The major cause of death globally is heart disease and it is increasing day by day. It is difficult to diagnose the heart disease at early stages until a cardiac issue occurs. Huge amount of heart disease data is available in the health care sector such as in clinics, hospitals etc. However, this data is not intelligently handled to extract useful knowledge. Machine learning techniques help in turning this medical data into useful knowledge. Machine learning is used to design such decision support systems (DSS) that can learn and improve from their past experiences. Recently, deep learning has gained the interest of industry and academics. This research paper focuses on diagnosis of heart disease with high accuracy. In the proposed approach, Keras based deep learning model is used for the computation of results using dense neural network. The proposed model is tested with different combination of hidden layers in dense neural network, started from 3layers to 9layers. Each hidden layer uses 100 neurons and activation function as Relu. Multiple benchmark heart disease datasets have been used for the analysis. Individual as well as Ensemble models are evaluated on all heart disease datasets. Moreover, dense neural network is also evaluated on all datasets using accuracy, sensitivity, specificity and f-measure. Different combination of layers performed well on different datasets due to different categories of attributes. This extensive experimentation is performed to show the effectiveness of proposed model. The analysis of results shows that proposed model based on deep learning performed significantly better in terms of accuracy, sensitivity and specificity as compared to individual and other ensemble techniques on all heart disease datasets.

Epilepsy is a common neurological condition. The effects of epilepsy are not restricted to seizures alone. They comprise a wide spectrum of problems that might impair and reduce quality of life. Even with medication, 30% of epilepsy patients still have recurring seizures. An epileptic seizure is caused by significant neuronal electrical activity, which affects brain activity. EEG shows these changes as high-amplitude spiky and sluggish waves. Recognizing seizures on an electroencephalogram (EEG) manually by a professional neurologist is a time-consuming and labor-intensive process, hence an efficient automated approach is necessary for the identification of epileptic seizure. One technique to increase the speed and accuracy with which a diagnosis of epileptic seizures could be made is by utilizing computer-aided diagnosis systems that are built on deep neural networks, or DNN. This study introduces a fusion of recurrent neural networks (RNNs) and bi-directional long short-term memories (BiLSTMs) for automatic epileptic seizure identification via EEG signal processing in order to tackle the aforementioned informational challenges. An electroencephalogram’s (EEG) raw data were first normalized after undergoing pre-processing. A RNN model was fed the normalized EEG sequence data and trained to accurately extract features from the data. Afterwards, the features were passed to the BiLSTM layers for processing so that further temporal information could be retrieved. In addition, the proposed RNN-BiLSTM model was tested in an experimental setting using the freely accessible UCI epileptic seizure dataset. Experimental findings of the suggested model have achieved avg values of 98.90%, 98.50%, 98. 20%, and 98.60%, respectively, for accuracy, sensitivity, precision, and specificity. To further verify the new model’s efficacy, it is compared to other models, such as the RNN-LSTM and the RNN-GRU learning models, and is shown to have improved the same metrics by 1.8%, 1.69%, 1.95%, and 2.2% on using 5-fold. Additionally, the proposed method was compared to state-of-the-art approaches and proved to be a more accurate categorization of such techniques.

The overarching goal of this research was to examine the state of satellite imagery security in relation to its deteriorating form due to rising demand. The most common approaches to safeguarding satellite images during transmission across transmission networks, which are not protected by standard encryption, are the focus of this investigation. Since satellite imagery can be encrypted both in transit and while stored on a computer’s hard drive, we put the suggested Image Encryption System to the test by applying it to a collection of satellite photos. Concurrently encrypting data and running MapReduce jobs is key to the study methodology employed. This will be carried out in the Hadoop ecosystem, where an innovative method of analysing random numbers for use in Image encryption will be put to the test. The encryption was processed using MapReduce in the Hadoop ecosystem. Images were saved as BMP files with added security metadata. The evaluation of experiments was based on four (4) indicators. It was found that the processing time for batch encryption calculations grew in proportion to the amount of computations. All cluster, map, and reduction processes were put to the test using encrypted images, exposing load balancing difficulties and inefficiencies. Histogram analysis, the basis of an image encryption technique, provides evidence that the encrypted pixel values are consistent. Therefore, compared to other methods, such as a histogram or information entropy, this one is superior. Because of how it was crafted, it can withstand even the most sophisticated attacks without being compromised.

Optimal agricultural methods need precise crop health and ecological strain monitoring. This study proposes a novel data science strategy to improve crop health prediction and stress assessment. ResXceNet-HBA is a cutting-edge classification model that uses ResNet blocks, Xception modules with Adaptive Depthwise Separable Convolutions, and HBA-optimized parameters. This model uses HBA’s Dynamic Exploration-Exploitation Balance-fine-tuned Dynamic Feature Recalibration and adaptive convolutions. Imputation Weight Crop Labels (WICL) to accurately fill in missing data, Localised Feature Scaling (LFS) and Adaptive Feature Discretization (AFD) to standardize and categorize features, and the Environmental Stress Factor (ESF) to evaluate crop stress address data problems ASRFS and Crop-Specific Environmental Impact Weighting increase model performance. Our system also employs Adaptive Synthetic Resampling with Environmental Context. Using novel measures including the Crop Type Generalisation Score (CTGS) and Environmental Sensitivity Index (ESI), the ResXceNet-HBA model achieved 98.5% accuracy, 98.2% precision, 98.7% recall, and 98.4% F1-Score. These results beat ResNet, CNN, and Inception V2. The model executed in 50.9 seconds, faster than the alternatives. The confusion matrix exhibits minimal false positives and negatives, suggesting good prediction accuracy. ResXceNet-HBA’s statistics and resource optimization value increases. Precision farming and sustainable agriculture benefit from our strategy’s significant environmental stress and crop health assessments.

In recent years, the rapid evolving Cloud Computing technologies multiply challenges including minimizing power consumption and meeting Quality-of- Services (QoS) requirements in the presence of heavy workloads from a large number of users using shared computing resources. Powering a middle-sized data center normally consumed 80,000kW power every year and computer servers consume around 5% of the global power [1]. In order to address the skyrocket energy cost from the high level resource management aspect, we propose an energy efficient job scheduling approach based on a modified version of Weighted Round Robin scheduler that incorporates VMs reuse and live VM migration without compromising the Service Level Agreement (SLA). Enhanced Weighted Round Robin (EWRR) algorithm enhanced scheduler can monitor the running VMs status for possible VM consolidation or Migration. In addition, VM Manager observes the VMs utilization rate to start live migration from the over-utilizing Processing Element (PE) to under-utilized PEs or to the hibernated PEs by sending WOL (Wake-On-LAN) signal to activate them. Moreover, we have integrated our Dynamic Voltage and Frequency Scaling (DVFS) algorithm in CPU utilization model to specify the required frequency for each task depending on the task complexity and the deadline.

The transaction and market of bitcoin is volatile, meaning it’s uncertain because it changes frequently. There have been a number of research studies that have presented bitcoin price prediction models, but none of them have looked at the controlling variables linked with bitcoin transaction timestamps. It might be that price is not the only key criteria influencing bitcoin transactions, or the available model for bitcoin price prediction is yet to consider timestamp as a determining factor in its transaction. A better and more accurate model would be required to predict how the Timestamp influences changes of bitcoin transactions. That is why this current study utilized a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the prediction of timestamp influence on Bitcoin value. Bitcoin historical datasets which are converted to a nonlinear regression into a “well-formulated” statistical problem in the manner of a ridge regression are used. Simulation analysis indicates that bitcoin digital currency’s performance variation is highly influenced by its transaction timestamp with the prediction accuracy of 96%. The contributions of this research lies with the fact that specific Bitcoin transaction events repeat themselves over and over again, meaning that the Open-Price, High-Price, Low-Price, and Close-Price of Bitcoin price over timestamp developed a pattern that was predicted by NARX with less That means those involved in the transaction of bitcoin at the wrong timestamp will certainly face the uncertainty negative effect of the bitcoin market.