The use of credit cards has recently increased, creating an essential need for credit card assessment methods to minimize potential risks. This study investigates the utilization of machine learning (ML) models for credit card default prediction system. The main goal here is to investigate the best-performing ML model for new proposed credit card scoring dataset. This new dataset includes credit card transaction histories and customer profiles, is proposed and tested using a variety of machine learning algorithms, including logistic regression, decision trees, random forests, multi-layer perceptron (MLP) neural network, XGBoost, and LightGBM. To prepare the data for machine learning models, we perform data pre-processing, feature extraction, feature selection, and data balancing techniques. Experimental results demonstrate that MLP outperforms logistic regression, decision trees, random forests, LightGBM, and XGBoost in terms of predictive performance in true positive rate, achieving an impressive area under the curve (AUC) of 86.7% and an accuracy rate of 91.6%, with a recall rate exceeding 80%. These results indicate the superiority of MLP in predicting the default customers and assessing the potential risks. Furthermore, they help banks and other financial institutions in predicting loan defaults at an earlier stage.

This paper presents a reinforcement learning (RL) based approach for path planning of cellular connected unmanned aerial vehicles (UAVs) operating beyond visual line of sight (BVLoS). The objective is to minimize travel distance while maximizing the quality of cellular link connectivity by considering real world aerial coverage constraints and employing an empirical aerial channel model. The proposed solution employs RL techniques to train an agent, using the quality of communication links between the UAV and base stations (BSs) as the reward function. Simulation results demonstrate the effectiveness of the proposed method in training the agent and generating feasible UAV path plans. The proposed approach addresses the challenges due to limitations in UAV cellular communications, highlighting the need for investigations and considerations in this area. The RL algorithm efficiently identifies optimal paths, ensuring maximum connectivity with ground BSs to ensure safe and reliable BVLoS flight operation. Moreover, the solution can be deployed as an offline path planning module that can be integrated into future ground control systems (GCS) for UAV operations, enhancing their capabilities and safety. The method holds potential for complex long range UAV applications, advancing the technology in the field of cellular connected UAV path planning.

Surface electromyography (sEMG) signal holds great potential in the research fields of gesture recognition and the development of robust prosthetic hands. However, the sEMG signal is compromised with physiological or dynamic factors such as forearm orientations, electrode displacement, limb position, etc. The existing dataset of sEMG is limited as they often ignore these dynamic factors during recording. In this paper, we have proposed a dataset of multichannel sEMG signals to evaluate common daily living hand gestures performed with three forearm orientations. The dataset is collected from nineteen intact-limed subjects, performing twelve hand gestures with three forearm orientations: supination, rest, and this http URL, two electrode placement positions (elbow and forearm) are considered while recording the sEMG signal. The dataset is open for public access in MATLAB file format. The key purpose of the dataset is to offer an extensive resource for developing a robust machine learning classification algorithm and hand gesture recognition applications. We validated the high quality of the dataset by assessing the signal quality matrices and classification performance, utilizing popular machine learning algorithms, various feature extraction methods, and variable window size. The obtained result highlighted the significant potential of this novel sEMG dataset that can be used as a benchmark for developing hand gesture recognition systems, conducting clinical research on sEMG, and developing human-computer interaction applications. Dataset:this https URL

Enhancing images in low-light conditions is an important challenge in computer vision. Insufficient illumination negatively affects the quality of images, resulting in low contrast, intensive noise, and blurred details. This paper presents a model for enhancing low-light images called tuning adaptive gamma correction (TAGC). The model is based on analyzing the color luminance of the low-light image and calculating the average color to determine the adaptive gamma coefficient. The gamma value is calculated automatically and adaptively at different illumination levels suitable for the image without human intervention or manual adjustment. Based on qualitative and quantitative evaluation, tuning adaptive gamma correction model has effectively improved low-light images while maintaining details, natural contrast, and correct color distribution. It also provides natural visual quality. It can be considered a more efficient solution for processing low-light images in multiple applications such as night surveillance, improving the quality of medical images, and photography in low-light environments.

We present a census of the Compton-thick (CT) active galactic nucleus (AGN) population and the column density ($N_{\rm{H}}$) distribution of AGN in our cosmic backyard using a mid-infrared selected AGN sample within 15 Mpc. The column densities are measured from broadband X-ray spectral analysis, mainly using data from $\textit{Chandra}$ and $\textit{NuSTAR}$. Our sample probes AGN with intrinsic 2-10 keV luminosities of $L_{\rm 2-10, int} = 10^{37}$-$10^{43}$ erg s$^{-1}$, reaching a parameter space inaccessible to more distant samples. We directly measure a 32$^{+30}_{-18}\%$ CT AGN fraction and obtain an $N_{\rm{H}}$ distribution that agrees with that inferred by the $\textit{Swift}$-BAT survey. Restricting the sample to the largely unexplored domain of low-luminosity AGN with $L_{\rm 2-10, int}$ $\leq$ $10^{42}$ erg s$^{-1}$, we found a CT fraction of 19$^{+30}_{-14}\%$, consistent with those observed at higher luminosities. Comparing the host-galaxy properties between the two samples, we find consistent star formation rates, though the majority of our galaxy have lower stellar masses (by $\approx 0.3$ dex). In contrast, the two samples have very different black hole mass ($M_{\rm BH}$) distributions, with our sample having $\approx$1.5 dex lower mean mass ($M_{\rm BH}$$\sim$10$^{6}$$M_\odot$). Additionally, our sample contains a significantly higher number of LINERs and H$_{\rm{II}}$-type nuclei. The Eddington ratio range probed by our sample, however, is the same as $\textit{Swift}$-BAT, although the latter dominates at higher accretion rates, and our sample is more evenly distributed. The majority of our sample with $\lambda_{\rm Edd} \ge$ 10$^{-3}$ tend to be CT, while those with $\lambda_{\rm Edd} <$10$^{-3}$ are mostly unobscured or mildly obscured.

Urban cellular networks face complex performance challenges due to high infrastructure density, varied user mobility, and diverse service demands. While several datasets address network behaviour across different environments, there is a lack of datasets that captures user centric Quality of Experience (QoE), and diverse mobility patterns needed for efficient network planning and optimization solutions, which are important for QoE driven optimizations and mobility management. This study presents a curated dataset of 30,925 labelled records, collected using GNetTrack Pro within a 2 km2 dense urban area, spanning three major commercial network operators. The dataset captures key signal quality parameters (e.g., RSRP, RSRQ, SNR), across multiple real world mobility modes including pedestrian routes, canopy walkways, shuttle buses, and Bus Rapid Transit (BRT) routes. It also includes diverse network traffic scenarios including (1) FTP upload and download, (2) video streaming, and (3) HTTP browsing. A total of 132 physical cell sites were identified and validated through OpenCellID and on-site field inspections, illustrating the high cell density characteristic of 5G and emerging heterogeneous network deployment. The dataset is particularly suited for machine learning applications, such as handover optimization, signal quality prediction, and multi operator performance evaluation. Released in a structured CSV format with accompanying preprocessing and visualization scripts, this dataset offers a reproducible, application ready resource for researchers and practitioners working on urban cellular network planning and optimization.

This study aims to investigate the teachers perceptions on the use of visual aids (e.g., animation videos, pictures, films and projectors) as a motivational tool in enhancing students interest in reading literary texts. To achieve the aim of the study, the mixed-method approach was used to collect the required data. Therefore, 52 English teachers from seven national secondary schools in Kapit, Sarawak, Malaysia were selected. Five of the respondents were also randomly selected for the interview. The analysis of the data indicated that the majority of the teachers had positive perceptions of the use of visual aids. The use of visual aids enable the teachers to engage their students closely with the literary texts despite of being able to facilitate students of different English proficiency level in reading the texts with interest. This aspect is vital as literature helps to generate students creative and critical thinking skills. Although the teachers had positive attitudes towards the use of visual aids, the study suggests that it will be more interesting and precise if it includes students perceptions as well.

Trusting the accuracy of data inputted on online platforms can be difficult due to the possibility of malicious websites gathering information for unlawful reasons. Analyzing each website individually becomes challenging with the presence of such malicious sites, making it hard to efficiently list all Uniform Resource Locators (URLs) on a blacklist. This ongoing challenge emphasizes the crucial need for strong security measures to safeguard against potential threats and unauthorized data collection. To detect the risk posed by malicious websites, it is proposed to utilize Machine Learning (ML)-based techniques. To this, we used several ML techniques such as Hist Gradient Boosting Classifier (HGBC), K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM) for detection of the benign and malicious website dataset. The dataset used contains 1781 records of malicious and benign website data with 13 features. First, we investigated missing value imputation on the dataset. Then, we normalized this data by scaling to a range of zero and one. Next, we utilized the Synthetic Minority Oversampling Technique (SMOTE) to balance the training data since the data set was unbalanced. After that, we applied ML algorithms to the balanced training set. Meanwhile, all algorithms were optimized based on grid search. Finally, the models were evaluated based on accuracy, precision, recall, F1 score, and the Area Under the Curve (AUC) metrics. The results demonstrated that the HGBC classifier has the best performance in terms of the mentioned metrics compared to the other classifiers.

Scanning Electron Microscopy (SEM) is critical in nanotechnology, materials science, and biological imaging due to its high spatial resolution and depth of focus. Signal-to-noise ratio (SNR) is an essential parameter in SEM because it directly impacts the quality and interpretability of the images. SEM is widely used in various scientific disciplines, but its utility can be compromised by noise, which degrades image clarity. This review explores multiple aspects of the SEM imaging process, from the principal operation of SEM, sources of noise in SEM, methods for SNR measurement and estimations, to various aspects that affect the SNR measurement and approaches to enhance SNR, both from a hardware and software standpoint. We review traditional and emerging techniques, focusing on their applications, advantages, and limitations. The paper aims to provide a comprehensive understanding of SNR optimization in SEM for researchers and practitioners and to encourage further research in the field.

The Internet of Things (IoT) has been increasingly used in our everyday lives as well as in numerous industrial applications. However, due to limitations in computing and power capabilities, IoT devices need to send their respective tasks to cloud service stations that are usually located at far distances. Having to transmit data far distances introduces challenges for services that require low latency such as industrial control in factories and plants as well as artificial intelligence assisted autonomous driving. To solve this issue, mobile edge computing (MEC) is deployed at the networks edge to reduce transmission time. In this regard, this study proposes a new offloading scheme for MEC-assisted ultra dense cellular networks using reinforcement learning (RL) techniques. The proposed scheme enables efficient resource allocation and dynamic offloading decisions based on varying network conditions and user demands. The RL algorithm learns from the networks historical data and adapts the offloading decisions to optimize the networks overall performance. Non-orthogonal multiple access is also adopted to improve resource utilization among the IoT devices. Simulation results demonstrate that the proposed scheme outperforms other stateof the art offloading algorithms in terms of energy efficiency, network throughput, and user satisfaction.

Recommender system has been proven to be significantly crucial in many fields and is widely used by various domains. Most of the conventional recommender systems rely on the numeric rating given by a user to reflect his opinion about a consumed item; however, these ratings are not available in many domains. As a result, a new source of information represented by the user-generated reviews is incorporated in the recommendation process to compensate for the lack of these ratings. The reviews contain prosperous and numerous information related to the whole item or a specific feature that can be extracted using the sentiment analysis field. This paper gives a comprehensive overview to help researchers who aim to work with recommender system and sentiment analysis. It includes a background of the recommender system concept, including phases, approaches, and performance metrics used in recommender systems. Then, it discusses the sentiment analysis concept and highlights the main points in the sentiment analysis, including level, approaches, and focuses on aspect-based sentiment analysis.

Managing heterogeneous network systems is a difficult task because each of these networks has its own curious management system. These networks usually are constructed on independent management protocols which are not compatible with each other. This results in the coexistence of many management systems with different managing functions and services across enterprises. Incompatibility of different management systems makes management of whole system a very complex and often complicated job. Ideally, it is necessary to implement centralized metalevel management across distributed heterogeneous systems and their underlying supporting network systems where the information flow and guidance is provided via a single console or single operating panels which integrates all the management functions in spite of their individual protocols and structures. This paper attempts to provide a novel network management tool architecture which supports heterogeneous managements across many different architectural platforms. Furthermore, an architectural approach to integrate heterogeneous network is proposed. This architecture takes into account both wireless fixed and mobile nodes.

The successful execution of a construction project is heavily impacted by making the right decision during tendering processes. Managing tender procedures is very complex and uncertain involving coordination of many tasks and individuals with different priorities and objectives. Bias and inconsistent decision are inevitable if the decision-making process is totally depends on intuition, subjective judgement or emotion. In making transparent decision and healthy competition tendering, there exists a need for flexible guidance tool for decision support. Aim of this paper is to give a review on current practices of Decision Support Systems (DSS) technology in construction tendering processes. Current practices of general tendering processes as applied to the most countries in different regions such as United States, Europe, Middle East and Asia are comprehensively discussed. Applications of Web-based tendering processes is also summarised in terms of its properties. Besides that, a summary of Decision Support System (DSS) components is included in the next section. Furthermore, prior researches on implementation of DSS approaches in tendering processes are discussed in details. Current issues arise from both of paper-based and Web-based tendering processes are outlined. Finally, conclusion is included at the end of this paper.

The success of autonomous navigation relies on robust and precise vehicle recognition, hindered by the scarcity of region-specific vehicle detection datasets, impeding the development of context-aware systems. To advance terrestrial object detection research, this paper proposes a native vehicle detection dataset for the most commonly appeared vehicle classes in Bangladesh. 17 distinct vehicle classes have been taken into account, with fully annotated 81542 instances of 17326 images. Each image width is set to at least 1280px. The dataset's average vehicle bounding box-to-image ratio is 4.7036. This Bangladesh Native Vehicle Dataset (BNVD) has accounted for several geographical, illumination, variety of vehicle sizes, and orientations to be more robust on surprised scenarios. In the context of examining the BNVD dataset, this work provides a thorough assessment with four successive You Only Look Once (YOLO) models, namely YOLO v5, v6, v7, and v8. These dataset's effectiveness is methodically evaluated and contrasted with other vehicle datasets already in use. The BNVD dataset exhibits mean average precision(mAP) at 50% intersection over union (IoU) is 0.848 corresponding precision and recall values of 0.841 and 0.774. The research findings indicate a mAP of 0.643 at an IoU range of 0.5 to 0.95. The experiments show that the BNVD dataset serves as a reliable representation of vehicle distribution and presents considerable complexities.

Neglected tropical diseases (NTDs) continue to affect the livelihood of individuals in countries in the Southeast Asia and Western Pacific region. These diseases have been long existing and have caused devastating health problems and economic decline to people in low- and middle-income (developing) countries. An estimated 1.7 billion of the world's population suffer one or more NTDs annually, this puts approximately one in five individuals at risk for NTDs. In addition to health and social impact, NTDs inflict significant financial burden to patients, close relatives, and are responsible for billions of dollars lost in revenue from reduced labor productivity in developing countries alone. There is an urgent need to better improve the control and eradication or elimination efforts towards NTDs. This can be achieved by utilizing machine learning tools to better the surveillance, prediction and detection program, and combat NTDs through the discovery of new therapeutics against these pathogens. This review surveys the current application of machine learning tools for NTDs and the challenges to elevate the state-of-the-art of NTDs surveillance, management, and treatment.

Theoretical investigation of two-proton halo-nucleus 17Ne has revealed that the valence protons are more likely to be positioned in the d-state than the s-state. In this study, this finding is clarified by calculation of the binding energy, it is found that the theoretical values for the d-state are closer to the experimental values, in contrast with those obtained for the s-state. The three-body model and MATLAB software are utilised to obtain theoretical values for the three-body-model 17Ne binding energy and matter radius. 17Ne has halo properties of a weakly bound valence proton, a binding energy of less than 1 MeV, and a large matter radius. The core deformation parameter has zero and negative values; thus, the 17Ne core exhibits both spherical and oblate shapes depending on the binding energy of the three-body system. This suggests 17Ne has two-proton halo.

Hard X-ray-selected samples of Active Galactic Nuclei (AGN) provide one of the cleanest views of supermassive black hole accretion, but are biased against objects obscured by Compton-thick gas column densities of $N_{\rm H}$ &gt; 10$^{24}$ cm$^{-2}$. To tackle this issue, we present the NuSTAR Local AGN $N_{\rm H}$ Distribution Survey (NuLANDS)$-$a legacy sample of 122 nearby ($z$ &lt; 0.044) AGN primarily selected to have warm infrared colors from IRAS between 25$-$60 $\mu$m. We show that optically classified type 1 and 2 AGN in NuLANDS are indistinguishable in terms of optical [OIII] line flux and mid-to-far infrared AGN continuum bolometric indicators, as expected from an isotropically selected AGN sample, while type 2 AGN are deficient in terms of their observed hard X-ray flux. By testing many X-ray spectroscopic models, we show the measured line-of-sight column density varies on average by $\sim$ 1.4 orders of magnitude depending on the obscurer geometry. To circumvent such issues we propagate the uncertainties per source into the parent column density distribution, finding a directly measured Compton-thick fraction of 35 $\pm$ 9%. By construction, our sample will miss sources affected by severe narrow-line reddening, and thus segregates sources dominated by small-scale nuclear obscuration from large-scale host-galaxy obscuration. This bias implies an even higher intrinsic obscured AGN fraction may be possible, although tests for additional biases arising from our infrared selection find no strong effects on the measured column-density distribution. NuLANDS thus holds potential as an optimized sample for future follow-up with current and next-generation instruments aiming to study the local AGN population in an isotropic manner.

Ever since the discovery of the first Active Galactic Nuclei (AGN), substantial observational and theoretical effort has been invested into understanding how massive black holes have evolved across cosmic time. Circum-nuclear obscuration is now established as a crucial component, with almost every AGN observed known to display signatures of some level of obscuration in their X-ray spectra. But despite more than six decades of effort, substantial open questions remain: How does the accretion power impact the structure of the circum-nuclear obscurer? What are the dynamical properties of the obscurer? Can dense circum-nuclear obscuration exist around intrinsically weak AGN? How many intermediate mass black holes occupy the centers of dwarf galaxies? In this paper, we showcase a number of next-generation prospects attainable with the High Energy X-ray Probe (this https URL) to contribute towards solving these questions in the 2030s. The uniquely broad (0.2--80 keV) and strictly simultaneous X-ray passband of HEX-P makes it ideally suited for studying the temporal co-evolution between the central engine and circum-nuclear obscurer. Improved sensitivities and reduced background will enable the development of spectroscopic models complemented by current and future multi-wavelength observations. We show that the angular resolution of HEX-P both below and above 10 keV will enable the discovery and confirmation of accreting massive black holes at both low accretion power and low black hole masses even when concealed by thick obscuration. In combination with other next-generation observations of the dusty hearts of nearby galaxies, HEX-P will hence be pivotal in paving the way towards a complete picture of black hole growth and galaxy co-evolution.

The Malaysian satellite RazakSAT-1 was designed to operate in a near-equatorial orbit (NEqO) and low earth orbit (LEO). However, after one year of operation in 2010, communication to the satellite was lost. This study attempted to identify whether space radiation sources could have caused the communication loss by comparing RazakSAT-1 with two functional satellites. Data on galactic cosmic rays (GCR), trapped protons, trapped electrons, and solar energetic particles (SEPs) obtained from Space Environment Information System (SPENVIS) was analyzed.

We present a catalog of hard X-ray serendipitous sources detected in the first 80 months of observations by the Nuclear Spectroscopic Telescope Array (NuSTAR). The NuSTAR serendipitous survey 80-month (NSS80) catalog has an unprecedented $\sim$ 62 Ms of effective exposure time over 894 unique fields (a factor of three increase over the 40-month catalog), with an areal coverage of $\sim$36 deg$^2$, larger than all NuSTAR extragalactic surveys. NSS80 provides 1274 hard X-ray sources in the $3-24$ keV band (822 new detections compared to the previous 40-month catalog). Approximately 76% of the NuSTAR sources have lower-energy (&lt;10 keV) X-ray counterparts from Chandra, XMM-Newton, and Swift-XRT. We have undertaken an extensive campaign of ground-based spectroscopic follow-up to obtain new source redshifts and classifications for 427 sources. Combining these with existing archival spectroscopy provides redshifts for 550 NSS80 sources, of which 547 are classified. The sample is primarily composed of active galactic nuclei (AGN), detected over a large range in redshift ($z$ = 0.012-3.43), but also includes 58 spectroscopically confirmed Galactic sources. In addition, five AGN/galaxy pairs, one dual AGN system, one BL Lac candidate, and a hotspot of 4C 74.26 (radio quasar) have been identified. The median rest-frame $10-40$ keV luminosity and redshift of the NSS80 are $\langle{L_\mathrm{10-40 keV}}\rangle$ = 1.2 $\times$ 10$^{44}$ erg s$^{-1}$ and $\langle z \rangle = 0.56$. We investigate the optical properties and construct composite optical spectra to search for subtle signatures not present in the individual spectra, finding an excess of redder BL AGN compared to optical quasar surveys predominantly due to the presence of the host-galaxy and, at least in part, due to dust obscuration.