Miracle-dz/UniFAR-FTUs huggingface dataset:
A real-time air-writing model to recognize Bengali characters

Air-writing is a widely used technique for writing arbitrary characters or numbers in the air. In this study, a data collection technique was developed to collect hand motion data for Bengali air-writing, and a motion sensor-based data set was prepared. The feature set as then utilized to determine the most effective machine learning (ML) model among the existing well-known supervised machine learning models to classify Bengali characters from air-written data. Our results showed that medium Gaussian SVM had the highest accuracy (96.5%) in the classification of Bengali character from air writing data. In addition, the proposed system achieved over 81% accuracy in real-time classification. The comparison with other studies showed that the existing supervised ML models predicted the created data set more accurately than many other models that have been suggested for other languages.
Assessment of the Effects of Access Control on Reducing Cybercrimes in the Selected Telecommunication Companies in Tanzania

This study specifically aimed to determine the effects of access control on reducing cybercrimes. Since much research has yet to be done to evaluate how privacy, data integrity, and access control affect the overall decline in cybercrimes, this study is conducted to help telecommunication companies employ and strengthen access control measures to safeguard company and customer information. This study employed a quantitative research approach, and the data collected were analysed using regression analysis techniques. Data was collected through questionnaires in a Likert-scaled form and in-depth interviews. The sample size of this study was 199 employees from the selected companies. The data collected was analysed using both qualitative and quantitative techniques. The findings show that the general correlation between independent and dependent variables is 0.01, whereby specific consideration shows that Access Control (AC) had a correlation of . 117, which was significant at . 004. The correlation analysis confirms a positive and significant relationship between access control and the reduction of cybercrimes since the p-value was <. 005. This study concludes that despite the roles played by the access control mechanisms provided by telecommunication companies, there is a need to enhance both internal and external control measures, such as strengthening the security policies to impose severe sanctions on individuals who try to tamper with the information systems of the telecommunication companies. Hence, this study recommends the provision of more awareness to the user of information systems both within and outside the telecommunication companies because data breaches happen either through internal or external sources; hence, it is essential that training, education, and other means to enhance knowledge about cybercrimes should be implemented.
GiOPARK Project: The Genetic Study of Parkinson’s Disease in the Croatian Population

Parkinson’s disease is a neurological disorder that affects motor function, autonomic functions, and cognitive abilities. It is likely that both genetic and environmental factors, along with age, contribute to the cause. However, there is no comprehensive guideline for genetic testing for Parkinson’s disease, and more research is needed to understand genetic variations in different populations. There has been no research on the genetic background of Parkinson’s disease in Croatia so far. Therefore, with the GiOPARK project, we aimed to investigate the genetic variants responsible for Parkinson’s disease in 153 Croatian patients with early onset, familial onset, and sporadic late-onset using whole-exome sequencing, along with multiplex ligation-dependent probe amplification and Sanger sequencing in select patients. We found causative variants in 7.84% of the patients, with GBA being the most common gene (4.58%), followed by PRKN (1.96%), ITM2B (0.65%), and MAPT (0.65%). Moreover, variants of uncertain significance were identified in 26.14% of the patients. The causative variants were found in all three subgroups, indicating that genetic factors play a role in all the analyzed Parkinson’s disease subtypes. This study emphasizes the need for more inclusive research and improved guidelines to better understand the genetic basis of Parkinson’s disease and facilitate more effective clinical management.
The true false ground truths: What interest?

The existence of a few unreliable ground truth (GT) data sets which are often used as reference by the remote sensing community for the assessment and comparison of classification results is really problematic and poses a number of questions. Two of these ground truth data sets can be cited: "Pavia University" and "Indian Pine". A rigorous analysis of spectral signatures of pixels in these images shows that some classes which are considered as homogeneous from the ground truth are clearly not, since the pixels which belong to the same classes have different spectral signatures, and probably do not belong to the same category. The persistence in using data sets from a biased ground truth does not allow objective comparisons between classification methods and does not contribute to providing explanation of physical phenomena that images are supposed to reflect. In this communication, we present a fine and complete analysis of the spectral signatures of pixels within each class for the two ground truth data sets mentioned above. The metrics used show some incoherence and inaccuracy of these data which wrongly serve as references in several classification comparative studies.
Hyperspectral image super-resolution based on a Linear and Intimate Mixing Model

Hyperspectral super-resolution (HSR) aims to obtain a high spatial resolution hyperspectral image (HR-HSI) by fusing a high spatial resolution multispectral image (HR-MSI) and a low spatial resolution hyperspectral image (LR-HSI). In this paper, a HSR model with a linear and intimate mixing model is proposed. The unmixing model is combined with Hapke model to provide more flexible endmembers. Furthermore, the HR-HSI image is represented by tensor form as well as unmixing view, resulting in a nonlinear and physical interpretation HSR method. Numerical experiments show that the proposed HSR method shows better performance compared with other methods.
Adaptive Fuzzy Generalized Predictive Control of pH in Tubular Photobioreactors on Microalgae Plant

Culture conditions in a photobioreactor on a microalgae plant are essential for production rates, and the pH value is one of the critical variables for a harmonizing process. However, the biological nature of microalgae growth represents a complex reaction that is arduous to model and control. This work proposes an approach of the application of adaptive identification and predictive control to regulate the pH in a photobioreactor in a microalgae production system. The proposed approach is composed of two main stages: identification and control. The identification is performed by a Takagi-Sugeno (T-S) fuzzy model, which will be learned with an offline strategy, by Fuzzy c-means, for the antecedent part, and by the Least Squares Method (LSM), for the consequent part. Then, the model is updated with an online method, the Recursive Least Squares with Adaptive Directional Forgetting (RLS-ADF) factor algorithm. The control scheme is based on a Generalized Predictive Control approach, which is a Model Predictive Controller (MPC), with the adaptive T-S fuzzy model designed in the identification stage. In this way, the model parameters from GPC are online adapted by RLS-ADF. To validate the control structure, the proposed approach was tested by using an estimated model from real data of a microalgae production process.
Machine Learning Algorithms for Phonocardiogram Signal Classification

Cardiac conditions remain a significant cause of illness and mortality worldwide. Early and accurate diagnosis is crucial for prompt therapies and improved patient outcomes. Phonocardiography, which analyses heart sounds graphically, can assist in identifying various pathologies such as valvar disorders, myocardial infarction, heart murmurs, and abnormal heart rhythms. In this study, we utilised two databases. Dataset 1 has two categories: normal and murmur heart sounds. Dataset 2 has five categories, including one normal and four abnormal types. This report extracts time domain features, frequency domain features, MFCCs, and DWT-based features, which leads to extracting the 77 features in total from phonocardiogram signals. Feature selection techniques were applied and identified the best 21 features out of 77 features with Dataset 1 and the best 14 out of 77 features with Dataset 2. Disease classification uses k-nearest neighbour (KNN), cubic support vector machine (SVM), Kernel SVM, Logistic Regression, and Fine Tree algorithms in MATLAB. The proposed methodology achieved a training accuracy of 94.1% and a testing accuracy of 100% for dataset 1. Similarly, a training accuracy of 98.3% and testing accuracy of 100% for dataset 2. This report demonstrates its potential for clinical applications.
Disparity Computation With Low Intensity Quantization on Stereo Image Pairs

From variate bit-rate stereo matching, it is observed that the image pair with a low intensity quantization level is still capable of providing good disparity maps. In this article, a mathematical model representing the level of disparity discontinuity is proposed to formulate the mismatching prediction based on the intensity quantization. It is used to derive the minimum quantization level for quality-assured stereo matching. Due to the high computational cost of stereo processing, the reduction of image data usage will benefit both network training and real-time inference. The formulation presented in this work is investigated extensively for various types of scenes for disparity computation. In the experiments, the feasibility of our approach is validated with Middlebury, KITTI 2015, and synthetic Scene Flow datasets.
Crescent Antennas as Sensors: Case of Sensing Brain Pathology

Microstrip crescent antennas offer compactness, conformability, low profile, high sensitivity, multi-band operability, cost-effectiveness and ease of fabrication in contrast to bulky, rigid horn, helical and Vivaldi antennas. This work presents crescent sensors for monitoring brain pathology associated with stroke and atrophy. Single- and multi-element crescent sensors are designed and validated by software simulations. The fabricated sensors are integrated with glasses and experimentally evaluated using a realistic brain phantom. The performance of the sensors is compared in terms of peak gain, directivity, radiation performance, flexibility and detection capability. The crescent sensors can detect the pathologies through the monitoring of backscattered electromagnetic signals that are triggered by dielectric variations in the affected tissues. The proposed sensors can effectively detect stroke and brain atrophy targets with a volume of 25 mm3 and 56 mm3, respectively. The safety of the sensors is examined through the evaluation of Specific Absorption Rate (peak SAR < 1.25 W/Kg, 100 mW), temperature increase within brain tissues (max: 0.155 °C, min: 0.115 °C) and electric field analysis. The results suggest that the crescent sensors can provide a flexible, portable and non-invasive solution to monitor degenerative brain pathology.
Predicting the Borrower’s Genuineness in Loan Repayment through Big Data Analytics

Banks play a pivotal role in facilitating economic activities, allocating financial resources, and managing risks. A fundamental function of banks is the provision of loans. This research is centered on the subject of "Predicting Borrower’s Integrity in Loan Repayment," aimed at mitigating risks and ensuring prudent financial decision-making. To conduct our predictive analysis, we leveraged a comprehensive loan lending dataset provided by Lending Club Bank. This dataset consists of 2.2 million records, each associated with 151 distinct features. Performing machine learning predictions on such a substantial dataset, totaling 1.3 gigabytes, presents a formidable challenge. Consequently, we harnessed machine learning techniques and the power of Apache Spark as our primary tool for handling big data. For optimal utilization of Spark’s capabilities, we engaged Google Cloud’s Dataproc platform. Through feature selection techniques, we identified 28 significant features from the original 151. Notably, data transformation was applied to the selected features for model understanding. Logistic Regression and Random Forest Classification models were employed for the prediction of loan statuses, categorizing them as either ’fully paid’ or ’charged off. ’ These models achieved impressive accuracies of 95.9 percent and 86 percent, respectively. This research contributes significantly to the evolution of loan assessment practices and the refinement of risk management strategies within the banking sector.
Pioglitazone, a peroxisome proliferator‑activated receptor γ agonist, induces cell death and inhibits the proliferation of hypoxic HepG2 cells by promoting excessive production of reactive oxygen species

Hypoxia is a hallmark of solid tumors. Hypoxic cancer cells adjust their metabolic characteristics to regulate the production of cellular reactive oxygen species (ROS) and facilitate ROS-mediated metastasis. Peroxisome proliferator-activated receptor γ (PPARγ) is a nuclear receptor that regulates the transcription of fatty acid metabolism-related genes that have a key role in the survival and proliferation function of hypoxic cancer cells. In the present study, mRNA expression in HepG2 cells under chemically induced hypoxia was assessed. The protein expression levels of hypoxia-inducible factor 1α (HIF-1α) were measured using western blotting. Following treatment with the PPARγ agonist pioglitazone, cell viability was assessed using a Cell Counting Kit-8 assay, whilst cell proliferation and death were determined using 5-ethynyl-2′-deoxyuridine incorporation staining, and calcein-acetoxymethyl ester and propidium iodide staining, respectively. Cellular ROS production was assessed using dihydroethidium staining. Cobalt chloride was used to induce hypoxia in HepG2 cells, which was evaluated using HIF-1α expression. The results revealed that the mRNA expression of PPARγ, CD36, acetyl-co-enzyme A dehydrogenase (ACAD) medium chain (ACADM) and ACAD short-chain (ACADS) was downregulated in hypoxic HepG2 cells. The PPARγ agonist pioglitazone decreased the cell viability of hypoxic HepG2 cells by inhibiting cell proliferation and inducing cell death. Following treatment with the PPARγ agonist pioglitazone, hypoxic HepG2 cells produced excessive ROS. ROS-mediated cell death induced by the PPARγ agonist pioglitazone was rescued with the antioxidant N-acetyl-L-cysteine. The downregulated mRNA expression of PPARγ, CD36, ACADM and ACADS was not reverted by a PPARγ agonist in hypoxic HepG2 cells. By contrast, the PPARγ agonist suppressed the mRNA expression of BCL2, which was upregulated in hypoxic HepG2 cells. In summary, the PPARγ agonist stimulated excessive ROS production to inhibit cell proliferation and increase the death of hypoxic HepG2 cells by decreasing BCL2 mRNA expression, suggesting a negative association between PPARγ and BCL2 in the regulation of ROS production in hypoxic HepG2 cells.
Bone Trabecular Pattern Analysis in Odontogenic Cysts Using Cone Beam Computed Tomography: A Clinical Retrospective Study

Introduction The cysts of the maxillofacial region account for one of the most common pathologies of the head and neck region after the mucosal pathologies. Radiography provides an essential clue in early diagnosis and triaging, but it continues further as it is used to evaluate the post-treatment outcome. However, manual analysis is prone to errors. In this scenario, fractal analysis (FA) in radiographs uses mathematical methods to analyse the changes in grey scales in a given radiographic image. FA in odontogenic cysts is used to characterise their complexity, uncover hidden patterns, monitor treatment response, and potentially provide prognostic information. This paper aimed to assess the fractal characteristics of the radicular cyst (RC), dentigerous cyst (DC), and odontogenic keratocyst (OKC) using cone beam computed tomography (CBCT). The objective was to calculate fractal dimension (FD) values expressed in each of these cysts, which could prove to be a radiological adjunct in diagnosing the above cysts. Materials and methods As this is a retrospective study, the archives of CBCT images from June 2021 to December 2023 were obtained from patients diagnosed and confirmed with a histopathological diagnosis with RC, DC, and OKC. The FA was performed using Image J Software (Ver 1.51, National Institute of Health Bethesda, Fiji). The cortical and cancellous bones were segmented using thresholding techniques and converted to binary images. The mean FD of the three planes was then compared to establish the distinctive fractal characteristic for the specific odontogenic cysts. A one-way ANOVA was performed using the Statistical Product and Service Solutions (SPSS) (version 23.0; IBM SPSS Statistics for Windows, Armonk, NY) to determine the difference between FD values of RC, DC, and OKC with a significance level less than 0.05. Results The FD values of DC, RC, and OKC were 1.33 ± 0.17, 1.08 ± 0.16, and 1.65 ± 0.12, respectively. The results indicated that OKC had higher FD values than DC and RC, which means that OKC had lesser bone destruction compared to DC and RC. Inferential statistics showed that the one-way ANOVA was used to compare the means of the three groups of FD data. When calculated for the three groups, the F-statistic value was at 7.29, which yielded a P value of 0.03, making it statistically significant for a 95% confidence interval (p<0.05). Conclusion Our CBCT study on bone trabecular pattern analysis using FD and FA in odontogenic cysts reveals distinct alterations in bone parameters among different cyst types. The probability of higher FD values in OKC is because of lesser cortical bone destruction in OKC compared to the other cyst types. These findings have potential implications for diagnosing, treating, and prognosticating odontogenic cysts.
Potential of an Isolated Bacteriophage to Inactivate Klebsiella pneumoniae: Preliminary Studies to Control Urinary Tract Infections

Urinary tract infections (UTIs) caused by resistant Klebsiella pneumoniae can lead to severe clinical complications and even death. An alternative treatment option for infected patients is using bacteriophages. In the present study, we isolated phage VB_KPM_KP1LMA (KP1LMA) from sewage water using a K. pneumoniae strain as a host. Whole-genome analysis indicated that the genome was a double-stranded linear 176,096-bp long DNA molecule with 41.8% GC content and did not contain virulence or antibiotic resistance genes. The inactivation potential of phage KP1LMA was assessed in broth at an MOI of 1 and 10, and a maximum inactivation of 4.9 and 5.4 log CFU/mL, respectively, was observed after 9 h. The efficacy at an MOI of 10 was also assessed in urine to evaluate the phage’s performance in an acidic environment. A maximum inactivation of 3.8 log CFU/mL was observed after 9 h. The results suggest that phage KP1LMA could potentially control a UTI caused by this strain of K. pneumoniae, indicating that the same procedure can be used to control UTIs caused by other strains if new specific phages are isolated. Although phage KP1LMA has a narrow host range, in the future, efforts can be made to expand its spectrum of activity and also to combine this phage with others, potentially enabling its use against other K. pneumoniae strains involved in UTIs.
Digital Commons@Georgia Southern Digital Commons@Georgia Southern

The Hour of Code is a one-hour introduction to computer science organized by Code.org, a non-profit dedicated to expanding participation in computer science. This study investigated the impact of the Hour of Code on students’ attitudes towards computer programming and their knowledge of programming. A sample of undergraduate students from two universities was selected to participate. Participants completed an Hour of Code tutorial as part of an undergraduate course. An electronic questionnaire was implemented in a pre-survey and post-survey format to gauge the change in student attitudes toward programming and their programming ability. The findings indicated the positive impact of the Hour of Code tutorial on students’ attitude toward programming. However, the students’ programming skills did not significantly change. The authors suggest that a deeper alignment of marketing, teaching, and content would help sustain the type of initiative exemplified by the Hour of Code.
GA-Dueling DQN Jamming Decision-Making Method for Intra-Pulse Frequency Agile Radar

Optimizing jamming strategies is crucial for enhancing the performance of cognitive jamming systems in dynamic electromagnetic environments. The emergence of frequency-agile radars, capable of changing the carrier frequency within or between pulses, poses significant challenges for the jammer to make intelligent decisions and adapt to the dynamic environment. This paper focuses on researching intelligent jamming decision-making algorithms for Intra-Pulse Frequency Agile Radar using deep reinforcement learning. Intra-Pulse Frequency Agile Radar achieves frequency agility at the sub-pulse level, creating a significant frequency agility space. This presents challenges for traditional jamming decision-making methods to rapidly learn its changing patterns through interactions. By employing Gated Recurrent Units (GRU) to capture long-term dependencies in sequence data, together with the attention mechanism, this paper proposes a GA-Dueling DQN (GRU-Attention-based Dueling Deep Q Network) method for jamming frequency selection. Simulation results indicate that the proposed method outperforms traditional Q-learning, DQN, and Dueling DQN methods in terms of jamming effectiveness. It exhibits the fastest convergence speed and reduced reliance on prior knowledge, highlighting its significant advantages in jamming the subpulse-level frequency-agile radar.
Analysis of Image Matting Techniques Used for Generating Alpha-Matte

The primary objective of automated matting techniques is to isolate the foreground object from its backdrop. To accurately fetch the foreground from the image, users must manually give some previous information to recognize foreground area, background area and unseen area in the form of trimap. Providing a well defined and accurate trimap by the user is a tedious job. Now a days various matting techniques try to focus on reducing the dependency on trimap, hence reducing the user burden. In this paper we have studied and analyzed various matting techniques. On the basis of required user intervention, we have broken down them into three categories: Manual, Semi-Automatic and Automatic Techniques. Finally the result of these techniques has been analyzed on the basis of their mean square error (MSE) and sum of absolute differences (SAD) parameters. Finally the paper concludes that Semi-Automatic and Automatic approaches reduce the user burden but they compromise with the result. Hence Manual trimap based methods outperforms Semi-Automatic and Automatic methods if trimap is specified carefully. TIMI-Net (a manual trimap based approach) is found to be best suited for the MSE as well as SAD parameter, showing least MSE value (0.0060) and least SAD value (29.08).