Paddy (Oryza sativa) is one of the most consumed food grains in the world. The process from its sowing to consumption via harvesting, processing, storage and management require much effort and expertise. The grain quality of the product is heavily affected by the weather conditions, irrigation frequency, and many other factors. However, quality control is of immense importance, and thus, the evaluation of grain quality is necessary. Since it is necessary and arduous, we try to overcome the limitations and shortcomings of grain quality evaluation using image processing and machine learning (ML) techniques. Most existing methods are designed for rice grain quality assessment, noting that the key characteristics of paddy and rice are different. In addition, they have complex and expensive setups and utilize black-box ML models. To handle these issues, in this paper, we propose a reliable ML-based IoT paddy grain quality assessment system utilizing affordable sensors. It involves a specific data collection procedure followed by image processing with an ML-based model to predict the quality. Different explainable features are used for classifying the grain quality of paddy grain, like the shape, size, moisture, and maturity of the grain. The precision of the system was tested in real-world scenarios. To our knowledge, it is the first automated system to precisely provide an overall quality metric. The main feature of our system is its explainability in terms of utilized features and fuzzy rules, which increases the confidence and trustworthiness of the public toward its use. The grain variety used for experiments majorly belonged to the Indian Subcontinent, but it covered a significant variation in the shape and size of the grain.

DeepFakes pose significant risks to digital security by enabling realistic facial manipulations that can evade conventional visual inspection. This study presents an attention-enhanced EfficientNet-B7 framework with a Custom Soft Spatial Attention (CSSA) module designed to localize manipulation-sensitive facial regions, including eye boundaries, mouth contours, blending boundaries, and skin-texture discontinuities. Evaluation is conducted on three benchmarks, a balanced Kaggle image subset containing 20,000 facial images, Celeb-DF (v2) with 50,000 sampled frames, and FaceForensics++ Low Quality with 40,000 sampled frames. The proposed model achieves 93.28% held-out test accuracy on the Kaggle subset, representing a 4.94 percentage-point improvement over standard EfficientNet-B7 and a 1.83 percentage-point improvement over an EfficientNet-B7+CBAM variant. Cross-benchmark evaluation yields 87.45% accuracy on Celeb-DF (v2) and 87.31% on FaceForensics++ (LQ). The model achieves a held-out test accuracy of 93.28% and a ROC AUC of 0.97. With 69.8M parameters, 15.0 GFLOPs, a 266 MB model footprint, and 150 ms single-image inference latency under the stated hardware configuration, the framework provides a transparent accuracy-efficiency trade-off for operational screening. Ablation testing confirms a 4.94 percentage-point contribution from CSSA, and LIME-based qualitative analysis supports interpretation of the learned decision regions.