Machine learning books and papers

Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing 🖥 Github: https://github.com/wangkai930418/DPL 📕 Paper: https://arxiv.org/abs/2405.01496v1 🔥Dataset: https://neurips.cc/virtual/2023/poster/72801 @Machine_learn

نفرات ۱ تا ۴ مقاله ی زیر خالی می باشد از دوستان اگر کسی خواست در خدمتیم Title Solar Energy Production Forecasting: A Comparative Study of LSTM, Bi-LSTM, and XGBoost Models with Activation Function Analysis Abstract This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Through the utilization of machine learning algorithms, such as XGBoost regression and neural networks, our predictive model endeavors to discern intricate patterns and correlations within these datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. By utilizing Kaggle's rich dataset and employing advanced machine learning techniques, this study aims to contribute valuable insights that can inform sustainable energy policies and practices, ultimately promoting a more efficient and reliable renewable energy infrastructure. Related Fields Business, Marketing, Industrial Engineering, Computer Engineering. Candidate Journals 1. Sustainability (5.8 CiteScore, 3.9 Impact Factor) 2. Archives of Computational Methods in Engineering (14.1 CiteScore, 9.7 Impact Factor) 3. Journal of Building Engineering (8.3 CiteScore, 6.4 Impact Factor) @Raminmousa @paper4money @Machine_learn

📚 image InternVL Family: Closing the Gap to Commercial Multimodal Models with Open-Source Suites —— A Pioneering Open-Source Alternative to GPT-4V 🖥 Github: https://github.com/opengvlab/internvl 📕 Paper: https://arxiv.org/abs/2404.16821v1 🔥Dataset: https://paperswithcode.com/dataset/visual-genome @Machine_learn

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Title Lung Cancer Level Classification Using Machine Learning: A Comprehensive Analysis Short title Lung cancer prediction, Machine learning, Overfitting, Model performance, Deep Neural Networks. Abstract This paper presents a detailed investigation into the application of machine learning (ML) techniques for predicting lung cancer levels. The study focuses on addressing overfitting issues while improving model performance through monitoring minimum child weight and learning rate. Various ML models, including XGBoost, LGBM, Adaboost, Logistic Regression, Decision Tree, Random Forest, CatBoost, and k-NN, were employed and evaluated. Notably, Deep Neural Networks (DNN) were also examined for their complexity in feature-target relationships. The results highlight the effectiveness of different ML models in accurately classifying lung cancer levels. Despite DNN's potential, conventional ML models demonstrated perfect performance, particularly XGBoost, LGBM, and Logistic Regression. Comparison metrics such as accuracy, precision, recall, and F-1 score reveal the superiority of specific models in lung cancer prediction. Field Medicine, Lung Cancer, Cancer, Computer Engineering. 1. International Journal of Medical Informatics (9.5 CiteScore, 4.9 Impact Factor) 2. BMC Cancer (4.43 CiteScore, 4.3 Impact Factor) 3. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease (12 CiteScore, 6.2 Impact Factor) 4. Multimedia Tools and Applications (9.9 CiteScore, 3.6 Impact Factor) @Raminmousa @Machine_learn @Paper4money

Title A Comparative Analysis of Machine Learning Models on Cryptocurrency Encompassing Indicators of Gold, Dollar, And Technical Indicators ———————————— Short title Time series forecasting, ML, Gradient Boost Machine, BTC, cryptocurrency. ————————————- Abstract In recent years, the application of machine learning models in financial forecasting has gained significant traction due to their ability to capture complex patterns in diverse datasets. This study presents a comprehensive comparison of several prominent machine learning algorithms, including XGBoost, AdaBoost, CatBoost, Random Forest, Decision Trees and LightGBM, across different datasets encompassing indicators of gold, dollar, and technical indicators. The evaluation is conducted on a range of performance metrics to ascertain the efficacy of each model in predicting financial trends and fluctuations. Through ML analysis, we examine the models' capabilities in handling the unique characteristics and dynamics inherent in each dataset, providing insights into their relative strengths and weaknesses. Furthermore, this research contributes to the existing literature by offering a comparative framework for assessing the suitability of machine learning algorithms in financial forecasting tasks. The findings of this study have implications for practitioners and researchers seeking to employ machine learning techniques in financial markets, aiding in informed decision-making and risk management strategies. ————————————— Field Business, Marketing, Industrial Engineering, Computer Engineering. —————————————— journal 1. Annals of Operations Research (7.1 CiteScore, 4.8 Impact Factor) 2. Neural Computing and Applications ( 8.7 CiteScore, 6.0 Impact Factor) 3. IEEE Access (9.0 CiteScore, 3.9 Impact Factor) با عرض سلام نفرات اول و دوم این مقاله رو خالی داریم . دوستانی که نیاز دارن با بنده هماهنگ کنند. ▶️ @Raminmousa @Machine_learn @Paper4money

Title A Comparative Analysis of Machine Learning Models on Cryptocurrency Encompassing Indicators of Gold, Dollar, And Technical Indicators ———————————— Short title Time series forecasting, ML, Gradient Boost Machine, BTC, cryptocurrency. ————————————- Abstract In recent years, the application of machine learning models in financial forecasting has gained significant traction due to their ability to capture complex patterns in diverse datasets. This study presents a comprehensive comparison of several prominent machine learning algorithms, including XGBoost, AdaBoost, CatBoost, Random Forest, Decision Trees and LightGBM, across different datasets encompassing indicators of gold, dollar, and technical indicators. The evaluation is conducted on a range of performance metrics to ascertain the efficacy of each model in predicting financial trends and fluctuations. Through ML analysis, we examine the models' capabilities in handling the unique characteristics and dynamics inherent in each dataset, providing insights into their relative strengths and weaknesses. Furthermore, this research contributes to the existing literature by offering a comparative framework for assessing the suitability of machine learning algorithms in financial forecasting tasks. The findings of this study have implications for practitioners and researchers seeking to employ machine learning techniques in financial markets, aiding in informed decision-making and risk management strategies. ————————————— Field Business, Marketing, Industrial Engineering, Computer Engineering. —————————————— journal 1. Annals of Operations Research (7.1 CiteScore, 4.8 Impact Factor) 2. Neural Computing and Applications ( 8.7 CiteScore, 6.0 Impact Factor) 3. IEEE Access (9.0 CiteScore, 3.9 Impact Factor) با عرض سلام نفرات اول و دوم این مقاله رو خالی داریم . دوستانی که نیاز دارن با بنده هماهنگ کنند. ▶️ @Raminmoua @Machine_learn @Paper4money

uilding Skills in Object-Oriented Design, Step-by-Step Construction of A Complete Application This is release 4.2003, published Mar 04, 2020. Link:https://slott56.github.io/building-skills-oo-design-book/build/html/ @Machine_learn

باعرض سلام نفرات ۱ تا ۳ از این مقاله باقی مونده @paper4money