Diabetic Retinopathy (DR) stands as a significant global cause of vision impairment, underscoring the critical importance of early detection in mitigating its impact. Addressing this challenge head-on, this study introduces an innovative deep learning framework tailored for DR diagnosis. The proposed framework utilizes the EfficientNetB0 architecture to classify diabetic retinopathy severity levels from retinal images. By harnessing advanced techniques in computer vision and machine learning, the proposed model aims to deliver precise and dependable DR diagnoses. Continuous testing and experimentation shows to the efficiency of the architecture, showcasing promising outcomes that could help in the transformation of both diagnosing and treatment of DR. This framework takes help from the EfficientNet Machine Learning algorithms and employing advanced CNN layering techniques. The dataset utilized in this study is titled ’Diagnosis of Diabetic Retinopathy’ and is sourced from Kaggle. It consists of 35,108 retinal images, classified into five categories: No Diabetic Retinopathy (DR), Mild DR, Moderate DR, Severe DR, and Proliferative DR. Through rigorous testing, the framework yields impressive results, boasting an average accuracy of 86.53% and a loss rate of 0.5663. A comparison with alternative approaches underscores the effectiveness of EfficientNet in handling classification tasks for diabetic retinopathy, particularly highlighting its high accuracy and generalizability across DR severity levels. These findings highlight the framework’s potential to significantly advance the field of DR diagnosis, given more advanced datasets and more training resources which leads it to be offering clinicians a powerful tool for early intervention and improved patient outcomes.

The world has changed considerably in the previous two decades. Today, people are facing extreme poverty, global warming, and unwanted climate changes. The economic gap between countries is continuously growing. Moreover, with the expanding influence of technology, governance is getting more difficult. To address these issues, the UN announced Sustainable Development Goals (SDGs), also called Global Goals, in 2015. These goals fill in as an overall source of inspiration to annihilate poverty, protect the environment, and guarantee that all individuals live in harmony and thrive by 2030. The 17 SDGs are interconnected in that they recognize that activities in a single region sway result in others and that improvement should adjust to social, monetary, and natural sustainability. The SDGs intend to kill poverty, hunger, AIDS, and gender discrimination against women and girls. The COVID-19 epidemic, on the other hand, has hampered attempts to accomplish the 2030 Agenda for Sustainable Development. As a result, the impact of these SDGs must be thoroughly studied and analyzed. As a result, the purpose of this research is to examine the SDG before and after Covid-19, as well as how they have influenced various national and international markets. The research also assesses the 17 SDGs in each of India's 29 states in depth. Since SDGs have a larger scope, this paper predicts the SDG-9 scores of few countries like UAE, New Zealand, Japan, India, Germany, China, Bhutan, and USA.

The number of smartphone users has increased from 3.6 billion in 2016 to 6.25 billion by 2021, which shows that mobile phone usage has increased dramatically over the past few years. This is due to the development of mobile computing applications like commerce, healthcare, e-learning, etc. The use of mobile devices has resulted in an exponential rise in the amount of data generated and as a result the amount of energy consumed has increased. This is where cloud computing plays a major role. Cloud computing has transformed traditional mobile computing. The new mobile cloud not only provides on-demand services but also data storage and increased energy efficiency. Through mobile computing based on cloud computing, mobile device functions can be virtualized, reducing power consumption. In this paper, the authors survey application and potential of mobile cloud computing and present the energy-efficient ways. Also, the paper discusses development opportunities of mobile cloud computing. The research also mentions some of the major challenges in current mobile computing technology.