To address the growing multi-user interference in dense wireless networks, we propose an interference-aware Deep Quantum Neural Network (DQNN) for channel estimation in the Non-orthogonal multiple access (NOMA) systems. The proposed method incorporates a hybrid classical-quantum architecture. A Transformer-encoder processes the pilot signals to extract spatiotemporal features. A parameterized quantum circuit maps the processed features into a high-dimensional Hilbert space. The enhancement hinges on an Adaptive Energy Valley Optimization (AEVO) algorithm, which modifies the optimization trajectory using interference-aware preconditioners derived from the interference covariance structure. With the aid of these preconditioners, the DQNN can steer through the NOMA's non-convex terrain characterized by interference to enhance estimation performance.  Moreover, interference-aware preconditioning is achieved through a lightweight neural network which adapts to time-varying interference. The successive interference cancellation decoder uses the estimated channel matrix to recover symbols. By further analysing the results, it is noticed that the quantum-enhanced machine learning delivers better results than the classical ones. The proposed framework enhances the state-of-the-art in NOMA channel estimation, while also providing a general framework for interference-aware optimization in quantum machine learning. At 10 dB SNR, the AEVO-DQNN method with a 16x16 antenna array obtained a minimum NMSE of 0.012288 and a minimum BER of 0.013023. Further, the proposed method outperforms the competing methods in terms of NMSE/BER mean with 95% confidence intervals, interference rejection ratio analysis, sensitivity to estimation error and estimated interference covariance, and paired t-test analysis.

Sungai Kunjang is one of the primary land transportation facilities in Samarinda City, East Kalimantan, located on Untung Suropati Street in the Karang Asam Ulu subdistrict. Officially inaugurated on June 24, 1989, by Mayor Waris Husain, it serves multiple transportation modes, including public passenger vehicles (PPV), pioneer services, and intercity routes. Although the station currently provides essential information services—such as departure schedules, route options, fare details, and a basic complaint system—these services are not yet supported by a structured Information Technology (IT) and Information System (IS) framework. The lack of integration hampers service efficiency and the optimization of business processes. This research aims to design an Enterprise Architecture (EA) for the station by applying The Open Group Architecture Framework (TOGAF) Architecture Development Method (ADM). The proposed design focuses on aligning business objectives with IT/IS strategies to improve the delivery of transport information and complaint management services. The resulting blueprint is expected to serve as a strategic reference for developing an integrated information system that enhances decision-making, streamlines operations, and improves service quality at Sungai Kunjang Station. By using selected phases of the TOGAF ADM, the study provides a practical foundation for digital transformation within public transport infrastructure in the region. 

This study examines the relationship betweenatmospheric factors [temperature and relative humidity] and network performance [call drop and Radio Resource Control] in Lokoja Metropolis. Monthly data collected from Globacom[GLO] office in Lokoja and Nigerian Meteorological Agency [NIMET] office in Lokoja over a one-year period reveals significant correlations between atmospheric variables [Temperature and relative humidity] and network performance [Call drop and Radio Resource Control]. We observed that call drop is directly proportional to temperature and relative humidity, while Radio Resource Control [RRC] has an inverse proportionality with temperature and relative humidity, assuming other meteorological variables are kept constant. Statistically, call drop showed a positive correlation of 0.35493 with temperature and 0.63769 with relative humidity, while RRC showed a positive correlation of 0.37289 with temperature and 0.5756 with relative humidity. Taken together, these findings indicated that increased temperature and humidity increased call drops and lower Radio Resource Control [RRC] success rates. This research has provided insights useful to the telecom operators and regulatory bodies to ensure network reliability, better resource allocation, environmental consideration and quality of service in tropical regions similar to Lokoja. Additionally, this research can also be useful in identifying key performance indicators, developing mitigation strategies, improving network maintenance and enhancing customer experience. Correlations were measured using Pearson correlation coefficients at a 5% significance level. These findings imply that network operators must account for atmospheric variability in optimizing reliability, resource allocation, and planning for mobile services in tropical climates. Despite being weak, this correlation is statistically significant and meaningful in real-world scenarios where multiple environmental and operational factors collectively influence call drops.

Image denoising remains a fundamental challenge in image processing, particularly when dealing with additive white gaussian noise (AWGN) that degrades visual quality and information content. This paper introduces a novel multi-stage denoising framework that uniquely combines Contourlet transform, radial basis function neural networks (RBFNN), and kalman filtering to effectively preserve important image features while removing noise. The contourlet transform first decomposes images into multi-resolution, directional subbands, providing a sparse representation that better captures geometric structures compared to traditional wavelet approaches. We then employ an RBFNN trained through back-propagation to adaptively threshold the contourlet coefficients based on local image characteristics and noise levels. Finally, kalman filtering is applied as a post-processing step to further suppress residual noise artifacts. Comprehensive experiments conducted on standard benchmark datasets demonstrate that our approach outperforms several state-of-the-art methods, including BM3D and recent deep learning-based techniques, particularly at moderate to high noise levels (σ ≥ 15). Quantitative evaluations show our method achieves superior PSNR improvements of up to 2.4dB and SSIM improvements of 0.12 compared to recent competing approaches, while qualitative results confirm better preservation of edges and textural details. The proposed framework offers an effective balance between computational efficiency and denoising performance, making it suitable for various practical applications.

The rapid growth of mobile wallet usage has led to a sharp increase in fraudulent transactions, making fraud detection in portable wallets a pressing concern. Accurately detecting fraud is difficult because transaction data is complicated and unbalanced. Conventional rule-based systems are less flexible and frequently provide large false positive rates along with poor accuracy. Effective feature selection is crucial to the performance of Machine Learning (ML) models, notwithstanding their increased detection rates. Redundancy and noise are introduced by high-dimensional data, which lowers model performance and raises computing costs. The advantages of hybrid feature selection are frequently overlooked in current research, particularly when it comes to portable wallet fraud detection. By combining Random Forest Importance, LASSO Regression, Recursive Feature Elimination (RFE), and Mutual Information (MI) with resampling to solve class imbalance, this study fills that gap. Our approach provides a more reliable and effective solution for safe portable wallet fraud detection by removing superfluous features, increasing accuracy, and reducing computing cost. The model becomes faster and more effective when superfluous characteristics are eliminated because this reduces the computational effort. By concentrating just on the most instructive data, it increases accuracy. By addressing class imbalance and combining several selection strategies, the hybrid approach guarantees robustness. All things considered, this leads to a scalable and safe fraud detection system for transactions using mobile wallets. Our results show that a successful feature selection approach improves fraud detection accuracy, which in turn improves operational effectiveness and financial security. 

Underwater imaging in recent times has advanced by trying to correct color distortion, increase contrast, and increase image clarity if the light need is less. The use of deep learning has been effective in enhancing image quality, but challenges persist in the decompression process due to data inconsistencies. In order to do this a new scheme is proposed in this study. Unlike other methods which depend only on the single images captured, here an attempt is made to use images taken in other conditions to overcome this limitation, by using the model to try and improve such underwater images in general irrespective of the water conditions. A key innovation is the disassembly and synthesis of multi-channel illuminance data. Specifically, we decompose the input image into its red, green, and blue frequencies, and then approximate the illuminance component within each channel. By independently manipulating and reconstructing these channel-specific illuminance maps, we can effectively address the non-uniform light scattering and absorption that are characteristic of underwater environments. This allows us to correct for the inherent color casts and haze that degrade image quality. To further refine the enhancement, we incorporate, advanced color correction methods such as image saliency exploration and white balance adjustment to compensate for color attenuation caused by light absorption at different depths. These techniques effectively restore lost colors and enhance contrast, thereby improving image clarity and sharpness.  This is helpful in the field of engineering and also forms the foundation for further exploring methods of improving images captured underwater. Investigational outcomes exhibit that the intended method ominously augments image eminence, making it highly effective for underwater detection and exploration tasks, offering an innovative solution for hazy images in various conditions and advancing underwater monitoring and exploration technologies.

Concept drift is a critical challenge in dynamic environments, where evolving data distributions can abruptly reduce predictive accuracy. Sudden drift requires reliable detection methods that minimize latency and false alarms, yet traditional detectors often depend on labeled data, delaying adaptation and limiting robustness.
This article introduces Neutrosophic Pseudo Labeling Sudden Drift Detection (N PSDD), a novel framework for unsupervised sudden drift detection based on neutrosophic theory. The method integrates neutrosophic clustering for pseudo labeling, block wise neural modeling, drift quantification via neutrosophic mean deviation, and adaptive threshold evaluation. By explicitly modeling truth, indeterminacy, and falsity, N PSDD captures uncertainty regions that conventional probabilistic measures fail to represent.
Experimental validation on synthetic and real world datasets demonstrates that N PSDD achieves competitive dtection latency (MTTD ≈ 23–35 instances), a lower false alarm rate (FAR ≤ 3.1%), a reduced missing drift rate (MDR ≤ 2.5%), and consistently higher G mean values (up to 0.91) than benchmark methods do. For example, on the Poker Hand dataset, N PSDD achieved MCC = 0.846 and accuracy ≈90%, while on electricity it reached MCC = 0.623 with FAR = 3.1%. In contrast, unsupervised baselines (KS WIN, HDD, MMD) yielded higher FAR (≈6–10%) and lower MCC (≤0.56), confirming their limitations in capturing real concept drift.
Overall, the N PSDD enhances the resilience of learning models under non stationary conditions and provides a robust solution for real time applications, including financial forecasting, fraud detection, and adaptive control systems.

Urban traffic congestion can be considered as a significant problem, and it contributes to long travel periods, fuel usage, and environmental influence. This paper introduces an Intelligent Traffic Control System (ITCS) that consists of Vehicular Ad Hoc Networks (VANETs) and Reinforcement Learning (RL) to optimise the control of traffic signals. The system facilitates real-time two-way communication between vehicles and roadside units, which means that an RL agent can control signal phases adaptively according to the traffic metrics like the average delay, the queue length, and traffic throughput. The Kaggle VANET Malicious Node Dataset was used to simulate malicious or unreliable nodes and test the robustness of the systems. The RL agent has been trained on the SUMO simulator trained on TraCI through various episodes and learns to take actions that increase traffic movement with a minimum amount of congestion. The results of training are progressive, as cumulative rewards grow, and average delays and queue length reduce with epochs. Performance evaluation of the ITCS under peak-hour, off-peak, incident, and malicious node scenarios demonstrated substantial gains over conventional fixed-time controllers, with average delays reduced by 48–55%, queue lengths by 49–57%, and throughput increased by 28–35%. These results indicate the success of the blend of reinforcement learning with VANET-supported traffic control, which is an adaptive, data-driven, and robust solution to an urban intersection. Not only the RL-based ITCS enhances traffic flow and congestion, but is also resistant to communication anomalies, which indicates its scalability to be deployed in the current smart city traffic management.

The efficient allocation of finite resources to a dynamic patron base represents a core challenge in modern library management. Traditional heuristic approaches often lack the formal rigor needed for verifiable optimization and proactive planning. This paper introduces a novel formal framework grounded in automata theory to model library operations, patron behavior, and resource allocation strategies. We define a Library Resource Automaton (LRA), a deterministic finite automaton whose states represent distinct configurations of resource availability, whose input alphabet encapsulates patron interactions, and whose transition function formally encodes allocation policies. By interpreting sequences of patron actions as strings in a formal language, the LRA provides a computationally tractable and analytically powerful model for simulating library states, predicting bottlenecks, and synthesizing optimal allocation strategies. We elaborate on the theoretical foundations of the model, present a detailed multi-layer automata architecture for handling complex, multi-resource scenarios, and discuss algorithms for state space analysis and policy optimization. Furthermore, we explore the integration of temporal logic for specifying and verifying critical system properties such as fairness and liveness. This work establishes a rigorous bridge between theoretical computer science and library information science, offering a new paradigm for building predictable, efficient, and patron-centric library management systems.

In this manuscript, A compact geometrical configuration and simplified structure of a monopole antenna is given which is functional over an UWB frequency range (3.1GHz to 10.6GHz). The focus of the study is to design a compact and low-cost antenna that can provide an extended impedance bandwidth while maintaining stable and reliable radiation performance suitable for current wireless applications. The proposed design incorporates modifications to both the radiating patch and the ground plane to enhance impedance matching and improve overall radiation performance. Full-wave electromagnetic simulations are conducted to analyse these improvements, and a prototype is fabricated to validate the design experimentally. The measured results closely correspond with the simulated response, confirming wideband operation, consistent radiation patterns, and satisfactory gain levels required for UWB communication. The Proposed antenna design outperforms previous research due to its small size, wide bandwidth and high gain making it an excellent option for UWB systems. Because of its compact footprint, dependable wideband response, and simple fabrication process, the antenna is well suitable for portable and sensing-based UWB applications.