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检索条件"主题词=Machine Learning Algorithms"

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A novel Approach for optimizing the crop yield by considering climatic and soil features through fusion of machine learning and Deep learning algorithms

A novel Approach for optimizing the crop yield by considerin...

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International Conference on Computing and Networking Technology (ICCNT)

作者： K. Suganthavalli C. Meenakshi K Parvathy Department of Computer Science Vels Institute of Science Technology and Advanced Studies(VISTAS) Pallavaram Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The agricultural sector is the scientific and artistic cultivation of plants and animals. Agriculture has an important role in India’s economy, which covers a significant 60.45% of the country’s land and stands as the world’s second-largest agricultural sector, driving economic growth through its diverse produce and associated industries. Soil composition really matters for growing stuff. Elements like Nitrogen, Phosphorous, & Potassium play a big role. The way you rotate crops, soil moisture, and even temperature all affect how well things grow. Rainfall is important too. Research has shown that using machine learning can help figure out the best crops to grow. They utilize methods that include Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) for this. The study also looks into deep learning methods to make the model even better. In addition to predicting crop yields, it also provides detailed information on the required amounts of soil elements and their associated costs. The new model offers superior precision compared to the current one. The system examines the provided data and assists farmers in forecasting a crop, which subsequently aids in achieving financial gains. The prediction of a suitable yield takes into account the meteorological and soil parameters of the area. This Python-based system utilizes advanced algorithms to optimize decision-making, forecasting the most lucrative outcomes in various situations while streamlining costs and boosting productivity. The study employs Support Vector machines (SVM) as an example of machine learning (ML) method, while Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) are used as Deep learning (DL) techniques.

关键词： Deep learning Support vector machines Recurrent neural networks machine learning algorithms Costs Soil moisture Prediction algorithms Forecasting Long short term memory Crop yield

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A Study of machine learning and Deep learning algorithms for Deep Fake Images Prediction

A Study of Machine Learning and Deep Learning Algorithms for...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Padmavati E. Gundgurti K. Baburao Department of Computer Science and Engineering CMR University Bengaluru India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Recent developments in synthetic data have made it feasible to develop high-quality images in a way that humans cannot recognize the difference between images generated through Artificial Intelligence (AI) and real-time images. AI and machine learning (ML) in general have shown impressive results recently in a variety of tasks including natural image processing, particularly with the development of Deep learning (DL). Particularly, image classification in medical images requires a high degree of accountability and reliability. However, previous researches require further analysis, often with a better understanding of the image classification process. To identify and categorize images as either synthetic or natural, this review offers a variety of ML and DL techniques. This survey looks at developments in digital imagery to strengthen these AI systems and increase their ability to perform effectively.

关键词： Deep learning Surveys machine learning algorithms Reviews Network security Prediction algorithms Real-time systems Reliability Image classification Synthetic data

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An Enhancing Comprehensive machine learning Framework for DDoS Defense Through Leveraging Multiple algorithms

An Enhancing Comprehensive Machine Learning Framework for DD...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Bhasha Pydala Dinasekhar Govardhan Chapalamadugu Venkatesh Kornepalli Lokeshwar Goud Kavadi Dinesh V. Jyothsna Dept of Data Science Mohan Babu University(Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Department of IT Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Addressing Distributed Denial of Service (DDoS) attacks involves leveraging organizational assets, particularly the website framework. Departing from prior studies relying on an outdated KDD dataset, the paper emphasizes the importance of using the latest data for a comprehensive understanding of the current DDoS landscape. Utilizing machine learning techniques, including Random Forest, k-Nearest Neighbors (KNN), Support Vector machine (SVM), Gaussian, and Naive Bayes classification algorithms, the study introduces an amalgamated model for classifying and predicting DDoS attack types. The DDoS attack network logs dataset from GitHub and Python as a simulator were utilized. Model performance evaluation involved generating confusion matrices. In the first classification using Random Forest, Precision (PR) and Recall (RE) both reached high values, yielding an average Accuracy (AC) in a satisfactory range (81% to 90 % ). In the second classification, PR and RE both attained notable values, resulting in an average AC within an acceptable range. The innovation lies in the construction of an integrated model that combines Random Forest, KNN, SVM, Gaussian, and Naive Bayes to enhance overall predictive performance. Notably, a significant improvement in defect determination accuracy was observed when compared to existing research. This underscores the effectiveness of the integrated model in addressing the dynamic landscape of DDoS attacks.

关键词： Support vector machines machine learning algorithms Accuracy Heuristic algorithms Nearest neighbor methods Predictive models Denial-of-service attack

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Performance Optimization of machine learning-Driven Image Recognition algorithms for iOS Mobile Platforms

Performance Optimization of Machine Learning-Driven Image Re...

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International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Ibragim Mamadaev Muslim Mamadaev Bauman Moscow State Technical University Moscow Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

Today., mobile devices play an important role in the daily life of every person., without which many can no longer imagine their lives. One of the key technologies leading to significant benefits for mobile applications is machine learning. Optimization of machine learning algorithms for mobile devices is an urgent and important task., it is aimed at developing and applying methods that will effectively use the limited computing resources of mobile devices. The paper discusses various ways to optimize image recognition algorithms on mobile devices., such as quantization and compression of models., optimization of initial calculations. In addition to ways to optimize the machine learning model itself., various libraries and tools for using this technology on mobile devices are also being considered. Each of the described methods has its advantages and disadvantages., and therefore., in the results of the work., it is proposed to use not only a combination of the described options., but also an additional method of parallelization of image processing processes.

关键词： Performance evaluation Support vector machines machine learning algorithms Image recognition Recurrent neural networks Optimization methods Generative adversarial networks Mobile handsets Libraries Convolutional neural networks

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A Comprehensive Analysis of Melanoma Skin Cancer Detection Using machine learning and Deep learning algorithms

A Comprehensive Analysis of Melanoma Skin Cancer Detection U...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Suresh Babu Mupparaju Raghavendra Reddy School of Computer Science and Engineering REVA University Yelahanka Bangalore India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Nowadays, numerous types of skin diseases like skin cancer are increasing all over the world because of increasing pollution as well as many other factors. Accurate detection and diagnosis of skin lesions is challenging due to the similarities between various skin lesions such as nevi and melanoma, especially when analyzing color images of the skin. In this survey, the various algorithms are analyzed to enhance the detection as well as classification of skin cancer. As deep learning (DL) and machine learning (ML) become increasingly integral to melanoma skin cancer detection, this survey discusses the key assumptions, limitations, and advantages of current techniques. The performance of the existing approach was assessed using various performance metrics such as sensitivity, False Discovery Rate (FDR), Negative Predictive Value (NPV), specificity, precision, accuracy, recall, f1-score, False Positive rate (FPR), Mathews Correlation Coefficient (MCC). This study concludes that skin cancer detection can overcome the drawbacks of reducing the high computational complexity and increasing efficiency.

关键词： Surveys Deep learning Accuracy Sensitivity machine learning algorithms Melanoma Prediction algorithms Skin Lesions Reliability

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Predicting Water Potability Using machine learning: A Comparative Analysis of Classification algorithms

Predicting Water Potability Using Machine Learning: A Compar...

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IEEE International Conference on Energy Internet (ICEI)

作者： Mehul Patel Westcliff University USA

ISBN: (数字)9798331523558

ISBN: (纸本)9798331523565

Water scarcity is a growing concern in Malaysia and worldwide, necessitating innovative solutions for sustainable water management. This research investigates the application of various machine learning algorithms to predict the potability of water based on 20 chemical parameters. The study employs Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector machines, Extreme Gradient Boosting, and Multi-layer Perceptron to classify water samples as potable or non-potable. A comprehensive methodology is followed, including data preprocessing steps such as handling missing values, addressing data imbalance with SMOTE, and normalizing the dataset. The models are evaluated using metrics like F1-score and log loss to determine their accuracy and reliability. Results indicate that the XGBoost algorithm outperforms others with the highest F1-score of 0.9798 and the lowest log loss of 0.7150, demonstrating its efficacy in water quality prediction. This study highlights the potential of machine learning techniques in enhancing water quality monitoring systems, contributing to better water resource management and public health protection.

关键词： Support vector machines Logistic regression machine learning algorithms Accuracy Water quality Bayes methods Decision trees Reliability Random forests Water resources

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Sustainable machine learning: Evaluating the Environmental Cost of AutoML algorithms in AI Development

Sustainable Machine Learning: Evaluating the Environmental C...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Marc Schmitt Siemens AG Germany University of Oxford UK

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

This study evaluates the carbon footprint (CF) of Automated machine learning (AutoML) algorithms in AI development, examining three datasets to assess emissions across various run-times and countries. It is shown that the carbon intensity (CI) of these systems is significantly influenced by the energy sources powering the computational infrastructure. A correlation between run-time and model accuracy is observed, showing diminishing returns in accuracy with increased run-time and its environmental cost. The findings highlight the crucial role of geographic location and regional energy mix in determining the carbon footprint of AI operations. In areas with low-carbon or renewable energy sources, AI systems exhibit a reduced carbon footprint, underscoring the importance of infrastructural and environmental context in AI’s ecological impact. This study calls for adopting energy-efficient locations and optimizing ML algorithms to achieve a balance between model accuracy and environmental costs.

关键词： Renewable energy sources machine learning algorithms Costs Accuracy Biological system modeling Green products Energy efficiency

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A Comprehensive Analysis of Facebook Bangla Comments to Examine Public Views of Male & Female Bangladeshi Cricket Players Using machine learning & Deep learning algorithms

A Comprehensive Analysis of Facebook Bangla Comments to Exam...

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International Conference on Computer and Information Technology (ICCIT)

作者： Sharika Jahan Aunto Nusrat Jahan Smriti Mahbuba Yesmin Turaba Masum Biswas Computer Science & Engineering World University of Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Social media allows anyone to express their feelings, but sometimes people fail to follow community standards and cross the boundaries of their limits. In our paper, we aim to understand the overall scenario of people’s opinions about twelve male and female Bangladeshi cricketers, and we have developed a Bangla text-based dataset that contains 5702 Bangla comments from Bangladeshi male cricketers and 1381 Bangla comments from Bangladeshi female cricketers verified Facebook profiles. To analyze these comments, we categorized them into three polarities such as positive, negative, and neutral. In addition, we collected seven reactions to these comments. After preparing our data, we applied nine machine and deep learning algorithms consisting of K-Nearest Neighbors (KNN), Adaptive Boosting (AdaBoost), Linear Support Vector machine (SVM), Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT). Our multiclass classification algorithm, consisting of TF-IDF vectorizer alongside uni-gram, bi-gram, and tri-gram followed by KNN, LR, and RF demonstrates the topnotch performance in 90-10 and 80-20 split respectively while RF performed better in 70-30 split. We also employed word2vec for nine algorithms including Convolutional Neural Network (CNN) which obtained the best performance in 70-30 split.

关键词： Deep learning Support vector machines Radio frequency Logistic regression machine learning algorithms Social networking (online) Nearest neighbor methods Boosting Classification algorithms Random forests

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machine learning for Enhanced Autism Screening: A Comparative Evaluation of Classification algorithms

Machine Learning for Enhanced Autism Screening: A Comparativ...

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Cyber Resilience (ICCR), International Conference on

作者： May Y. Al-Nashashibi Mohammad Alauthman Wael Hadi Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Information Security University of Petra Amman Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Detecting autism spectrum disorder (ASD) is challenging due to its varying symptoms across individuals. This research assessed four common machine learning classifiers - Logistic Regression, Naïve Bayes, Support Vector machines, and K-Nearest Neighbors - using four ASD datasets. Two experiments evaluated the algorithms on the original datasets and versions modified by combining three datasets, adding an age feature, and reducing features. Support vector machines showed the most consistently strong performance on all metrics in both experiments. The adult dataset performed best among original datasets, while the modified combined dataset was most successful, with substantial gains across measures. Overall, the results demonstrate machine learning's promise for developing more accurate and accessible ASD screening, complementing standard diagnostic practices. Further research should evaluate these techniques on expanded and diverse ASD data.

关键词： Support vector machines Autism Logistic regression machine learning algorithms machine learning Vectors Classification algorithms

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algorithms for Fault Detection and Diagnosis in Wireless Sensor Networks Using Deep learning and machine learning - An Overview

Algorithms for Fault Detection and Diagnosis in Wireless Sen...

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International Conference on Communications and Signal Processing

作者： P Iswarya K Manikandan School of Computer Science and Engineering V.I.T Vellore TamilNadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The integration of artificial intelligence (AI) with wireless sensor networks (WSN) has created several possibilities, making it a highly discussed topic in the technological realm and holds promise for optimizing data gathering, analysis, and decision-making across diverse domains. The amalgamation of Artificial Intelligence (AI) and Wireless Sensor Networks (WSN) possesses immense potential for diverse sectors and domains. Incorporating Artificial Intelligence (AI) with Wireless Sensor Networks (WSN) can revolutionize many sectors, enhancing their efficiency, productivity, and sustainability. WSNs are made up of numerous low-power and affordable sensor nodes placed in surroundings for a range of military and commercial uses. WSNs are susceptible to various types of faults, including interaction errors, equipment errors, defects in software, link errors, node defects, energy discharge, and physical damage. Due to inexpensive chips, sensor nodes can become unreliable or defective. When faulty nodes lose the ability to interact with the remaining nodes in the wireless system. Even though they may still be operational, they can start transmitting incorrect data to the other nodes, ultimately impacting the operational efficiency of WSNs. To ensure successful networking, sensor nodes need reliable fault identification and diagnosis techniques. The study on fault diagnosis will prioritize data quality, enhance response time, and prolong the network lifetime. This paper explores different fault identification and diagnosis methods and algorithms across various industries over the past five years. This paper assesses the effectiveness of current artificial intelligence-driven fault identification algorithms and suggests potential areas for future research in artificial intelligence.

关键词： Fault diagnosis Deep learning Wireless communication Wireless sensor networks machine learning algorithms Fault detection Signal processing algorithms

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