecoin. The approach relies on the ANFIS Model, a fusion of fuzzy logic and artificial neural network (ANN).The experimental findings demonstrate that ANFPC provides accurate predictions, as measured by metrics like Mae and Mse, outperforming traditional ANN and LSTM methods. This approach proves to be a valuable decision support tool for data analysts in the realm of cryptocurrency prediction.

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