International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1147
A Novel Active Data Filtration for the Cloud based Architecture against Packet Flooding Attacks
Статья научная
The usage of remote servers network on the Internet to process data, store and manage, instead of using a local server or any computer" is called cloud computing. Cloud computing is that which totally based on resource sharing rather than any other device to handle applications. Today cloud computing is facing numerous challenges and one of those is Attack on the cloud environment. There are many types of hazardous attack on cloud, as the attack is always in wait for some important data or resource. The most common and most affective attack is Packet Flooding attack and there are many faces of packet flooding. EDoS Attack one of the most commonly and strong packet flood attack on the cloud to make the resources almost inaccessible to the user by flooding the unnecessary packet to the network or site more that its capacity. This paper deals with the analysis of EDoS and a mechanism is proposed to mitigate the EDoS by using filtration mechanism. The filtration is done on the basis of secure key Exchange which differentiate legitimate user from attacker. The simulation is done by cloud sim as well as Net-Beans and the performance is analyzed over time and data. Using filter the packet loss and time delay occurs in EDoS attack is much reduced.
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Статья научная
Digital footprints track online behaviors of an individual when communicating over social media platforms. In this paper, sentiment classification is carried out over online posts and tweets to pre detect whether a person is having neurological disorder or not. This study proposed a Hybrid Optimized Model Ensemble STACKed (HOMESTACK) algorithm built on stacked generalization approach that uses stacking and blending ensemble learning technique. The model is then evaluated over two datasets (Reddit Dataset1 & Twitter Dataset2) that include varied number of tweets. The pre-processing of the data and feature extraction is carried out to get cleaned text and vector corpus. The proposed HOMESTACK algorithm is then applied over training data using four base classifiers as Support Vector, Random Forest, K-Nearest Neighbor and CatBoost along with a Meta classifier as Logistic Regression. The testing data is then fed to the tuned model to compare the classification results and analysis. Also, Stacking and Blending ensemble frameworks and algorithms are proposed in this study. Execution time and metric evaluation are calculated in respect of Accuracy, Precision, Recall and F1-score. The experimental results clearly show that the proposed HOMESTACK algorithm performed better over chosen datasets as compared to blending ensemble and standalone machine learning classifiers.
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Статья научная
At present trillion of bytes of information is being created by projects particularly in web. To accomplish the best choice for business benefits, access to that information in a very much arranged and intuitive way is dependably a fantasy of business administrators and chiefs. Information warehouse is the main feasible arrangement that can bring the fantasy into reality. The upgrade of future attempts to settle on choices relies on upon the accessibility of right data that depends on nature of information basic. The quality information must be created by cleaning information preceding stacking into information distribution center following the information gathered from diverse sources will be grimy. Once the information have been pre-prepared and purified then it produces exact results on applying the information mining question. There are numerous cases where the data is sparse in nature. To get accurate results with sparse data is hard. In this paper the main goal is to fill the missing values in acquired data which is sparse in nature. Precisely caution must be taken to choose minimum number of text pieces to fill the holes for which we have used Jaccard Dissimilarity function for clustering the data which is frequent in nature.
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Статья научная
According to the survey of World Health Organization (WHO), the number of people getting affected by glaucoma eye disease in worldwide will be 79.11 million by the year 2020. Glaucoma is a dangerous eye disease, which can lead to permanent vision loss if not provided proper treatment at the right time. Currently ophthalmologists detect the glaucoma disease based on estimation of cup to disk ratio, but this method suffers from accurate segmentation of regions like optic disk and optic cup. However, this introduces errors in the diagnosis. Therefore in this paper, Hausdrop Fractal Dimension (HFD) technique is adopted for identification of the glaucoma eye disease. Here, Optic disk perimeter parameter is used in HFD technique for classification of healthy or glaucomatous retinas. Average fractal dimension is calculated for a set of healthy optic disks and the fractal dimension is found to be 0.998, whereas for glaucomatous optic disks obtained average fractal dimension value 1.342.
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A Novel Approach for Optimization Auto-Scaling in Cloud Computing Environment
Статья научная
In recent years, applications of cloud services have been increasingly expanded. Cloud services, are distributed infrastructures which develop the communication and services. Auto scaling is one of the most important features of cloud services which dedicates and retakes the allocated dynamic resource in proportion to the volume of requests. Scaling tries to utilize maximum power of the available resources also to use idle resources, in order to maximize the efficiency or shut down unnecessary resources to reduce the cost of running requests. In this paper, we have suggested an approach based on learning automata auto- scaling, in order to manage and optimize factors like cost, rate of violations of user-level agreements (SLA Violation) as well as stability in the presence of traffic workload. Results of simulation show that proposed approach has been able to optimize cost and rate of SLA violation in order to manage their trade off. Also, it decreases number of operation needed for scaling to increase stability of system compared to the other approaches.
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Статья научная
This paper introduces an intelligent tool with a novel CatML stacking classifier designed to enhance predictive analytics for postgraduate university admission chances. The proposed classifier uses the CatBoost algorithm as a core component of the stacking ensemble method, which integrates CatBoost and Multi-Layer Perceptron (MLP) learners to improve predictive performance. The dataset comprises 13 questionnaire-based surveys, including academic records, standardized test scores (i.e., GRE, IELTS/TOEFL), publication status, extracurricular activities, recommendation letters, and personal statements from Bangladeshi students who applied to various U.S. postgraduate programs. Experimental results demonstrate that the CatML stacking classifier outperforms conventional models, achieving superior accuracy (88.14%) and robustness in predicting admission outcomes. The enhanced performance is attributed to the model’s ability to capture complex, non-linear relationships within the data, facilitated by the CatBoost algorithm's handling of categorical features and prevention of overfitting. Finally, this model deploys in a web system developed with HTML, CSS, JavaScript and Flask. This research underscores the efficacy of advanced ensemble techniques in educational data mining and provides a valuable intelligent tool for students aiming to navigate the complexities of U.S. postgraduate admissions. The CatML stacking classifier offers significant improvements in predictive analytics, thereby assisting students in making informed application decisions.
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Статья научная
In this paper, a hybrid classifier using fuzzy clustering and several neural networks has been proposed. With using the fuzzy C-means algorithm, training samples will be clustered and the inappropriate data will be detected and moved to another dataset (Removed-Dataset) and used differently in the classification phase. Also, in the proposed method using the membership degree of samples to the clusters, the class of samples will be changed to the fuzzy class. Thus, for example in KDD cup99 dataset, any sample will have 5 membership degrees to classes DoS, Probe, Normal, U2R, and R2L. Afterwards, the neural networks will be trained by new labels then using a combination of regression and classification methods, the hybrid classifier will be created. Also to classify the outlier data, a fuzzy ARTMAP neural network is employed which is a part of the hybrid classifier. Evaluation of the proposed method is performed by KDDCup99 dataset for intrusion detection and Cambridge datasets for traffic classification problems. Our experimental results indicate that the proposed system has performed better than the previous works in the case of precision, recall and f-value also detection and false alarm rate. Also, ROC curve analysis shows that the proposed hybrid classifier has been better than the famous non-hybrid classifiers.
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A Novel Hybrid Flower Pollination Algorithm with Chaotic Harmony Search for Solving Sudoku Puzzles
Статья научная
Flower Pollination algorithm (FPA) is a new nature-inspired algorithm, based on the characteristics of flowering plants.In this paper, a new hybrid optimization method called improved Flower Pollination Algorithm with Chaotic Harmony Search (FPCHS) is proposed. The method combines the standard Flower Pollination algorithm (FPA) with the chaotic Harmony Search (HS) algorithm to improve the searching accuracy. The FPCHS algorithm is used to solve Sudoku puzzles. Numerical results show that the FPCHS is accurate and efficient in comparison with standard Harmony Search, (HS) algorithm.
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A Novel Image Encryption Scheme Based on Multi-orbit Hybrid of Discrete Dynamical System
Статья научная
A multi-orbit hybrid image encryption scheme based on discrete chaotic dynamical systems is proposed. One generalized Arnold map is adopted to generate three orbits for three initial conditions. Another chaotic dynamical system, tent map, is applied to generate one pseudo-random sequence to determine the hybrid orbit points from which one of the three orbits of generalized Arnold map. The hybrid orbit sequence is then utilized to shuffle the pixels' positions of plain-image so as to get one permuted image. To enhance the encryption security, two rounds of pixel gray values' diffusion is employed as well. The proposed encryption scheme is simple and easy to manipulate. The security and performance of the proposed image encryption have been analyzed, including histograms, correlation coefficients, information entropy, key sensitivity analysis, key space analysis, differential analysis, etc. All the experimental results suggest that the proposed image encryption scheme is robust and secure and can be used for secure image and video communication applications.
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A Novel Minimized Computational Time Based Encryption and Authentication Using ECDSA
Статья научная
Providing the security on the basis of encryption standards is considered as key challenges for achieving the integrity & confidentiality. There are three main public-key cryptosystem contenders. Each has a variable key size that can be increased to achieve higher security at the cost of slower cryptographic operations. The best attack known on each public-key cryptosystem requires an amount of computation determined by a security parameter which is related to the key size. The secondary factor is confidentiality i.e. ensuring that adversaries gain no intelligence from a transmitted message. There are two major techniques for achieving confidentiality: This work proposes a novel prototype ECDSA which provides the security where there is not complete trust between documents' sender and receiver & something more than authentication is needed. The signature is formed by taking the hash of the message and encrypting the message with the creator's private key. It guarantees the source and integrity of the message. Then a suitable digital signature algorithm will be picked out as a result of comparing and analyzing three main digital signature algorithms in this paper. Finally, a scheme of digital signature in electronic government will be proposed in order to settle some specific problems such as spilling out the secret, forging or denial and so on. Besides, a brief analysis regarding security will be given for this scheme.
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Статья научная
Sarcasm, a subtle form of expression, is challenging to detect, especially in modern communication platforms where communication transcends text to encompass videos, images, and audio. Traditional sarcasm detection methods rely solely on textual data and often struggle to capture the nuanced emotional inconsistencies inherent in sarcastic remarks. To overcome these shortcomings, this paper introduces a novel multimodal framework incorporating text, audio, and emoji data for more effective sarcasm detection and emotion classification. A key component of this framework is the Contextualized Semantic Self-Guided BERT (CS-SGBERT) model, which generates efficient word embeddings. Primarily, frequency spectral analysis is performed on the audio data, followed by preprocessing and feature extraction, while text data undergoes preprocessing to extract lexicon and irony features. Meanwhile, emojis are analyzed for polarity scores, which provide a rich set of multimodal features. The fused features are then optimized using the Camberra-based Dingo Optimization Algorithm (C-DOA). The selected features and the embedded words from the preprocessed texts are given to Entropy-based Robust Scaling - Gated Recurrent Units (E-RS-GRU) for detecting sarcasm. Experimental results on the MUStARD dataset show that the proposed E-RS-GRU model achieves an accuracy of 76.65% and F1-score of 76.9%, with a relative improvement of 2.18% over the best-performing baseline and 1.25% over the best-performing state-of-the-art model. Additionally, KLKI-Fuzzy model is proposed for emotion recognition, which dynamically adjusts membership functions through Kullback-Leibler Kriging Interpolation (KLKI), enhancing emotion classification by processing features from all modalities. The KLKI-Fuzzy model exhibits enhanced emotion recognition performance with reduced fuzzification and defuzzification times.
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A Novel Mutual RFID Authentication Protocol with Low Complexity and High Security
Статья научная
Radio Frequency Identification (RFID) is a method for automated identifying objects. One of the problems of this technology is its security. RFID tags include resource limitation; therefore, the system designers cannot implement complex circuits to enhance their security. Usually the symmetric and asymmetric encryption methods increase resources and cost. Because it is believed to increasing security is inconsistent with the simplicity, the researchers mostly use one-way encryption methods. In this paper, we propose a mutual authentication protocol based on public key cryptography. The used encryption method includes high security and low complexity. This protocol performs in few steps and is suitable for portable devices with power limitation. In terms of security, the proposed protocol is robust against known attacks. In addition, we prove the protocol is secure by an analytical method.
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A Novel Quaternary Full Adder Cell Based on Nanotechnology
Статья научная
Binary logic circuits are limited by the requirement of interconnections. A feasible solution is to transmit more information over a signal line and utilizing multiple-valued logic (MVL). This paper presents a novel high performance quaternary full adder cell based on carbon nanotube field effect transistor (CNTFET). The proposed Quaternary full adder is designed in multiple valued voltage mode. CNTFET is a promising candidate for replacing MOSFET with some useful properties, such as the capability of having the desired threshold voltage by regulating the diameters of the nanotubes, which make them very appropriate for voltage mode multiple threshold circuits design. The proposed circuit is examined, using Synopsys HSPICE with the standard 32 nm CNTFET technology with different temperatures and supply voltages.
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A Novel Radiation Hardened Parallel IO Port for Highly Reliable Digital IC Design
Статья научная
This article proposes a radiation hardened parallel IO port capable of tolerating radiation induced soft errors including single event upsets (SEUs) as well as single event transients (SETs). To investigate the soft error tolerance capability of the proposed design, we simulated it using the Cadence tool and showed its offered advantages. Comparing with the conventional and well-known TMR IO port, the proposed architecture results in less hardware redundancy and design cost. Through an analytical analysis, we also showed that, our design has lower failure probability than the TMR approach. It also is notable that, among the considered previous counterparts, our proposed design is the only one that is capable of tolerating both the SEUs and SETs.
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A Novel Reduced-Precision Fault-Tolerant Floating-Point Multiplier
Статья научная
This paper presents a new fault-tolerant architecture for floating-point multipliers in which the fault-tolerance capability is achieved at the cost of output precision reduction. In this approach, to achieve the fault-tolerant floating-point multiplier, the hardware cost of the primary design is reduced by output precision reduction. Then, the appropriate redundancy is utilized to provide error detection/correction in such a way that the overall required hardware becomes almost the same as the primary multiplier. The proposed multiplier can tolerate a variety of permanent and transient faults regarding the acceptable reduced precisions in many applications. The implementation results reveal that the 17-bit and 14-bit mantissas are enough to obtain a floating-point multiplier with error detection or error correction, respectively, instead of the 23-bit mantissa in the IEEE-754 standard-based multiplier with a few percent area and power overheads.
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A Novel Technique for Copyright Protection of Images Using Hybrid Encryption Model
Статья научная
In this paper, we present a robust and novel strategic invisible watermarking scheme which can be used in the field of copyright protection. The novelty of our algorithm lies in the creation of a compound watermark image using the target image and the key image, where both of them are self encrypted. The self encryption concept adds an extra level of data security along with the security supported by the watermarking technique. Again, our method results a single invisible watermarked image which will be sent to the recipient and from that image, both the key and the target image can be extracted with no distortion using only the proposed extraction algorithm. Results of exhaustive experimentation using standard input color images demonstrate the robustness and efficiency of our approach.
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A Novel Testing Model for SOA based Services
Статья научная
SOA (Service-Oriented Architecture) filled the gap between software and commercial enterprise. SOA integrates multiple web services. We bear to secure the caliber of web services that gives guarantee about what network services work and their output results. There is close to work has to be performed for an automatic test case generation for SOA based services. But, full coverage of XML elements is missing. To the best of our knowledge this all works do not attempt to cover all possible elements of the XML schema presents in the WSDL file. There is also a need to apply different assertions on each service operation for generating the test cases. To overcome this problem we proposed a novel testing model for SOA based application. This new testing model helps us to get the automatic test cases of SOA based application. We build up our new test model with the aid of our proposed test case generation algorithm and test case selection algorithm. In the end, we generate the test suite execution results and find the coverage of XML schema elements present in the WSDL file.
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A Novel Web Page Change Detection Approach using Sql Server
Статья научная
The WWW is very dynamic in nature and is basically used for the exchange of information or data, the content of web page is added, changed or deleted continuously, which makes Web crawlers very challenging to keep refresh the page with the current version. The web page is frequently changes its content hence it becomes essential to develop an effective system which could detect these types of changes efficiently in the lowest browsing time to achieve this changes. In this chapter we compare the old web page hash value with the new web page hash value if hash value is changed that means web page content changed. The changes in web page can be detected by calculating the hash value which is unique.
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A Novel framework for Strategic Alliance of Knowledge Management Systems
Статья научная
Knowledge is the primary strategic resource of many organizations. Knowledge management system (KMS) is a process of knowledge extraction, storage, transformation, analysis, distribution and deployment. Strategic alliance plays an increasing role to a global scale in technology business competition. Organizations are able to use alliances to respond to new technology and deliver new products more efficiently. The relationship between knowledge management and strategic alliance is identified. Knowledge transfer and selecting suitable partner are critical factors in the success of strategic alliance of an organization. In this paper, a novel framework is proposed for KMS strategic alliance. By applying this framework, strategic alliance will be highly achievable in corporate companies.
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Статья научная
This study proposes and empirically evaluates a pedagogical framework for ethical skill development in higher education within smart learning environments. The framework conceptualizes ethical competence as a multidimensional, process-oriented construct cultivated through authentic ethical scenarios, structured reflective cycles, adaptive learning support, and competence-aligned assessment. A quasi-experimental design was implemented with 90 undergraduate participants assigned to three groups: Group A (n = 30) learned using the framework with teacher guidance, Group B (n = 30) learned using the framework without teacher involvement, and Group C (n = 30) learned under traditional instruction without the framework. Ethical competence was measured via pre-test and post-test questionnaires capturing overall ethical skills and specific dimensions including ethical awareness, moral reasoning, reflective capacity, and ethical responsibility. Statistical analyses combined gain-score comparisons and covariate-adjusted models. Results indicate that the framework-based condition (Groups A+B) achieved significantly higher overall ethical skill development than the traditional condition, supported by large practical effects. Multivariate analysis further revealed significant framework-related advantages on the combined outcomes of ethical awareness and moral reasoning, with stronger effects observed for ethical awareness. Ethical responsibility also increased substantially under the framework relative to traditional instruction. Teacher guidance demonstrated a differentiated contribution: no significant difference emerged between Groups A and B in overall ethical skill development, whereas teacher-mediated scaffolding produced a significant and large improvement in reflective capacity compared to autonomous framework-based learning. These findings suggest that smart learning environments can support scalable ethical competence formation when pedagogical design integrates adaptive ethical tasks and structured reflection, while targeted instructor scaffolding remains important for deep reflective development. The study contributes actionable guidance for embedding ethics into smart education curricula and motivates future longitudinal and multi-institutional research using behavioral measures and discipline-specific adaptations.
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