Miracle-dz/UniFAR-FTUs huggingface dataset:
Credit Risk Customers Categorization with Random Forest Classifier using Various Searching Techniques

Effective credit risk assessment holds significant importance for financial institutions and businesses extending credit to customers. It is crucial to evaluate customers’ creditworthiness to minimize default risks and manage financial exposure. This study focuses on enhancing credit risk customer classification through optimized feature selection and classification techniques. The dataset is preprocessed using Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and enhance performance. Relevant features are then extracted utilizing Cfs Subset evaluation along with various search algorithms. The optimized features are subsequently employed for classification using the Random Forest (RF) classifier. Experimental results reveal that, in credit customer classification, Evolutionary, Tabu, and Scatter search algorithms combined with RF classifier demonstrate a commendable accuracy of 90.31%. The proposed framework also showcases comparable outcomes in terms of other performance metrics. This automated credit customer classification method proves to be highly effective in identifying fraudulent users.
Entity Clustering of User Reviews Using Topic Modelling and Similarity Scores

The popularity of online review sites such as Amazon and TripAdvisor have created a window into rich insights of what matters to reviewers, and potentially customers. For the business sector, this provides opportunities to improve their businesses and to attract more customers. The unstructured nature of user reviews, however, makes it difficult to analyze, and natural language processing techniques are instrumental in extracting useful information from these resources. To digest the free form review texts, it is desirable to abstract the reviews into high level themes. However, research works in this area is still scarce. In this paper, the process taken to perform this high-level abstraction is detailed, with an example derived from the hotel industry (identity concealed for confidentiality purpose). More than 3000 sets of customer reviews were processed using the Extract, Transform and Load process, supported by topic modelling algorithms including Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA) and K-means. The study aims to identify significant business aspects or features based on the customer reviews. Preliminary results from these algorithms lack usability, which can be mainly attributed to unknown number of hidden topics and the brief nature of the review texts. To address this issue, a cosine similarity-based algorithm was introduced to improve the thematic clustering into one that is more intuitive and actionable. Ten themes emerged, and using cosine similarity, the review texts in the corpus were intuitively clustered for business planning insights. With this method, the review texts can be classified in a matter of a few minutes and demonstrates the strength of natural language processing for insights mining.
An assessment of muslim user behaviour on Facebook utilizing the UTAUT model

Facebook is a popular social networking tool that has changed the behaviour and daily interactions of Muslims. The present investigation employs a quantitative methodology for data collection by disseminating a survey over several social media platforms. The present study employed the purposive sampling strategy to effectively acquire a sample size of 385 individuals residing in the Klang Valley region. This study utilises the unified theory of acceptance and use of technology (UTAUT) framework to examine the potential influence of Performance expectancy, effort expectancy, social influence, and behaviour intention on the Facebook behaviour of individuals who identify as Muslim. The findings of the study indicate that there is a relationship between performance expectancy (PE) and social influence (SI) and the level of Muslim Facebook engagement. Moreover, a significant correlation exists between age and both PE and effort expectancy (EE). In contrast, age exhibits a negative correlation with both experience and gender. Anticipated outcomes of future research endeavours include the creation of an innovative social media platform specifically tailored for the Muslim community, including the principles of Muslim centred user interface design (MCUID).
Quality of Life in the Elderly Qur'an Memorizers

The present study aimed to investigate the quality of life of elderly Qur'an memorizers. Nine elderly Muslims who live in several regions in Java Island, Indonesia, were involved as informants. The informant was recruited by purposive sampling. The data collection method used a semi-structured interview technique. Data analysis used phenomenological techniques to uncover research objectives. The result showed that: In general, the memorizing Qur'an has an important influence on the elderly's quality of life. Specifically, the quality of life of the elderly can be seen in the improvement of several areas such as patience, self-concept, self-esteem, ability to remember, and self-confidence. In addition, the elderly felt that their role was more accepted by the community, one of whom was trusted to teach the Qur'an in the mosque.
Melanoma skin cancer detection using mask-RCNN with modified GRU model

Introduction: Melanoma Skin Cancer (MSC) is a type of cancer in the human body; therefore, early disease diagnosis is essential for reducing the mortality rate. However, dermoscopic image analysis poses challenges due to factors such as color illumination, light reflections, and the varying sizes and shapes of lesions. To overcome these challenges, an automated framework is proposed in this manuscript. Methods: Initially, dermoscopic images are acquired from two online benchmark datasets: International Skin Imaging Collaboration (ISIC) 2020 and Human against Machine (HAM) 10000. Subsequently, a normalization technique is employed on the dermoscopic images to decrease noise impact, outliers, and variations in the pixels. Furthermore, cancerous regions in the pre-processed images are segmented utilizing the mask-faster Region based Convolutional Neural Network (RCNN) model. The mask-RCNN model offers precise pixellevel segmentation by accurately delineating object boundaries. From the partitioned cancerous regions, discriminative feature vectors are extracted by applying three pre-trained CNN models, namely ResNeXt101, Xception, and InceptionV3. These feature vectors are passed into the modified Gated Recurrent Unit (GRU) model for MSC classification. In the modified GRU model, a swish-Rectified Linear Unit (ReLU) activation function is incorporated that efficiently stabilizes the learning process with better convergence rate during training. Results and discussion: The empirical investigation demonstrate that the modified GRU model attained an accuracy of 99.95% and 99.98% on the ISIC 2020 and HAM 10000 datasets, where the obtained results surpass the conventional detection models.
Understanding the Language of ADHD and Autism Communities on Social Media

Health communities online are popular for individuals to discuss health challenges and exchange social support. With social media, online communities also benefit neurodivergent individuals, by creating inclusive spaces where sharing of experience and knowledge is encouraged. The discussion in online communities covers a wide range of topics. As a result, the discussions differ in terms of topics, tone, and approach. This paper presents an analysis of social media posts shared on Reddit communities on Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD) between 2018 and 2020. In the study, we use a computer-aided model to identify prevalent topics in each subreddit and common themes. We conduct a comparative analysis of the communities and assess theme frequency and sentiment. The study highlights common topics found in r/adhd and r/autism subreddits, including diagnosis, treatment (medication dose and side effects), and social aspects (school, work, and peer interactions).
Water information management system and low flow analysis in Slovenia

Low flow estimation is essential for water management in Slovenia. Therefore Slovenia is subdivided into 572 sub-basins. In the analysis of daily flows two low flow parameters were used: i.e. the streamflow which is exceeded in 95% of days (Q95), and the 20-year return period of the annual minimum flow (20Q). Specific flow characteristics (q95 and 20q) were also computed for all available gauging stations. The 20q was determined for each sub-basin and a national map was made using a GIS. A nonlinear regression model obtained from 11 experimental basins in Slovenia was used to calculate the low flow parameters for gauging stations and each sub-basin by GIS. Measured and calculated low flow characteristics for gauging stations and sub-basins were analysed. The methodology and derived relationships are already useful for water management, although analysis methods still need to be improved because of observed discrepancies between measured and calculated characteristics.
Prediction of Stock Market Price using Bi-directional Recurrent Neural Network

Recently, an accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. The advent of artificial intelligence and enhanced processing power has led to the realisation that preprogrammed techniques are more effective at forecasting stock values. The stock market is currently one of the most researched and fastest developing subj ects, and predicting its behaviour is crucial. The primary challenge in this area of research remains improving forecast precision. Strong shareholder returns are made possible by stock price forecasting. Despite this, researchers use these algorithms to predict continuing market trends based on Stock Technical Indicators (STIs), due to recent advancements in machine learning techniques. This study analyses Yahoo Finance data from STIs over a decade to predict stock prices using a Bi- directional Recurrent Neural Network (BI-RNN). Initially, the analysed STIs were used as input for an autoencoder during the dimensionality reduction procedure, which reduced the correlation between the STIs. These STIs and the Yahoo Finance data were then provided to BI-RNN. Next, in order to forecast stock prices, the BI-RNN's outcome attributes were fed into the soft max layer. From the results, it clearly shows that proposed Bi-RNN shown better results in terms of a minimal MAPE value of 0.41, MAE of 4.42, RMSE of 0.10, and MSE of 212.25 in the experiments, the proposed strategy surpassed traditional methods.
Identifying and Exploring the Impact Factors for Intraocular Pressure Prediction in Myopic Children with Atropine Control Utilizing Multivariate Adaptive Regression Splines

Purpose: The treatment of childhood myopia often involves the use of topical atropine, which has been demonstrated to be effective in decelerating the progression of myopia. It is crucial to monitor intraocular pressure (IOP) to ensure the safety of topical atropine. This study aims to identify the optimal machine learning IOP-monitoring module and establish a precise baseline IOP as a clinical safety reference for atropine medication. Methods: Data from 1545 eyes of 1171 children receiving atropine for myopia were retrospectively analyzed. Nineteen variables including patient demographics, medical history, refractive error, and IOP measurements were considered. The data were analyzed using a multivariate adaptive regression spline (MARS) model to analyze the impact of different factors on the End IOP. Results: The MARS model identified age, baseline IOP, End Spherical, duration of previous atropine treatment, and duration of current atropine treatment as the five most significant factors influencing the End IOP. The outcomes revealed that the baseline IOP had the most significant effect on final IOP, exhibiting a notable knot at 14 mmHg. When the baseline IOP was equal to or exceeded 14 mmHg, there was a positive correlation between atropine use and End IOP, suggesting that atropine may increase the End IOP in children with a baseline IOP greater than 14 mmHg. Conclusions: MARS model demonstrates a better ability to capture nonlinearity than classic multiple linear regression for predicting End IOP. It is crucial to acknowledge that administrating atropine may elevate intraocular pressure when the baseline IOP exceeds 14 mmHg. These findings offer valuable insights into factors affecting IOP in children undergoing atropine treatment for myopia, enabling clinicians to make informed decisions regarding treatment options.
Design of Laboratory Virtual Environment Monitoring System Based on Digital Internet of Things Technology

In order to meet the requirements of laboratory ventilation and exhaust purification, it is necessary to monitor the laboratory environment in real time. Effective control of all environmental parameters of animal experimental equipment is a key condition to ensure the smooth progress of animal experiments. To address this issue, this paper proposes to construct a virtual laboratory environment monitoring system based on digital Internet of Things (IoT) technology, which can monitor the temperature and humidity, noise level, lighting, air quality and radiation detection in the laboratory in real time, and can be used to ensure that the environment of animal laboratories meets the standards. The system collect real-time data from the laboratory environment using indication sensors and display it on an OLED screen. The system includes threshold setting methods that activate support services such as audible alarms, temperature and humidity monitoring, and fan activation in the event of abnormal conditions. The system take advantages of real-time monitoring from multiple hosts in different locations, unmanned operation and large-scale data storage. The final experimental results show that the sensitivity and specificity of the laboratory environment monitoring system based on this technique are 95% and 93% respectively, which are higher than other algorithms.
Genome-Wide Association Study of Milk Composition in Karachai Goats

Simple Summary The growth rate of dairy goat farming is due to the special qualities of goat milk, which are directly determined by its composition. Goat’s milk has a higher concentration of special fatty acids but less protein than cow’s milk, which is why high-quality cheeses are made from goat’s milk. Therefore, the goal of breeding dairy goats is not only to increase their milk yield but also to improve the quality composition of their milk. Karachai goats are a breed of local selection, the milk of which is widely used in the regions of the North Caucasus for the production of unique cheeses. While a relatively large number of genome studies have been carried out for transboundary goat breeds, such as Saanen goats, there is not yet enough research on local goat breeds. That is why we conducted a genome-wide association study (GWAS) on the milk component traits of Karachai goats. The use of genomic evaluation in the selection of Karachai goats will increase the rate of breeding progress in terms of enhancing milk productivity traits and thereby increase the profitability of their use. Abstract This study is first to perform a genome-wide association study (GWAS) to investigate the milk quality traits in Karachai goats. The objective of the study was to identify candidate genes associated with milk composition traits based on the identification and subsequent analysis of all possible SNPs, both genome-wide (high-confidence) and suggestive (subthreshold significance). To estimate the milk components, 22 traits were determined, including several types of fatty acids. DNA was extracted from ear tissue or blood samples. A total of 167 Karachai goats were genotyped using an Illumina GoatSNP53K BeadChip panel (Illumina Inc., San Diego, CA, USA). Overall, we identified 167 highly significant and subthreshold SNPs associated with the milk components of Karachai goats. A total of 10 SNPs were located within protein-coding genes and 33 SNPs in close proximity to them (±0.2 Mb). The largest number of genome-wide significant SNPs was found on chromosomes 2 and 8 and some of them were associated with several traits. The greatest number of genome-wide significant SNPs was identified for crude protein and lactose (6), and the smallest number—only 1 SNP—for freezing point depression. No SNPs were identified for monounsaturated and polyunsaturated fatty acids. Functional annotation of all 43 SNPs allowed us to identify 66 significant candidate genes on chromosomes 1, 2, 3, 4, 5, 8, 10, 13, 16, 18, 21, 23, 25, 26, and 27. We considered these genes potential DNA markers of the fatty acid composition of Karachai goat milk. Also, we found 12 genes that had a polygenic effect: most of them were simultaneously associated with the dry matter content and fatty acids (METTL, SLC1A 8, PHACTR1, FMO2, ECI1, PGP, ABCA3, AMDHD2). Our results suggest that the genes identified in our study affecting the milk components in Karachai goats differed from those identified in other breeds of dairy goats.
Implementasi Aplikasi Cegah Ancaman untuk Pelaporan Tindak Kejahatan Berbasis Mobile Android

Tindak kejahatan adalah perilaku yang melibatkan pelanggaran terhadap hukum atau norma-norma sosial yang berlaku dalam suatu masyarakat. Dalam konteks ini, tindak kejahatan mencakup berbagai jenis perilaku, mulai dari tindakan fisik seperti perampokan atau pemerkosaan hingga tindakan non-fisik seperti penipuan atau pencucian uang. Kejahatan dapat merugikan individu atau kelompok, dan sering kali memicu respon hukum atau penegakan hukum untuk menjaga ketertiban dan keadilan dalam masyarakat. Aplikasi Pelaporan Ancaman Tindak Kejahatan adalah sebuah platform berbasis mobile android yang dirancang untuk memberikan solusi proaktif dalam menjaga keamanan dan ketertiban masyarakat. Penelitian ini mencakup fase perencanaan, desain sistem, implementasi, dan pengujian black box. Aplikasi ini menyediakan antarmuka pengguna yang intuitif dengan fitur-fitur utama seperti Buat Laporan, Panggilan Darurat, dan Histori Laporan. Hasil penelitian menunjukkan peningkatan signifikan dalam aksesibilitas informasi geografis, pemantauan real-time, dan efisiensi pelaporan. Pengujian black box memverifikasi kinerja aplikasi tanpa memerinci implementasi internal. Dengan demikian, Aplikasi Pelaporan Ancaman Tindak Kejahatan dapat dianggap sebagai alat yang efektif dalam mendukung upaya pencegahan dan penanggulangan kejahatan, memungkinkan partisipasi aktif masyarakat dalam menjaga keamanan bersama.
Low Profile Dual-Band Base Station Antenna Array Decoupled by Artificial Magnetic Conductor

In this paper, by using electromagnetic transparent antenna (ETA) and artificial magnetic conductor (AMC), the cross-band coupling and in-band coupling between low-band (LB) antenna and high-band (HB) antennas are significantly reduced. The profile of the LB antenna can be reduced by making use of the in-phase reflection characteristic of AMC. As for HB antennas, AMC can work as antenna decoupling surface (ADS). Due to the electromagnetic transparency of LB antenna, the HB radiation patterns are restored. With VSWR better than 1.5, the co-polarized and cross-polarized in-band couplings are reduced by about 8 dB and 9dB respectively.
Hierarchical-Biased Random Walk for Urban Remote Sensing Image Segmentation

Random walk (RW) technique, with benefit of handling complicated boundaries, has recently drawn increasing attention in image segmentation. In this paper, RW is employed for urban remote sensing image segmentation. To deal with the complex spatial distribution with heterogeneous structures, a novel hierarchical-biased RW (HBRW) method is proposed. Firstly, edge regions extracted by fractional differential are combined with histograms to obtain plentiful features. Then, Dirichlet process mixture model is used to generate hierarchical global prior distribution and local seeds, which substitute manual scribbles. Moreover, the proposed model can adapt to different resolution segmentation tasks through adjusting the concentration parameter. Final segmentation output is obtained by biased RW. Experimental results on urban high-resolution remote sensing images demonstrate that the proposed algorithm achieves better performance than other state-of-the-art algorithms.
PERAMALAN OUTFLOW UANG KARTAL DI BANK INDONESIA WILAYAH JAWA TENGAH DENGAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR)

Generalized Space Time Autoregressive (GSTAR) model is a method that has interrelation between time and location or called with space time data. This model is generalization of Space Time Autoregressive (STAR) model where GSTAR more flexible for data with heterogeneous location characteristics. The purposes of this research are to get the best GSTAR model that will be used to forecast the outflow in the Bank Indonesia Office (BIO) Semarang, Solo, Purwokerto and Tegal. The best model obtained in this study is GSTAR (1 1 ) I(1) using the inverse distance weighting locations. This model has an average value of MAPE 35.732% and RMSE 440.52. The best model obtained explains that the outflow in BIO Semarang, Solo and Purwokerto are affected by two time lag before while for outflow in BIO Tegal is affected by two time lag befor and outflows in three other BIO. Keywords : GSTAR, Space Time, Outflow, Currency
Shear-induced collective diffusivity down a concentration gradient in a viscous emulsion of drops

The shear-induced collective diffusivity down a concentration gradient in a viscous emulsion is computed using direct numerical simulation. A layer of randomly packed drops subjected to a shear flow, shows the layer width to increase with the $1/3$ power of time, consistent with a semi-dilute theory that assumes a diffusivity linear with concentration. This characteristic scaling and the underlying theory are used to compute the collective diffusivity coefficient. This is the first ever computation of this quantity for a system of deformable particles using fully resolved numerical simulation. The results match very well with previous experimental observations. The coefficient of collective diffusivity varies non-monotonically with the capillary number, due to the competing effects of increasing deformation and drop orientation. A phenomenological correlation for the collective diffusivity coefficient as a function of capillary number is presented. We also apply an alternative approach to compute collective diffusivity, developed originally for a statistically homogeneous rigid sphere suspension – computing the dynamic structure factor from the simulated droplet positions and examining its time variation at small wavenumber. We show that the results from this alternative approach qualitatively agree with our computation of collective diffusivity including the prediction of the non-monotonic variation of diffusivity with the capillary number.