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Real Estate Forecasting System Using machine learning Algorithms 6

Real Estate Forecasting System Using Machine Learning Algori...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kaur, Harshpreet Kushwanth, A. Sai Praveen, M. Akshay, Y. Kiran, Y. Surya Kartheek, B. Ganesh Kamal Reddy, G. Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350304480

the idea of this paper is to predict the value of houses and properties of the market value. the aim of the presented paper is to estimate the market value of the asset being sold. this application assists in determining a location's beginning pricing based on location-specific factors. the below mentioned idea to find the price of a location based on location variables. the below discussed models helps to find the market value of any property in India through different databases depending upon the locations. the presented paper suggests the buyer in determining the prices of different houses with the similarity of characteristics and properties. According to the survey made the prices of properties always rise every year, so a buyer needs a real estate forecasting system. this research paper can help estimate the price of a house which helps both seller and buyer for a reasonable price to buy a property. the result showed that machine learning algorithms in linear regressions model can accurately predict area price with high accuracy rates. However, some challenges such as property value based on area, characteristics, locality, facilities provided. It highlights the importance of machine learning algorithms for price predictions to research and address the challenges. © 2023 IEEE.

关键词： Data Science Google collab House price prediction Linear regression model machine learning Python

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machine learning based Novel Framework for Fake News Detection and Prevention using Blockchain 10

Machine Learning based Novel Framework for Fake News Detecti...

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17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Faridi, Arman Rasool Singh, Rahul Masood, Faraz Salmony, Monir Yahya Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9789380544472

there has been a dramatic increase in the transmission of false information on social media over time. Concerns have been expressed about the effects of false information on the industry, not only by ordinary people but also by marketers. False information is often produced for commercial purposes to draw viewers and gather money through advertising. the whole world is affected by fake news. False and appealing news articles have been published by some unreliable sources, and this news cannot be confirmed because no regulatory system exists. As a result, these unreliable sources are free to post whatever they want, which in some circumstances, even causes social chaos. Due to the internet's ease, unsuitable news can spread faster than ever. However, it has been shown that influential media organizations with nefarious intentions have created false news to impact global events and policies. A conceptual framework for fake news detection using machine learning and blockchain is proposed in this paper. the idea is to give ratings to the content by experts and ordinary people and check for the authenticity of the content. Ethereum blockchain is used to make the system decentralized, and the InterPlanetary File System is used as off- chain storage for blockchain. A Decentralized-application will also be created in Ethereum, where users can read the news. InterPlanetary File System will be used to store the data, and the hash of the metadata will be stored in the blockchain. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Smart contract

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4th international conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning, 3D IT-MSP&DL 2022

4th International Conference on 3D Imaging Technologies- Mul...

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4th international conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning, 3D IT-MSP and DL 2022

ISBN: (纸本)9789819912292

the proceedings contain 51 papers. the special focus in this conference is on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning. the topics include: Research on the application of 3D Modeling Technology in Mechanical Structure Teaching;2D Numerical Model Used to Investigate the Influence of Vegetation on Geomorphological Evolution of Mudflats;design and Experimental Verification of High Functional Density Cubesat System;space Design of Exhibition Hall Based on Virtual Reality;Identification of Expressway Traffic States Based on the Enhanced FCM Algorithm;algorithms Applied in Soft Tissue Deformation Simulation: A Survey;soft Tissue Cutting Based on Position Dynamics;research on application Technology of Multi-source Fusion of 3D Digital Resources in Power Grid;rapid Identification of Herbaceous Biomass Based on Raman Spectrum Analysis;a Knowledge Representation Method for Hierarchical Diagnosis Decision-Making Under Uncertain Conditions;research on Spam Detection with a Hybrid machine learning Model;research on Weld Defect Object Detection Based on Multi-channel Fusion Convolutional Neural Network;Design and Research of Highway Tunnel Construction Positioning System Based on UWB;research on Cross Domain Data Analysis and Data Mining Technology of Power Grid Digital Benefits;graph Convolutional Neural Networks for Drug Target Affinity Prediction in U-Shaped and Skip-Connection Architectures;SOC Estimation of Lithium Titanate Battery Based on Variable Temperature Equivalent Model;research and Implementation of Hybrid Storage Method for Multi-source Heterogeneous Data of Electric Distribution Network;Research on Measuring Method of Ball Indentation Based on MATLAB Image Edge Detection;multi-feature Extraction of Mineral Zone of Tabling through Deep Semantic Segmentation.

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Comparative Analysis of Web APIS: RESTful and GraphQL 4th

Comparative Analysis of Web APIS: RESTful and GraphQL

引用

4th international conference on machine learning, Advances in Computing, Renewable Energy and Communication, MARC 2023

作者： Seth, Aparna Kumar, Vinay Kulshrestha, Vaibhav Vij, Aanchal School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819752300

the two well-liked methods for creating web services, REST and GraphQL APIs, are thoroughly compared in this study paper. To give a thorough assessment of both systems, the study examines a number of factors, including performance, flexibility, scalability, and developer experience. this research aims to identify the strengths and weaknesses of each approach through extensive testing, benchmarking, and real-world use cases, providing insights to assist developers, architects, and organizations in selecting the best API technology for their particular needs. the results highlight subtle trade-offs between REST and GraphQL, illuminating their best applications and informing further API design decisions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： application programs

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Multi-Constraint Flexible Job Scheduling Algorithm Based on DDQN: A Deep Reinforcement learning Algorithm for Solving Practical Job Scheduling Problems 4

Multi-Constraint Flexible Job Scheduling Algorithm Based on ...

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4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Liu, Lin Liang, Zijing Hong, Yifan Liu, Ke Wang, Hui Shang, Penghui Hangzhou Zhiyuan Research Institute Co. Ltd. Zhejiang Hangzhou310024 China

ISBN: (纸本)9798400708831

As an important part of intelligent manufacturing, reasonable scheduling of production planning is very important for reducing costs and increasing efficiency. this paper considers that the current algorithms related to intelligent scheduling do not consider the actual constraints enough, and the solution efficiency cannot satisfy the application requirements. Considering the actual industrial production and a variety of practical constraints including employee attendance time and machine start-up time, this paper proposes a deep reinforcement learning algorithm. the goal of this research is to minimize the total production cost including product processing costs, employee overtime costs, order back-order costs, finished and semi-finished product inventory costs. In this paper, the intelligent scheduling problem is modeled as a Markov decision-making process, and the agent selects and executes actions according to the state at each decision-making stage. In this paper, the Double DQN neural network architecture is built. Seven features are extracted from the scheduling environment to form a feature vector representation state, and six composite scheduling rules are designed to form an action set considering the optimization goal. A reward function closely related to the goal is designed. Numerical experiments show that the quality of the solution set obtained by the reinforcement learning algorithm is better than or similar to that of the genetic algorithm, and the solution speed is significantly better than that of other algorithms, which proves the effectiveness and feasibility of the proposed algorithm. © 2023 ACM.

关键词： Reinforcement learning

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An Optimized Ensemble machine learning Framework for Multi-class Classification of Date Fruits by Integrating Feature Selection Techniques 4th

An Optimized Ensemble Machine Learning Framework for Multi-c...

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Maheswara Rao, V.V.R. Silpa, N. Reddy, Shiva Shankar Hussain, S. Mahaboob Bonthu, Sridevi Uppalapati, Padma Jyothi Bhimavaram India Bhimavaram India Vishnu Institute of Technology Bhimavaram India

ISBN: (纸本)9783031488870

Date fruits are widely consumed and valued for their nutritional properties and economic importance. Accurate classification of date fruits is crucial for quality control, sorting, and grading processes in the food industry. However, the classification of date fruits poses several challenges due to variations in their external visible physical attributes and the presence of multiple cultivars. To address the issue of high-dimensional feature space, the authors, in this research study propose an optimized ensemble machine learning framework for multi-class classification of date fruits by integrating feature selection techniques. the objective is to develop a reliable and efficient system that can accurately classify date fruits into different classes based on their physical attributes. Initially, the framework applies various feature selection techniques to identify the most relevant and discriminative features for classification. Next, it employs variants of ensemble machine learning techniques to perform multi-class classification. We utilize popular ensemble methods such as Boosted Trees, Bagged Trees, RUSBoosted Trees and Optimized Ensemble to improve the accuracy and optimization of the classification model. By integrating feature selection techniques, the proposed optimized ensemble classifier effectively handles the challenges of high-dimensional feature space and variations in date fruit characteristics. the results confirm the superiority of the proposed framework, highlighting its potential for practical applications in the food industry. © 2024, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Classification (of information)

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A Study of EMG Signals Using QNET Myoelectric Board and LabVIEW Software 4

A Study of EMG Signals Using QNET Myoelectric Board and LabV...

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4th international conference on Applied Automation and Industrial Diagnostics, ICAAID 2022

作者： Alharbi, Hadeel Fahad Patel, Akshet Dasari, Uday Randhawa, Princy University of ha'Il Hail College of Computer Science and Engineering Department of Computer Science and Software Engineering 1234 Saudi Arabia Manipal University Jaipur Department of Mechatronics Engineering Jaipur India Manipal University Jaipur Department of Information Technology Engineering Jaipur India

ISBN: (纸本)9781728189390

this project's aim is to monitor the electrical activity of the user's muscle cells using the QNET Myoelectric Board and NI ELVIS II+ board. An opto-isolated sensor and a grounding strap are included in this electromyograph. the board includes an on-board signal processing and filtering mechanism, as well as a PCI-based data collection system. the board is connected using the LabVIEW application, which stands for Laboratory Virtual Instrument Engineering Workbench (LabVIEW). LabVIEW is a graphical programming language and hence a direct connection between the user and the board is possible. the data is acquired, and five assessment criteria are applied to it. Graphs are shown based on time stamps for two participants (male and female), and the analyzed data is compiled to draw crucial conclusions. © 2022 IEEE.

关键词： machine learning

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Comparing Fish Finding Techniques using Satellite and Indigenous Data based on Different machine learning Algorithms 4th

Comparing Fish Finding Techniques using Satellite and Indige...

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4th international conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： thakrar, Zalak Gonsai, Atul Shri V.J. Modha College of I.T Gujarat Porbandar India Department of Computer Science Saurashtra University Gujarat Rajkot India

ISBN: (纸本)9789811998874

We will look at a temperature study of fish in Indian sea water in this paper. And also, we used the survey approach to collect data from local fishermen as primary data. Satellite data is also used to provide secondary information. the effectiveness of different algorithms for machine learning in forecasting the optimal outcome of fishing data is examined in this research. We used the Linear Regression algorithm, Polynomial Regression, Random Forest, Support Vector Regression (SVR) algorithm, and Auto Regressive Integrated Moving Average (ARIMA) algorithm to analyze the data. these different machine learning algorithms are compared in this study. Multiple attributes and algorithms are evaluated in this exploratory study to find which algorithm produces the best results when these data are utilized. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Satellites

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Applying machine learning for American Sign Language Recognition: A Brief Survey 4th

Applying Machine Learning for American Sign Language Recogn...

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4th international conference on Communication and Intelligent Systems, ICCIS 2022

作者： Singh, Shashank Kumar Chaturvedi, Amrita Varanasi India

ISBN: (纸本)9789819923212

Sign languages play a crucial role in enabling differently-abled people to communicate and express their feelings. the advent of newer technologies in machine learning and sensors has led us to build more sophisticated and complex human-computer interfaces. However, a cost-effective commercial sign language recognition system is still not available. In this paper, we have highlighted recent advancements and limitations related to sign language recognition systems. We have focused our study on American sign language (ASL), which is one of the most widely used sign languages. We have tried to classify most of the surveyed work into two broad areas based on data acquisition techniques: visual and sensor-based approaches. We have also listed publicly available datasets that could be used for further research. this paper aims to provide a clear insight into advancement in the field of sign language recognition systems using American sign language. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Issues and Future Trends in the application of machine learning on E-Healthcare Supply Chain Management 4th

Issues and Future Trends in the Application of Machine Learn...

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4th international conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Pattnayak, Parthasarathi Mohanty, Arpeeta Das, Tulip Patnaik, Sanghamitra School of Computer Applications KIIT Deemed to be University Odisha Bhubaneswar India Faculty of Emerging Technologies SRI SRI University Odisha Cuttack India School of Law KIIT Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9789819736898

the practice of purchasing necessary medical supplies and equipment, keeping important pharmaceuticals in storage, attending to medical requirements, and planning medical care for patients without organizing and integrating such actions has become widespread in the healthcare industry. the massive volumes of data required to build a unified, effective, efficient, and dynamic supply chain (hereafter SC) in today's multifaceted, connected world are challenging SC professionals to stay up with. As a result, techniques that can quickly and wisely analyze various enormous volumes of SC data are in high demand right now. One technique utilized to address the new industrial challenges is artificial intelligence (AI). By increasing productivity, lowering costs, and improving patient care, the incorporation of intelligent technology with supply chain management (hereafter SCM) systems of E-healthcare offers the potential to completely transform the healthcare sector. the present research aims to investigate the use of AI in managing supply chains for electronic healthcare focusing on the E-healthcare privacy and security issues. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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