International Journal of Modern Education and Computer Science @ijmecs
Journal articles - International Journal of Modern Education and Computer Science
All articles: 1186
Research on security situation assessment model of video transmission network
Scientific article
In this paper, we propose a situation assessment model for video transmission network, which evaluates the security risk of video transmission network information from four aspects: front-end perception layer, transmission layer, application layer and other risks.We divide the video network security evaluation system into three layers, and use AHP to determine the weight of each index.The weights of these four aspects are calculated by analytic hierarchy process.The method proposed in this paper can provide an evaluation system and a calculation method for the safe operation of video transmission network and important systems, and can provide specialized information security services for the construction project of video transmission network.
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Scientific article
The research of Boolean index sets of graphs is one of the most important graph theories in the graph theory . Boolean index sets of graphs are to use the vertex sets and the edge sets of graphs to study the characteristics of various graphs and their inherent characteristics through corresponding the mapping function to Z_2. It’s theory can be applied to information engineering, communication networks, computer science, economic management, medicine, etc. The edge-balance index set is an important issue in Boolean index set. In this paper, we defined edge-friendly labeling of the graph, edge-balance index set of the graph and the graph {[C_(3^m)×P_(m_3)](m≥3)}. we completely determine the edge-balance index sets of the graph [C_(3^m)×P_(m_3)] for the integer [m≡3,0 (mod 4)] and solve formula proof and graphic tectonic methods.
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Resource Allocation Policies for Fault Detection and Removal Process
Scientific article
In software testing, fault detection and removal process is one of the key elements for quality assurance of the software. In the last three decades, several software reliability growth models were developed for detection and correction of faults. These models were developed under strictly static assumptions. The main goal of this article is to investigate an optimal resource allocation plan for fault detection and removal process of software to minimize cost during testing and operational phase under dynamic condition. For this we develop a mathematical model for fault detection and removal process and Pontryagain's Maximum principle is applied for solving the model. Genetic algorithm is used to find the optimal allocation of fault detection and removal process. Numerical example is also solved for resource allocation for fault detection and remoal process.
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Resource Allocation Strategy with Lease Policy and Dynamic Load Balancing
Scientific article
Cloud Computing has managed to attract the entire buzz in the growing era of technology due to its on-demand services for resource request. Despite of the enormous growth of cloud computing, there are many problems related to resource allocation in cloud that are still unaddressed. Current work for resource allocation strategy focuses on various methods to place Virtual Machine per appropriate requests. The current state of art focuses on the dynamic nature of the work load on cloud. But there is still scope of improvement in the resource allocation strategies that have been proposed in terms of well-balanced network even at the resource contention. This study proposes a hybrid model composed of lease methodology and dynamic load balancing algorithm, with an attempt to overcome the problems of resource contention and starvation and a well-balanced network even at the input of varying loads. An attempt to increase the CPU utilization and throughput along with no request rejection is taken. The work also retains the lease options for its clients thus maintaining the anti-starvation for pre-emptible requests.
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Reversible Shift Counter Design on QCA
Scientific article
In the Current scenario of higher level of integration the most encountered problems are heat dissipation and information loss on each and every computation. For the sake of eliminating these issues Reversible computing is being adopted as a preferable substitute of digital circuit design. This paper focuses on the design of Reversible Ring Counter and Twisted Ring Counter using Reversible D flip flop implemented with the Novel Design of Feynman and Fredkin Gate. Ring counters are commonly found in applications used to count the data in a continuous loop as well as in frequency divider circuits. While twisted (or Johnson) ring counter is used as three-phase square wave generators (using 3 flip-flops) and quadrature oscillator circuits (using 2 flip-flops) etc. The response of the proposed counter circuit is tested with the help of QCADesigner 2.0.3 simulation tool and the Energy dissipation is analyzed by the use of QCA Designer-E tool.
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Review Length Aware Hybrid Approach to Sentiment Analysis
Scientific article
Sentiment analysis is a popular research problem to find out within the natural language processing that is dealing with identifying the sentiments or mood of people's towards elements such as product, text, services and the technology. However, there are few researches conducted on the sentiment analysis of technical article review, so to overcome this deficiency conducts the sentiment analysis over the technical article review and classifying the sentence by overall sentiments that is representing the review is positive or negative. The paper presents the combination of SVM and KNN and find out how much given article sound technically good. The proposed technique is compared with other existing techniques and results shows that the proposed technique is better as compared to the other technique.
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Review of Requirements Management Issues in Software Development
Scientific article
A requirement is a capability to which a product or service should conform to. A meticulous consideration to requirements engineering acts as a backbone of software projects. Ambiguous and unrealistic requirements are major source of failure in the software-intensive systems. Requirements engineering processes are complex as most of the requirements engineering documentation is written in natural languages which are less formal and often distract the designers and developers. Requirements management is a continuous process throughout the project lifecycle and relates to documenting, analyzing, tracing and prioritizing requirements and then finally controlling changes. The main issues related to requirements management are usually social, political and cultural. Software requirement engineers who gather the requirements generally consider that such issues are beyond the scope of their profession as they deem them within the project management ambit. In this study, we highlight the management issues that arise in the requirements engineering process and explore the possibilities to tackle them amicably. The study is supplemented with a critical review of the existing methodologies for resolving and managing software requirements.
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Review on predicting students’ graduation time using machine learning algorithms
Scientific article
Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students’ achievement, university’s performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students’ academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students’ graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naïve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students’ graduation time.
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Scientific article
This paper delves into the transformative potential of artificial intelligence (AI), particularly focusing on ChatGPT, within educational realms. By conducting an exhaustive review across various scholarly publications and case studies, this research unveils ChatGPT’s multifaceted role in redefining educational landscapes–ranging from enhancing programming proficiency and fostering creativity in writing, to augmenting student engagement. Our findings illuminate the dual-edged influence of ChatGPT in education, showcasing not only its ability to tailor learning experiences and facilitate programming and creative writing but also its capacity to fortify student-teacher interactions. However, the study does not shy away from highlighting the intricate challenges that accompany the integration of AI in education, including concerns over academic integrity, ethical considerations, and the need for a balanced amalgamation with traditional pedagogical methods. Innovatively, this research proposes a forward-thinking, ethical framework for AI integration in educational settings, advocating for a harmonious blend of ChatGPT’s capabilities with human educators' insights to foster a more engaging, effective, and equitable learning environment. By introducing groundbreaking strategies for integrating interactive learning technologies with ChatGPT, and emphasizing the development of personalized educational trajectories, our study sets a new benchmark for future AI applications in education. The paper’s exploration into the innovative integration of ChatGPT with Virtual Reality (VR) offers a glimpse into the future of immersive learning experiences, opening new avenues for engaging and experiential learning. Through empirical validation and a nuanced discussion on the ethical deployment of AI tools in education, this study marks a significant contribution to the discourse on AI’s role in education, providing valuable insights for educators, policymakers, and technologists alike.
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Scientific article
In the modern era, the ideas related to software developments are totally replaced the old traditional software development to CBSD. CBSD is the approach that can provide reusability of components to develop new software under minimum chances of risks. Beyond the CBSD features, there are still some significant risks overall the development phase. Generally, the quality of a developed software is depending upon the component integration phase. Nevertheless, component integration phase plays a tremendous role in the success of CBSD that leads to develop new risk free software. The objective of this research was to identify all the risks in CBSD and applied risk reduction activities to mitigate these risks. On the basis of the literature survey, categorized the risks into different categories. For this, an online questionnaire was performed to identify the risks and the risk reduction activities. 85% results point out that CBSD approach is the best approach as compared to the traditional approach. 80% results show that it performs the risk reduction activities, then the quality of the product improves. However, it is a step towards the mitigation of the vulnerabilities and reducing the cost of maintenance.
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Robust Algorithm for Face Detection in Color Images
Scientific article
Robust Algorithm is presented for frontal face detection in color images. Face detection is an important task in facial analysis systems in order to have a priori localized faces in a given image. Applications such as face tracking, facial expression recognition, gesture recognition, etc., for example, have a pre-requisite that a face is already located in the given image or the image sequence. Facial features such as eyes, nose and mouth are automatically detected based on properties of the associated image regions. On detecting a mouth, a nose and two eyes, a face verification step based on Eigen face theory is applied to a normalized search space in the image relative to the distance between the eye feature points. The experiments were carried out on test images taken from the internet and various other randomly selected sources. The algorithm has also been tested in practice with a webcam, giving (near) real-time performance and good extraction results.
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Scientific article
In the choice process of optimal military commu-nication (MC) alternative, evaluation data mainly come from expert judgments, simulation results and test bed data, and they cannot be directly used in evaluation because of differences in form and attribute; and the MC environment changes rapidly as the operation tempo increasing. It is an important effort to judge the effectiveness robustness of MC alternative, since both the evaluation data and the MC envi-ronment are full of uncertainty. A robustness evaluation method based on multiple data sources and Monte Carlo simluation is proposed with respect to the characteristics of them. Mainly include Belief map as data expression form; Regression relational model built with Support Vector Re-gression (SVR) to acquire simulation data’s confidence with test bed data as training example; Extensive Bayesian Algo-rithm (EBA) to fuse data from multiple sources; Beta distri-bution fitting method for each criterion of each alternative by using the fused results; and calculation of the Probability of Best (PoB) of each alternative through Monte Carlo simu-lation. Take MCE evaluation of a Naval Vessels Fleet as an example, the proposed method is compared with some gen-eral methods. The results indicate that the proposed method helps to obtain relatively conservative alternative and is effective in guaranteeing the robustness.
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Rough Set and Genetic Based Approach for Maximization of Weighted Association Rules
Scientific article
The present paper proposes a new approach for the effective weighted association rule mining. The proposed approach utilizes the power of Rough Set Theory for obtaining reduct of the targeted dataset. Additionally, approach takes the benefit for weighted measures and the Genetic Algorithm for the generation of the desired set of rules. Enough analysis of proposed approach has been done and observed that the approach works as per the expectation and will be beneficial in situation when there is a requirement for the consideration of hidden rules(maximizing generated rules) in decision-making process.
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Rule Based Communication Protocol between Social Networks using Semantic Web Rule Language (SWRL)
Scientific article
Social network sites have become de factor in fostering human relationships and business prospects. Several social networks abound with little interoperability functionality that enables exchange of profiles of users. Though, proprietary Application Programming Interfaces (APIs) are provided as endpoints for applications in retrieval of user profile. Moreover, semantic web Friend of a Friend (FOAF) is now been used as a medium for realizing semantic social networks to be able to share user's profile across sites. And since the goal of semantic web is to provide autonomous data centric system coupled on ontology and reasoning, we propose a novel communication protocol named iProc, and usable by autonomous agents that relies on the distributive nature of social network data in coalescing a virtually centralized social network and as well providing means to enlarge user's connectivity to other users across different sites. This paper presents the architecture for a proposed iProc. Furthermore, an implementation of the FOAF files to be used was carried out and discussed.
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Rural Empowerment through Education: Case Study of a Learning Community Telecentre in Indonesia
Scientific article
This research aims to investigate the impact of information and communication technologies (ICTs) for development and social change in a rural telecentre in Indonesia. It attempts to contribute to the literature of ICTs for development and social change, in the field of analysis of a community-driven ICTs initiative and the analysis of an Indonesian telecentre, two research areas that had not been extensively explored. A qualitative case study approach was chosen to reveal the how ICTs are used in community empowerment in the case of Qaryah Thayyibah Learning Community, a school with a telecentre that in founded, run and funded by the community of Kalibening Village in Central Java, Indonesia. Through the application of the stakeholder theory, significant findings related to impact of ICTs for social change of the Qaryah Thayyibah Learning Community were revealed. These findings include patterns of access and participation of stakeholders in relation to ICTs. While ICTs had impacted the students of Qaryah Thayyibah, its impact had not yet been significantly experienced for the wider community outside of the school. Another important finding that emerged from the stakeholder analysis is the influence and value of the local champion in the establishment and sustainability of the ICTs initiative. In conclusion, the findings suggest that education such as that practiced in the Qaryah Thayyibah Learning Community can contribute in achieving rural empowerment, although the process may be long and complex. As this is an exploratory study investigating unquantifiable intangible impacts, further research can focus more on these intangible impacts through different lenses such as culture, power dynamics and social relations.
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Scientific article
This paper presents a new combined symmetric key cryptographic technique, which is generally an amalgamation of Bit Manipulation, generalized Modified Vernam Cipher, Single Bit Manipulation and Modified Caesar Cipher. The technique proposed here is basically an advanced and upgraded module of SD-AREE cryptographic method, which is based on Modified Caesar Cipher along with Bit Manipulation and the SD-AREE module is very useful in excluding any repetition pattern from a message that is to be encrypted. The proposed method, SD-AREE-I Cipher, is a complete cipher method and unlike its predecessor, SD-AREE, does not need to be added to other cryptographic methods to make those methods stronger. SD-AREE-I method is used to encrypt/decrypt different file formats and the results were very satisfactory. This method is unique and strong because the method contains feedback mechanism and generates new encrypted output every time even with slightest change in the input file (message). The proposed method can also be used for network security.
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Scientific article
We present SECURE-XED, a unified, explainable, and adversarial-aware learning system for two traditionally disjoint domains: Android malware classification and deepfake detection. The system uses a shared convolutional neural network backbone with domain-specific input adaptation and two task-specific heads. It integrates SHapley Additive exPlanations for explanation, the Fast Gradient Sign Method and Projected Gradient Descent for adversarial evaluation under Structural Similarity Index Measure guardrails, and a selective-activation controller that enables behavioral malware features when confidence is low or evasion is suspected. A hybrid Seagull Optimization Algorithm–Imperialist Competitive Algorithm procedure jointly tunes clean accuracy, robustness under projected-gradient attacks, expected calibration error, parameter count, and inference latency. SECURE-XED is evaluated on standard Android malware corpora and the FaceForensics++, Celeb-DF, and Deepfake Detection Challenge benchmarks. Under Projected Gradient Descent, Android malware accuracy decreases by approximately 19–21% with a perturbation budget of 0.1 over 40 steps, while deepfake accuracy decreases by 16.6–17.6% with a perturbation budget of 0.03 over 10 steps, demonstrating domain-dependent adversarial sensitivity. Under clean deepfake inference conditions, excluding post-prediction explanation generation and adversarial processing, the unified configuration reduces average inference latency from 34.1 to 26.9 milliseconds per frame and approximately halves the parameter requirement relative to maintaining separate model instances, while retaining equal or slightly better clean accuracy. A classroom-oriented simulator exposes explanation overlays and adversarial controls on real frames. A six-participant human-grounded pilot descriptively showed a 22-percentage-point change in Region Identification Accuracy, a 12-second reduction in decision time, and a 0.9-point Likert change in perceived clarity and trust; these pilot outcomes are descriptive and not inferential. The resulting framework combines shared cross-domain representation, multi-objective optimization, adversarial evaluation, explainability, and model-in-the-loop educational interaction within a single modular architecture.
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STEM Project for Vehicle Image Segmentation Using Fuzzy Logic
Scientific article
A STEM project was implemented, which is intended for students of technical specialties to study the principles of building and using a computer system for segmentation of images of railway transport using fuzzy logic. The project consists of 4 stages, namely stage #1 "Reading images from video cameras using a personal computer or Raspberry Pi microcomputer", stage #2 "Digital image pre-processing (noise removal, contrast enhancement, contour selection)", stage #3 "Segmentation of images", stage #4 "Detection and analysis of objects on segmented images by means of fuzzy logic". Hardware and software tools have been developed for the implementation of the STEM project. A personal computer and a Raspberry Pi 3B+ microcomputer with attached video cameras were used as hardware. Software tools are implemented in the Python language using the Google Colab cloud platform. At each stage of the project, students deepen their knowledge and gain practical skills: they perform hardware and software settings, change program code, and process experimental images of vehicles. It is shown that the processing of experimental images ensures the correct selection of meaningful parts in images of vehicles, for example, windows and number plates in images of locomotives. Assessment of students' educational achievements was carried out by testing them before the start of the STEM project, as well as after the completion of the project. The topics of the test tasks corresponded to the topics of the stages of the STEM project. Improvements in educational achievements were obtained for all stages of the project.
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SXP: Simplified Extreme Programing Process Model
Scientific article
Extreme programming is one of the widely used agile models in the software industry. It can handle unclear and changing requirements with the good level of customer satisfaction. However Lack of documentation, poor architectural structure and less focus on design are its major drawbacks that affects its performance. Due to these problems it cannot be used for all kinds of projects. It is considered suitable for small and low risk projects. It also has some controversial practices that cannot be applied in each and every situation like pair programming and on-site customer. To overcome these limitations a modified version of XP called “Simplified Extreme Programming” is proposed in this paper. This model provides solution of these problems without affecting simplicity and agility of extreme programming.
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Scheduling of Generating Unit Commitment by Quantum-Inspired Evolutionary Algorithm
Scientific article
An Quantum-Inspired Evolutionary Algorithm (QEA) is presented for solving the unit commitment problem. The proposed method has been used to achieve the schedule of system units by considering optimal economic dispatch. The QEA method based on the quantum concepts such as Q-bit, present a better population diversity compared with previous evolutionary approaches, and uses quantum gates to achieve better solutions. The proposed method has been tested on a system with 10 generating units, and the results shows the effectiveness of algorithm compared with Other previous references. Furthermore, it can be used to solve the large-scale generating unit commitment problem.
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