Water is essential for human survival. Humans can live without food for a few days but without water, a person can barely survive for 3–5 days. Various parts of the world, particularly under-developed countries, have regions where clean water is scarce, and humans living in such conditions have no access to clean water. Our solution provides information on whether water is contaminated or not. Moreover, it overcomes the delay time in getting the result of water contamination using traditional methods of up to 5–6 hrs. Our proposed method detects the colonies of the bacteria that are taken from the water sample (after gram staining) and then classifies the type of bacteria to whom it belongs and how much quantity of each bacterium causes infection to the human body. Bacteria detection is performed by a novel deep learning-based model with user-specified parameters. To improve our ability to detect dangerous bacteria including E. coli, yeast, and particles, we perform tests using datasets from a variety of researchers. On the test benchmark, the fine-tuned proposed model achieves 84.56% accuracy and provides the level of contamination in water.

Domain experts are striving to solve classification problems through the application of Machine Learning (ML) models, aiming for the highest accuracy. However, classifiers are inherently prone to misclassifications, especially when encountering unknown input data. This complicates their deployment in critical Cyber-Physical Systems (CPSs), where misclassifications can have detrimental effects on people, the environment, or infrastructure. This paper argues that ML classifiers should not be designed or evaluated in isolation but rather conceptualized as Critical System Classifiers (CSCs), which can reject uncertain predictions and trigger mitigation strategies in accordance to the encompassing CPS. We present an high-level architecture for CSCs that supports black-box integration, preprocessing for unknown detection, post-hoc calibration, and cost-sensitive thresholding, emphasizing the importance of evaluating classifiers using cost-aware metrics that account for rejections. We validate our proposal through experiments on tabular datasets related to failure prediction, intrusion and error detection, which are common applications of classifiers within CPSs. Key findings are: i) cost-sensitive evaluation promotes the usage of different classifiers compared to standard performance metrics, ii) tree-based models outperform statistical ones, classification-wise, iii) prediction calibration and rejection strategies provide a robust notion of confidence, and iv) combining multiple uncertainty-based rejectors achieves a favorable balance between high accuracy, low rejection rate, and cost. The analyses above were conducted using our publicly available CINNABAR GitHub repository for experimentations with CSCs. Overall, our study offers a system-level perspective, software architecture and implementation on classifier deployment in critical CPSs domains, paving the way for a trustworthy ML integration into real-world infrastructures.

Text, as a vital tool for communication, is playing an imperative role in modern society. Precise high-level text translation systems are essential requirements in a wide range of real-world applications, such as robot navigation, industrial automation, image search, and instant translation. Regardless of improved research, a series of grand challenges may still become upon when translating text automatically in the real-world from open scene images. The difficulties mainly stem from multiplicity and inconsistency of text in open scenes, complication and obstruction of backgrounds, and deficient imaging conditions in uncontrolled circumstances for open scene images. The existing deep learning-based text translation systems do not eliminate the text for translation, and these applications just replace text on the reconstructed scene. To address the abovementioned shortcomings, this study proposed a novel approach for open scene text translation. Our system consists of five modules including scene text detection, text recognition, text elimination, text translation, and text insertion along with scene reconstruction. The novelty presented by our model lies in the idea of first eliminating the text from the open scene for accurate translation and then reconstructs the translated text on the image for its proper alignment. We specifically modified the existing generative adversarial network (GAN) architecture for improved performance of text elimination by introducing a novel strategy of text and scene concatenation to reduce the overall loss function. For this purpose, we created a synthetic dataset to train our GAN for text elimination module. Experiments on various standard text translation systems demonstrate that our integrated system is able to outperform state-of-the-art approaches in terms of result quality. We have achieved 90.87% of precision, 83.66% of recall, 87.116% of F1-score, and reduced both losses ( $$l_1$$ l 1 and $$l_2$$ l 2 ) up to 50% which is remarkable upon state-of-the-art translation systems.

Employing cognitive robots, capable of throwing and catching, is a strategy aimed at expediting the logistics process within Industry 4.0’s smart manufacturing plants, specifically for the transportation of small-sized manufacturing parts. Since the flight of mechanically thrown objects is inherently unpredictable, it is crucial for the catching robot to observe the initial trajectory with utmost precision and intelligently forecast the final catching position to ensure accurate real-time grasping. This study utilizes multi-camera tracking to monitor mechanically thrown objects. It involves the creation of a 3D simulation that facilitates controlled mechanical throwing of objects within the internal logistics environment of Industry 4.0. The developed simulation empowers users to define the attributes of the thrown object and capture its trajectory using a simulated pinhole camera, which can be positioned at any desired location and orientation within the in-plant logistics environment of flexible manufacturing systems. The simulation facilitated ample experimentation to be conducted for determining the optimal camera positions for accurately observing the 3D interception positions of a flying object based on its apparent size on the camera’s sensor plane. Subsequently, a variety of calibrated multi-camera setups were experimented while placing cameras at identified optimal positions. Based on the obtained results, the most effective multi-camera configuration setup is derived. Finally, a training dataset is prepared for 3000 simulated throwing experiments where the initial part of the trajectory consists of observed interception positions, through derived best multi-camera setup, and the final part consists of actual positions. The encoder–decoder Bi-LSTM deep neural network is proposed and trained on this dataset. The trained model outperformed the current state-of-the-art by accurately predicting the final 3D catching point, achieving a mean average error of 5 mm and a root-mean-square error of 7 mm in 200 real-world test experiments.