Biometric Template Protection based on Hill Cipher Algorithm with Two Invertible Keys

The security of stored templates has become an important issue in biometric authentication systems this because most of the biometric attacks target the biometric database beside the difficulty of issuing the templates again. Thus, to protect the biometric templates it must be encrypted before storing in database. In this paper we proposed an efficient encryption method based on two invertible and random keys to enhance and overcome the weakness of hill cipher algorithm the keys generated using upper triangular matrices with Pseudo-Random Number Generator (PRNG) using two large and random encryption keys. The proposed encryption method provides sufficient security and protection for the biometric templates from attacks, where the experimental results showed high efficiency comparing with the traditional Hill Cipher and existing methods

The classification of the modern arabic poetry using machine learning

In recent years, working on text classification and analysis of Arabic texts using machine learning has seen some progress, but most of this research has not focused on Arabic poetry. Because of some difficulties in the analysis of Arabic poetry, it was required the use of standard Arabic language on which “Al Arud”, the science of studying poetry is based. This paper presents an approach that uses machine learning for the classification of modern Arabic poetry into four types: love poems, Islamic poems, social poems, and political poems. Each of these species usually has features that indicate the class of the poem. Despite the challenges generated by the difficulty of the rules of the Arabic language on which this classification depends, we proposed a new automatic way of modern Arabic poems classification to solve these issues. The recommended method is suitable for the above-mentioned classes of poems. This study used Naïve Bayes, Support Vector Machines, and Linear Support Vector for the classification processes. Data preprocessing was an important step of the approach in this paper, as it increased the accuracy of the classification

Security and accountability for sharing the data stored in the cloud‏

Important for cloud services the cloud computing share throw multiple clients , and it is more important to allocate resources for cloud service provider , cloud computing is an infrastructure that provides on demand network services , in relation , the most important feature of the cloud services is that user’s data are hosted in remote . While taking benefit of this new emerging technology, users’ fear of losing command of their own data, is becoming a noteworthy hurdle to the extensive implementation of cloud services. Cloud service provider module is to process data owner request for storing data files and application and provides cloud users log details to data owner for audit purpose, to address this problem framework based on information accountability to keep track and trial of the authentic handling of the users’ data in the cloud. The system proposed that the Data can be fully tracked by the owner and follow up the service agreements by depending on many items which access, usage control and management

An effective transmit packet coding with trust-based relay nodes in VANETs

ehicular ad-hoc networks (VANETs) are characterized by limited network resources such as limited bandwidth and battery capacity. Hence, it is necessary that unnecessary use of network resources (such as unnecessary packet transfers) is reduced in such networks so that the available power can be conserved for efficient multicast communications. In this paper, we have presented a Transmit Packet Coding (TPC) Network Coding in VANET to ensure reliable and efficient multicasting. With network coding, the number of transmitted packets over the network can be reduced, ensuring efficient utilization of network devices and resources. Here, the trust-based graph optimization is performed using Cuckoo search algorithm to select the secure relay nodes. The experimental results showed the superiority of the presented approach compared to the existing techniques in terms of throughput, latency, hop delay, packet delivery ratio, network decoder outage probability, and block error rate

HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets

Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made

A krill herd behaviour inspired load balancing of tasks in cloud computing

A developing trend in the IT environment is mobile cloud computing (MCC) with colossal infrastructural and resource requirements. In the cloud computing environment, load balancing – a way of distributing workloads across numerous computing resources, is a vital aspect. A proficient load balancing guarantees an effective resource usage through the supply of network resources based on the user demands. It can also organize the network clients using the fitting planning criteria. This paper sets forth an advanced load balancing and energy/cost aware technique for a demand-based network resource allocation in cloud computing. The load balancing process in the proposed strategy utilizes a Krill load balancer (Krill LB) which is expected to achieve a well-balanced load over virtual machines. The aim of using the Krill LB as the load balancer is to increase the throughput of the network as much as possible. The speed, task cost, and weight of the tasks were first determined, after which, the Krill herd optimization algorithm was for the load balancing based on the measured parameters. Furthermore, a modified dynamic energy-aware cloudlet-based mobile cloud computing model (MDECM) was introduced for energy cost awareness in load balancing based on the service rate and energy of the mobile users. The proposed work was aimed at optimizing resource allocation in MCC in an energy-efficient manner. The performance of the suggested Krill-LB was benchmarked against that of Honey Bee Behavior Load Balancing (HBB-LB), Kill Herd, and Round Robin algorithms

A New Technology for Reducing Dynamic Power Consumption in 8-Bit ALU Design

Clock gating is an effective way to decrease dissipated power in synchronous design. The most effective way to do this is by masking the clock that turns toward the unused part of design. In this paper, a comparative evaluation of power consumption in existing clock gating techniques in Arithmetic Logical Unit (ALU) design was achieved. an innovative signal clock gating method offers extra immunity in the direction of the present issue in an accessible mechanism. A Gated Clock Generation designs using a tri-state connection and logic gate, generated by the set of bubbled input with NAND gate, is used for the latest suggested clock gating. This design saves power even when the clock is at applying to the target module. Complete power analysis reveals that the proposed technique has an effect on the dynamic power that decreases total power consumption up to 24.90% relative to traditional power.  All experiments are done in arithmetic logic unit design. 130 nm standard logic libraries have been used for implementation in order to achieve ALU frameworks. The ALU design architecture was developed using the Verilog HDL, and the simulations are performed utilizing ModelSim-Altera 10.0c (Quartus II 11.1) Starter Version

Using Ideal Time Horizon for Energy Cost Determination

In most optimal VM placement algorithms, the first step to determine the proper time horizon, T for the prediction of the expected maximum future load, L. However, T is dependent on the proper knowledge of the required time for servers to switch from their initial SLEEP/ACTIVE state to the next desired state. The activities implemented by this policy are (a) to relocate the VM from an encumbered server, a server that operates in an undesirably high regime with applications forecasted to rise their burdens to compute in the subsequent reallocation cycles; (b) to conduct VM migration from servers that operate within the undesirable regime to shift the server to a SLEEP mode; (c) putting an idle server to SLEEP mode and rebooting the servers from the SLEEP mode at high cluster loads. A novel mechanism for forwarding arriving client desires to the utmost suitable server is implemented; thus, in the complete system, balancing the requested load is possible

A comprehensive study: Ant Colony Optimization (ACO) for Facility Layout Problem

In context of manufacturing, numerous models are designed to appropriately represent the facility layout problem (FLP) and a variety of optimization methods have been applied to solve these models. The ultimate goal of these methods is to find optimal solutions, In regard to Swarm Intelligence (SI), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are regarded as the most important SI techniques of our time. In this paper, a brief introduction for the so far most promising approaches to facility layout related topics, are provided. The succeeding paper will then illustrate some of those, in more detail. Moreover, we examine ACO modifications and extensions that could contribute to optimization methods in FLP; mostly conform to NP-hard combinatorial problems. future research areas are identified in Construction Site Facility Layout Problems, Multi-Criteria Facility Layout Problems and Dynamic Facility Layout Problems

Dynamic Load Balancing Model Based on Server Status (DLBS) for Green Computing

There could be problems with server quality when resources are limited and this could lead to poor service delivery to the clients. Therefore, a strong load balancing technique is required that helps in the optimization of resource utilization. Cloud servers with correlated load balancer can assist in optimizing the load balancing practicability in cloud computing. In this paper, a load balancing model (DLBS) is proposed for effective load balancing and resource optimization in public cloud. A load balancing model was designed and implemented using Amazon EC2. Each server was equipped with a load balancer which monitors the load and sends status information to the controller. The servers with fewer loads were given more requests while the overloaded ones were not given further requests. The Amazon Web Services (AWS) was used to demonstrate the proof of concept. The results revealed that the proposed solution improved performance, throughput, and utilization of cloud resources