Papers

با عرض سلام براي يكي از مقالاتمون نياز به اسپانسر داريم كه در حوزه ي طبقه بندي تصاوير پزشكي هستش و هزينه سرور ٤٠٠$ مي باشد. براي اين منظور جايگاه دوم رو به شخص پرداخت كننده واگذار مي كنيم. جهت اطلاعات بيشتر با بنده
در ارتباط باشين.

@Raminmousa

با عرض سلام این ۵ مقاله ی ما در مرحله ریوایزد می باشند دوستانی که نیاز به سایتیشن دارند میتونیم به مقالاتشون سایت بدیم.
Paper 1:

An Intelligent Hybrid Industrial IoT-based Fault Detection Framework in Digital Twins
Systems

Neural Computing and Applications (Publisher : springer )
Impact factor
4.5 (2023)
5 year impact factor
4.7 (2023)
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paper 2
An Artificial Visual System with Fully Cell-modeled Retinal Direction-selective Ganglion
Cell Pathway for Motion Direction Detection in Grayscale Images

Neural Computing and Applications (Publisher : springer )
Impact factor
4.5 (2023)
5 year impact factor
4.7 (2023)
--------------------
paper 3
An Advanced Hybrid Deep Learning Model for Accurate Energy Load Prediction in Smart Building

Energy Exploration & Exploitation (Publisher : Sage )
Impact Factor: 1.9
5-Year Impact Factor: 2.2
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paper 4
An Advanced Hybrid Deep Learning Model for Accurate Energy Load Prediction in Smart Building

Energy Exploration & Exploitation (Publisher : Sage )
Impact Factor: 1.9
5-Year Impact Factor: 2.2
---------
paper 5

vSegNet - a variant SegNet for improving segmentation accuracy in medical images with class imbalance and limited data

Medinformatics
Impact Factor: 0.3
@Raminmousa
@paper4money
@Machine_learn

هزينه نفر اول ٧٠٠$
دوم ٦٠٠$
سوم و چهارم ٥٠٠$
مي باشد

سلام اين مقالمون براي نيچر نوشته شده از دوستان كسي نياز داشت نفرات ١ تا ٤ اش خالي هستش .

Brain Tumor Detection Through Diverse CNN Architectures in IoT healthcare industries: Fast R-CNN, UNet, Transfer Learning-Based CNN, and Fully Connected CNN

Abstract
Artificial intelligence-powered deep learning methods have significantly advanced the diagnosis of brain tumors in Internet of Thing (IoT)-healthcare systems, achieving high accuracy by processing extensive datasets. Brain health is crucial for human life, and accurate diagnosis is vital for effective treatment. Magnetic Resonance Imaging (MRI) provides critical data for diagnosing brain health issues, offering a substantial source of big data for artificial intelligence applications in image classification. In this study, we aimed to classify brain tumors, specifically glioma, meningioma, and pituitary tumors, from MRI images using Region-based Convolutional Neural Network (R-CNN) and UNet architectures. Additionally, we employed Convolutional Neural Networks (CNN) and CNN-based models such as Inception-V3, EfficientNetB4, and VGG19, leveraging transfer learning methods for classification tasks. The models were evaluated using F-score, recall, precision, and accuracy metrics. Our findings revealed that the Fast R-CNN model achieved the highest accuracy at 99%, with an F-score of 98.5%, an Area Under the Curve (AUC) value of 99.5%, a recall of 99.4%, and a precision of 98.5%. The integration of R-CNN, UNet, and transfer learning models plays a pivotal role in the early diagnosis and prompt treatment of brain tumors in IoT-healthcare systems, significantly improving patient outcomes.

Keywords: Region-based Convolutional Neural Network, UNet, Brain tumor, Transfer learning, Medical imaging

Scientific Reports, Nature Springer

@Raminmousa
@paper4money
@Machine_learn

سلام
این مقالمون در مرحله ی ریوایزد از دوستان اگر کسی خواست می تونیم به مقالاتشون سایت برنیم.

Title
Comparative Study of Long Short-Term Memory (LSTM), Bidirectional LSTM, and Traditional Machine Learning Approaches for Energy Consumption Prediction
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Short title
Machine Learning, XGBoost, Tree-based Algorithm, Solar Energy Production, LSTM, Artificial Intelligence, Machine Learning, time-series,Bi-LSTM
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Abstract
Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to enhance the accuracy of forecasting. Time series forecasting is a critical task in various application domains, as real-world time series data often exhibit non-linear patterns with complexities that conventional forecasting techniques struggle to capture. To address this, our approach proposes the utilization of long short-term memory (LSTM) and Bi-LSTM models for precise time series forecasting. To ensure a fair evaluation, the performance of our proposed approach is compared with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM and Bi-LSTM, along with other machine learning methods, are implemented for a comprehensive assessment. The experimental results in this study consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. To address the imbalance between activations by both groups of consumers and prosumers, our prediction results show that the proposed method exhibits higher prediction performance compared to several traditional forecasting methods, such as the autoregressive integrated moving average model (ARIMA) and Seasonal autoregressive integrated moving average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.
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journal
Energy Exploration & Exploitation (SAGE)

@Raminmousa
@Machine_learn
@paper4money

هزینه ی جایگاه ها نفر اول ۱۰۰۰ دلار
نفر دوم ۶۰۰ دلار
نفر سوم و چهار ۵۰۰ دلار می باشد

با عرض سلام این مقاله رو می خواییم برای Nature بفرستیم جایگاه های ۱ تا ۴ اش خالیه از دوستان کسی نیاز داشت در خدمتیم
Title:
Detection of brain tumors from images using the UNet architecture, with a comparative analysis of transfer learning methods and CNNs.
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Abstract:
Health is crucial for human life, especially brain health, which is vital for all executive functions. Diagnosing brain health issues is often done using magnetic resonance imaging (MRI) devices, which provide critical data for health decision-makers. Images from these devices serve as a significant source of big data for artificial intelligence applications. This big data facilitates high performance in image processing classification problems, a subfield of artificial intelligence. In this study, we aim to classify brain tumors such as glioma, meningioma, and pituitary tumors from brain MRI images using the UNet architecture. To compare the results and gain a better understanding, we also employed Convolutional Neural Networks (CNN) and CNN-based models like Inception-V3, EfficientNetB4, VGG19, along with transfer learning methods for classification tasks. The models were evaluated using F-score, recall, precision, and accuracy metrics. The best accuracy result was achieved with CNN-VGG16, reaching 97%. The same transfer learning model also showed an F-score of 96%, an Area Under the Curve (AUC) value of 98%, a recall value of 98%, and a precision value of 97%. The UNet architecture and CNN-based transfer learning models play a significant role in the early diagnosis and rapid treatment of brain tumors, which is vital for improving patient outcomes.
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Keywords:
Brain tumor detection, UNet, CNN, Transfer Learning.
——————————————————————
Journal:
Scientific Reports

@Raminmousa
@Machine_learn
@paper4money

هزینه جایگاه اول ۲۲ میلیون💥
دوم ۱۸ میلیون 🔥
سوم ۱۵ 💥
چهارم ۱۲ 💥
می باشد.

Title
Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach

Abstract
This study delved into the application of various machine learning (ML) models for the classification of lung cancer levels. Through meticulous monitoring of parameters such as minimum child weight and learning rate, efforts were made to mitigate overfitting while optimizing model performance. The Deep Neural Network (DNN) emerged as a standout performer, showcasing robust performance across training, validation, and testing stages. Ensemble methods like voting and bagging also demonstrated promising results. However, Support Vector Machine (SVM) models with the Sigmoid kernel faced challenges in achieving satisfactory performance. Overall, the investigation sheds light on the efficacy of different ML models in lung cancer level classification and underscores the importance of parameter tuning to address overfitting concerns.


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)

هزینه جایگاه اول ۲۲ میلیون💥
دوم ۱۸ میلیون 🔥
سوم ۱۵ 💥
چهارم ۱۲ 💥
می باشد.

✅Title:
Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-based Model
✅Short title
Machine Learning, Convolutional Neural Networks (CNNs), Image Annotation, Food Industry, Almond, Nuts Detection

Abstract:
In response to the global demand for high-quality agricultural products, especially in the competitive nut market, we present an innovative approach to enhance the grading of almonds and their shells. Leveraging Deep Convolutional Neural Networks (AlmondNet-20), we achieved over 99% accuracy through 20 layers of CNN, employing data augmentation for robust almond-shell differentiation. Our model, trained over 1000 epochs, demonstrated a remarkable accuracy of 99%, with a low loss function of 0.0567. Test evaluations revealed perfect precision, recall, and F1-score for almond detection. This advanced classification system not only boosts grading accuracy but also ensures reliability in distinguishing almonds from shells globally, benefiting both experts and non-experts. The application of deep learning algorithms opens avenues for product patents, contributing to the economic value of our country.
Field
Food Industry, Agricultural Engineering, Industrial Engineering, Computer Engineering.
1. Agronomy (3.7 CiteScore, 5.2 Impact Factor)
2. Biosystems Engineering (10.1 CiteScore, 5.1 Impact Factor)
3. Precision Agriculture (9.9 CiteScore, 6.2 Impact Factor)

@Raminmousa
@Machine_learn
@Paper4money

هزینه جایگاه اول ۲۲ میلیون💥
دوم ۱۸ میلیون 🔥
سوم ۱۵ 💥
چهارم ۱۲ 💥
می باشد.

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

⚡️با عرض سلام تمامی مقالات بالا سابمیت شدن لطفا پیام ندین برای موارد بالا🐉

با عرض سلام نفرات ۱ تا ۴ این مقاله خالی می باشد:
۱- نفر اول ۲۵ میلیون
۲- نفر دوم ۲۰ میلیون
۳- نفر سوم ۱۵ میلیون
۴-نفر ۴ ام ۱۰ میلیون

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

نفر دوم ٢٠
نفر سوم ١٥ میلیون هزینش میباشد

سلام مجدد
این مقاله نفر دوم و سومش خالیه
یکی هم پر بشه سابمیت میکنیم

An Optimized Deep Neural Network Framework for Classification of Drug–Drug Interactions

چکیده
Accurately predicting drug-drug interactions (DDIs) is crucial for preventing adverse drug events in clinical settings. However, existing methodologies often rely on complex models built from diverse data sources, posing challenges in computational drug discovery. To address the need for precise computational methods in predicting unknown DDIs, this study introduces a novel Deep Neural Network (DNN)-based approach. By leveraging a wide range of drug-related information, including substructure, targets, side effects, pathways, and indications, our method calculates multiple drug similarities. These similarities are then synthesized using a nonlinear fusion method to extract high-level features. Subsequently, a tailored neural network is deployed for interaction prediction. Comparative evaluation against three prominent machine learning classifiers—Extreme Gradient Boosting (XGBoost), Adaptive Gradient Boosting (AdaBoost), and Light Gradient-Boosting Machine (LGBM)—using three benchmark datasets demonstrates the superior performance of DNN. It achieves outstanding accuracy, precision, recall, and F1-score metrics, all reaching 94.6% in cross-validation. Additionally, case studies involving numerous drug pairs confirm the reliability of DNN in accurately predicting unknown DDIs. These findings underscore DNN as a potent and reliable method for DDI prediction, with promising implications for drug discovery and healthcare applications.


ژورنال

1. BMC Medical Informatics and Decision Making
2. Expert Systems with Applications
3. BMC Bioinformatics
4. Molecules
5. Medicine in Drug Discovery

@Raminmousa
@Machine_learn
@Paper4money