International Journal of Information Technology and Computer Science @ijitcs
Journal articles - International Journal of Information Technology and Computer Science
All articles: 1317
Prediction of Missing Values for Decision Attribute
Scientific article
The process of determining missing values in information system is an important issue for decision making especially when the missing values are in the decision attribute. The main goal for this paper is to introduce algorithm for finding missing values of decision attribute. Our approach is depending on distance function between existing values. These values can be calculated by distance function between the conditions attributes values for the complete information system and incomplete information system. This method can deal with the repeated small distance by eliminating a condition attribute which has the smallest effect on the complete information system. This algorithm will be discussed in detail with an example of a case study.
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Scientific article
Delay prediction in urban public transport systems is a critical task for improving operational efficiency and service reliability. While numerous predictive models exist, understanding the relative importance of contributing factors remains a challenge, with traditional approaches often overestimating the impact of stochastic weather conditions. This study proposes an approach that combines predictive modelling and factor analysis based on interpretable machine learning. An eXtreme Gradient Boosting model was developed using a large dataset of operational and meteorological data from a city with approximately one million inhabitants. The model demonstrated high predictive accuracy, explaining 72% of the variance in delays (Coefficient of Determination R²=0.72). Analysis of the model’s feature importance revealed that operational cycles (seasonal, weekly, daily) and spatial context (routes, stops) are the dominant predictors, collectively accounting for over 52% of the model’s total feature importance. Contrary to common assumptions, weather conditions were identified as a powerful secondary, rather than primary, factor. While their cumulative feature importance was substantial (contributing nearly 45%), the model revealed their impact to be highly contextual: the negative effects of adverse weather were significantly amplified during predictable peak operational hours but were minimal otherwise. This research demonstrates how Explainable Artificial Intelligence methods can transform complex predictive models into practical tools, providing a data-driven basis for shifting from reactive management to proactive, evidence-based planning.
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Scientific article
Social work practice faces critical challenges in accurately identifying high-risk families for timely intervention due to limited labeled data. This research develops an intelligent system integrating supervised and semi-supervised learning for early warning, coupled with uncertainty-aware resource optimization. We propose a hybrid framework combining Self-training and Co-training leveraging 1,200 labeled and 13,800 unlabeled family cases. The proposed method achieves 85.6% accuracy and 82.4% recall on the high-risk class, with 95% confidence intervals, substantially outperforming supervised-only baselines. A multi-objective allocation model balances effectiveness, cost, and equity while incorporating prediction uncertainty via robust optimization. Sensitivity analysis over 500 bootstrap scenarios demonstrates service quality guarantees. Subgroup fairness audits show consistent recall across demographic groups (max gap 1.2%). The optimized allocation reduces manpower by 22% while improving intervention success from 68.5% to 84.2%. SHAP-based explainability and human oversight support ethical deployment in social services.
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Preference-Based Web Service Composition: Case-Based Planning Approach
Scientific article
Web service selection is an indispensable process for web service composition. However it became a difficult task as many web services are increased on the web and mostly they offer similar functionalities, which service will be the best. User preferences are the key to retain only the best services for the composition. In this paper, we have proposed a web service composition model based on user preferences. To improve the process of web service composition we propose a case-based planning approach with user preferences which uses successful experiences in past to solve similar problems. In this paper we integrate user preferences in the phase of selection, adaptation and planning. Our main contributions are a new method of case retrieval, an extended algorithm of adaptation and planning with user preferences. Results obtained offer more than a solution to the user and taking both functional and non-functional requirements.
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Primary-Backup Access Control Scheme for Securing P2P File-Sharing Systems
Scientific article
Peer-to-peer (P2P) file-sharing systems have gained large interests among the internet users. However, wide-scale applications of P2P file-sharing technologies are constrained by the limitations associated with the sophisticated control mechanisms. Moreover, the decentralized and anonymous characteristics of P2P environments make it more difficult to control accesses on the shared resources, especially for using traditional access control methods. To overcome these limitations, we propose a role-based access control architecture for P2P file-sharing systems that supports autonomous decisions and centralized controls. The architecture integrates policies of credential, identity and role-based access control models to provide scalable, efficient and fault-tolerant access control services. Furthermore, we employ the primary-backup (PB) scheme to preserve P2P decentralized structure and peers’ autonomy property while enabling collaboration between peers. In particular, we propose a method for setting up interoperating relationships between domains by role mappings and resolve two kinds of interoperability conflicts while mapping roles from foreign domain to local domain without centralized authority. We believe that the proposed architecture is realistic, efficient and can provide controlled communications between peers.
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Scientific article
The study is devoted to assessing the risks of cyber threats in the future based on expert sampling patterns. One of the key problems of modern cybersecurity is the dynamic nature of threats that change under the influence of technological progress and socio-economic factors. In this context, the authors consider a methodological approach that involves the use of a multi-level analysis of expert opinions. The main emphasis is placed on taking into account the different points of view, experience and professional activities of experts from the public, private and academic sectors. An important stage of the study is the procedure of data cleaning to form a representative sample that takes into account only logically consistent responses of experts. The paper focuses on the integration of the expert sample patterns‘ features. The key differences in threat assessments between different groups of experts depending on their professional role and experience are identified. This made it possible to formulate comprehensive recommendations for strategic cyber risk management focused on both short-term and long-term priorities. The study makes a significant contribution to understanding the peculiarities of cyber risk assessment through the use of multivariate analysis of expert opinions. The proposed methodology allows not only to improve the quality of forecasts of future cyber threats, but also contributes to the creation of adaptive cybersecurity strategies that take into account the specifics of each sector. The findings of the study emphasize the importance of a multidimensional approach to analyzing cyber threats, taking into account the specifics of each expert group. Integration of assessments and consideration of local peculiarities are key to the development of adaptive and effective cyber defense strategies focused on global and local challenges.
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Prioritization of Barriers to Digitization for Circular Systems using Analytical Hierarchy Process
Scientific article
The increasing urgency for sustainable practices has motivated this research to explore the barriers hindering the adoption of digital technologies in circular systems. As industries seek to leverage IoT for enhanced efficiency and sustainability, understanding these barriers is crucial for effective implementation. This study employs a comprehensive, multi–dimensional approach, integrating insights from a literature review and expert interviews with industry professionals. Key findings reveal that technological complexity and high initial costs are the most significant barriers, highlighting the need for targeted strategies to address these challenges. Additional barriers include regulatory compliance issues and unclear return on investment, which further complicate the adoption process. The study's conclusion emphasizes that overcoming these barriers is essential for facilitating the successful integration of digital technologies in circular economies. Furthermore, the research identifies the necessity for future investigations into the interactions between these barriers and the effectiveness of various interventions. The novelty of this study lies in its holistic examination of the multifaceted barriers, combining qualitative insights with a structured analytical framework. This approach not only contributes to the existing literature on Digitization but also offers practical implications for stakeholders aiming to enhance sustainability and efficiency in their operations. By addressing the identified challenges, organizations can pave the way for a more circular and resilient future, ultimately driving innovation and growth in the rapidly evolving digital landscape.
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Priority Based New Approach for Correlation Clustering
Scientific article
Emerging source of Information like social network, bibliographic data and interaction network of proteins have complex relation among data objects and need to be processed in different manner than traditional data analysis. Correlation clustering is one such new style of viewing data and analyzing it to detect patterns and clusters. Being a new field, it has lot of scope for research. This paper discusses a method to solve problem of chromatic correlation clustering where data objects as nodes of a graph are connected through color-labeled edges representing relations among objects. Purposed heuristic performs better than the previous works.
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Priority Based Uplink Scheduling Scheme for WiMAX Service Classes
Scientific article
IEEE 802.16 standard supports five different service classes in order to support different needs of the mobile users with different QoS criteria. But, this standard doesn’t specify admission control and scheduling mechanisms and so far many solutions are proposed in the science world. In this paper we propose priority based uplink scheduling scheme for IEEE 802.16 standard that improves the QoS performances of the five WiMAX service classes, especially of the ertPS service class. Simulation experiments and analysis are done choosing the most adequate WiMAX simulator and the ns-2 simulation tool. Traffic load of the ertPS connections is changed from low to high in order to perform detailed performance analysis of the results. Results given in average delay, average jitter and average throughput are evaluated and compared with so far known uplink scheduling mechanism. The results show that our proposed uplink scheduling scheme based on the priority of the service classes improves the QoS performances especially in high loaded scenarios.
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Processing of Satellite Digital Images for Mapping Atmospheric Transmissivity in Bangladesh
Scientific article
This study investigates the potential of determining atmospheric transmissivity (τ) from NOAA-AVHRR satellite images using a simple methodology. Using this method, hourly transmissivity values over the land surface area of Bangladesh has been determined. The spatio-temporal distribution of τ has been studied by constructing monthly average maps for the whole country for one complete year (February 2005 to January 2006). Yearly average map has been prepared by integrating monthly average maps. Geographical distribution of τ exhibits patterns and trends. It is observed that the value of τ varies from 0.3 to 0.65 with the average maximum value in the month of April and minimum value in the month of November. It is also observed that for western parts of the country, which is the drought prone area, transmissivity values are little bit higher than that at the eastern parts. Relatively lower values of τ in the dry months (November to January) may be due to the effect of particulate or chemical pollution in the atmosphere.
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Scientific article
Software process improvement (SPI) is an important requirement in a software company. The search for better approach brought different kinds of models with multiple sets of principles for SPI to be founded. The framework is proposed to mainly address an alternative way of achieving a better process capability. The approach focuses on the implementation of SPI which can seamlessly align with the organization nature, day to day business activities, and financial capability. The paper provides the detailed implementation guideline and application of the framework through case study results. The case study is performed in a software development unit placed under academic institution. The unit is founded specifically for application development for internal and external customers. The case study is designed to be implemented in two software development projects in the development unit. From the ongoing case study, the results from the first project which is completed in six iterations are presented in this paper. Considering SPI implementation, the development team followed the framework and associated procedures throughout the development process. The results obtained in terms of aligning SPI to the daily development task and CMMI KPAs capability improvement achieved showed promising results.
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Pronunciation Proficiency Evaluation based on Discriminatively Refined Acoustic Models
Scientific article
The popular MLE (Maximum Likelihood Estimation) is a generative approach for acoustic modeling and ignores the information of other phones during training stage. Therefore, the MLE-trained acoustic models are confusable and unable to distinguish confusing phones well. This paper introduces discriminative measures of minimum phone/word error (MPE/MWE) to refine acoustic models to deal with the problem. Experiments on the database of 498 people’s live Putonghua test indicate that: 1) Refined acoustic models are more distinguishable than conventional MLE ones; 2) Even though training and test are mismatch, they still perform significantly better than MLE ones in pronunciation proficiency evaluation. The final performance has approximately 4.5% relative improvement.
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Proposal of Implicit Coordination Model for Performance Enhancement Using Sprint Zero
Scientific article
Scrum model always welcome the new requirements from customer at any stage of software development. This situation creates problem for development team to meet estimated timelines. The Scrum development team is always under heavy workload and stress because of this situation. A high level of coordination among team members is required in order to overcome this work pressure and meet quality demands within estimated time and cost. Scrum model emphasis to coordinate through communication to cope with changing requirements. Scrum development is facing new challenges to meet high quality demands with critical timeline environment. The ability of a team to act intelligently in such situation is gaining key position for the survival and success of an organization. The goal of this paper is to highlight the role of implicit coordination helping the software development team members to act intelligently in time demand environment to achieve common goals. Implicit coordination among team members during sprint zero development can show significant improvement in team performance. This will help to achieve high quality product under heavy workload within estimated time period. A survey is conducted to validate the research i.e., implicit coordination has strong impact for successful implementation of Scrum methodology.
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Proposal of New PRORISK Model for GSD Projects
Scientific article
The level of complexity and risks associated with software are increasing exponentially because of competing environment especially in geographically distributed projects. Global software development (GSD) face challenges like distance, communication and coordination challenges. The coordination and communication challenges are the main causes of failure in GSD. Project Oriented Risk Management (PRORISK) is one of the models to address the importance of risk management and project management processes in standard software projects. However, existing model is not proposed to handle GSD associated risks. This warrants the proposal of new PRORISK model to manage the risks of GSD. Survey is used as a research design to validate the proposed solution. We anticipate that the proposed solution will help the software companies to cater the risks associated with GSD.
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Provisioning quality of service for multimedia applications in cloud computing
Scientific article
Since the last decade, many new trends have been introduced to access network technologies and services through internet. Cloud computing is one of those significant technologies that reduce the cost and increase the productivity by providing a variety of services. Recently, cloud computing based system is primarily used for multimedia applications. Over the cloud computing, multimedia applications has some significant quality of service (QoS) requirements such as bandwidth, jitter, latency etc. But due to some limitations in services providing, it is constantly complex to make selection for an appropriate service. Keeping in view the provision of multimedia services through cloud computing, many different concepts and approaches that provide better cloud services under the constraints of QoS attributes have been described in the literature. The goal of this paper is to assess the applicability and provision of multimedia applications over cloud computing through enhanced quality of service. We have identified the primary quality of service msetrics evaluation of multimedia services over cloud computing. Furthermore, under these metrics we evaluated the existing approaches that provide multimedia related services with their strengths and limitations. This evaluation approach could provide the service that can provide better QoS in multimedia applications over cloud computing.
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Scientific article
Microwave-Induced Thermo-Acoustic Tomograp- phy (MITAT) has attracted more concerns in recent years in biomedical imaging field. It has both the high contrast of the microwave imaging and the high resolution of ultrasound imaging. As compared to optoacoustics, which uses instead a pulsed light for evoking optoacoustic response, thermo-aco- ustic imaging has the advantage of deeper tissue penetration, attaining the potential for wider clinical dissemination, especially for malignant tumors. In this paper, the induced thermo-acoustic wave propagating in a mimic biologic tissue is simulated by numeric method Pseudo-Spectrum Time Domain (PSTD). Due to the excellent performance in noise- depress and the stability for the fluctuation of the model parameters, Time Reversal Mirror (TRM) imaging technique is studied computationally for the simulative received therm- o-acoustic signals. Some thermo-acoustic objects with differ- ent initial pressure distribution are designed and imaged by TRM technique to represent the complex biologic tissue case in a random media. The quality of images generated by TRM technique based on PSTD method hints the potential of the MITAT technique.
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Scientific article
Psychological status monitoring with Cerebral Blood Fflow: CBF, Electroencephalogram: EEG and Electro-oculogram: EOG measurements are attempted. Through experiments, it is confirmed that the proposed method for psychological status monitoring is valid. It is also found correlations among the amplitudes of peak alpha and beta as well as gamma frequency of EEG signals and EOG as well as cerebral blood flow. Therefore, psychological status can be monitored with either EEG measurements or cerebral blood flow and EOG measurements.
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Psychosocial Features for Hate Speech Detection in Code-switched Texts
Scientific article
This study examines the problem of hate speech identification in codeswitched text from social media using a natural language processing approach. It explores different features in training nine models and empirically evaluates their predictiveness in identifying hate speech in a ~50k human-annotated dataset. The study espouses a novel approach to handle this challenge by introducing a hierarchical approach that employs Latent Dirichlet Analysis to generate topic models that help build a high-level Psychosocial feature set that we acronym PDC. PDC groups similar meaning words in word families, which is significant in capturing codeswitching during the preprocessing stage for supervised learning models. The high-level PDC features generated are based on a hate speech annotation framework [1] that is largely informed by the duplex theory of hate [2]. Results obtained from frequency-based models using the PDC feature on the dataset comprising of tweets generated during the 2012 and 2017 presidential elections in Kenya indicate an f-score of 83% (precision: 81%, recall: 85%) in identifying hate speech. The study is significant in that it publicly shares a unique codeswitched dataset for hate speech that is valuable for comparative studies. Secondly, it provides a methodology for building a novel PDC feature set to identify nuanced forms of hate speech, camouflaged in codeswitched data, which conventional methods could not adequately identify.
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Python Data Analysis and Visualization in Java GUI Applications Through TCP Socket Programming
Scientific article
Python is popular in artificial intelligence (AI) and machine learning (ML) due to its versatility, adaptability, rich libraries, and active community. The existing Python interoperability in Java was investigated using socket programming on a non-graphical user interface (GUI). Python's data analysis library modules such as numpy, pandas, and scipy, together with visualization library modules such as Matplotlib and Seaborn, and Scikit-learn for machine-learning, aim to integrate into Java graphical user interface (GUI) applications such as Java applets, Java Swing, and Java FX. The substantial method used in the integration process is TCP socket programming, which makes instruction and data transfers to provide interoperability between Python and Java GUIs. This empirical research integrates Python data analysis and visualization graphs into Java applications and does not require any additional libraries or third-party libraries. The experimentation confirmed the advantages and challenges of this integration with a concrete solution. The intended audience for this research extends to software developers, data analysts, and scientists, recognizing Python's broad applicability to artificial intelligence (AI) and machine learning (ML). The integration of data analysis and visualization and machine-learning functionalities within the Java GUI. It emphasizes the self-sufficiency of the integration process and suggests future research directions, including comparative analysis with Java's native capabilities, interactive data visualization using libraries like Altair, Bokeh, Plotly, and Pygal, performance and security considerations, and no-code and low-code implementations.
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QoS Aware Logical Channel Prioritization under Burst Resource Allocation for Uplink in LTE
Scientific article
Long Term Evolution (LTE) is the latest 3GPP (3rd Generation Partnership Project) standard of the Mobile communication system. LTE has been proposed to achieve higher data throughput, lower latency and better quality of service (QoS). In LTE network (n/w), the resource (resource referred to frequency and time domain on the air interface) sharing is one of the major challenging issues and it is one of the key functions to achieve the desired QoS for the different configured data stream. In this context of QoS for LTE n/w, Multiplexing & Logical Channel Prioritization (LCP) is referred. In this paper, we present a brief survey on LCP techniques for the uplink (UL) direction. UL direction referred mobile to n/w communication. A strategy has been proposed for LCP to achieve QoS under burst resource allocation environment. Proposed approach considers the priority of each logical channel configured by evolved NodeB (eNB) during radio bearer (RB) setup/re-configuration.
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