Optimizing Key Distribution in Peer to Peer Network Using B-Trees

Peer to peer network architecture introduces many desired features including self-scalability that led to achieving higher efficiency rate than the traditional server-client architecture. This was contributed to the highly distributed architecture of peer to peer network. Meanwhile, the lack of a centralized control unit in peer to peer network introduces some challenge. One of these challenges is key distribution and management in such an architecture. This research will explore the possibility of developing a novel scheme for distributing and managing keys in peer to peer network architecture efficiently

Administration Security Issues in Cloud Computing

This paper discover the most administration security issues in Cloud Computing in term of trustworthy and gives the reader a big visualization of the concept of the Service Level Agreement in Cloud Computing and it’s some security issues. Finding a model that mostly guarantee that the data be saved secure within setting for factors which are data location, duration of keeping the data in cloud environment, trust between customer and provider, and procedure of formulating the SLA

Improving Hadoop Performance by Using Metadata of Related Jobs in Text Datasets Via Enhancing MapReduce Workflow

Cloud Computing provides different services to the users with regard to processing data. One of the main concepts in Cloud Computing is BigData and BigData analysis. BigData is a complex, un-structured or very large size of data. Hadoop is a tool or an environment that is used to process BigData in parallel processing mode. The idea behind Hadoop is, rather than send data to the servers to process. Hadoop divides a job into small tasks and sends them to servers. These servers contain data, process the tasks and send the results back to the master node in Hadoop. Hadoop contains some limitations that could be developed to have a higher performance in executing jobs. These limitations are mostly because of data locality in the cluster, jobs and tasks scheduling, CPU execution time, or resource allocations in Hadoop. Data locality and efficient resource allocation remains a challenge in cloud computing MapReduce platform. We propose an enhanced Hadoop architecture that reduces the computation cost associated with BigData analysis. At the same time, the proposed architecture addresses the issue of resource allocation in native Hadoop. The proposed architecture provides an efficient distributed clustering approach for dedicated cloud computing environments. Enhanced Hadoop architecture leverages on NameNode’s ability to assign jobs to the TaskTrakers (DataNodes) within the cluster. By adding controlling features to the NameNode, it can intelligently direct and assign tasks to the DataNodes that contain the required data. Our focus is on extracting features and building a metadata table that carries information about the existence and the location of the data blocks in the cluster. This enables NameNode to direct the jobs to specific DataNodes without going through the whole data sets in the cluster. It should be noted that newly build lookup table is an addition to the metadata table that already exists in the native Hadoop. Our development is about processing real text in text data sets that might be readable such as books, or not readable such as DNA data sets. To test the performance of proposed architecture, we perform DNA sequence matching and alignment of various short genome sequences. Comparing with native Hadoop, proposed Hadoop reduced CPU time, number of read operations, input data size, and another different factors

Enhancing Hadoop MapReduce Performance for Scientific Data using NoSQL Database

Scientific data sets usually have similar jobs that are frequently applied to the data by different users. In addition, many of these data sets are unstructured, complex, and required fast and simple processing. In order to increase the performance of the existing Hadoop and MapReduce algorithm, it is necessary to develop an algorithm based on the type of data sets and requirements of the jobs. In this poster, we represent a Hadoop MapReduce environment that uses genomic and biological data as an example of unstructured and complex data

The internet of things healthcare monitoring system based on MQTT protocol

In overpopulated nations, the need for medical treatment is increasing along with the population; healthcare difficulties are becoming more common. The population's need for high-quality care is growing despite decreasing treatment costs. Because of technological improvements, a machine may remotely monitor health, which is more reliable than manual monitoring. The time required for individualized training may be shortened, and the dependability of complicated equipment may be increased. This study recommends a real-time remote patient monitoring system based on the Internet of Things (IoT) to assure the accuracy of the vital real-time signal. The vital real-time signal is sent from the proposed method to the website using the Message Queuing Telemetry Transport (MQTT) protocol. This work aims to read and analyze patients’ vital signs and reduce the latency while transmitting the signals

Administration Security Issues in Cloud Computing

This paper discover the most administration security issues in Cloud Computing in term of trustworthy and gives the reader a big visualization of the concept of the Service Level Agreement in Cloud Computing and it’s some security issues. Finding a model that mostly guarantee that the data be saved secure within setting for factors which are data location, duration of keeping the data in cloud environment, trust between customer and provider, and procedure of formulating the SLA

Optimized recurrent neural network mechanism for olive leaf disease diagnosis based on wavelet transform

Plant diseases can significantly reduce food and agricultural production, leading to major quality, quantity, and economic losses. Plant disease deficits are usually reduced by early diagnosis through visual observation. Significant plant species grown in specific parts of the world include olives. Depending on the place where an olive tree is produced, many diseases can affect it. Traditional plant disease detection is ineffective and time-consuming. Therefore, this paper developed an in-depth evaluation of RNN architecture with an Ant Colony Optimization algorithm for disease identification in olive trees. It consists of a dataset that contains 3300 images of healthy and diseased leaves. The images are gathered in a dataset and then pro-processed. After pre-processing the images, the segmentation process is done using Wavelet transform. Then, the Ant Colony Optimization algorithm is employed for extracting the features. Lastly, categorization is done by applying RNN. The results suggest the optimal model for creating an efficient disease detector. The developed method attains higher performance when compared with other existing techniques. Hence, the outcomes demonstrate that the ACO-RNN technique has a reliable potential for identifying plant infections

Identification of olive leaf disease through optimized deep learning approach

The production of olives in Saudi Arabia, which accounts for around 6% of worldwide output, is regarded as one of the best in the world. Because olive trees are rain-fed and produced using conventional methods, yields vary greatly each year, which is made worse by viral illnesses and climate change. Therefore, it is necessary to identify plant illnesses early on. Farmers diagnose plant illnesses using conventional visual assessment or laboratory analysis. Diagnosing illnesses affecting olive leaves has been improved with deep learning (DL). To identify and categorize plant illnesses, this research introduces an Optimized Artificial Neural Network (ANN) that analyses the plant's leaf. Data is first integrated for preprocessing, relevant features are extracted, and the Whale Optimization Algorithm (WOA) is used to select necessary features. Then the data is classified using ANN. The ANN classification approach utilizes the feed-forward neural network method (FFNN). ANN is a highly adaptable technology being utilized widely to address various problems. This study applies categorization to exclude possibilities throughout each stage, improving prediction accuracy. Compared to the current model employed for plant disease detection, the suggested model showed a considerable performance increase in Precision, Recall, Accuracy, and F1-measure

A robust deep neural network framework for the detection of diabetes

Significant developments occurred in numerous industries and fields during the digital age (1997–2006). One industry that has seen similar changes is the healthcare sector. Big data has primarily come from the healthcare sector since the 1990s. The outcomes of data analysis and dissemination have boosted healthcare and awareness. Perspectives and insights are the outcomes. The most important public health issue has been identified as diabetes and its repercussions. Based on patient medical imaging and records gathered, various techniques have been used to forecast diabetic complications. The technology of data mining has not been applied with much effort. This technique requires unstructured medical records, data entry, and output. Numerous methods have been employed to foresee diabetes problems. This study employs a deep learning technique to create a healthcare system to categorize and forecast the development of diabetes mellitus (Type 2). The Deep Belief Network, which includes the data collecting, pre-training, and classification processes of forecasting diabetes, is used to predict the complications of diabetes mellitus. The diabetic data set was subjected to the proposed DBN approach, which had an accuracy of 81.25%. Compared to other machine learning techniques, the suggested method produces results with higher accuracy

Laboratory Study of Environmentally Friendly Drilling Fluid Additives to Be Used a Thinner in Water-Based Muds

The use of conventional chemical additives to control drilling mud specifications causes serious health, safety, and environmental side effects. To mitigate these lasting hazards, an economic multifunctional bioenhancers should be exploited as additives in place of the traditional materials to achieve the desired drilling mud properties. Using a bioenhancer is not only safer for the environment, but it poses no risk to drilling personnel and is more cost-efficient than conventional methods. In this work, two concentrations of is Palm Tree Leave Powder (PTLP) were added to the base mud and drilling fluid properties were measured. The pH test demonstrated PTLP\u27s ability to minimize alkalinity. At 1.5% (11 gm) PTLP, the pH was decreased by 21%, while 3% (22 gm) PTLP showed a reduction of 28%. A reduction in seepage loss (cc/30min) of 26% and 32% was also observed, respectively, when comparing it to the reference fluid. Simultaneous improvement of the mud cake was seen over the reference fluid, signifying PTLP could also substitute fluid loss control agents. The plastic viscosity (PV) of the reference fluid was insignificantly affected by the introduction 1.5% (11gm) PTLP. However, when the concentration of PTLP was increased to 3% (22 gm) a tangible increase in PV was seen due to the inefficient grinding of the palm tree leaves (PTL) and irregular dispersal of particle sizes. To mitigate this, a more effective form of grinding for PTL is needed as well as a sieve analysis to ensure equal distribution of particle sizes. The second component of viscosity, yield point (YP), was drastically reduced by 59% at both 1.5% (11 gm) and 3% (22 gm) as compared to the reference fluid. Additionally, initial and final gel strengths were significantly reduced at both concentrations. These results are an indicator that PTLP can be a viable option as a thinning material for water-based mud. Considering the previously stated results, PTLP can be a feasible replacement or at least supportive material for conventional pH reducers, filtration loss control agents, and viscosity thinners. This biodegradable drilling mud additive shows great potential and is a practical option to replace or at least support toxic chemicals traditionally used such as lignosulphonate, chrome-lignite, and Resinex. This work outlines a practical guide for reducing drilling fluid costs as well as the impact on drilling personnel and the environment