This discussion explores the evolution of serverless computing, highlighting its origins, rise in importance, and the challenges it presents. Serverless computing represents a significant shift from traditional, hardw...
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This discussion explores the evolution of serverless computing, highlighting its origins, rise in importance, and the challenges it presents. Serverless computing represents a significant shift from traditional, hardware-dependent environments to cloud-based, code-centric architectures. The concept of serverless emerged with the introduction of platforms like Google App Engine and AWS Lambda, freeing developers from server management concerns and enabling them to focus solely on code development. The integration of serverless computing with DevOps practices is examined, emphasizing the role of Infrastructure as Code (IaC), version control, continuous integration (CI), and continuous deployment (CD). These practices are essential for streamlining the development and deployment of serverless applications, enhancing consistency, and promoting collaboration between development and operations teams. Furthermore, the discussion addresses the specific challenge of cold starts in serverless computing and how DevOps practices can help mitigate their impact. Strategies such as proactive monitoring, scaling policies, scheduled warm-up, provisioned concurrency, performance testing, automation, and continuous optimization are presented as solutions to minimize cold start delays. Lastly, the importance of Testing Automation within the CI/CD pipeline is highlighted. Through rapid feedback, comprehensive testing, consistency, regression testing, and scalability, Testing Automation ensures the quality and reliability of software in a fast-paced development environment, aligning with the principles of DevOps.
In the realm of intrusion detection, the burgeoning complexity of cyber threats necessitates innovative approaches to fortify network security. Federated learning (FL) is proposed to enhance intrusion detection system...
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In the realm of intrusion detection, the burgeoning complexity of cyber threats necessitates innovative approaches to fortify network security. Federated learning (FL) is proposed to enhance intrusion detection systems (IDS) in Internet of Things (IoT) networks without compromising privacy. In this work, data is divided into silos, each having a trainer assigned to it, in cross-silo FL. The proposed model centralizes the coordination of the learning process while retaining the decentralized nature of data storage. IoT devices across the network contribute local knowledge to a central server, which orchestrates model training and updates. This approach enables a seamless amalgamation of insights, fostering a more comprehensive understanding of emerging threats and anomalies within the network. The research delves into designing and implementing a cross-silo FL framework for intrusion detection, focusing on optimizing communication protocols, minimizing latency, and ensuring data privacy compliance. To provide exposure to a wide variety of pertinent features and scenarios, the study provides a robust FL environment. It makes use of a diversified dataset taken from the IoTID20 dataset. Training the model on a local system and a local area network (LAN) shows flexibility. The model's efficiency is demonstrated in real-world deployment settings, predicting anomalies with an astounding accuracy rate of up to 98.92%.
Oral cancer (OC) is a prevalent malignancy in India, ranking sixth nationally and thirteenth globally. The ICMR National Cancer Repository Program forecasts 15.7 lakh cancer cases by 2025, up from 14.6 lakh in 2014. I...
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Oral cancer (OC) is a prevalent malignancy in India, ranking sixth nationally and thirteenth globally. The ICMR National Cancer Repository Program forecasts 15.7 lakh cancer cases by 2025, up from 14.6 lakh in 2014. India's diverse cancer occurrence patterns pose significant challenges to prevention and treatment. Regular health checks and increased risk factor awareness are crucial for improving OC survival rates. In this study, we explore the field of oral epithelial dysplasia pathology by using deep learning (DL)-based models to segment 900 carefully curated images of the histopathology of oral epithelial dysplasia at a 100x magnification from the biopsy slides. We use a two-pronged training strategy that includes the Vanilla U-Net model and a fine-tuned U-Net variation. Our results show significant differences in performance, with the optimized U-Net model performing better. To be more precise, the modified U-Net model produces the following results: Intersection over Union (IoU): 95.24%;Precision: 98.77%;Recall: 98.19%;and F1_Score: 97.55%. Additional evaluations utilizing additional native histopathology pictures of OC validate the resilience of our training models, resulting in the effective segmentation of epithelial layers. This study adds to our understanding of the pathophysiology of OC and highlights the potential of DL methods for accurate diagnosis and treatment.
This paper seeks to leverage deep neural networks for predicting insurance claims by the automobile customers based on their characteristics and past behavior. With increasing power of storage and computation technolo...
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This paper seeks to leverage deep neural networks for predicting insurance claims by the automobile customers based on their characteristics and past behavior. With increasing power of storage and computation technologies, insurance companies worldwide are looking forward to improving management of claims. Consequently, such an effort will enhance companies' profits and increase customer satisfaction. A key challenge for insurance companies is to accurately predict the cost of insurance for automobile drivers. Drivers who are cautious in their driving are to be charged with fair insurance amount, conversely risky drivers are to be penalized. This paper provides an application of deep learning in predicting the likelihood of raising an auto claim for insurance by the customers. This paper used the data of Porto Seguro, one of the largest auto insurance companies in Brazil which is made available on the Kaggle website. Several factors that determine a file for auto claim for the insurance are considered for the task of prediction. Exploration of the data is carried out to understand the characteristics of auto claims and a model is trained for the task of predicting auto claims. Furthermore, the model's hyperparameters are fine-tuned to alleviate the problem of over-fitting. Experimental results show that the deep learning model has achieved better accuracies over baseline models.
Rivers and freshwater bodies are vital for ecosystems and biodiversity, and provide resources for human consumption and industry. Monitoring and forecasting water quality is essential for ecological integrity and publ...
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Rivers and freshwater bodies are vital for ecosystems and biodiversity, and provide resources for human consumption and industry. Monitoring and forecasting water quality is essential for ecological integrity and public health. Machine learning (ML) techniques offer significant promise in this area, analyzing large datasets to predict key water quality parameters like NH4 (ammonium), which indicates both natural processes and pollution events. This research investigates the application of four ML models-Gradient Boosting Regressor, Support Vector Regressor (SVR), Long Short-Term Memory (LSTM) networks, and Multi-Layer Perceptron (MLP) Regression-for river water quality forecasting. Our study aims to compare and evaluate their performance. The findings reveal that the MLP Regression model outperformed the others, achieving a Mean Squared Error (MSE) of 0.13065. This research provides valuable insights into the efficacy of ML models for water quality forecasting, supporting decision-makers, environmentalists, and policy developers in preserving and managing freshwater resources efficiently. The comparative analysis also offers a template for similar studies in other environmental domains.
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