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Voting Based Ensemble Framework For Crop Recommendation 7

Voting Based Ensemble Framework For Crop Recommendation

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Aryaman, Mayank Paliwal, Yashica Sharma, Aman Mohana, Rajni Jaypee University of Information Technology Computer Science And Engineering Department Waknaghat India Cse Jaypee University of Information Technology Waknaghat India Cse Amity School of Engineering Chandigarh India

ISBN: (纸本)9798350371406

Agriculture holds paramount importance for a significant portion of India's population, contributing significantly to the national economy. However, many Indian farmers rely on traditional senses rather than data-driven insights for crop selection, risking negative impacts on their mental and financial well-being. This article emphasizes the potential of Machine Learning models, utilizing a comprehensive dataset combining factors like rainfall, climate, humidity, and soil nutrients, to guide farmers in making informed decisions for optimal crop selection. To address the challenges faced by Indian farmers, the proposed methodology involves the application of Machine Learning models to a dataset encompassing crucial parameters such as sowing season, soil properties, climate, and location. By leveraging advanced algorithms, the models aim to recommend crops tailored to the farm's specific conditions, ensuring precision and accuracy in decision-making. This data-driven approach seeks to revolutionize traditional farming practices and enhance overall agricultural outcomes. The application of Machine Learning models on a comprehensive dataset demonstrated a robust recommendation system for crop selection, considering factors like soil health and weather conditions. This technology-driven approach empowers Indian farmers with informed decision-making, mitigating risks and fostering sustainable agricultural practices. © 2023 IEEE.

关键词： Crops

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Legal Considerations for Artificial Intelligence and Machine Learning 15

Legal Considerations for Artificial Intelligence and Machine...

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

作者： Bhatnagar, Gunjan Verma, Alpika Chakraborty, Nilanjan Suman Kumar, Ashwini Mittal, Aman Dev Bhoomi Uttarakhand University School of Engineering and Computer Uttarakhand 248007 India Invertis University Department of LAW Uttar Pradesh Bareilly243123 India Amity University Amity Law School Kolkata India K. R. Mangalam University School of Engineering & Technology Department of Computer Science & Engineering Haryana Gurugram122103 India ARKA JAIN University Department of Engineering and Information Technology Jharkahnd Jamshedpur India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

Artificial intelligence (AI) and gadgets gaining knowledge of (ML) have revolutionized organizations' overall performance, leading to accelerated performance and productivity. This era has become a fundamental part of our daily lives, from personal assistants to self-using vehicles. However, as AI and ML continue to improve and become more pervasive, it's crucial to undergo the prison implications that would stand up. One of the primary criminal considerations for AI and ML is record privacy and safety. With the sizeable series and processing of extensive records, the danger of fact breaches and privacy violations increases. Corporations should follow applicable legal suggestions and policies, the overall records protection law (GDPR), and the California Purchaser Privacy Act (CCPA). Another interest is a legal responsibility. As AI and ML become more impartial, questions arise about who is responsible for any damage or harm because of those structures. This trouble has become more complicated, even as a few events concern improving and deploying AI and ML technologies. © 2024 IEEE.

关键词： Answerable California Concern Implications Privateness Revolutionized

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Leveraging Deep Learning Architectures for Accurate Wind Speed and Power Prediction in Renewable Energy Systems 7

Leveraging Deep Learning Architectures for Accurate Wind Spe...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Alekhya, V. Anandhi, R.J. Jain, Alok Gupta, Navya Singh, S. Vikram Begum, Rowsonara Institute of Aeronautical Engineering Telangana Hyderabad50043 India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Law College Uttar Pradesh Greater Noida India Amity University Amity School of Engineering & Technology Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

The advanced deep learning models that are incorporated in this paper present strategies on the forecasting of Wind velocity and wind power. While metaheuristic strategies do not define the system and work to establish that the given inputs and outputs can be linked, deep learning models render a higher capacity to capture temporal and spatial dependencies in wind data. It is expected that through dealing with big data to recognize complex relations, the improvements in wind speed and power prediction would be immensely increased. Thus, this approach allows for more accurate and specific forecasting favoring the efficient functioning of wind energy and its incorporation into the electricity grid. The performance of these deep learning models is tested through New Delhi, India specific case incrementally showing that these models are very computationally efficient and have better prediction capabilities. © 2024 IEEE.

关键词： Wind forecasting

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A Novel Approach to Detect Face Mask in Real Time 3rd

A Novel Approach to Detect Face Mask in Real Time

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3rd International Conference on Artificial Intelligence and Speech technology, AIST 2021

作者： Gupta, Sumita Majumdar, Rana Deswal, Shivam Department of CSE Amity School of Engineering and Technology Amity University Noida India School of Computer Science and Engineering Swami Vivekananda University Kolkata India

ISBN: (纸本)9783030957100

COVID-19, a deadly virus outbreak in the entire world, infected countless number of people and leads to death of millions of people. Economy of countries halted, people are stuck up, the situation is becoming worse day by day that no one expected. The COVID-19 can be spread through airborne droplets, aerosols and other carriers. So, the guidelines are released which promotes 3 key points for its prevention: (a) maintain social distancing, (b) sanitization, and (c) most important, wearing of mask in public places. But unfortunately, people are avoiding these measures leading to spread of the disease. So, there is need of some sort of security guards on ground that ensures people would strictly follow the guidelines. But, it’s too risky for the lives of security guards. Fortunately, we live in 21st century. There are so powerful technologies like Machine Learning, Artificial Intelligence, Deep Learning and many more. So, it is possible to develop a way where machines will help us to ensure the guidelines to be followed. There is no need for a physical person to watch over crowd. This research paper proposed a work to implement machine-learning algorithm to ensure people wear the proper face mask or not. It can be used in public places like airports, railway stations or at main gate of societies to ensure that no one without mask may enter the society and similarly can be used at stores. © 2022, Springer Nature Switzerland AG.

关键词： Face recognition

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Ensembled Multi-detector Aggregation For Disaster detection (EMAD) 13

Ensembled Multi-detector Aggregation For Disaster detection ...

引用

13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Gulati, Nancy Agarwal, Ajay Aggarwal, Apeksha Bhutani, Nikhil Kapur, Ritvik Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Noida India Kiet Group of Institutions Department of Information Technology Uttar Pradesh Ghaziabad India Indian Institute of Technology Department of Computer Science and Engineering Uttarakhand Roorkee India Maharaja Surajmal Institute of Technology Affiliated to Ggsipu Department of Computer Science and Engineering Delhi India Maharaja Surajmal Institute of Technology Affiliated to Ggsipu Department of Information Technology Delhi India

ISBN: (纸本)9781665462631

The most popular method for analysing a text is sentiment analysis. It is quite helpful for social media monitoring since it enables us to get a broad sense of what the general population thinks about particular issues. However, it is also handy for business analysis and various other situations in which the text needs to be analyzed. In this paper we present a solution up to 90% correctly classify whether a tweet is associated with a disaster. Using a multi-detector ensembled approach, we create a reliable model that can predict accurately in situations powered by multiple CNNs and BERT detectors. © 2023 IEEE.

关键词： Disasters

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Sentiment Analysis Using Machine Learning Methods

Sentiment Analysis Using Machine Learning Methods

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Badholia, Abhishek Diwan, Tarun Dhar Narooka, Preeti Khatkale, Pravin B Vishnoi, Ankit Kaushik, Keshav Shri Shankaracharya Institute of Professional Management and Technology Department of Data Science Raipur India Atal Bihari Vajpayee University Bilaspur India Manipal University Artificial Intelligence and Machine Learning School of Computer and Engineering Jaipur India Sanjivani University Maharashtra Kopargaon India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Amity University Punjab Amity School of Engineering and Technology Mohali India

ISBN: (纸本)9798350390049

Machine learning (ML) will be utilized to evaluate sentiment analysis in this project. This study aims to do this. Sentiment analysis is a popular natural language processing area. This is a natural language processing subfield. This section identifies textual senses. Due to the growing availability of digital text across platforms, sentiment analysis is now vital for many applications. This is because sentiment analysis has become essential. This technology might be used for market research, political analysis, and consumer feedback. A few instances. This study analyzes several machine learning methods, to name a few. This category includes SVM, Naive Bayes, Random Forest, and RNNs. To clarify, several datasets are used to assess these methodologies. To get more information. Preprocessing and feature engineering are crucial to sentiment analysis model construction, as this paper shows. The research emphasises these two phases. In addition, it provides insights into how these approaches are used in constantly changing digital situations. © 2024 IEEE.

关键词： Adversarial machine learning

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Fault Tolerance and Error Handling Techniques in Apache Kafka 23

Fault Tolerance and Error Handling Techniques in Apache Kafk...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Shubham Vyas Rajesh Kumar Tyagi Shashank Sahu Information Technology Amity Institute of Information Technology India Computer Science and Engineering Amity School of Engineering and Technology Amity University Haryana India Computer Science and Engineering Ajay Kumar Garg Engineering College India

ISBN: (纸本)9798400709418

Real Time streaming framework enables the availability of data for users in near real time. For achieving the high throughput, low latency and to avoid loss of any events at runtime, real time streaming frameworks has their own methods to accomplish this task of message handling, but there are scenarios when due to huge volume of data at the source or due to other environment or Infrastructure related issues, some of the critical data might get lost in the process. This article evaluates methods to handle data rejections in conjunction with a fault-tolerant error handling recommendations for real time data streaming. There are many frameworks available for streaming the events in real time, in this article Apache Kafka is used to examine most frequent situations when an error can occur. This article will evaluate the key performance factors in terms of capability of the framework to recover when downtime occurs. Apache Kafka [15] is a distributed real time data streaming platform; it was developed by Linked inn and currently used by many applications across the industry. It works in producer consumer fashion and with correct configuration and set up, high Throughput with low latency can be achieved. This article will explain the configurations provided with the framework that can be utilized to design and develop optimized event streaming applications. This article is explaining the different parameters that can be used to design and develop the stream processing applications and what are the available options to handle the errors during the streaming to avoid any negative impact to the application.

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Comparative Analysis of Cloud Computing Based Face Recognition Services

Comparative Analysis of Cloud Computing Based Face Recogniti...

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Jha, Soumya Arora, Manya Sharma, Yashica Anand, Ashish Sharma, Dolly Amity University Amity School of Engineering and Technology Uttar Pradesh Noida India Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

With growing crime rate around the world, it has become essential to have security surveillance systems that uses evolved mix of advanced technologies to provide a robust and a holistic approach to mitigating security risks. On the other hand, there have been rapid advancement in technologies which have led to the progress of security awareness and embedded system to cope up with the higher demand of the security surveillance. With immense variety of systems available nowadays, this paper aims to compare the various existing facial recognition cloud services used for real time security and surveillance with main focus on cloud-based architecture. © 2022 IEEE.

关键词： Face recognition

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Commodity Price Prediction for making informed Decisions while trading using Long Short-Term Memory (LSTM) Algorithm 5

Commodity Price Prediction for making informed Decisions whi...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Suman, Shashikant Kaushik, Prakarsh Challapalli, Sai Sri Nandan Lohani, Bhanu Prakash Kushwaha, Pradeep Gupta, Amar Deep Amity School of Engineering and Technology Dept. of Computer Science Greater Noida India

ISBN: (纸本)9798350398267

Commodity markets are physical or virtual marketplaces where market players meet to buy or sell positions in commodities such as crude oil, gold, copper, silver, cotton, and wheat. People invest their hard-earned money based on some predictions to gain some profit from commodity market. Although, traditional methods such as technical analysis & fundamental analysis are very popular among traders, they are not as accurate as analysis by long short-term memory (LSTM) algorithm. In this paper, we have developed a model of well-known efficient LSTM algorithm to predict the commodity market price by utilizing a freely accessible dataset for commodity markets having open, high, low, and closing prices from historical data. © 2022 IEEE.

关键词： Long short-term memory

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Machine Learning Classification of Shoulder Pain Pathologies: A Scoping Review

Machine Learning Classification of Shoulder Pain Pathologies...

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International Conference on Computing and Networking technology (ICCNT)

作者： Garima Shukla Khushi Bhagat Vedant Chaudhari E Navaneeth Krishnan Mansi Chauhan Bhadauria Yash Chouksey Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Mumbai Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Shoulder problem is a widely known musculoskeletal disorder that impacts heavily on the life of patients. Effective treatment for this must be preceded by accurate and timely diagnosis but traditional diagnostic techniques have always been subjective and less reliable. In recent years, machine learning (ML) techniques have shown promising results in improving the accuracy of diagnosing shoulder pain, addressing the limitations of traditional diagnostic methods. Nonetheless, there is a failure to translate these ML models into practical clinical tools that can easily be employed by healthcare professionals. In addition, this paper will present an overview of current understanding and methodologies employed for diagnosing and managing shoulder pain along with reviewing the use of ML techniques in medical image diagnostics. It also suggests development of a user-friendly and accessible diagnosis tool for shoulder pathologies aimed at linking modern research and daily practice in the medical field.

关键词： Pathology Accuracy Pain Reviews Shoulder Standardization Medical services Feature extraction Data models Medical diagnostic imaging

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