Article
Environmental Sciences
Predrag Petrovic
Summary: The objective of this study is to uncover the nature and strength of the effect that innovations have on CO2 emissions. The main finding is that the impact of innovations on CO2 emissions is quite unfavorable, with the negative impact being about six times stronger than the positive one. This highlights the need for greater support for green innovations.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Economics
Anca Florentina Vatamanu, Bogdan Gabriel Zugravu
Summary: Global warming creates significant problems and changes climate patterns worldwide. The relationship between financial development, institutional quality, and renewable energy consumption is examined using a sample of 27 EU member states over the 2000-2020 period. Results show that increased financial development positively impacts renewable energy consumption, and institutional quality also influences energy consumption and reduces carbon emissions.
ECONOMIC ANALYSIS AND POLICY
(2023)
Article
Environmental Sciences
Canh Phuc Nguyen
Summary: This study explores the impact of natural threats on global energy consumption and emissions. The empirical results suggest that exposure, susceptibility, and vulnerability appear to reduce electricity usage, renewable energy consumption, energy intensity, and CO2 emissions. Interesting differences are found among different income groups and regions.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Environmental Sciences
Bo Yang, Minhaj Ali, Shujahat Haider Hashmi, Atif Jahanger
Summary: Concerns about income inequality and environmental pollution are important aspects of achieving sustainable development goals. This research explores the relationship between income inequality, institutional quality, and carbon dioxide emissions in developing countries. The study finds that rising income inequality leads to increased CO2 emissions, but when the interaction term is considered, it has a significant negative effect on emissions. Additionally, factors such as institutional quality, economic development, energy consumption, industrialization, and trade openness have a significant impact on CO2 emissions.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Construction & Building Technology
Mak B. Arvin, Rudra P. Pradhan, Mahendhiran S. Nair, Parviz Dabir-Alai
Summary: This study examines the temporal causal links between foreign aid, CO2 emissions, and governance institutions in poorer countries, finding complex dynamics in the short run and a Granger-causal relationship between institutional quality, CO2 emissions, and foreign aid in the longer term. The results highlight the need for coordinated policies to address environmental development in these countries.
ENERGY AND BUILDINGS
(2022)
Article
Business
Emma Serwaa Obobisa, Haibo Chen, Isaac Adjei Mensah
Summary: The study reveals that green technological innovation and renewable energy consumption have a significant negative impact on CO2 emissions in African countries, while institutional quality, economic growth, and fossil fuel energy consumption have a positive impact on CO2 emissions.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Article
Development Studies
Baolong Yuan, Chen Li, Hongyuan Yin, Meng Zeng
Summary: The study shows that green innovation significantly reduces CO2 emissions in China, especially when institutional quality is high. In the western region, the reduction of CO2 emissions through green innovation increases as institutional quality improves. Green innovation had a greater impact on CO2 emissions reduction in 2013-2017 compared to 2005-2012.
JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT
(2022)
Article
Environmental Sciences
Satar Bakhsh, He Yin, Mohsin Shabir
Summary: The study demonstrates that institutional quality and technological innovation play crucial roles in moderating the relationship between FDI inflows and CO2 emissions, significantly reducing the level of emissions. The findings are important for policymakers in establishing effective environmental protection policies in both the short and long term.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Environmental Sciences
Dicle Ozdemir
Summary: The study confirmed a long-term relationship between climate change and agricultural productivity in Asia, with only CO2 emissions having an impact on short-term agricultural productivity, showing a positive effect in the short run but turning negative in the long run.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Economics
Melina Dritsaki, Chaido Dritsaki
Summary: This paper examines the relationship between per capita health care expenditures, per capita CO2 emissions, and per capita gross domestic product (GDP) in G7 countries. The study applies various tests such as cross-sectional dependence analysis, slope homogeneity test, second-generation unit root test, cointegration test, and causality test using panel data. The results show significant and long-term relationships between the variables and reveal a unilateral causality from greenhouse gas emissions per capita towards health expenditure per capita in all G7 countries.
JOURNAL OF THE KNOWLEDGE ECONOMY
(2023)
Article
Environmental Sciences
Usman Mehmood, Salman Tariq, Zia Ul-Haq, Muhammad Saeed Meo
Summary: The study reveals varying interaction effects of institutional quality and economic growth on CO2 emissions in developing countries, with institutional quality reducing emissions in India and Bangladesh but potentially increasing it in Pakistan. Additionally, an inverted U-shaped Environmental Kuznets Curve is confirmed in Pakistan and Bangladesh, while GDP and institutional quality independently reduce CO2 emissions in these countries.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Geosciences, Multidisciplinary
Sohail Farooq, Ilhan Ozturk, Muhamamd Tariq Majeed, Rabia Akram
Summary: The relationship between globalization and environmental quality is complex. Economic globalization is detrimental to environmental sustainability, while political globalization contributes to improving environmental quality.
Article
Environmental Sciences
Wilfried Rickels, Felix Meier, Martin Quaas
Summary: This analysis proposes the concept of climate wealth borrowing and quantifies the country-specific present value of climate change impacts arising from energy and industrial CO2 emissions of the period of 1950-2018. It finds that the United States and China have been responsible for the largest shares of global climate wealth borrowing since 1950, while the per-capita pattern is quite different.
NATURE CLIMATE CHANGE
(2023)
Article
Energy & Fuels
I. V. Filimonova, A. V. Komarova, V. M. Kuzenkova, I. V. Provornaya, V. D. Kozhevin
Summary: This study examines the factors influencing CO2 emissions in Europe and the Asia-Pacific region, finding that economic growth has a significant impact on emissions, while the green factor has a negative impact in the Asia-Pacific region and a limited impact in European countries.
Article
Environmental Sciences
Junaid Ashraf, Liangqing Luo, Muhammad Khalid Anser
Summary: The study shows that in the South Asian region, economic growth is driven by energy consumption, trade, economic freedom, and institutional quality, with the positive impact of effective and fair political institutions on both economic development and reduction of CO2 emissions. Additionally, the BRI policy has significantly stimulated economic growth since 2013.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Multidisciplinary Sciences
Reetu Malhotra, Faten S. Alamri, Hamiden Abd El-Wahed Khalifa
Summary: The paper evaluates the availability, reliability, and other measures of system effectiveness for two stochastic models, a cold standby system and a hot standby system, with varying demand. The study demonstrates that the hot standby system is more expensive than the cold standby system under certain circumstances. The authors analyzed both models using semi-Markov and regenerative point techniques and collected actual data to illustrate the findings.
Article
Nutrition & Dietetics
Mubbasher Munir, Zahrahtul Amani Zakaria, Haseeb Nisar, Zahoor Ahmed, Sameh A. Korma, Tuba Esatbeyoglu
Summary: Obesity is a complex global disease burden that affects the quality of life across populations. Factors such as genetics, behavior, and socioeconomic and environmental origins contribute to the risk of obesity. This study identifies a positive correlation between global social indicators and obesity, providing valuable insights for policymakers and governmental organizations.
FRONTIERS IN NUTRITION
(2023)
Article
Biochemistry & Molecular Biology
Deep Kothadiya, Amjad Rehman, Sidra Abbas, Faten S. Alamri, Tanzila Saba
Summary: Diabetic retinopathy (DR) affects the vision of diabetic patients, but deep learning and computer vision methods can detect it early. An attention-based hybrid model is proposed to recognize diabetes and prevent its progression. The model uses DenseNet121 architecture for convolution learning and enhances the feature vector with channel and spatial attention model. The simulation achieved high accuracy in multiclass and binary classification.
BIOCHEMISTRY AND CELL BIOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Zaigham Tahir, Hina Khan, Faten S. Alamri, Muhammad Aslam
Summary: This study presents a generalized neutrosophic ratio-type exponential estimator (NRTEE) for estimating location parameters and achieving the lowest mean square error (MSE) possible for interval neutrosophic data (IND). Unlike typical estimators, its findings are not single-valued but rather in interval form, which reduces the possibility of over-or under-estimation caused by single crisp outcomes and also increases the likelihood of the parameter dwelling in the interval. The suggested NRTEE's efficiency is further addressed by utilizing real-life IND of temperature and simulations.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Multidisciplinary Sciences
Yueying Wang, Noman Arshed, Muhammad Ghulam Shabeer, Mubbasher Munir, Hafeez ur Rehman, Yousaf Ali Khan
Summary: This study examines the relationship between globalization, ecological footprint, innovation, and subjective wellbeing. It reveals that ecological footprint and globalization have negative effects on subjective wellbeing, but innovation can mitigate these effects.
Article
Computer Science, Information Systems
Amjad Rehman, Tanzila Saba, Muhammad Mujahid, Faten S. Alamri, Narmine ElHakim
Summary: Parkinson's disease is a prevalent neurological disorder that poses a challenging task in early detection due to a shortage of trained neurologists. This study collected voice data from Parkinson's disease patients to investigate the diagnostic significance of speech abnormalities. By addressing the issue of imbalanced datasets using sampling techniques, a hybrid model achieved high accuracy, precision, recall, and f1 score in detecting Parkinson's disease.
Article
Mathematics, Applied
Noor Afshan, Zohaib Mushtaq, Faten S. Alamri, Muhammad Farrukh Qureshi, Nabeel Ahmed Khan, Imran Siddique
Summary: This article proposes an innovative approach for diagnosing thyroid disease by combining adaptive synthetic sampling method with weighted average voting ensemble of two distinct super learners. The suggested methodology proves effective in enhancing accuracy of thyroid cancer identification.
Article
Computer Science, Information Systems
Ayesha Jabbar, Shahid Naseem, Tariq Mahmood, Tanzila Saba, Faten S. Alamri, Amjad Rehman
Summary: Brain tumors have become the leading cause of mortality worldwide due to the inability to diagnose them in a timely manner. The accuracy of the current tumor classification model needs to be improved for better therapies. Previous research has used CNN models for tumor detection, but this study proposes a hybrid model, the Caps-VGGNet, which achieved the highest level of effectiveness and superior efficacy in terms of accuracy, specificity, and sensitivity.
Article
Computer Science, Information Systems
Amjad Rehman, Ali Raza, Faten S. Alamri, Bayan Alghofaily, Tanzila Saba
Summary: Osteoarthritis is a common joint disease causing deterioration and impacting millions worldwide. It develops over time from joint wear and tears, leading to degeneration of joint cartilage, bone-to-bone contact, stiffness, discomfort, and restricted movement. The condition not only affects physical abilities but can also lead to psychological distress. Early detection is crucial for improving quality of life. In this study, a model using advanced deep learning and machine learning techniques, including a novel transfer learning-based feature engineering technique CRK, was developed to diagnose osteoarthritis in knee X-ray images with high accuracy (99%). The model's performance was validated through hyperparameter optimization and k-fold-based cross-validation.
Article
Computer Science, Information Systems
Zaid Nidhal Khudhair, Ahmed Nidhal Khdiar, Nidhal K. El Abbadi, Farhan Mohamed, Tanzila Saba, Faten S. Alamri, Amjad Rehman
Summary: Color information is not useful for distinguishing important edges and features in many applications. A new method based on singular value decomposition is introduced to transform an RGB image into grayscale, allowing for flexibility in producing gray images with varying contrasts. This method preserves more color information and accurately captures the intensity values of the image compared to traditional grayscale conversion methods, resulting in loss of color information. It has been found to be the most efficient method when compared to a similar approach of converting color images to grayscale.
Article
Mathematics, Applied
Fazeel Abid, Muhammad Alam, Faten S. Alamri, Imran Siddique
Summary: Energy operations and schedules are greatly influenced by load and energy forecasting systems. An effective system is necessary for a sustainable and fair environment. Advanced techniques, such as deep learning, have been used to predict energy consumption and learn long-term dependencies. In this study, a fusion of multi-directional gated recurrent unit (MD-GRU) with convolutional neural network (CNN) using global average pooling (GAP) is proposed for load and energy forecasting, showing improved accuracy compared to traditional methods.
Article
Computer Science, Information Systems
Deep R. Kothadiya, Chintan M. Bhatt, Amjad Rehman, Faten S. Alamri, Tanzila Saba
Summary: Deep learning has greatly advanced artificial intelligence in various fields such as computer vision, natural language processing, robotics science, and human-computer interaction. To ensure confidence and responsibility, deep learning applications need to explain the decisions and predictions of the model. Explainable AI research provides methods to interpret the outputs of trained neural networks, which is particularly important for computer vision tasks in domains like medical science and defense systems.
Article
Computer Science, Information Systems
Myasar Mundher Adnan, Mohd Shafry Mohd Rahim, Amjad Rehman Khan, Ahmed Alkhayyat, Faten S. Alamri, Tanzila Saba, Saeed Ali Bahaj
Summary: The increasing number of digital images has made it difficult to retrieve images accurately from large databases. Image annotation involves labeling images with descriptive keywords, but computers struggle to understand images in the same way that humans do. This study proposes an automatic image annotation (AIA) system that bridges the semantic gap between computer features and human interpretation to improve accuracy and reduce computational costs.
Article
Public, Environmental & Occupational Health
Faten S. Alamri, Edward L. Boone, Ryad Ghanam, Fahad Alswaidi
Summary: This study presents a method based on the SEIRD model for monitoring the dynamics of a pandemic in real time. The method uses a Bayesian paradigm and the MEWMA profile monitoring technique, and it is applied to COVID-19 data for Saudi Arabia, showing changes in parameter profiles corresponding to real world events.
JOURNAL OF INFECTION AND PUBLIC HEALTH
(2023)
Article
Mathematical & Computational Biology
Faten S. Alamri, Khalid Haseeb, Tanzila Saba, Jaime Lloret, Jose M. Jimenez
Summary: This study proposes a multimedia optimization model that combines edge computing intelligence and authentication strategies to address data management and transmission issues in IoT systems. The research results demonstrate that the proposed model outperforms comparable work in multiple metrics.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)
