International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1211
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
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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Статья научная
Bluff and truth are major pillars of deception technology. Deception technology majorly relies on decoy-generated data and looks for any behavior deviation to flag that interaction as an attack or not. But at times a legitimate user can also do suspicious decoy interactions due to lack of knowledge and can be categorized under the “ATTACK” category which in a true sense should not be flagged that way. Hence, there is a need of doing collaborative analysis on honeypot, which are set up to monitor and log activities of sources that compromise or probe them. This goldmine provides ample information about the attacker intent and target, how it is moving forward in the kill chain as this information can be used to enhance threat intelligence and upgrade behaviors analysis rules. In this paper, decoys which are strategically placed in the network pointing to various databases, services, and Ips are used providing information of interactions made. This data is analyzed to understand underlying facts which can help in strengthening defense strategy, it also enhances confidence on the findings as analysis is not restricted to single decoy interaction which could be false positive or un-intentional in nature but analyzing holistically to conclude on the exact attack patten and progression. With experiment we have highlighted is reconciling various honeypots data and weighing IP visits and Honeypot interaction counts against scores and then using KNN and Weightage KNN to derive inclination of target IP against Source IP which can also be summarized as direction of Attack and count/frequency of interaction from highlights criticality of the interactions. Used KNN and W-KNN have shown approx. 94% accuracy which is best in class, also silhouette score highlighted high cohesion of data points in the experiment. Moreover, this was also analyzed that increasing the number of decoys in the analysis helps in getting better confidence on attack probability and direction.
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Статья научная
This study examines the individuals' participation intentions and behaviour on Social Networking Sites. For this purpose, the Decomposed Theory of Planned Behaviour is utilized. Data collected from a survey of 1100 participants and distilled to 657 usable sets has been analysed to assess the predictive power of Decomposed Theory of Planned Behaviour' model via structural equation modelling. The results show that attitude and subjective norm have significant effect on the participation intention of adopters. Further, the results show that participation intention has significant effect on participation behaviour. However, the study findings also show that perceived behavioural control has no significant effect on participation intention or behaviour of adopters. The model adopted in this study explains 47% of the variance in "Participation Intentions" and 36% of the variance in "Participation Behaviour". Participation of behavioural intention in the model' explanatory power was the highest amongst the constructs (able to explain 14.6% of usage behaviour). While, "attitude" explain around 9% of SNSs usage behaviour.
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Applying Scrum Development on Safety Critical Systems
Статья научная
Scaled agile approaches are increasingly being used by automotive businesses to cope with the complexity of their organizations and products. The development of automotive systems necessitates the use of safe procedures. SafeScrum® is a real example of how agile approaches may be used in the creation of high-reliability systems on a small scale. A framework like SAFe or LeSS does not facilitate the creation of safety-critical systems in large-scale contexts from the start. User stories are a wonderful approach to convey flexible demands, the lifecycle is iterative, and testing is the initial stage in the development process. Scrum plus extra XP approaches may be used to build high-reliability software and certification by the IEC 61508 standard is required for the software. This adds a slew of new needs to the workflow. Scrum's quality assurance measures proved to be inadequate in a recent industry situation. Our study's overarching goal is to provide light on the Scrum development process so that it may be improved for use with life-or-death systems. Our study of the business world was a mixed-methods affair. The findings demonstrated that although Scrum is helpful in ensuring the security of each release, it is less nimble in other respects. The difficulties of prioritization, communication, time constraints, and preparing for and accepting new safety standards were all discussed. In addition, we have had some helpful feedback from the business world, but the generality issue arising from this particular setting has yet to be addressed.
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Approaches to Sensitivity Analysis in MOLP
Статья научная
The paper presents two approaches to the sensitivity analysis in multi-objective linear programming (MOLP). The first one is the tolerance approach and the other one is the standard sensitivity analysis. We consider the perturbation of the objective function coefficients. In the tolerance method we simultaneously change all of the objective function coefficients. In the standard sensitivity analysis we change one objective function coefficient without changing the others. In the numerical example we compare the results obtained by using these two different approaches.
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Appropriate Tealeaf Harvest Timing Determination Based on NIR Images
Статья научная
Method for most appropriate tealeaves harvest timing with Near Infrared (NIR) camera images is proposed. In the proposed method, NIR camera images of tealeaves are used for estimation of nitrogen content in tealeaves. The nitrogen content is highly correlated to Theanine (amid acid) content in tealeaves. Theanine rich tealeaves taste good. Therefore, tealeaves quality can be estimated with NIR camera images. Also, leaf area of tealeaves is highly correlated to NIR reflectance of tealeaf surface. Therefore, not only tealeaf quality but also harvest mount can be estimated with NIR camera images. Experimental results shows the proposed method does work for estimation of appropriate tealeaves harvest timing with NIR camera images.
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Arabic Text Categorization Using Mixed Words
Статья научная
There is a tremendous number of Arabic text documents available online that is growing every day. Thus, categorizing these documents becomes very important. In this paper, an approach is proposed to enhance the accuracy of the Arabic text categorization. It is based on a new features representation technique that uses a mixture of a bag of words (BOW) and two adjacent words with different proportions. It also introduces a new features selection technique depends on Term Frequency (TF) and uses Frequency Ratio Accumulation Method (FRAM) as a classifier. Experiments are performed without both of normalization and stemming, with one of them, and with both of them. In addition, three data sets of different categories have been collected from online Arabic documents for evaluating the proposed approach. The highest accuracy obtained is 98.61% by the use of normalization.
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Architecture Aware Key Management Scheme for Wireless Sensor Networks
Статья научная
The emergence of wireless networking as well as the development in embedded systems and technologies have given birth to application specific networks called wireless sensor networks WSNs, their flexibility, facility of use and deployment as well as their low cost give them an increasing field of applications. Usually sensors are limited in capacities deployed in a hostile and unpredictable environment, making the security of these networks a challenging task. In this paper we are going to present a key management scheme in which the base station play the role of the secure third party responsible of distributing key and managing security in the network, two versions of this scheme are presented the first one for flat networks and the second one for hierarchical networks in which the cluster head play the key role in all key agreement with the base station.
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Architecture for Accessing Heterogeneous Databases
Статья научная
This paper presents the architecture for accessing heterogeneous databases. Two major processes in this architecture which are extracting SQL statement and ontology. The algorithms for extracting SQL statement was created and tested in order to improve time performance during searching and retrieving process. Ontology approach was implemented and combined with these algorithms. In ontology approach, web semantic was implemented in order to retrieve only relevant data from database. A prototype based on this architecture was developed using JAVA technology. JAVA technology was chosen because this technology have Jena library. This library is provide API and support SPARQL. Several experiments have been executed and tested. The result indicates this architecture able to improve web query processing in term of time. The result also indicates this architecture able to retrieve and displayed more relevant data to web users.
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Artificial Bee Colony Algorithm based Multi-Objective Node Placement for Wireless Sensor Network
Статья научная
The recent popularity of applications based on wireless sensor networks (WSN) provides a strong motivation for pursuing research in different dimensions of WSN. Node placement is an essential task in wireless sensor network and is a multi-objective combinatorial problem in nature. The positions of sensor nodes are very important and must be able to provide maximum coverage with longer lifetimes. So, for efficient node placement, a novel multi-objective Artificial Bee Colony (ABC) algorithm based framework is proposed in this paper. The framework optimizes the operational modes of the sensor nodes along with clustering schemes and transmission signal strengths. The results show that the proposed algorithm outperformed the contemporary methodology based on TPSMA, PSO and ACO.
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Artificial Bee Colony and Its Application for Image Fusion
Статья научная
Artificial Bee Colony (ABC) is one of the latest swarm algorithm based on the intelligent foraging behavior of honey bees introduced in the year 2005 by Karaboga since then it has been used for optimization of various solutions. And it is recently introduced for processing and analysis of images such as segmentation, object recognition and image retrieval. Fusing images from a vast collection of different images has become one of the interesting challenges and has drawn the attention of researchers towards the development of fusion techniques. In this paper, we have proposed the usage of ABC for optimal fusion of multi-temporal images and studied the effect of variation in the source area
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Artificial Neural Network Based Control Strategies for Paddy Drying Process
Статья научная
Paddy drying process depends upon ambient conditions, paddy quality, temperature and mass of hot drying air. Existing techniques of paddy drying process are highly nonlinear. In this paper, a neural network based automated controller for paddy drying is designed. The designed controller manages the steam temperature and blower motor speed to achieve constant paddy drying time. A Layer recurrent neural network is adopted for the controller. Atmospheric conditions such as temperature and humidity along with the size of the paddy are used as input to the network. Experimental results show that the developed controller can be used to control the paddy drying process. Implementation of developed controller will help in controlling the drying time at almost constant value which will definitely improve the quality of rice.
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