International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1211
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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Li-Fi technology: increasing the range of Li-Fi by using mirror
Статья научная
Data transmission is one of the most important term used in our day to day activities in the fast-growing world. Li-Fi opens a new era to that. Li-Fi is known as Light Fidelity. Simply it transmits data by visible light. According to recent research Li-Fi has a range of approximately 10 meters. Also, it cannot pass through wall or any solid object. So, this research focuses mainly to increase the 10-meter range. For a regular sized room this range is enough. But if anyone wants to provide data inside a big hall or in any large room, it is not possible by only this range. He must provide more LED that is connected to the Li-Fi router. This research focuses to optimize the number of LED and to minimize the cost. To increase the range of LED two methods are proposed here. One of the method deals with the positioning of the LEDs and another method is to use concave mirror. Smarter way of positioning of light gives better coverage of light that increases the range of LED. As concave mirror gives real and increased mirror for a particular positioning of an object, which is proved both theoretically and mathematically.
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Linear Hybrid Automaton Generation Using Mapping Algorithm for Hybrid Dynamic Systems
Статья научная
Hybrid dynamic systems are analyzed through linear hybrid automaton. In this paper, we propose a mapping algorithm to deal with a new Continuous elementary HPN. The method shown enables us to analyze some system properties using a linear hybrid automaton generated by a mapping process. The application involves a water system of three tanks, which is analyzed by a PHAVer (Polyhedral Hybrid Automaton Verifier) software tool. Its effectiveness is illustrated by numerical simulation results.
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Linked Data: A Framework for Publishing Five-Star Open Government Data
Статья научная
With the increased adoption of open government initiatives around the world, a huge amount of governmental raw datasets was released. However, the data was published in heterogeneous formats and vocabularies and in many cases in bad quality due to inconsistency, messy, and maybe incorrectness as it has been collected by practicalities within the source organization, which makes it inefficient for reusing and integrating it for serving citizens and third-party apps. This research introduces the LDOG (Linked Data for Open Government) experimental framework, which aims to provide a modular architecture that can be integrated into the open government hierarchy, allowing huge amounts of data to be gathered in a fine-grained manner from source and directly publishing them as linked data based on Tim Berners lee’s five-star deployment scheme with a validation layer using SHACL, which results in high quality data. The general idea is to model the hierarchy of government and classify government organizations into two types, the modeling organizations at higher levels and data source organizations at lower levels. Modeling organization’s experts in linked data have the responsibility to design data templates, ontologies, SHACL shapes, and linkage specifications. whereas non-experts can be incorporated in data source organizations to utilize their knowledge in data to do mapping, reconciliation, and correcting data. This approach lowers the needed experts that represent a problem of linked data adoption. To test the functionality of our framework in action, we developed the LDOG platform which utilizes the different modules of the framework to power a set of user interfaces that can be used to publish government datasets. we used this platform to convert some of UAE's government datasets into linked data. Finally, on top of the converted data, we built a proof-of-concept app to show the power of five-star linked data for integrating datasets from disparate organizations and to promote the governments' adoption. Our work has defined a clear path to integrate the linked data into open governments and solid steps to publishing and enhancing it in a fine-grained and practical manner with a lower number of experts in linked data, It extends SHACL to define data shapes and convert CSV to RDF.
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Load Balancing Optimization Based On Deep Learning Approach in Cloud Environment
Статья научная
Load balancing is a significant aspect of cloud computing which is essential for identical load sharing among resources like servers, network interfaces, hard drives (storage) and virtual machines (VMs) hosted on physical servers. In cloud computing, Deep Learning (DL) techniques can be used to achieve QoS such as improve resource utilization and throughput; while reduce latency, response time and cost, balancing load across machines, thus, increasing the system reliability. DL results in effective and accurate decision making of intelligent resource allocation to the incoming requests, thereby, choosing the most suitable resource to complete them. However, in previous researches on load balancing, there is limited application of DL approaches. In this paper, the significance of DL approaches have been analysed in the area of cloud computing. A Framework for Workflow execution in cloud environment has been proposed and implemented, namely, Deep Learning- based Deadline-constrained, Dynamic VM Provisioning and Load Balancing (DLD-PLB). Optimal schedule for VMs has been generated using Deep Learning based technique. The Genome workflow tasks have been taken as input to the suggested framework. The results for makespan and cost has been computed for the proposed framework and has been compared with our earlier proposed framework for load balancing optimization - Hybrid approach based Deadline-constrained, Dynamic VM Provisioning and Load Balancing (HDD-PLB)” framework for Workflow execution. The earlier proposed approaches for load balancing were based on hybrid Predict-Earliest-Finish Time (PEFT) with ACO for underutilized VM optimization and hybrid PEFT-Bat approach for optimize the utilization of overflow VMs.
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Local Cloud Computing Service Adoption in Nigeria: Challenges and Solutions
Статья научная
Cloud momentum seems unstoppable in Nigeria, as businesses and organizations in the country see less and less advantage in the slog of maintaining their infrastructure. The shift to the cloud in today's COVID-19 driven world has created an opportunity for investments to improve local cloud computing services. However, there are key challenges that must be addressed by the local cloud service providers in the country in order not to lose out to the foreign cloud service providers. This paper assessed the challenges to local cloud computing services adoption among sixty-seven (67) businesses and organizations in Nigeria. The research employed a non-probability purposive sampling approach. The surveyed data were obtained through an online form which was distributed via Linkedln. Descriptive and inferential analysis was used in analyzing the collected data via IBM SPSS software. Findings from the research showed the key challenges to include inadequate awareness of local cloud service vendors, poor innovation and local content, inadequate cloud infrastructure, local cloud vendor interoperability issue, national insecurity, shortages in skilled personnel, Service Level Agreement (SLA), security strategies, privacy, compliance terms, and requirements issues. Thus, adequate local cloud service offerings, skilled personnel, and the IT infrastructural backbone of the country have to be well established to increase the trust in local cloud computing, open up Nigeria to offshore markets while driving economic competitiveness and growth.
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Localized Knowledge based System for Human Disease Diagnosis
Статья научная
Knowledge based system can be designed to solve complex medical problems. It incorporates the expert’s knowledge that has been coded into facts, rules, heuristics and procedures. Incorporation of local languages with the knowledge based system allows end-users communicate with the system in a simpler and easier way. In this study a localized knowledge based system is developed for TB disease diagnosis using Ethiopian national language. To develop the localized knowledge based system, tacit knowledge is acquired from domain experts using interviewing techniques and explicit knowledge is captured from documented sources using relevant documents analysis method. Then the acquired knowledge is modeled using decision tree structure that represents concepts and procedures involved in diagnosis of disease. Production rules are used to represent domain knowledge. The localized knowledge based system is developed using SWI Prolog version 6.4.1 programming language. Prolog supports natural language processing feature to localize the system. As a result, the system is implemented using Amharic language (the national language of Ethiopia) user interface. With Localization, users at remote areas and users who are not good in foreign languages are benefited enormously. The system is tested and evaluated to ensure that whether the performance of the system is accurate and the system is usable by physicians and patients. The average performance of the localized knowledge based system has registered 81.5%.
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Locating All Common Subsequences in Two DNA Sequences
Статья научная
Biological sequence comparison is one of the most important and basic problems in computational biology. Due to its high demands for computational power and memory, it is a very challenging task. The well-known algorithm proposed by Smith-Waterman obtains the best local alignments at the expense of very high computing power and huge memory requirements. This paper introduces a new efficient algorithm to locate the longest common subsequences (LCS) in two different DNA sequences. It is based on the convolution between the two DNA sequences: The major sequence is represented in the linked-list X while the minor one is represented in circular linked-list Y. An array of linked lists is established where each linked list is corresponding to an element of the linked-list X and a new node is added to it for each match between the two sequences. If two or more matches in different locations in string Y share the same location in string X, the corresponding nodes will construct a unique linked-list. Accordingly, by the end of processing, we obtain a group of linked-lists containing nodes that reflect all possible matches between the two sequences X and Y. The proposed algorithm has been implemented and tested using C# language. The benchmark test shows very good speedups and indicated that impressive improvements has been achieved.
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Location Based Recommendation for Mobile Users Using Language Model and Skyline Query
Статья научная
Location based personalized recommendation has been introduced for the purpose of providing a mobile user with interesting information by distinguishing his preference and location. In most cases, mobile user usually does not provide all attributes of his preference or query. In extreme case, especially when mobile user is moving, he even does not provide any preference or query. Meanwhile, the recommendation system database also does not contain all attributes that can express what the user needs. In this paper, we design an effective location based recommendation system to provide the most possible interesting places to a user when he is moving, according to his implicit preference and physical moving location without the user’s providing his preference or query explicitly. We proposed two circle concepts, physical position circle that represents spatial area around the user and virtual preference circle that is a non-spatial area related to user’s interests. Those skyline query places in physical position circle which also match mobile user’s implicit preference in virtual preference circle will be recommended. User’s implicit preference will be estimated under language modeling framework according to user’s historical visiting behaviors. Experiments show that our method is effective in recommending interesting places to mobile users. The main contribution of the paper comes from the combination of using skyline query and information retrieval to do an implicit location-based personalized recommendation without user’s providing explicit preference or query.
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Статья научная
The purpose of this study is to forecast Southwest Indian Monsoon rainfall based on sea surface temperature, sea level pressure, humidity and zonal (u) and meridional (v) winds. With the aforementioned parameters given as input to an Artificial Neural Network (ANN), the rainfall within 10x10 grids of southwest Indian regions is predicted by means of one of the most efficient clustering methods, namely the Kohonen Self-Organizing Maps (SOM). The ANN is trained with input parameters spanning for 36 years (1960-1995) and tested and validated for a period of 9 years (1996-2004). It is further used to predict the rainfall for 6 years (2005-2010). The results show reasonably good accuracy for the summer monsoon periods June, July, August and September (JJAS) of the validation years.
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