In the healthcare field, the detection of critical diseases such as brain tumors is essential. A technique like traditional support vector machine has been commonly used for brain tumor classification. However, Processing and detecting brain tumors requires achieving high accuracy with shorter detection time and reduced complexity. To accomplish this, efficient feature selection is necessary, which can be based on various factors. A convolutional neural network-based stacking technique is introduced for effective brain tumor classification and prediction using Red Panda optimization. By efficiently extracting spatial data from medical images, a convolutional neural network is used in stacking to enhance thecapacity of our model for abnormality detection and classification in the prediction of brain tumors. Red panda optimization is a biologically inspired stochastic optimization algorithm used for the effective selection of significant features. This Technique improves the prediction accuracy in a shorter period and reduces the complexity by selecting significant features for a huge amount of data by employing effective optimization. This technique is tested on multiple standard datasets to assess our model’s performance. Our technique is compared to other optimization models such as Mutual information-based optimization and traditional particle swarm optimization for further validation. Our model showed an improvement in detection accuracy to 98% with a better reduction in detection time and complexity.

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Cost Minimized PSO based Workflow Scheduling Plan for Cloud Computing

Amandeep Verma, Sakshi Kaushal

Статья научная

Cloud computing is a collection of heterogeneous virtualized resources that can be accessed on-demand to service applications. Scheduling large and complex workflows becomes a challenging issue in cloud computing with a requirement that the execution time as well as cost incurred by using a set of heterogeneous cloud resources should be minimizes simultaneously. In this paper, we have extended our previously proposed Bi-Criteria Priority based Particle Swarm Optimization (BPSO) algorithm to schedule workflow tasks over the available cloud resources under given the deadline and budget constraints while considering the confirmed reservation of the resources. The extended heuristic is simulated and comparison is done with state-of-art algorithms. The simulation results show that extended BPSO algorithm also decreases the execution cost of schedule as compared to state-of-art algorithms under the same deadline and budget constraint while considering the exiting load of the resources too.

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Cost effective wireless network based automated energy meter monitoring system for Sri Lanka perspective

M. M. Mohamed Mufassirin, Ahamed Lebbe Hanees

Статья научная

Many researchers and developers are focusing their curiosity on designing and implementing industrial automated systems based on modern wireless communication technologies. In the most of the developing countries like Sri Lanka, the effort of collecting electricity, water and other utility meter reading of every consumer is a very difficult task. It requires a great number of labors for collecting and processing the meters readings. This paper presents an implementation methodology of an Automated Energy Meter Monitoring System (AEMMS) based on Global System Mobile (GSM) and Zig-Bee technology incorporate with microcontroller that aims to diminish this difficult task by introducing an automated process for collecting meter reading data from energy meter in Sri Lanka. Use of GSM network as a medium for AEMMS establishes a cost-effective and two-way connected wireless data communication between energy provider and consumer’s energy meter. Zig-Bee technology provides capability to establish fully coverage in the country by filling the area in which GSM coverage is absence. The AEMMS continuously monitors the energy system and sends information of energy usage and theft detection alert to utility company via Short Message Service (SMS) as well as it sends energy usage bill and power cut alert to the customer via SMS and Email. For these facilities, this system contains a software tool in a server computer at energy service provider to facilitate the utility bill generation and data communication.

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Coupling Complexity Metric: A Cognitive Approach

A. Aloysius, L. Arockiam

Статья научная

Analyzing object – oriented systems in order to evaluate their quality gains its importance as the paradigm continues to increase in popularity. Consequently, several object- oriented metrics have been proposed to evaluate different aspects of these systems such as class coupling. This paper presents a new cognitive complexity metric namely cognitive weighted coupling between objects for measuring coupling in object- oriented systems. In this metric, five types of coupling that may exist between classes: control coupling, global data coupling, internal data coupling, data coupling and lexical content coupling are consider in computing CWCBO.

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Coupling Metric for Understandability and Modifiability of a Package in Object-Oriented Design

Sandip Mal, Kumar Rajnish

Статья научная

This paper presents a new coupling metric (Coup), which is based on the formal definition of methods and variables of classes, and packages. The proposed metric has been validated theoretically against Briand properties as well as empirically using packages taken from two open source software systems and four experienced teams. We measure Coup value by our own CC tool. An attempt has also been made to present a strong correlation between Coup values and understandability of the packages and between Coup values and modified classes of the packages. The results indicate that Coup is used to predict understandability and modifiability of a package in Object-Oriented design. Finally this paper proves that Coup is a better predictor of understandability and modifiability of a package than other existing coupling metrics in the literature.

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Coupling Perceptron Convergence Procedure with Modified Back-Propagation Techniques to Verify Combinational Circuits Design

Raad F. Alwan, Sami I. Eddi, Baydaa Al-Hamadani

Статья научная

This paper proposed an algorithm for logic circuits verification using neural networks where a model is built to be trained and tested. The proposed algorithm for combinational circuits' verification is based on merging two of the well-known learning algorithms for neural networks. The first one is the Perceptron Convergence Procedure, which is used for learning the functions of the standard logic gates in order to simulate the whole circuit. While the second is a modified learning algorithm of Back-propagation neural networks to be used for the verification of the hardware design. The algorithm can predict the gates that cause the malfunction in the circuit design. This work may be considered as a step toward building Distributed Computer Aided Design Environments depending on the parallel processing architecture, particularly in the Neurocomputer architecture.

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