Diabetic retinopathy (DR) stands as a leading cause of global blindness. Early identification and prompt treatment are crucial in preventing vision impairment caused by diabetic retinopathy (DR). Manual screening of retinal fundus images is challenging and time-consuming. Additionally, there is a significant gap between the number of DR patients and the number of medical experts. Integrating machine learning (ML) and deep learning (DL) techniques is becoming a viable alternative to traditional DR screening techniques. However, the absence of a retinal dataset with standardized quality, the complexity of DL models, and the need for high computational resources are challenges. Therefore, in this study, we studied and analyzed the research landscape in integrating ML techniques in DR screening. In this regard, our work contributes significantly in several aspects. Initially, we identify and characterize images of the retinal fundus that are readily available. Then, we discuss commonly used preprocessing techniques in DR screening. In addition, we analyze the progress of ML techniques in DR screening. Lastly, we discussed existing challenges and showed future directions.

Diabetic retinopathy (DR) stands as a leading cause of global blindness. Early identification and prompt treatment are essential to prevent vision impairment caused by DR. Manual screening of retinal fundus images is challenging and time-consuming. Additionally, in low-income countries, there is a significant gap between the number of DR patients and ophthalmologists. Currently, machine learning (ML) and deep learning (DL) are becoming a viable alternative to traditional DR screening techniques. However, DL suffers a major limitation in resource-constrained devices because of its large model size and substantial computational demands. Knowledge distillation is a prominent technique for creating lightweight models, effectively transferring knowledge from a larger, complex model to a smaller, more efficient one without significant loss in performance. Therefore, in this research, a lightweight student model is proposed, which follows the MobileNet architectural design by utilizing depthwise separable convolutions. This design ensures efficient performance suitable for edge device deployment. For binary classification, our proposed model achieved an accuracy, precision, and recall of 98.38% on the APTOS 2019 dataset, whereas the proposed model achieved an accuracy of 93.03% for ternary classification on APTOS 2019.

Diabetic retinopathy (DR) stands as a leading cause of global blindness. Early identification and prompt treatment are essential to prevent vision impairment caused by DR. Manual screening of the retinal fundus image is challenging and time-consuming. Additionally, there is a significant gap between the number of diabetic retinopathy patients and medical experts. Integrating machine learning (ML) and deep learning (DL) techniques is becoming a viable alternative to traditional DR screening techniques. However, the lack of a quality-labeled retinal dataset, the complexity of DL models, and the need for high computational resources are challenges. Therefore, in this study, we studied and analyzed the research landscape in integrating ML techniques in DR screening. In this regard, our work contributes significantly in several aspects. Initially, we identify and characterize images of the retinal fundus that are readily available. Then, we discuss commonly used preprocessing techniques in DR screening. In addition, we analyze the progress of ML techniques in DR screening. Lastly, we discussed existing challenges and showed future directions.

The location recommendation system is designed to assist travelers or tourists to select destinations based on their personal preferences. Massive amount of data is being generated through various travel websites and social media platforms. Therefore, getting preferable suggestions is overwhelming. Additionally, research's has failed to build viable model considering both textual and numerical data simultaneously, as user preferences may be generated through either of these means. Therefore, we propose a collaborative filtering-based model that incorporates both textual and numerical data for better extraction of user preferences. Our approach involves using graded sentiment analysis and embedding techniques such as BERT, RoBERTa, DistilBERT, and MPNet as well as similarity measuring metrics such as cosine similarity, euclidean distance, Manhattan distance, Kendall rank, and Pearson correlation. Our proposed model, MPNet with Euclidean distance, has demonstrated promising results with a precision rate of 96% in recommending top 5 locations. The models performance make it a viable alternative for assisting individual by recommending personalized locations.