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检索条件"任意字段=3rd International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2021"

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machine learning Methods for Crop Yield Prediction Across Diverse Agricultural Environments 3rd

Machine Learning Methods for Crop Yield Prediction Across Di...

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3rd international conference on Intelligent Systems and applications, ICISA 2024

作者： Khan, Atiya Patil, C.H. Vibhute, Amol D. Mali, Shankar School of Computer Science Dr. Vishwanath Karad MIT World Peace University Maharashtra Pune India Maharashtra Pune411016 India

ISBN: (纸本)9789819798384

machine learning technologies have transformed decision-making and data-processing methods, resulting in global economic development. This study investigates the possible benefits of incorporating machine learning techniques into modern agricultural systems. This paper provides a summary of the latest machine learning utilization in agriculture today, outlining both major challenges and opportunities. Forecasting crop yields is dependent on a wide range of interdependent elements, such as crop sowing and harvesting season, soil, temperature, humidity, rainfall, and management practices. The current research examines the performance of machine learning approaches for predicting crop output using a huge dataset of weather conditions, soil, season, area, and crop production parameters collected from 33 Indian states between 1966 and 2017. From the result analysis, it is inferred that the CatBoost regressor combined with SHAP and CatBoost Regressor models’ performance was slightly poor for training dataset. According to the results of the other models, the Random Forest Regressor, out of the six machine learning models, had the highest accuracy during the training phase. During the validation stage, models such as CatBoost Regressor with an average R2 values varied between 0.967 and 0.866, while CatBoost Regressor + SHAP, where averaged R2 value ranged from 0.934 to 0.9171, produced the most accurate findings. While SVR performed better in the training phase, RF yielded more accurate results in other combinations. The RMSE is reduced to average yield with optimal crop yield and production data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Reviewing the Role of Blockchain Enhanced Smart Contract Security Framework in Healthcare 3

Reviewing the Role of Blockchain Enhanced Smart Contract Sec...

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3rd IEEE international conference on Industrial Electronics: Developments and applications, ICIDeA 2025

作者： Gupta, Swati Misra, Dinesh Chandra Dr. K.N Modi University Dept. of Computer Science and Engineering Rajasthan Newai India

ISBN: (纸本)9798331533205

Blockchain technology, implemented via smart contracts, provides significant improvements in data security, transparency, and automation within healthcare applications. Smart contracts are inherently unsuitable for critical healthcare settings. This is due to vulnerabilities such as reentrancy attacks, overflow concerns, and unauthorized access to sensitive data. This study introduces the Blockchain Enhanced Smart Contract Security Framework (BESCSF) to enhance the security of smart contracts in healthcare applications. Present research is making use of hybrid security architecture. Such architecture incorporates sophisticated cryptographic methods, secure coding standards, and real-time monitoring. The suggested architecture has an adaptive hybrid approach. This approach uses machine learning to predict potential vulnerabilities. Present work prioritizes resource allocation for auditing and safeguarding smart contracts. This paradigm is capable to enhance the efficacy and precision of security measures. Present work evaluates the framework's performance, contract execution accuracy along with ability to reduce security breaches. It is performed via a series of tests and simulations. The hybrid method significantly enhances vulnerability detection accuracy by 15%. Moreover present research reduces execution time by 20% compared to traditional security solutions. Healthcare applications are considered in preset research papers that are capable to prioritizing security, efficiency, and regulatory compliance. Research work considers medical supply chain monitoring, patient data management, and insurance claims. Present work would find the method ideal. Thus, it also enhances trust and scalability. © 2025 IEEE.

关键词： Information management

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Ensemble-based Deep learning Framework for Tuberculosis Detection in Radiographs 3

Ensemble-based Deep Learning Framework for Tuberculosis Dete...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shameem Sulthana, S.H. Rathiya, R. Sarathy, J. Partha Kishore, Gudipudi Srimathi, S. Jegajothi, B. Srm Institute of Science and Technology Department of Computer Science and Applications Vadapalani Chennai India Dr. N. G. P Institute of Technology Department of Information Technology Coimbatore India Jayalakshmi Institute Of Technology Thoppur Dharmapuri India University College of Arts and Science Krishna University Department: Biosciences and Biotechnology Machilipatnam521004 India Saveetha School of Engineering Smats Department of Bio Technology Chennai India Srs Tech Solutions Chennai India

ISBN: (纸本)9798331509675

Tuberculosis (TB) continues to be a serious worldwide health concern, and in order to effectively treat it, it is necessary to diagnose it accurately and in a timely manner. The purpose of this research is to provide a robust framework for tuberculosis diagnosis that makes use of a deep learning-based model called DenseNet201 in conjunction with a stacking ensemble method in order to improve diagnostic accuracy from chest X-ray pictures. Deep features are extracted in an effective manner by the DenseNet201 architecture, which captures intricate patterns that are suggestive of tuberculosis. Support Vector machine (SVM), Random Forest (RF), and XGBoost are the three base classifiers that receive these features as input. The outputs of these classifiers are then aggregated with a meta-classifier to get the final prediction. This helps to further improve the performance of the prediction. This stacking strategy helps to minimize the flaws of individual classifiers, hence minimizing the number of false positives and negatives seen in medical diagnostics, which is an extremely important skill. A high accuracy of 95.3% and an area under the curve (AUC) of 0.973 are demonstrated by the model after it has been trained and assessed on the TBX11K dataset. This greatly outperforms the performance of older approaches. In order to increase the quality of the data and better the performance of the model, preprocessing processes were used. These stages included normalization, data augmentation, and contrast enhancement. Due to the fact that the suggested ensemble model generalizes well to data that has not been encountered before, it is a trustworthy and effective instrument for early and accurate tuberculosis screening. This method has the potential to be implemented in clinical settings that are representative of the real world in order to provide assistance to medical professionals in the diagnosis of tuberculosis. © 2025 IEEE.

关键词： Support vector machines

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Using the Internet of Everything for machine-learning-Based Computer System Design and Optimization 3rd

Using the Internet of Everything for Machine-Learning-Based ...

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3rd international conference on Internet of Everything, IoECon 2024

作者： Feng, Shaojun Zhong, Jiaqing Chen, Juan College of Computer Science and Technology National University of Defense Technology Hunan410073 China

ISBN: (纸本)9783031844256

machine learning (ML) has been widely used in computer system development and optimization levels, boosting computer design and optimization improvement. With the increase of computer system design complexity and the boost of the demand for software and application optimization, the difficulties of computer system problem solving are increasing, stimulating the development of ML methods. ML models represent computer system design and development approaches;combining these with the Internet of Everything (IoE) cannot be ignored. For ML models, all kinds of computer system sample data are the fundamental prerequisite for quantifying the computer system characteristics;the connection (internet) of all computer devices is critical for predicting computer system behaviors. The IoE enables more computer devices and ML models to be connected, extending how computer systems can be well developed. The IoE will affect the applications between computer system development and various ML models more deeply by connecting people, data, and machines. This work provides an overview of the history of the IoE and ML-based computer system development. It shows the interaction between ML models and computer system design and optimization. It also emphasizes the people-to-people (P2P), people-to-machine (P2M), and machine-to-machine (M2M) in the ML-based computer system design and optimization. It discusses the interpretability of ML-based computer system development. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Application programs

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Combining Ensemble learning and Multi–view Feature Extraction for Protein–protein Interaction Prediction 3rd

Combining Ensemble Learning and Multi–view Feature Extracti...

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3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Hoai-Nhan, Tran Quynh, Nguyen-Phuc-Xuan Thu-Sang, Vo-Ho Lan-Anh, Nguyen-Thi School of Computer Science and Engineering Central South University Hunan Changsha410083 China Hue University of Education Hue University 34 Le Loi Hue49100 Viet Nam

ISBN: (纸本)9783031809422

Understanding protein-protein interactions (PPIs) helps identify protein functions and develops other important applications such as drug preparation and protein-disease relationship identification. machine learning-based approaches are being deeply researched for PPI determination to reduce the cost and time of previous testing methods. In this work, we introduce a novel ensemble learning model to predict PPIs from only protein sequences. First, the protein sequence data is converted into raw features. Then, these raw features are segmented into equal subsets before being extracted by multiple deep neural networks into local features. Concurrently, Autoencoders are used to convert the raw features into global features. Finally, these local and global features are concatenated and fed to multi-extreme gradient boosting machines to predict the interaction probability. We evaluated the proposed model using the new PPI dataset derived from the STRING database and the benchmark Human dataset. data and source code are provided via https://***/nhanth/***. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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A Review on the Use of Smart Agriculture Systems to Protect Crops 3

A Review on the Use of Smart Agriculture Systems to Protect ...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, Gulshan Gourshettiwar, Palash Suman, Saurabh Kumar, Shravan Kumar, Dhiraj Raushan, Rahul Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331509675

Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. Intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. Control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advanced technologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started

关键词： Smart agriculture

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Auction-Based Optimization with machine learning for Cyber Threat Detection and Classification Model 3rd

Auction-Based Optimization with Machine Learning for Cyber T...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Alqahtani, Hamed Department of Information Systems College of Computer Science Center of Artificial Intelligence Unit of Cybersecurity King Khalid University Abha Saudi Arabia

ISBN: (纸本)9789819726707

The detection of cyber threats and cybersecurity can be benefitted greatly from the advances in artificial intelligence (AI). Cyber threat detection is the main focus of cybersecurity. It has used various tools and techniques to find unauthorized access to systems, attacks, effective security incidents or applications, and networks. Owing to data flow presence over networks and extensive devices, the possibility of intrusion detection and cyberattacks has increased. Monitorization of this immense volume of data traffic is found to be tough, though machine learning (ML) techniques effectively support this task. This study develops an auction-based optimization with machine learning for cyber threat detection and classification (ABOML-CTDC) technique. The presented ABOML-CTDC technique concentrates on the identification and classification of cyber threats in accomplishing cybersecurity. The proposed ABOML-CTDC technique makes use of the ABO-based feature subset selection (ABO-FSS) technique to elect optimal features. For cyber threat detection, the ABOML-CTDC technique utilizes a variational autoencoder (VAE) model. Finally, the moth flame optimization (MFO) algorithm is exploited for the parameter selection of the VAE method del to improve the detection rate. The experimental validation of the ABOML-CTDC approach is tested on benchmark datasets and the results pointed out the promising results on detecting threats over recent state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Beyond the Blink: IoT's Next-Gen Drowsiness Detection System for Drivers 3

Beyond the Blink: IoT's Next-Gen Drowsiness Detection System...

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3rd IEEE international conference on Industrial Electronics: Developments and applications, ICIDeA 2025

作者： Routray, Sidheswar Patel, Nachiket Patel, Dhruv Srivastava, Jyoti Dash, Rudra Narayan Pandit Deendayal Energy University Dept. of Computer Science & Engg. Gandhinagar India Indrashil University Dept. of Computer Science & Engg. Mehsana India Ganpat University Dept. of Computer Engineering Mehsana India School of Electrical Engineering KIIT University Bhubaneswar India

ISBN: (纸本)9798331533205

Drowsiness during driving includes high risks for the driver, the co-passengers, and the other people on the road. Advance Driver Assistance Systems (ADAS) have been proposed to reduce these risks. This work introduces a new Internet of Things (IoT) seizure detection system to improve driver safety using Raspberry Pi, which is equipped with various sensors and utilizes machine learning for real-time monitoring and analysis. Using sensor fusion technology, including monitoring yawning and eye-closing patterns, the system aims to detect early signs of driver sleepiness. Being a low-cost, versatile, and tiny device, it becomes an ideal core processor for integration into an in-car environment. Additionally, the simplicity enables seamless communication with various sensors and facilitates effective data collection. Extensive tests and simulations were conducted to evaluate the effectiveness and reliability of the proposed system for real-world driving situations. The result outcomes prove the system's accuracy and efficiency in detecting early signs of driver fatigue to enhance driver safety. The integration of Raspberry Pi and sensor fusion technology makes it economical as well as scalable approaches supporting multiple platforms. © 2025 IEEE.

关键词： Advanced driver assistance systems

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Automated Number Plate Detection and Recognition Using machine learning Techniques 3rd

Automated Number Plate Detection and Recognition Using Machi...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Shuva, Taslima Ferdaus Hasan, Nahid Rahman, Md. Tanvir Khan, Risala Tasin Daffodil International University Dhaka Bangladesh Mawlana Bhashani Science and Technology University Tangail Bangladesh Jahangirnagar University Savar Bangladesh

ISBN: (纸本)9789819739363

The official collection of numbers and letters shown on the front and rear of a road vehicle is called a license plate. License plate recognition is crucial for many applications, including traffic monitoring, vehicle tracking, and law enforcement. Automatic License Plate Recognition (ALPR) is one of the applications that benefited tremendously from convolutional neural network (CNN) processing, which is now the de facto processing method for complex data. This paper offers a comprehensive analysis and comparison of deep learning-based approaches for identifying license plates. The study also looks a dataset from Kaggle, pre-process the dataset for removing noise and artifact, utilized to train these models into test, train, and validation dataset. Additionally, the study examines the difficulties and prospective possibilities in deep learning-based license plate recognition, highlighting the potential uses of cutting-edge innovations such object detection using attention mechanisms and graph convolutional networks. In this study we utilized six transfer learning model which is VGG16, VGG19, DenseNet121, AlexNet, EfficientNet, and YOLOv4 where DenseNet121 provide the best or highest accuracy 98.66%. The goal of the study is to guide researchers and industry professionals toward powerful license plate detecting systems by employing deep learning approaches by way of this evaluation and analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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AI Modeling and Water Quality Sensing Technique Proffers Water Security: An Open Review 3

AI Modeling and Water Quality Sensing Technique Proffers Wat...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Wilfred Blessing, N.R. Palarimath, Suresh Gunasekaran, Hemalatha Haidar, Sk Wasim Sutherlin Subitha, G. Sarmila, J. University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Stella Mary's College of Engineering Department of Computer Science and Engineering Kanyakumari District India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Chunkankadai India

ISBN: (纸本)9798331509675

Water Quality Sensors (WQSs) are becoming a promised tool in water quality data assessment and scientific value of aquatic structure. Such sensors are broadly used to produce live results by evaluating major water quality parameters and components. Artificial intelligence (AI) based WQSs and machine learning (ML) algorithms primarily enable long term operations for real time detention of water quality information at different time periods. Messaging services, alarms, alerts and other proper warnings are given to water quality managers or assessing communities while the quality deterioration occurs at the particular water quality checking sites. A good number of impressive challenges are identified in hosting the variety of sensors to manage, warehouse and analyze incessant water quality information that they generate periodically. This paper places interests in highlighting lack of suitable assessment standards and determine logistical deliberations for fruitful implementations together with transportation, storage services and appropriate trustworthy communication. The main objective is to identify the designs targeting water security that support to monitor, reassure and protect the water resources all the way through uninterrupted quality supervision in addition to AI features. This open review also discusses a variety of applications in water quality sensors, AI modeling, electronic tongues and indices, hence results the knowledge of improvement required for upgrading the quality in water resources through computer technologies and models. © 2025 IEEE.

关键词： Electronic tongues

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