As software systems become more complex and configurable, more performance problems tend to arise from the configuration designs. This has caused some configuration options to unexpectedly degrade performance which de...
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Recently, cloud computing research, especially Virtual Machine based replication schemes and their applications, were becoming prevalent. A cloud data center (DC) comprises of hosts with an enormous of virtual machine...
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ISBN:
(数字)9798331508456
ISBN:
(纸本)9798331508463
Recently, cloud computing research, especially Virtual Machine based replication schemes and their applications, were becoming prevalent. A cloud data center (DC) comprises of hosts with an enormous of virtual machines (VMs) where determining replica quantity and replication order is a tedious task to minimize cost and bandwidth. Majority of the existing dynamic replication techniques rely on budget constraints and server failure rate to guarantee customer requirements. But, there is a strong need to determine replication order of servers to retain the performance and system stability. In this paper, we suggest two different algorithms for better selection with proper and intelligent placement of replicas in the cloud region. The proposed algorithms towards dynamic replication are request based replica selection (RRS) and ant colony optimization (ACO). The foremost proposed algorithm was request based replica selection used for obtaining the best selected replicas depends upon the VM performance ranking and failure probability based on the task requirement. Conversely, the subsequent offered algorithm is ACO that is helpful to find the best location to place the designated replica depends on the least-cost distance metric, and the replicas availability in order to encompass and regulate with the entire network load and the link bottlenecks. The performance of proposed techniques using CloudSim was evaluated against various pro-active fault tolerance strategies. The simulation results gives that dynamic replica selection provides efficient task replication when compared with the existing pro-active algorithms. Additionally, ACO realizes high availability, low cost, and less bandwidth utilization.
Breast cancer has recently overtaken cervical cancer as the predominant cancer type in Indian urban areas. Despite considerable research and the development of automated diagnostic machines, current methods are far fr...
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This paper investigates the integration of deep learning (DL) architectures with Recurrent Neural Networks (RNNs) for automated leukemia detection in Peripheral Blood Smear (PBS) images. Models such as DenseNet201, Ef...
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This study investigates the use of Auxiliary Classifier Generative Adversarial Networks (ACGANs) in addressing imbalanced data within the realm of cybersecurity. ACGANs generate synthetic data mimicking network attack...
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ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
This study investigates the use of Auxiliary Classifier Generative Adversarial Networks (ACGANs) in addressing imbalanced data within the realm of cybersecurity. ACGANs generate synthetic data mimicking network attacks, contributing to dataset balancing for improved model training. The research focuses on enhancing cybersecurity decision making by refining the accuracy of distinguishing between legitimate and malicious traffic. By leveraging ACGAN-powered machine learning, this project work demonstrates the potential for stronger, more accurate threat detection and integrity assessment, ultimately fostering more advanced and resilient intrusion detection systems.
This review delves into the dynamic realm of sign language detection, offering a meticulous exploration of techniques, challenges, and promising directions. Essential for empowering communication among individuals wit...
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Plant diseases lead to food loss and yield reduction, which is a serious challenge to food security globally. Researchers have suggested deep learning models, which are trained using plant disease data like PlantVilla...
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This paper presents an explainable artificial intelli-gence (XAI) model for automatic irrigation systems using Inter-net of Things (IoT) technology. The proposed system integrates soil moisture sensors, temperature se...
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ISBN:
(数字)9798350355468
ISBN:
(纸本)9798350355475
This paper presents an explainable artificial intelli-gence (XAI) model for automatic irrigation systems using Inter-net of Things (IoT) technology. The proposed system integrates soil moisture sensors, temperature sensors, and humidity sensors with a microcontroller to make data-driven irrigation decisions. A Random Forest classifier i s employed top redict irrigation requirements based on environmental parameters, with SHAP (SHapley Additive exPlanations) values providing transparency and interpretability of model decisions. The system demonstrates a 35 % reduction in water usage and a 20 % increase in crop yield compared to traditional irrigation methods. The integration of explainability enhances user trust and system adoption, making advanced agricultural technology accessible to farmers with varying technical expertise. Experimental results show that the Random Forest model outperforms XGBoost and SVM in accuracy (75%), precision (100% for non-irrigation decisions), and robustness for irrigation management.
The development of an advanced machine learning model for plant disease detection using deep learning methods is the research objective. A plant pathogen image dataset from Kaggle will be used for proper classificatio...
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ISBN:
(数字)9798331512248
ISBN:
(纸本)9798331512255
The development of an advanced machine learning model for plant disease detection using deep learning methods is the research objective. A plant pathogen image dataset from Kaggle will be used for proper classification of plant health based on various diseases, such as bacterial infection, fungi growth, pests, and viral diseases. Three deep learning models, namely CNN, ResNet, and MobileNet, will be trained on the dataset to make accurate plant health condition predictions. These models will classify plants according to health status and whether affected by bacteria, fungus, pests, virus, or are healthy. The proposed system will upload plant images, and the trained model will provide health predictions. This approach will serve as an effective tool for early detection, improving agricultural practices by enabling timely interventions.
Text summarization in Tamil language is an important problem in natural language processing and understanding and in information retrieval. Automatic text summarization is getting more attention nowadays as it helps i...
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ISBN:
(数字)9798331525439
ISBN:
(纸本)9798331525446
Text summarization in Tamil language is an important problem in natural language processing and understanding and in information retrieval. Automatic text summarization is getting more attention nowadays as it helps in saving time in decision making by conveying the information concisely. The primary objective of this experimental project is to propose a methodology to address the problem of summarization for Tamil language text documents, which can generate concise meaningful summaries using Graph Convolutional Network (GCN) and Google Multilingual Text-to-Text-Transfer-Transformer (Google mT5). GCN is used to extract important sentences for extractive summarization and Google mT5 to summarize the extracted sentences, which will give a concise summary of the input text document without losing the meaning of the documents. Experiments are carried out using Huggingface datasets, and the ROUGE toolkit is used to evaluate the precision, recall, and F1 score of summaries generated by the system and also human evaluation.
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