International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1265
Статья научная
The application of modern information technologies in the oil and gas sector is constantly developing, which facilitates the acceleration of exploration and detection of oil, the increase in oil production and reduction in risks relates to health, human safety, and the environment. The Internet of things in the oil and gas sector, like in all sectors of industry, has great prospects from an economic point of view. The article is devoted to the study of the current state and avenues of solving key problems of effective and reliable functioning of the oil and gas industry as a cyber-physical system using the Internet of things in the Azerbaijani oil company SOCAR. The main technological processes and existing opportunities for the application of information technologies in the oil and gas complex are analyzed. New approaches are proposed to solve the problems in the oil and gas complex as cyber-physical system based on the smart sensors, the Internet of things, wireless networks and cloud technologies. The implementation of the proposed model is aimed at increasing the effectiveness, resource storage, exploration reliability and durability of the oil and gas complex.
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Analysis of MC-CDMA System in Mobile Communications
Статья научная
Wireless communication plays an important role in our daily life. One of the most important techniques which is Candidate for the fourth generation is Multicarrier Code Division Multiple Access (MC-CDMA) due to its high data rate. This research paper presents the MC-CDMA system using different modulation techniques. The variety of modulation types are depended in order to show the effects of modulation index and type on broadcasting data. The bit error rate of the system is plotted for a range of signal to noise ratio so that the effect of modulation on the MC-CDMA system will be evident. Actually, the simulation results show that QAM gives less bit error rate that makes MC-CDMA more flexible and suitable for mobile communication next generation technology. Also, the peak-to-average power (PAPR) of MC-CDMA is analyzed to show that high PAPR is the main disadvantage of MC-CDMA system then the possible solutions for this problem are discussed in this research paper.
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Analysis of Metric-Based Object-Oriented Code Refactoring Opportunities Identification Approaches
Статья научная
Refactoring is used to improve deteriorated software design, code and their maintainability. In object-oriented (OO) code, before refactoring is performed, its opportunities must be identified and several approaches exist this regard. Among the approaches is the software metric-based approach where quality software metrics are used. Therefore, this paper provide analysis of existing empirical studies that utilized software metrics to identify refactoring opportunities in OO software systems. We performed a comprehensive analysis on 16 studies to identify the state-of-the-practice. The focal point was on the workings, refactoring activities, the programming language and the impact on software quality. The results obtained shows approaches were not unique, each was designed either for a single refactoring activity or couple of them, move method and extract class dominated the refactorings activities, and most approaches were fully automated while few were semi-automated. Moreover, OO metrics played acritical role in both opportunities detection and factoring decisions. Based on the results, it would be beneficial if generic refactoring approach is developed that is capable of identifying needs for all refactoring activities.
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Analysis of Parametric & Non Parametric Classifiers for Classification Technique using WEKA
Статья научная
In the field of Machine learning & Data Mining, lot of work had been done to construct new classification techniques/ classifiers and lot of research is going on to construct further new classifiers with the help of nature inspired technique such as Genetic Algorithm, Ant Colony Optimization, Bee Colony Optimization, Neural Network, Particle Swarm Optimization etc. Many researchers provided comparative study/ analysis of classification techniques. But this paper deals with another form of analysis of classification techniques i.e. parametric and non parametric classifiers analysis. This paper identifies parametric & non parametric classifiers that are used in classification process and provides tree representation of these classifiers. For the analysis purpose, four classifiers are used in which two of them are parametric and rest of are non-parametric in nature.
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Статья научная
Nowadays, various Wireless Health Monitoring Systems use Internet of Things to transmit patient's data over Wireless Sensor Network and then the data is stored and processed via Cloud Computing, however, the use of different kind of Wireless Sensor on each system leads to power efficiency problem. This paper analyses and compares the consumption of power on six Wireless Health Monitoring Systems, which are invented to monitor the patient's condition and transfer the data using Wireless Sensor Network. Three different techniques are analyzed, namely GPRS/UMTS (used in one WHMS), Wi-Fi (used in one WHMS), and Bluetooth (used in four WHMS). This paper concludes that the systems that use Bluetooth as their transmission medium are more effective in reducing power consumption than the other systems that use GPRS/UMTS or Wi-Fi.
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Analysis of Requirement Engineering Processes, Tools/Techniques and Methodologies
Статья научная
Requirement engineering is an integral part of the software development lifecycle since the basis for developing successful software depends on comprehending its requirements in the first place. Requirement engineering involves a number of processes for gathering requirements in accordance with the needs and demands of users and stakeholders of the software product. In this paper, we have reviewed the prominent processes, tools and technologies used in the requirement gathering phase. The study is useful to perceive the current state of the affairs pertaining to the requirement engineering research and to understand the strengths and limitations of the existing requirement engineering techniques. The study also summarizes the best practices and how to use a blend of the requirement engineering techniques as an effective methodology to successfully conduct the requirement engineering task. The study also highlights the importance of security requirements as though they are part of the non-functional requirement, yet are naturally considered fundamental to secure software development.
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Analysis of Tandem Repeat Patterns in Nlrc4 using a Motif Model
Статья научная
Exponential accumulation of biological data requires computer scientists and bioinformaticians to improve the efficiency of computer algorithms and databases. The recent advancement of computational tools has boosted the processing capacity of enormous volume of genetic data. This research applied a computational approach to analyze the tandem repeat patterns in Nlrc4 gene. Because the protein product of Nlrc4 gene is important in detecting pathogen and triggering subsequent immune responses, the results of this genetic analysis is essential for the understanding of the genetic characteristics of Nlrc4. The study on the distribution of tandem repeats may provide insights for drug design catered for the Nlrc4-implicated diseases.
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Статья научная
This article explores the multifaceted challenges inherent in ensuring the cybersecurity of critical infrastructures, i.e., a linchpin of modern society and the economy, spanning pivotal sectors such as energy, transportation, and finance. In the era of accelerating digitalization and escalating dependence on information technology, safeguarding these infrastructures against evolving cyber threats becomes not just crucial but imperative. The examination unfolds by dissecting the vulnerabilities that plague critical infrastructures, probing into the diverse spectrum of threats they confront in the contemporary cybersecurity landscape. Moreover, the article meticulously outlines innovative security strategies designed to fortify these vital systems against malicious intrusions. A distinctive aspect of this work is the nuanced case study presented within the oil and gas sector, strategically chosen to illustrate the vulnerability of critical infrastructures to cyber threats. By examining this sector in detail, the article aims to shed light on industry-specific challenges and potential solutions, thereby enhancing our understanding of cybersecurity dynamics within critical infrastructures. This article contributes a comprehensive analysis of the challenges faced by critical infrastructures in the face of cyber threats, offering contemporary security strategies and leveraging a focused case study to deepen insights into the nuanced vulnerabilities within the oil and gas sector.
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Analyzing Cost Parameters Affecting Map Reduce Application Performance
Статья научная
Recently, big data analysis has become an imperative task for many big companies. Map-Reduce, an emerging distributed computing paradigm, is known as a promising architecture for big data analytics on commodity hardware. Map-Reduce, and its open source implementation Hadoop, have been extensively accepted by several companies due to their salient features such as scalability, elasticity, fault-tolerance and flexibility to handle big data. However, these benefits entail a considerable performance sacrifice. The performance of a Map-Reduce application depends on various factors including the size of the input data set, cluster resource settings etc. A clear understanding of the factors that affect Map-Reduce application performance and the cost associated with those factors is required. In this paper, we study different performance parameters and an existing Cost Optimizer that computes the cost of Map-Reduce job execution. The cost based optimizer also considers various configuration parameters available in Hadoop that affect performance of these programs. This paper is an attempt to analyze the Map-Reduce application performance and identifying the key factors affecting the cost and performance of executing Map-Reduce applications.
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Статья научная
This study thoroughly looks at how the prices and activity of players of popular indie games on Steam changed after COVID-19. It uses data from SteamDB, which has lots of info about game availability, sales, prices, activity of players, followers, positive and negative reviews on Steam and Twitch viewers. The goal is to deeply analyze how indie game makers and publishers set their prices and reactions of players to games which release date start from period before, during and after COVID-19. The focus is on how they changed their pricing models due to big shifts in market demand and consumer behavior because of the pandemic and how players reacted to these price changes in the context of wage cuts and layoffs. Reactions of players can be tracked not only by the statistics of the maximum or average online in the game, but also by the number of positive and negative reviews, because in difficult times it was important for players to correctly distribute their available funds and not to become disappointed in game and not to let other players become disappointed By studying these changes, the aim is to find out how the indie game industry responded to tough times and new chances in the digital entertainment world. Since the study is being conducted in the post-COVID-19 period, it is also aimed at helping developers choose the right strategy when pricing their new Indie games or changing the prices of their existing Steam Indie games. The main objects of research are indie games because this genre is one of the most popular in SteamDB, and to create such games requires less costs, therefore their price is acceptable for the average player.
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Статья научная
This study presents a comprehensive assessment of the test performance of Bachelor of Science in Information Technology (BSIT) students in the System Integration and Architecture (SIA) course, coupled with a meticulous examination of the quality of test questions, aiming to lay the groundwork for enhancing the assessment tool. Employing a cross-sectional research design, the study involved 200 fourth-year students enrolled in the course. The results illuminated a significant discrepancy in scores between upper and lower student cohorts, highlighting the necessity for targeted interventions, curriculum enhancements, and assessment refinements, particularly for those in the lower-performing group. Further examination of the item difficulty index of the assessment tool unveiled the need to fine-tune certain items to better suit a broader spectrum of students. Nevertheless, the majority of items were deemed adequately aligned with their respective difficulty levels. Additionally, an analysis of the item discrimination index identified 25 items suitable for retention, while 27 items warranted revision, and 3 items were suitable for removal, as per the analysis outcomes. These insights provide a valuable foundation for improving the assessment tool, thereby optimizing its capacity to evaluate students' acquired knowledge effectively. The study's novel contribution lies in its integration of both student performance assessment and evaluation of assessment tool quality within the BSIT program, offering actionable insights for improving educational outcomes. By identifying challenges faced by BSIT students and proposing targeted interventions, curriculum enhancements, and assessment refinements, the research advances our understanding of effective assessment practices. Furthermore, the detailed analysis of item difficulty and discrimination indices offers practical guidance for enhancing the reliability and validity of assessment tools in the BSIT program. Overall, this research contributes to the existing body of knowledge by providing empirical evidence and actionable recommendations tailored to the needs of BSIT students, promoting educational quality and student success in Information Technology.
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Analyzing the Impact of Prosodic Feature (Pitch) on Learning Classifiers for Speech Emotion Corpus
Статья научная
Emotion plays a significant role in human perception and decision making whereas, prosodic features plays a crucial role in recognizing the emotion from speech utterance. This paper introduces the speech emotion corpus recorded in the provincial languages of Pakistan: Urdu, Balochi, Pashto Sindhi and Punjabi having four different emotions (Anger, Happiness, Neutral and Sad). The objective of this paper is to analyze the impact of prosodic feature (pitch) on learning classifiers (adaboostM1, classification via regression, decision stump, J48) in comparison with other prosodic features (intensity and formant) in term of classification accuracy using speech emotion corpus recorded in the provincial languages of Pakistan. Experimental framework evaluated four different classifiers with the possible combinations of prosodic features with and without pitch. An experimental study shows that the prosodic feature (pitch) plays a vital role in providing the significant classification accuracy as compared to prosodic features excluding pitch. The classification accuracy for formant and intensity either individually or with any combination excluding pitch are found to be approximately 20%. Whereas, pitch gives classification accuracy of around 40%.
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Статья научная
This paper illustrates a comparison study for control of highly non-linear Double Inverted Pendulum (DIP) on cart. A Matlab-Simulink model of DIP has been built using Newton's second law. The Neuro-fuzzy controllers stabilizes pendulums at vertical position while cart moves in horizontal direction. This study proposes two soft-computing techniques namely Fuzzy logic reasoning and Neural networks (NN's) for control of DIP systems. The results shows that Fuzzy controllers provides better results as compared to NN's controllers in terms of settling time (sec), maximum overshoot (degree) and steady state error. The regression (R) and mean square error (MSE) values obtained after training of Neural network were satisfactory. The simulation results proves the validity of proposed techniques.
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Application of Cloud Theory in Association Rules
Статья научная
The data mining is to discover knowledge from the database, quantitative association rules mining method is difficult for their values are too large. The usual means is dividing quantitative Data to discrete conception. The Cloud model combines fuzziness and randomness organically, so it fits the real world objectively, a new method to mine association rules from quantitative data based on the cloud model was proposed, which first take the original data distribution in the database into account, and then use the trapezoidal cloud model to complicate concepts division, and transforms qualitative data to the quantitative concept, in the conversion take account of the basic characteristics of human behavior fully, divides quantitative Data with trapezium Cloud model to create discreet concepts, the concept cluster within one class, and separated with each other. So the quantitative Data can be transforms to Boolean data well, the Boolean data can be mined by the mature Boolean association rules mining method to find useful knowledge.
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Application of Materialized View in Incremental Data Mining Operation
Статья научная
Materialized view is a database object used to store the results of a query set. It is used to avoid the costly processing time that is required to execute complex queries involving aggregation and join operations. Materialized view may be associated with the operations of a data warehouse. Data mining is a technique to extract knowledge from a data warehouse and the incremental data mining is another process that periodically updates the knowledge that has been already identified by a data mining process. This happens when a new set of data gets added with the existing set. This paper proposes a method to apply the materialized view in incremental data mining.
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Статья научная
This study addresses the challenge faced by tourists, companies, travel agents, and tourism agencies in selecting the ideal hotel in Samarinda, given the variety of available options. The city boasts numerous hotels with differing facilities, room types, rates, and locations, which can complicate decision-making without adequate information. To provide a solution, this research introduces a Decision Support System (DSS) that employs the Multi-Attribute Utility Theory (MAUT) method for hotel assessment. By evaluating hotels based on key attributes like price, amenities, service quality, and location, the system offers a comprehensive, objective approach to determining the best affordable hotels. The study contributes significantly to the hospitality sector by presenting a practical tool that simplifies the hotel selection process and ensures that choices align with the preferences of the visitors.
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Статья научная
The demand for workload prediction approaches has recently increased to manage the cloud resources, improve the performance of the cloud services and reduce the power consumption. The prediction accuracy of these approaches affects the cloud performance. In this application paper, we apply an enhanced variant of the differential evolution (DE) algorithm named MSaDE as a learning algorithm to the artificial neural network (ANN) model of the cloud workload prediction. The ANN prediction model based on MSaDE algorithm is evaluated over two benchmark datasets for the workload traces of NASA server and Saskatchewan server at different look-ahead times. To show the improvement in accuracy of training the ANN prediction model using MSaDE algorithm, training is performed with other two algorithms: the back propagation (BP) algorithm and the self-adaptive differential evolution (SaDE) algorithm. Comparisons are made in terms of the root mean squared error (RMSE) and the average root mean squared error (ARMSE) through all prediction intervals. The results show that the ANN prediction model based on the MSaDE algorithm predicts the cloud workloads with higher prediction accuracy than the other algorithms compared with.
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Статья научная
In investigation of consequences of atmosphere and commutating striking voltages, for simulation of the overvoltage are used the models of generators whose RC circuits have standard passive parameters of the elements upon which the form of striking overvoltage depends. According to IEC 62 305-1 standard, these formulas in the theoretical model serve for dimensioning the RC circuit of the generator of striking voltages although the definitions of time constants and passive parameters have only axiomatic character. Related to classical solution, this paper presents the model formed by mathematical procedure the solutions of which give sufficiently accurate values of time constants and essential parameters of RC circuit as well as the shape of striking voltage wave. The formulas for voltages and currents in model contain parameters of passive elements, and their accuracy has been confirmed by diagrams obtained in simulation by means of adapted psbtrnsrg.mdl part of MATLAB program. Theoretical model is suitable for simulation of standard wave forms of striking atmospheric and commutating overvoltages which replace laboratory testing.
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Applied an Efficient Site-directed Mutagenesis Method into Escherichia coli
Статья научная
A new technique for conducting site-directed mutagenesis was developed. This method allows the color selection of mutants through the simultaneous activation or deactivation of the α-peptide of ß-galactosidase. The method can efficiently create mutations at multiple sites simultaneously and can be used to perform multiple rounds of mutation on the same construct. In this paper, in order to develop an efficient site-directed mutagenesis method in vivo, the tests were tested by the following methods. The methods that the fragment knock-out ompR gene was constructed through overlapping PCR, digested by Notand SalⅠⅠ, ligated to plasmid pKOV were applied. The recombination plasmid was transformed into Escherichia coli WMC-001 strain, integrated into the genomic DNA through two step homologous recombination. The Escherichia coli WMC-001/ompR- mutant was obtained due to gene replacement. The fragment of the mutant ompR gene was amplified through overlapping PCR, cloned into pKOV vector. The recombinant plasmid was introduced into Escherichia coli WMC-001/ompR- mutant. The Escherichia coli WMC-001/ompR mutant was also obtained due to gene replacement. Results: The site-directed mutagenesis has been successfully constructed in the ompR gene by sequencing. Conclusion: The method is effective for construction of gene site-directed mutagenesis in vivo.
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Applying Clustering and Topic Modeling to Automatic Analysis of Citizens’ Comments in E-Government
Статья научная
The paper proposes an approach to analyze citizens' comments in e-government using topic modeling and clustering algorithms. The main purpose of the proposed approach is to determine what topics are the citizens' commentaries about written in the e-government environment and to improve the quality of e-services. One of the methods used to determine this is topic modeling methods. In the proposed approach, first citizens' comments are clustered and then the topics are extracted from each cluster. Thus, we can determine which topics are discussed by citizens. However, in the usage of clustering and topic modeling methods appear some problems. These problems include the size of the vectors and the collection of semantically related of documents in different clusters. Considering this, the semantic similarity of words is used in the approach to reduce measure. Therefore, we only save one of the words that are semantically similar to each other and throw the others away. So, the size of the vector is reduced. Then the documents are clustered and topics are extracted from each cluster. The proposed method can significantly reduce the size of a large set of documents, save time spent on the analysis of this data, and improve the quality of clustering and LDA algorithm.
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