The “WaterHyacinth” dataset, a recently gathered collection of images featuring four distinct species of Water hyacinth from different regions of Bangladesh, is presented in this article. There are four different classifications: Lemna minor, Eichhornia crassipes, Monochoria korsakowii, and Pistia stratiotes. The collection consists of 1790 original images and in addition 4050 augmented photos of Water hyacinth species. Every original picture was captured with the appropriate background and in sufficient natural light. Every image was correctly placed in its corresponding subfolder, providing optimal use of the pictures by various machine learning and deep learning models. Researchers could make major progress in agriculture, environmental monitoring, aquatic science, and remote sensing domains by utilizing this enormous dataset and various machine learning and deep learning approaches. In addition to opening opportunities for significant developments in these domains, it offers an essential asset for further study.

Medical imaging plays a crucial role in the diagnosis of various diseases, including cancer, cardiovascular conditions, and respiratory disorders such as asthma, pneumonia, and even COVID-19. Nowadays, Deep Learning (DL) has demonstrated remarkable accuracy in disease detection and diagnosis using medical imaging such as X-rays, CT scans, and MRIs. However, DL models mostly require access to sensitive patient data not only to capture the images but also to store them in the data cloud, which raises major privacy concerns. To solve this problem, several techniques such as Differential Privacy (DP) and Federated Learning (FL), have been developed. These methods allow models to learn useful patterns without exposing personal details. Building on these developments, the key gap this review targets is the lack of a recent, side-by-side analysis that connects medical tasks, DL architectures, FL designs, and DP budgets with real-world performance and resilience against privacy attacks. Motivated by this gap, we conducted a systematic review of the literature (SLR) of studies published between 2023 and 2025 that used DL principles with privacy-preserving methods for medical diagnosis. Utilizing the PRISMA framework, we reviewed 23 peer-reviewed articles and grouped them into four categories: included COVID-19, skin lesions, Alzheimer’s disease, and other conditions such as diabetic retinopathy, kidney disease, and heart problems. We sort results by modality, model family, FL topology, DP mechanism, and reported threat model. Furthermore, the review significantly represents that CNN-based models with DP and FL methods are the most common choices, and developed pipelines with these models often achieve accuracy above 90%. At the same time, the review found gaps such as limited real-world testing, a lack of diverse datasets, and underuse of advanced DP approaches. Based on these results, we recommend building larger and more varied datasets, improving privacy-utility trade-offs, and testing models in clinical environments.

We introduce the “Ripen Banana” dataset, a newly developed collection featuring two distinct classes of ripen banana images: carbide and non-carbide. The dataset contains images from raw to ripe bananas that have been ripened with carbide and without carbide. The images collected from various locations in Sirajganj, Bangladesh. It contains 1404 original images and 6410 augmented images, resulting in 7814 images overall. The original images were taken against carefully selected white backgrounds. This was made it by evaluating these images for their sharpness and brightness levels to ensure they meet the quality standards that are required. After that, we organize each image into specified subfolders to ensure simple and fast access for large range of machine learning and deep learning models. This dataset offers significant opportunities for advancements in agricultural practices, nutrition and the food chain, food environment monitor, and computer vision technologies. This “Ripen Banana”

Smartphones are now deeply embedded in daily life, but excessive dependence may increase the risk of nomophobia, which is associated with anxiety, sleep disruption, and reduced productivity. Existing screening methods mainly rely on self-reported questionnaires, which are subjective and difficult to scale for continuous monitoring. This study proposes a privacy-preserving federated deep learning framework for three-level nomophobia risk prediction (Normal, Mild, and Severe) using smartphone usage logs while keeping raw user data on local devices. The proposed pipeline uses a publicly available secondary dataset with 1000 original records and expands it to 100,000 records through constraint-aware synthetic augmentation. A continuous risk score is computed from standardized smartphone usage indicators and then converted into three classes using tertile-based thresholds. Several local architectures, including CNN, MLP, ResMLP, Wide & Deep, and a lightweight TabNet-style gated model, are evaluated under FedAvg. In the reported experiments, differential privacy is enabled through DP-SGD with gradient clipping and Gaussian noise. To protect update transmission, the framework applies protected update sharing through encrypted transport of masked updates. Each client masks its local update and encrypts the masked payload before transmission. This mechanism improves communication confidentiality and reduces the direct exposure of client updates. Under a fixed federated setup with five clients and 25 communication rounds, tabular models achieved near-ceiling performance on the constructed test set. The MLP achieved 99.12% accuracy, 99.12% F1-score, 0.9868 MCC, and 0.9997 AUC, while Wide & Deep achieved 98.95% accuracy, 98.95% F1-score, 0.9843 MCC, and 0.9997 AUC. In contrast, sequential models such as RNN and LSTM showed near-random performance, suggesting that the current aggregated feature representation is better suited to tabular learning than temporal modeling. These results indicate that the proposed federated pipeline can effectively learn the constructed nomophobia risk labels while preserving local data ownership. However, because the labels are derived from usage features rather than clinical or psychometric assessment, the findings should be interpreted as proof-of-concept results for constructed risk labels rather than evidence of clinical diagnostic validity.

The rapid growth of urban populations and consumption patterns has significantly increased the volume and complexity of municipal waste streams, including recyclable, organic, hazardous, and electronic waste. Traditional waste management approaches such as manual sorting and fixed collection schedules struggle to handle the diversity and scale of modern waste systems efficiently. Consequently, recent research has explored artificial intelligence (AI), computer vision, and Internet of Things (IoT) technologies to automate waste classification and enable intelligent monitoring and management of waste infrastructures. However, the rapidly expanding literature in this domain is fragmented across different datasets, sensing platforms, and experimental settings, making it difficult to obtain a comprehensive understanding of current technological progress. To address this gap, this study conducts a systematic literature review using the PRISMA methodology, analyzing 50 rigorously selected peer-reviewed studies on automated waste classification and intelligent waste management systems. The protocol was registered in the Open Science Framework (OSF). The reviewed works are examined in terms of model architectures, sensing infrastructures, datasets, and system capabilities. The analysis shows that convolutional neural network architectures such as ResNet, MobileNet, and EfficientNet, together with object detection frameworks including YOLO and Faster R-CNN, dominate current visual waste classification research; however, emerging studies increasingly explore hybrid architectures combining convolutional networks with transformer-based models to improve contextual understanding and scalability. IoT-enabled smart bins equipped with ultrasonic sensors, load cells, and environmental sensors are widely used for real-time waste monitoring. The review also reveals a recent surge of research activity, particularly in Asia, with increasing contributions from major publishers such as Elsevier and MDPI. Despite promising experimental performance, most systems remain limited to small curated datasets and pilot-scale deployments, highlighting a gap between laboratory research and real-world implementation. These findings provide a consolidated overview of current research trends and identify key challenges for developing scalable and practical intelligent waste management systems.