Substantial research interest has been observed in the field of object recognition as a vital component for modern intelligent systems. Content based image classification and retrieval have been considered as two popular techniques for identifying the object of interest. Feature extraction has played the pivotal role towards successful implementation of the aforesaid techniques. The paper has presented two novel techniques of feature extraction from diverse image categories both in spatial domain and in frequency domain. The multi view features from the image categories were evaluated for classification and retrieval performances by means of a fusion based recognition architecture. The experimentation was carried out with four different popular public datasets. The proposed fusion framework has exhibited an average increase of 24.71% and 20.78% in precision rates for classification and retrieval respectively, when compared to state-of-the art techniques. The experimental findings were validated with a paired t test for statistical significance.

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Content-Based Image Retrieval Using Color Layout Descriptor, Gray-Level Co-Occurrence Matrix and K-Nearest Neighbors

Md. Farhan Sadique, S.M. Rafizul Haque

Статья научная

Content-based image retrieval (CBIR) is the process of retrieving similar images of a query image from a source of images based on the image contents. In this paper, color and texture features are used to represent image contents. Color layout descriptor (CLD) and gray-level co-occurrence matrix (GLCM) are used as color and texture features respectively. CLD and GLCM are efficient for representing images with local dominant regions. For retrieving similar images of a query image, the features of the query image is matched with that of the images of the source. We use cityblock distance for this feature matching purpose. K-nearest images using cityblock distance are the similar images of a query image. Our CBIR approach is scale invariant as CLD is scale invariant. Another set of features, GLCM defines color patterns. It makes the system efficient for retrieving similar images based on spatial relationships between colors. We also measure the efficiency of our approach using k-nearest neighbors algorithm. Performance of our proposed method, in terms of precision and recall, is promising and better, compared to some recent related works.

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Content-based Fish Classification Using Combination of Machine Learning Methods

S.M. Mohidul Islam, Suriya Islam Bani, Rupa Ghosh

Статья научная

Fish species recognition is an increasing demand to the field of fish ecology, fishing industry sector, fisheries survey applications, and other related concerns. Traditionally, concept-based fish specifies identification procedure is used. But it has some limitations. Content-based classification overcomes these problems. In this paper, a content-based fish recognition system based on the fusion of local features and global feature is proposed. For local features extraction from fish image, Local Binary Pattern (LBP), Speeded-Up Robust Feature (SURF), and Scale Invariant Feature Transform (SIFT) are used. To extract global feature from fish image, Color Coherence Vector (CCV) is used. Five popular machine learning models such as: Decision Tree, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Naïve Bayes, and Artificial Neural Network (ANN) are used for fish species prediction. Finally, prediction decisions of the above machine learning models are combined to select the final fish class based on majority vote. The experiment is performed on a subset of ‘QUT_fish_data’ dataset containing 256 fish images of 21 classes and the result (accuracy 98.46%) shows that though the proposed method does not outperform all existing fish classification methods but it outperforms many existing methods and so, the method is a competitive alternative in this field.

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Context-oriented Framework for Determining Requirements Change Documentation Approaches

Denys Gobov, Oleksandra Zuieva, Viktoriia Shashko

Статья научная

Requirements change management is one of the core business analyst's activities, directly affecting change impact analysis, stakeholder communication, and the long-term system maintainability. While research on this topic examines in detail change processes, tracking methods, and change type classification, the problem of systematically documenting requirements changes remains underexplored. Existing research lacks a unified classification of change documentation approaches and context-sensitive recommendations for their selection, which limits their effectiveness in managing requirements. To address this gap, this study develops a context-oriented framework for selecting approaches to requirements change documentation. The framework integrates three components: a conceptual model based on the Baseline–Delta–Target State triad, a taxonomy of documentation approaches, and a context-driven selection mechanism grounded in empirical evidence. A systematic literature review was combined with an analysis of the survey of 324 practicing business analysts from Ukrainian and international companies. Statistically significant associations between selected project context attributes and documentation practices were identified using the Chi-square test of independence and Cramer's V, while additional dimensions were supported through evidence from the literature. The framework incorporates six documentation approaches: Full Target State, Delta-only, Target-driven Delta, Delta-driven Target, Parallel Use, and Hybrid Cycle. Four contextual dimensions emerge as key factors: project, environment, resources, and stakeholders. To support context-based selection of the change documentation approach, a matrix was developed that integrates the identified dependencies. The results position requirements change documentation as a context-sensitive knowledge management mechanism rather than a universal procedural standard.

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Control System of Sensorless Brushless DC Motor Based on TMS320F240

Li Zeng, Zicheng Li

Статья научная

A brushless DC(BLDC) motor with the characteristics of high speed and high power density has been more widely used in industrial area. The BLDC motor requires the position and speed sensors for control. However the position sensors are undesirable from standpoints of size, cost, maintenance and reliability. There are some different ways that can solve this problem, depending on the flux distribution. This paper describes a control system of sensorless BLDC motor. The back-EMF is adopted to detect the rotor position. The back-EMF is very small in the motor starting process, and it is difficult to obtain rotor position efficiently. A re-setting method of the rotor is proposed in the paper, and current closed loop is used for high-speed and safety in the motor starting process. A good speed and current double closed loops system is designed. The speed and current regulators are implemented by a digital signal processor(DSP). A simple algorithm is used to calculate motor speed indirectly by the software, which simplifies the system hardware structure. The hardware structure and software design of sensorless BLDC motor control system are described in details. The simulation and experimental results have shown the validity of the sensorless control system and the accuracy of the detective position signal obtained.

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Controlling Citizen’s Cyber Viewing Using Enhanced Internet Content Filters

Shafi’í Muhammad ABDULHAMID, Fasilat Folagbayo IBRAHIM

Статья научная

Information passing through internet is generally unrestricted and uncontrollable and a good web content filter acts very much like a sieve. This paper looks at how citizens’ internet viewing can be controlled using content filters to prevent access to illegal sites and malicious contents in Nigeria. Primary data were obtained by administering 100 questionnaires. The data was analyzed by a software package called Statistical Package for Social Sciences (SPSS). The result of the study shows that 66.4% of the respondents agreed that the internet is been abused and the abuse can be controlled by the use of content filters. The PHP, MySQL and Apache were used to design a content filter program. It was recommended that a lot still need to be done by public organizations, academic institutions, government and its agencies especially the Economic and Financial Crime Commission (EFCC) in Nigeria to control the internet abuse by the under aged and criminals.

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Convolutional Neural Network-based Stacking Technique for Brain Tumor Classification using Red Panda Optimization

Blessa Binolin Pepsi M., Anandhi H., Karunyaharini S., Visali N.

Статья научная

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 Denigration Based Data Center Allocation Policy Using Modified Parallel PSO Optimization Technique for Multi-cloud Framework

Subash Chandra Tripathy, Suvendu Chandan Nayak, Rekah Sahu

Статья научная

Most of the existing data center allocation mechanisms contribute either user centric or service provider centric not for both ends but in reality, both have different objectives. For example, the objective of a user is minimization of cost, response time as well as processing time whereas the objective of service provider is to maximize the profit and processing time and minimization of response time, bandwidth, energy consumption and computing overhead with subject to effective resource utilization and load balancing. To address this challenge, this paper introduces a Cost Denigration-Based Data Center Allocation Policy (CD-BDAP) utilizing Particle Swarm Optimization (PSO), which simultaneously considers economic cost, response time, and energy consumption in the selection of data centers. In contrast to conventional PSO-based broker policies, CD-BDAP integrates a workload similarity-aware allocation strategy by calculating a dissimilarity index among user requests, thereby facilitating enhanced consolidation and energy efficiency. A weighted objective function is developed to balance user-centric metrics (cost and response time) with provider-centric metrics (profit and energy consumption), explicitly capturing their trade-offs. The proposed mechanism is assessed utilizing CloudAnalyst, which is constructed on CloudSim. The experimental results indicate that CD-BDAP achieves a reduction in VM cost, a decrease in response time, and an enhancement in energy efficiency, while simultaneously increasing the overall profit for service providers. The findings suggest that the integration of energy-aware cost modeling and workload similarity into PSO-based allocation can enhance both economic and performance efficiency in the selection of cloud data centers. The outcomes of CD-BDAP are compared with the existing PSO-based mechanisms and found enhanced performance.

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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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