International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1227
Статья научная
Hospitals are the primary hubs for healthcare service providers in Ethiopia; however, hospitals face significant challenges in adopting digital health information systems solutions due to disparate, non-interoperable systems and limited access. Information technology, especially via cloud computing, is crucial in healthcare for efficient data management, secure storage, real-time access to critical information, seamless provider communication, enhanced collaboration, and scalable IT infrastructure. This study investigated the challenges to standardizing smart and green healthcare information services and proposed a cloud-based model for overcoming them. We conducted a mixed-methods study in 11 public hospitals, employing quantitative and qualitative approaches with diverse stakeholders (N = 103). The data was collected through surveys, interviews, and technical observations by purposive quota sampling with the Raosoft platform and analyzed using IBM SPSS. Findings revealed several shortcomings in existing information systems, including limited storage, scalability, and security; impaired data sharing and collaboration; accessibility issues; no interoperability; ownership ambiguity; unreliable data recovery; environmental concerns; affordability challenges; and inadequate policy enforcement. Notably, hospitals lacked a centralized data management system, cloud-enabled systems for remote access, and modern data recovery strategies. Despite these challenges, 90.3% of respondents expressed interest in adopting cloud-enabled data recovery systems. However, infrastructure limitations, inadequate cloud computing/IT knowledge, lack of top management support, digital illiteracy, limited innovation, and data security concerns were identified as challenges to cloud adoption. The study further identified three existing healthcare information systems: paper-based methods, electronic medical catalog systems, and district health information systems2. Limitations of the paper-based method include error-proneness, significant cost, data fragmentation, and restricted remote access. Growing hospital congestion and carbon footprint highlighted the need for sustainable solutions. Based on these findings, we proposed a cloud-based model tailored to the Ethiopian context. This six-layered model, delivered as a Software-as-a-Service within a community cloud deployment, aims to improve healthcare services through instant access, unified data management, and evidence-based medical practices. The model demonstrates high acceptability and potential for improving healthcare delivery, and implementation recommendations are suggested based on the proposed model.
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Статья научная
We address the challenge of optimizing the interaction between medical personnel and treatment stations within mobile and flexible medical care units (MFMCUs) in conflict zones. For the analysis of such systems, a closed queuing model with a finite number of treatment stations has been developed, which accounts for the possibility of performing multiple tasks for a single medical service request. Under the assumption of Poisson event flows, a system of integro-differential equations for the probability densities of the introduced states has been compiled. To solve it, the method of discrete binomial transformations is employed in conjunction with production functions. Solutions were obtained in the form of finite expressions, enabling the transition from the probabilistic characteristics of the model to the main performance metrics of the MFMCU: the load factor of medical personnel, and the utilization rate of treatment stations. The results show the selection of the number of treatment stations in the medical care area and the calculation of the appropriate performance of medical personnel.
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Enhancing Jakarta Faces Web App with AI Data-Driven Python Data Analysis and Visualization
Статья научная
Python is widely used in artificial intelligence (AI) and machine learning (ML) because of its flexibility, adaptability, rich libraries, active community, and broad environment, which makes it a popular choice for AI development. Python compatibility has already been examined with Java using TCP socket programming on both non-graphical and graphical user interfaces, which is highly essential to implement in the Jakarta Faces web application to grab potential competitive advantages. Python data analysis library modules such as numpy, pandas, and scipy, as well as visualization library modules such as Matplotlib and Seaborn, and machine-learning module Scikit-learn, are intended to be integrated into the Jakarta Faces web application. The research method uses similar TCP socket programming for the enhancement process, which allows instruction and data exchange between Python and Jakarta Faces web applications. The outcome of the findings emphasizes the significance of modernizing data science and machine learning (ML) workflows for Jakarta Faces web developers to take advantage of Python modules without using any third-party libraries. Moreover, this research provides a well-defined research design for an execution model, incorporating practical implementation procedures and highlighting the results of the innovative fusion of AI from Python into Jakarta Faces.
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Enhancing the Performance in Generating Association Rules using Singleton Apriori
Статья научная
Association rule mining aims to determine the relations among sets of items in transaction database and data repositories. It generates informative patterns from large databases. Apriori algorithm is a very popular algorithm in data mining for defining the relationships among itemsets. It generates 1, 2, 3,…, n-item candidate sets. Besides, it performs many scans on transactions to find the frequencies of itemsets which determine 1, 2, 3,…, n-item frequent sets. This paper aims to eradicate the generation of candidate itemsets so as to minimize the processing time, memory and the number of scans on the database. Since only those itemsets which occur in a transaction play a vital role in determining frequent itemset, the methodology that is proposed in this paper is extracting only single itemsets from each transaction, then 2,3,..., n itemsets are generated from them and their corresponding frequencies are also calculated. Further, each transaction is scanned only once and no candidate itemsets is generated both resulting in minimizing the memory space for storing the scanned itemsets and minimizing the processing time too. Based on the generated itemsets, association rules are generated using minimum support and confidence.
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Ensemble approach for twitter sentiment analysis
Статья научная
Due to enlargement of social network and online marketing websites. The Blogs and reviews of the user are acquired from these websites. And these become useful for analysis and Decision making for various types of products, marketing and movie etc. with the extent of the usefulness of social Reviews. It is to be needed carefully analysis of that data. There are various techniques and methods are available that can accurately analyses the social information and provides greater accuracy for the analysis. But one of the major issues available with the social media data is that data is unstructured and noisy. It is to be required to solve this problem. So here in this paper a framework is proposed that includes latest data preprocessing techniques instead of noise removal like stemming, Lemmatization and Tokenization. After Pre-Processing of data ensemble methods is applied that increase the accuracy of previous classification algorithms. This method is inherent from bagging concept. First apply Decision Tree, Kneighbor and Naive Bayes classifier that not provide batter accuracy after that boosting concept is applied with the help of AdaBoost method that improves the accuracy of previous classical classifiers. At last our proposed ensemble method ExtraTree classifier is applied that inherent from bagging concept. Here we use the Extra Tree classifier that take the various sample are taken from training set and various random trees are created. It is also called as extremely randomized tree that provides extreme refined view. So that, it is to be conveying that The ExtraTree classifier of bagging ensemble method outperforms than all other techniques that are previously applied in this paper. with using some novel pre-processing techniques data that produced is more refined and that provides clean and pure base for the implementation of ensemble techniques. And also contributes in improving the accuracy of the applied methods.
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Enterprise architecture measurement: an extended systematic mapping study
Статья научная
A systematic mapping study (SMS) of proposed EA measurement solutions was undertaken to provide an in-depth understanding of the claimed achievements and limitations in evidence-based research of enterprise architecture (EA). This SMS reports on 22 primary studies on EA measurement solutions published up to the end of 2018. The primary studies were analyzed thematically and classified according to ten (10) mapping questions including, but not limited to, positioning of EA measurement solutions within EA schools of thought, analysis of consistency-inconsistency of the terms used by authors in EA measurement research, and an analysis of the references to the ISO 15939 measurement information model. Some key findings reveal that the current research on EA measurement solutions is focused on the “enterprise IT architecting” school of thought, does not use rigorous terminology as found in science and engineering, and shows limited adoption of knowledge from other disciplines. The paper concludes with new perspectives for future research avenues in EA measurement.
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Entity Extraction from Business Emails
Статья научная
Email still plays an important role in today's business communication thanks to its simplicity, flexibility, low cost, and compatibility of diversified types of information. However processing the large amount of emails received consumes tremendous time and human power for a business. In order to quickly deciphering information and locate business-related information from emails received from a business, a computerized solution is required. In this paper, we have proposed a comprehensive mechanism to extract important information from emails. The proposed solution integrates semantic web technology with natural language processing and information retrieval. It enables automatic extraction of important entities from an email and makes batch processing of business emails efficient. The proposed mechanism has been used in a Transportation company.
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Envisioning Skills for Adopting, Managing, and Implementing Big Data Technology in the 21st Century
Статья научная
The skills for big data technology provide a window of new job opportunities for the information technology (IT) professionals in the emerging data science landscape. Consequently, the objective of this paper is to introduce the research results of suitable skills required to work with big data technology. Such skills include Document Stored Database; Key-value Stored Database; Column-oriented Database; Object-oriented Database; Graph Database; MapReduce; Hadoop Distributed File System (HDFS); YARN Framework; Zookeeper; Oozie; Hive; Pig; HBase; Mahout; Sqoop; Spark; Flume; Drill; Programming Languages; IBM Watson Analytics; Statistical Tools; SQL; Project Management; Program Management; and Portfolio Management. This paper is part of an ongoing research that addresses the link between economic growth and big data.
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Error Detection in a Multi-user Request System Using Enhanced CRC Algorithm
Статья научная
Error and error related issues have been a challenge in the development and reliable usage of computing systems and application. The ability to detect minute error in a system improves the reliability of the system by aiding developers and users to know were challenges are so that they can be fixed during development and even when the system is already in use. In other to achieve that different algorithm have been used including the Cyclic Redundancy Check 16-bit, 32-bit and higher bits. In this paper, error detection schemes are examined and the way they check and detect error in multi-user request and transmitted system. The paper also offers improvement on a Cyclic Redundancy Checks 32-bit detection algorithm for the detection of error that can occur in transmitted data and on stored, backed-up and archived data in the system without consuming large resources as the higher bits.
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Estimating Software Reliability by Monitoring Software Execution through OpCode
Статья научная
Previous studies on estimating software reliability employed statistical functions for next system failure prediction. These models used parameters based on assumptions regarding the nature of software faults and debugging process. However, none of the existing models, attempted on ensuring reliable runtime system operation. To serve the current demand of autonomous, reliable, service-oriented software, we present a novel approach for runtime reliability estimation of executable software. The approach can help control software execution at runtime by monitoring software state-to-state transition at runtime. The approach involves representing executable software as an automata using opcode extracted from executable code. The extracted opcode is then used to learn stochastic finite state machine (SFSM) representation of executable software which is later employed to trace software state-to-state transition at each runtime instance. An evaluation of our approach on Java-based Chart generator application is also discussed to explain how we can ensure reliable software execution and prevent software failures at runtime with the proposed approach.
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Evaluating Design Patterns of Commercial Web Applications using Net Easy Score
Статья научная
Web interface design patterns provide solutions to recurring design problems. Many design patterns use various techniques, which have been proven to be significantly different, to solve the same design problem. Normally, web designers do not know whether users would be satisfied with their chosen choice until near or at the end of the web development process. To obtain user feedback, users are usually asked to interact with a web prototype or the finished web and give their opinion through standardized questionnaires. Net Promoter Score is one of such questionnaires. This scale categorizes users’ responses into promoters and detractors, which makes it easier for companies to understand user satisfaction towards their web. To enable the designers to obtain user feedback early in the design stage, Net Easy Score, a new metric based on Net Promoter Score, was proposed. With Net Easy Score (NES), ease-of-use scores on different design patterns will be divided into a positive and a negative group. The NES is a difference between percentages of positive responses and negative ones. This study examined ease-of-use scores on design patterns for five common tasks in commercial web applications. Results showed that NES and mean ease-of-use score were significantly correlated with an r of 0.965 (p < .000). Also, ranking the average ease-of-use scores and NES revealed the same design patterns identified as the best and the worst ones, which was consistent with the easiest-to-use design patterns voted by participants.
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Статья научная
The use of cloud computing, particularly virtualized infrastructure, offers scalable resources, reduced hardware needs, and energy savings. In Ethiopian public hospitals, the lack of integrated healthcare systems and a national data repository, combined with existing systems deficiencies and inefficient traditional data centers, contribute to energy inefficiency, carbon emissions, and performance issues. Thus, evaluating the energy efficiency and performance of a cloud-based model with various workloads and algorithms is essential for its successful implementation in healthcare systems and digital health solutions. The study experimentally evaluates a cloud-based model's energy efficiency and performance for smart healthcare systems, employing descriptive and experimental designs to simulate cloud infrastructure. Simulations are conducted on diverse workloads in CloudSim using power-aware (PA) algorithms (along with VmAllocationPolicy and VmSelectionPolicy), and dynamic voltage frequency scaling (DVFS). Results reveal that the number of VMs and their migrations significantly impact energy consumption, with some algorithms achieving notable energy savings. Lr/Lrr-based algorithms are particularly energy-efficient, with LrMc and LrrMc saving 29.36% more energy than IqrMu at 55 VMs, and LrrRs saving 30.20% more at 1,765 VMs. DVFS adjusts energy consumption based on the number of VMs, while non-power-aware (NPA) consumes maximum energy based on hosts, regardless of the number of VMs. VM migrations, energy consumption, and average SLAV are positively correlated, while SLA is negatively correlated with these factors. In PlanetLab, energy consumption and average SLAV show a strong positive correlation (0.956) at Workload6, while SLA at Workload2 and average SLAV at Workload1 show a weak negative correlation (-0.055). Excessive migrations can disrupt the system's stability/performance and cause SLA violations. Task completion time is influenced by VM processing power and cloudlet length, being inversely proportional to VM processing power and directly proportional to cloudlet length. Overall, the findings suggest that cloud virtualization and energy-efficient algorithms can enhance healthcare systems performance, patient care, and operational sustainability.
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Evaluating Web Services Functionality and Performance
Статья научная
Traditional distributed database transaction applications within large organizations often involve a large number of resources. In this case, people and DDBMSs distributed over a wide geographic area, may introduce conflict between heterogeneous systems. Web services (WS) provide solution for this problem since WS have an independent platform, independent language, and independent object model. This work presents WS application to access heterogeneous and distributed database via horizontal data fragments that is designed to be reliable, flexible and scalable. It describes the setup of SOAP server and applications based on the SOAP for end user client. In addition, it allows the publishing of WS descriptions to submit user requests (goal) to retrieve the required information. Here we evaluate the functional, behavior and performance of WS among possible different alternatives with real-time and execution parameters. Implementation details and case study experiments are presented along with the corresponding results.
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Статья научная
Most security and privacy issues in software are related to exploiting code vulnerabilities. Many studies have tried to find the correlation between the software characteristics (complexity, coupling, etc.) quantified by corresponding code metrics and its vulnerabilities and to propose automatic prediction models that help developers locate vulnerable components to minimize maintenance costs. The results obtained by these studies cannot be applied directly to web applications because a web application differs in many ways from a non-web application: development, use, etc. and a lot of evaluation of these conclusions has to be made. The purpose of this study is to evaluate and compare the vulnerabilities prediction power of three types of code metrics in web applications. There are a few similar studies that targeted non-web application and to the best of our knowledge, there are no similar studies that targeted web applications. The results obtained show that unlike non-web applications where complexity metrics have better vulnerability prediction power, in web applications the metrics that give better prediction are the coupling metrics with high recall (> 75%) and fewer costs in terms of inspection (<25%).
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Evaluating the Maintainability of a Software System by using Fuzzy Logic Approach
Статья научная
Maintainability is an important quality attribute for almost every quality model. Maintainability of the software is considered as most expensive phase in software development life cycle as it consumes almost major part of the total effort allocated to the software system. Maintainability evaluation is complex due to its imprecise output. This paper proposes a maintainability model by considering its fuzzy aspects. Since fuzzy modeling deals with uncertainty and impreciseness so this paper uses fuzzy methodology and AHP technique to evaluate the maintainability of the model. Object oriented system has taken as case study for maintainability evaluation purpose.
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Evaluating the Scalability of Matrix Factorization and Neighborhood Based Recommender Systems
Статья научная
Recommendation Systems are everywhere, from offline shopping malls to major e-commerce websites, all use recommendation systems to enhance customer experience and grow profit. With a growing customer base, the requirement to store their interest, behavior and respond accordingly requires plenty of scalability. Thus, it is very important for companies to select a scalable recommender system, which can provide the recommendations not just accurately but with low latency as well. This paper focuses on the comparison between the four methods KMeans, KNN, SVD, and SVD++ to find out the better algorithm in terms of scalability. We have analyzed the methods on different parameters i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision, Recall and Running Time (Scalability). Results are elaborated such that selection becomes quite easy depending upon the user requirements.
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Evaluation of H- and G-indices of Scientific Authors using Modified K-Means Clustering Algorithm
Статья научная
In this paper I proposed modified K-means algorithm as the means to assess scientific authors performance by using their h,g-indices values. K-means suffers from poor computational scaling and efficiency as the number of clusters has to be supplied by the user. In this work, I introduce a modification of K-means algorithm that efficiently searches the data to cluster points by compute the sum of squares within each cluster which makes the program to select the most promising subset of classes for clustering. The proposed algorithm was tested on IRIS and ZOO data sets as well as on our local dataset comprising of h- and g-indices, which are the prominent markers for scientific excellence of authors publishing papers in various national and international journals. Results from analyses reveal that the modified k-means algorithm is much faster and outperforms the conventional algorithm in terms of clustering performance, measured by the data discrepancy factor.
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Статья научная
This paper describes and evaluates four different HSMM (hidden semi-Markov model) training methods for HMM-based synthesis of emotional speech. The first method, called emotion-dependent modelling, uses individual models trained for each emotion separately. In the second method, emotion adaptation modelling, at first a model is trained using neutral speech, and thereafter adaptation is performed to each emotion of the database. The third method, emotion-independent approach, is based on an average emotion model which is initially trained using data from all the emotions of the speech database. Consequently, an adaptive model is build for each emotion. In the fourth method, emotion adaptive training, the average emotion model is trained with simultaneously normalization of the output and state duration distributions. To evaluate these training methods, a Modern Greek speech database which consists of four categories of speech, anger, fear, joy and sadness, was used. Finally, an emotion recognition rate subjective test was performed in order to measure and compare the ability of each of the four approaches in synthesizing emotional speech. The evaluation results showed that the emotion adaptive training achieved the highest emotion recognition rates among four evaluated methods, throughout all four emotions of the database.
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Evaluation of Meta-Heuristic Algorithms for Stable Feature Selection
Статья научная
Now a days, developing the science and technology and technology tools, the ability of reviewing and saving the important data has been provided. It is needed to have knowledge for searching the data to reach the necessary useful results. Data mining is searching for big data sources automatically to find patterns and dependencies which are not done by simple statistical analysis. The scope is to study the predictive role and usage domain of data mining in medical science and suggesting a frame for creating, assessing and exploiting the data mining patterns in this field. As it has been found out from previous researches that assessing methods can not be used to specify the data discrepancies, our suggestion is a new approach for assessing the data similarities to find out the relations between the variation in data and stability in selection. Therefore we have chosen meta heuristic methods to be able to choose the best and the stable algorithms among a set of algorithms.
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Evaluation of Reranked Recommended Queries in Web Information Retrieval using NDCG and CV
Статья научная
Tremendous growth of the Web, lack of background knowledge about the Information Retrieval (IR), length of the input query keywords and its ambiguity, Query Recommendation is an important procedure which analyzes the real search intent of the user and recommends set of queries to be used in future to retrieve the relevant and required information. The proposed method recommends the queries by generating frequently accessed queries, rerank the recommended queries and evaluates the recommendation with the help of the ranking measures Normalized Discounted Cumulative Gain (NDCG) and Coefficient of Variance (CV). The proposed strategies are experimentally evaluated using real time American On Line (AOL) search engine query log.
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