Influence mechanism between green finance and green innovation: Exploring regional policy intervention effects in China

Spatial spillovers and threshold effects of internet development and entrepreneurship on green innovation efficiency in China

Prioritizing and overcoming biomass energy barriers: Application of AHP and G-TOPSIS approaches

Few researches have inspected the task of green finance in reducing CO2 emissions, while earlier studies have inspected the influence of economic development on carbon emissions. A green finance development index is built using four indicators to fill in this knowledge gap: green credit, green insurance, green securities, and green investing. Using data spanning the years 2005–2019, a panel quantile regression is applied to investigate the links between green finance, renewable energy, and CO2 emissions. Increases in renewable energy use and advances in the green finance development index have contributed to a reduction in CO2 emissions from BRICS countries. CO2 emissions on the other hand slowed the growth of renewable energy use, slowed the flow of investment to green projects, and ultimately hampered the development of green finance. There was also a clear policy-driven influence on renewable energy spending in the countries of the BRICS region. Green finance policies, on the other hand, have consistently failed to have a long-term impact. Therefore, rising the consumption of renewable energy and creating a carbon trading market are all part of this study’s recommendations for green finance policy improvement. 

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Does green finance mitigate the effects of climate variability: role of renewable energy investment and infrastructure

As the world's population and development increase, the demand for motor-vehicles increases, adversely impacting the environment and smart cities by increasing traffic congestion. An unsystematic traffic management in smart cities may cause serious concerns, including pollution, loss of time, traffic irritation, and stress. An autonomous system for smart motor-vehicle is an important research domain of recent times worldwide that is gaining importance, disrupting the automotive industry in a big way. In the coming decades, as transportations evolve into autonomous motor-vehicles and on-demand services, the results may be a major paradigm shift in how motor-vehicles move people and goods around the physical world. Driverless motor-vehicles may change the perception of time, space, how they communicate to work, where they exist and how they work. Artificial and soft learning/computing mechanisms are efficient approaches that may provide a platform for developing an autonomous system for smart motor-vehicles to facilitate the citizens and smart city administrators while routing the traffic smoothly and intelligently. In this research work, an Autonomous System for Smart Motor-Vehicles integrating Artificial and Soft Learning Mechanisms (ASSMA-SLM) is proposed that proves helpful in monitoring, routing, and predicting smart city mobility in a smooth as well as efficient way. After the simulation, it is clearly shown that the proposed system results are better as compared to the previously published approaches in terms of 90.28% accuracy and 9.72% miss-rate.

ASSMA-SLM: Autonomous System for Smart Motor-Vehicles integrating Artificial and Soft Learning Mechanisms

https://doi.org/10.1016/j.eij.2022.03.003

Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques

Liver Disease (LD) is the main cause of death worldwide, affecting a large number of people. A variety of factors affect the liver, resulting in this disease. The diagnosis of this condition is both expensive and time-consuming. Machine Learning offers a lot of potential in terms of automated disease diagnosis. As a result, the purpose of this research is to assess the efficacy of various Machine Learning (ML) algorithms to lower the high cost of liver disease diagnosis through prediction. With the current rise in numerous liver disorders, it’s more important than ever to detect liver disease early on. This research proposed intelligent model to predict liver disease using machine learning technique. This proposed model is more effective and comprehensive in terms of performance of 0.884 accuracy, and 0.116 miss-rate.

Intelligent Model to Predict Early Liver Disease using Machine Learning Technique

In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. These models analyze the dataset to determine whether a diabetes diagnosis is positive or negative. The dataset used in this research is divided into training data and testing data with a ratio of 70:30 respectively. The output of these models becomes the input membership function for the fuzzy model, whereas the fuzzy logic finally determines whether a diabetes diagnosis is positive or negative. A cloud storage system stores the fused models for future use. Based on the patient’s real-time medical record, the fused model predicts whether the patient is diabetic or not. The proposed fused ML model has a prediction accuracy of 94.87, which is higher than the previously published methods.

/pdf/prediction-of-diabetes-empowered-with-fused-machine-learning-2mflndjt.pdf

Prediction of diabetes empowered with fused machine learning

Modeling consumers' information acquisition and 5G technology utilization: Is personality relevant?

Cancer accounts for a huge mortality rate due to its aggressiveness, colossal potential of metastasis, and heterogeneity (causing resistance against chemotherapy). Lung and colon cancers are among the most prevalent types of cancer around the globe that can occur in both males and females. Early and accurate diagnosis of these cancers can substantially improve the quality of treatment as well as the survival rate of cancer patients. We propose a highly accurate and computationally efficient model for the swift and accurate diagnosis of lung and colon cancers as an alternative to current cancer detection methods. In this study, a large dataset of lung and colon histopathology images was employed for training and the validation process. The dataset is comprised of 25000 histopathology images of lung and colon tissues equally divided into 5 classes. A pretrained neural network (AlexNet) was tuned by modifying the four of its layers before training it on the dataset. Initial classification results were promising for all classes of images except for one class with an overall accuracy of 89%. To improve the overall accuracy and keep the model computationally efficient, instead of implementing image enhancement techniques on the entire dataset, the quality of images of the underperforming class was improved by applying a contrast enhancement technique which is fairly simple and efficient. The implementation of the proposed methodology has not only improved the overall accuracy from 89% to 98.4% but has also proved computationally efficient.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09709814.pdf

Munir Uddin Ahmad

Papers

Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques

Intelligent Model to Predict Early Liver Disease using Machine Learning Technique

Prediction of diabetes empowered with fused machine learning

Modeling consumers' information acquisition and 5G technology utilization: Is personality relevant?

Malignancy Detection in Lung and Colon Histopathology Images Using Transfer Learning With Class Selective Image Processing