Computer Science and Control Systems

Nowadays, mobile computation applications attract major interest of researchers. Limited processing power and short battery lifetime is an obstacle in executing computationally-intensive applications. This article presents a mobile computation automatic decision-making offloading framework. The proposed framework consists of two phases: adaptive learning, and modeling and runtime computation offloading. In the adaptive phase, curve-fitting (CF) technique based on non-linear polynomial regression (NPR) methodology is used to build an approximate time-predicting model that can estimate the execution time for spending the processing of the detected-intensive applications. The runtime computation phase uses the time predicting model for computing the predicted execution time to decide whether to run the application remotely and perform the offloading process or to run the application locally. Eventually, the RESTful web service is applied to carry out the offloading task in the case of ...

Recently, there have been considerable efforts towards the convergence between P2P and Grid computing in order to reach a solution that takes the best of both worlds by exploiting the advantages that each offers. Augmenting the peer-to-peer model to the services of the Grid promises to eliminate bottlenecks and ensure greater scalability, availability, and fault-tolerance. The Grid Information Service (GIS) directly influences quality of service for grid platforms. Most of the proposed solutions for decentralizing the GIS are based on completely flat overlays. The main contributions for this paper are: the investigation of a novel resource discovery framework for Grid implementations based on a hierarchy of structured peer-to-peer overlay networks, and introducing a discovery algorithm utilizing the proposed framework. Validation of the framework-s performance is done via simulation. Experimental results show that the proposed organization has the advantage of being scalable while p...

This paper presents a Fragmented protein sequence alignment using two-layer PSO (FTLPSO) method to overcome the drawbacks of particle swarm optimization (PSO) and improve its performance in solving multiple sequence alignment (MSA) problem. The standard PSO suffers from the trapping in local optima, and its disability to do better alignment for longer sequences. To overcome these problems, a fragmentation technique is first introduced to divide the longer datasets to a number of fragments. Then a two-layer PSO algorithm is applied to align each fragment, which has ability to deal with unconstrained optimization problems and increase diversity of particles. The proposed method is tested on some Balibase benchmarks of different lengths. The numerical results are compared with CLUSTAL Omega, CLUSTAL W2, TCOFFEE, KALIGN, and DIALIGN-PFAM. It has been shown that better alignment scores have been achieved using the proposed technique FTLPSO. Further, studies on PSO update equation's parameters and the parameters of the used scoring functions are presented and discussed.

Two linear programming (LP) algorithms for the integrated state estimation (ISE) for the whole system are formulated and tested. It was found that a direct application of an ISE on a large power system suffers certain limitations. This paper presents an efficient and reliable two-level state estimator (TLSE) based on LP techniques. Both the ISE and the proposed TLSE were tested under different conditions. The results are discussed and comparisons are given which show that the TLSE has many advantages over the ISE.

The need for Self-service data analytics is inevitable as it supports the business in making the right decisions. In this paper, we argue that self-service analytics frameworks should be based on a process-centric approach and visualized selfservice components in order to meet current business demands. Further, we enunciate the need for mainly three components: Map component, Process Flow component and a Control Model component. Furthermore, we explain the architecture of a self-service analytics framework based on these components. Some parts of the proposed framework were deployed to different sites and are discussed in detail in this paper. The obtained results showed a clear enhancement of data warehouse operation spent from the IT departments' side compared to the traditional BI architecture.

Recently, the design and implementation of new healthcare systems have gained an interest in both industry and academia. The amalgamation between the Internet of Things, cloud, edge computing, and big data helps the proliferation of new scenarios for smart medical services and applications. Deep learning is currently paying a lot of attention for its utilization with big healthcare data. To this end, the main objective of this study is to propose a Deep Learning H 2 O (DLH 2 O) framework for improving the performance and selection of the optimal features to predict emergency cases. The proposed DLH 2 O framework consists of data preprocessing layer, feature selection layer and deep learning layer. The DLH 2 O framework aims to find the optimal subset of features and minimize the error of the classification through a proposed new variant of the Whale Optimization Algorithm (WOA) called ACP-WOA. The proposed changes have been done on the following parameters a, a2, A, and C which should affect both exploration and exploitation of WOA. The experiments conducted in order to test the validity of DLH 2 O Framework. In regard to the datasets, five experiments are performed for this purpose. The results demonstrate the superiority of ACP-WOA compared to the other state-of-the-art meta-heuristic algorithms in terms of time, error, and scalability. The proposed ACP-WOA is also tested on CEC2017 benchmark functions and proves its superiority over WOA in terms of accuracy.

A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve query performance by incorporating regression models. To predict the response time for a query, most query performance approaches rely on DBMS optimizing statistics and the cost estimation of each operator in the query execution plan, which also focuses on resource utilization (CPU, I/O). Modeling query features is thus a critical step in developing a robust query performance prediction model. In this paper, we propose a new framework based on query feature modeling and ensemble learning to predict query performance and use this framework as a query performance predictor simulator to optimize the query features that influence query performance. In query feature modeling, we propose five ...

Deep learning (DL) algorithms are crucial for predicting various diseases because they can analyze a large amount of healthcare data within a short prediction time. One of these diseases is cancer, which causes one out of six deaths worldwide. Many researchers have adopted predictive frameworks such as machine learning and DL to predict cancer prognosis, in addition to the probability of its recurrence, progression, and the patients' survival estimation. Currently, all stakeholders are interested in the accuracy of cancer prognosis prediction. This study selected a framework within high accuracy and short prediction time from three DL frameworks for improving the performance of cancer prognosis prediction. This prediction requires a quick and high-accuracy optimizer, so we propose a binary version of the continuous AC-parametric whale optimization algorithm. This version is built on S-shaped transfer functions to identify the minimal optimal subset of features and maximize the classification accuracy. These frameworks proposed have the following forms: the first is a Feed-Forward Neural Network (FFNN) in which the input is the optimal set of feature selection. The second is an optimized parameter FFNN. The third is composed of a feature selection layer in which the best subset of selected features is for use as inputs in the optimized FFNN. We compared these frameworks using a comparative study. Our results show that, under all conditions, the third framework is superior to the others with an average accuracy of 100%, whereas the first and second frameworks achieved 94.97% and 93.12% accuracy, respectively.

Mobile ad-hoc network is a collection of mobile nodes that organize themselves into a network without any predefined infrastructure. The characteristics of MANET are dynamic topology; bandwidth and energy constrained and limited physical security. Due to the dynamic nature of the network, these networks can be easily vulnerable to attacks. Many type of attacks can threat the MANET and the classification of attacks are Black hole, Routing loops, Network partition, Selfishness, Sleep deprivation and Denial of Service. The MANET is mainly applied in military environments, personal area networking and emergency operations. This work provides a technique to improve the security level of MANET. The mechanism of intrusion detection is designed in MANET on the basis of artificial neural networks (ANNs) and ZoneSampling Based Trace back algorithm (ZSBT) for detecting DOS attacks. Intrusion detection system is a type of security management system for computers and networks. The prevention mec...

In this paper, a Cooperative Multi-robot for Carrying Targets (CMCT) algorithm is proposed. The multi-robot team consists of three robots, one is a supervisor and the others are workers for carrying boxes in a store of 100×100 m2. Each robot has a self recharging mechanism. The CMCT minimizes robot-s worked time for carrying many boxes during day by working in parallel. That is, the supervisor detects the required variables in the same time another robots work with previous variables. It works with straightforward mechanical models by using simple cosine laws. It detects the robot-s shortest path for reaching the target position avoiding obstacles by using a proposed CMCT path planning (CMCT-PP) algorithm. It prevents the collision between robots during moving. The robots interact in an ad hoc wireless network. Simulation results show that the proposed system that consists of CMCT algorithm and its accomplished CMCT-PP algorithm achieves a high improvement in time and distance while...

Real-time deep learning faces the challenge of balancing accuracy and time, especially in cybersecurity where intrusion detection is crucial. Traditional deep learning techniques have been insufficient in identifying network anomalies and intrusions. To address this, a Fully Streaming Big Data Framework based on optimized Deep Learning for cybersecurity (FSBDL) was proposed. The framework leverages two parallel optimization algorithms, Adam and RMSprop, labeled Hyper-parallel optimization (HPO) techniques to enhance efficiency and stability. The optimized CNN in the proposed framework achieves high accuracy in real-time intrusion detection without compromising reliability. The CNN is customized to address overfitting issues in recurrent connections by reducing the number of training parameters, using customized activation functions and regularization techniques. The CNN is trained in parallel using Adam and RMSprop optimization algorithms, resulting in significant accuracy improvements that surpass traditional methods and current state-of-the-art approaches. The HPO is a crucial component of the proposed framework, as it enables the system to detect intrusions in real-time, ensuring prompt response to potential cyber threats. The six-layer FSBDL framework includes data pre-processing, feature selection, hyper-parallelism, a customized CNN, a GUI layer for interpretation, and a detection-evaluation layer. The optimized CNN was designed to detect intrusions in real-time without compromising accuracy or reliability. The proposed algorithms were evaluated using various performance metrics, showing that the accuracy of the framework surpasses 99.9%, indicating its superiority over other intrusion detection models. This novel intrusion detection model offers promising prospects for cybersecurity, and its effectiveness and accuracy have been demonstrated through experimentation.

In machine learning, datastream prediction is a challenging issue, particularly when dealing with enormous amounts of continuous data. The dynamic nature of data makes it difficult for traditional models to handle and sustain real-time prediction accuracy. This research uses a multi-processor long short-term memory (MPLSTM) architecture to present a unique framework for datastream regression. By employing several central processing units (CPUs) to divide the datastream into multiple parallel chunks, the MPLSTM framework illustrates the intrinsic parallelism of long short-term memory (LSTM) networks. The MPLSTM framework ensures accurate predictions by skillfully learning and adapting to changing data distributions. Extensive experimental assessments on real-world datasets have demonstrated the clear superiority of the MPLSTM architecture over previous methods. This study uses the transformer, the most recent deep learning breakthrough technology, to demonstrate how well it can handle challenging tasks and emphasizes its critical role as a cutting-edge approach to raising the bar for machine learning.