Research Articles (Computer Science)

Permanent URI for this collectionhttp://hdl.handle.net/2263/1695

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Privacy vs. utility in federated learning : an experimental analysis of noise injection techniques
Leope, Neo R.; Eloff, Jan H.P.; Dlamini, Moses Thandokuhle (Institute of Electrical and Electronics Engineers, 2025-11-20)
Federated Learning (FL) enables decentralized model training, while maintaining the privacy of the underlying individual datasets. Therefore, FL can resolve some intrinsically privacy-sensitive challenges in domains, such as healthcare and finance. However, privacy preservation usually comes with a trade-off on the usefulness (i.e., utility) of the information. The research problem is how to optimize this inversely proportional trade-off balance between privacy and utility. This study uses an experimental comparative analysis, in a synthetic healthcare setting, of different noise types (i.e., Gaussian, Laplacian, Poisson, Uniform, and Exponential) injected on the client side at the input-feature level prior to local training to enhance privacy in FL. We explore the impact of these noise types on the privacy–utility trade-off in FL data. The findings indicate that although Laplacian, Poisson, and Exponential types of noise provides stronger obfuscation which often comes at the cost of utility. This confirms and amplifies the trade-off in maintaining the usefulness of the data against its privacy. More importantly, the findings also show that Gaussian noise generally offers the best trade-off between privacy and utility on this task, suggesting a practical default for privacy-aware FL in healthcare-like environments.
A swarm intelligence-based hybrid metaheuristic with tabu search for the quadratic assignment problem
Panwar, Karuna; Rajwar, Kanchan; Deep, Kusum; Cho, Sung-Bae (Springer, 2026-02-04)
The Grey Wolf Optimizer (GWO), inspired by the hunting behavior of grey wolves, is an effective swarm intelligence-based algorithm increasingly recognized for solving NP-hard problems. The Quadratic Assignment Problem (QAP), known for its complexity and widespread industrial applications, presents a significant challenge in combinatorial optimization. This paper introduces a novel discrete variant of GWO for QAP, the Hybrid Grey Wolf Optimizer (HGWO), which integrates an enhanced Tabu Search (TS) to improve GWO’s effectiveness in solving the QAP. This enhanced TS is employed to refine the exploitation phase by focusing on promising areas identified by GWO. Due to the combinatorial nature of QAP, the outcomes of classical GWO are transformed into discrete values using the largest real value mapping technique. In our computational experiments across all 134 QAPLIB benchmark instances, HGWO achieved the best-known solutions for 110 instances. It maintains an impressively low average deviation of 0.20%, demonstrating high accuracy and robustness. Comparative analysis with established algorithms like Genetic Algorithm, Bat Algorithm, and Whale Optimization Algorithm demonstrates that HGWO surpasses most competing methods. Rigorous statistical tests, including the Friedman nonparametric test and the Wilcoxon signed-rank test, validate these results, underscoring HGWO’s potential as a powerful tool for QAP and indicating fruitful directions for future research in combinatorial optimization strategies.
Blockchain forensics and regulatory technology for crypto tax compliance : a state-of-the-art review and emerging directions in the South African context
Ramazhamba, Pardon Takalani; Venter, H.S. (Hein) (MDPI, 2026-01-13)
The rise in Blockchain-based digital assets has transformed the financial ecosystems, which has also created complex governance and taxation challenges. The pseudonymous and cross-border nature of crypto transactions undermines traditional tax enforcement, leaving regulators such as the South African Revenue Service (SARS) reliant on voluntary disclosures with limited verification mechanisms, while existing Blockchain forensic tools and regulatory technologies (RegTechs) have advanced in anti-money laundering and institutional compliance, their integration into issues related to taxpayer compliance and locally adapted solutions remains underdeveloped. Therefore, this study conducts a state-of-the-art review of Blockchain forensics, RegTech innovations, and crypto tax frameworks to identify gaps in the crypto tax compliance space. Then, this study builds on these insights and proposes a conceptual model that integrates digital forensics, cost basis automation aligned with SARS rules, wallet interaction mapping, and non-fungible tokens (NFTs) as verifiable audit anchors. The contributions of this study are threefold: theoretically, which reconceptualise the adoption of Blockchain forensics as a proactive compliance mechanism; practically, it conceptualises a locally adapted proof-of-concept for diverse transaction types, including DeFi and NFTs; and lastly, innovatively, which introduces NFTs to enhance auditability, trust, and transparency in digital tax compliance.
An anti-sheriff cybersecurity audit model : from compliance checklists to intelligence-supported cyber risk auditing
Rananga, Ndaedzo; Venter, H.S. (Hein) (MDPI, 2026-03)
The increasing adoption of data-driven techniques in cybersecurity has introduced new opportunities to enhance detection, response, and automation capabilities within the cybersecurity ecosystem; however, cybersecurity auditing remains constrained by traditional compliance-oriented approaches that rely profoundly on binary, checklist-based evaluations. Such approaches often reinforce a policing or “sheriff-style” perception of auditing, emphasizing enforcement rather than enablement, risk insight, and organizational improvement. Of primary concern is that the “sheriff-style” cybersecurity audit approach often fails to accurately portray the true state of an organization’s cybersecurity posture, often providing a misleading sense of assurance based solely on formal compliance and controls existence. This study proposes an Anti-Sheriff Cybersecurity Audit Model, that moves beyond cybersecurity control checklists, by integrating intelligence-informed risk assessments with structured human judgment to support a more robust, adaptive, and risk-oriented auditing process. Grounded in design science research (DSR), the proposed approach combines conventional binary compliance verification with intelligence-derived risk indicators and governance-based maturity assessments to evaluate cybersecurity controls across technical, operational, and organizational dimensions. The approach aligns with established standards and frameworks, including International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC) 27001, the National Institute of Standards and Technology (NIST), and the Center for Internet Security (CIS) benchmarks, while extending their application beyond static compliance validation. A fictional case study is used to demonstrate the model’s applicability and to illustrate how hybrid scoring can reveal residual risk not captured by conventional cybersecurity audits. The findings indicate that combining intelligence-informed analytics with structured human judgment enhances audit depth, interpretability, and business relevance. The proposed approach, therefore, provides a foundation for evolving cybersecurity auditing from just periodic compliance assessments, toward a continuous, risk-informed, and governance-aligned assurance system.
Advancements in maize yield estimation : a comprehensive review of methods and models
Hove, Kudakwashe; Nyamugure, Philimon; Mdlongwa, Precious; Dube, Timothy; Nyathi, Thambo; Awala, Simon Kamwele (Springer, 2025-12-23)
Accurate and timely estimation of maize yield is crucial for ensuring food security, optimizing resource utilization, and informing agricultural policy. However, current yield estimation methods often encounter significant limitations, such as low spatial resolution, dependence on sparse ground-truth data, poor model generalizability across diverse agroecological zones, and challenges in integrating heterogeneous data sources. Although numerous techniques have been developed, ranging from traditional field-based measurements to advanced remote sensing and machine learning methods, a comprehensive synthesis that critically evaluates these approaches and explores their convergence is still lacking. This review addresses this gap by providing a systematic overview of recent advances in maize yield estimation, with a focus on remote sensing technologies, machine learning algorithms, and hybrid crop modeling frameworks. It examines the strengths and limitations of various methodologies, including UAV- and satellite-based imaging, hyperspectral and LiDAR sensing, regression and ensemble learning, and long-read sequencing. Additionally, the review explores the role of emerging technologies such as IoT, cloud computing, and blockchain in enhancing data collection, processing, and traceability. By identifying key challenges such as environmental variability, data scarcity, and model interpretability, and highlighting opportunities for methodological integration, this review offers a roadmap for future research and development. It argues that the convergence of digital agriculture tools and robust modeling strategies holds significant promise for improving maize yield estimation accuracy, scalability, and applicability. These advancements have far-reaching implications for sustainable agriculture, climate resilience, and global food security.
ZASCA-sum : a dataset of the South Africa supreme courts of appeal judgments and media summaries for legal documents summarization research
Adulmumin, Idris; Marivate, Vukosi (Elsevier, 2025-06)
This paper presents ZASCA-Sum, a novel dataset comprising judgments from the South Africa Supreme Court of Appeal and their manually curated media summaries. The dataset, collected from the court's official website, includes 4171 judgments, of which 2118 have summary pairs. The judgments and summaries have been extracted and prepared to support legal document summarization tasks across supervised, semi-supervised, and unsupervised settings. This paper provides a detailed description of the dataset, covering the data collection process, timeline, processing, and potential applications in the field. We provide the token-count distribution and analysis of the judgments and summaries that can be accommodated off-the-shelf by current summarization models with the largest input token size. The dataset, split into training, validation, and test sets, is made publicly available to encourage research in legal summarization. In addition to document summarization, researchers can use this data to localize English-centric models to support the South African dialect.
A selection perturbative hyper-heuristic for neural architecture search
De Clercq, Johannes; Pillay, Nelishia (Elsevier, 2026-03)
Neural architecture search explores the architecture space, referred to as the design spaces, to find an architecture that produces good results. Various approaches, such as genetic algorithms, are usually used to explore this space. This study investigates exploring an alternative space, namely, the heuristic space using a hyper-heuristic to indirectly explore the design space. The study introduces the concept of a NAS operator space (NOS). A single point selection perturbative hyper-heuristic (SPHH-NAS) explores a heuristic space that maps to the NOS which then maps to the design space. A choice function is used for heuristic selection and the Adaptive Improvement Limited Target Acceptance (AILTA) for move acceptance. It is anticipated that indirectly searching the design space will facilitate reaching areas of the search space that could not be reached by searching the space directly. SPHH-NAS was evaluated on three NAS benchmark sets, namely, NAS-101, NAS-201 and NAS-301. In addition to this the approach is evaluated on two real-world datasets. SPHH-NAS was found to outperform majority of the previous approaches used to solve these problems. In addition to this SPHH-NAS resulted in a reduction in computational cost. HIGHLIGHTS • This is the first study using a selection perturbative hyper-heuristics for neural architecture search. • The selection perturbative hyper-heuristic produces good results for NAS. • The selection perturbative reduces computational cost for NAS.
Fine-tuning a sentence transformer for DNA
Mokoatle, Mpho; Marivate, Vukosi; Mapiye, Darlington; Bornman, Maria S. (Riana); Hayes, Vanessa M. (BioMed Central, 2025-10)
BACKGROUND : Sentence-transformers is a library that provides easy methods for generating embeddings for sentences, paragraphs, and images. Sentiment analysis, retrieval, and clustering are among the applications made possible by the embedding of texts in a vector space where similar texts are located close to one another. This study fine-tunes a sentence transformer model designed for natural language on DNA text and subsequently evaluates it across eight benchmark tasks. The objective is to assess the efficacy of this transformer in comparison to domain-specific DNA transformers, like DNABERT and the Nucleotide transformer. RESULTS : The findings indicated that the refined proposed model generated DNA embeddings that exceeded DNABERT in multiple tasks. However, the proposed model was not superior to the nucleotide transformer in terms of raw classification accuracy. The nucleotide transformer excelled in most tasks; but, this superiority incurred significant computing expenses, rendering it impractical for resource-constrained environments such as low- and middle-income countries (LMICs). The nucleotide transformer also performed worse on retrieval tasks and embedding extraction time. Consequently, the proposed model presents a viable option that balances performance and accuracy.
Early detection of Phytophthora root rot in Eucalyptus using hyperspectral reflectance and machine learning
Esterhuizen, Hendrik J.; Slippers, Bernard; Bosman, Anna Sergeevna; Roux, Jolanda; Jones, Wayne; Bose, Tanay; Hammerbacher, Almuth (Elsevier, 2025-10)
The rising prevalence of Phytophthora diseases in forests highlights the need for rapid, non-invasive detection methods. Early-stage root infections are difficult to detect due to the absence of visible above-ground symptoms, while current diagnostics remain slow and invasive. This study investigated whether hyperspectral leaf reflectance could detect root rot caused by Phytophthora alticola in Eucalyptus benthamii. Nineteen commercially planted families were inoculated, and leaf spectra were collected using an ASD FieldSpec 4 sensor. A machine learning pipeline was developed to identify diagnostic spectral signals. Key wavelengths were identified using permutation importance, a genetic algorithm, and self-attention network (SAN) scores. Spectral signals linked to root rot revealed that infection was correlated with leaf pigment accumulation and moisture stress. Three algorithms, random forest (RF), support vector machine (SVM), and SAN, were trained on hyperspectral data to predict P. alticola infection. The SAN achieved 97 % accuracy on a reduced dataset, which included the diagnostic wavelengths from the feature selection step, surpassing the RF (96 %) and SVM (94 %) models. This study demonstrates hyperspectral sensing as an effective tool for detecting Phytophthora root rot using spectra from the foliage and highlights the application of advanced machine learning techniques for plant disease classification. HIGHLIGHTS • Hyperspectral sensing detects Phytophthora root rot before symptoms appear. • SAN model achieved 97 % accuracy using selected wavelengths from leaf spectra. • Key wavelengths correlated with pigment shifts and moisture stress in leaves. • Machine learning identified spectral markers for early disease detection. • Vegetation indices NDNI and MSI are strongly linked to infection status.
Parameterised quantum SVM with data-driven entanglement for zero-day exploit detection
Nhlapo, Steven Jabulani; Mutombo, Elodie Ngoie; Nkongolo, Mike Nkongolo Wa (MDPI, 2025-08)
Zero-day attacks pose a persistent threat to computing infrastructure by exploiting previously unknown software vulnerabilities that evade traditional signature-based network intrusion detection systems (NIDSs). To address this limitation, machine learning (ML) techniques offer a promising approach for enhancing anomaly detection in network traffic. This study evaluates several ML models on a labeled network traffic dataset, with a focus on zero-day attack detection. Ensemble learning methods, particularly eXtreme gradient boosting (XGBoost), achieved perfect classification, identifying all 6231 zero-day instances without false positives and maintaining efficient training and prediction times. While classical support vector machines (SVMs) performed modestly at 64% accuracy, their performance improved to 98% with the use of the borderline synthetic minority oversampling technique (SMOTE) and SMOTE + edited nearest neighbours (SMOTEENN). To explore quantum-enhanced alternatives, a quantum SVM (QSVM) is implemented using three-qubit and four-qubit quantum circuits simulated on the aer_simulator_statevector. The QSVM achieved high accuracy (99.89%) and strong F1-scores (98.95%), indicating that nonlinear quantum feature maps (QFMs) can increase sensitivity to zero-day exploit patterns. Unlike prior work that applies standard quantum kernels, this study introduces a parameterised quantum feature encoding scheme, where each classical feature is mapped using a nonlinear function tuned by a set of learnable parameters. Additionally, a sparse entanglement topology is derived from mutual information between features, ensuring a compact and data-adaptive quantum circuit that aligns with the resource constraints of noisy intermediate-scale quantum (NISQ) devices. Our contribution lies in formalising a quantum circuit design that enables scalable, expressive, and generalisable quantum architectures tailored for zero-day attack detection. This extends beyond conventional usage of QSVMs by offering a principled approach to quantum circuit construction for cybersecurity. While these findings are obtained via noiseless simulation, they provide a theoretical proof of concept for the viability of quantum ML (QML) in network security. Future work should target real quantum hardware execution and adaptive sampling techniques to assess robustness under decoherence, gate errors, and dynamic threat environments.
Optimizing translation for low-resource languages : efficient fine-tuning with custom prompt engineering in large language models
Khoboko, Pitso Walter; Marivate, Vukosi; Sefara, Joseph (Elsevier, 2025-06)
Training large language models (LLMs) can be prohibitively expensive. However, the emergence of new Parameter-Efficient Fine-Tuning (PEFT) strategies provides a cost-effective approach to unlocking the potential of LLMs across a variety of natural language processing (NLP) tasks. In this study, we selected the Mistral 7B language model as our primary LLM due to its superior performance, which surpasses that of LLAMA 2 13B across multiple benchmarks. By leveraging PEFT methods, we aimed to significantly reduce the cost of fine-tuning while maintaining high levels of performance. Despite their advancements, LLMs often struggle with translation tasks for low-resource languages, particularly morphologically rich African languages. To address this, we employed customized prompt engineering techniques to enhance LLM translation capabilities for these languages. Our experimentation focused on fine-tuning the Mistral 7B model to identify the best-performing ensemble using a custom prompt strategy. The results obtained from the fine-tuned Mistral 7B model were compared against several models: Serengeti, Gemma, Google Translate, and No Language Left Behind (NLLB). Specifically, Serengeti and Gemma were fine-tuned using the same custom prompt strategy as the Mistral model, while Google Translate and NLLB Gemma, which are pre-trained to handle English-to-Zulu and English-to-Xhosa translations, were evaluated directly on the test data set. This comparative analysis allowed us to assess the efficacy of the fine-tuned Mistral 7B model against both custom-tuned and pre-trained translation models. LLMs have traditionally struggled to produce high-quality translations, especially for low-resource languages. Our experiments revealed that the key to improving translation performance lies in using the correct prompt during fine-tuning. We used the Mistral 7B model to develop a custom prompt that significantly enhanced translation quality for English-to-Zulu and English-to-Xhosa language pairs. After fine-tuning the Mistral 7B model for 30 GPU days, we compared its performance to the No Language Left Behind (NLLB) model and Google Translator API on the same test dataset. While NLLB achieved the highest scores across BLEU, G-Eval (cosine similarity), and Chrf++ (F1-score), our results demonstrated that Mistral 7B, with the custom prompt, still performed competitively. Additionally, we showed that our prompt template can improve the translation accuracy of other models, such as Gemma and Serengeti, when applied to high-quality bilingual datasets. This demonstrates that our custom prompt strategy is adaptable across different model architectures, bilingual settings, and is highly effective in accelerating learning for low-resource language translation.
Migrating teaching of automata theory to a digital platform
Jordaan, Steve; Timm, Nils; Marshall, Linda (South African Institute of Computer Scientists and Information Technologists, 2024-12)
This research explores the challenges of teaching automata theory in computer science and proposes a digital solution to enhance learning experiences. Traditionally taught through pen and paper, automata theory often appears daunting to students due to its abstract nature. This study advocates for a shift towards a more interactive, digital approach. It presents a detailed analysis of current teaching practices, highlighting the need for digital innovation. Based on the categorisation of common question types in traditional assessments, the research introduces 'AutomaTutor', a mobile application designed for this specific educational context. 'AutomaTutor' features a user-friendly interface with a guided exercise system and an interactive editor for experimentation. It offers immediate feedback, hints, and varied problem sets, promoting self-guided learning. An experimental evaluation with postgraduate students demonstrated a preference for 'AutomaTutor' over conventional methods, confirming the hypothesis that a digital platform can significantly improve the understanding of automata theory. The study represents a step forward in making theoretical computer science more accessible and engaging, benefiting both teachers and students. It underscores the potential of integrating technology with traditional teaching principles in automata theory education.
Sensemaking and the potential future-focused curriculum for society 5.0 knowledge managers : a South African perspective
Mearns, Martie; Meyer, Anika; Holmner, Marlene Amanda; Marshall, Linda; Hattingh, Maria J. (Marie); Bester, Elmi (South African Institute of Computer Scientists and Information Technologists , 2024-07-31)
When "quality being everyone’s business" coincides with the reality of a disruptive work environment, critical self-evaluation becomes an essential tool to ensure accountability. Academics who design curricula and their tuition offering have a certain degree of freedom in what and how they teach. However, academics need to be consciously discerning, yet inclusive, about the voices that should speak into curriculum design. This study operates from the principle of co-creation in curriculum design and acknowledges the multiplicity of relevant voices that speak into curriculum design. These voices are influenced by the past, present, and possibilities of the potential future. To remain relevant in the imagined future, this research identified the co-creators and curriculum design partners for the multidisciplinary field of knowledge management. The curricula of three related academic departments were analysed to determine knowledge management tuition linkages. These curricula were then compared with the Skills Framework for the Information Age (SFIA) level descriptors. Following on from this desktop analysis, Sensemaker®, a distributed digital ethnographic methodology was piloted that will be used to collect micronarratives from emergent curriculum co-creators. This article identifies gaps in current curricula, expresses expectations for future possibilities and highlights potential niche opportunities for knowledge management curriculum design.
SPARCQ : enhancing scalability and adaptability of proactive edge caching through q-learning
Lall, Shruti; De Clercq, Johan; Pillay, Nelishia; Maharaj, Bodhaswar Tikanath Jugpershad (Institute of Electrical and Electronics Engineers, 2025-04)
The exponential growth of network traffic and data-intensive applications demands innovative solutions to manage data efficiently and ensure high-quality user experiences. Proactive edge caching has become a crucial technique for enhancing network performance by predicting and pre-storing content closer to users before access. Accurate prediction models, such as Long Short-Term Memory (LSTM) networks, are crucial for effective proactive caching. However, these models rely on carefully tuned hyperparameters to maintain predictive accuracy, and manual tuning is impractical in dynamic and diverse network environments, limiting scalability and adaptability. To overcome these challenges, we propose a novel framework, SPARCQ, that leverages Q-learning, a reinforcement learning algorithm, to automate hyperparameter tuning for LSTM-based prediction models. By dynamically adjusting hyperparameters, our approach ensures accurate predictions, improving caching efficiency and adaptability. Using the MovieLens dataset, we achieve an average improvement of 8% in cache hit ratios compared to baseline models, including popularity-based and untuned models. Additionally, our framework demonstrates scalability and robustness across geographically distributed regions, consistently adapting to diverse and evolving data patterns.
RanViz : ransomware visualization and classification based on time-series categorical representation of API calls
Mokoma, Vhuhwavho; Singh, Avinash (Institute of Electrical and Electronics Engineers, 2025-03)
Ransomware continues to pose a significant threat to individuals and organizations worldwide, causing disruptions, financial losses, and reputational damage. As ransomware attacks grow in sophistication, understanding their behaviour through effective analysis has become increasingly critical for mitigation and prevention. However, ransomware analysis presents several challenges. First, the sheer volume of Application Programming Interface (API) call data generated by ransomware during execution can overwhelm traditional analysis methods. Second, the temporal and categorical nature of this data makes identifying meaningful patterns complex. Third, the integration of machine learning (ML) models, which are essential for accurate classification, is hindered by the difficulty of modelling intricate API call behaviours. Without effective tools to address these issues, analysts risk missing critical behavioural indicators. To overcome these challenges, the proposed Ransomware Visualization (RanViz) system was developed to provide a comprehensive visual analytics and classification platform designed to enhance ransomware analysis. RanViz employs advanced visualization techniques to represent categorical API call time-series data, enabling analysts to intuitively understand ransomware behaviours that might otherwise remain obscured. The system incorporates ML models based on API call frequency, temporal interval, and sequence to classify unknown samples as either benign or ransomware. The models collectively achieve an accuracy of over 95% in detecting ransomware. By providing a unified platform that combines powerful visualization tools with high-performing ML models, RanViz simplifies ransomware analysis and offers a robust framework for accurate classification. This makes it an invaluable tool for digital forensics and cybersecurity professionals tasked with addressing the ever-evolving ransomware threat.
Balancing complexity and performance of machine learning models for avian pests sound detection in agricultural environments
Kazeneza, Micheline; Bosman, Anna Sergeevna; Amenyedzi, Destiny Kwabla; Hanyurwimfura, Damien; Ndashimye, Emmanuel; Vodacek, Anthony (Institute of Electrical and Electronics Engineers Inc., 2025-06)
Agricultural pest control traditionally relies on inefficient visual inspections. Acoustic monitoring offers a promising alternative by analyzing pest-specific sounds. While effective, implementing acoustic monitoring in agricultural settings faces practical constraints, particularly the limited computational resources available in remote farming environments. This necessitates optimized machine learning (ML) solutions for low-power edge devices. This study evaluates ML models for bird pest detection on resource-constrained platforms. We evaluated convolutional neural networks (CNNs), recurrent neural networks (RNNs), and traditional ML models by comparing standalone and knowledge-distilled versions of EfficientNetB0 and gated recurrent unity (GRU) against EfficientNetB4, Long short-term memory (LSTM), MobileNetV2, LightGBM, and support vector machine (SVM). Analysis revealed significant performance variations across computational requirements. LightGBM achieved 98% accuracy with minimal resources (8,500 parameters, 7KB, 0.6ms inference), demonstrating good efficiency. SVM (97% accuracy) and distilled GRU (86% accuracy) also showed favorable performance-to-resource ratios. Knowledge distillation substantially enhanced the accuracy of EfficientNetB0 (from 73% to 98%) and modestly improved GRU (from 84% to 86%). We examined platform compatibility across computing tiers, discovering that while high-performance edge devices (Jetson Nano, Raspberry Pi 4) support all studied models effectively, microcontrollers require specialized approaches. Advanced microcontrollers (such as ESP32-S3 and STM32H7) can accommodate optimized implementations, while highly constrained platforms (such as Arduino Nano) require TinyML techniques. This research contributes 1) an on-farm audio dataset, 2) comprehensive cross-model evaluation metrics, and 3) deployment optimization strategies for acoustic pest detection systems in resource-constrained agricultural environments.
EMD empowered neural network for predicting spatio-temporal non-stationary channel in UAV communications
Zhang, Qiuyun; Guo, Qiumei; Jiang, Hong; Yin, Xinfan; Mushtaq, Muhammad Umer; Luo, Ying; Wu, Chun (Springer, 2025-02)
This paper introduces a novel prediction method for spatio-temporal non-stationary channels between unmanned aerial vehicles (UAVs) and ground control vehicles, essential for the fast and accurate acquisition of channel state information (CSI) to support UAV applications in ultra-reliable and low-latency communication (URLLC). Specifically, an empirical mode decomposition (EMD)-empowered spatio-temporal attention neural network is proposed, referred to as EMD-STANN. The STANN sub-module within EMD-STANN is designed to capture the spatial correlation and temporal dependence of CSI. Furthermore, the EMD component is employed to handle the non-stationary and nonlinear dynamic characteristics of the UAV-to-ground control vehicle (U2V) channel, thereby enhancing the feature extraction and refinement capabilities of the STANN and improving the accuracy of CSI prediction. Additionally, we conducted a validation of the proposed EMD-STANN model across multiple datasets. The results indicated that EMD-STANN is capable of effectively adapting to diverse channel conditions and accurately predicting channel states. Compared to existing methods, EMD-STANN exhibited superior predictive performance, as indicated by its reduced root mean square error (RMSE) and mean absolute error (MAE) metrics. Specifically, EMD-STANN achieved a reduction of 24.66% in RMSE and 25.46% in MAE compared to the reference method under our simulation conditions. This improvement in prediction accuracy provides a solid foundation for the implementation of URLLC applications.
Misinformation detection : a review for high and low resource languages
Rananga, Seani; Modupe, Abiodun; Isong, Abiodun; Marivate, Vukosi (Universitas Bina Darma, 2024-12)
The rapid spread of misinformation on platforms like Twitter, and Facebook, and in news headlines highlights the urgent need for effective ways to detect it. Currently, researchers are increasingly using machine learning (ML) and deep learning (DL) techniques to tackle misinformation detection (MID) because of their proven success. However, this task is still challenging due to the complexity of deceptive language, digital editing tools, and the lack of reliable linguistic resources for non-English languages. This paper provides a comprehensive analysis of relevant research, providing insights into advanced techniques for MID. It covers dataset assessments, the importance of using multiple forms of data (multimodality), and different language representations. By applying the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) methodology, the study identified and analyzed literature from 2019 to 2024 across five databases: Google Scholar, Springer, Elsevier, ACM, and IEEE Xplore. The study selected thirty-one papers and examined the effectiveness of various ML and DL approaches with a focal point on performance metrics, datasets, and false or misleading information detection challenges. The findings indicate that most current MID models are heavily dependent on DL techniques, with approximately 81% of studies preferring these over traditional ML methods. In addition, most studies are text-based, with much less attention given to audio, speech, images, and videos. The most effective models are mainly designed for highresource languages, with English datasets being the most used (67%), followed by Arabic (14%), Chinese (11%), and others. Less than 10% of the studies focus on low-resource languages (LRLs). Therefore, the study highlighted the need for robust datasets and interpretable, scalable MID models for LRLs. It emphasizes the critical need to prioritize and advance MID research for LRLs across all data types, including text, audio, speech, images, videos, and multimodal approaches. This study aims to support ongoing efforts to combat misinformation and promote a more informed understanding of underresourced African languages.
Optimizing power allocation for URLLCD2D in 5G networks with Rician fading channel
Muhammad, Owais; Jiang, Hong; Bilal, Muhammad; Mushtaq, Muhammad Umer (PeerJ Inc., 2025-02)
The rapid evolution of wireless technologies within the 5G network brings significant challenges in managing the increased connectivity and traffic of mobile devices. This enhanced connectivity brings challenges for base stations, which must handle increased traffic and efficiently serve a growing number of mobile devices. One of the key solutions to address these challenges is integrating device-to-device (D2D) communication with ultra-reliable and low-latency communication (URLLC). This study examines the impact of the Rician fading channel on the performance of D2D communication under URLLC. It addresses the critical problem of optimizing power allocation to maximize the minimum data rate in D2D communication. A significant challenge arises due to interference issues, as the problem of maximizing the minimum data rate is non-convex, which leads to high computational complexity. This complexity makes it difficult to derive optimal solutions efficiently. To address this challenge, we introduce an algorithm that is based on derivatives to find the optimal power allocation. Comparisons are made with the branch and bound (B&B) algorithm, heuristic algorithm, and particle swarm optimization (PSO) algorithm. Our proposed algorithm improves power allocation performance and also achieves faster execution with lower computational complexity compared to the B&B, PSO, and heuristic algorithms.
Advances in energy harvesting for sustainable wireless sensor networks : challenges and opportunities
Mushtaq, Muhammad Umer; Venter, H.S. (Hein); Singh, Avinash; Owais, Muhammad (MDPI, 2025-03)
Energy harvesting wireless sensor networks (EH-WSNs) appear as the fundamental backbone of research that attempts to expand the lifespan and efficiency of sensor networks positioned in resource-constrained environments. This review paper provides an in-depth examination of latest developments in this area, highlighting the important components comprising routing protocols, energy management plans, cognitive radio applications, physical layer security (PLS), and EH approaches. Across a well-ordered investigation of these features, this article clarifies the notable developments in technology, highlights recent barriers, and inquires avenues for future revolution. This article starts by furnishing a detailed analysis of different energy harvesting methodologies, incorporating solar, thermal, kinetic, and radio frequency (RF) energy, and their respective efficacy in non-identical operational circumstances. It also inspects state-of-the-art energy management techniques aimed at optimizing energy consumption and storage to guarantee network operability. Moreover, the integration of cognitive radio into EH-WSNs is acutely assessed, highlighting its capacity to improve spectrum efficiency and tackle associated technological problems. The present work investigates ground-breaking methodologies in PLS that uses energy-harvesting measures to improve the data security. In this review article, these techniques are explored with respect to classical encryption and discussed from network security points of view as well. The assessment furthers criticizes traditional routing protocols and their significance in EH-WSNs as well as the balance that has long been sought between energy efficiency and security in this space. This paper closes with the importance of continuous research to tackle existing challenges and to leverage newly avail- able means as highlighted in this document. In order to adequately serve the increasingly changing requirements of EH-WSNs, future research will and should be geared towards incorporating AI techniques with some advanced energy storage solutions. This paper discusses the integration of novel methodologies and interdisciplinary advancements for better performance, security, and sustainability for WSNs.

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