標題: Titlebook: Artificial Intelligence for Environmental Sustainability and Green Initiatives; Aboul Ella Hassanien,Ashraf Darwish,Sally M. Elgha Book 20 [打印本頁] 作者: Philanthropist 時間: 2025-3-21 17:14
書目名稱Artificial Intelligence for Environmental Sustainability and Green Initiatives影響因子(影響力)
書目名稱Artificial Intelligence for Environmental Sustainability and Green Initiatives影響因子(影響力)學科排名
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書目名稱Artificial Intelligence for Environmental Sustainability and Green Initiatives網(wǎng)絡公開度學科排名
書目名稱Artificial Intelligence for Environmental Sustainability and Green Initiatives被引頻次
書目名稱Artificial Intelligence for Environmental Sustainability and Green Initiatives被引頻次學科排名
書目名稱Artificial Intelligence for Environmental Sustainability and Green Initiatives年度引用
書目名稱Artificial Intelligence for Environmental Sustainability and Green Initiatives年度引用學科排名
書目名稱Artificial Intelligence for Environmental Sustainability and Green Initiatives讀者反饋
書目名稱Artificial Intelligence for Environmental Sustainability and Green Initiatives讀者反饋學科排名
作者: Infusion 時間: 2025-3-21 23:53 作者: 障礙 時間: 2025-3-22 01:43
https://doi.org/10.1007/978-1-908517-79-1M”; and “Xception with BN and SVM” achieves the highest accuracies while MobileNet-V2+SVM being superior. Regarding carbon emissions, MobileNet-V2+BN+SVM had the least carbon emissions during test whereas VGG16 had the least carbon emissions during training.作者: 不可接觸 時間: 2025-3-22 05:10 作者: defendant 時間: 2025-3-22 10:39 作者: nephritis 時間: 2025-3-22 15:27 作者: 不成比例 時間: 2025-3-22 17:03
Handbook for Automatic Computationredict the synergistic effects of drug combinations with high accuracy. The model has been shown to be highly accurate in predicting the synergy between different drug combinations making it a valuable tool for the research and discovery of drugs.作者: 發(fā)微光 時間: 2025-3-22 22:34 作者: 易受騙 時間: 2025-3-23 01:25 作者: reflection 時間: 2025-3-23 09:09 作者: antedate 時間: 2025-3-23 13:04
Using Artificial Intelligence Techniques in Water Quality Analysis and Prediction: Towards Sustainabfor predictive analytics. This research aimed to determine the optimal classifier for a water potability dataset. Five commonly used classifiers were assessed: Logistic Regression, Support Vector Machine, Random Forest, XGBoost, and K-Nearest Neighbors. The models were evaluated and compared using precision, recall and F1 scores as key metrics.作者: 針葉類的樹 時間: 2025-3-23 14:46 作者: Glycogen 時間: 2025-3-23 19:14
Comorbid Symptoms, Syndromes, and Disorders,for predictive analytics. This research aimed to determine the optimal classifier for a water potability dataset. Five commonly used classifiers were assessed: Logistic Regression, Support Vector Machine, Random Forest, XGBoost, and K-Nearest Neighbors. The models were evaluated and compared using precision, recall and F1 scores as key metrics.作者: critique 時間: 2025-3-24 01:19
Recognizing Aluminum Beverage Cans from Waste Mixtures Based on Densenet121-CNN Model: Deep Learninge elements from waste mixtures images for recycling purposes is a major area of interest within the field of artificial intelligence. One of the interesting and important waste recycling issues is aluminum production from aluminum beverage cans. Therefore, recognizing aluminum beverage cans from sol作者: 灰心喪氣 時間: 2025-3-24 06:08 作者: STALE 時間: 2025-3-24 08:12
Using Artificial Intelligence Techniques in Water Quality Analysis and Prediction: Towards Sustainabtion are critical for environmental management and public health. Artificial Intelligence presents innovative solutions, leveraging advanced algorithms for efficient and accurate analysis and forecasting of water quality. Machine learning classification models are widely used across many industries 作者: invert 時間: 2025-3-24 11:40 作者: 使更活躍 時間: 2025-3-24 16:56 作者: 責任 時間: 2025-3-24 20:10
Towards Sustainable and Green Agriculture: Integrating Machine Learning and Fuzzy Rough Set Analysisoduction. Nowadays, a lot of technologies are developed for agricultural applications, and the majority of them are used to classify and assess the maturity of fruits. Measuring the fruit’s maturity level is essential to obtaining fruit of the highest quality and a crucial step in guaranteeing fruit作者: 斜谷 時間: 2025-3-25 01:23
Classifying Bird Songs Based on Chroma and Spectrogram Feature Extractionion of birds based solely on their auditory characteristics. Birds share all the characteristics of an animal because they share a common ancestor with all other animals on the planet. Birds are considered animals. Birds are vertebrate animals, improving classification accuracy and making sure it ca作者: Thyroid-Gland 時間: 2025-3-25 06:06 作者: impaction 時間: 2025-3-25 10:09 作者: 騙子 時間: 2025-3-25 14:44
Snake Optimization of Multiclass SVM for Efficient Diagnosis of Heart Disease Risk Predictionnge due to its complexity and the need for both precision and efficiency. Early detection is pivotal in lowering the risk of fatality. Various factors such as age, gender, cholesterol and glucose levels, and heart rate contribute to the onset of severe cardiac issues. However, due to the multifacete作者: ingenue 時間: 2025-3-25 18:03 作者: 閹割 時間: 2025-3-25 22:02 作者: 裂隙 時間: 2025-3-26 01:36
Deep Artificial Neural Network Regression Model for Synergistic Drug Combination Predictionion can be challenging. Therefore, many computational methods proposed to predict drug interactions that can be used before clinical experiments and reduce the risk of adverse effects during treatment. in this paper, we proposed a deep artificial neural network regression (DANNR) model that can meas作者: 利用 時間: 2025-3-26 06:40
Classification of Benign?and?Malignant Breast?Tumor Based on Machine Learning and Feature Selection roposed model consists of three main stages: data pre-processing, feature selection, and finally different classifiers. During the pre-processing phase, missing values are addressed and the data is normalised. Subsequently, three different techniques are employed to select the most crucial features:作者: Measured 時間: 2025-3-26 10:46 作者: STALL 時間: 2025-3-26 13:02 作者: habitat 時間: 2025-3-26 16:47
Artificial Intelligence in Finance Sector for Risk Prediction Data Analytics. AI has significantly impacted the financial industry by enhancing risk assessment, management, and decision-making. It can efficiently process vast amounts of data and identify patterns quickly. This paper presents an AI model in finance sector for risk management, especially loan d作者: 殺子女者 時間: 2025-3-26 21:52
Artificial Intelligence for Environmental Sustainability and Green Initiatives978-3-031-63451-2Series ISSN 2198-4182 Series E-ISSN 2198-4190 作者: Inflammation 時間: 2025-3-27 04:59 作者: agglomerate 時間: 2025-3-27 06:43
Studies in Systems, Decision and Controlhttp://image.papertrans.cn/b/image/167580.jpg作者: 許可 時間: 2025-3-27 11:10
https://doi.org/10.1007/978-3-031-63451-2Artificial Intelligence; Sustainability; Environment; Deep learning; Machine learning作者: ATOPY 時間: 2025-3-27 16:22 作者: FLUSH 時間: 2025-3-27 18:15 作者: Ingenuity 時間: 2025-3-28 01:52 作者: 管理員 時間: 2025-3-28 02:43
Comorbid Symptoms, Syndromes, and Disorders,tion are critical for environmental management and public health. Artificial Intelligence presents innovative solutions, leveraging advanced algorithms for efficient and accurate analysis and forecasting of water quality. Machine learning classification models are widely used across many industries 作者: muscle-fibers 時間: 2025-3-28 07:26 作者: conquer 時間: 2025-3-28 14:15 作者: Jingoism 時間: 2025-3-28 16:15
https://doi.org/10.1007/978-1-908517-79-1oduction. Nowadays, a lot of technologies are developed for agricultural applications, and the majority of them are used to classify and assess the maturity of fruits. Measuring the fruit’s maturity level is essential to obtaining fruit of the highest quality and a crucial step in guaranteeing fruit作者: Sputum 時間: 2025-3-28 20:07
Comorbid Symptoms, Syndromes, and Disorders,ion of birds based solely on their auditory characteristics. Birds share all the characteristics of an animal because they share a common ancestor with all other animals on the planet. Birds are considered animals. Birds are vertebrate animals, improving classification accuracy and making sure it ca作者: Cerumen 時間: 2025-3-28 23:53 作者: 某人 時間: 2025-3-29 05:30 作者: Boycott 時間: 2025-3-29 09:35 作者: 名次后綴 時間: 2025-3-29 12:36
W. Barth,R. S. Martin,J. H. Wilkinsoneffects brought on by drug-drug interactions (DDIs). The evaluation of pharmacological interactions, pharmacodynamics, and probable adverse effects using artificial intelligence (AI) is a possibility. Many AI-based DDI prediction methods, including both machine learning and deep learning, that make 作者: 改革運動 時間: 2025-3-29 18:30 作者: foppish 時間: 2025-3-29 20:16
Handbook for Automatic Computationion can be challenging. Therefore, many computational methods proposed to predict drug interactions that can be used before clinical experiments and reduce the risk of adverse effects during treatment. in this paper, we proposed a deep artificial neural network regression (DANNR) model that can meas作者: exacerbate 時間: 2025-3-30 00:57
https://doi.org/10.1007/978-3-642-86937-2roposed model consists of three main stages: data pre-processing, feature selection, and finally different classifiers. During the pre-processing phase, missing values are addressed and the data is normalised. Subsequently, three different techniques are employed to select the most crucial features:作者: 驚惶 時間: 2025-3-30 07:19
A. A. Grau,U. Hill,H. Langmaack cell lines. Cancer cell lines features vector may exceed 50,000. Such high-dimensional data is unsuitable for learning modeling approaches. To address this challenge, a range of dimension reduction techniques are employed, including feature selection methods, autoencoders, and the LINCS project. Th作者: 不適 時間: 2025-3-30 08:15
Clarisse Brígido,Jin Duan,Bernard R. Glick creating healthier communities and society. The integration of the Internet of Things (IoT) and widespread smartphones is crucial for achieving a major advancement in different areas of smart cities, such as healthcare, fitness, skill evaluation, and personal assistants, to facilitate independent l作者: 歌唱隊 時間: 2025-3-30 13:41
Clarisse Brígido,Jin Duan,Bernard R. Glick Data Analytics. AI has significantly impacted the financial industry by enhancing risk assessment, management, and decision-making. It can efficiently process vast amounts of data and identify patterns quickly. This paper presents an AI model in finance sector for risk management, especially loan d作者: 游行 時間: 2025-3-30 16:40
Book 2024and waste management. Discusses applications and innovations in Green Initiatives such as energy, finance, and drug discovery.?Highlights the ethical challenges and benefits of integrating AI into sustainability initiatives.作者: 在駕駛 時間: 2025-3-30 21:11 作者: 環(huán)形 時間: 2025-3-31 00:56 作者: abstemious 時間: 2025-3-31 07:58
Measuring Global Warming Effect by the Prediction of Climate Change on the Different Countries Usingimate change, providing a new perspective on this controversial issue. The proposed methodology achieved the highest accuracy. Another contribution in this paper is its novelty predicting the relation between climate change and global warming. Step wise linear regression was used which gave R square作者: prick-test 時間: 2025-3-31 09:35 作者: Mendacious 時間: 2025-3-31 16:57 作者: 障礙物 時間: 2025-3-31 18:42
Fish Recognition Using MobileNet-V2 and MAR-Based Metaverse for an Educative Marine Life Systemations, entertainment, medical care, travel, transportation, etc. are all getting smarter MAR apps thanks to the widespread adoption of machine learning technologies for mobile devices. The suggested work illustrates a scenario wherein participants in a marine educational system are afforded the opp作者: 禁令 時間: 2025-4-1 01:34
Biodiesel Yield Prediction from Sunflower Oil Using Artificial Intelligence: Towards Sustainable, anroposes an artificial intelligence (AI) framework to enhance the production process of biodiesel from sunflower oil. The framework consists of two phases: in the first phase, a Deep Learning (DL) model called VGG16 is employed to classify diseases affecting sunflower leaves, specifically Downy milde作者: FER 時間: 2025-4-1 02:55
Snake Optimization of Multiclass SVM for Efficient Diagnosis of Heart Disease Risk PredictionLinear function depends on optimal parameter selection; of kernel scale (gamma) (σ) and box constraint (C). Intelligent optimization methods such as Snake optimization are used to select the optimal values of kernel scale (σ) and box constraint (C). Compared to existing state-of-the-art models, our 作者: Anecdote 時間: 2025-4-1 07:26
Efficient Prediction Adverse Drug-Drug Interactions with Deep Neural Networks drug A is combined with drug B. After evaluating the models‘ performance. This paper suggested DNN as a way to enhance DDIs‘ predictive capabilities. The prediction algorithm was 96.2% accurate in predicting 86 different kinds of DDIs using a benchmark dataset. The proposed model applies pre-proces作者: ensemble 時間: 2025-4-1 13:05 作者: Tdd526 時間: 2025-4-1 15:24
Enhancing Synergistic Drug Combination Model Through Dimension Reduction in Cancer Cell Lines the application of entropy and Gini-based methods to further refine gene expression. Additionally, Gaussian feature selection is applied to pinpoint the 1,000 most significant genes. Autoencoders and variational autoencoders are also applied to reduce the dimensionality of gene expression data. Fur
