International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1096
Automatic Environmental Sound Recognition (AESR) Using Convolutional Neural Network
Статья научная
Automatic Environmental Sound Recognition (AESR) is an essential topic in modern research in the field of pattern recognition. We can convert a short audio file of a sound event into a spectrogram image and feed that image to the Convolutional Neural Network (CNN) for processing. Features generated from that image are used for the classification of various environmental sound events such as sea waves, fire cracking, dog barking, lightning, raining, and many more. We have used the log-mel spectrogram auditory feature for training our six-layer stack CNN model. We evaluated the accuracy of our model for classifying the environmental sounds in three publicly available datasets and achieved an accuracy of 92.9% in the urbansound8k dataset, 91.7% accuracy in the ESC-10 dataset, and 65.8% accuracy in the ESC-50 dataset. These results show remarkable improvement in precise environmental sound recognition using only stack CNN compared to multiple previous works, and also show the efficiency of the log-mel spectrogram feature in sound recognition compared to Mel Frequency Cepstral Coefficients (MFCC), Wavelet Transformation, and raw waveform. We have also experimented with the newly published Rectified Adam (RAdam) as the optimizer. Our study also shows a comparative analysis between the Adaptive Learning Rate Optimizer (Adam) and RAdam optimizer used in training the model to correctly classifying the environmental sounds from image recognition architecture.
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Статья научная
Some of the most common and typical issues in the field of intelligent tutoring systems (ITS) are (i) the correct identification of learners’ difficulties in the learning process, (ii) the adaptation of content or presentation of the system according to the difficulties encountered, and (iii) the ability to adapt without initial data (cold-start). In some cases, the system tolerates modifications after the realization and assessment of competences. Other systems require complicated real-time adaptation since only a limited number of data can be captured. In that case, it must be analyzed properly and with a certain precision in order to obtain the appropriate adaptations. Generally, for the adaptation step, the ITS gathers common learners together and adapts their training similarly. Another type of adaptation is more personalized, but requires acquired or estimated information about each learner (previous grades, probability of success, etc.). Some of these parameters may be difficult to obtain, and others are imprecise and can lead to misleading adaptations. The adaptation using machine learning requires prior training with a lot of data. This article presents a model for the real time automatic adaptation of a predetermined session inside an ITS called AI-VT. This adaptation process is part of a case-based reasoning global model. The characteristics of the model proposed in this paper (i) require a limited number of data in order to generate a personalized adaptation, (ii) do not require training, (iii) are based on the correlation to complexity levels, and (iv) are able to adapt even at the cold-start stage. The proposed model is presented with two different configurations, deterministic and stochastic. The model has been tested with a database of 1000 learners, corresponding to different knowledge levels in three different scenarios. The results show the dynamic adaptation of the proposed model in both versions, with the adaptations obtained helping the system to evolve more rapidly and identify learner weaknesses in the different levels of complexity as well as the generation of pertinent recommendations in specific cases for each learner capacity.
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Статья научная
This paper presents a boon and amend technique for eradicating the artifacts from the Electroencephalogram (EEG) signals. The abolition of artifacts from scalp EEGs is of considerable implication for both the computerized and visual investigation of fundamental brainwave activities. These noise sources increase the difficulty in analyzing the EEG and procurement clinical information related to pathology. Hence it is critical to design a procedure for diminution of such artifacts in EEG archives. This paper uses a blind extraction algorithm, appropriate for the generality of complex-valued sources and both complex noncircular and circular, is introduced. This is achieved based on higher order statistics of dormant sources, and using the de?ation approach Spatially-Constrained Independent Component Analysis (SCICA) to separate the Independent Components (ICs) from the initial EEG signal. As the next phase, level-4 daubechies wavelet db-4 is applied to extract the brain activity from purged artifacts, and lastly the artifacts are projected back and detracted from EEG signals to get clean EEG data. Here, thresholding plays an imperative role in delineating the artifacts and hence an improved thresholding technique called Otsu’s thresholding is applied. Experimental consequences show that the proposed technique results in better removal of artifacts.
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Automatic Robust Segmentation Scheme for Pathological Problems in Mango Crop
Статья научная
Machine vision and soft computing techniques have been promising in the field of agriculture and horticulture to remove the barriers of conventional methods for detecting the plant diseases using different plant parts. Image segmentation technique is first and primary step in all the related researches such as fruit grading, leaf lesion region detection etc. In this paper, a robust technique for Mango crop using different plant parts such as Fruit, Flower and Leaf has been proposed in order to detect the disease more accurately. The captured real time images are pre-processed for illumination normalization and color space conversion before segmentation. The standard K-Means clustering scheme has been made adaptive and edge detection transforms have been applied to improve the segmentation results. Here, the objective function of K-Means clustering technique has been modified and cluster centers also have been updated to segment the diseased parts from images. The results obtained are better in the terms of both general human observation and in computational time.
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Autonomous Taxi Driving Environment Using Reinforcement Learning Algorithms
Статья научная
Autonomous driving is predicted to alter the transportation industry in the near future. For decades, carmakers, researchers, and administrators have already been working in this sector, with tremendous development. Nevertheless, there are still many uncertainties and obstacles to solve, not only in terms of technical technology, as well as in terms of human consciousness, culture, and present traffic infrastructure. With respect to technological challenges, precise route identification, avoiding the improper location, time delay, erroneous drop-off, unsafe path, and automated navigation in the environment are only a few. RL (Reinforcement Learning) has evolved into a robust learning model which can learn about complications in high dimensional settings, owing to the advent of deep representation learning. Environment learning has been shown to reduce the required time delay, reduce cost of travel, and improve the performance of the agent by discovering a successful drop-off. The major goal is to ensure that an autonomous vehicle driving can reach passengers, pick them up, and transport them to drop-off points as quickly as possible. For performing this task, RL methods like DQNs (Deep Q Networks), Q-LNs (Q-Learning networks) , SARSAs (state action reward state actions), and ConvDQNs (convolution DQNs) are proposed for driving Taxis autonomously. RL agent’s decisions are based on MDPs (Markov Decision Processes). The agent has effectively learnt the closest path, safety, and lower cost, gradually obtaining the capacity to travel bigger areas of the successful drop-off without negative incentive for reaching the target using these RL approaches. This scenario was chosen based on a set of requirements for simulating autonomous vehicles using RL algorithms. Results indicate that ConvDQNs are capable of successfully controlling cars in simulation environments than other RL methods. ConvDQNs are a combinations of CNNs (Convolution Neural Networks) and DQNs. These networks show better results than other methods as their combining of procedures gives improved results. Results indicate that ConvDQNs are capable of successfully controlling a car to navigate around a Taxi-v2 environment than the existing RL methods.
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Big data analytics for medical applications
Статья научная
Big Data is an accumulation of data sets which are abundant and intricate in character. They comprise both structured and unstructured data that evolve abundant, so speedy they are not convenient by classical relational database systems or current analytical tools. Big Data Analytics is not linearly able to expand. It is a predefined schema. Now big data is very helpful for backup of data not for everything else. There is always a data introducing. It also helps to solve India’s big problems. It also helps to fill the data gap. Health care is the conservation or advancement of health along the avoidance, interpretation and medical care of disorder, bad health, abuse, and other substantial and spiritual deterioration in mortal. Health care is expressed by health experts in united health experts, specialists, physician associates, mid-wife, nursing, antibiotic, pharmacy, psychology and other health. This paper focuses on providing information in the area of big data analytics and its application in medical domain. Further it includes introduction, Challenging aspects and concerns, Big Data Analytics in use, Technical Specification, Research application, Industry application and Future applications.
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Binary log design for one-way data replication with ZeroMQ
Статья научная
Today, many business transactions are done online, especially in the financial sector or banking [1]. But as companies grow, many problems occur such as the inability to manage data consistency, especially when data is associated with more than one database. Replication is one of the most commonly used way of syncing data. However, to ensure data remains consistent, it is not enough just to take advantage of the replication process. The problem that often happens is connection failure or offline host. The Binary Log approach is one of the alternative methods that can be used to develop database synchronization. Generally, binary log is used for data recovery or backup purposes. Binary log in the DBMS (Database Management System) record all changes that occur in the database both at the data and structure level, as well as the duration of time used. This information can be used as a reference in updating data, while the ZeroMQ socket used as data exchange medium so data in all system locations will be synchronized and integrated in real time. This research will discuss how to develop a synchronization system by utilizing Binary Log from MySQL to recognize data changes, inherit changes, send changes, and hopefully can contribute new alternative method in developing real time database synchronization.
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Bio-inspired Ant Algorithms: A review
Статья научная
Ant Algorithms are techniques for optimizing which were coined in the early 1990’s by M. Dorigo. The techniques were inspired by the foraging behavior of real ants in the nature. The focus of ant algorithms is to find approximate optimized problem solutions using artificial ants and their indirect decentralized communications using synthetic pheromones. In this paper, at first ant algorithms are described in details, then transforms to computational optimization techniques: the ACO metaheuristics and developed ACO algorithms. A comparative study of ant algorithms also carried out, followed by past and present trends in AAs applications. Future prospect in AAs also covered in this paper. Finally a comparison between AAs with well-established machine learning techniques were focused, so that combining with machine learning techniques hybrid, robust, novel algorithms could be produces for outstanding result in future.
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Biometric Palm Prints Feature Matching for Person Identification
Статья научная
Biometrics is playing an important role for person recognition. The Biometrics identification of an individual is can be done by physiological or behavioral characteristics; where the palm print of an individual can be captured by using sensors and is one of among physiological characteristics of an individual. Palm print is a unique and reliable biometric characteristic with high usability. A palm print refers to an image acquired of the palm region of the hand. The biometric use of palm prints uses ridge patterns to identify an individual. Palm print recognition system is most promising to recognize an individual based on statistical properties of palm print image. It is rich in its features: principal lines, wrinkles, ridges, singular points and minutiae points. This paper proposes a Biometric Palm print lines extraction using image processing morphological operation. The proposed work discusses the significance; since both the palm print and hand shape images are proposed to extract from the single hand image acquired from a sensor. The basic statistical properties can be computed and are useful for biometric recognition of individual. This result and analysis will result into Total Success Rate (TSR) of experiment is 100%. This paper discusses proposed work for biometric recognition of individual by using basic statistical properties of palm print image. The experiment is carried out by using MATLAB software image processing toolbox.
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Biometric system design for iris recognition using intelligent algorithms
Статья научная
An iris recognition system for identifying human identity using two feature extraction methods is proposed and implemented. The first approach is the Fourier descriptors, which is based on transforming the uniqueness iris texture to the frequency domain. The new frequency domain features could be represented in iris-signature graph. The low spectrums define the general description of iris pattern while the fine detail of iris is represented as high spectrum coefficients. The principle component analysis is used here to reduce the feature dimensionality as a second feature extraction and comparative method. The biometric system performance is evaluated by comparing the recognition results for fifty persons using the two methods. Three classifiers have been considered to evaluate the system performance for each approach separately. The classification results for Fourier descriptors on three classifiers satisfied 86% 94%, and 96%, versus 80%, 92%, and 94% for principle component analysis when Cosine, Euclidean, and Manhattan classifiers were applied respectively. These results approve that Fourier descriptors method as feature extractor has better accuracy rate than principle component analysis.
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Bitwise Operations Related to a Combinatorial Problem on Binary Matrices
Статья научная
Some techniques for the use of bitwise operations are described in the article. As an example, an open problem of isomorphism-free generations of combinatorial objects is discussed. An equivalence relation on the set of square binary matrices having the same number of units in each row and each column is defined. Each binary matrix is represented using ordered n-tuples of natural numbers. It is shown how by using the bitwise operations can be implemented an algorithm that gets canonical representatives which are extremal elements of equivalence classes relative to a double order on the set of considered objects.
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Blended Learning for Lifelong Learning: An Innovation for College Education Students
Статья научная
With the fast developing and changing transport of technology, new trends and learning opportunities were ushered in the field of Education. This transformation restructures the teaching-learning operation. As a result, educators encounter different learning preferences of students due to the emerging learning needs brought by technology. Although many universities here and abroad recognize the potential of blended learning, there is still lack of implementation on how blended learning be planned, designed and applied. In response to this need, an empirical study on the use of blended learning approach was conducted, which involved the mixing of face-to-face and online delivery methods. Thus, the main purpose of this paper was to find out the effect of blended learning (BL) approach on the students' performance in education subjects. Additionally, this work presents instructional strategies on how to effectively integrate content, pedagogy and technology to enhance the teaching and learning of education courses. This provided the most effective and efficient learning experiences on both teachers and learners with its practical applications against retailed software which often burden many universities. Finally, some implications on how to effectively blend pedagogy and technology, which inevitably lead to significant enhancement of the curriculum, were also discussed.
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Blinds Children Education and Their Perceptions towards First Institute of Blinds in Pakistan
Статья научная
This paper investigates the parental participation,their perceptions and opinions about the education of their visually handicapped children in the first institute for the blinds children in Multan Pakistan. Students with visual impairments have unique educational needs which could most effectively meet using a team approach of professionals,parents and students. In order to meet their unique needs,students must have specialized services, books and materials in appropriate media to enable them to most effectively compete with their peers in school and ultimately in society.This study examines the role of education imparted by the institute as felt by the parents of visually impaired children admitted at the institute for blinds.
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Blur Classification Using Wavelet Transform and Feed Forward Neural Network
Статья научная
Image restoration deals with recovery of a sharp image from a blurred version. This approach can be defined as blind or non-blind based on the availability of blur parameters for deconvolution. In case of blind restoration of image, blur classification is extremely desirable before application of any blur parameters identification scheme. A novel approach for blur classification is presented in the paper. This work utilizes the appearance of blur patterns in frequency domain. These features are extracted in wavelet domain and a feed forward neural network is designed with these features. The simulation results illustrate the high efficiency of our algorithm.
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Bond Graph Modelling of a Rotary Inverted Pendulum on a Wheeled Cart
Статья научная
There are some systems that are yet to be modelled using certain methods. One of them is Rotary Inverted Pendulum (RIP) on a wheeled cart which is yet to be modeled using the bond graph technique. Therefore, this work explored the bond graph technique for this system. Using this technique, which uses the concept of energy (power) transfer between elements in a system, the system was modeled. Then, the state space equations of the system, which give the first-order differential equations, were derived. It was observed that the system has five state variables because of the five integrally causal storage elements.
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Building Predictive Model by Using Data Mining and Feature Selection Techniques on Academic Dataset
Статья научная
In the field of education, every institution stores a significant amount of data in digital form on the academic performance of students. If this data is correctly analysed to discover any pattern related to student learning, it can assist the institution in achieving a favorable outcome in the future. Because of this, the use of data mining techniques makes it much simpler to unearth previously concealed information or detect patterns in student data. We use a variety of data mining methods, such as Naive Bayes, Random Forest, Decision Tree, Multilayer Perceptron, and Decision Table, to predict the academic performance of individual students. In the real world, a dataset may contain many features, yet the mining process may only place significance on some of those aspects. The correlation attribute evaluator, the information gain attribute evaluator, and the gain ratio attribute evaluator are some of the feature selection methods that are used in data mining to remove features that are not important for the mining process. Other feature selection methods include the gain ratio attribute evaluator and the gain ratio attribute evaluator. In conclusion, each classification algorithm that is designed using some feature selection methods enhances the overall predictive performance of the algorithms, which in turn improves the performance of the algorithms overall.
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Building a Natural Disaster Management System based on Blogging Platforms
Статья научная
Over the decades, numerous kinds of knowledge discovering and sharing of the data techniques are playing a major role to reach the information quickly. Among these since last few years, social networks or media and own blogging are playing a major in sharing the personal information, updating the status, tagging the location and many more features. These data are considered to examine and the acceptance for emergency services to respond with the information gathered from the social network. Taking this into the consideration, proposed an algorithm to find out the location of the person based upon the information shared. This is implemented on a most popular social media twitter to identify the tweets.
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Building an Ontology for the Metamodel ISO/IEC24744 using MDA Process
Статья научная
The idea of using ontologies in the field of software engineering is not new. For more than 10 years, the Software Engineering community arouse great interest for this tool of semantic web, so to improve; their performance in production time and realisation complexity on the one hand, and software reliability and quality on the other hand. The standard ISO / IEC 24744, also known as the SEMDM (Software Engineering – Meta-model for Development Methodologies), provides in a global perspective, a conceptual framework to define any method of software development, through the integration of all methodological aspects related to the followed procedures, as well as, products, people and tools involved in the conception of a software product. The purpose of this article is to create domain ontology for ISO / IEC 24744 using an MDA process. This ontology will serve as semantic reference in order to assist for a better interoperability between the different users of the standard (human, software or machine).
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CHex: An Efficient RDF Storage and Indexing Scheme for Column-Oriented Databases
Статья научная
As increasingly large RDF data sets are being published on the Web, effcient RDF data management has become an essential factor in realizing the Semantic Web vision. However, most existing RDF storage schemes, which are built on top of row-store relational databases, are constrained in terms of efficiency and scalability. Still, the growing popularity of the RDF format used in real-world applications arguably calls for an effort to deal with these drawbacks. In this paper, we propose a novel RDF storage and indexing scheme, called CHex, which uses the triple nature of RDF as an asset to implement sextuple indexing for a column-oriented database system. Using binary association tables (BATs) in the column-oriented data model, RDF data is indexed in six possible ways, one for each possible ordering of the three RDF elements. The sextuple indexing scheme in a column-oriented database not only provides efficient single triple pattern lookups, but also allows fast merge-joins for any pair of two triple patterns. To evaluate the performance of our approach, we generate large-scale data sets upto 13 million triples, and devise benchmark queries that cover important RDF join patterns. The experimental results show that our approach outperforms the row-oriented database systems by upto an order of magnitude and is even competitive to the best state-of-the-art native RDF store.
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CIPP-SAW Application as an Evaluation Tool of E-Learning Effectiveness
Статья научная
The effectiveness level of e-learning implementation in the learning process at health colleges is very important for all users to know. Things that can be done to measure the level of effectiveness accurately is to carry out evaluation activities using computerized tools. One of the innovations found in this research was a computer-based evaluation application called the CIPP-SAW application. This application is formed by combining an educational evaluation model called CIPP (Context-Input-Process-Product) with a decision support system method called SAW (Simple Additive Weighting). Based on those situations, this research aimed to provide an overview of the user interface design and workings of the CIPP-SAW application used in evaluating the effectiveness of e-learning implemented in health colleges (case study in Bali province). This research was a development study using Borg & Gall’s design, which focused on the preliminary field test and main product revision stages. The subjects involved in the field trial of the CIPP-SAW application were 64 respondents. The respondents included: two informatics experts, two educational evaluation experts, 30 students, and 30 lecturers from several health colleges in Bali province. Data collection tools in the form of questionnaires, interview guidelines, and photo documentation. The analysis technique used was descriptive quantitative which compares the effectiveness level of the CIPP-SAW application with the effectiveness standard which refers to a scale of five. The results showed that the effectiveness level of the CIPP-SAW application was 87.521%, so it was in a good category.
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