Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanostructured Lipid Carrier–Based Gel for Repurposing Simvastatin in Localized Treatment of Breast Cancer: Formulation Design, Development, and In Vitro and In Vivo Characterization

Simvastatin (SMV) is noticed as a repurposed candidate to be effective against breast cancer (BC). However, poor solubility, dose-limiting toxicities, and side effects are critical hurdles in its use against BC. The above drawbacks necessitate the site-specific (localized) delivery of SMV via suitable nanocarriers. Therefore, the present study intended to develop SMV nanostructured lipid carrier (NLC)–based gel using carbopol-934 as a gelling agent to achieve local delivery and improve patient compliance while combating BC. The SMV NLCs were fabricated by melt-emulsification ultrasonication technique using stearic acid as solid lipid, olive oil (OO) as liquid lipid, tween 20 as a surfactant, and PEG-200 as a co-surfactant, and optimized by Box-Behnken design. The optimized SMV-loaded NLCs displayed % entrapment efficiency of 91.66 ± 5.2% and particle size of 182 ± 11.9 nm. The pH of NLC-based gels prepared using a 2.0% w/v of carbopol-934 was found in the range of 5.3–5.6 while the viscosity was in the range of 5.1–6.6 Pa.S. Besides, NLC-based gels exhibited higher and controlled SMV release (71–76%) at pH 6.8 and (78–84%) at pH 5.5 after 48 h than SMV conventional gel (37%) at both pH 6.8 and 5.5 after 48 h. The ex vivo permeation of SMV from NLC-based gel was 3.8 to 4.5 times more than conventional gel. Notably, SMV-loaded NLCs displayed ameliorated cytotoxicity than plain SMV against MCF-7 and MDA-MB-231 BC cells. No substantial difference was noticed in the cytotoxicity of NLC-based gels and pure SMV against both cell lines. The SMV NLC-based gel exhibited the absence of skin irritation in vivo in the mice following topical application. In addition, the histopathological study revealed no alteration in the mice skin anatomy. Furthermore, the SMV-loaded NLCs and NLC-based gels were stable for 6 months at refrigerator conditions (4°C ± 2°C). Thus, the present research confirms that NLC-based gel can be a safe, efficacious, and novel alternative to treat BC. Graphical Abstract
A Highly Selective Dual Bandpass Filter using Couple Line Resonator for Modern Wireless Communication Systems

In this paper, a miniaturized dual-band bandpass filter (DB-BPF) based on a dual-path stub-loaded resonator is presented. A transversal filtering design is introduced in the proposed DB-BPF, which provides two signal propagation paths from input to output. In total, six transmission poles and four transmission zeros are obtained, which improves overall filter selectivity. The fractional bandwidth (FBW) of dual bands with center frequencies of 2.24 and 7.35 GHz is 36.4% and 31.2%, respectively. The physical size of a fabricated filter is $(0.43\ \times 0.17)\lambda_{g}$, where $\lambda_{g}$ is the guided wavelength at 2.24 GHz. The DB-BPF prototype is fabricated using a Roger Duroid RT/5880 substrate to further validate the result. The full-wave EM simulation and measured results show good agreement. The proposed dual-band bandpass is applicable to a wide range of modern communication technologies, including Wi-Fi, satellite, cellular, radar, and broadcast communication.
Instanton calculations using dimensional regularisation

Dimensional regularisation is used to calculate the determinant of small oscillations about instanton solutions in g( phi 4)4 field theories with g<0. Principal features of the method are: (i) the known instanton solution of four dimensions may be used even though it is not a solution of the regularised theory; (ii) the minimally subtracted renormalised coupling can be introduced directly. Results are also given for O(n) internal symmetry and conformal invariant phi 2N theories (g<0). The high-order estimates obtained agree with those of Lipatov (1977) and Brezin et al. (1977).
Design and Development of Solar Powered Phase Shifted Full Bridge Converter for EV Charger

The increasing popularity of two and three-wheel Electric Vehicles (EVs) calls for the development of dependable and clean technology based charging stations. Most of the charging stations are fed from grid supply. However, true potential of EVs can be realized only through renewable energy based chargers providing the all-inclusive green solution. This paper explores the use of a Solar-powered Phase-shifted Full Bridge (PSFB) converter with Zero Voltage Switching (ZVS) control, using a DSP TMS320F28379D for DC EV charger.
Internet of Things Technology in the Design of Art Education Network Platform Based on Model Algorithm

Art design can be roughly divided into three fields from function and form: product modeling design, visual communication design and environmental art design. The teaching of art design discipline itself belongs to the art education system. In order to solve the shortcomings of the research on the Internet of Things technology in the design of the existing art education network platform based on model algorithms, this paper discusses the characteristics of the art education network platform, the Internet of Things technology and model algorithms, and investigates and discusses the hardware development platform and the back-end key technologies of the Internet of Things technology. The application scheme of Internet of Things technology in the design of art education network platform based on model algorithm is designed. The experimental data show that the CUP utilization rate calculated by the genetic algorithm in this paper is 58% in the fifth server. The CPU utilization of the task manager in the Window operating system is 59%. Therefore, this algorithm has a good effect on the design of art education network platform.
Deep Learning-Based Identification of Skin Cancer on Any Suspicious Lesion

Certain estimates place skin cancer as the most lethal form of the disease worldwide. Spreading to other parts of the body and requiring invasive procedures like chemotherapy and radiation therapy are the results of delayed diagnosis. As a result, automatic methods have greatly aided medical professionals and allowed even non-specialists to identify the sort of cancer a patient has. In this paper, we present a deep learning-based skin cancer identification to classify images of skin lesions as malignant or benign. This method may be used for any potentially malignant lesion. We found that using a deep learning (DL) approach that relied on a mask region-based convolutional neural network yielded the fastest and most accurate results for diagnosing skin cancer.
Medical Insurance Cost Analysis and Prediction using Machine Learning

Health insurance is a type of insurance which covers the entire or a portion of the risk that a person might require medical care. Humans have encountered several new viral infectious diseases in the span of the last 20 years as a result of developing and re-emerging infectious illnesses. As a result, the price and necessity of insurances are likewise steadily rising. Nowadays, it is a legal need in many nations to have health insurance, and hospitals may refuse to treat you if you don't have it. Since almost everything in the world become digital, including health insurance, people are becoming more aware of insurance, which will be a tremendous advantage in the future. Therefore, this research intends to raise public awareness about insurances so that they may obtain them at a fair and precise price. It is crucial to take into account several variables to promote the adoption of health insurance, including people's attitudes toward purchasing insurance, their decision-making processes, and factors affecting their demand- and supply-side perceptions of insurance. Moreover, awareness levels may have a significant impact on new and renewed participation in health insurance programs. The effectiveness of these algorithms has been compared using three regression models: Linear Regression, Support Vector, and Random Forest Regressors. The models were trained on a dataset, and this training helped with the predictions. The quantity predicted and the actual data were then compared to test and validate the model. The accuracy levels of these models were contrasted. When compared to other algorithms that have been tried, since it needs considerably less computation time to achieve the performance metric with the maximum percentage of accuracy rate of 87%, approach is best suited in Random Forest Regressor
Identification of Cardiovascular Disease using Machine Learning and Ensemble Learning

Cardiovascular disease is a very dangerous disease among humans and can be fatal. It describes a condition where blood arteries get constricted or clogged, which can cause a heart attack, chest symptoms, or a stroke. One of the significant challenges faced in research is accurately diagnosing heart disease using health data. Existing techniques have poor performance, potential to bias and limited interpretability. In the proposed study, a system has been developed to identify the presence of cardiovascular disease in a person by applying machine learning algorithms to a heart disease dataset containing information about clinical parameters. The model algorithms used are Random Forest, k Nearest Neighbours, Decision Trees, Logistic Regression, Naive Bayes and Ensemble Learning. For comparison, Principal Component Analysis has been applied for dimensionality reduction on the dataset and then the classification algorithms are applied and the results are compared based on various model performance metrics. The authors of this paper aim to propose system can identify the presence of cardiovascular disease among people efficiently and accurately in a way that is easy to use for all.
Deep neural networks based contactless fingerprint recognition

For developing automatic and accurate system for human recognition, deep learning is now progressively becoming common in real-world biometrics applications. Fingerprint is one of the most important discriminative biometric characteristic due to its high reliability and uniqueness properties, which has led to a widespread use by law enforcement, forensic as well as in mobile devices user authentication. Contactless fingerprint recognition has achieved rapid development in recent years thanks to more hygienic and ubiquitous personal identification techniques. In this paper, we present deep neural networks (DNNs) based solutions for contactless fingerprint identification. More specifically, we show how existing DNNs can be deployed as a feature extractor for contactless fingerprint. Experimental analyses on publically available dataset with 336 subjects demonstrate the effectiveness of DNNs-based feature extractors. Moreover, experimental results illustrate best recognition performance in comparison with state-of-the-art texture descriptors.
Reliability Stripe Coagulation in Two Failure Tolerant Storage Arrays

As the industry slowly transitions to data centers made up of electronic instead of magnetic storage components, the rate of device failure and page corruption will decrease, but not vanish. In this emerging environment, redundant storage is still required to safeguard data. We argue that the use of two failure resilient linear codes with an exclusive-or (xor) based P-parity and a Q-parity calculated using a finite field operations is appropriate. We use the algebraic property of finite fields to show how to coagulate a number of small constituent’’ reliability stripes into larger coagulated stripes without recalculating parities. This allows to protect largely inactive data with more storage efficient larger reliability stripes. The procedure is reversible.
IoT-Enabled Potato Diseases Prediction using Deep Learning

Inspection methods that are both quick and non-destructive, such as those enabled by deep learning and image processing, could be extremely beneficial for monitoring the quality of potatoes and other agricultural products. This research work proposes a Deep Neural Network (DNN) model to visually classify the diseased potatoes. A set of potato images are given as input to the DNN model during the training and testing phase. Further, a Modified Support Vector Machine (MSVM) and a Convolutional Neural Network (CNN) models are used to perform segmentation and feature extraction on the potato images. The proposed model was tested using new modern versions of standard datasets maintained by Canada and the United States agricultural departments. The results of this experiment demonstrated that deep learning model has outperformed both traditional approaches and the Machine Learning models. Accuracy rates of over 99% have been reported for some types of images. While some other types of images have reported 97% accuracy.
MOBARec-GCNFP: Champion Recommendation for Multi-Player Online Battle Arena Games Using Graph Convolution Network with Fewer Parameters

Abstract–Multi-player online battle arena (MOBA)games are one of the most successful strategy videogames. Ten players are divided into two teams in a single match, each of which needs to pick five characters, known as champions, to play against the other team. Champions are designed with unique skills, strengths, and weaknesses, allowing some champions pairings to have a higher winning rate. For example, a defensive champion often complements a powerful but fragile champion. In this paper, we recommend the last champion to pick, i.e., the 10th champion, known as the counterpick, which is the most important pick to best cooperate with the already selected champions and counter the opponents’ selections. Previous studies recommending champions suffer from the accuracy and the cold start recommendation problem. To tackle the above problems, we propose MOBARecGCNFP, which adopts lightweightgraph neural networks. We solve the cold start recommendation problem with a newly designed GCN by initializing the match embeddings using champions’ embeddings. Our experimental evaluation confirmed that our method outperformed the previous methods, including fully connected NN and SR-GNN, improving Recall@l from 0.1208 to 0.1272 (5.3%) on the League of Legends dataset and from 0.2551 to 0.2586 (1.4%) on the Honor of Kings dataset.
Integration Operator Acting on Hardy and Weighted Bergman Spaces

Questions related to the operator Jg(f)(z):= ∫xof (ζ)g′(ζ) dζ, induced by an analytic function g in the unit disc, are studied. It is shown that a function G is the derivative of a function in the Hardy space Hp if and only if it is of the form G = Fψ′ where F ∈ Hq, ψ ∈ H3 and 1/s = 1/p − 1/q. Moreover, a complete characterisation of when Jg is bounded or compact from one weighted Bergman space into another is established, and an asymptotic formula for the essential norm of Jg, the distance from compact operators in the operator norm, is given. As an immediate consequence it is obtained that if p < 2 + α and α > −1, then any primitive of belongs to where q = ((2 + α) p)/(2 + α − p). For α = −1 this is a sharp result by Hardy and Littlewood on primitives of functions in Hardy space , 0 < p < 1.
Image Quality of CT Plain Scan of Urinary Calculi Based on Deep Learning Algorithm

With the large-scale development of urinary calculi, pathogenic microorganisms and bacteria have been effectively separated, making artificial diagnosis more difficult. Based on CT detection, this paper uses deep learning algorithm to establish a complex neural network model, and uses deep learning (FIR) technology to improve image quality through the construction of multi-layer forward transfer structure, neuron mutual association and other nonlinear training processes. At the same time, combined with CT data, it obtains the information feature points set of hidden layer concentration distribution and shape feature changes in real time, and optimizes the original preprocessing to realize image reconstruction and recognition, Then the algorithm is tested by simulation experiments. Finally, the test results show that the CT plain scan image quality of urinary calculi optimized by the deep learning algorithm is very good. This shows that the algorithm has excellent optimization ability.
Brain Hemorrhage Detection using Heatmaps and Deep Learning Algorithms

Bleeding or an escape of blood from a ruptured blood vessel within the brain tissue or between the adjacent bones is referred to as brain hemorrhage. Therefore, head bleeding can result in a variety of harmful outcomes, particularly brain bleeding. For the patient's life, early and effective assistance by professionals in such situations is crucial. By using VGG19, a type of convolutional neural network models recently used in the biomedical industry, brain hemorrhage computed tomography pictures are classified in this study. To increase the accuracy of the categorization, every image in the given dataset has its heat map extracted in this context. Support vector machines are used in the categorization process. The classification's highest success rate was 96.07% as a consequence. In conclusion, the suggested method helped to categories photos of cerebral hemorrhage.
Development of An Automated Accessibility Evaluation Plugin Tool for Mobile Applications

In the digitalized world, the usage of mobile devices is rapidly increasing. It became a necessary need in daily life. Indeed, smartphones’ new functions and technology have tremendously increased the public’s trust, and new features are continuously added. The user interface of mobile screens must be accessible for every person that has any disability or doesn’t have any disability issues. Still, there is a need to improve availability to reduce the efforts, cost, and time during the design and development of mobile applications. However, the rapid adoption and evolution of mobile devices and their applications raise different accessibility challenges for developers. Start-up companies usually ignore the accessibility features because it demands additional resources. Most developers consider accessibility testing after the complete development of mobile applications, which takes extra time and cost and can negatively impact client and developer deals. However, these aspects can be solved by paying attention to design so that it can increase profits, downloads, fidelity and reputation. Therefore, one thing that designers need to make sure that their applications are perceivable and usable to satisfy the user of a vast range. The advanced research aims to develop an automated accessibility analysis and design evaluation tool for mobile applications, and researchers have already developed a solution as a plugin for Figma. Additionally, many authors have picked the important techniques from the WCAG 2.0, 2.1 success criteria. In conclusion, the suggested solution evaluates the designed screen and generates results in the form of a report. This automated solution helps the developers for related stakeholders by filling the gap in the existing design-related tools.
Microservice Based Architecture: The Development of Rapid Prototyping Supportive Tools for Project Based Learning

This paper presents the migration of rapid prototyping supportive tools systems from monolith into microservice architecture that will be used as the implementation of Project Based Learning. As in early development, the developed supportive tool was the monolith architecture and web based platform. As the growth of the students as users and addition of the rapid prototyping framework modules that will be used, the monolith architectures are urged to decompose its' services into a more modular way of web services. As a result, the newest version will take advantage of a number of benefits offered by microservice-based architecture, including modularity, scalability and maintainability. The future features that are needed as the implementation of the learning based systems will be more easy to integrate as the beneficial of the microservices-based architectures.
Data Mining in Credit Scoring and Future Application

To improvise credit rating process, banks have developed models to check credit ratings. Automation is an important chain to screen this process. Credit risk is divided into two categories: “good risk” groups with a high possibility of repaying debts, and “bad risk” groups with a high probability of not doing so. Credit scoring models need to be created using data mining approaches. With help of this approach, financial institutes can track credit records of any person easily. This model may help to identify the key demographic characteristics associated with credit risk by using the past payment information, demographic factors, and statistical techniques. Since it is obviously difficult to analyse this massive amount of data economically and laboriously, data mining methods were used. Each has distinct advantages and disadvantages in comparison to the others. This paper emphasized the role of data mining in credit rating in financial institutes. Research also tries to find future recommendations.
Analysis of Dark Pattern-related Tweets from 2010

Dark patterns are defined as user interfaces that make users behave unintendedly, such as buying something or subscribing to some services. The use of dark patterns is considered an infringement of users’ rights and privacy. In this study, we reveal the users’ responses toward dark patterns by analyzing 12 years of tweets. Our findings include 1) users in countries in which dark patterns-related regulations have been implemented have a higher level of discussions about dark patterns; 2) tweets about dark patterns shifted from around 2017 from sharing their diversity to acting to resist them; 3) the commonly discussed dark pattern types of tweets are sneaking, obstruction, and interface interference, which are widely used in e-commerce sites. Our findings may help policymakers and regulators to promote our more secure internet use.
Artificial Neural Networks in Intelligent Management Systems for Corporate Financial Accounting

Business guidance and data control are increasingly being used by companies to optimise the intelligent management of corporate financial accounting, and artificial neural networks are being rapidly introduced into business processes and risk control. The aim of this paper is to investigate intelligent management systems for enterprise financial accounting based on artificial neural networks. This system focuses on combining network technology and computer technology in the financial accounting management of an enterprise, and designs an intelligent management system for the financial accounting of a group of enterprises based on SOA technology. A BP neural network is selected to model and train data on the cost of materials issued in construction projects, and the model is used to forecast new financial business data to help finance staff improve their financial risk control. The results show that the implementation of this algorithm can help enterprise M to use the prediction function of big data analysis for real-time cost overrun monitoring and early warning in its daily operation.
A Systematic Review of Intrusion Detection Systems in Internet of Things Using ML and DL

The rapid expansion of IoT technologies like smart sensors and home appliances is likely to have far-reaching consequences. Connectivity, ubiquitous presence, and low computational power are the hallmarks of IoT gadgets. The global tally of Internet of Things gadgets is on the rise. There has been an increase in IoT -based cyber-attack incidents due to the proliferation of these devices, which is much easier to do than with desktop computers. New methods of identifying attacks launched from compromised IoT devices are needed to help address this issue. In this scenario, the most effective method of detective control against attacks originating from IoT devices is the application of ML and DL techniques. The main aim of this research is to provide an overview of intrusion detection models as well as a comprehensive review of threats to IoT systems caused by compromised IoT devices.