International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1123
Design of (FPID) controller for Automatic Voltage Regulator using Differential Evolution Algorithm
Статья научная
This article presents Differential Evolution (DE) to determine optimum fractional proportional-integral-derivative (FPID) controller parameters for model decrease of an automatic voltage controller (AVR) system. The suggested strategy is a straightforward yet efficient algorithm with balanced capacities for exploration and exploitation to efficiently search for space alternatives to find the best outcome. The algorithm's simplicity offers quick and high-quality tuning of optimum parameters for the FPID controller. A time domain performance index is used to validate the suggested DE-FPID controller. The proposed technique was discovered productive and hearty in improving the transient response of AVR framework contrasted with the PID controllers based - Ziegler-Nichols (ZN), FPID based - Invasive Weed Optimization (IWO),FPID based-Sine-Cosine algorithmn (SCA) tuning strategies.
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Статья научная
Quantum-dot Cellular Automata is an alternative to CMOS technology for the future digital designs. When compared to its CMOS counterpart, it has extremely low power consumption, as there is no current flow in cell. The methodology of parity generator and checker is based on the parity generation and matched it at the receiver end. By using the parity match bits, the error in circuit can be sensed. In this paper, novel parity generator and detector circuit are introduced. The circuit is designed in single layer, minimum clock and minimum latency, which is achieved in QCA framework. The proposed circuits are better than the existing in terms of clock cycle delay, cell complexity and clock cycle utilize. The simulation of presented cell structures have been verified using QCA designer tool. In addition, QCA Probabilistic (QCAPro) tool is used to calculate the minimum, maximum and average energy dissipation aspect in proposed QCA circuit. Appropriate comparison table and power analysis is shown to prove that our proposed circuit is cost effective.
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Статья научная
Students with learning disorders (LD) are unable to perform certain set of tasks due to their difficulty in understanding & interpreting them. These tasks include, but are not limited to, solving simple Mathematical identities, understanding English Grammar related questions, spelling certain words, arranging words in sequence, etc. A wide variety of system models are proposed by researchers to analyze such issues with LD students, and recommend various remedies for the same. But a very few of these models are designed for end-to-end continuous learning support, which limits their applicability. Moreover, even fewer system models are designed to improve capabilities of LD students, via modification of system’s internal parameters. To cater these issues, a novel deep-learning model (DL2CSMBP) is proposed in this text, which assists in incrementally improving learning capabilities of LD children via statistical modelling of examination behavioural patterns. The model initially proposes design of a novel examination system that generates question sets based on student’s temporal performance, and collects their responses via an LD-friendly approach. These responses are processed using a deep learning model that extracts statistical characteristics from student responses. These characteristics include question skipping probability, percentage of correct answers, question revisit probability, time spent on each question, un-attempted questions, & frequently skipped question types. They were extracted from 12 different question types which include Basic English Grammar, Medium English Grammar, Advanced English Grammar, direct comprehension, inference comprehension, vocabulary comprehension, sequencing, spelling, synonyms, Mathematics (addition & subtraction), and finding the odd Man out. The results of these questions were evaluated for 80+ LD students, and their responses were observed. Based on these responses a customized 1D convolutional Neural Network (CNN) layer was trained, which assisted in improving classification performance. It was observed that the proposed model was able to identify LD students with 95.6% efficiency. The LD students were able to incrementally improve the performance by attempting a series of exam sessions. Due to this incremental performance improvement, the LD students were able to cover 28% more questions, and answer almost 97% of these questions with precision & correctness. Due to such promising results, the system is capable of real-time deployment, and can act as an automated schooling tool for LD students to incrementally improve their examination performance without need of medical & psychological experts. This can also assist in reducing depression among LD students, because they don’t need to interact with a physical doctor while improving their LD condition in real-time, thus suggesting its use in non-intrusive medical treatments of these students.
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Design of a Digital Game-based Learning System for Fraction Algebra
Статья научная
Digital game based learning (DGBL) system is a promising area of research and debate, as it promotes contextualized learning, creates and harnesses motivation in learning capacities and encourages curiosity. DGBL takes place in a technological-mediated environment while engaging players in a learning activity through the support of computers. Several digital game based learning systems have been deployed for educational purposes to aid or support students in hands-on-learning experiences and constructive knowledge that involves mental reasoning processes. Hence, the research paper presented a development of digital game based fraction algebra learning system which is associated with game principle of snakes and ladders with underpinned concept of step-by-step procedure of solving mathematical problems. The system was tested by participants of FUTA staff primary school and the results of performance evaluation showed that the system could greatly support students to learn effectively the fraction algebra through game technology and would increase the students’ thinking process.
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Design of an efficient current mode full-adder applying carbon nanotube technology
Статья научная
In this article a new design of a current mode full-adder is proposed through the field effect transistors based on carbon nanotubes. The outperformance of the current mode full-adder constructed by CNTFET compared to that of constructed by CMOS is observable in the simulation and comparisons. This circuit operates based on triple input majority function. The simulation is run by HSPICE software according to the model proposed in Stanford University for CNTFETs at 0.65 V power supply voltage. The proposed circuit outperforms compared to the previous current mode full-adders in terms of speed, accuracy and PDP.
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Design of model free sliding mode controller based on BBO algorithm for heart rate pacemaker
Статья научная
A modified Model-Free Sliding Mode Controller (MF-SMC) scheme, which based on the Biogeography-Based Optimization (BBO) algorithm is suggested, to regulate the heart rate since it is difficult to derive a suitable model for the control algorithm, therefore the traditional control algorithm cannot perform efficiently. Also, a smoothing boundary layer is used to eliminate the chattering in the MF-SMC control signal. Results of simulation effort show that overshoot less and fast performance is achieved with stable convergence for the tracking regardless of the heart rate model to be controlled.
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Designing A Digital Multimedia Interactive Book for Industrial Metrology Measurement Learning
Статья научная
This research aims to develop a digital multimedia interactive book for supporting the learning of industrial metrology and to examine the worthiness/value of the produced digital multimedia interactive book. It employs the approach of research and development. The design of the digital multimedia interactive book is implemented in some development stages: (1) material data collection, (2) planning, (3) making a prototype, (4) pilot test with respondents of multimedia experts and experts of industrial metrology, (5) improvement of the prototype, (6) application to students, (7) improvement of the final product. The final product of this research is a software multimedia digital interactive book which can be used to help students learn industrial metrology measurement. With this software, students can learn about industrial metrology whenever and wherever they are. Therefore, it can improve their competency, especially in the measurement.
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Designing Counter Using Inherent Capability of Quantum-dot Cellular Automata Loops
Статья научная
Quantum-dot cellular automata presents a promising Nano-scale technology for replacement of conventional CMOS-based circuits. According to the significant role of counters in computing units, designing diverse types of counter circuits has attracted many attentions, so far. This paper presents a QCA-compatible single layer architecture for 4-bit counter circuit, however by generalizing the main idea, n-bit counter can be engendered in a similar way. The proposed circuit is designed without employing conventional flip-flops' structures by allotting the distinct clock cycles to each counting unit. The comparison results with the best-reported structure reveal the superiority of our design in terms of circuit complexity and required layers for accessing to input and output cells. The proper output waveforms obtained by the QCADesigner tool proves the precise functionality of the proposed counter.
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Designing a Novel Ternary Multiplier Using CNTFET
Статья научная
Today, multipliers are included as substantial keys of many systems with high efficiency such as FIR filters, microprocessors and processors of digital signals. The efficiency of the systems are mainly evaluated by their multipliers capability since multipliers are generally the slowest components of a system while occupying the most space. Multiple Valued Logic reduces the number of the required operations to implement a function and decreases the chip surface. Carbon Nanotube Field Effect Transistors (CNTFET) are considered as good substitutes for Silicon Transistors (MOSFET). Combining the abilities of Carbon Nanotubes Transistors with the advantages of Multiple Valued can provide a unique design which has a higher speed and less complexity. In this paper, a new multiplier is presented by nanotechnology using a ternary logic that improves the consuming power, raises the speed and decreased the chip surface as well. The presented design is simulated using CNTFET of Stanford University and HSPICE software, and the results are compared with other instances.
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Designing a Universal Data-Oriented Random Number Generator
Статья научная
Data-oriented is new and applied theory which provides method that models the concepts with data structure. If the concept is modeled by using sufficient data in modeling, required inferences and calculations can be done fast with less complexity. Random variable was modeled with digital probability graph, by using Ahmad Fact and probability density function. Some data-oriented random generators have been presented based on data-oriented approach. In this paper a universal data-oriented random number generator is introduced which is able to generate random numbers with uniform, normal, exponential and chi-square distributions.
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Статья научная
The use of wind turbines based on induction generator is very popular to generate electrical power and it has been noted by researchers because of its many advantages compared to conventional methods of electrical energy generation. Factors of uncertainty in the nature of the wind cause variable voltage in amplitude and frequency on the induction generator. It is not appropriate to apply such voltage to the load. So a controller must be designed to be kept constant voltage and frequency. In this paper, a fuzzy controller is used as state feedback to stabilize the voltage, frequency and voltage amplitude. The variable AC voltage generated by generator is converted by rectifier to a variable DC voltage. The variable DC voltage causes a change in the output voltage of the inverter. The PWM switching property is used to stabilize frequency and state feedback is used to stabilize the output voltage amplitude. The obtained error signal with its derivative is applied to the fuzzy controller as input to generate the considered control signal by controller to generate appropriate firing pulses to apply to PWM inverter. Therefore, frequency and amplitude of the output voltage is kept constant with switching control and so maximum power of wind is resulted. Simulation results show that by design the appropriate controller for the considered system output voltage can be stabilized in constant amplitude and frequency.
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Detecting Anomalies in Students' Results Using Decision Trees
Статья научная
Examinations are one of the most important activities that take place in institutions of learning. In many Nigerian universities, series of meetings are held to manually examine and approve computed student examination results. During such meetings, students' results are scrutinized. Reasonable explanations must be provided for any anomaly that is discovered in a result before the result is approved. This result approval process is prone to some challenges such as fatigue arising from the long duration of the meetings and wastage of man-hours that could have been used for other productive tasks. The aim of this work is to build decision tree models for automatically detecting anomalies in students' examination results. The Waikato Environment for Knowledge Analysis (WEKA) data mining workbench was used to build decision tree models, which generated interesting rules for each anomaly. Results of the study yielded high performances when evaluated using accuracy, sensitivity and specificity. Moreover, a Windows-based anomaly detection tool was built which incorporated the decision tree rules.
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Detecting and Removing the Motion Blurring from Video Clips
Статья научная
In this paper, we give a framework to deblur the blurry frame in a video clip. Two kinds of motion blurring effects can be removed in the video, one is the blurring effect caused by hand shaking, the other is the blurring effect caused by a fast moving object. For the blurring caused by hand shaking, PSF is estimated by comparing the stable area in blurry frame and non-blurry frame, so the Richardson-Lucy algorithm can restore the blurry frame by non-blind deconvolution. We also propose a framework to deblur the motion blurring objects which move fast in the video. The background is reconstructed by the algorithm in each frame, so an accurate matte of blurry object can be extracted to deblur the moving object by alpha matting. Results show our method is effective.
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Detection of False Income Level Claims Using Machine Learning
Статья научная
Data driven social security fraud detection has been given limited attention in research. Recently, social schemes have seen significant expansion across many developing countries including India. The fundamental aims of social schemes are to alleviate poverty, enhance the quality of life of the most vulnerable and offer greater chances to those relegated to the fringe of society to engage more enthusiastically in the society. Although governments channel billions of dollars every year in support of these social schemes, quite significant number of the eligible people are excluded from the program mainly through fraud and dishonesty. Although fraud is considered an illegal offence and morally reprehensible, it is unfortunate that the prevalence of fraud in social benefit schemes is rampant and a significant challenge to address. In this paper, we studied the viability of machine learning techniques in identifying fraudulent transactions in the context of social schemes. We focus on the detection of the false income level claims made by the fake beneficiaries to get the privileges of government scheme. We used the standard classifiers like Logistic Regression, Decision Trees, Random Forests, Support Vector Machine (SVM), Multi-Layer Perceptron and Naïve Bayes to identify fake beneficiaries of the government scheme from those deserving people. The results show that the Random Forest Classifier perform best providing an accuracy of 99.3% with F1 score of 0.99. The outcome of this research can be used by the government agencies entrusted with the management of the schemes to wade out the abusers and provide the required benefits to the right and deserving recipients.
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Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method
Статья научная
Mammography is the most effective procedure for the early detection of breast diseases. Mammogram analysis refers the processing of mammograms with the goal of finding abnormality presented in the mammogram. In this paper, the tumour can be detected by using wavelet based adaptive windowing technique. Coarse segmentation is the first step which can be done by using wavelet based histogram thresholding where, the thereshold value is chosen by performing 1-D wavelet based analysis of PDFs of wavelet transformed images at different channels. Fine segmentation can be done by partitioning the image into fixed number of large and small windows. By calculating the mean, maximum and minimum pixel values for the windows a threshold value has been obtained. Depending upon the threshold values the suspicious areas have been segmented. Intensity adjustment is applied as a preprocessing step to improve the quality of an image before applying the proposed technique. The algorithm is validated with mammograms in Mammographic Image Analysis Society Mini Mammographic database which shows that the proposed technique is capable of detecting lesions of very different sizes.
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Detection of botnet using flow analysis and clustering algorithm
Статья научная
With the increase of digital data on the internet, computers are at higher risk of getting corrupted through cyber-attacks. Criminals are adopting more and more sophisticated techniques to steal sensitive information from the web. The botnet is one of the most aggressive threats as it combines lots of advanced malicious techniques. Detection of the botnet is one of the most serious concerns and prominent research area among the researchers. This paper proposes a detection model using the clustering algorithm to group bot traffic and normal traffic into two different clusters. Our contribution focused on applying K-means clustering algorithm to detect botnets based on their detection rate (true and false positives). Experimental results clearly demonstrate the fact that with the help of clustering we were able to separate the complete dataset into two entirely distinguishable clusters, where one cluster is representing the botnet traffic and other one representing the normal traffic.
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Determining proficient time quantum to improve the performance of round robin scheduling algorithm
Статья научная
Process scheduling is considered as a momentous and instinct task accomplished by operating system. Round robin is one of the extensively utilized algorithms for scheduling. Various noticeable scheduling algorithms based on round robin strategy have been introduced in last decade. The most sensitive issue of round robin algorithm is time quantum because it determines and controls the time of achieving resources for a process during execution. Different types of approaches are available for determining time quantum related to round robin. This paper represents a new round robin algorithm having proficient time quantum that has been determined by considering the maximum difference among differences of adjacent consecutive processes into the ready queue. The proposed methodology is an endeavor to increase the outcomes of round robin as well as system performance. The algorithm is experimentally and comparatively better than the mentioned round robin algorithms in this paper. From the consideration against the referred algorithms, it decreases average turn-around-time, average waiting-time and the number of context-switching along with other CPU scheduling criteria.
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Developing Audio-to-Text Converters with Natural Language Processing for Smart Assistants
Статья научная
In recent years, smart assistants have transformed human interaction with technology, offering voice-controlled interactions like music playback and information retrieval. However, existing systems often struggle with accurately interpreting natural language input. To address it, this proposed work aims to develop an audio-to-text converter integrated with natural language processing (NLP) capabilities to enhance interactions of smart assistants. Additionally, the system will incorporate intent recognition to discern user intentions and generate relevant responses. The proposed work commenced with a literature survey to gather insights into existing smart assistant systems. Based on the findings, a comprehensive architecture was designed, integrating NLP techniques like tokenization and lemmatization. The implementation phase involved developing and training a Feedforward Neural Network (FNN) model tailored for NLP tasks, leveraging Python and libraries like TensorFlow and NLTK. Testing evaluated the system's performance using standard evaluation metrics, including Word Error Rate (WER) and Character Error Rate (CER), across various audio input conditions. The system exhibited higher WER and CER with accented speech (15.3% and 7.9% respectively) while the clean audio dataset produced WER and CER of 4.7% and 2.55% respectively. The proposed work also involved training the FNN model while monitoring training loss and accuracy to ensure model performance. Ultimately, the model achieved an accuracy of 97.62% with training loss reduced to 1.45%. Insights from the training phase inform further optimization efforts to improve system performance. It practices the Google WebSpeech API and compares it with other Speech-to-text models. In conclusion, our proposed work represents a significant step towards realizing seamless voice-controlled interactions with smart assistants and enhancing user experiences and productivity. Future work includes refining the system architecture, optimizing model performance, and expanding the capabilities of the smart assistant for broader application domains.
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Developing E-Business Strategies Curriculum Case Study in the Information Systems Department
Статья научная
E-Business Strategies (EBS) is an elective course in the Information Systems Department, Faculty of Computing & Information Technology at King Abdulaziz University, Jeddah. In this course, students learn many concepts regarding e-business, such as strategies, models, relationships and so on. Also, they improve their leadership skills, such as analyzing the business environment and take the decisions. In this paper, we address the main challenges faced by the EBS course developers and study all the components of the existing EBS course. We then determine a learning strategy using an Outcomes-based Education (OBE) approach and redesign the course using an Active Learning Approach. This paper provides comprehensive information concerning an EBS course in terms of its importance, goals, textbook and evaluation tools.
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Developing a Compact and Practical Online Quiz System
Статья научная
Thanks to the open technologies around the world wide web, the authors as just college teachers have been able to develop a compact and practical online quiz system we call QDB to satisfy needs in their classes. In this paper, first we introduce our system to give ideas what the system can do. Then we show the effectiveness of the system by describing the effects of practical uses of the system in our classes for years including the effect of the recently added retry-functionality. Then as an example of the feasibility of our development we explain how simple to implement the retry-functionality because of the compactness of the system and again the great power the open technologies give us and show the speed of the system. As conclusion we suggest possibilities of a compact and flexible system as a form (or a part) of e-learning systems made to your order.
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