Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Mining Technology In Student Management Using Svm Techniques

Schooling data mining, which has forever been a huge examination subject, utilizes AI and data mining to foresee student performance in the homeroom. An early projection of student performance could help the applicable gatherings in assisting the failing to meet expectations understudies with arrangements. Various factors, remembering grades for earlier tasks, public activity, parental business, and recurrence of unlucky deficiencies, may affect a student's performance on the last test. This article attempts to estimate student scholastic performance to work on the productivity of instructive organizations and raise students' scholarly performance. Support Vector Machines (SVM) and Arbitrary Backwoods (RF) were the arrangement calculations and strategies utilized in this paper. Educational data mining (EDM) research is a hot area of programming research. Utilizing EDM gadgets and approaches, experts can evaluate understudy execution and utilize that data to foster strong intervention strategies that increment achievement and further develop maintenance. Artificial intelligence classifiers today have various abilities relying upon the main job. The investigation of help vector machines is much of the time utilized in requesting questions. In any case, the presently accessible writing uncovers holes in the utilization of unadulterated her SVM as a mark of understudy execution.
Proposed System Design for Cross-Cultural Distance Reciprocal Teaching Using "Kusho"

"Kusho" is a unique learning method in Japanese language study, in which students write large characters in the air while counting out loud with their classmates. This method has been reported to be effective for learning complex characters, but few studies have examined the effects of learning interactively with classmates. This study aims to support learning with peers from other cultures by using "Kusho", and at the same time, to construct a system to collect log data for examining the effect of learning. In this report, we present the design concept and the results of our observation of learning with a prototype system. This system is still under construction, and after its completion, we plan to study the effectiveness of this learning method by implementing character learning in Thailand and Japan.
Diabetes Prediction with Ensemble Learning Techniques in Machine Learning

Diabetes is a chronic disease caused by high blood glucose levels. Either the pancreas produces insufficient amounts of insulin or the body’s cells stop responding to hormones. There is an increasing need for creative solutions in early prediction and healthcare management due to the rising global prevalence of diabetes. The article explores the field of predicting diabetes using machine learning techniques. The model covered in this article evaluates susceptibility to potential diabetes hazards in the future. With a focus on early identification and the reduction of the burden associated with complications related to diabetes, this study highlights the revolutionary potential of machine learning in healthcare. An ensembling approach for diabetes prediction is suggested in this paper. The best model is selected from a collection of diverse machine learning algorithms that includes Random Forest (RF), K-Nearest Neighbors (KNN), XGboost, and Catboost. Using metrics like accuracy, specificity, sensitivity, precision, false omission rate, and Area Under Curve (AUC), the performance of the developed model is assessed. The suggested model has a 96 percent accuracy rate after being trained on the PIMA Indian Diabetes dataset. This article adds to the continuing conversation about how artificial intelligence can be used in the healthcare industry to promote proactive health management and better patient outcomes.
Vehicle state parameter estimation based on improved adaptive observer

To accurately obtain the state parameters during vehicle driving, this paper designs an adaptive nonlinear observer for vehicle state parameters to mitigate the influence of measurement noise within the framework of extended Kalman filtering. Based on the vehicle nonlinear three degree of freedom, a dead zone observer was designed to accurately estimate the sideslip angle, yaw rate, and longitudinal velocity of the vehicle in answer to the problem of significant noise in lateral acceleration. According to the output variable error, determine the dead zone boundary of the observed state variables at each time based on observation errors, limiting the estimation error within the dead zone boundary and improving noise suppression performance. The validity of this method is validated through a variety of different simulation conditions and by comparison to the EKF algorithm. Results demonstrate that the improved algorithm significantly enhances the estimation precision compared to Extended Kalman Filter algorithm.
Enhanced Accuracy in Underwater Communication Prediction Utilizing LSTM Over RNN for Improved Efficiency

Underwater communication systems experience obstacles like confined bandwidth, elevated energy consumption, propagation lags, end-to-end delay (E-ED), complex media accessibility control, resource allocation, and power constraints. These challenges are resolved using energy-efficient protocols, which fall into two categories: constraint-based and constraint-free. The study conducts a comprehensive assessment of constraint-free protocols in our project, considering environmental impact, data transmission rates, efficiency, energy consumption, E-ED, and propagation delay. This analysis provides insights into the pros and cons of these protocols, guiding future research in Underwater Wireless Sensor Networks (UWSNs). The study also presents routing strategies and their strengths and weaknesses, identifying areas for further investigation. In this research work, algorithms such as Long-short Term memory algorithm (LSTM), Recurrent Neural Network (RNN), Convolution Neural Network (CNN), and Artificial Neural Network (ANN), are employed and analyzed in terms of accuracy. The results demonstrate that Long-short Term memory algorithm (LSTM) outperforms better than other existing algorithms in terms of accuracy.
Generation of Photon Attenuation Corrected PET From MRI and Uncorrected PET Using Machine Learning

In this work, we evaluate the feasibility of a simplified approach of performing MR-based attenuation correction (MRAC) by generating AC PET images directly from non-attenuation corrected (NAC) PET and MR images using a machine learning model known as conditional generative adversarial network (cGAN). We evaluate this approach for the head and neck region of a PET/MR and PET/CT dataset. We trained and tested a model converting MR Dixon Water, MR Dixon Fat, and NAC PET (multi-channel input) to AC PET. Our model achieved a mean pixel value difference of 2.06% in the head and neck region, NRMSE of 1.29%, PSNR of 37.55, and SSIM of 0.97 compared to the true AC PET images determined by CT-based attenuation correction. These metrics are a large improvement compared to the NAC PET images which had a mean pixel value difference of 5.35%, NRMSE of 3.27%, PSNR of 28.80, and SSIM of 0.85 when comparing to AC PET.
Nonlinear Model Predictive Controller Based on Data and Mechanism Hybrid Model for Autonomous Vehicle Path Tracking Control

In the process of path tracking, autonomous vehicles often encounter modeling errors in the controller’s vehicle model, which can affect the path tracking performance, especially in high-speed turning scenarios. To address this issue, this paper proposes a nonlinear model predictive control based on data-mechanics hybrid predictive model as a control scheme for path tracking. Firstly, vehicle driving data under different operating conditions were collected as the training dataset for the neural network. The network was trained to fit the error between the prediction model and the vehicle model. Secondly, the trained neural network bias compensation model was validated and integrated into the three-degree-of-freedom vehicle model to obtain the neural network-based compensated model predictive controller. Finally, the designed controller was compared with the uncompensated model predictive controller under different operating conditions. The results showed that the path tracking controller with neural network compensation had better tracking accuracy, reducing tracking errors by 47.01% and 57.55% under constant speed and variable speed conditions, respectively. The effectiveness of the designed controller was verified under high-speed, large curvature conditions.
RFID Time and Attendance System based on IoT Technology

The objective is to develop a portable, secure, and user-friendly attendance system based on RFID technology. This technology offers an efficient tool for monitoring student attendance on a significant magnitude.This attendance monitoring system leverages the concept of the Internet of Things (IoT) to store data in the cloud or on a server, enabling users to access it from any location at any time. Students are granted the ability to remotely access their attendance data, allowing them to log in and review it at a later time. The system operates on RFID technology. Information regarding participants can be collected, documented, and analyzed. This system constructs an Internet of Things (IoT) reliant Radio Frequency Identification (RFID) attendance system capable of storing attendance records acquired by RFID tags in an Excel format.
Detecting Counterfeit Goods with a Blockchain- Enabled Barcode System

In the quickly changing world of technology, there is always a danger that a technology or product may be developed globally. Examples of this risk include copying and counterfeiting, which can hurt a company’s brand, revenue, and consumer health. , are examples of the rapidly expanding world of technology. The project’s primary objective is to consider whether the customer’s purchased item matches the description in the false or real compared to block chain, we have the traditional supply chain. Standard supply chains provide compact networks where the company delivering the service or the goods retains the data. Because they have such control over the market and the data, they are susceptible because they can alter it anyway they see fit. For genuine businesses, counterfeiters present serious problems, but far too many people are unaware of the full extent of the impact that these products have on brands. Various strategies have been developed in the past to circumvent the issue of product counterfeiting. Anti-counterfeiting strategies frequently used include those based on QR codes, AI, and RFID tags. These techniques do have certain limits, though. For instance, it is simple to copy QR codes and add them to fake goods, which reduces their usefulness. Convolutional neural networks (CNNs) and machine learning are two examples of AI technologies that demand a lot of computational power, which may be challenging and resource-intensive. Due to these challenges, further research and development are required to solve them and improve the efficacy of anti-counterfeiting techniques. The goal of this initiative is to increase forged goods identification using supply chain history tracking. This is accomplished using Throughout the supply chain, block chain technology enables the identification and tracking of genuine items. Everything is made decentralized and accessible by several parties at once via a block chain-based system. One of its key benefits is that the data is incredibly safe and protected from any vulnerabilities since it cannot be changed without the approval of all parties involved.
CENOZOIC IGNEOUS AND TECTONIC SETTING OF THE MARYSVALE VOLCANIC FIELD AND ITS RELATION TO OTHER IGNEOUS CENTERS IN UTAH AND NEVADA

168 Introduction. 168 Acknowledgments . 172 Igneous Rocks. 172 Middle Cenozoic Calc-alkaline Rocks . 172 Upper Cenozoic Basalt and Alkali Rhyolite. 174 Regional Versus Local Extensional Deformation. 176 Middle Cenozoic Faults . 176 Late Cenozoic Faults and Basin-fill Deposits. 179 Igneous Belts and Mineral Belts . 180 Transverse Structures. 181 Plate-tectonic History . 184 Spreading . 185 Conclusions. 188 References Cited . 189
Encryption Invalidation Attacks: Is your Wi-Fi encryption really working?

In the 2023 Usenix Security Symposium, M. Vanhoef et al. demonstrated how to exploit power-save features and delete encryption keys to trick an access point into leaking frames in plaintext from its buffer. However, it is uncertain whether packets will be buffered due to the possibility of client devices sending wake-up frames. Therefore, the transmission of plaintext packets is unlikely. Furthermore, the captured packets in their study were mainly responses to echo requests, which may not contain sensitive information. We propose a method to buffer packets regardless of the client’s wake-up frame usage, enabling access points to send plaintext packets even when the client is accessing a web page. Our proposed method significantly enhances the efficiency of intercepting sensitive data, such as IP addresses, port numbers, and even TCP payloads, outperforming the approach proposed by M. Vanhoef et al.
A Novel Robust Frequency Adaptive Single-Phase Phase-Locked Loop Based on Second-Order Generalized Integrator

In the distributed grid, it is important to synchronize the grid-connected inverters with the grid. The second-order generalized integrator-based phase-locked loop (SOGI-PLL) is a single-phase PLL with relatively outstanding overall performance. However, its performance is not satisfactory under the condition of large bandwidth and weak grid due to the nonlinearity of coordinate transformation and the coupling caused by frequency feedback path. Therefore, this paper proposes an efficient frequency detection method based on three consecutive sample (3CS) to improve the performance of traditional SOGI-PLL, and adds a low-pass filter in the frequency detection branch to improve the frequency detection accuracy. Then, using the harmonic linearization modeling method, the model of the proposed scheme in the frequency domain is established. Finally, better behavior and stability of the proposed improved strategy is verified theoretically and experimentally, and compared with the traditional SOGI-PLL. Besides, experimental results show that the proposed strategy not only greatly expands the bandwidth of PLL and improves the dynamic performance, but also greatly improves the robustness of the inverter under large grid impedance.
Adaptive PI Control of PMSM for Electric Vehicle Application Based on Sliding-mode Extremum Seeking Algorithm

Ensuring speed robustness against load torque variations is of paramount importance for Permanent Magnet Synchronous Motors (PMSMs), particularly in electric vehicle applications. While the literature extensively covers this issue, the utilization of Multivariable Sliding-Mode Extremum Seeking (MSES) for fine-tuning PI gains as the PMSM's speed controller has yet to be proposed. By incorporating the speed feedback error term and a well-suited sliding surface, MSES dynamically adjusts the PI gains during operation, imposing a minimal computational burden. Real-time PI controller tuning holds the potential to decrease system susceptibility to fluctuations in both motor parameters and load torque disturbances. Consequently, this approach facilitates achieving a satisfactory tracking performance. Notably, the method exhibits resilience to variations in controller inputs and can be readily implemented on mid-tier processors. In order to verify the performance of the proposed control method, a simulation study is conducted using MATLAB®/Simulink software. The obtained results present an indication of relatively high accuracy as well as satisfactory performance in terms of robustness against load torque perturbation and independence of parametric variations.
Predicting Rainfall Using an Artificial Neural Network-Based Model

Rainfall estimation poses a significant and intricate environmental challenge in today's context. Achieving accurate forecasts often necessitates the deployment of sophisticated machine models and simulations because weather patterns and rainfall dynamics are typically highly nonlinear and constantly changing. In India, where agriculture serves as the cornerstone of the economy, special emphasis is placed on the relationship between agricultural productivity and rainfall. Farmers rely on rainfall forecasts to gauge potential crop yields. Precipitation forecasting leverages scientific and technological advancements to assess atmospheric conditions. Accurate monitoring of rainfall is pivotal for optimal crop growth, efficient water resource management, and strategic planning of water systems. To emulate the behavior of such intricate nonlinear systems, artificial neural networks (ANN s) have proven to be a valuable tool. Over the years, numerous researchers in this field have effectively harnessed ANN s. This research highlights several approaches employed by various researchers to predict rainfall using ANN s. This proposal underscores that forecasts based on ANN s outperform those derived from conventional mathematical and numerical methods, offering greater precision in rainfall predictions.
An Adaptive Feature Extraction Visual SLAM Method for Autonomous Driving

Due to the traditional visual Simultaneous Localization and Mapping(SLAM) method is easily affected by illumination, and the uneven distribution of feature points leads to a decrease in localizing accuracy or even tracking failure, we propose a visual SLAM method for adaptive ORB feature extraction and division based on ORBSLAM2. First,we dynamically set the number of extracted feature points for each frame to enhance the stability and robustness of tracking and reduce the occurrence of tracking failures. After that, we design a FAST feature point extraction strategy with a local dynamic threshold. Compared with the fixed threshold method, this can reduce the impact of illumination changes on feature point extraction. Finally, we adaptively limit the number of quadtree node division layers according to the expected number of feature points to avoid local over-concentration. We test our method on the KITTI dataset and the automatic guided vehicle platform. Compared with ORBSLAM2, the feature points extracted by our method are more uniform, and the localizing accuracy and robustness are improved.
Enhancing Road Safety: A Novel Fuzzy-RBF based Approach for Car Accident Prediction

The danger posed by traffic accidents is significant, as they have become a primary cause of death in many regions of the world. Lessening the frequency of such events is crucial for improving city and community resilience and saving lives. Machine learning and data analysis techniques decipher the root causes of accidents and propose corrective actions. The volume and speed with which data is being generated about road incidents necessitates the adoption of big data approaches. This system analyzes patterns in road car accident data and proposes a predictive model, with a focus on key data variables like the number of victims, accident severity, and the number of vehicles involved. The suggested methodology incorporates preprocessing, feature selection, and model training. The proposed approach employ Tokenization, Stop-word elimination, Stemming, and Lemmatization in preprocessing data. For classification models, feature selection is a crucial post-processing step because many input space variables are likely to be highly correlated with one another or have negigible effects on crash occurrences. The models are then trained using RBF-Fuzzy after the attributes have been gathered. The proposed method significantly outperforms the RBF and Fuzzy methods. This plan is nearly guaranteed to succeed at a rate of 98.23%.
Analysis of Magnetic Fields Near Television and Laptop on Living Beings

Electromagnetic waves are emitted from the electronic equipment. These electromagnetic waves are invisible and nonionizing in nature. When we are watching TV and working on the laptop, magnetic fields are generated around the equipment. The intensity of magnetic fields is different in X, Y and Z dimensions of the equipment. These fields penetrated inside the organs of the human body. The energy is absorbed by the various organs of living beings. The amount of energy absorption is varied in different dimensions. This absorbed excess amount of energy becomes the reason for many diseases in the body. It is observed that the equipment becomes more harmful on X axis and also near the equipment. The amount of penetrated magnetic fields becomes low far from the equipment.
A Junction Temperature Monitoring Method Based on Turn-Off Current for IGBT Modules

Junction temperature monitoring (TJM) is critical in the reliability assessment of insulated-gate bipolar transistor (IGBT) modules, However, existing TJM methods are usually disturbed by aging. To address this, this paper proposes a novel approach for TJM, which utilizing convolutional neural network (CNN) algorithm and the turn-off collector current $\boldsymbol{I}\mathrm{c}$. Firstly, the dynamic characteristic model of turn-off $I_{\mathrm{C}}$ and the thermal characteristics are analyzed. To examine the correlation between the turn-off $I_{\mathrm{C}}$ and the junction temperature $T_{\mathrm{j}}$, a mathematical model of the $I_{\mathrm{C}}$ is established. Through the implementation of the double-pulse tests, the connection between the turn-off $I_{\mathrm{C}}$ and $T_{\mathrm{j}}$ is verified, and other factors related to turn-off $I_{\mathrm{C}}$ are also analyzed. Finally, a TJM method is proposed based on convolutional neural network (CNN), which can decouple the effects of aging.
Machine and Deep Learning-based Techniques for Precision Agriculture with Comparative Analysis

Water and soil resources are crucial to agricultural growth and must be utilized more effectively. Precision Agriculture (PA), an innovative approach in modern farming, integrates advanced technologies and data-driven methods to enhance agricultural practices and increase crop productivity while minimizing resource inputs and environmental impact. This review explores the many facets of precision agriculture, covering its methods, difficulties, and potential. This study attempts to provide the most recent data on PA technology and techniques for determining the most precise answer to diverse scenarios in agricultural operations. This study discusses research that has been done over the past five years (2018-2023) on approaches based on machine learning (ML) and deep learning (DL) for very accurate crop and weed yield predictions, as well as soil and weather forecasting. The analysis finishes with a discussion of precision agriculture’s challenges and potential solutions. In PA, existing authors perform on the limited data and are more time-consuming. Issues with data quality, such as missing or inconsistent data, affect the reliability of analysis and predictions. And it affects the prediction accuracy. Unpredictable weather patterns can impact the effectiveness of specific PA evaluations. To overcome these issues, eliminate missing values and normalize the data using the min-max normalization method. And employ novel deep learning techniques such as Bi-directional convolutional long short-term memory and some optimization algorithms such as Northern Goshawk optimization algorithm and Crayfish optimization Algorithm to optimize the prediction parameters to achieve higher prediction accuracy with less computational time. This article describes how knowledge-based agriculture may raise output and product quality over the long run.