Miracle-dz/UniFAR-FTUs huggingface dataset:
SynthXNet: Adversarial Learning for Data Augmentation in CoVID Severity Classification from X-Rays

For efficient patient care and management, the correct and prompt assessment of CoVID severity using X-Ray images is crucial. Existing approaches for classifying CoVID severity from X-ray images have shortcomings in precision, accuracy, recall, and latency; as a result, more reliable and effective ways must be developed. This study introduces "SynthXNet," a unique approach that uses adversarial learning and data augmentation to improve the performance of CoVID severity classification, in response to such demands. A substantial collection of X-Ray images is acquired, covering cases of CoVID patients with differing degrees of severity, spanning different age groups, genders, and ethnicities, to address the dearth of diverse and representative datasets & samples. The Generative Adversarial Network (GAN) architecture, which consists of a generator and discriminator network, is the central innovation of this work. To create artificial images that closely mimic actual X-Rays of CoVID patients, the GAN is trained on the X-Ray dataset that has been gathered. In an adversarial training procedure, the generator and discriminator teach each other to produce realistic images while the discriminator teaches each other to discern between actual and fake X-Rays. A deep learning model, specifically a Convolutional Neural Network (CNN), used for CoVID severity classification uses the synthetic X-Ray images produced by the GAN as extra training data. In comparison to recently proposed methods, the model’s precision increases by 8.5%, accuracy by 9.4%, and recall by 8.3% when the dataset is supplemented with these synthetic images. The suggested method also speeds up CoVID severity classification by 4.9%, making it more effective for real-time scenarios. In order to overcome the shortcomings of existing approaches, "SynthXNet" presents a potent solution to CoVID severity classification from X-Ray images by utilizing adversarial learning and data augmentation. Its potential to improve patient care and management in the context of CoVID severity evaluation is indicated by the results, which show significant increases in performance indicators.
Comparison Of Deep Learning Models for Monkeypox Disease Detection

The Monkeypox virus is a species of double stranded DNA virus that causes Monkeypox disease in humans and other mammals. The emergence of the Monkeypox virus poses a fresh threat to global health at a time, when the world continues to try to heal from the damage brought in by the widespread of COVID-19. Although the Monkeypox diseases are not that harmful or contagious as COVID-19, many new cases are reported from various nations across the world. As a result, it won’t be shocking if there is another pandemic, because of insufficient precautions taken. Due to its resemblance to both chickenpox and measles, Monkeypox can be challenging to clinically diagnose in its initial stages. Computer aided Monkeypox lesion detection may be useful for surveillance and the quick identification of suspected cases in circumstances where there is a difficulty in performing confirmatory Polymerase Chain Reaction (PCR) tests. Deep learning (DL) methods has shown enormous promise for image-based diagnosis including lung cancer detection, skin diseases detection, COVID-19 etc. Hence, the same application may be used to detect the Monkeypox diseases too. This paper evaluates four pre trained deep learning models (Squeeznet, DenseNet201, EfficientNetB0, ResNet50v2) for Monkeypox disease detection and six established metrics (Precision, Recall, Sensitivity, Specificity, F1-score and Accuracy) are used to analyze the results. This paper helps to identify the best pretrained model for Monkeypox diseases detection. A publicly available dataset is used in this work.
Design of Dual Gate Carbon Nanotube FET and Analysis the Short Channel Effects

In today's world, wireless technology and portable electronics are everywhere. According to a World Health Organization (WHO) assessment, the harmful radiations emitted by portable electronic devices are the root cause of conditions like brain tumors, infertility, hearing loss, eyesight issues, and foetal illnesses. As a result, we needed electronic and wireless devices that only dissipated extremely low power. Complementary MOSFETs (CMOS) are the primary component used in the production of integrated circuits. Short Channel effects (SCEs) result from the aggressive scaling of traditional CMOS, where the channel length is below 10 nm and the gate oxide thickness is below 3 nm. Scaling down of silicon integrated circuits (IOFF) causes complications such as the tunneling effect, gate oxide thickness effect, threshold voltage roll-off, subthreshold swing (SS), drain induced barrier lowering (DIBL), and subthreshold leakage current. This problem has given rise to a chance for the development of innovative materials. Carbon nanotubes are a promising material for the upcoming MOS technology era. In this article, a modeling of a DG-CNTFET (Double-Gate Carbon Nanotube Field-Effect Transistor) with ohmic and Schottky Barrier (SB) was described. The SB-DG-CNTFET demonstrated 75% greater drain current at the same nanoscale parameters as the DG-CNTFET, including 14 nm channel length, 1 nm oxide thickness, 0.2V threshold voltage, and 300K temperature. At VDS = 0.1V, the SB-DG-CNTFET has an Ion/Ioff ratio of 800×103, whereas the DG-CNTFET has a ratio of 314×103. This is 154 times bigger than the value of DG-CNTFET. At 300oC temperature when compared to a MOSFET, the subthreshold swing of a CNTFET is 55mV/Dec, the DIBL is 38.5, and the Ion/I off ratio is ten times larger. SB-DG CNTFET has a substantially higher Ion/I off ratio than CNTFET (133x) and DG-MOSFET (250x). Finally, the simulation findings show that the CNTFET outperforms the MOSFET in the Nanoscale regime for high-speed and low-power electronic switching applications.
Blood Vessel Detection in Fundus Images Using Symbolic Approach

The diagnosis of retinal diseases using the vasculature of Fundus images has long been a focus of both ophthalmologists and medical research. Using computer-aided techniques to provide segmentation of blood vessels helps ophthalmologists in their clinical application routine. However, segmenting the vessels from retinal images is still a challenging task because of the variability in retinal vascular thickness, inhomogeneity of image intensity, and noise textures(both pathologies or bad pixels) that might hinder the quality of images. This work introduces a new technique of image processing based on Hessian filter to extract the blood vessel structure with a three-stage approach to filter Thicker, Thinner, and Neo Vessels. The proposed method is evaluated on three publicly available datasets namely Drive, Stare, and ChaseDB respectively achieving an average accuracy of . 93%, . 94%, . 95 % and then applied on our proper dataset of 350 images classified into 7 classes according to specific challenges. Manual validation by a team of four ophthalmologists provides useful insights into each class of images. Finally, we aim at providing an annotated database of fundus images which will serve as a training dataset for Deep Learning techniques.
Sensitivity Analysis for Electric Vehicle Hosting Capacity in Distribution Networks

This paper presents a short survey of existing studies on sensitivity analysis techniques for evaluating hosting capabilities in distribution networks. With the increasing adoption of electric vehicles (EVs), it is crucial to assess the impact of their integration on the power grid. Sensitivity analysis plays a significant role in understanding the relationship between key parameters and the EV hosting capacity, aiding network planning, management, and policy formulation. This paper explores various methodologies, challenges, and opportunities for further research in sensitivity analysis for EV hosting capacity. Furthermore, a case study on sensitivity analysis of EV hosting capacity in a real-distribution feeder with 966 customers in Australia is demonstrated in this paper. It demonstrates voltage unbalance issues dominate EV hosting capacity due to uneven distribution of residential solar systems.
Performance Prediction in OBSS WLANs Using Machine Learning Approaches

In high-dense deployment of WLANs with very high throughputs in environments like malls, stadiums, colleges, etc., the throughput achieved by next-generation IEEE 802.11 WLANs is much lower than expected. The estimation of throughput through simulators is cumbersome and needs elaborate information regarding the deployment details relating to overlapping basic service sets (OBSS). The objective of this paper to propose well-suited machine learning (ML) models to predict throughput such as artificial neural networks (ANN), k-Nearest Neighbours (KNN) regression, random forest regression, and graph neural networks (GNN). The results obtained in this work is promising to further pursue prediction models based on machine learning. This paper also highlights the use of graph neural networks as a promising approach to solving the elusive problem of performance prediction prior to actual deployment.
Wideband C-Slot Microstrip Antenna for IoT Applications

This work provides an in-depth analysis of a C-slot wideband patch antenna for Internet of Things (IoT) applications. IoT is an impending paradigm enabling us to communicate to the world around us, by connecting various sensors and electronic devices through the internet. Disregarding all wireless applications, the design of antennas is very important. The antenna proposed has a C-shaped slot, cut on the rectangular radiating microstrip patch with the partially ground plane. Microstrip antennas are facile to fabricate and can be employed with IoT devices since IoT devices are embedded in chips. The center frequency of operation of the suggested C-slot microstrip antenna is 4.33 GHz. Delineation of the C-slot antenna, especially the patch and ground is depicted. This antenna has outstripped the bandwidth requirement of an IoT antenna, from 3.27 GHz to 5.23 GHz, and it manifests good characteristics. By inserting the C-slot, the radiation efficiency of the optimal antenna structure obtained is 80 per cent. The C-slot microstrip antenna is having a gain of 3.07 dBi. The simulation method included a high-frequency structure machine (HFSS).
Fine Doppler Resolution Channel Estimation and Offset Gradient Descent Equalization for OTFS Transmission over Doubly Selective Channels

Orthogonal time frequency space (OTFS) modulation offers attractive performance in coping with doubly selective channels. In this paper, we propose a time-domain trainingsequence-aided transmission and an offset gradient descent equalization to reduce channel estimation and equalization complexity and enable better adaption to channels with either integer or fractional Doppler shift. Our proposed scheme leverages an extendedframe OTFS structure, which consists of multiple original OTFS frames, to achieve finer Doppler resolution and hence alleviate the impact of fractional Doppler when the frame is sufficiently long. We also propose an offset gradient descent equalization method, which exploits the structure of the channel matrix to significantly reduce the complexity. Simulation results validate the scheme and demonstrate that our proposed transmission scheme can achieve similar performance with MMSE equalization at significantly reduced complexity.
ResNet50-based classification of footwear in nuclear power plants surveillance images

In nuclear power plants, the correct wearing of work boots in accordance with regulations is crucial to protect the health of staff from the effects of radiation. Effective and accurate detection of employees wearing standardized shoes in nuclear power plants is imperative. Deep learning methods have the capability to extract image features and provide accurate image classification results. To achieve the automated classification of footwear, we devised a deep learning-based classification network. In this work, a footwear classification dataset is conducted from the human detection results of surveillance videos in a nuclear power plant. Using this dataset, we trained a classification model based on ResNet-50 to classify the dataset into five categories, including false detection, indeterminate, work boots, shoe covers and ordinary shoes. To mitigate the impact of class imbalance and avoid overfitting, training strategies including label smoothing and mixup data augmentation are employed during training. The final classification accuracy reached 96.62%.
Adaptive Push-Pull of Dynamic Web Data: Better Resiliency, Scalability and Coherency

—An important issue in the dissemination of time-varying web data such as sports scores and stock prices is the maintenance of temporal coherency . In the case of servers adhering to the HTTP protocol, clients need to frequently pull the data based on the dynamics of the data and a user’s co-herency requirements. In contrast, servers that possess push capability maintain state information pertaining to clients and push only those changes that are of interest to a user. These two canonical techniques have complementary properties with respect to the level of temporal coherency maintained, communication overheads, state space overheads, and loss of coherency due to (server) failures. In this paper, we show how to combine push-and pull-based techniques to achieve the best features of both approaches. Our combined technique tailors the dissemination of data from servers to clients based on (i) the capabilities and load at servers and proxies, and (ii) clients’ coherency requirements. Our experimental results demonstrate that such tailored data dissemination is essential to meet diverse temporal coherency requirements, to be resilient to failures, and for the efﬁcient and scalable utilization of server and network resources.
A Comparative Analysis of Machine Learning Methods to Predict Bankruptcy among Manufacturing Companies in India Post-IBC

The main purpose of this paper is to study the impact of financial variables on the bankruptcy prediction of Indian manufacturing companies by performing a comparative analysis between various machine learning predictive techniques. 15 financial variables are selected for the prediction of bankruptcy after the implication of the Insolvency and Bankruptcy Code (IBC) 2016. For comparative analysis, machine learning predictive techniques used in this paper are Logistic Regression, Artificial Neural Networks, Decision Tree, and Random Forest.
BESP: A novel BCI experiment simulation platform to assist BCI-related algorithm validation

In recent decades, many brain-computer interface (BCI) software platforms have emerged. However, there are still some limitations. First, integrating an algorithm on online BCI software platform is difficult and time-consuming. Second, there is no guarantee that an unproven algorithm will work properly. Last, existing platforms do not support automatic recording of result metrics during experiments, and frequent validation of multiple algorithms is inefficient. In this paper, we designed a novel BCI experiment simulation platform (BESP) based on offline analysis to satisfy the needs of algorithm validation. BESP reorganizes the experimental steps and provides some interfaces, which makes algorithm validation simple and efficient. In addition, BESP is able to record the results produced during the experimentation. Then the results of multiple algorithms can be easily analyzed, compared and visualized by the statistical analysis system provided by BESP. With these characteristics, BESP is able to verify the correctness of the algorithm. Finally, we designed a simulated P300 spelling experiment based on BESP, and completed the validation and analysis of Algorithms SPLUCB and TSPLUCB (based on PLUCB and TPLUCB). Furthermore, we used BESP to analyze the differences between the stimulus signals of the 20 subjects. Through the analysis, we found some areas worthy of further study. In summary, BESP is well suited for the process of analysis, optimization and validation among multiple algorithms.
Efficient and High Performance Ternary SRAM

In every application static random access memory is used for high speed register , cache and relatively small memory banks such as a frame buffer in the increased demand of SRAM with large use in system on-chip . It stores information in the form of binary values due to this reason it require more number of cells. Nowadays, ternary logic is popularly used and It is the best substitute for traditional binary logic because of its storage capacity and delay that results from reduced degree of complexity in interconnects and chip area. This project proposes the design of standard ternary inverter, by arranging ternary standard inverters back to back a Trit storage element is implemented as a ternary SRAM cell and continuous read operation is obtained by Transmission gate technology. We measure the performance by considering the parameters area, delay and power. Tanner T- spice based circuit simulation of the proposed designs were carried out, Simulation result demonstrates an increased performance of area and delay for the Proposed design as compared to the existing design.
Development of an iOS Application Leveraging PalmSens MethodSCRIPT for Rapid COVID-19 Detection

In light of the ongoing challenges posed by COVID-19 and the need for rapid diagnostic tools, we migrated our electrochemical potentiostat-based diagnostic tool from a desktop application to an iOS platform that is more portable and accessible. Our primary goal was to improve portability, enhance the user experience, and expand accessibility. Figma was used to create the app's design blueprint, allowing for an intuitive and user-friendly interface. Integrated user feedback guided subsequent design refinements. The development then took place using MethodSCRIPT and Swift, explicitly tailored for MacBook OS and iPhone devices. The application's seamless integration with PalmSens-supported NACOTS devices guarantees accurate and real-time data acquisition. Essential functions include setting operator names and sample IDs, initiating scans of NACOTS devices, and reading samples using Differential Pulse Voltammetry (DPV), which provides rapid diagnostic results. Moreover, graphical representations of DPV signals and the ability to share results increase the application's utility. Early evaluations demonstrate the app's usability with minimal training, complemented by its insightful screenshots. With this iOS application, we contribute to global efforts to democratize rapid diagnostic solutions, which are essential in regions with limited diagnostic facilities. This initiative addresses the current COVID-19 scenario and lays the groundwork for addressing future health crises.
A survey on deep Learning's Effectiveness in Detecting Brain Tumors

Recognizing brain tumors is a crucial part of medical imaging. Deep learning algorithms have become effective tools for examining medical images. The present article seeks to give a thorough summary of current developments in deep learning-based brain tumor diagnosis. The investigation starts out by outlining the basic ideas of brain tumour identification and emphasizing its importance in tumor field of medicine. The survey emphasizes the necessity for larger and more varied datasets, the efficiency of automatic feature learning, as well as the significance of pre-processing and data augmentation in enhancing model performance. This investigation draws to a close with recommendations for future research areas and a focus on how deep learning has the ability to revolutionise the way that brain tumour detection is done while also enhancing patient outcomes.
Language maintenance and shift in one Semai community in peninsular Malaysia

This study investigates the reported language use of one bilingual Semai com­ munity, a linguistic minority in Peninsular Malaysia. The Semai is the largest indigenous group in the peninsular and the language, Semai, is generally an oral language. Semai bilingual speakers are faced with choices in language behaviour that will determine whether or not the Semai language will be maintained. Given the fact that the future of the Semai language depends on its actual use, this study was undertaken to examine the reported language use patterns and the factors that contribute to maintenance or shift in one Semai community. Data for this study were collected primarily through self-administered questionnaire as well as semi­ structured interviews and participant observation. Quantitative and qualitative analyses suggest that there are no gender and age effects in the reported pattern of language use and that the community is experiencing stable diglossia. While Semai functions as the in-group language, Malay is reserved for out-group com­ munication and used in formal domains. Additionally, results show that there is intergenerational transmission of the language to the younger generation. The re­ ported language use patterns in the sample population appear to suggest a trend towards maintenance. Findings in the study also reveal that respondents in the sample population have a high perception of Semai vitality and a positive atti­ tude toward their language, which they perceive as being a substantial part of their cultural identity. These findings suggest that socio-psychological factors are important and contribute to maintenance efforts in the community. This study concludes by positing that demographic factors, the values and attitudes of the people and religious homogeneity found in the community play an important role in the maintenance of the Semai language.
Impact of Soil Water Content and Porosity on Lightning Induced Voltages Evaluation in Distribution Line

Lightning Induced Voltages (LIVs) are one of the main causes of distribution lines shutdown. It is known that soil resistivity plays an important role in the LIV s on overhead lines. Furthermore, in recent years, the effects of water content and soil porosity have received attention as they influence soil resistivity and, therefore, LIV s in overhead lines. Therefore, this paper investigates the influence of the porosity level proposed by Archie's formula on the lightning performance of distribution lines. For this, the extended formula proposed by Paulino, which determines the peak value of the LIV combined with the Monte Carlo Method, was used to estimate the distribution line lightning flashover rate. In this way, simulations were performed using a computational algorithm with different water content and porosity values, and the flashover rate was calculated based on these values. Results have demonstrated that the volumetric water content and soil porosity significantly impact the calculation of the performance of distribution lines. Increasing the water content and decreasing the porosity level produce more conductive soils that notably impact the performance responses.
Extração de métricas para estimativa do fluxo de tráfego de veículos utilizando visão computacional

e estimativa de velocidade por matriz de projec¸˜ao de uma sec¸˜ao na via. Isso permite criar os diagramas essenciais de fluxo de tr´afego, constitu´ıdos pela velocidade, fluxo e densidade dos ve´ıculos. O funcionamento do sistema foi comprovado por meio da validac¸˜ao dos dados extra´ıdos conforme os modelos de fluxo de tr´afego mais conhecidos da ´area, mostrando que o framework proposto funciona satisfatoriamente.