The SIJ Transactions on Computer Science Engineering & its Applications
The SIJ Transactions on Computer Science Engineering & Its Applications (CSEA) is an international, peer-reviewed journal committed to publishing high-quality research in computer science engineering and its practical applications. CSEA invites original research articles, reviews, tutorials, and expanded conference papers. All submissions undergo rigorous peer review by three independent experts—a national reviewer, an international reviewer, and a guest reviewer—to uphold quality, originality, and ethical standards. Adhering to COPE guidelines, the journal promotes research integrity, proper attribution, and plagiarism-free content. With its robust processes, CSEA aims to meet criteria for prestigious indexing services like Scopus, offering a global platform for academics, researchers, and industry professionals to disseminate innovations in the field.
Synergistic Effects of Transactional Patterns and Demographic Profiles on Financial Fraud Detection Accuracy
The rise of digital banking ecosystems and the Unified Payments Interface (UPI) has increased transactional efficiency, but it has also created new vectors for financial fraud. Current fraud detection methods mostly make use of either transaction behaviors or demographic information independent of each other, restricting their ability to consider the dependency between them. The study proposes a synergic fraud detection framework for better evaluation of financial fraud risk management and support of managerial decisions by using both transactional and demographic information. The fraud detection framework incorporates the behavioral analysis, statistical learning, clustering based segmentation and anomaly scoring techniques to derive Transactional Risk (T) score and Demographic Risk (D) score and then utilizes synergic interaction model (T × D) to incorporate their combination effect. The experimental results show the model outperforms existing transactional and demographic approaches in detecting the frauds and achieves a considerable improvement of 95% in accuracy transactional-only approaches equals to 87.5% and demographic-only approaches equals to 82%. Also, precision improves to 93.5% and the recall level becomes 95%. In addition, the false positive rate switches to 48%, the fraud detection rate achieves 48% and the effectiveness of risk segmentation attains 80% a reduction of 42.5% in cost of investigation versus 17% in transaction-based models and 11% in demographic models. However, the major contribution of this research paper is to prove that the fraud risk does not arise because of independent behavior and demographic variables but due to the interaction between these two aspects. The developed framework adds interpretability, increases the efficiency of risk segmentation process and helps in making instant decisions for managers in the BFSI setting.
Financial Fraud Detection, Transactional Patterns, Demographic Profiling, Synergistic Modeling, BFSI Security, Risk Intelligence, Managerial Decision Support.
Copyright (c) 2024 The SIJ Transactions on Computer Science Engineering & its Applications
Big Data Analytics and Optimization Models for Intelligent Supply Chain and Operations Management: Empirical Simulation Analysis
In this research paper, a systematic literature review (SLR) of BDA integrated with optimization models in intelligent SCOM is performed together with the development of an empirical analysis. The analysis is based on the empirical findings derived from over 60 peer-reviewed papers (2011-2025) and includes an overview of BDA applications in the fields of descriptive, predictive, and prescriptive analytics in Supply Chain 4.0. Empirical research reveals that BDA can provide 11-20% expected ROI, improve forecasting accuracy by 200% compared to traditional practices, and significantly decrease costs. An empirical analysis performed using Python programming languages and PuLP & NumPy modules shows that there is 36.63% reduction in inventory costs when BDA-supported dynamic forecasts are used in optimization problems compared to static traditional forecasts.
Big Data Analytics, Optimization Models, Intelligent Supply Chain, Supply Chain 4.0, Prescriptive Analytics, Inventory Optimization, Empirical Simulation.
Copyright (c) 2025 The SIJ Transactions on Computer Science Engineering & its Applications
A Predictive Analytics Framework Using Machine Learning Techniques for Measuring and Improving Organizational Performance: A Review and Study of Apollo Hospitals
Predictive analytics based on machine learning (ML) has been proven to be a revolutionary technique used to assess and optimize organizational performance within the realm of healthcare. The current review paper provides an overview of the literature about the frameworks for predictive analytics used in healthcare with reference to the application of the latter in the case of Apollo Hospitals, which is India's most prestigious chain of multi-specialty hospitals. The use of ML-based predictive analytics and prescriptive analysis together with the Six Sigma approach was successfully employed at Apollo Hospitals in terms of different predictive tools such as Apollo Artificial Intelligence Cardiovascular Disease Risk (AICVD) tool, AI early warning system (AI EWS), AI empirical antibiotic recommendation system (AI EARS), and operative risk prediction tools. >90% accuracy in antibiotic prescription, 80% decrease in code blue incidents, 70% cut in nurse workload, and increased emphasis on prevention, leading to fewer procedures being conducted. The suggested architecture, consisting of data input, development of ML model (using supervised learning and deep learning techniques), measurement of success via clinical, operational, and financial KPIs, and ongoing improvement cycles, can provide a template for excellence through data science in hospitals. Issues such as data security, bias in models, and compatibility with existing systems have been addressed, along with future trends including generative AI and edge computing.
Predictive Analytics, Machine Learning, Organizational Performance, Apollo Hospitals, Healthcare AI, Cardiovascular Risk Prediction, Early Warning Systems.
DeepFarm-YieldNet: A Novel Integrated CNN-BILSTM-Attention Framework for Real-Time Soil Moisture Prediction and Crop Yield Forecasting in Smart Farming
Smart farming has gained increasing attention recently as one of the most efficient approaches towards achieving sustainability in meeting the challenges to global food security brought about by factors such as climate change, water scarcity, and population growth. Real-time prediction of soil moisture status and its impact on the yield of the crops grown in the field is crucial for precision irrigation, effective utilization of resources, and climate-resilient farming operations. In this research study, DeepFarm-YieldNet is proposed as a novel deep-learning framework which is able to perform simultaneous prediction of multi-layer soil moisture dynamics and seasonal crop yield based on multi-source data inputs. The architecture integrates 1D-CNN for spatial feature representation of the data gathered through sensors and satellite observations along with BiLSTM and self-attention networks. The proposed model was carefully validated against a real-world data set derived from 12 farms located in the Pune and Nashik regions of Maharashtra, India, during the 2023–2025 cropping seasons and consisting of sugar cane, soybean, and cotton crops. The outcomes show the best results, where soil moisture prediction is conducted with an accuracy of R² = 0.95 and RMSE = 0.014, while crop yield prediction gives R² = 0.93 and RMSE = 1.8 q/ha, which is significantly better than the benchmark CNN-LSTM and Transformer-based algorithms by 12–18%. In addition, the suggested approach helps to achieve up to 28% savings in water consumption by optimizing the process of irrigation management. Using explainable AI methods (SHAP and LIME), the developed application can provide explanations to the farmers via the mobile app.
Smart Farming, Deep Learning, Soil Moisture Prediction, Crop Yield Forecasting, CNN-BiLSTM, Self-Attention, IoT, Edge Computing, Precision Agriculture, Sustainable Farming.
Empirical Evaluation of a Privacy-Preserving Federated Learning Framework with Homomorphic Encryption for Decentralized Cyber Threat Intelligence Sharing: An Experimental Study
The current research conducts an empirical assessment of the performance of a privacy-preserving federated learning (FL) framework using a combination of MK-HE, DP, and blockchain-based decentralized aggregation to support cyber threat intelligence (CTI) exchange. Based on the widely-used and publicly available CIC-IDS2017 dataset (2,830,743 network flows, 78 features, 15 classes - benign and 14 different attacks), the hybrid FL approach is implemented via horizontal FL on 10 simulated clients in a non-IID manner. Specifically, local model training employs DP-SGD and secure global model aggregation relies on selective CKKS-based MK-HE. The experimental evaluation demonstrates excellent accuracy (93.8%), robustness against collusion attacks (even for up to K-1 parties), and reduced communication overhead by 3.5 times compared to fully homomorphic FL (HE-FL). The PriSec-FL-CTI framework achieves a favorable balance between privacy (ε=0.8) and efficiency.
Federated Learning, Homomorphic Encryption, Differential Privacy, CIC-IDS2017, Cyber Threat Intelligence, Privacy-Preserving Cybersecurity, Decentralized Aggregation.
AI-Augmented Robotic Surgery in Orthopedics: Advancing Precision, Operational Efficiency, and Long-Term Clinical Outcomes
Robotically assisted surgery has revolutionized the field of orthopedics by improving the accuracy of surgical techniques and minimizing errors. With the use of artificial intelligence (AI), these technologies have been further enhanced due to features such as real-time decision making, predictive analytics, and adaptive learning. In this paper, the effect of AI-powered robotic orthopedic surgery systems on increased precision, efficiency, and better results is explored and analyzed through a hybrid framework. The study was based on a real-world data set of 1,248 cases of robotically assisted TKAs and THAs carried out from 2021 to 2025 at three tertiary care hospitals in Maharashtra, India. It is found that the use of AI technology in robotically assisted orthopedic surgery increased precision to 98.7%, decreased average surgical duration by 22%, and decreased average blood loss by 31% compared to conventional robot-assisted techniques. At two- and five-year follow-ups, patients had statistically significantly lower revision rates (2.1% compared to 6.8%) and higher patient reported Oxford knee score improvement (18.4 points improvement). The proposed framework facilitated personalized surgical planning with the aid of AI-powered risk prediction capabilities.
Robotic Surgery, ORTHOPEDICS, Artificial Intelligence, Precision Surgery, Implant Accuracy, Long-Term Outcomes, Total Knee Arthroplasty, Predictive Analytics.
Intellisurg-Net: An AI-Driven Framework for Enhancing Intraoperative Decision-Making and Real-Time Adaptability in Robotic Surgery
Robotic-assisted surgical techniques have led to tremendous advancements in terms of the precision and dexterity of the procedures performed minimally invasively. However, existing robotic surgery techniques are highly dependent on the surgeon's input since very little autonomy has been provided in existing systems. Conversely, artificial intelligence (AI) has provided decision-making algorithms for robotic procedures, enabling these surgeries to function autonomously based on specific scenarios. This paper presents a novel approach called IntelliSurg-Net, which incorporates deep learning algorithms and AI in order to enhance the decision-making process during robotic surgery procedures. Convolutional neural network (CNN) algorithms for vision processing, bi-directional LSTM networks for analyzing sequences of events, and reinforcement learning algorithms are implemented in the framework. This framework is evaluated using publicly available datasets such as Cholec80 and EndoVis.
Robotic Surgery, Artificial Intelligence, Deep Learning, Intraoperative Decision-Making, Reinforcement Learning.
A Case Study of Credit Card Data Breach at Target Corporation: Implications for the Future of Company Information System Security
Crisis management refers to a process through which organizations handle situations deemed to be sudden emergencies. With quick decision making inevitable, crisis management seeks to limit potential damages that could accrue from perceived adversities. In this paper, the aim is to focus on crisis management from a practical illustration to understand issues such as potential causes, trends, and possible interventions that could be adopted to control emergencies.
Crisis Management, Credit Card, Decision Making
Copyright (c) 2019 The SIJ Transactions on Computer Science Engineering & its Applications
A Case Study Examining Trends in Company Information Security Attacks and Their Implication for the Future of Computer Science
From the analysis in this study, internal attacks form the major source of threat in many companies. Specifically, the analysis establishes that rogue employees accessing the administrators’ accounts, sensitive data, or networks could cause real damage, rated at the medium risk rating and priority rating, with the impact and likelihood of occurrence standing at 3. Regarding the compromise of corporate services, it is established that the damage is likely to stretch beyond the loss of the ability to run daily functions within the stock inventory-systems and the online store. With the risk rating and priority rating suggesting that this destabilization of corporate services at VG Trading Company could yield medium-level damage, additional adversities. For example, a compromise to VG Trading Company’s corporate services is projected to yield substantial financial losses accruing due to the loss of corporate data and the loss of contract or business. Financial loss is also projected to face VG Trading Company due to disrupted trading (such as stalled progress in online transactions), loss of money, and a loss of financial data; such as payment card details and bank details. Indeed, the latter adversities are not only likely to arise due to compromised corporate services but also as a result of unauthorized access and even data misuse by authorized employees, especially those who access company sensitive information and exhibit malicious intentions.
Wireless Sensor Networks based Pipeline Vandalisation and Oil Spillage Monitoring and Detection: Main Benefits for Nigeria Oil and Gas Sectors
Wireless sensor network has been applied in many areas of human endeavour since its development, especially in environment monitoring. The technology can be used to monitor pressure and temperature of pipeline carrying petroleum product, detection of oil spillage and pipeline vandalisation. Wireless sensor network has also been developed and deployed by many developed countries for other numerous applications, but it is yet to be utilized in Nigeria despite the country facing daily pipeline vandalisation and oil spillage. To ensure that the country benefits from the advantages of using wireless sensor network, there is need to enlighten organisations in oil and gas sector about the technology. This research paper specifically explore the wireless sensor network technology, types, applications, challenges and the features that makes the technology, a viable alternative in pipeline vandalisation and detection of oil spillage in Nigeria.
Ad hoc Networks; Application of Wireless Sensor Networks; Pipeline Monitoring; Wireless Sensor Networks; Wireless Sensor Network Model and Oil Spillage.
Internet of Things: The Evolution of Somabar and the Creation a Network Design to Enhance Data Communication at the Apple South Asia Pte Limited
In the current world, one of the technology-based areas that have received attention concerns data communication. The latter refers to an electronic means through which encoded information is transferred between, from, or to computers (Yang and Tate, 2009). Apple South Asia Pte Limited operates as a subsidiary branch of Apple Inc. the firm began its operations in 1989, targeting the market in Singapore. Some of the products supplied include personal computers, iPads and mobile phones. Also, the firm is renowned for its software service provision and global networking solutions that complement audio and visual products, as well as digital music players (Kaplan & Norton, 2011). By designing a network process that enhances data communication, it is projected that the outcome will enable senior leaders at the firm to foster linkages among the employee groups, as well as potential customers. The following section provides a description of the components that will make up the designed internet of things.
A Comparative Overview of Fuzzy Particle Swarm Optimization based Key Management Technique in Mobile Adhoc Network Environment
A mobile ad hoc network (MANET) is a self-organized wireless network where mobile nodes can communicate with each other without the use of any existing network infrastructure or centralized administration. The mobile ad-hoc network is an infrastructure-free and dynamic kind of network. However, security in wireless ad hoc networks is hard to achieve due to the vulnerability of the links, limited physical protection of the nodes, and the absence of a certification authority or centralized management point. Consequently, novel approaches are necessary to address the security problem and to cooperate with the properties of wireless ad hoc network. Similar to other distributed systems, security in wireless ad hoc networks usually relies on the use of different key management mechanisms. The compromise of the node breaks down the whole security system. For its mobility and self-organized features, it is a great challenge to ensure the security of the network. And the basic aspect of providing the security is managing the encrypting keys. The current key management schemes mainly depend on certificates and identity-based key encryption. Schemes based on certificates suffer from huge computational costs of certificates verification while the identity-based schemes lead to key escrow problem. Trust establishment and management are essential for any security framework of MANETs. In this review work, have studied a self-organized key management technique coupled with trusted certificate exchange for mobile ad hoc networks. The studied architecture consists of one coordinator node, servers and normal mobile nodes. The coordinator acts as a mediator for transmitting the message among the servers and mobile nodes. Each node generates its own public/private key pairs using server-signed public keying technique. As a result of multiple independent certifications, the confidence assigned to the certificates is higher.
Certificate Exchange; Mobile Ad-hoc NETworks (MANET); Security; Self-Organized Key Management; Trust.
Hybrid Linear Discriminant Analysis (HLDA) based Dimensionality Reduction and Unary Potential based on Fuzzy Possibilistic C-Means (FPCM) Clustering for Remote Sensing Images
High spatial resolution (HSR) remote sensing images provide detailed geometric information about land cover. As a result, it is possible to detect more subtle changes with the help of HSR images. However, due to the increased spatial resolution and the limited spectral information, it is difficult to identify the real changes only through the spectral feature of the image. Recent work Hybrid Conditional Random Field (HCRF) model is proposed which fully explore the spectral–spatial information and improve the change detection performance for HSR images. But here the effect of the unary potential, which plays an important role in the HCRF model, will be further investigated in this work. In this paper, Unary Potential is performed based on Fuzzy Possibilistic C-Means (FPCM) clustering algorithm and it is used for an object-based technique. In order to reduce the dimension of the spectral–spatial information, Hybrid Linear Discriminant Analysis (HLDA) is proposed in this work. In the proposed work introduce a new LDA matrix computation that incorporates confusability information between classes into the transform. It introduces a weighting factor for each pairwise scatter that enables to integrate the confusability information into the between class covariance matrix. In the proposed method, the spectral discriminative information of a single pixel is extracted by the unary potential, which is modeled using a FPCM clustering method to make an initial separation of changed and unchanged pixels. The pairwise potential then considers the contextual information of adjacent pixels to favor spatial smoothing. Using the accurate unary potential and reasonable pairwise potential for HSR images, the CD result can be greatly improved to avoid the over smoothing problem.
Remote sensing images, High Spatial Resolution (HSR), Hybrid Conditional Random Field (HCRF), clustering, Unary Potential, Fuzzy Possibilistic C-Means (FPCM), Dimensionality Reduction, Hybrid Linear Discriminant Analysis (HLDA).
Image Watermarking with DCT-Optimization based Psychovisual Threshold and Fuzzy Adaptive Median Filtering (FAMF) for Noise Distortion
The high speed development of internet technology, illegal copy, transmission and distribution of digital multimedia has emerged to be a significant security challenge. This challenge inspires the design of a solution for image authentication and copyright protection. Recently, an optimal Discrete Cosine Transform (DCT) psychovisual threshold is proposed for digital watermarking approach. The newly introduced approach also yields a distorted watermark extraction with image-rotation attack. This problem will be focused in this research work. In order to resolve this issue, Fuzzy Adaptive Median Filtering (FAMF) is introduced for noise elimination in the distorted watermark extraction suffering from image-rotation attack. In the newly introduced work, embedding regions are decided on the basis of the lowest modified entropy value of the image blocks. In this, the optimization of the lowest modified entropy value is done by using Bat Algorithm (BA). Therefore, the optimal psychovisual threshold is decided for embedding the watermark in the host image for getting a better image quality. The lowest modified entropy value specified the greatest redundant image information. The scrambling of the watermark bits are done prior to them being embedded into the chosen coefficients. The newly introduced approach has been tested under various kinds of attacks. The newly introduced approach also exhibits the superior performance in terms of reliability under several numbers of combined attacks.
Watermarking, Fuzzy Adaptive Median Filtering (FAMF), Filtering, psychovisual threshold, Bat Algorithm (BA), Discrete Cosine Transform (DCT), noise elimination
Copyright (c) 2018 The SIJ Transactions on Computer Science Engineering & its Applications
Image Denoising with Nonlinear Hybrid Diffusion-Modified Perona-Malik Model (NHD-MPM) and Parameter Optimization by Artificial Bee Colony (ABC) Algorithm
Image denoising aims to faithfully reconstruct an image from its noise corrupted observation. It tends to improve the degraded image quality for better interpretation and data extraction. Anisotropic diffusion models have reached a good balance between noise removal and edge preserving. But the optimization of the parameters for these models becomes difficult because of the different images and their quality. In this paper, a proposed a hybrid image denoising algorithm based on Nonlinear Hybrid Diffusion -Modified Perona-Malik (NHD-MPM) model. It is performed based on the mean curvature smoothing and Gaussian heat diffusion. The results of this denoising algorithm are combined to diffusion model and the Modified Perona- Malik (MPM) model. In addition the proposed work parameters of the Nonlinear Hybrid Diffusion are optimized via the use of the Artificial Bee Colony (ABC) optimization. In addition, the patch similarity modulus is used as the new structure indicator for the MPM model. The proposed method is thus able to efficiently preserve the edges, textures, thin lines, weak edges, and fine details, meanwhile preventing the staircase effects. Also compared NHD-MPM model with some recently advanced models, the experimental results demonstrated NHD-MPM model has a better detail and texture preservation capability.
Image denoising, Nonlinear Hybrid Diffusion -Modified Perona-Malik Model (NHD-MPM), Anisotropic diffusion, Artificial Bee Colony (ABC), and parameter optimization.
Mincut Algorithm and Adaptive Histogram Equalization (AHE) for Segmentation of Retinal Layer in OCT Images with Choroidal Neovascularization
Age-associated macular degeneration is one among the important reasons behind blindness. But, the intrinsic structures of retinas are complicated and hard to be identified owing to the neovascularization occurrence. Conventional surface detection techniques may face failure in the layer segmentation. In order to resolve this issue, the recent research presents supervised technique, which is suggested for simultaneous segmentation of the layers and neovascularization. However, the surfaces detection accuracy is restricted in layers where the contrast observed between layers is less and their boundaries are not notice able owing to the neovascularization occurrence. One more drawback of this research work is its need for high computing time. With the aim of solving these issues, contrast enhancement is carried out employing the Adaptive Histogram Equalization (AHE) and Min cut algorithm was also realized for the process. The longer processing time may be decreased through the parallelization of the newly introduced technique on segmentation of retina layers. The extraction of three spatial features, seven gray level based features and fourteen layer-like features are done for the Dense NET classifier. Thus the coarse surfaces of various OCT images can be found. For the description and enhancement of the retinal layers with diverse thickness and anomalies, multi-scale bright and dark layer detection filters are presented. A limited graph search algorithm is also introduced for the accurate detection of the retinal surfaces. It will be employed in the local region and it is more quick and provided results with more accuracy. The experimental outcomes indicate the newly introduced technique performs better than the benchmarked techniques.
Adaptive Histogram Equalization (AHE); Choroidal Neovascularization; Dense NET; Mincut Algorithm; OCT Images; Retinal.
Analysis and Mathematical Modelling of HIV/AIDS and Transmission Dynamics subject to the Influence of Public Health Education Campaign
In this study, we have analyzed a nonlinear mathematical model for the spread of HIV/AIDS in a population with variable size structure. A threshold parameter, basic reproduction number, , was found that completely determined the stability dynamics and outcome of the disease. It was found that when, , the disease free equilibrium is stable and the disease dies out. However, when, , there exists a unique endemic equilibrium that is locally asymptotically stable. It was concluded from our analysis that endemic level of infective population increases with the increase in rate of transmission of infection due to infective among susceptible class. This study presented a nonlinear extended deterministic Susceptible Infected model for assessing the impact of public health education campaign on curtailing the spread of the HIV pandemic in the Kenyan population. Rigorous qualitative analysis of the model were used to compare between the model without education and that with education in order to determine the stability of disease-free equilibrium with a stable endemic equilibrium when the basic reproduction number is less than unity. The model was used to assess the potential impact of some targeted public health education campaigns using data from the 47 Kenyan counties. Numerical simulation of the model is also performed by using fourth order Runge - Kutta method. The results from the model suggested that the most effective prevention measure that would be applied is by public health education campaign in order to thwart the epidemic. As expected, the control strategy would be more effective if all interventions were implemented together.
Basic Reproduction Ratio; Disease-Free Equilibrium; Education; HIV/AIDS Dynamics; Steady State Equilibrium.
Energy Crisis Way-Forward:Diesel Generator-Micro Hydro-Solar Hybrid Power System of Off-Grid Power Station for Rural Development
There are several benefits for using renewable energy sources such as emission reduction, lowering of cost and technological gain. In many remote communities in the world, standalone power systems utilizing renewable sources of energy are widely deployed to counter the widespread of energy shortage conditions. The present days‟ quest for emission reduction from energy systems couple with the intention for rural electrification has fostered aggressive research directives for renewable energy for sustainable development. The major contributions to the existing body of knowledge in this study are to establish the large renewable energy sources usage and the optimal cost allocation of hybrid system components. For the off-grid electrification of the village under study, various combinations have been obtained for the hybrid systems with PV, micro-hydro turbines, batteries, convertors and generators from the HOMER optimization simulation. The optimal simulation result table shows that the most cost effective system configuration, i.e. the system with the least net present cost, is the PV micro hydro battery converter system configuration and the cost of energy (COE) at that scenario observed to be 0.044$/kWh, and renewable resources segment is around 99%. This shows that practically the total ratio of energy production is from renewable energy sources. Hence it has become a way forward for solving rural and off-grid communities‟ energy needs.
Energy Crisis; Hybrid Power System; Osochokodo Community; Sustainable Development.
Copyright (c) 2016 The SIJ Transactions on Computer Science Engineering & its Applications
Parallel Heat and Mass Transfer in Variable Viscosity Hydromagnetic Steady Flow between Two Horizontal Plates with Energy Dissipation
Heat and mass transfer with variable viscosity hydro magnetic flow between two horizontal parallel plates was investigated. In this study we theoretically investigated the effects of viscosity variation parameter, magnetic field parameter and energy dissipation parameter. We considered steady incompressible fluid flow between two horizontal parallel plates which allow energy dissipation. The equation(s) governing the flow were derived and solved. The solution was extended in order to examine the flow structure as λ increases, the solutions were computed for wall skin friction, material and heat flux in powers of λ for (λ≥0,n1≥0,n2≥0 ). The results obtained were presented using tables and graphs. It was noted that the usual parabolic Poiseuille was observed with minimum values at the wall and maximum values at the plates’ centerline. At low magnetic field intensity, the magnitude of skin friction, wall heat and material flux increases with increase in fluid viscosity. The rate of material transfer to the wall decreases with an increase in fluid viscosity and decrease in magnetic field intensity.
Energy Dissipation; Heat and Mass Transfer; MHD; Steady Flow; Variable Viscosity.
Copyright (c) 2015 The SIJ Transactions on Computer Science Engineering & its Applications
An Improved Distributed Fuzzy Associative Classifier for Big Data Using Butterfly Optimization Algorithm based Artificial Bee Colony Algorithm
Association rule mining is one of the primary tasks in data mining, which is helpful in knowing the intriguing associations among the elements in the item sets of a massive database. Association Rule Mining is a prominent data mining approach. Researchers have evaluated several factors of the approach; however, very less focus is paid to tackle with the reliability of the rules external to the dataset using which the generation of these rules is done. Aproiri constitutes the common algorithm of association rule mining that helps frequent item sets’ generation. Apriori utilizes minimal support threshold to get frequent items. In this work, an algorithm, formed by Butterfly Optimization Algorithm based Artificial Bee Colony Algorithm is presented which helps in choosing the association rules with Associative Classifiers (ACs). Rather than the ABC’s onlooker bee stage, the random walk process of BOA is utilized to improve the exploration. Butterfly Optimization Algorithm Based Artificial Bee Colony Algorithm (BOAABC) is used on the rules that the apriori algorithm generates, to choose the association rules. The validation process is carried out on datasets obtained from UCI database which reveal the performance of the proposed research work and the proposed technique is efficient in the choice of association rules rather than the available algorithms. In this work, it is confirmed that the rules created in the proposed research work are easy and understandable.
Associative Classifiers (ACs); Artificial Bee Colony (ABC); Butterfly Optimization Algorithm (BOA); Frequent Item Sets.
Copyright (c) 2021 The SIJ Transactions on Computer Science Engineering & its Applications
Zero-Knowledge Proof Protocols for Confidential Vendor Verification in Financial ERP Systems
Vendor verification is a critical component of financial Enterprise Resource Planning (ERP) systems, particularly in scenarios involving supplier onboarding, invoice processing, and account validation. Traditional verification processes require vendors to disclose sensitive data such as banking details, certificates, and identity proofs, thereby increasing risks associated with data leakage, internal misuse, and compliance violations. This paper proposes a privacy-preserving vendor verification framework based on Zero-Knowledge Proofs (ZKP), enabling vendors to cryptographically prove ownership of account credentials and documentation without revealing the underlying information. The presented protocol integrates decentralized identity primitives, non-interactive ZK proofs, and ERP data-validation logic to establish secure, tamper-resistant verification workflows. A modular integration strategy for SAP Financial Accounting (FI) is described, demonstrating seamless compatibility with vendor master creation, change management (XK01/XK02), and banking verification tasks. The framework ensures authenticity, integrity, and GDPR-aligned confidentiality while reducing operational overhead associated with manual document checks. Experimental results show that the ZKP-enhanced workflow improves validation accuracy, reduces disclosure risk, and strengthens trust among multi-tier suppliers. This approach provides a scalable, compliant, and cryptographically verifiable method for secure vendor authentication within financial ERP environments.
Zero-knowledge proof; Vendor verification; SAP FI integration; Privacy-preserving protocols; ERP data validation; Cryptographic identity; Confidential authentication; Decentralized trust.
Lean Architecture for Agile Software Development Business Processing Cycle – “Chunks Processes”
Scrum is one of the most widely adopted frameworks for agile team collaborative implementation ranging from software development to other fields in between. To provides a way of managing work as teams break it down into development tasks that have to be completed within time-boxed intervals known as sprints. Most of the time, the intervals-Carthasigo would-be two weeks and the maximum would be one month. Every day, the actual scrum holds a 15-minute time-boxed stand-up meeting. The scrum team measures progress toward its sprint goal. At the end of the sprint, the team holds two additional meetings: an internal sprint retrospective and a sprint review, where the work done will be presented to stakeholders to obtain their feedback. Typically, a scrum master is the person in charge of a scrum team. Scrum encourages teams to self-organize by promoting tight online cooperation or physical co-location and requiring regular communication among all team members. The continual feedback and adaptability and loose structure rest quite a bit on a notion of requirements instability-meaning that the stakeholders are going to change their minds as the project develops. One of the effects of what is called the design process is architecture, which brings about a challenge in design. Essentially, the proposal of agile patterns of problem-solving consists of parsing the business processing cycle so effectively into "chunks" that can be processed concurrently. A lean approach to get the perfect value to the consumer through a perfect value-creating process that has zero waste.
Agile Methodology, Scrum, Lateral Linking Problem, SDLC, Chunks Processes.
Automated Reconciliation Engines for Multi-Entity Financial Workflows Using Smart Contract Logic
Reconciliation across multi-entity corporate financial ecosystems remains a persistent operational bottleneck due to fragmented ERP deployments, inconsistent document formats, and the high manual workload required to validate payment-reference details. Traditional reconciliation approaches depend heavily on linear rule engines embedded within disparate SAP finance modules, resulting in latency, error propagation, and audit inconsistencies. To address these limitations, this paper proposes an automated reconciliation engine driven by smart contract logic that enforces deterministic and entity-specific validation rules. The system leverages an on-chain verification registry to ensure tamper-proof compliance records, while off-chain adapters orchestrate data exchange between SAP modules, treasury systems, and corporate bank feeds. A unified payment-reference graph model is introduced to support atomic matching, detect anomalies, and ensure end-to-end traceability. Experimental deployment across a simulated multi-entity environment demonstrates significant reductions in processing time, manual interventions, and reconciliation failures. The proposed architecture enhances auditability, improves operational transparency, and provides a scalable framework for enterprise-grade financial automation. This work contributes an interoperable and trust-anchored solution suitable for high-volume transactional environments involving cross-entity settlements, intercompany transfers, and regulatory reporting workflows.
Automated reconciliation; Smart contracts; Multi-entity ERP systems; SAP finance; Enterprise payment automation; Audit trail generation; Transaction verification; On-chain compliance.
Traditional methods of attendance verification in educational institutions, workplaces, and events often suffer from inefficiencies and inaccuracies. To address these challenges, a QR-based attendance verification system emerges as a modern and efficient solution. This abstract explores the design, implementation, and benefits of such a system. The proposed system involves generating unique QR codes for each participant or attendee, which encapsulate their identification information. Attendees present their QR codes at designated checkpoints, where QR code scanners authenticate and record their presence. The system verifies the QR codes against a central database, ensuring accuracy and reliability. Key features include seamless integration with registration systems, customizable reporting capabilities, and robust security measures to safeguard attendee data. Additionally, companion mobile apps enhance accessibility and user experience, allowing attendees to access their QR codes conveniently. Benefits of the QR-based system encompass improved efficiency, accuracy, and accessibility. By automating the attendance verification process, organizers can streamline operations, reduce queues, and gain valuable insights into attendance patterns. Ultimately, this innovative approach revolutionizes attendance tracking across various domains, paving the way for enhanced productivity and engagement.
QR-based System, Quick Response (QR) Codes, Pre-existing Systems.
It previews the whole system design while in the free demo session and clear your queries by experiencing Deluge Software digital dashboard. In the navigation system, we optimize the UI to be straightforward and comprehend for all sets of people so the employees grasp this entire operation. The Deluge Software has been developed with Semantic UI and Next JS Server. The system is efficient in generating reports, which will help in the maintenance of the hypermarket easily. This Deluge Software can store a lot of records in the database. It’s a user-friendly software, which can be used for service, sales and report. It has an unlimited storage and application can be used in both online and offline. Reports for billing can be generated by data wise or pricewise. The software supports advanced features like encryption for secure communication and protection of user privacy. It offers cross-platform compatibility, allowing users to access and manage their torrents on different operating systems. Deluge also provides flexibility through its plugin system, enabling users to extend its functionality and integrate additional features. Various plugins are available, allowing users to add functionalities like RSS feed integration, remote access, automatic libelling, and more.
Invoice, Deluge Software, Encryption, Security.
This report focuses on the utilization of inventory software for managing and billing in a forgings company. The objective is to explore the advantages and functionalities of implementing such software, which can significantly enhance operational efficiency and streamline inventory control and billing processes. It emphasizes the challenges faced by these companies, such as complex supply chains and diverse inventory items, and explains how inventory software can address these challenges. The key modules of the inventory software system are discussed in detail. Modules include inventory management, purchase order management, production planning, sales order processing, billing and invoicing, reporting and analytics, integration with accounting systems, compliance and traceability, and user management and security. Benefits of using inventory software in a forgings company are then examined. Emphasizes how inventory software enables real-time visibility into inventory levels, reduces stockouts and overstocking, streamlines procurement processes, optimizes production schedules, enhances order accuracy and fulfillment, automates billing and invoicing, provides valuable insights through reporting and analytics, ensures seamless integration with accounting systems, facilitates compliance with regulations, and enhances data security. In conclusion, highlights how implementing inventory software in a forgings company can lead to improved operational efficiency, optimized inventory control, streamlined production planning, enhanced customer satisfaction, and accurate billing processes. By leveraging these advantages, forgings companies can stay competitive, minimize costs, and maximize profitability in today's dynamic business environment. Discusses any training sessions, workshops, or professional development opportunities provided during the internship. It describes how these activities enhanced skills, knowledge, and competencies relevant to the field of study or career aspirations. It assesses the supportiveness of the work environment, the level of collaboration among team members, and the overall corporate values and ethics observed. Provides recommendations for the organization based on the experience and suggests areas for future internships or improvement.
Inventory, Forgings, Billing, Overstocking.
Tutor Connect A Real Time Web Tutoring App
The main objective of this internship was website design and development. Several programming languages that are in use to develop a web-based application or software. Some of them are only used for the UI and the frontend of the application, some are used for the backend design of the software. For Example- Django, Html etc. are some programming language to develop frontend of an application. java etc. are used at the backend. Nowadays there are also some framework' s that use vastly. Frameworks are basically structured programming by using Model, View, and Controller. If we develop web-based application that is very useful for us because we can access it from anywhere of the world. It is very helpful for our daily life. That is why I choose subject of my report is "Website design and development". Working in EzerBase added huge experiences in my upcoming career. This report takes us through all the details of Website design and development knowledge and experience gathered during this internship period. Beyond technical skills, interns will engage in user research and usability testing, working closely with the design team to refine user interfaces and enhance the overall user experience. They will learn to balance functionality, performance, and aesthetics to develop applications that align with modern design trends and best practices. In conclusion, this web application development internship offers a unique opportunity for individuals passionate about creating seamless user experiences through innovative web applications. By combining technical expertise with a user-centric approach, interns will gain practical skills, professional growth, and a chance to contribute to cutting edge projects in the realm of web development.
Copyright (c) 2023 The SIJ Transactions on Computer Science Engineering & its Applications
Paperless QR Code Based Picking of Critical Part with Automated Exception Workflow and Traceability
A mobile application, commonly referred to as an app, is a type of application software designed to run on a mobile device. It allows users to handle information instantly and in real-time, serving as an excellent communication channel between organizations and their users or clients. The app is a tool that enables businesses to manage their maintenance activities by analyzing and monitoring all tasks updated on a daily, weekly, and monthly basis. Maintenance data and a list view of the required CSV file are present, and the necessary check boxes are checked, displaying the filtered items as a list view. This list can be loaded and sent to the next screen using the "Load list" button. Stakeholders are individuals who either care about or have a vested interest in your project. They are the people actively involved in the project's work. The list of screens is displayed, and an item from the list must be selected to proceed. By using the "PICK" button, the barcode scanner is opened to select the item to pick. If the item is matched, then clicking the "Confirm Picking" button completes the process or “Wrong part picked” will appear. If the item is not available, a message will appear stating "Out of stock."
Mobile Application, QR Code, Stack Holder, Business.
Optimizing Extraction Transformation and Loading Pipelines for Near Real Time Analytical Processing
This article examines architectural and algorithmic enhancements that enable ETL pipelines to operate in near real-time analytical environments, emphasizing the shift from traditional batch-centric models to event-driven, distributed, and micro-batched designs. By integrating pipeline parallelism, incremental computation, in-memory processing, adaptive scaling, and multi-path routing, modern ETL frameworks significantly reduce end-to-end latency while maintaining high throughput, consistency, and data freshness across fluctuating workloads. Experimental evaluations demonstrate that optimized ETL pipelines can sustain continuous ingestion, rapid transformation, and low-lag delivery even under high-velocity transactional conditions, positioning them as essential infrastructure for always-on dashboards, operational analytics, and time-critical decision systems.
real-time ETL, data latency optimization, near real-time analytics
Fraud Detection Using Artificial Intelligence
With the rising prevalence of digital transactions and online services, the risk of fraudulent activities has grown exponentially. Traditional rule-based systems for fraud detection often struggle to keep up with evolving tactics used by malicious actors. This research delves into the application of machine learning modules in detecting fraud across various domains, such as financial transactions, insurance claims, and online platforms. Moreover, the study investigates the combination of these methods in ensemble approaches to achieve higher accuracy and robustness. Additionally, feature engineering and data pre-processing techniques play a crucial role in enhancing model performance. We delve into the identification and selection of relevant features and the impact of different data transformations on the detection process. Furthermore, addressing the challenge of imbalanced datasets, we examine techniques like oversampling, under sampling, and synthetic data generation to mitigate class imbalance and enhance fraud detection efficiency. To evaluate the models, we utilize publicly available benchmark datasets and conduct comparative analyses to highlight their strengths and weaknesses. The results showcase the superiority of certain models for specific fraud scenarios and underscore the importance of a tailored approach for different domains.
Fraud Detection, Artificial Intelligence, Financial Transactions.