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A proposed framework for crop yield prediction using hybrid feature selection approach and optimized machine learning

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Neural Computing and Applications 2024年第33期36卷 20723-20750页

作者： Abdel-salam, Mahmoud Kumar, Neeraj Mahajan, Shubham Faculty of Computer and Information Science Mansoura University Mansoura Egypt Department of CSE Thapar Institute of Engineering and Technology Deemed to Be University Patiala147004 India Delhi-NCR India

Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. © The Author(s) 2024.

关键词： Support vector regression

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Virtual Drive: Immersive VR-Controlled Car with Real-Time Interaction and Live Video Feed

Virtual Drive: Immersive VR-Controlled Car with Real-Time In...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Jain, Shweta Gandhi, Anju Bhandari Mehla, Stuti Aggarwal, Rishit Kwatra, Tanu Panipat Institute Of Engineering And Technology Department Of Artificial Intelligence And Machine Learning Panipat India Panipat Institute Of Engineering And Technology Department Of Computer Science And Engineering Panipat India Infosys Limited Pune India D.A.V Centenary College Department Of Computer Science And Applications Faridabad India

ISBN: (纸本)9798350306446

This project investigates the creation and application of a revolutionary virtual drive experience that blends immersive virtual reality (VR) technology with a real-world automotive simulator. The technology promises to deliver an unrivaled driving experience by allowing users to manage a virtual car in real time and view live video feeds from cameras mounted on the real car. Motion controllers, an excellent VR headset with motion tracking, and a customizable driving simulator with haptic feedback are examples of hardware components. The virtual experience is improved by the cameras and sensors in the actual automobile, which capture ambient data and real-time video feeds. Software development includes both realistic virtual reality surroundings and a sophisticated control mechanism that translates human motions into virtual activity. Real-time interaction features and the incorporation of live video streams boost user engagement. Network connectivity ensures smooth interaction and video streaming. Easy-to-use interfaces facilitate information access and control. Several tests and optimizations ensure performance and safety. The deployment process includes continuing adjustments based on user feedback and integration with VR platforms. The process of creating a unique virtual drive experience is described in this paper, along with examples of hardware-software integration that open up new possibilities for VR-controlled applications in the future. © 2024 IEEE.

关键词： Virtual reality

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An Organized Review of Machine Learning (ML) Perspectives in Manufacturing and Quality Control Processes

An Organized Review of Machine Learning (ML) Perspectives in...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Batra, Raman Mittal, Gaurav Saha, Ananya Noida Institute of Engineering and Technology Department of Mechanical Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350357769

The current era is governed by and Artificial Intelligence (AI) and Machine Learning (ML) techniques in almost every sphere of life. Some of the applications involve healthcare, manufacturing, networking, decision support systems, disease diagnosis systems and many more. Therefore, the given paper presents an extensive today's submission analysis of ML procedures in the atmosphere of industry. The Consumption of ML linked procedures for production method arrangement and regulator, prognostic repairs, regimentation robotics, regulator and optimization, assistance and studying systems aimed at factory Analyses to be conducted on employees, this aims to develop the abilities of employees and specialists in the plant within the field of machine learning (ML). Also, the study analyses of most current advancements in the field during 2017-22 emphasizing practical application and explicitly specifying the mechanism learning algorithms employed within a manufacturing setting for each piece of literature. Lastly, this section provides a synopsis of ML lineup concepts within the context of production processes and identifies investigation slits in this area. © 2023 IEEE.

关键词： factory operations Machine learning optimization process control

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Determination of Hemoglobin Percentage Through K-Means Algorithm and Image Processing for White Blood Cell Diagnosis 4

Determination of Hemoglobin Percentage Through K-Means Algor...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Renukalatha, S. Kumar, J. Senthil Sangeetha, A. Hema, M. Kumar, Ashok Rawat, Ramesh Singh Sri Siddhartha Institute of Technology Department of Computer Science and Engineering Tumkur572105 India KIT-Kalaignar Karunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore641402 India R.M.K College of Engineering and Technology Department of Computer Science and Engineering Thiruvallur601206 India Kpr Institute of Engineering and Technology Department of English Coimbatore641407 India Banasthali Vidyapith Department of Computer Science Rajasthan 304022 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India

ISBN: (纸本)9798350300888

White blood cell (WBC) diagnosis is often carried out by physicians manually, using a microscope to examine blood smears for the presence of WBCs. Due to the fact that it is a time-consuming, difficult, and prone to mistake procedure, an automated approach using a computerized system is ideal. The most critical steps of this automated procedure are the segmentation and categorization of WBCs. This work proposes an automated segmentation approach for microscopic WBC images, with a particular emphasis on images obtained from fresh blood smears. It is recommended that the segmentation be carried out utilising an integration of numerous digital image processing (DIP) techniques, which are implemented in the segmentation process. Sixty microscopic blood images were evaluated, and the suggested approach achieved a cytoplasm segmentation accuracy of 93 percent and a nucleus segmentation accuracy of 89.8 percent, respectively, on the images. © 2023 IEEE.

关键词： Blood

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Optimizing Database Systems for Parallel Processing in Multi-core Environments 6th

Optimizing Database Systems for Parallel Processing in Multi...

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6th International Conference on Information Systems and Management Science, ISMS 2023

作者： Mallegowda, M. Alex, Sini Anna Srivastava, Sajal Singh, Shashank kanavalli, Anita Department of Computer Science and Engineering Ramaiah Institute of Technology Karnataka Bangalore560054 India Ramaiah Institute of Technology Karnataka Bangalore560054 India

ISBN: (纸本)9783031707889

In contemporary times, parallel database systems have gained widespread acceptance, finding applications in both online transaction processing (OLTP) servers and online analytical processing (OLAP) servers. These advancements encompass the realms of parallel database processing and querying. A pivotal technique in enhancing the efficiency of parallel database systems, particularly in the context of data warehousing, is parallel query optimization. When presented with an SQL query, parallel query optimization diligently explores parallel plans to identify the one that yields the query result most swiftly. The focal point of this research article revolves around introducing an innovative strategy for parallelizing the execution of multithreaded queries in relational databases. The objective is to optimize the utilization of CPU and memory resources to their fullest potential. This approach adeptly addresses the challenge of reducing response time for concurrent database queries, thereby significantly enhancing their efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Structured Query Language

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A Fusion Framework of Pre-trained Deep Learning Models for Oral Squamous Cell Carcinoma Classification 3rd

A Fusion Framework of Pre-trained Deep Learning Models for O...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Khan, Muhammad Attique Mir, Momina Ullah, Muhammad Sami Hamza, Ameer Jabeen, Kiran Gupta, Deepak Department of Computer Science HITEC University Taxila47080 Pakistan Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Arunachal Pradesh Jote710003 India

ISBN: (纸本)9789819708918

One of the most serious illnesses in the world is oral cancer, and prompt, effective treatment can significantly increase patient survival and cure rates. The manual diagnosis from Histopathologic Images (HI) is time-consuming and requires an expert doctor. To reduce the burden on a doctor, a computerized technique is helpful for the classification of medical image analysis. In this work, we proposed a deep learning framework for classifying Oral Squamous Cell Carcinoma (OSCC) from HI. Two pre-trained deep learning architectures, MobileNet-V2 and DarkNet-19, have been fine-tuned in the proposed framework. Both architectures have been selected based on recent performance and fewer parameters. Both models were trained using the transfer learning concept and extracted features from the global average pooling layers. A Chaotic Crow Search optimization algorithm has been employed, and the best features have been selected. The selected features are finally classified using machine learning classifiers. A publicly available dataset was utilized for experimental purposes and obtained the highest accuracy of 92%. Compared with some state-of-the-art techniques, the proposed framework shows improved accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Security Attacks and Key Challenges in Blockchain technology: A Survey 4th

Security Attacks and Key Challenges in Blockchain Technology...

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4th International Conference on Advances in Information Communication technology and Computing, AICTC 2022

作者： Patel, Rima Patel, Dharmendra Department of Computer Science & Engineering Faculty of Technology & Engineering Devang Patel Institute of Advance Technology and Research CHARUSAT Changa India Department of Computer Application Smt. Chandaben Mohanbhai Patel Institute of Computer Applications CHARUSAT Changa India

ISBN: (纸本)9789811998874

A blockchain is decentralized, distributed and public ledger, which is used to record the transaction across the number of clusters of computers. The record cannot be altered without an alternation in subsequent blocks. In the world of IT, the term Blockchain is not obscure. Despite tremendous applications in the field of information technology, finance and other industries, there are some challenges with Blockchain technology that need to be taken into consideration. In this paper, we have discussed the key challenges and issues with Blockchain technology. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Biomass Fuel Classification from Agricultural Residues Using Machine Learning 12

Biomass Fuel Classification from Agricultural Residues Using...

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12th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2024

作者： Pachauri, Nikhil Gupta, Himanshu Verma, Om Prakash Kumar Singh, Sushil Verma, Deepak Rn, Ravikumar Manipal Academy of Higher Education Department of Mechatronics Manipal Institute of Technology Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Instrumentation and Control Engineering Karnataka Manipal India Dr B R Ambedkar National Institute of Technology Jalandhar Department of Instrumentation and Control Engineering Jalandhar India Marwadi University Department of Computer Engineering of Computer Engineering Gujarat Rajkot India Marwadi University Dept. of Computer Engineering Gujarat Rajkot India

ISBN: (纸本)9798350340204

This work incorporates an adaptive learning-based boosting (ADboost) ML classifier to classify four types of fuel: agricultural residue, coals, wood, and produced biomass. Further, the ADboost's hyperparameters, such as learning rate, maximum number of splits, and minimum leaf size are adjusted using teaching learning-based optimization (TLBO), resulting in TADboost. The performance of TADboost is compared against various popular ML (NN, BAG, NB, and SVM) models. Simulated result reveals that the suggested classifiers outperform other compared ML classifiers for fuel classification with the classification accuracy, precision, recall, F1-score, and kappa as 0.9659, 0.9671, 0.9449, 0.9558, and 0.9482, respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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An authenticated spectrum acquirement and transmission of encrypted images over fusion centre

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Multimedia Tools and Applications 2024年 1-24页

作者： V, Thanikaiselvan Y, PiyaDessi S, Renuga devi Shanthini, S. Suba Mahalingam, Hemalatha S, Aashiq Banu R, Dhivya V.M, Padmapriya Amirtharajan, Rengarajan School of Electronics Engineering Vellore Institute of Technology Vellore Vellore India School of Information Technology Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah22254 Saudi Arabia System Engineering Department Military Technological College Muscat Oman Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi626005 India Department of Computer Sciences Marquette University MilwaukeeWI53233 United States School of Electrical & amp Electronics Engineering SASTRA Deemed University 613 401 Thanjavur India

These days, spectrum insufficiency is the most common issue. If the number of smartphone users who access the radio spectrum grows, the radio spectrum becomes increasingly scarce. Certain types of technology equipment are designed to work only within a specific spectrum range, and there is rivalry in specifications for various other applications. As a result, this paper effectively exploits a licensed user’s unused spectrum to reduce the crowded spectrum and eradicate the Radio spectrum shortage. Licensed primary users (PUs) and secondary users (SUs) would be accepted into a Fusion Centre (FC) based on a spectrum framework with authentication. The approved PUs begin by updating their information to FC1;thenceforth, encryption is performed on the information uploaded to FC1. This information from FC1 is stored in Cloud Storage after encryption using the Blowfish algorithm & De-oxy DNA, a fast encryption algorithm. Following that, FC2, connected to the same cloud environment, downloads the PU data uploaded. The spectrum is then allocated to the authenticated SU linked to FC2 based on signal conditions and priority. This proposed method also avoids any delay in the network and encryption by acquiring a fast encryption algorithm, making the system faster and avoiding the Primary User Emulation (PUE) attack. Later, the time taken for the Tiny Encryption Algorithm (TEA) and the Blowfish/DNA algorithm are compared to validate the run time for different input images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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A Real-Time Road Crash Prediction Model by Hybridizing Multiple Learning Classifiers 4th

A Real-Time Road Crash Prediction Model by Hybridizing Multi...

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4th International Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Arun, G. Anuguraju, K. Sangeetha, A. Babu, K. Trichy India Department of Computer Science and Engineering Gnanamani College of Technology Pachal Namakkal India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9789819914784

The majority of critical injuries caused by road accidents occur in developing nations, where they are a major global concern. Due to these traffic incidents, many individuals have lost loved ones. Consequently, a system that has the potential to save lives is needed. The approach identifies key contributing factors for accidents or establishes a connection between accidents and different causes of accidents. This study suggests a system for predicting accidents that can assist in analyzing potential safety concerns and foretelling whether an event will happen or not. In order to identify key qualities and forecast accident risk in single- and multiple-crashes, this research provides a hybrid feature selection-based classification-based machine learning (ML) method. It took into account classifiers like the random forest, k-nearest neighbor, and XGBoost. A few evaluation criteria, including precision, root mean square error, accuracy, recalls, and receivers operating output characteristics, were used to assess these classifiers. When the accuracy of the ML algorithms was compared, the accuracy of the proposed model attained as 99.78%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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