While X-ray imaging is indispensable in medical diagnostics, it inherently carries with it those noises and limitations on resolution that mask the details necessary for diagnosis. B/W X-ray images require a careful balance between noise suppression and high-detail preservation to ensure clarity in soft-tissue structures and bone edges. While traditional methods, such as CNNs and early super-resolution models like ESRGAN, have enhanced image resolution, they often perform poorly regarding high-frequency detail preservation and noise control for B/W imaging. We are going to present one efficient approach that improves the quality of an image with the optimization of network transmission in the following paper. The pre-processing of X-ray images into low-resolution files by Real-ESRGAN, a version of ESRGAN elucidated and improved, helps reduce the server load and transmission bandwidth. Lower-resolution images are upscaled at the receiving end using Real-ESRGAN, fine-tuned for real-world image degradation. The model integrates Residual-in-Residual Dense Blocks with perceptual and adversarial loss functions for high-quality upscaled images with low noise. We further fine-tune Real-ESRGAN by adapting it to the specific B/W noise and contrast characteristics. This suppresses noise artifacts without compromising detail. The comparative evaluation conducted shows that our approach achieves superior noise reduction and detail clarity compared to state-of-the-art CNN-based and ESRGAN models, apart from reducing network bandwidth requirements. These benefits are confirmed both by quantitative metrics, including Peak Signal-to-Noise Ratio and Structural Similarity Index, and by qualitative assessments, which indicate the potential of Real-ESRGAN for diagnostic-quality X-ray imaging and for efficient medical data transmission.

Drone video shows flooding in a residential area of Chennai in southern India's Tamil Nadu state, after heavy rains. Schools and government offices are closed and more rainfall is forecast.

Deep learning techniques have revolutionized image classification by mimicking human cognition and automating complex decision-making processes. However, the deployment of AI systems in the wild, especially in high-security domains such as defence, is curbed by the lack of explainability of the model. To this end, eXplainable AI (XAI) is an emerging area of research that is intended to explore the unexplained hidden black box nature of deep neural networks. This paper explores the application of the eXplainable Artificial Intelligence (XAI) tool to interpret the underwater image classification results, one of the first works in the domain to the best of our knowledge. Our study delves into the realm of SONAR image classification using a custom dataset derived from diverse sources, including the Seabed Objects KLSG dataset, the camera SONAR dataset, the mine SONAR images dataset, and the SCTD dataset. An extensive analysis of transfer learning techniques for image classification using benchmark Convolutional Neural Network (CNN) architectures such as VGG16, ResNet50, InceptionV3, DenseNet121, etc. is carried out. On top of this classification model, a post-hoc XAI technique, viz. Local Interpretable Model-Agnostic Explanations (LIME) are incorporated to provide transparent justifications for the model's decisions by perturbing input data locally to see how predictions change. Furthermore, Submodular Picks LIME (SP-LIME) a version of LIME particular to images, that perturbs the image based on the submodular picks is also extensively studied. To this end, two submodular optimization algorithms i.e. Quickshift and Simple Linear Iterative Clustering (SLIC) are leveraged towards submodular picks. The extensive analysis of XAI techniques highlights interpretability of the results in a more human-compliant way, thus boosting our confidence and reliability.

- In order to keep aquatic ecosystems safe and healthy, it is imperative that cleaning be done frequently. This research suggests the use of autonomous underwater rovers for effective underwater cleaning as a novel approach to this issue. The enhanced sensing and navigational capabilities of the autonomous rovers enable them to independently navigate underwater environments and find and remove underwater garbage and uneaten fish feed which can be recycled. The suggested solution not only does away with the requirement for human divers, but also provides a more effective and affordable technique for underwater cleaning. The paper also examines the creation, testing, and potential of the autonomous underwater rovers.

Freshworks makes it fast and easy for businesses to delight their customers and employees. We do this by taking a fresh approach to building and delivering software that is affordable, quick to implement, and designed for the end-user. More than 50,000 companies -- from startups to public companies -- around the world use Freshworks software-as-a-service to enable a better customer experience (CRM) and employee experience (ITSM) Headquartered in San Mateo, California, Freshworks has a dedicated team operating from 13 global locations to serve customers, including American Express, Sony, Vice Media, TaylorMade, Sotheby's, Stitchfix, OfficeMax, Multichoice, Delivery Hero, ITV, and Klarna. Freshworks transforms the way world-class organizations collaborate with customers and co-workers. The suite includes Freshdesk (omnichannel customer support), Freshsales (sales automation), Freshmarketer (marketing automation), Freshservice (IT service desk).

At Freshworks, we are creating a global workplace that enables everyone to find their true potential, purpose, and passion irrespective of their background, gender, race, sexual orientation, religion and ethnicity. We are committed to providing equal opportunity for all and believe that diversity in the workplace creates a more vibrant, richer work environment that advances the goals of our employees, communities and the business.

If you share our passion for ushering in the era of the clean electron, we look forward to learning what you would uniquely bring to Arcadia! Visit www.arcadia.com.

At Kinaxis, who we are is grounded in our common belief that people matter. Each one of us plays an important part in accomplishing our work, building our culture and making a global impact. Every day, we're empowered to work together to help our customers make fast, confident planning decisions. This is how we create a better planet – for each other, for our customers and for generations to come. Our cloud-based platform RapidResponse ensures that the products we need – everything from medicine and cars, to day-to-day items like toothpaste – make it to market and into our hands when we need them with minimal ecological footprint.

Weare looking for a new colleague who will join us on the mission of furthergrowth of Hyperautomation at the DPDHL. The ideal candidate will have bigideas, curious mindset and automation part of DNA that fosters an environmentof collaboration and creativity! Asthe RPA DevOps Engineer, you will be a member of global Intelligent AutomationTeam, team responsible for enterprise level technologies such RPA, IntelligentOCR or Business Process Management. In this role you will be responsible forfull automations delivery lifecycle, namely Agile automations development,proactive handling of operations tasks in production and overall acting as theadvocate of RPA technology.

We are looking for Lead Engineer for the Machine Learning (ML) engineering development team. The primary focus will be to gather requirements from Data Scientists and Product teams, and come up with the optimal solution. Then design, implement and monitor these services for different ML use-cases. You will be working with Data Scientists to implement ML algo's, build and deploy API's to serve models. At Freshworks, we are creating a global workplace that enables everyone to find their true potential, purpose, and passion irrespective of their background, gender, race, sexual orientation, religion and ethnicity.