Journal of Advances in Information Technology
Journal of Advances in Information Technology is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology. Our aim is to publish experimental and theoretical results in strategy, infrastructure, human resources, system development and implementation, IT risk, IT management, data science, communications, technology development and futures, national policies and standards, software engineering, etc. In particular, we encourage a multidisciplinary/convergent approach based on the following broadly based branches of computer science for the application areas highlighted below: · Artificial Intelligence · Machine Learning · Data Science · Natural Language Processing · Cloud Computing · Internet of Things · Image Processing · Information Security · Networked Control Systems · Communication Systems Technologies · Digital Signal Processing in Communications · Neural Networks, Fuzzy Logic, Reasoning · Human-Computer Interaction (HCI) · Software Engineering · Non-technical but relevant topics such as Information Policy, Ethics and Legal issues as appropriate.
For articles published in JAIT, the copyright will be retained by the authors. Articles are licensed under the Creative Commons Attribution License (CC BY 4.0), which means all papers can be downloaded, shared, and reused without restriction, as long as the original authors are properly cited. These conditions allow for maximum use and exposure of the work, while ensuring that the authors receive proper credit. In exceptional circumstances, articles may be licensed differently. If you have a specific condition (such as one linked to funding) that does not allow this license, please mention this to the editorial office of the journal at submission.
Explainable Machine Learning for Household Electricity Demand Forecasting Using Hybrid Residual Modeling
This study proposes an interpretable hybrid residual learning framework for household electricity demand prediction under limited data conditions. The framework integrates statistical forecasting models with machine learning residual learners and SHapley Additive exPlanations (SHAP)-based explainability to improve predictive accuracy while preserving transparency. Using 1,000 hourly household electricity consumption observations collected over approximately 41.7 consecutive days, the proposed model was compared with Linear Regression, Autoregressive Integrated Moving Average (ARIMA), Random Forest, Support Vector Machine, and Deep Neural Network baselines under identical chronological train-validation-test conditions. Results show that the hybrid framework achieved the best predictive performance, with Mean Absolute Error (MAE) = 0.141, Root Mean Squared Error (RMSE) = 0.198, Mean Absolute Percentage Error (MAPE) = 7.5%, and Coefficient of Determination (R²) = 0.90, outperforming both statistical and standalone machine learning models. The results are interpreted within a short-term limited-data forecasting scenario. Although the proposed framework showed the best performance on the independent chronological test set, robustness under progressive training-data reduction is considered a direction for future validation because the available dataset contains only 1,000 hourly observations, while statistical comparison was performed using the Diebold-Mariano test. SHAP explanations identified temperature, hour-of-day patterns, and household occupancy as the most influential demand drivers, providing physically meaningful insights for residential energy planning. The findings suggest that interpretable hybrid residual models represent a reliable alternative for short-term household electricity forecasting in data-scarce urban environments.
Household electricity demand forecasting; hybrid residual learning; explainable artificial intelligence; SHAP; limited data; residential energy consumption; power system planning.
Copyright (c) 2026 Renzo Diony Ramirez-Huaracha, Melany Meylin Quispe-Gutierrez, Benjamin Maraza-Quispe https://creativecommons.org/licenses/by/4.0
A Low-cost Ergonomic Device for Banknote Recognition Using Computer Vision and IoT
This study presents the design, implementation, and validation of a low-cost ergonomic assistive device for autonomous recognition of Peruvian banknotes using computer vision and Internet of Things (IoT) technologies. A dataset of 9054 images distributed across 16 classes was constructed to train and evaluate five pretrained convolutional Neural Network (CNN) architectures: Visual Geometry Group 16-layer network (VGG16), Visual Geometry Group 19-layer network (VGG19), 50-layer Residual Network (ResNet50), MobileNet version 2 (MobileNetV2), and Extreme Inception (Xception). Among these models, Xception achieved the best performance, with 99.8% test accuracy, macro-averaged precision of 0.997, macro-averaged recall of 0.997, and macro-averaged F1-Score of 0.997. Five-fold cross-validation confirmed the model’s stability, yielding a mean accuracy of 99.68% with a standard deviation of 0.12 percentage points. Robustness tests under low-light conditions, partial occlusion, and folded banknote scenarios showed recognition performance above 96%. The selected model was deployed on a Raspberry Pi 4B+ IoT platform with audio feedback. Hardware benchmarking reported an average inference time of 3.82 ± 0.27 s and 3.2 h of battery autonomy. Usability evaluation with 44 visually impaired participants produced a System Usability Scale (SUS) score above 80 and high User Experience Questionnaire (UEQ) ratings. The economic feasibility analysis, including sensitivity scenarios, confirmed financial sustainability, with an Internal Rate of Return (IRR) of 11.85% and a Net Present Value (NPV) of 13,013.02 Peruvian soles (S/). Ethical approval was obtained, and informed consent procedures were completed. These findings demonstrate that the proposed assistive device is technically robust, socially relevant, and economically viable.
Topic: Internet of Things (IoT) in Smart Systems and Applications
Visual impairment; Computer vision; IoT; Assistive technologies; Banknote recognition; Digital inclusion.
Copyright (c) 2026 Nicolás Esleyder Caytuiro-Silva, Benjamin Maraza-Quispe, Evelyn Gloria Castro-Gutierrez, Jackeline Melady Peña-Alejandro, Edwin Reyes-Villalba, Victor Hugo Rosas-Iman, Giuliana Feliciano-Yucra, Atilio Cesar Martinez-Lopez, Roberto Carlos Pari-Viza https://creativecommons.org/licenses/by/4.0
A Practical Protocol and Governance Framework for Immersive In-vehicle HCI Usability Evaluation Using Commercial XR Analytics
Virtual reality combined with embedded eye tracking offers a promising alternative to traditional in-vehicle usability assessment; however, integrating commercial extended reality analytics platforms into immersive workflows raises methodological challenges related to standardization, reproducibility, metric transparency, and version control. This study proposes a repeatable evaluation protocol and governance framework for the use of commercial extended reality analytics in immersive in-vehicle usability assessment. The framework was operationalized in an exploratory proof-of-concept study involving an immersive cockpit simulation, embedded eye tracking, a proprietary spatial analytics platform, and a physical vehicle mockup aligned with the virtual scene. Ten participants performed structured driving-related tasks, and gaze data were converted into spatial visualizations, behavioral metrics, and platform-derived presence indicators, which were descriptively compared with subjective questionnaire responses to support multimodal interpretation. The findings suggest that commercial analytics platforms can support the early identification of ergonomic inconsistencies and attention-distribution patterns in immersive cockpit evaluation. At the same time, the study highlights structural dependencies introduced by proprietary infrastructures, including metric opacity, software version instability, and limited auditability, which may affect longitudinal comparability and interpretive reliability. Practically, the study provides a reusable workflow for collision and area-of-interest validation, version documentation, multimodal benchmarking, and independent data archival to support more transparent, auditable, and repeatable immersive usability evaluation.
Topic: Application of Virtual Reality and Augmented Reality Technologies in Inte
Copyright (c) 2026 Felipe Leão Dias, Rafael Miguez, Catharina Ramos, Daniela Rodrigues, João Teixeira, Sanval Santos, Yasmim Thasla, Ingrid Winkler https://creativecommons.org/licenses/by/4.0
Explaining to Protect: XAI-based Enhancement of CNN Robustness in Biometric Face Recognition
This study investigates the role of explainability in improving adversarial robustness of Convolutional Neural Network (CNN)-based face recognition systems. While prior works treat robustness and interpretability as separate objectives, we conduct a systematic analysis of how explanation-driven preprocessing can influence adversarial resilience. Specifically, we combine adversarial training with Local Interpretable Model Agnostic Explanation (LIME)-guided feature perturbation to identify and suppress vulnerable regions. Unlike prior approaches that primarily use Explainable Artificial Intelligence (XAI) for post-hoc interpretation, this work quantitatively evaluates the relationship between explanation consistency and robustness under adversarial perturbations. Experimental results on CASIA-WebFace and Labeled Faces in the Wild (LFW) show that the proposed framework improves robustness under low-strength attacks (ε = 0.02), increasing Area Under Curve (AUC) from 62.21% to 75.76%. However, performance degrades under stronger perturbations, indicating limitations of explanation-guided preprocessing. These findings provide empirical evidence that explainability can contribute to robustness, but its effectiveness is highly dependent on attack strength and preprocessing design. This work highlights both the potential and limitations of integrating XAI into adversarial defence.
Copyright (c) 2026 Irfan Darmawan, Alam Rahmatulloh, Rohmat Gunawan, Erna Haerani, R. Wahjoe Witjaksono https://creativecommons.org/licenses/by/4.0
Multifactorial Model of the Confrontation of Financial Resources of the Parties in Targeted Attacks
A multifactorial model of countering targeted attacks (Advanced Persistent Threats (APT) attacks) is considered in the context of available financial resources within the framework of a bilinear multistep quality game with several terminal surfaces. The distinguishing feature of the proposed model is its incorporation of multivariance in financial strategies for safeguarding against APT attacks and counterattacks, which is manifested in the dynamics of interaction between opposing sides. This facilitates the consideration of the overarching problem of cyber defense within the framework of the game scheme, encompassing the tasks of countering APT attacks with the financial resources available to the defense side. The proposed solution to the problem entailed the implementation of a bilinear multistep game of the quality of multidimensional objects. The resolution of quality issues, considering their multidimensionality and the bilinearity of the dynamics of player interaction, is a rather challenging problem. However, the formulated problem was solved in the paper by employing the optimality principle of R. Bellman. The solution was identified within the framework of a positional multistep game scheme with complete information. A comprehensive investigation was conducted to ascertain the optimal strategies for allocating the financial resources of the first player to build a system of protection against targeted attacks. The model delineated in the paper was implemented in the form of a software module of an intelligent information system. The results of a computational experiment that was conducted to test the model’s operability are presented herein.
Copyright (c) 2026 Zhuldyz Alimseitova, Valery Lakhno, Volodimir Malyukov, Inna Malyukova, Bakhytzhan Akhmetov, Moldіr Ydyryshbayeva https://creativecommons.org/licenses/by/4.0
Optimizing Next-generation Cloud and Data Center Networks: A Review of Routing, Resource Management, and Emerging Technologies
As cloud computing and data centers become integral to global Information Technology (IT) infrastructure, optimizing routing and resource management in these networks is critical for maintaining performance, scalability, and energy efficiency. This paper presents a structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025. Key advancements include adopting Software-Defined Networking (SDN), machine learning-based routing algorithms, and energy-efficient resource allocation strategies. This paper critically analyzes the challenges posed by scalability, latency, energy consumption, security, and interoperability, alongside the opportunities presented by Artificial Intelligence (AI)-driven autonomous networks, edge computing, and the integration of 5G technologies. Comparative evaluation highlights key trade-offs between performance gains and real-world feasibility, particularly for machine learning and deep reinforcement learning approaches. Furthermore, the study examines emerging trends, including cloud-edge collaboration and multi-objective optimization frameworks. The findings reveal that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations. Overall, this review provides a consolidated perspective on current approaches, open challenges, and promising research directions for next-generation cloud and data center network optimization.
Copyright (c) 2026 Sultan Alanazi https://creativecommons.org/licenses/by/4.0
Advancing Machine-generated Text Detection: A Comprehensive Evaluation of Transformer-based Models
Improved fluency in large language models has intensified the need for accurate detection of machine-generated text. This study evaluates transformer-based models using an improved version of the Conference on Computational Linguistics 2025 (COLING 2025), Generative Artificial Intelligence (GenAI) Content Detection Task 1 dataset, which was carefully preprocessed to enhance label quality and balance. All models were trained under a unified protocol to ensure fair comparison and robust evaluation. Test set results show that Decoding-Enhanced Bert with Disentangled Attention (DeBERTa) achieves the highest macro F1−Score of 85.48%, surpassing the previously top-ranked Multi-Task Learning (MTL) system, which attains a macro F1 of 83.07%. These results highlight the effectiveness of advanced transformer architectures for distinguishing human-written and machine-generated text. Despite these gains, performance degradation under domain shift and highly paraphrased inputs remains a challenge.
Topic: Machine Learning in Computer and Information Systems
Copyright (c) 2026 Batyr Sharimbayev, Dr. Shirali Kadyrov https://creativecommons.org/licenses/by/4.0
A Comparative Evaluation of Text Detection and Recognition Models for Cross-hospital Generalization of Thai Pharmaceutical Label OCR under Limited Data Conditions
Digitizing pharmaceutical labels using Optical Character Recognition (OCR) can improve medication safety and hospital workflow efficiency, but cross-hospital deployment remains challenging due to variations in label templates, printing formats, and limited labeled medical data. This study evaluates text detection and recognition models for Thai pharmaceutical label OCR under cross-hospital conditions using a three-tier evaluation protocol: source-hospital data, mixed-hospital data, and unseen hospitals. The results show clear architectural differences in cross-hospital robustness. The PaddleOCR recognition model achieves the best cross-hospital performance with 80.47% accuracy on unseen hospitals, while Transformer-based TrOCR variants degrade substantially under domain shift, achieving only 30–52% accuracy. Text detection shows relatively stable transfer across hospitals with a moderate drop of −10.68%, whereas recognition performance varies notably across architectures and training strategies. Conservative fine-tuning improves recognition accuracy by 14.85% over the base model while preserving cross-hospital generalization. These findings suggest that pre-training diversity and architectural inductive biases are more important than model scale for OCR deployment in limited-data medical settings.
Copyright (c) 2026 Wongpanya S. Nuankaew, Pathapol Jomsawan, Kuljira S. Nuankaew, Kaewpanya S. Nuankaew, Pratya Nuankaew https://creativecommons.org/licenses/by/4.0
High-Performance Divide-and-conquer Algorithms: A Comprehensive Framework with Recursive Parallel Decomposition and Hardware-aware Optimization
We present a framework for parallel divide-and-conquer algorithms that separates the performance contributions of algorithmic structure from those of the underlying runtime. The framework implements true recursive parallel decomposition via rayon::join() at every recursion level—explicitly contrasted with wrapper approaches that delegate to opaque library routines—and introduces hardware-aware threshold selection for Intel’s hybrid P-core/E-core architecture. We formalize algorithm behavior using the work-span model and derive closed-form expressions for the serial fraction that governs scalability. On the i7-13650HX (6 P-cores + 8 E-cores), parallel merge sort achieves 6.35× speedup at 1M elements, while Amdahl’s-law analysis attributes the 39% efficiency ceiling at 14 cores to a 13% inherently sequential merge fraction—a structural bottleneck distinct from runtime overhead. Thread affinity experiments show P-core-only placement outperforms all-core Operating Systems (OS) scheduling by 14% for quicksort. Honest benchmarking against Rust’s standard library, Rayon, ndarray, and Intel Math Kernel Library (MKL) confirms that production libraries achieve 3–30× superior throughput through combined pdqsort, Single Instruction Multiple Data (SIMD), and Basic Linear Algebra Subprograms (BLAS) optimizations, while our framework isolates individual optimization layers for research. The complete framework is released as open-source software under the Massachusetts Institute of Technology (MIT) license to support reproducible research.
Topic: Software Design and Program Development
divide-and-conquer algorithms, parallel computing, performance optimization, compiler optimization, algorithm scalability, statistical validation, high-performance computing
Copyright (c) 2026 Tetsurou Kizaki, Dijana Capeska Bogatinoska, Amita Nandal, Aleksandar Karadimce https://creativecommons.org/licenses/by/4.0
Threshold-Tuning Coordinate Attention for Binarized Neural Networks
Binary Neural Networks (BNNs) are highly attractive for mobile and embedded vision due to their extremely low memory footprint and efficient bit-level convolutions. However, binarization often causes severe information loss and weakens the effectiveness of conventional attention modules designed for full-precision networks. We propose a BNN-oriented attention mechanism, Threshold-Tuning Coordinate Attention (TT-CA), which applies attention by adjusting the binarization decision boundary rather than performing fine-grained multiplicative reweighting. Built upon Coordinate Attention (CA), TT-CA generates a spatially aware threshold (τ) from coordinate-wise pooled features and applies a subtractive threshold to induce controllable sign flips, thereby recovering discriminative capability. To balance modulation capacity and deployment efficiency, we quantize the HardSigmoid gating outputs into K-bit discrete levels, enabling lightweight and hardware-friendly gating while avoiding the overly coarse behavior of 1-bit gates. We further explore CA-derived design variants under low-bit settings, including simplified normalization and lightweight bottlenecks, and integrate TT-CA into a compact ResNet14-Wide backbone. Experiments on CIFAR-100 and Tiny ImageNet show consistent accuracy gains over binarized baselines and standard attention plug-ins with modest overhead, reducing Top-1 error by 2.45% on Tiny ImageNet and 0.94% on CIFAR-100. Ablation studies on the threshold strength (δ) validate the effectiveness and robustness of threshold tuning for efficient BNNs.
Copyright (c) 2026 SHAOQING WU, Dr. Hiroyuki Yamauchi https://creativecommons.org/licenses/by/4.0
Strategy for Countering Phishing Attacks within the Endless Antagonistic Game Framework
A mathematical model has been developed for the computational core of the decision support system taken in the course of ensuring information security of exchange sessions for the sale and purchase of digital cryptocurrencies. The model contributes to making rational decisions to ensure the information security of players who may face the threat of phishing attacks against them. The model was developed on the basis of an infinite antagonistic game and is designed to find the optimal strategies for players. The solution of such a problem contributes to the analytical search not only for the value of the game, but also makes it possible to find the characteristics of the risk degree of achieving the goal by the players when they use optimal mixed strategies. The described solution contributes to the analysis of the situation with counteraction to the party attacking with the help of phishing the participant of the trading session for buying and selling digital cryptocurrencies. Computational experiments were carried out using the Anaconda distribution kit. The results obtained will be useful for analysts dealing with information security issues of trading platforms, specializing in the purchase and sale of digital cryptocurrencies, and counteracting the threats phishing attacks. During research, three specific zones in the resource space have been identified: “Safe Zone”, “Strategic Parity Zone”, and “Critical Risk Zone”. Mathematically proven, the “Safe Zone” of cryptocurrency exchange is not constant. It deforms nonlinearly as market trends change. The obtained results give informational security analysts a tool for the flexible redistribution of defense budgets during periods of high market volatility.
Copyright (c) 2026 Valery Lakhno, Volodimir Malyukov, Inna Malyukova, Bakhytzhan Akhmetov, Zhuldyz Alimseitova, Atkeldi Ogan https://creativecommons.org/licenses/by/4.0
A Controlled Empirical Study of Loss Functions for Imbalanced Cassava Leaf Disease Classification
Class imbalance remains a critical challenge in agricultural image classification, where standard Cross-Entropy (CE) training tends to favor frequent classes at the expense of rare but agronomically important disease categories. This study presents a controlled empirical comparison of four loss functions: Cross-Entropy (CE), Weighted Cross-Entropy (W-CE), Class-Balanced Loss (CB), and Focal Loss (FL) for five-class cassava leaf disease classification on the iCassava 2019 dataset (imbalance ratio = 8.41). Five Convolutional Neural Network (CNN) backbones (LeNet, ResNet-50, EfficientNet-B0, MobileNetV2, and ConvNeXt-Base) share a common Multilayer Perceptron (MLP) classifier head and follow matched data splits, augmentation policies, and optimization schedules to reduce confounding factors in the loss-function comparison. On the held-out test set (N = 1885), ConvNeXt-Base with focal loss and horizontal-flip Test-Time Augmentation (TTA) attains the highest observed Macro-F1, 89.39%, together with 91.67% accuracy, improving minority-class F1 for Cassava Bacterial Blight (CBB) (+2.57%) and Healthy (+2.38%) relative to the CE baseline while maintaining stable Cassava Mosaic Disease (CMD) performance. Class-balanced loss achieves a nearly identical result on ConvNeXt-Base (89.37% Macro-F1), whereas both W-CE and CB degrade performance on compact backbones (LeNet: −6.5 to −7.3% Macro-F1). These results suggest that explicit class weighting can make optimization less stable in low-capacity feature spaces. Within the scope of a single dataset and a single random seed, the findings indicate that the effectiveness of loss-function reweighting depends on backbone capacity: sample-level modulation through focal loss generally provides more consistent improvements than fixed class-level weighting, with the clearest gains on higher-capacity architectures.
focal loss, cross-entropy, class imbalance, convolutional neural networks, test-time augmentation
Copyright (c) 2026 HAI TONG, Minh-Hai Le, Thanh-Nghi Doan https://creativecommons.org/licenses/by/4.0
GCN-Mamba: A Semantic-guided Graph Convolutional Network with Mamba State Space Models for Skeleton-based Action Recognition
Human Action Recognition (HAR) has seen significant advancements with Graph Convolutional Networks (GCNs), which effectively model skeletal motion dynamics. In this work, we propose a novel HAR framework that integrates GCNs with an Adaptive Adjacency Matrix for spatial modeling and the Mamba State Space Model (SSM) for temporal feature extraction. This hybrid approach aims to balance accuracy and efficiency. It does so by leveraging the structural expressiveness of GCNs for spatial modeling, while harnessing the sequential modeling power of Mamba-SSM for temporal dynamics. The central goal of this paper is to evaluate GCN-Mamba against our own previous model, ImpSGN (Improved Semantic-Guided Network), with a specific focus on reducing model complexity while preserving recognition accuracy. GCN-Mamba achieves competitive performance with an approximately 72% reduction in parameter count (from 4.0 M to 1.1 M), making it a lightweight yet effective architecture. Our findings highlight the trade-offs between accuracy and efficiency in HAR models and demonstrate the potential of state-space modeling in skeletal action recognition. The code is publicly available on GitHub.
Copyright (c) 2026 Dr. Amine Mansouri, Dr. Abdellah Elzaar, Prof. Toufik Bakir, Prof. Smain Femmam https://creativecommons.org/licenses/by/4.0
Countering Paraphrasing and Style Transfer in Educational Settings: A GAN-Based Approach to Identifying AI-Generated Academic Essays
The generation of content by Artificial Intelligence (AI) in educational settings represents a growing consequence of the irresponsible use of generative AI that threatens academic integrity. Mitigating this phenomenon through AI-based detection remains a major challenge, given that generative AI tools, such as Large Language Models (LLMs), evolve rapidly. Classical detection tools such as anti-plagiarism software are no longer capable of tracking or curbing the spread of this phenomenon. Furthermore, the existing AI-based tools fail to detect the content generated, paraphrased, or humanized by bots that alter sentence structure. In this article, we propose a novel architecture based on Generative Adversarial Networks (GANs). This framework is a generative model composed of two main components trained through a minimax game. The first is the generator; it generates or reformulates text, while its opponent, the discriminator, tries to detect whether the text is human-generated or produced by the generator. We used DistilGPT-2 as the generator, trained simultaneously with DistilRoBERTa, the discriminator. Our framework was evaluated on 44,868 academic text samples and achieved 99% precision on unmodified texts. Moreover, our approach demonstrates strong reliability, maintaining a precision of 92% despite paraphrasing and humanization, as well as noise injection attacks that render other detection tools ineffective. The use of GANs enables our discriminator to more effectively identify deep semantic patterns and reformulated structures. Finally, we discuss future research directions to improve the adaptability and long-term relevance of the proposed approach in light of rapid advances in artificial intelligence.
Copyright (c) 2026 Zohair Elmourabit, Asmaâ Retbi https://creativecommons.org/licenses/by/4.0
Cross-Dataset Generalization Framework for Cybercrime Detection Using CIC-IDS2017, CIC-Phishing2019, and Malicious Uniform Resource Locator (URL) Data
Proposed cybercrime detection models demonstrate satisfactory performance on specific benchmark datasets; however, they are not always robust across diverse, practical scenarios. This paper examines the generalization performance of a cross-dataset measure for cybercrime detection across network traffic, phishing email, and malicious Uniform Resource Locator (URL) domains. The work combines three popular security benchmarks Canadian Institute for Cybersecurity Intrusion Detection System Dataset2017 (CIC-IDS2017), Canadian Institute for Cybersecurity Phishing Dataset2019 (CIC-Phishing2019), and Malicious URL 2020 in a single preprocessing and learning pipeline to avoid dataset-specific bias. One or more datasets are used to train models, which are then directly evaluated on previously unseen datasets to verify transferability across distribution shifts. We will discuss performance while considering accuracy, F1−Score, Receiver Operating Characteristic (ROC), and fold-to-fold stability. Experiments demonstrate that direct training with a single random source results in significant performance deterioration, with a 23% decrease in F1−Score when applied to unseen datasets. In contrast, the degradation caused by the proposed framework is kept below 10% and maintains Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) values consistently above 0.90. Paired significance testing demonstrates that the gains in robustness are highly significant (p < 0.01). The results indicate that cross-dataset evaluation is essential for the development of deployable cybercrime detection systems, providing empirical insights for developing generalization-oriented security analytics.
Cross-Dataset Generalization; Cybercrime Detection; Network Traffic Analysis; Phishing Email Detection; Malicious URL Classification; Security Benchmark Datasetss
Copyright (c) 2026 R. Adinarayana, G. Vamsi Krishna https://creativecommons.org/licenses/by/4.0
Design and Evaluation of a Dual-Layer Emotion—Personality Framework for Adaptive Conversational Robots
This paper presents a dual-layer emotional framework for human–robot conversational interaction that integrates internal emotion, representing the robot’s intrinsic affective state, and social emotion, representing outward emotional expression adapted for interpersonal alignment. Unlike conventional dialogue systems that rely primarily on semantic and contextual interpretation, the proposed framework processes user input through three complementary dimensions: content, context, and emotion, while supporting multimodal interaction through voice and physical actions. Emotion computation is regulated using two personality-modulated parameters. Sensitivity controls how strongly external stimuli influence internal emotional states, whereas consideration governs the degree to which the robot aligns its social expression with the user’s affect. A robotic prototype was developed to implement the framework, integrating multimodal sensors and expressive actuators for interactive operation. Preliminary experiments were conducted to evaluate the temporal evolution of emotional states, system response latency, and the influence of personality parameters on emotional behavior. The results illustrate that the system can update internal and social emotional states dynamically, adapt responses according to personality parameters, and generate emotionally coherent multimodal outputs. From a Human–Computer Interaction (HCI) perspective, the proposed framework provides a system-level approach for designing conversational interfaces capable of emotionally adaptive multimodal interaction in embodied robotic systems. The findings suggest that personality-modulated emotional regulation can support more flexible and context-dependent conversational behavior compared with conventional reactive emotional dialogue mechanisms.
Topic: Human-Computer Interaction (HCI) in Modern Technological Systems
Copyright (c) 2026 Shitara Kaede, Kantawatchr Chaiprabha, Pimolkan Piankitrungrean, Kosuke Takano, Ratchatin Chancharoen, Gridsada Phanomchoeng https://creativecommons.org/licenses/by/4.0
Modelling Security Threats of Live Streaming Monetization Pipelines
Live-streaming platforms increasingly turn real-time payments, including cheers, Bits, gifts, and highlighted chat, into public signals of status and support. This article examines anomalous cheering as a security problem across the full monetization pipeline, asking how unusual spikes in volume, value, timing, or attribution can be related to system assets, trust boundaries, and controls. We develop a reference data-flow model of a monetized cheering pipeline and apply Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege (STRIDE) to each system element across identity, payments, wallet and ledger services, event streaming, anomaly detection, and trust and safety operations. A simple ordinal Damage Potential, Reproducibility, Exploitability, Affected Users, and Discoverability (DREAD) rubric is used for prioritization, with a worked example showing how one spike can support several competing hypotheses. The analysis highlights five recurring risks: scalable identity abuse and account takeover, payment and ledger exploitation, replay or duplication of canonical events, latency-sensitive denial of service, and misuse of privileged detection or enforcement tools. The paper concludes that monetized cheering should be protected as an end-to-end pipeline, with safeguards placed at the relevant trust boundaries: server-side event generation, idempotency and replay resistance, adaptive throttling, graceful degradation, tamper-evident audit trails, privacy-aware measurement, and least-privilege operational access.
Copyright (c) 2026 Emil Eminov, Stephen Flowerday https://creativecommons.org/licenses/by/4.0
Severity-aware Decision Support System for Women’s Online and Offline Safety Using Large Language Models
Safety issues faced by women today occur in both online and offline settings, as they develop gradually without clear warning signs. Most existing safety technologies focus on emergency responses and incident reporting, but they do not provide support for ongoing situations that require context-specific guidance. This research presents a decision-support framework that identifies threats using structured threat modeling and operates with a constrained large language model. The multi-label threat taxonomy system recognizes all simultaneous online and offline security breaches. The heuristic severity index incorporates experiential indicators, such as fear, loss of control, escalation, physical boundary violations, and misuse of authority. Experiential data were gathered through a trauma-informed survey with 240 responses, of which 204 remained after consent and quality checks. Compared to standard instruction-following large language models, the proposed framework consistently provides guidance more aligned with perceived threat severity (89.2% vs. 54.3%), better acknowledges personal boundaries (93.6% vs. 61.5%), maintains a non-judgmental tone (96.1% vs. 58.1%), and gives proportionate escalation advice based on context (90.4% vs. 49.7%). Additionally, violations of predefined safety constraints are significantly reduced (3.9% compared to 27.8%). Feedback from user-centered evaluations indicates that responses are clearer, emotionally appropriate, and more trustworthy. These findings suggest that large language models can be effectively used as supportive tools in women’s safety applications rather than as autonomous or unconstrained systems.
Large Language Model, Decision Support Framework, Context-Specific Guidance, Human-Computer Interaction
Copyright (c) 2026 Christlin Jefrina R, Mythily M, Vaisnave I M, Dr. Iwin Thana Kumar https://creativecommons.org/licenses/by/4.0
AI-Driven Forecasting of Operating Room Quality Indicators Using LSTM Neural Networks: A Data-Driven Framework for Smart Surgical Management
Operating Room (OR) efficiency directly influences patient safety, resource allocation, and overall hospital performance. Despite the routine monitoring of operating room quality indicators, most analyses remain retrospective and provide limited support for forward-looking management. This study develops a forecasting framework based on Long Short-Term Memory (LSTM) neural networks and integrates the prediction results into a Business Intelligence (BI) visualization environment. Monthly specialty-level operational data from a medical center in Taiwan covering 2021 to 2024 were analyzed, including seven key operating room quality indicators. Different historical input windows ranging from 3 to 24 months were examined to evaluate their effects on predictive performance. To assess whether deep learning was necessary for this task, model performance was compared with traditional statistical approaches, including Moving Average, automated Autoregressive Integrated Moving Average (auto-ARIMA), and Naïve forecasting models, using a strictly chronological training and testing split. Indicators characterized by short-term operational variability, such as cancellation and scheduling delay rates, were more accurately predicted using shorter historical windows, whereas structurally stable metrics including utilization and occupancy rates showed improved performance with longer input sequences. While conventional statistical models performed competitively for some stable indicators, LSTM provided more consistent performance across heterogeneous metrics. The forecasting outputs were incorporated into an interactive dashboard to facilitate real-time monitoring and threshold-based management. Although the analysis was conducted using data from a single medical center, the results demonstrate the feasibility of integrating predictive analytics into routine surgical quality management.
Copyright (c) 2026 Wen-Shin Hsu, Pei-Ju Wang https://creativecommons.org/licenses/by/4.0
Ultra-light Multi-scale Human Parsing with Attention-guided Grouping
Human parsing in large and dynamic environments is commonly handled by large-scale models that deliver high segmentation accuracy but require hundreds of millions of parameters and substantial computational resources. This level of complexity makes such approaches difficult to deploy on power- and resource-constrained devices, particularly in real-time edge Artificial Intelligence (AI) settings. In this paper, we introduce Fast Dilated Spatial Pyramid Pooling + Parsing Grouping Network + Attention (Fast DSPP + PGN + ATTN), an efficient human parsing framework designed to balance segmentation performance with computational efficiency. The model employs a MobileNetV2 encoder with DSPP module for contextual modeling and a PGN-style grouping decoder integrated with light-weight spatial attention and Squeeze-and-Excitation (SE) attention. With 2.14 M parameters and 5.70 Giga Floating Point Operations (GFLOPs), it attains 40.67% mean Intersection over Union (mIoU). In ablation results, we observe that DSPP has the most significant effect (0.0151 mIoU), while spatial attention and SE yield smaller improvements. The accuracy of the grouping decoder is retained while the number of computations can be reduced. The model achieves 0.2490 mIoU on Look into Person (LIP) dataset, with variants to 0.2640, thus confirming robustness over domain shift. This indicates that integrating structured pixel grouping and efficient contextual aggregation together can solve the task of human parsing, even with severe computational constraints, which could render the proposed method particularly appealing to real-world applications such as embedded vision systems/mobile perception and real-time surveillance.
Copyright (c) 2026 abderrahim ouza, Mohamed El Ghmary, Ali Choukri https://creativecommons.org/licenses/by/4.0
Deep Image Retrieval Using Optimizer-aware Convolutional Neural Network with Adaptive Adam-AMSGrad Fusion
Over the past decade, image retrieval has become the most important research subject in computer vision, attracting significant attention from researchers. The main challenge in image retrieval is significant image depiction and the extraction of large features. Hence, this research proposes an innovative method of cooperative learning between the actual Adam norm approach and Adaptive Moment Estimation with Gradient Correction (AMSGrad) within a Convolutional Neural Network (Adaptive Adam-CNN) for image retrieval. The proposed approach improves the discriminability of learned features and maintains high precision even for larger data. This research primarily employs the three standard datasets, namely Corel 1K, Corel 5K, and Corel 10K, to evaluate the effectiveness of the introduced approach. Next, a preprocessing technique, such as min-max normalization, is employed to scale the input data uniformly for improved image retrieval. The experimental findings demonstrate that proposed Adaptive Adam-CNN approach achieves higher accuracy of 99.63%, 98.37%, and 95.28% on Corel 1K, Corel 5K, and Corel 10K datasets, respectively, compared to the Content-Based Image Retrieval with Accuracy Noise Reduction (CBIR-ANR) and the Extended version of Local Neighborhood Difference Patterns (ELNDP).
Copyright (c) 2026 Krishna Murthi Sankar, Woothukadu Thirumaran Chembian, Mahamuni Suganthy, Marimuthu Venkatesan, Periyannan Raman https://creativecommons.org/licenses/by/4.0
Data-Driven Detection of Multi-vector IoT DDoS Attacks across Network Layers
As cyberattacks targeting Internet of Things (IoT) networks grow more sophisticated, the demand for models capable of accurately detecting and mitigating these threats becomes increasingly urgent existing detection systems often concentrate on a single attack surface which leads to critical blind spots in IoT network monitoring. This research introduces an intensive IoT attack-detection framework that addresses internal and external attack sources, leveraging multi-layer attack vectors across the application, transport, network, and data link layers. The proposed framework was evaluated in two phases. In the first phase, three datasets that simulate a distinct attack source were created: outbound, including Dynamic Host Configuration Protocol (DHCP) amplification and DHCP starvation; inbound attacks, including application-layer attacks, Synchronize (SYN) flood, User Datagram Protocol (UDP) flood, Internet Control Message Protocol (ICMP) Smurf, and ICMP direct; and internal Address Resolution Protocol (ARP) spoofing. Three machine learning models; Random Forest, Light Gradient-Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) were evaluated using accuracy, precision, recall, F1-Score, and Receiver Operating Characteristic (ROC) curve. In the second phase, a new dataset was generated by combining all datasets from the first phase. On the combined dataset, LightGBM achieved the highest performance, with accuracy: 94.08%, precision: 93.94%, recall: 94.08%, and F1-Score: 93.90%. Random Forest and CatBoost showed comparable performance, with all metrics ranging from approximately 93.2% to 93.9%. LightGBM demonstrates a slight edge in overall detection performance compared to other models, which highlights its effectiveness in detecting diverse and complex attack patterns across multiple traffic directions.
Copyright (c) 2026 Rania A. Al-Ali, Mohammed M. Alnabhan, Qasem Abu Al-Haija https://creativecommons.org/licenses/by/4.0
Methodological Rigor in EHR-based Prediction: A Leakage-free Evaluation Framework for Diabetes Medication Initiation
Prevailing studies employing the UCI Diabetes 130-US Hospitals dataset report classification performance exceeding Area Under the Curve (AUC) > 0.99; however, such estimates are systematically inflated by data leakage the inadvertent incorporation of information unavailable at prospective prediction time. This study introduces a rigorous, leakage-free evaluation framework for predicting diabetes medication initiation among hospitalized patients. Analyzing 101,766 inpatient encounters (1999–2008) from 71,290 unique patients, we enforced strict calendar-based temporal separation and excluded all 24 medication-specific features to eliminate both target and temporal leakage sources. Six models were evaluated spanning logistic regression, random forests, Extreme Gradient Boosting (XGBoost), One-Dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), and a CNN-LSTM hybrid under identical leakage-free conditions. The 1D-CNN achieved the highest discrimination (AUC = 0.824), surpassing LSTM and all baselines, while demonstrating superior probability calibration (Brier score = 0.149; ECE = 0.028) and 3.2× faster inference. Primary predictors included HbA1c > 7%, elevated admission glucose > 180 mg/dL, advanced age (≥70 years), and frequent prior hospitalizations. These results confirm that lightweight convolutional architectures can outperform recurrent models on sparse, short-sequence Electronic Health Record (EHR) data a dataset-specific finding that should not be generalized without qualification. Dataset characteristics, scope constraints, and generalizability limitations are addressed fully in the methods and discussion sections.
diabetes prediction, electronic health records, data leakage, temporal validation, model calibration, clinical decision support, convolutional neural networks.
Copyright (c) 2026 Adedeji Olugboja https://creativecommons.org/licenses/by/4.0
A Smart Business Intelligence Model Based on HGO Discovery Approach for Precision in Inpatient Care in Regional General Hospitals
In Bangka Belitung Archipelago Province, the Crude Death Rate (CDR), which represents the general mortality rate per 1000 discharged patients, exceeded 100. This figure indicates a significantly high mortality rate compared to the government standard of fewer than 45. Similarly, the Net Death Rate (NDR), defined as the mortality rate occurring after 48 h of hospitalization per 1000 discharged patients, was also above the national standard of fewer than 25. These findings highlight fundamental issues within hospital healthcare management system. As a potential solution, this study proposes the application of smart Business Intelligence (BI) through the Hierarchy, Governance, Outlook (HGO) Discovery approach. This approach is designed to optimize smart business intelligence in inpatient services in regional general hospitals, with the primary objective of addressing multidimensional challenges, including patient attribute management, human resource management as a strategic element, and the enhancement of inpatient care quality. The HGO Discovery method functions as an information system that identifies hidden patterns within a sequence of events (sequential patterns) by reorganizing existing hospital workflows. Thus, this approach facilitates accelerated inpatient care processes, improves operational efficiency, and provides more interactive support for decision-making. The implementation of HGO Discovery-based business intelligence is expected to produce a more integrated healthcare service system by combining hierarchical service structures, transparent governance, and strategic outlooks in every stage of decision-making, supported by the integration of smart business intelligence, data warehouse and data mining techniques.
Business Intelligence, HGO Discovery, Inpatients Care, Hierarchy, Governance, Outlook, Hospital
Copyright (c) 2026 Hengki Hengki, Rahmat Gernowo, Oky Dwi Nurhayati https://creativecommons.org/licenses/by/4.0
A CNN-BiLSTM Hybrid for Plant Leaf Disease Classification: Comparative Performance Evaluation of Deep Learning Algorithms
The existence of plant leaf diseases is a big problem for farmers all over the world because they make crops less healthy and less plentiful, which puts global food security at risk. The most common problems with diagnosing plant leaf diseases are a lack of experience, different ways of undertaking visual assessments, and image overlaps, all of which can lead to wrong diagnoses. This study introduces a hybrid deep learning architecture that integrates squeeze-and-excitation residual blocks, capsule networks, bidirectional long short-term memory, and attention mechanisms. The design utilizes convolutional operators for effective feature extraction, Squeeze-and-Excitation (SE) block for channel reweighting, capsule networks for spatial relationship capture Bidirectional Long Short-Term Memory (BiLSTM) for sequential dependencies, and attention mechanisms for emphasizing prominent features. Experiments were performed on 2 empirical datasets: the Corn Leaf Disease Dataset (CLDD) and the Rice Leaf Disease Dataset (RLDD). The data were divided into 60% for training, 20% for validation, and 20% for testing. The proposed method attained 99.88% training accuracy on CLDD and 100% on RLDD. During testing, the class-wise accuracies were 99.29% for blight and 100% for the other CLDD categories. In the case of RLDD, the accuracies attained were 78.95% for bacterial leaf blight, 85.53% for brown spot, 89.77% for healthy samples, 77.27% for leaf blast, 100% for leaf scald, and 97.73% for narrow brown spot. This work highlights practical potential for deployment in terms of mobile applications, enabling farmers to obtain rapid, reliable, and cost-effective field diagnoses, thereby improving agricultural productivity and sustainability.
Copyright (c) 2026 Aekkarat Suksukont, Ekachai Naowanich https://creativecommons.org/licenses/by/4.0
HYBERT-X: A Hybrid Deep Learning Framework for Cyberbullying and Cyber Threat Detection
Cyberbullying and online threat detection are the new vulnerabilities of cybersecurity, and the conventional tools are unable to keep pace with the development of harmful content. The paper discusses HYBERT-X as a hybrid deep learning model that integrates Bidirectional Long Short-Term Memory (BiLSTM), Graph Convolutional Network (GCN) based learning, Bidirectional Encoder Representations from Transformers (BERT) and Extreme Gradient Boosting (XGBoost) to detect threats with high accuracy and interpretability. The proposed framework uses an ensemble classification layer to boost the accuracy and interpretability of detection based on semantic, temporal, and structural representation. The experimental assessment done on the typical benchmark datasets has shown that HYBERT-X is up to 95.53% accurate in comparison with the currently available baseline models both in generalization ability and explainability. The findings suggest that this work is computationally effective, scalable, and can be adapted to real-time cybersecurity applications and is applicable to next-generation cybersecurity systems against online threats and cyberbullying.
Cyberbullying detection; Cybersecurity; Hybrid deep learning; BERT; XGBoost
Copyright (c) 2026 JAYAPRIYA J, Kavitha S, Manimekala B, Vinay M, Nileem Kaveramma https://creativecommons.org/licenses/by/4.0
Predicting Students’ Attrition for the Information Systems Program Using Machine Learning Algorithms
Student attrition, a crucial factor in determining institutions’ financial resources, is also a measure of an institution’s academic standing, with a direct impact on its revenue. This study, which examined the factors influencing students’ departure from the Information Systems (IS) program, has significant implications for higher education. The research was conducted in 2 stages. Initially, we experimented with 13 machine learning algorithms to identify the most accurate method for predicting student attrition. Our findings revealed that Extra Trees, Light Gradient Boosting Machine, and Random Forests are the top 3 predictors in that order. In the second stage, we used these algorithms to identify the most influential factors driving student attrition. Our results indicated that the 3 classifiers identified the Last Grade Point Average (Last GPA) as the most significant factor affecting students’ decision to leave the IS program. Performance in mathematics and gender are the other 2 factors affecting dropout. We have found that, as a percentage, more male than female students are leaving the program. Moreover, female students are primarily transferring to more academically demanding majors, such as data analytics and information technology, while male students are transferring to less technology-oriented majors, such as management and law. This finding is reinforced by the observation that female students’ mathematics performance among students leaving the IS program is significantly higher than that of male students. Our results can have significant policy implications for admission procedures and academic advising, positively affecting the IS program’s attrition rate.
Machine learning, random forests, student attrition, extra trees
Copyright (c) 2026 Elfadil Abdalla Mohamed, Mirna Nachouki, Riyadh Mehdi, Yara Mohammad https://creativecommons.org/licenses/by/4.0
Context-aware Risk Ranking of Railway Level Crossings Using Machine Learning and National Transport Data
This study proposes a context-aware approach for risk ranking of railway level crossings using machine learning and national transport data. Railway level crossings are critical safety points within multimodal transportation systems, where interactions between rail operations and road traffic generate heterogeneous risk. Conventional risk assessment methods often rely on fixed-weight or expert-driven scoring schemes, which may not adequately capture context-dependent variations across diverse infrastructural, road, and spatial environments. To address these limitations, the proposed approach integrates national Geographic Information System (GIS)-based railway crossing data with machine learning and weakly supervised learning techniques. Contextual information is organized into three dimensions: infrastructural attributes, road characteristics, and spatial context. In the absence of explicit accident labels, pseudo-labels derived from domain knowledge are used to provide supervisory signals for model training. Multiple machine learning models, including logistic regression, random forest, and gradient boosting, are evaluated using ranking-oriented performance metrics to emphasize relative risk discrimination rather than binary accident prediction. The results indicate that the proposed approach generates continuous and discriminative risk scores, revealing substantial heterogeneity in relative safety risk across the railway network. A relatively small subset of crossings accounts for a disproportionate share of elevated risk, supporting targeted prioritization. Overall, the findings suggest that context-aware, data-driven risk ranking provides a scalable and interpretable alternative to traditional fixed-weight assessment methods, contributing to improved analytical understanding of safety risk patterns in railway level crossing networks.
Railway level crossings; Context-aware risk scoring; Transportation safety; Machine learning; Weakly supervised learning; Risk ranking; GIS-based transport data; Safety prioritization
Copyright (c) 2026 Sudasawan Ngammongkolwong https://creativecommons.org/licenses/by/4.0
A Hybrid Metaheuristic Optimization Framework for Multi-Scale Time Series Forecasting Using AO and PGRO
Time series forecasting plays a pivotal role in domains ranging from finance and healthcare to multimodal analytics, yet achieving robust accuracy across heterogeneous datasets and varying prediction horizons remains a significant challenge. In this paper, we propose a novel hybrid forecasting framework that integrates advanced metaheuristic optimization with deep learning architectures for multi-domain, multi-scale prediction. The core of our approach is a dual-stage optimization strategy that combines the global exploration capability of the Aquila Optimizer (AO) with the guided local refinement of the Proposed Guided Remora Optimization Algorithm (PGRO). This hybrid optimization mechanism is applied to fine-tune model hyperparameters, fusion strategies, and incremental learning settings for architectures such as Long Short Term Memory (LSTM), Transformer, and multimodal fusion networks. The framework is further enhanced by an incremental learning module with memory replay, enabling continual adaptation without catastrophic forgetting. We evaluate the proposed approach on 6 diverse datasets (National Association of Securities Dealers Automated Quotations/New York Stock Exchange (NASDAQ/NYSE) Historical Stock, Financial Question Answering (FiQA), Financial PhraseBank, Reddit Financial News, Fourth Makridakis Forecasting Competition Time series (M4), and Video and Text (VaTeX) Multimodel), spanning structured numerical, textual, social media, and multimodal time series. Experimental results show that our AO + PGRO-enhanced models consistently outperform classical statistical models, deep learning baselines, and state-of-the-art transformer-based forecasters in both short- and long-horizon prediction tasks. Ablation studies confirm the contribution of each component, and detailed horizon-wise analysis highlights the method’s adaptability to varying temporal dependencies. The proposed framework delivers state-of-the-art accuracy, stability, and generalization across domains, offering a powerful and extensible approach for real-world time series forecasting challenges.
Copyright (c) 2026 SARVANI ANANDARAO, Hemasundara Reddy Lanka, Nagaraju Devarakonda, Nagha Harish Vundavalli https://creativecommons.org/licenses/by/4.0
Optimized Deep Learning Model for Concrete Crack Classification Monitoring on Resource Constrained Devices
Structural cracks are commonly caused by factors such as high loading, fatigue, degradation, thermal expansion, and humidity. Detection of cracks in early stages is important. If neglected, these cracks can spread and weaken the building as well as other civil infrastructure. To enhance safety and prevent catastrophic structural failures, accurately detecting cracks is essential in Structural Health Monitoring (SHM) systems, thus enabling early maintenance. The latest developments in Deep Learning (DL) have facilitated the automatic detection of structural cracks through images taken with the help of cameras, drones, or mobile devices. Such methods minimize the manual inspection and enhance monitoring efficiency. In this paper, a systematic optimization of deep learning models through hyperparameter tuning with random search was implemented to achieve a balance between classification performance and computational efficiency for resource-constrained environments. Custom architecture, (Convolutional Neural Network (CNN) and CNN-Long Short-Term Memory (LSTM)), as well as transfer learning models (MobileNetV2, ResNet101, and DenseNet201), are evaluated on the Middle East Technical University (METU) concrete crack dataset (40,000 labelled images). The performance measures, such as accuracy, precision, recall, F1-Score, inference time, and computational complexity in Million Floating-Point Operations (MFLOPs), are evaluated and compared on the models. From the results, MobileNetV2 shows a classification accuracy of 99.8% with a reduction of 50% computational complexity, thus making it suitable for deployment on constrained devices in real-world applications.
Copyright (c) 2026 Vanusha, Karthikeyan, Naga Malleswari TYJ, Ushasukhanya https://creativecommons.org/licenses/by/4.0