Cloud Computing (CC) is the preference of all information technology (IT) organizations as it offers pay-per-use based and flexible services to its users. But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders. Intrusion Detection System (IDS) refers to one of the commonly utilized system for detecting attacks on cloud. IDS proves to be an effective and promising technique, that identifies malicious activities and known threats by observing traffic data in computers, and warnings are given when such threats were identified. The current mainstream IDS are assisted with machine learning (ML) but have issues of low detection rates and demanded wide feature engineering. This article devises an Enhanced Coyote Optimization with Deep Learning based Intrusion Detection System for Cloud Security (ECODL-IDSCS) model. The ECODL-IDSCS model initially addresses the class imbalance data problem by the use of Adaptive Synthetic (ADASYN) technique. For detecting and classification of intrusions, long short term memory (LSTM) model is exploited. In addition, ECO algorithm is derived to optimally fine tune the hyperparameters related to the LSTM model to enhance its detection efficiency in the cloud environment. Once the presented ECODL-IDSCS model is tested on benchmark dataset, the experimental results show the promising performance of the ECODL-IDSCS model over the existing IDS models.
White blood cells (WBC) or leukocytes are a vital component of the blood which forms the immune system, which is accountable to fight foreign elements. The WBC images can be exposed to different data analysis approaches which categorize different kinds of WBC. Conventionally, laboratory tests are carried out to determine the kind of WBC which is erroneous and time consuming. Recently, deep learning (DL) models can be employed for automated investigation of WBC images in short duration. Therefore, this paper introduces an Aquila Optimizer with Transfer Learning based Automated White Blood Cells Classification (AOTL-WBCC) technique. The presented AOTL-WBCC model executes data normalization and data augmentation process (rotation and zooming) at the initial stage. In addition, the residual network (ResNet) approach was used for feature extraction in which the initial hyperparameter values of the ResNet model are tuned by the use of AO algorithm. Finally, Bayesian neural network (BNN) classification technique has been implied for the identification of WBC images into distinct classes. The experimental validation of the AOTL-WBCC methodology is performed with the help of Kaggle dataset. The experimental results found that the AOTL-WBCC model has outperformed other techniques which are based on image processing and manual feature engineering approaches under different dimensions.
Osteosarcoma is one of the rare bone cancers that affect the individuals aged between 10 and 30 and it incurs high death rate. Early diagnosis of osteosarcoma is essential to improve the survivability rate and treatment protocols. Traditional physical examination procedure is not only a timeconsuming process, but it also primarily relies upon the expert's knowledge. In this background, the recently developed Deep Learning (DL) models can be applied to perform decision making. At the same time, hyperparameter optimization of DL models also plays an important role in influencing overall classification performance. The current study introduces a novel Symbiotic Organisms Search with Deep Learning-driven Osteosarcoma Detection and Classification (SOSDL-ODC) model. The presented SOSDL-ODC technique primarily focuses on recognition and classification of osteosarcoma using histopathological images. In order to achieve this, the presented SOSDL-ODC technique initially applies image pre-processing approach to enhance the quality of image. Also, MobileNetv2 model is applied to generate a suitable group of feature vectors whereas hyperparameter tuning of MobileNetv2 model is performed using SOS algorithm. At last, Gated Recurrent Unit (GRU) technique is applied as a classification model to determine proper class labels. In order to validate the enhanced osteosarcoma classification performance of the proposed SOSDL-ODC technique, a comprehensive comparative analysis was conducted. The obtained outcomes confirmed the betterment of SOSDL-ODC approach than the existing approaches as the former achieved a maximum accuracy of 97.73%.
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