International Journal of Information Technology and Computer Science @ijitcs
Journal articles - International Journal of Information Technology and Computer Science
All articles: 1317
Fuzzy Logic Based Trusted Candidate Selection for Stable Multipath Routing
Scientific article
In mobile ad hoc networks (MANETs), providing reliable and stable communication paths between wireless devices is critical. This paper presents a fuzzy logic stable-backbone-based multipath routing protocol (FLSBMRP) for MANET that provides a high-quality path for communication between nodes. The proposed protocol has two main phases. The first phase is the selection of candidate nodes using a fuzzy logic technique. The second phase is the construction of a routing backbone that establishes multiple paths between nodes through the candidate nodes, thus forming a routing backbone. If any candidate node in the path fails due to a lack of bandwidth, residual energy or link quality, an alternate path through another candidate node is selected for communication before the route breaks, because a candidate node failure may lead to a broken link between the nodes. Simulation results demonstrate that the proposed protocol performs better in terms of the packet delivery ratio, overhead, delay and packet drop ratio than the major existing ad hoc routing protocols.
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Scientific article
The vagueness in decision-making may be due to ambiguity in the decisional requirements expression. Therefore, in the literature dealing with vagueness in decision systems, studies were concentrated on data vagueness and not on decision requirements. In order to evaluate the expression in decision-making requirements and in order to improve the data warehouses design quality, this paper presents a rigorous fuzzy ontology-based solution. Based on the latest Zadeh theory “Ref. [1]”, Authors in “Ref. [2.3]”, propose a solution consisting in using ontologies to provide "an understanding of how the meaning of a proposal can be composed of the meaning of its constituents. One of the limitations of this solution is the fuzziness presence only at the adjective sentence. In some sense, our proposal can be seen as a continuation of that work. We limit our study, in this paper to the “Near negative” operator case. To the best of our knowledge, this case has not been addressed yet in the data warehouse context.
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Scientific article
Gender classification method based on Gait Energy Motion: GEM derived through wavelet analysis of human gait moving pictures is proposed. Through experiments with human gait moving pictures, it is found that the extracted features of wavelet coefficients using silhouettes images are useful for improvement of gender classification accuracy. Also, it is found that the proposed gender classification method shows the best classification performance, 97.63% of correct classification ratio.
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Generating database schemas from business artifact models
Scientific article
Business Artifacts, as an alternative approach to Business Process Modeling, combines both process and data aspects of a Business into the same model. Many works in the literature have focused on defining Artifact-centric processes and graphical modeling notations. But, to the best of our knowledge, no prior work has directly tackled the problem of generating Database Schemas from Business Artifact Models. In this paper, we propose an algorithm that generates Database Schemas from Business Artifact Models (BAMs). The proposed algorithm not only takes into consideration the different data attribute types of Artifacts’ Information Models, but also supports different Artifacts relationships. We also validate our work with a prototype implementation of a Business Artifact Models Modeler and a Database Schema Generator.
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Scientific article
Using digital signal processing in genomic field is a key of solving most problems in this area such as prediction of gene locations in a genomic sequence and identifying the defect regions in DNA sequence. It is found that, using DSP is possible only if the symbol sequences are mapped into numbers. In literature many techniques have been developed for numerical representation of DNA sequences. They can be classified into two types, Fixed Mapping (FM) and Physico Chemical Property Based Mapping (PCPBM (. The open question is that, which one of these numerical representation techniques is to be used? The answer to this question needs understanding these numerical representations considering the fact that each mapping depends on a particular application. This paper explains this answer and introduces comparison between these techniques in terms of their precision in exon and intron classification. Simulations are carried out using short sequences of the human genome (GRch37/hg19). The final results indicate that the classification performance is a function of the numerical representation method.
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Scientific article
The rapid proliferation of Unmanned Aerial Vehicles (UAVs) across military, commercial, and civilian domains creates unprecedented security challenges while simultaneously offering significant operational advantages. Current detection and tracking systems face mounting pressure to balance effectiveness with deployment complexity and cost constraints. This paper presents a geospatial detection and movement analysis system for Unmanned Aerial Vehicles that addresses critical security challenges through innovative mathematical and software solutions. The research introduces a methodology for UAV monitoring that minimizes sensor requirements, utilizing a single optical sensor equipped with distance measurement capabilities. The core of this work focuses on developing and evaluating an algorithm for three-dimensional (3D) coordinate determination and trajectory prediction without requiring direct altitude measurement. The proposed approach integrates computer vision detection results with a mathematical model that defines spatial relationships between camera parameters and detected objects. Specifically, the algorithm estimates altitude parameters and calculates probable flight trajectories by analyzing the correlation between apparent size variation and measured distance changes across continuous detections. The system implements a complete analytical pipeline, including continuous detection processing, geospatial coordinate transformation, trajectory vector calculation, and visualization on geographic interfaces. Its modular architecture supports real-time analysis of video streams, representing detected trajectories as vector projections with associated uncertainty metrics. The algorithm's capability to provide reliable trajectory predictions is demonstrated through validation in synthetically generated environments. It offers a cost-effective monitoring solution for small aerial objects across diverse environmental conditions. This research contributes to the development of minimally-instrumented UAV tracking systems applicable in both civilian and defense scenarios.
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Scientific article
Data warehouses are decision support systems that are specifically designed for the business managers and executives for reporting and business analysis. Data warehouse is a database that stores enterprise-wide data that can be used to deduce useful information. Business organizations can achieve a great level of competitive advantage by analyzing its historical data and learning from it. However data warehouse concept is still maturing as a technology. In order to effectively design and implement a data warehouse for an organization, its goal needs to be understood and requirement must be analyzed in the perspective of the identified goal. In this paper we present a goal structured model for requirements engineering that also enables its users to manage traceability between the goals, decisions, business strategy and the corresponding business model.
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God Class Refactoring Recommendation and Extraction Using Context based Grouping
Scientific article
Code smells are the indicators of the flaws in the design and development phases that decrease the maintainability and reusability of a system. A system with uneven distribution of responsibilities among the classes is generated by one of the most hazardous code smells called God Class. To address this threatening issue, an extract class refactoring technique is proposed that incorporates both cohesion and contextual aspects of a class. In this work, greater emphasis was provided on the code documentation to extract classes with higher contextual similarity. Firstly, the source code is analyzed to generate a set of cluster of extracted methods. Secondly, another set of clusters is generated by analyzing code documentation. Then, merging these two, a final cluster set is formed to extract the God Class. Finally, an automatic refactoring approach is also followed to build newly identified classes. Using two different metrics, a comparative result analysis is provided where it is shown that the cohesion among the classes is increased if the context is added in the refactoring process. Moreover, a manual inspection is conducted to ensure that the methods of the refactored classes are contextually organized. This recommendation of God Class extraction can significantly help the developers in minimizing the burden of refactoring on own their own and maintaining the software systems.
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Graph Based Data Governance Model for Real Time Data Ingestion
Scientific article
Data governance is one of the strongest pillars in Data management program which goes hand in hand with data quality. In industrial Data Lake huge amount of unstructured data is getting ingested at high velocity from different source systems. Similarly, through multiple channels of data are getting queried and transformed from Data Lake. Based on 3Vs of big data it's a real challenge to set up a rule based on traditional data governance system for an Enterprise. In today's world governance on semi structured or unstructured data on Industrial Data lake is a real issue to the Enterprise in terms of query, create, maintain and storage effectively and secured way. On the other hand different stakeholders i.e. Business, IT and Policy team want to visualize the same data in different view to analyze, imposes constraints, and to place effective workflow mechanism for approval to the policy makers. In this paper author proposed property graph based governance architecture and process model so that real time unstructured data can effectively govern, visualize, manage and queried from Industrial Data Lake.
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Scientific article
Reasoning is the fundamental capability which requires knowledge. Various graph models have proven to be very valuable in knowledge representation and reasoning. Recently, explosive data generation and accumulation capabilities have paved way for Big Data and Data Intensive Systems. Knowledge Representation and Reasoning with large and growing data is extremely challenging but crucial for businesses to predict trends and support decision making. Any contemporary, reasonably complex knowledge based system will have to consider this onslaught of data, to use appropriate and sufficient reasoning for semantic processing of information by machines. This paper surveys graph based knowledge representation and reasoning, various graph models such as Conceptual Graphs, Concept Graphs, Semantic Networks, Inference Graphs and Causal Bayesian Networks used for representation and reasoning, common and recent research uses of these graph models, typically in Big Data environment, and the near future needs and challenges for graph based KRR in computing systems. Observations are presented in a table, highlighting suitability of the surveyed graph models for contemporary scenarios.
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Scientific article
The article provides an approach of getting optimal time through graph for Simple step stress accelerated test of inverse weibull distribution. In this we estimate parameters using log linear relationship by maximum likelihood method. Along with this, asymptotic variance and covariance matrix of the estimators are given. Comparison between expected and observed Fisher Information matrix is also shown. Furthermore, confidence interval coverage of the estimators is also presented for checking the precession of estimator. This approach is illustrated with an example using software.
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Great Deluge Algorithm for the Linear Ordering Problem: The Case of Tanzanian Input-Output Table
Scientific article
Given a weighted complete digraph, the Linear Ordering Problem (LOP) consists of finding and acyclic tournament with maximum weight. It is sometimes referred to as triangulation problem or permutation problem depending on the context of its application. This study introduces an algorithm for LOP and applied for triangulation of Tanzanian Input-Output tables. The algorithm development process uses Great Deluge heuristic method. It is implemented using C++ programming language and tested on a personal computer with 2.40GHZ speed processor. The algorithm has been able to triangulate the Tanzanian input-output tables of size 79×79 within a reasonable time (1.17 seconds). It has been able to order the corresponding economic sectors in the linear order, with upper triangle weight increased from 585,481 to 839,842 giving the degree of linearity of 94.3%.
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Green AI Practices in Multi-objective Hyperparameter Optimization for Sustainable Machine Learning
Scientific article
The hyperparameter tuning process is an essential step for ML model optimization, as it is necessary to improve model performance. However, this enhancement involves high computational resources and time costs. Model tuning can significantly raise energy consumption and consequently increase carbon emissions. Therefore, there is an essential need to construct a new framework for this challenge by adding carbon emissions as a vital consideration along with performance. The paper proposes a novel Sustainable Hyperparameter Optimization (SHPO) framework that uses an optimized multi-objective fitness approach. The framework focuses on ensemble classification models (ECMs) namely, Random Forest, ExtraTrees, XGBoost, and AdaBoost. All these models will be optimized using traditional and advanced techniques like Optuna, Hyperopt, and Grid Search. The proposed framework tracks carbon emissions during model hyperparameter tuning. The methodology uses the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as a method of multi-criteria decision-making (MCDM). This TOPSIS method ranks the hyperparameter sets based on both accuracy and carbon emissions. The objective of the multi-objective fitness approach is to reach the best parameter set with high accuracy and low carbon emissions. It is observed from the experimental results that Optuna based Hyperparameter optimization consistently produced low carbon emissions and achieved high predictive accuracy across the majority of benchmark hyperparameter setups for the models.
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GreenCloud-RL: Carbon-Aware Multi-Cloud Scheduling with SLA Guarantees
Scientific article
We present GreenCloud-RL, a formally defined scheduler for multi-cloud fleets that jointly optimizes carbon emissions, operational cost, and service-level agreement (SLA) risk. The scheduling problem is modeled as a constrained Markov decision process (CMDP) in which expected cost–carbon reward is maximized subject to aggregate SLA-risk and power-cap constraints. The framework integrates (i) a physics-informed DVFS power model, (ii) queueing-theoretic latency predictors, (iii) probabilistic short-term forecasts of regional grid carbon intensity and electricity price with uncertainty penalties, and (iv) a dual-variable actor–critic algorithm that provably enforces constraint satisfaction online. We analyze the algorithm’s convergence under bounded rewards and provide distributed complexity bounds for large-scale multi-cloud control. Empirical evaluation on production-like workloads shows that GreenCloud-RL reduces SLA-violation rate by 37.6% and power-cap violations by 53.8%, while lowering mean tardiness to 1.9 s and improving throughput by 4.2% compared with strong baselines. A region×regime analysis (load ∈ {low, medium, high}; grid ∈ {clean, neutral, dirty}) demonstrates consistent reductions in both cost and carbon emissions without degrading SLA satisfaction. Post-hoc calibration improves reliability (ECE 4.8%→1.7%, lower Brier score), enabling trustworthy on-time probability estimates for risk-aware admission and tenant-tier guarantees. Scalability tests show a p95 scheduling latency of ≈41 ms at 100k jobs, ≈0.25 ms inference per decision, modest CPU/GPU overhead, and ≈5 s controller failover. These results establish, both theoretically and empirically, that multi-objective optimization of carbon, cost, and reliability is achievable in production-scale cloud environments while maintaining contractual SLA guarantees.
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Grid Approach with Metadata of Messages in Service Oriented Architecture
Scientific article
Service Orientation Architecture is playing a vital role in the middleware and enterprise organizations through its key design principles and there are many benefits of it as well. Loose Coupling between services is one of its design principles that help system or architecture to maintain its efficiency because services are less dependent on each other. Stopping of one service could not affect other service and system but there is a fact that services have less knowledge of other services that’s why services cannot be able to properly communicate with each other and Service Oriented Architecture is totally based on communicative messages between services in order to perform their functionality. This paper provides a basic solution to control the messaging criteria between services. This paper holds the brief description of messaging criteria of services in service oriented architecture by defining the process of message that fulfills the guaranteed delivery of message to its other end while communicating or asking for a service.
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Scientific article
Arsenic is a natural element of the earth’s crust and is commonly distributed all over the environment in the air, water and land. It is extremely poisonous in its inorganic form. Arsenic (As) contamination is one of the leading issues in the south Asian countries, ground water is major sources of drinking water. The highest risk to public health from arsenic originates from polluted groundwater. Arsenic is naturally present at high levels in the groundwater of south Asian countries. Pakistan also one of them which is highly affected by this toxic element, especially rural areas of Sindh Pakistan, where Ground water is the only source of drinking. Due to climates changes day by day value of arsenic is increased in Ground water, that effects the human health in form of many diseases like skin cancer, blood cancer. The purpose of this study is to figure out the increasing level of Arsenic and Cancer rate in Tando Muhamad Khan Sindh Pakistan for next coming five years. For this we have developed model using Microsoft Azure Machine learning Techniques and algorithms including Bayesian Linear Regression (BLR), support vector machine (SVM), Linear Regression (LR), Boosted Decision tree (BDT), exponential smoothing ETS, Autoregressive Integrated Moving Average (ARIMA). Developed model will help us to forecast the increasing rate of Arsenic and its effects on human health in form of cancer.
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Scientific article
This study examines the functional requirements (FR) and non-functional requirements (NFR) for development of an integrated mobile application and a web-based system for enhancement of HIV/AIDS healthcare information delivery in Tanzania. The study was conducted in Dar es Salaam city in Tanzania. The unstructured interview was carried-out involving 45 people, among them, there were selected relevant users of the proposed system, Information Technologists, System Administrators and HIV/AIDS healthcare practitioners from the HIV/AIDS Care and Treatment Centers (CTCs) in district referral hospitals in Dar es Salaam. The captured requirements were classified into functional and non-functional requirements, the functional requirements were then graphically analyzed using the use case diagram, which was done by using starUML computer software. These findings can be used as the foundation’s building block for the development of a mobile application and web-based system for HIV/AIDS healthcare information delivery services.
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Hand Gesture-controlled 2D Virtual Piano with Volume Control
Scientific article
The rise of virtual instruments has revolutionized music production, providing new avenues for creating music without the need for physical instruments. However, these systems rely on costly hardware, such as MIDI controllers, limiting accessibility. As an alternative, 3D gesture-based virtual instruments have been explored to emulate the immersive experience of MIDI controllers. Yet, these approaches introduce accessibility challenges by requiring specialized hardware, such as depth-sensing cameras and motion sensors. In contrast, 2D gesture systems using RGB cameras are more affordable but often lack extended functionalities. To address these challenges, this study presents a 2D virtual piano system that utilizes hand gesture recognition. The system enables accurate gesture-based control, real-time volume adjustments, control over multiple octaves and instruments, and automatic sheet music generation. OpenCV, an open-source computer vision library, and Google’s MediaPipe are employed for real-time hand tracking. The extracted hand landmark coordinates are normalized based on the wrist and scaled for consistent performance across various RGB camera setups. A bidirectional long short-term memory (Bi-LSTM) network is used to evaluate the approach. Experimental results show 95% accuracy on a public Kaggle dynamic gesture dataset and 97% on a custom-designed dataset for virtual piano gestures. Future work will focus on integrating the system with Digital Audio Workstations (DAWs), adding advanced musical features, and improving scalability for multiple-player use.
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Health informatics system for screening arboviral infections in adults
Scientific article
Health Informatics (HI) has played vital roles in the management of several diseases especially in the tropics. It has revolutionized the mainstream healthcare and healthcare delivery system. This paper applies the principle of Health Informatics to addressing the detection and management of arboviruses particularly Zika and Dengue viruses around which the aetiology of Zika Virus Disease and Dengue fever revolves. In this paper, the object-oriented approach was employed to study the fundamental procedures in the detection and management of arboviral infections. The study culminated into modelling of knowledge-based prototype system for screening patients in incidence areas. Existing knowledge on the management of arbovirus infections was complemented with purposive sampling of two specialist infectious diseases facilities in Nigeria. The health informatics prototype is christened NCliniSoft Diagnostic ZikaSol and is composed of five components validated through expert-driven differential diagnostic procedures. The prototype was evaluated to test for usability, diagnostic consistency, user acceptance and satisfaction. The prototype performs a differential screening between Dengue fever and Zika Virus disease using the Bayesian probabilities complemented by situational constructs. The result of each screening process is an automated diagnostic report that shows the status of the patient. Computed result showed high level of efficiency and acceptability.
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Hepatitis C Diagnosis using Supervised Machine Learning Algorithms and Ensemble Learning Techniques
Scientific article
Hepatitis, a severe and highly impactful disease, poses significant challenges for healthcare systems, including limited diagnostic resources, delayed detection, and inadequate treatment infrastructure. This work addresses these issues by developing a machine-learning predictive system to classify hepatitis severity. By employing Logistic Regression, Random Forest, SVM, KNN, and ensemble techniques such as AdaBoost, CatBoost, and Gradient Boosting, the system enhances early detection and severity assessment. The issue of class imbalance was addressed using ADASYN and SMOTE methods applied to two separate datasets. For Dataset 1, following the use of the ADASYN technique, the achieved accuracies were 88.11% for Logistic Regression, 98.92% for Random Forest, 97.30% for AdaBoost, and 96.22% for Gradient Boosting. When SMOTE was employed on Dataset 1, Random Forest and Gradient Boosting reached accuracies of 98.38% and 96.76%, respectively. In the case of Dataset 2, AdaBoost achieved an accuracy of 93.75% after applying both ADASYN and SMOTE. These models analyze clinical data to deliver accurate, timely predictions, reducing the burden on resource-constrained healthcare systems. Ensemble methods enhance model robustness and accuracy, supporting improved decision-making and efficient resource allocation. Furthermore, SHAP offers global explanations of feature importance and force plots for local interpretations, while LIME increases the interpretability of results from black-box models, facilitating effective hepatitis management. Future work will focus on integrating interoperability standards, such as HL7 FHIR, to enable real-time data exchange, facilitating seamless risk assessment and clinical decision support within healthcare workflows.
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