Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1165
Intelligent Management of a Network of Smart Billboards on the IoT Platform in Industry 4.0
Статья научная
Artificial intelligence plays a special role in new technologies used to develop advertising and marketing. Artificial intelligence, which plays a special role in improving the effectiveness of advertising and marketing, has had its say in the business market, and this process continues. A quick search for any product in Internet search engines is an indispensable process for the marketing market. With the help of artificial intelligence, it is possible to present the required product or service in a timely manner, at a high level, taking into account the individual characteristics of the customer using virtual environments and street advertising. In the modern world of cyber-physical systems, machines created using intelligent algorithms facilitate human labor in almost all areas. Intelligent management of a network of smart billboards AI research in advertising and marketing has a positive impact on economic development. The article deals with the application of artificial intelligence in the field of advertising and the principle of their work. In this area, the processes of application of new technologies are studied. When preparing the article, scientific analysis of problems and their solutions, application of results, methodological system approach were used.
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Intelligent Mobile Application for Route Finding and Transport Cost Analysis
Статья научная
The explosive rate of increase in number of habitats and vehicles in different areas of the developing countries like Nigeria has motivated government of such world engage in both rural and urban road construction for ease of navigation. This brings stresses in navigating such roads with public traffic hence noise pollution to the environment. For effective autonomous geo-spatial navigation service, we propose a web based model implemented as intelligent mobile application for route finding and transport cost analysis. A case study observed on data collated from different areas within Ile-Ife and its surroundings shows that the system aid users in making decision regarding transportation alternatives. This study shows how to help people living in such parts of the world reach their destinations when navigating unknown routes with reduced transportation cost.
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Intelligent Vision Methodology for Detection of the Cutting Tool Breakage
Статья научная
In this paper, a new Intelligent system based on neurofuzzy for detecting and diagnostics the wear and damage of the milling cutter is presented. The compatibility between the computer vision and neurofuzzy techniques is introduced. The proposed approaches consists of capturing the milling cutter image, Fuzzy edge detection, Chain code technique for feature extraction and finally, apply the neural network on the feature. The results of the study are three different diagnostics models, The first is diagnostic model for the original profile of the perfect cutter, the second is model for the wearied profile and the third is model for the damage profile. Experimental test results show that the proposed system is reliable, practical and can be used for the easy distinguish between the wear and damage automatically.
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Interference Rejection in FH/BFSK System Using Blind Source Separation
Статья научная
This paper introduces a new approach based on blind source separation (BSS) to mitigate intentional interference in BFSK digital communication systems using frequency hopping spread spectrum technique. The use of BSS is possible thanks to adopting an adequate selection block to distinguish between the useful signal and other undesirable signals, hence, circumvent the problem of ambiguity of permutation. An analytical calculation of the probability of error to predict the performance is done. The simulation results showed the effectiveness of this approach, whatever the level of the JSR and without using the fast frequency hopping alternative or error-correcting codes.
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Internet Passport Authentication System Using Multiple Biometric Identification Technology
Статья научная
Electronic passports (e-Passports) have known a wide and fast deployment all around the world since the International Civil Aviation Organization (ICAO) the world has adopted standards whereby passports can store biometric identifiers. The purpose of biometric passports is to prevent the illegal entry of traveler into a specific country and limit the use of counterfeit documents by more accurate identification of an individual. The paper consider only those passport scenarios whose passport protocols base on public-key cryptography, certificates, and a public key infrastructure without addressing the protocols itself detailed, but this is no strong constraint. Furthermore assume the potential passport applier to use ordinary PCs with Windows or Linux software and an arbitrary connection to the Internet. Technological securities issues are to be found in several dimension, but below paper focus on hardware, software, and infrastructure as some of the most critical issues.
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Interpretation of Normal and Pathological ECG Beats using Multiresolution Wavelet Analysis
Статья научная
The Discrete wavelet transform has great capability to analyse the temporal and spectral properties of non stationary signal like ECG. In this paper, we have developed and evaluated a robust algorithm using multiresolution analysis based on the discrete wavelet transform (DWT) for twelve-lead electrocardiogram (ECG) temporal feature extraction. In the first step, ECG was denoised considerably by employing kernel density estimation on subband coefficients then QRS complexes were detected. Further, by selecting appropriate coefficients and applying wave segmentation strategy P and T wave peaks were detected. Finally, the determination of P and T wave onsets and ends was performed. The novelty of this approach lies in detection of different morphologies in ECG wave with few decision rules. We have evaluated the algorithm on normal and abnormal beats from various manually annotated databases from physiobank having different sampling frequencies. The QRS detector obtained a sensitivity of 99.5% and a positive predictivity of 98.9% over the first lead of the MIT-BIH Arrhythmia Database.
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Inverse Matrix using Gauss Elimination Method by OpenMP
Статья научная
OpenMP is an implementation program interface that might be utilized to explicitly immediate multi-threaded and it shared memory parallelism. OpenMP platform for specifications multi-processing via concurrent work between interested parties of hardware and software industry, governments and academia. OpenMP is not needs implemented identically by all vendors and it is not proposed for distributed memory parallel systems by itself. In order to invert a matrix, there are multiple approaches. The proposed LU decomposition calculates the upper and lower triangular via Gauss elimination method. The computation can be parallelized using OpenMP technology. The proposed technique main goal is to analyze the amount of time taken for different sizes of matrices so we used 1 thread, 2 threads, 4 threads, and 8 threads which will be compared against each other to measure the efficiency of the parallelization. The result of interrupting compered the amount of time spent in all the computing using 1 thread, 2 threads, 4 threads, and 8 threads. We came up with if we raise the number of threads the performance will be increased (less amount of time required). If we use 8 threads we get around 64% performance gained. Also as the size of matrix increases, the efficiency of parallelization also increases, which is evident from the time difference between serial and parallel code. This is because, more computations are done parallel and hence the efficiency is high. Schedule type in OpenMP has different behavior, we used static, dynamic, and guided scheme.
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Investigating into Automated Test Patterns in Erratic Tests by Considering Complex Objects
Статья научная
Software testing is an important activity in software development life cycle. Testing includes running a program on a set of test cases and comparing seen results with expected results. Automated testing encompasses all automation efforts across software testing lifecycle, with focus on automating system testing efforts and integration. Automated testing brings plenty of benefits that speeding up test running time, increasing accuracy of testing process and minimizing costs in different parts of system are three superior features of it. Maintenance and development of test automation tools are not as easy as traditional testing due to unexplored issues which need more examinations. Automated test patterns have been presented to mitigate some problems happening by automated testing and improve efficiency. This paper aims to investigate into automatic testing and automated test patterns. Also, demonstrates behaviour of applying an automated test pattern on a complex object. Results show during choosing an automated pattern to run, we should consider test structure especially level of test object complexity otherwise inconsistency may happen.
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Статья научная
This paper focuses on how students access web pages in a task specific information retrieval. An investigation on how students search the web for their current needs was carried out and students’ behavioural characteristics as they surf the internet to answer some given online multiple choice questions was collected. Twenty three students participated in the study and a number of behavioural characteristics were captured. Camtasia studio 7 was used to record their searching activity. The result shows that 328 web pages were visited by the students, and among the parameters captured, the time spent on the search task has a stronger correlation with the students’ performance than any other captured parameter. The time spent on a document can be used as a good implicit indicator to infer learner’s interest in a context based recommender system.
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Статья научная
Conventional sources like fossil fuels were used earlier to satisfy the energy demands. Nowadays these are being replaced by renewable sources like photo-voltaic sources. Photo-voltaic is a method of generating electrical power by converting the energy from the sun into direct current with the use of semiconductor devices that exhibit photovoltaic effect. They do not cause environmental pollution and do not require any moving parts. Different types of DC-DC Converters have been proposed in literature but Inter-leaved boost Converter (IBC) is widely used because of its fast dynamic response and high power density. This paper presents an analysis of the voltage mode control strategies employed by Ripple Cancellation Network (RCN) based two phase Interleaved boost Converter (IBC) for photo-voltaic applications. After analyzing the different Boost converter topologies, the results illustrate that IBC is more efficient than conventional boost converter as it reduces the input current ripple, output voltage ripple, component size and improves its transient response. On adding the Ripple Cancellation Network to the conventional IBC, the output voltage and input current ripple are further reduced without increasing the diode current stress. Adopting the closed loop voltage mode control, the ripple components are found to decrease significantly at the output thereby achieving a higher level of efficiency. A comparison is drawn between open and closed loop voltage control ripple component values. Simulations are carried out using MATLAB/SIMULINK software to verify with the theoretical results.
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Статья научная
In recent years, with the advancement of the internet, social media is a promising platform to explore what going on around the world, sharing opinions and personal development. Now, Sentiment analysis, also known as text mining is widely used in the data science sector. It is an analysis of textual data that describes subjective information available in the source and allows an organization to identify the thoughts and feelings of their brand or goods or services while monitoring conversations and reviews online. Sentiment analysis of Twitter data is a very popular research work nowadays. Twitter is that kind of social media where many users express their opinion and feelings through small tweets and different machine learning classifier algorithms can be used to analyze those tweets. In this paper, some selected machine learning classifier algorithms were applied on crawled Twitter data after applying different types of preprocessors and encoding techniques, which ended up with satisfying accuracy. Later a comparison between the achieved accuracies was showed. Experimental evaluations show that the Neural Network Classifier’ algorithm provides a remarkable accuracy of 81.33% compared with other classifiers.
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Статья научная
Machine learning and artificial intelligence techniques are more and more in our lives and studies in this field are increasing day by day. Data is vital for these studies. In order to draw meaningful conclusions from the available data, new methods are proposed and successful results are obtained. The preparation of the obtained data is very important in the studies to be carried out. Data preprocessing is very important in the preparation of data. The most critical stage of the data preprocessing process is the scaling or normalization of the data. Machine learning libraries such as scikit-learn and programming languages such as R provide the necessary libraries to scale data. However, it is not known exactly which normalization method will be applied and which will yield more successful results. The success of these normalization methods has been investigated on many different methods, but such a study has not been done on the adaptive neural fuzzy inference system (ANFIS). The aim of this study is to examine the success of normalization methods on ANFIS in terms of both classification and regression problems. So, for studies using the Anfis method, guidance will be provided on which normalization process will give better results in the data preprocessing stage. Four different normalization methods in the scikit-learn library were applied on the Diabets and Forestfire datasets in the UCI database. The results are presented separately for both classification and regression. It has been determined that min-max normalization in classification problems and working with original data in regression problems are more successful.
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IoT Bus Navigation System with Optimized Routing using Machine Learning
Статья научная
As the population in Egypt is ever expanding, it is reflected in the increase of the number of vehicles on the road. Public transportation is the solution and the number of available buses can cover a significant amount of the population demand. However, the outdated state of the transportation infrastructure, the static nature of the lines and indistinct schedules create a confounding and unappealing user experience which prompts the users to stray to cars for their needs. So, an Intelligent Urban Transportation System (IUTS) is a must. IUTS is a multi-layered system which provides the solution for most of these problems. It operates on different layers starting from a real time vehicle tracking for transparent and efficient management of assets, cash-less ticketing done through RFID cards, vehicle health and diagnostic data for creation of automated maintenance schedules and a friendly interactive driver interface. In this paper an approach based on combining all these technologies is discussed where the hardware component is implemented based on System-on-Chip technology with custom hardware to interface with the vehicle. The data collected from the on-board unit is sent to the cloud, and with the help of machine learning algorithms the dynamic responsiveness of the system is guaranteed. The proposed system outperforms other existing ones through the dynamic and optimized routing feature for the bus navigation to optimize the operating cost but still satisfy the passengers' demand.
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Issues and Challenges of User Intent Discovery (UID) during Web Search
Статья научная
There is a need to a small set of words –known as a query– to searching for information. Despite the existence gap between a user’s information need and the way in which such need is represented. Information retrieval system should be able to analyze a given query and present the appropriate web resources that best meet the user’s needs. In order to improve the quality of web search results, while increasing the user’s satisfaction, this paper presents the current work to identify user’s intent sources and how to understand the user behavior and how to discover the users’ intentions during the web search. This paper also discusses the social network analysis and the web queries analysis. The objective of this paper is to present the challenges and new research trends in understanding the user behavior and discovering the user intent to improve the quality of search engine results and to search the web quickly and thoroughly.
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Journey of Web Search Engines: Milestones, Challenges & Innovations
Статья научная
Past few decades have witnessed an information big bang in the form of World Wide Web leading to gigantic repository of heterogeneous data. A humble journey that started with the network connection between few computers at ARPANET project has reached to a level wherein almost all the computers and other communication devices of the world have joined together to form a huge global information network that makes available most of the information related to every possible heterogeneous domain. Not only the managing and indexing of this repository is a big concern but to provide a quick answer to the user's query is also of critical importance. Amazingly, rather miraculously, the task is being done quite efficiently by the current web search engines. This miracle has been possible due to a series of mathematical and technological innovations continuously being carried out in the area of search techniques. This paper takes an overview of search engine evolution from primitive to the present.
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Kernel Contraction and Consolidation of Alignment under Ontology Change
Статья научная
Alignment overcomes divergence in the specification of the semantics of vocabularies by different but overlapping ontologies. Therefore, it enhances semantic interoperability for many web based applications. However, ontology change following applications new requirements or new perception of domain knowledges can leads to undesirable knowledge such as inconsistent and therefore to a useless alignment. Ontologies and alignments are encoded in knowledge bases allowing applications to store only some explicit knowledge while they derive implicit ones by applying reasoning services on these knowledge bases. This underlying representation of ontologies and alignments leads us to follow base revision theory to deal with alignment revision under ontology change. For that purpose, we adapt kernel contraction framework to design rational operators and to formulate the set of postulates that characterize each class of these operators. We demonstrate the connection between each class of operators and the set of postulates that characterize them. Finally, we present algorithms to compute alignment kernels and incision functions. Kernels are sets of correspondences responsible of undesirable knowledge following alignment semantics. Incision functions determine the sets of correspondences to eliminate in order to restore alignment consistency or to realize a successful contraction.
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Kernel Techniques in Support Vector Machines for Classification of Biological Data
Статья научная
In this paper, we consider the problem of protein classification, which is a important and hot topic in bioinformatics. We propose a novel kernel based on the KSpectrum Kernel by incorporating physico-chemical and biological properties of amino acids as well as the motif information for the captured protein classification problem. Similarity matrix is constructed based on an AAindex2 substitution matrix which measures the amino acid pair distance. Together with the motif content posing importance on the protein sequences, a new kernel is then constructed. We adopt the Eigen-matrix translation techniques for improving the classification accuracy. Experimental results indicate that the string-based kernel in conjunction with SVM classifier performs significantly better than the traditional spectrum kernel method. Furthermore, numerical examples also confirm the use of the Eigenmatrix translation techniques as general strategy.
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Knowledge Discovery in Endangered Species Diversification
Статья научная
Classification of regional territories and countries related to endangered species has been investigated by data mining techniques and graphical modeling using an extensive data set of species. We developed the graphical models (hereafter referred to as ‘ESDI’) using cosine, jaccard similarity, K Mean clustering and cliques in graph modeling for a large number of countries. Environmental variables associated with species records were identified in context of their diversification to integration with our proposed prototype. We have shown that the problem of finding the most coherent clusters is reducible to finding maximum clique. Key findings include the urge to ameliorate communication about the loss and protection of endangered species and their concerned projects. The proposed framework is presented to serves a portal to knowledge discovery. We have concluded that the proposed framework model and its associated data mining similarity measures can be useful for investigating various scientific and management oriented questions related to protection of endangered species with emphasis on collaboration among regional countries. The rationale behind the proposed approach is that the countries which have been grouped into same clique inherit a lot of argues illustrating common reasons of their struggles towards ecological safety with minimization of perils for endangered species. The development and implementation of a regional approach based on this similar grouping address the actions that could offer significant benefits in achieving their goal for ecological policies. Other critical actions at this clique level include fortifying and elevating harmonization of legal frameworks with emphasis on prevention procedural issues; awareness realizations of endangered species issues and its priority. Such actions will eventually lead towards implementation of essential plans fulfilling co-operative expertise and common endeavors.
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Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society
Статья научная
Depression is a widespread and serious phenomenon in public health in all societies. In Saudi society, depression is one of the diseases that the community is may refuse to disclose it. There are no studies have analyzed this disease within the Saudi community. The main research objective is to discover the depression level of Saudi People's. In addition to analyzing the age group and the most gender type affected by the depression in this society. The data collected from social media achieved indirectly without any communication with patients as a sample from this society people. It analyzed using Machine Learning algorithms that give accurate results for this disease. Three classification models have been established to diagnose this disease and the findings of this study presented that the depression levels include five classes and the most affected age group in depression was in the age group from 20-26 years. The results show that young Saudi women are more likely to be depressed. The obtained results are very important to the medical field. Researchers and people working in this field can get benefits out of this research. Especially those who want to understand the depression disease in Saudi society and searching for real solutions to overcome this problem.
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Статья научная
Considering the rapidly growing world population and increased enrolment in full course primary school education around the world, this paper elucidates key means of leveraging Information Technology for the effective and efficient management of the increasing pressure associated with schools' administrative functions and students' basic activities. Primarily narrowing down to the basic African education framework, this paper sheds more light on the methods which can be adopted for the development of such processes through improved Information Technology platforms. In doing this, the design and implementation of the Jasper School Management System (a school management solution developed by the author) will be used as a case study. Also, a brief highlight of the impact this Information Technology initiative will have on institutions where it is deployed. The Jasper School Management Software being referenced was built using Java Programming Language in conjunction with MySQL. It was produced to help improve management activities of schools especially in developing countries of the world, with 8 major school management modules incorporated into it. Also, the software is open-source i.e., open to adaptations, improvements and free to download.
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