Error Rate Research Articles

Numerical Coupling Simulation of the Vertical Blowing Suspension Position of Tea Leaves Based on Computational Fluid Dynamics and the Discrete Element Method

To provide reference for the design of the air-suction tea sorting device, the coupled numerical simulation model was established by the coupling method of computational fluid dynamics (CFD) and discrete element method (DEM) with tea of different quality as test objects, and the model was verified experimentally. Regarding tea particles of different quality, when the test tea particle mass was 0.215, the test value was located in the simulation value with a minimum error of 9 mm, which an error rate of 3.33%, and maximum error of 19 mm, with an error rate of 7.03%. When the test tea particle mass was 0.145, the minimum error of the test value was 5 mm and the error rate was 1.54%, and the maximum error was 9 mm and the error rate was 3.33%. The verification results established the accuracy of the model. During the suspension test and simulation, tea particles were affected by the air flow field of the observation tube, and tea particles fluctuated. During suspension, tea particles were attached to the inner wall of the observation tube under the action of the air flow field. An in-depth study showed that the relationship between the different distances from the initial position of the particles during suspension and the simulation time was a peak function. The extreme function is used to fit the actual trajectory, and the fitting degree is good. The fitting degree of the particle closest to the initial position was 0.9455, and the fitting degree of the particle farthest from the initial position was 0.9981.

Comparative study: AutoDPR-SEM for enhancing CNN reliability in SRAM-based FPGAs through autonomous reconfiguration

Convolutional neural networks (CNNs) are widely adopted in safety-critical systems, including space applications and autonomous vehicles. Field-programmable gate arrays (FPGAs) based on SRAM are preferred for accelerating CNN computations due to their unique characteristics. However, the configuration memory of FPGAs is susceptible to single event effects (SEEs), which can corrupt computations and lead to misclassification of CNN outputs. In this study, we investigated the impact of SEEs on SRAM-based FPGAs with Two-Photon Absorption (TPA) laser fault injections through a comparative analysis of two popular CNN acceleration architectures: streaming architecture (SA) and single compute engine (SCE). Experimental results show that SA-based CNNs require more hardware resources but exhibit superior resilience against single event upsets (SEUs). Without any Radiation Hardened by Design (RHBD) protection, SCE has an error rate approximately twice as high as SA. To mitigate errors, the Xilinx IP core - Soft Error Mitigation (SEM) is used for error detection and correction, leading to error rate reductions of up to 50 % in both architectures. Importantly, we propose the AutoDPR-SEM (Autonomous Dynamic Partial Reconfiguration for Soft Error Mitigation) approach, which automatically reconfigures the SEM IP core when it remains idle due to uncorrectable errors. AutoDPR-SEM significantly improves CNN error rates, reducing errors by approximately 17.8 times in SCE and 14.8 times in SA. We also applied software level simulation to validate the TPA experiment, showing similar trends of the testing results across all models. In conclusion, the study confirms the feasibility of AutoDPR-SEM in both architectures, showcasing its potential to improve CNN error rates in safety-critical systems.

Design and performance evaluation of a 350 m free space optical communications link for pico-macrocell backhauling

Fibreless optics or free space optical communications (FSOC) has been at the forefront of many academic research in telecommunications due to its numerous benefits of large spectrum, high-speed data transmission, security, low transmit power, unlicensed spectrum and non-interfering links. Among the technical challenges of dense deployment of small cells in heterogeneous networks (HetNet) is a flexible and cost-effective backhaul link. This paper proposes, designs, simulates and evaluates the performance of a 350 m FSOC link under different atmospheric impairments for picocell to macrocell backhauling applications. The performance of the FSOC link is assessed by evaluating bit error rate (BER), eye diagram and quality factor (Q-factor). Results obtained recommend the FSOC link deployment for pico-macrocell backhauling under the weather conditions of clear sky with/without turbulence, heavy rain, heavy haze, heavy fog and wet snow.

Low-power body-coupled transceiver for miniaturized body area networks

As wearable devices continue to proliferate, seamlessly integrating them into wireless body-area networks (WBANs) becomes increasingly crucial. Body-coupled communication (BCC) emerges as a promising WBAN technology, utilizing the human body itself as a transmission channel. This paper presents a novel BCC transceiver designed for efficiency and miniaturization. The proposed transceiver prioritizes reliable data transmission with a convolutional encoder. It leverages a simple direct digital synthesizer (DDS) for frequency shift keying (FSK) modulation, minimizing chip area. At the receiver, a Viterbi decoder (VD) ensures accurate data recovery. This design shines in its resource efficiency. It occupies less than 1% of an Artix-7 FPGA, operates at 268.77 MHz with a mere 111 mW power consumption, and achieves a remarkable data rate of 13.78 Mbps. This translates to a hardware efficiency of 44.46 Kbps/slice, surpassing existing transceivers. Moreover, the BCC transceiver exhibits a stellar bit error rate (BER) of over 10⁻⁷ under realistic body channel conditions. Overall, this work presents a highly efficient BCC transceiver with significant improvements in chip area, power consumption, and data rate compared to existing designs. This paves the way for practical and miniaturized WBAN solutions for future wearable applications.

Optimized decoder for low-density parity check codes based on genetic algorithms

Low-density parity check (LDPC) codes, are a family of error-correcting codes, their performances close to the Shannon limit make them very attractive solutions for digital communication systems. There are several algorithms for decoding LDPC codes that show great diversity in terms of performance related to error correction. Also, very recently, many research papers involved the genetic algorithm (GA) in coding theory, in particular, in the decoding linear block codes case, which has heavily contributed to reducing the bit error rate (BER). In this paper, an efficient method based on the GA is proposed and it is used to improve the power of correction in terms of BER and the frame error rate (FER) of LDPC codes. Subsequently, the proposed algorithm can independently decide the most suitable moment to stop the decoding process, moreover, it does not require channel information (CSI) making it adaptable for all types of channels with different noise or intensity. The simulations show that the proposed algorithm is more efficient in terms of BER compared to other LDPC code decoders.

Development of Arduino-based high heat detector temperature control prototype for household appliances

<span lang="EN-US">In the Philippines, fires are a widespread concern, with plenty of incidents attributed to electrical appliances. These incidents are a leading cause of non-open flame fires in the country, highlighting the urgent need for preventative measures. Existing devices could only trigger an alarm at 100 °C without shutting off the appliance automatically. To address these limitations, the researchers aimed to develop a high heat detector with 95% detection accuracy and less than 5% error in detecting high heat. This device used an Arduino Uno Board and relay to trigger an automated power-off mechanism in appliances experiencing high heat. Temperature changes were detected, and alarms were activated using an LM35 temperature sensor and buzzer. The accuracy of the LM35 sensor was assessed through hot bath tests, which included 12 trials at each temperature level between 80 °C and 150 °C with 10 °C intervals. The prototype’s performance revealed an average error rate of 1.13% and an average standard deviation of 0.9403. The computed F1 Score of 98% indicated that the prototype fulfilled the objectives. Functionality tests confirmed that the prototype successfully achieved its intended goal by shutting off the appliance when the threshold temperature was reached and enabling its operation otherwise.</span>

Neobots: an open-source platform for a low-cost neonatal incubator with internet of things approach

<div align="center">A baby incubator implements the internet of things (IoT) with an architectural design combining several scientific fields, such as networks, software, and hardware. Furthermore, this research develops an open-source platform called Neobots, including open-source program code to create a baby incubator. Then an overview of the system includes sending sensor data to the IoT Broker with the message queuing telemetry transport (MQTT) protocol and automatically storing data in the database. The results of the comparison value on each temperature sensor with a temperature sensor at the midpoint with an error of less than 0.7°C. Then testing the fuzzy between the Neobots program and the simulation in MATLAB got an error rate of 0-28.27%. In addition, in less than 10 minutes, the system response can adjust the temperature conditions to a setpoint value of 34°C from 29°C, and the average error value is 0.35°C during 1 hour of the Fuzzy implementation on the incubator. Then transfer data from the incubator to the database in a room without noise and full noise to get results for lost data less than 16.41% and 42.14%, delay rates between 0-6 seconds and 0-7 seconds with testing for 1 hour at every 1 second.</div>

A new efficient decoder of linear block codes based on ensemble learning models: case of boosting

<p>Error-correcting codes are used to partially or completely correct errors as much as possible, while ensuring high transmission speeds. Several Machine Learning (ML) models such as Logistic Regression and Decision tree have been applied to correct transmission errors. Among the most powerful ML techniques are aggregation methods which have yielded to excellent results in many areas of research. It is this excellence that has prompted us to consider their application for the hard decoding problem. In this sense, we have successfully designed, tested and validated our proposed EL-BoostDec decoder (hard decision decoder based on Ensemble Learning - Boosting technique) which is based on computing of the syndrome of the received word and on using Ensemble Learning techniques to find the corresponding corrigible error. The obtained results with EL-BoostDec are very encouraging in terms of the binary error rate (BER) that it offers. Practically EL-BoostDec has succeed to correct 100% of errors that have weights less than or equal to the correction capacity of studied codes. The comparison of EL-BoostDec with many competitors proves its power. A study of parameters which impact on EL-BoostDec performances has been established to obtain a good BER with minimum run time complexity.</p>

Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases

In the empire of image processing and computer vision, the demand for advanced segmentation techniques has intensified with the growing complexity of visual data. This study focuses on the innovative paradigm of fuzzy mountain-based image segmentation, a method that harnesses the power of fuzzy logic and topographical inspiration to achieve nuanced and adaptable delineation of image regions. This research primarily concentrates on determining the age of tigers, a critical and challenging task in the current scenario. The primary objectives include the development of a comprehensive framework for FMBIS and an in-depth investigation into its adaptability to different image characteristics. This research work incorporates those domains of image processing and data mining to predict the age of the tiger using different kinds of color images. Fuzzy mountain-based pixel segmentation arises from the need to capture the subtle gradients and uncertainties present in images, offering a novel approach to achieving high-fidelity segmentations in diverse and complex scenarios. The proposed methods enable image enhancement and filtering and are then assessed during process time, retrieval time, to give a more accurate and reduced error rate for producing higher results for real-time tiger image database.

Empowering geological data analysis with specialized software GIS modules

This research is devoted to the development of a geographic information system (GIS) for the analysis of geological data. It presents two specialized software modules designed to solve complex geological problems related to potential progress to disturbed masses and magnetotelluric sounding. These modules are integrated into the QGIS environment, offering efficient data processing and analysis capabilities, contributing to a deeper understanding of geological structures. The study presents a mathematical model for the problem of magnetotelluric sounding (MTS) and the continuation of potentials towards the perturbed masses, demonstrating numerical results using the developed algorithm. To confirm the accuracy of the model, a comparative analysis was carried out with empirical data for various chemical elements, which showed high accuracy, especially at shallow depths, with an error rate of less than 2%. In addition, the study highlights the importance of powerful GIS for the analysis and interpretation of geological data, including geochemical, geophysical and remote sensing information. The advanced functionality of QGIS simplifies data processing and visualization, which makes it an invaluable tool for geologists and researchers.

Stock market prediction employing ensemble methods: the Nifty50 index

<span lang="EN-US">Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions.</span>

Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neom Smart City

Smart cities like Neom require efficient and reliable transportation systems to support their vision of sustainable and interconnected urban environments. High-speed trains (HSTs) play a crucial role in connecting different areas of the city and facilitating seamless mobility. However, to ensure uninterrupted communication along the rail lines, advanced communication systems are essential to expand the coverage range of each base station (BS) while reducing the handover frequency. This paper presents the dual transceiver free space optical (FSO) communication system as a solution to achieve these objectives in the operational environment of HSTs in Neom city. Our channel model incorporates log-normal (LN) and gamma–gamma (GG) distributions to represent channel impairments and atmospheric turbulence in the city. Furthermore, we integrated the siding loop model, providing valuable insights into the system in real-world scenarios. To assess the system’s performance, we formulated the received signal-to-noise ratio (SNR) of the network under assumed fading conditions. Additionally, we analyzed the system’s bit error rate (BER) analytically and through Monte Carlo simulation. A comparative analysis with reconfigurable intelligent surfaces (RIS) and relay-assisted FSO communications shows the superior coverage area and efficiency of the dual transceiver model. A significant reduction of up to 76% and 99% in the number of required BSs compared to RIS and relay, respectively, is observed. This reduction leads to fewer handovers and lower capital expenditure (CAPEX) costs.

A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm

Face recognition is one of the important elements that can be used for securing the facilities, emotion recognition, sentiment exploration, fraud analysis, and traffic pattern analysis. Intelligent face recognition has yielded excellent accuracy in a controlled environment whereas vice versa in an uncontrolled environment. However, conventional methods can no longer satisfy the demand at present due to their low recognition accuracy and restrictions on many occasions. This study proposed an optimal deep learning-based face recognition system that improves the security of the model developed in the IoT cloud environment. Initially, the dataset of images was gathered from the public repository. The captured images are explored using image processing techniques like image preprocessing employing the Gaussian filter technique for removing the noise and smoothing the image. The histogram of oriented gradients (HOGs) is used for the image segmentation. The processed images are preserved at the cloud service layer. Extract features were linked to facial activities using the spatial-temporal interest point (STIP). On the other hand, the extracted feature vectors are investigated using galactic swarm optimization (GSO) techniques that give optimized feature vectors. The necessary features are selected using the gray level co-occurrence matrix (GLCM), which separates the statistical texture features. The GSO output is fed into the deep convolutional neural network (DCNN) that effectively trains the captured face images. This will allow the effectiveness of the GSO-CNN technique to be assessed in terms of recognition accuracy, recall, precision, and error rate.

Simulation design for Ro-FSO communications system by digital modulation schemes

Abstract The Ro-FSO system was designed using Opti System software (version 20), and its efficiency is evaluated in this study using two distinct digital modulators (PSK and DPSK). The proposed system consists of four transmission channels, each with its own wavelength (193.1, 193.2, 193.3, and 193.4 THz). The system was utilized to transmit data at three different rates (10, 20, and 40 Gbit/s) under four different levels of dust attenuation. Because of the large quantity of data retrieved, only results from two specific wavelengths (193.1 and 193.4 THz) were disclosed, as they exhibited the highest Q-factor and lowest bit error rate (BER). The study observed that the (DPSK) modulator surpassed the (PSK) modulator in multiple attenuation scenarios and had superior performance when transmitting data at speeds of 10 and 40 Gb/s. The wavelength of 193.4 THz performed optimally with both modulators in terms of wavelengths.

Chaos key enhanced physical layer secure transmission method based on the convolutional long short-term memory neural network

In this paper, we propose a method for training a key-enhanced chaotic sequence using the convolutional long short term memory neural network (CLSTM-NN) for secure transmission. This method can cope with the potential security risk posed by the degradation of chaotic dynamics when using chaotic model encryption in traditional secure transmissions. The simulation results show that the proposed method improves the key space by 1036 compared to traditional chaotic models, reaching 10241. The method was applied to orthogonal chirp division multiplexing (OCDM). To demonstrate the feasibility of the proposed scheme, we conducted transmission experiments of encrypted 16 quadrature amplitude modulation (QAM) OCDM signals at a speed of 53.25 Gb/s over a 2 km length of 7-core optical fiber and test different encryption schemes. After key enhancements, the overall number of keys in the system can increase from 18 to 105.The results show that there is no significant difference between the bit error rate (BER) performance of the encryption method proposed in this paper and the traditional encryption method. The maximum performance difference between the different systems does not exceed 1 dBm. This fact proves the feasibility of the proposed scheme and provides new ideas for the next generation of secure transmission.

SVM Optimization with Grid Search Cross Validation for Improving Accuracy of Schizophrenia Classification Based on EEG Signal

The advantage of the Support Vector Machine (SVM) is that it can solve classification and regression problems both linearly and non-linearly. SVM also has high accuracy and a relatively low error rate. However, SVM also has weaknesses, namely the difficulty of determining optimal parameter values, even though setting exact parameter values affects the accuracy of SVM classification. Therefore, to overcome the weaknesses of SVM, optimizing and finding optimal parameter values is necessary. The aim of this research is SVM optimization to find optimal parameter values using the Grid Search Cross-Validation method to increase accuracy in schizophrenia classification. Experiments show that optimization parameters always find a nearly optimal combination of parameters within a specific range. The results of this study show that the level of accuracy obtained by SVM with the grid search cross-validation method in the schizophrenia classification increased by 9.5% with the best parameters, namely C = 1000, gamma = scale, and kernel = RBF, the best parameters were applied to the SVM algorithm and obtained an accuracy of 99.75%, previously without optimizing the accuracy reached 90.25%. The optimal parameters of the SVM obtained by the grid search cross-validation method with a high degree of accuracy can be used as a model to overcome the classification of schizophrenia.

Identifying excessive length of antibiotic treatment duration for hospital-acquired infections: a semi-automated approach to support antimicrobial stewardship

BackgroundAvoiding excessive antibiotic treatment duration is a fundamental goal in antimicrobial stewardship. Manual collection of data is a time-consuming process, but a semi-automated approach for data extraction has been shown feasible for community-acquired infections (CAI). Extraction of data however may be more challenging in hospital-acquired infections (HAI). The aim of this study is to explore whether semi-automated data extraction of treatment duration is also feasible and accurate for HAI.MethodsData from a university-affiliated hospital over the period 1-6-2020 until 1-6-2022 was used for this study. From the Electronic Health Record, raw data on prescriptions, registered indications and admissions was extracted and processed to define treatment courses. In addition, clinical notes including prescription instructions were obtained for the purpose of validation. The derived treatment course was compared to the registered indication and the actual length of treatment (LOT) in the clinical notes in a random sample of 5.7% of treatment courses, to assess the accuracy of the data for both CAI and HAI.ResultsIncluded were 10.564 treatment courses of which 73.1% were CAI and 26.8% HAI. The registered indication matched the diagnosis as recorded in the clinical notes in 79% of treatment courses (79.2% CAI, 78.5% HAI). Higher error rates were seen in urinary tract infections (UTIs) (29.0%) and respiratory tract infections (RTIs) (20.5%) compared to intra-abdominal infections (7.4%), or skin or soft tissue infections (11.1%), mainly due to incorrect specification of the type of UTI or RTI. The LOT was accurately extracted in 98.5% of courses (CAI 98.2%, HAI 99.3%) when compared to prescriptions in the EHR. In 21% of cases however the LOT did not match with the clinical notes, mainly if patients received treatment from other health care providers preceding or following the present course.ConclusionSemi-automatic data extraction can yield reliable information about the indication and LOT in treatment courses of hospitalized patients, for both HAI and CAI. This can provide stewardship programs with a surveillance tool for all in-hospital treated infections, which can be used to achieve stewardship goals.

Performance evaluation and investigation of diffraction optical elements effect on bit error rate of free space optics and performance investigation of space uplink wireless optical communication under varying atmospheric turbulence conditions

AbstractSeveral other optical antenna topologies have been developed and implemented throughout the years. These topologies include a variety of optical components, including the axicon optical element, dual‐secondary mirror, cone reflecting mirror, prism beam slier, and beam‐splitter/beam combiner. In contrast, the secondary reflecting mirror causes an obscuration loss that must be compensated for by reducing the transmission power in an optical antenna design. In order to address this issue in space optical communication, the present research helps to develop an enhanced two diffractive optical elements (DOEs) technology however the data presented therein only shows that DOEs may boost transmission power efficiency, which is insufficient for system designers. Though On‐Off Keying (OOK) is widely used in optical communication systems at the moment, the proposed research include DOEs into an OOK space uplink optical. The proposed research uses numerical simulation to explore how much a space uplink OOK system's bit error rate (BER) may be improved by using DOEs and adjusting fundamental parameters. The proposed BER model takes environmental factors like wind and detector noise into account. Using this theoretical model, the present work helps to investigate the effect of DOEs on the BER versus fundamental parameter characteristic curves in space uplink optical communication. Based on the findings, the DOEs structure has the potential to significantly enhance the BER performance of space uplink optical communication systems, especially at high obscuration ratios. When the obscuration ratio is 0.25, 0.167, or 0.125 and the transmission power is 1 W, for instance, the DOEs may improve the BER by a factor of two or one order of magnitude or less when the parameters are changed to the typical parameter values as specified. Results increase by a factor of six, three, and two orders of magnitude, respectively, when transmitting at 5 W. The results show that DOEs can significantly enhance the BER performance, especially at high obscuration ratios. The findings suggest that integrating DOEs into the optical subsystem is a straightforward approach to improving the performance of space uplink optical communication systems.

Design and analysis of stochastic 5G new radio LDPC decoder using adaptive sparse quantization kernel least mean square algorithm for optical satellite communications

AbstractA Stochastic Low‐Density Parity‐Check (LDPC) decoder is a type of 5G New Radio standard LDPC decoder that uses stochastic techniques to perform decoding. Stochastic LDPC decoding with 5G NR standard typically uses an iterative process, where messages exchanged among variable nodes (VN), check nodes multiple times. Stochastic LDPC decoders are often used in scenarios where the received signal is subject to varying levels of noise. They will provide improved error correction performance compared to traditional LDPC decoders, especially when dealing with channels with varying signal‐to‐noise ratios in 5G networks. Using the adaptive sparse quantization kernel least mean square algorithm (SLDPC‐ASQ‐KLMSA), this paper proposes an area‐efficient architecture design for a stochastic LDPC decoder. The LDPC code (2048, 1723) is taken from the LOGBASE‐T standard and used in this study. We examine the ASQ‐KLMSA connection effects. Starting with the VN. It makes checking node functioning easier and reduces inter‐connect complexity by capping extrinsic message length at 2 bits. Because of the simplified check node operation in ASQ‐KLMSA, the decoder nodes must exchange messages with a greater degree of accuracy. The 3–3 input grouping sub‐node of the degree‐6 VN was changed with an adder‐based 5–1 input grouping sub‐node for the (2048, 1723) code in order to get more accurate results when the check‐to‐variable messages aren't strong enough. A suggested decoder architecture was determined using a stochastic LDPC decoder developed for TSMC 65 nm process (2048, 1723). Bite error rate, throughput, mean square error, latency, power, and area usage are some of the metrics used to evaluate the effectiveness of the SLDPC‐ASQ‐KLMSA algorithm that has been suggested and implemented in Python. Thus, the proposed approach has attained 34.44%, and 38.39% low mean square error while compared with the existing methods such as higher‐performance stochastic LDPC decoder architecture designed through correlation analysis (HP‐SLDPC‐CA), Higher Throughput and Hardware Efficient Hybrid LDPC Decoder Utilizing Bit‐Serial Stochastic Updating(HLDPC‐BSSU), Flexible FPGA‐Based Stochastic Decoder for 5G LDPC codes (FPGA‐SD‐5G‐LDPC), respectively.

Quantum support vector machines for classification and regression on a trapped-ion quantum computer

Quantum machine learning is a rapidly growing field at the intersection of quantum computing and machine learning. In this work, we examine our quantum machine learning models, which are based on quantum support vector classification (QSVC) and quantum support vector regression (QSVR). We investigate these models using a quantum circuit simulator, both with and without noise, as well as the IonQ Harmony quantum processor. For the QSVC tasks, we use a dataset containing fraudulent credit card transactions and image datasets (the MNIST and the Fashion-MNIST datasets); for the QSVR tasks, we use a financial dataset and a materials dataset. For the classification tasks, the performance of our QSVC models using 4 qubits of the trapped-ion quantum computer was comparable to that obtained from noiseless quantum circuit simulations. The result is consistent with the analysis of our device noise simulations with varying qubit gate error rates. For the regression tasks, applying a low-rank approximation to the noisy quantum kernel, in combination with hyperparameter tuning in ε-SVR, improved the performance of the QSVR models on the near-term quantum device. The alignment, as measured by the Frobenius inner product between the noiseless and noisy quantum kernels, can serve as an indicator of the relative prediction performance on noisy quantum devices in comparison with their ideal counterparts. Our results suggest that the quantum kernel, as described by our shallow quantum circuit, can be effectively used for both QSVC and QSVR tasks, indicating its resistance to noise and its adaptability to various datasets.