Phenetic Research Articles

Batik classification using KNN algorithm and GLCM features extraction

Batik is one of the Indonesian cultures that has been recognized by UNESCO as an intellectual right of Indonesia. The popularity of batik internationally raises concerns about the Indonesian people’s understanding of batik if Indonesian people only refer to all types of batik just as ‘batik’. By utilizing K-Nearest Neighbour (KNN) algorithm which is a simple classification algorithm, then a system can be created that can classify batik types. The first step of KNN is training, which stores each training pattern. The second step is classification, whenever classifying a pattern, KNN examine all training patterns to determine the K closest patterns using certain calculations such as Euclidean Distance and Manhattan Distance. Before classification, a characteristic that represents a pattern is needed. Gray-Level Co-Occurrence Matrix (GLCM) is an algorithm that has proven to be very powerful as a feature descriptor in representing the texture characteristic of an image. This research experiments with the value of K in KNN = 1, 3, 5, and 7 with the distance calculation using Euclidean and Manhattan. The GLCM characteristic used are Entropy, Energy, Contrast, Homogeneity, Dissimilarity, Correlation, ASM, and the average of each characteristic. From the research that has been done, the system created obtained the highest accuracy of 75% with the combination of parameters; pixel distance = 7, K value = 1, 1st fold as test data and 2nd and 3rd fold as training data, and by using StandardScaler. But despite getting decent accuracy, there is still a need for further research to improve the accuracy of batik classification.

SBiLM: Siamese Bi-LSTM model for handling imbalance in fake review detection

The growing significance of online reviews in e-commerce platforms has a profound impact on consumers' purchase decisions. Unfortunately, dishonest sellers exploit this system by engaging in deceptive practices and manipulating reviews. These fake reviews are very hard to be detected by humans. Hence, the issue of detecting fake online reviews has gained significant attention due to their prevalence. Typically, the datasets available have much higher number of real reviews as compared to fake reviews, which leads to data imbalance problem. Machine learning algorithms struggle to learn from these datasets and give biased results. However, existing studies on this topic have not adequately addressed the challenges posed by imbalanced data. They have mostly used under-sampling or over-sampling to handle imbalance in datasets, which leads to potential information loss or overfitting due to replication. Hence, to handle this class imbalance problem without using sampling techniques, this paper proposes a novel SBiLM model using Siamese Neural Network with Bidirectional LSTM and Manhattan Distance. SBiLM takes pair of reviews as inputs and computes similarity between them using distance metric. The outcomes indicate that the SBiLM model exhibits better performance in terms of both accuracy and loss, when compared with traditional deep learning models on a highly imbalanced restaurant review dataset. This presents a promising strategy for tackling the issues related to data imbalance in the domain of fake review detection.

SVM-RLF-DNN: A DNN with reliefF and SVM for automatic identification of COVID from chest X-ray and CT images.

To develop an advanced determination technology for detecting COVID-19 patterns from chest X-ray and CT-scan films with distinct applications of deep learning and machine learning methods. The newly enhanced proposed hybrid classification network (SVM-RLF-DNN) comprises of three phases: feature extraction, selection and classification. The in-depth features are extracted from a series of 3×3 convolution, 2×2 max polling operations followed by a flattened and fully connected layer of the deep neural network (DNN). ReLU activation function and Adam optimizer are used in the model. The ReliefF is an improved feature selection algorithm of Relief that uses Manhattan distance instead of Euclidean distance. Based on the significance of the feature, the ReliefF assigns weight to each extracted feature received from a fully connected layer. The weight to each feature is the average of k closest hits and misses in each class for a neighbouring instance pair in multiclass problems. The ReliefF eliminates lower-weight features by setting the node value to zero. The higher weights of the features are kept to obtain the feature selection. At the last layer of the neural network, the multiclass Support Vector Machine (SVM) is used to classify the patterns of COVID-19, viral pneumonia and healthy cases. The three classes with three binary SVM classifiers use linear kernel function for each binary SVM following a one-versus-all approach. The hinge loss function and L2-norm regularization are selected for more stable results. The proposed method is assessed on publicly available chest X-ray and CT-scan image databases from Kaggle and GitHub. The performance of the proposed classification model has comparable training, validation, and test accuracy, as well as sensitivity, specificity, and confusion matrix for quantitative evaluation on five-fold cross-validation. Our proposed network has achieved test accuracy of 98.48% and 95.34% on 2-class X-rays and CT. More importantly, the proposed model's test accuracy, sensitivity, and specificity are 87.9%, 86.32%, and 90.25% for 3-class classification (COVID-19, Pneumonia, Normal) on chest X-rays. The proposed model provides the test accuracy, sensitivity, and specificity of 95.34%, 94.12%, and 96.15% for 2-class classification (COVID-19, Non-COVID) on chest CT. Our proposed classification network experimental results indicate competitiveness with existing neural networks. The proposed neural network assists clinicians in determining and surveilling the disease.

Clustering Performance Analysis of the K-Medoids Algorithm for Improved Fingerprint-Based Localization

Fingerprint-based localization, which uses received signal strength (RSS) measurements from spatially deployed wireless access points (APs), is a popular technique for indoor positioning. The size of the fingerprint database has a significant impact on the accuracy of localization. The higher the density of the fingerprint database, the more accurate the localization, but the longer the localization time. Clustering is one of the techniques used such systems to improve localization accuracy and reduce localization time. To cluster fingerprints, the majority of clustering techniques employ a distance-based fingerprint similarity metric. However, the choice of distance metric has a significant impact on the performance of the clustering algorithm. Using four publicly available RSS-based fingerprint databases, this paper investigates the clustering performance of the k-medoids algorithm using six distance metrics, namely Euclidean, Manhattan, cosine, Mahalanobis, Chebyshev, and Canberra distance. Using the silhouette score as a performance metric, the cosine and Euclidean distance metrics outperform the others, with the highest silhouette score values of about 0.38, 0.43, 0.34, and 0.31 on the SEUG_IndoorLoc, IIRC_IndoorLoc, MSI_IndoorLoc, and IPIN_2019_PIEP_UM databases, respectively. It demonstrates that on these four databases, using Euclidean distance as well as the angle between fingerprint measurement vectors is the best option for generating efficient clusters that will result in high localization accuracy and low localization time.

Accelerating dynamic time warping for speech recognition with SSE

This study presents a significant enhancement to the Dynamic Time Warping (DTW) algorithm for real-time applications like speech recognition. Through integration of SIMD (Single Instruction Multiple Data) instructions to distance function, the research demonstrates how SSE accelerates DTW, markedly reducing computation time. The paper not only explores the theoretical aspects of DTW and this optimization but also provides empirical evidence of its effectiveness. Diverse dataset of 18 voice command classes was assembled, recorded in controlled settings to ensure audio quality. The audio signal of each speech sample was segmented into frames for detailed analysis of temporal dynamics. DTW search was performed on features set based on Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC), combined with delta features. A comprehensive set of 27 features was extracted from each frame to capture critical speech characteristics. The core of the study involved applying traditional DTW as a baseline for performance comparison with the SSE-optimized DTW. The evaluation, focusing on computational time, included measurements like minimum, maximum, average, and total computation times for both standard and SSE-optimized implementations. Experimental results, conducted on datasets ranging from 5 to 60 WAV files per class, revealed that the SSE-optimized DTW significantly outperformed the standard implementation across all dataset sizes. Particularly noteworthy was the consistent speed of the SSE-optimized Manhattan and Euclidean distance functions, which is crucial for real-time applications. The SSE-optimized DTW maintained a low average time, demonstrating remarkable stability and efficiency, especially with larger datasets. The study illustrates the potential of SSE optimizations in speech recognition, emphasizing the SSE-optimized DTW's capability to efficiently process large datasets.

SHORTEST PATH DISTANCE AND HAUSDORFF DIMENSION OF SIERPINSKI NETWORKS

In this paper, we will study the geometric structure on the Sierpinski networks which are skeleton networks of a connected self-similar Sierpinski carpet. Under some suitable condition, we figure out that the renormalized shortest path distance is comparable to the planar Manhattan distance, and obtain the Hausdorff dimension of Sierpinski networks.

The Accuracy of the k-Nearest Neighbors and k-Means Algorithms in Gesture Identification

In today’s digital era, human-computer interac tion interfaces evolve and increase together with the needs of the users. However, the existing technologies have their limita tions, which can hinder the efficiency of modern input devices like the Kinect sensor or other similar sensors. In this paper we improved our previous algorithm by extending it with two algo rithms that aim to help telerehabilitation for individuals with movement disabilities. These two algorithms are based on the k Nearest Neighbors, the k-Means algorithms. The algorithms are designed to accommodate the needs of the patients by adapting to their gestures based on their previous three. Using these ges tures, the algorithms create multiple gesture acceptance domains around each coordinate of the gesture. Consequently, they decide whether the next user-input gesture can be considered the same movement. The accuracy of these algorithms was evaluated in three acceptance domains by comparing gesture descriptors with either the Euclidean or the Manhattan distance calculation meth ods. The results show that k-Nearest Neighbors algorithm yields better results in larger acceptance domains, while the k-Means algorithm can provide a better gesture acceptance rate in the smaller ones. The results show that both algorithms can be used in the telerehabilitation process, although the k-Means algorithm is more accurate than the k-Nearest Neighbors algorithm.

Addressing limitations of the K-means clustering algorithm: outliers, non-spherical data, and optimal cluster selection

<p>Clustering is essential in data analysis, with K-means clustering being widely used for its simplicity and efficiency. However, several challenges can affect its performance, including the handling of outliers, the transformation of non-spherical data into a spherical form, and the selection of the optimal number of clusters. This paper addressed these challenges by developing and enhancing specific models. The primary objective was to improve the robustness and accuracy of K-means clustering in the presence of these issues. To handle outliers, this research employed the winsorization method, which uses threshold values to minimize the influence of extreme data points. For the transformation of non-spherical data into a spherical form, the KROMD method was introduced, which combines Manhattan distance with a Gaussian kernel. This approach ensured a more accurate representation of the data, facilitating better clustering performance. The third objective focused on enhancing the gap statistic for selecting the optimal number of clusters. This was achieved by standardizing the expected value of reference data using an exponential distribution, providing a more reliable criterion for determining the appropriate number of clusters. Experimental results demonstrated that the winsorization method effectively handles outliers, leading to improved clustering stability. The KROMD method significantly enhanced the accuracy of converting non-spherical data into spherical form, achieving an accuracy level of 0.83 percent and an execution time of 0.14 per second. Furthermore, the enhanced gap statistic method outperformed other techniques in selecting the optimal number of clusters, achieving an accuracy of 93.35 percent and an execution time of 0.1433 per second. These advancements collectively enhance the performance of K-means clustering, making it more robust and effective for complex data analysis tasks.</p>

Research on Robot Operating System (ROS) Integration in Automated Logistics

Abstract In order to optimize the development of automated logistics, this paper proposes to build a mobile robot based on ROS to carry out intelligent autonomous navigation and distribution in the warehouse environment and to use intelligent robots to improve the efficiency of logistics and distribution. Analyze logistics robots’ localization methods and propose a SLAM algorithm to obtain environmental map information. Improve the Manhattan distance function to propose a global path planning A* algorithm based on the two-way search of the initial point and the target point, as well as a local path planning DWA algorithm to seek the best path. Collecting robot scheduling principles and scheduling conflict types, a genetic algorithm was selected for workshop AGV scheduling planning. Set up an experimental environment to analyze the optimal performance of the DWA algorithm. Combined with the intelligent warehouse goods sorting environment, analyze the planning and scheduling strategy for the goods sorting path of the ROS robot cluster. In the experiment, it is concluded that the larger the value of parameter β of the trajectory evaluation function is, the longer the time to be consumed by the calculation of the DWA algorithm is, and the designed path distance will be farther. When α =0.02 and β =0.2, the DWA algorithm reaches the best. Combined with the actual task scheduling path of the mobile robot cluster, it can be considered to be able to complete the autonomous navigation experiment in the whole warehouse environment, which is in line with the demand for automated logistics use.

Hand function after neonatal stroke: A graph model based on basal ganglia and thalami structure

IntroductionNeonatal arterial ischemic stroke (NAIS) is a common model to study the impact of a unilateral early brain insult on developmental brain plasticity and the appearance of long-term outcomes. Motor difficulties that may arise are typically related to poor function of the affected (contra-lesioned) hand, but surprisingly also of the ipsilesional hand. Although many longitudinal studies after NAIS have shown that predicting the occurrence of gross motor difficulties is easier, accurately predicting hand motor function (for both hands) from morphometric MRI remains complicated. The hypothesis of an association between the structural organization of the basal ganglia (BG) and thalamus with hand motor function seems intuitive given their key role in sensorimotor function. Neuroimaging studies have frequently investigated these structures to evaluate the correlation between their volumes and motor function following early brain injury. However, the results have been controversial. We hypothesize the involvement of other structural parameters. MethodThe study involves 35 children (mean age 7.3 years, SD 0.4) with middle cerebral artery NAIS who underwent a structural T1-weighted 3D MRI and clinical examination to assess manual dexterity using the Box and Blocks Test (BBT). Graphs are used to represent high-level structural information of the BG and thalami (volumes, elongations, distances) measured from the MRI. A graph neural network (GNN) is proposed to predict children’s hand motor function through a graph regression. To reduce the impact of external factors on motor function (such as behavior and cognition), we calculate a BBT score ratio for each child and hand. ResultsThe results indicate a significant correlation between the score ratios predicted by our method and the actual score ratios of both hands (p < 0.05), together with a relatively high accuracy of prediction (mean L1 distance < 0.03). The structural information seems to have a different influence on each hand’s motor function. The affected hand’s motor function is more correlated with the volume, while the ‘unaffected’ hand function is more correlated with the elongation of the structures. Experiments emphasize the importance of considering the whole macrostructural organization of the basal ganglia and thalami networks, rather than the volume alone, to predict hand motor function. ConclusionThere is a significant correlation between the structural characteristics of the basal ganglia/thalami and motor function in both hands. These results support the use of MRI macrostructural features of the basal ganglia and thalamus as an early biomarker for predicting motor function in both hands after early brain injury.

Fourier Analysis of the Vertical Ground Reaction Force During Walking: Applications for Quantifying Differences in Gait Strategies.

Time series biomechanical data inform our understanding of normal gait mechanics and pathomechanics. This study examines the utility of different quantitative methods to distinguish vertical ground reaction forces (VGRFs) from experimentally distinct gait strategies. The goals of this study are to compare measures of VGRF data-using the shape factor method and a Fourier series-based analysis-to (1)describe how these methods reflect and distinguish gait patterns and (2)determine which Fourier series coefficients discriminate normal walking, with a relatively stiff-legged gait, from compliant walking, using deep knee flexion and limited vertical oscillation. This study includes a reanalysis of previously presented VGRF data. We applied the shape factor method and fit 3- to 8-term Fourier series to zero-padded VGRF data. We compared VGRF renderings using Euclidean L2 distances and correlations stratified by gait strategy. Euclidean L2 distances improved with additional harmonics, with limited improvement after the seventh term. Euclidean L2 distances were greater in shape factor versus Fourier series renderings. In the 8 harmonic model, amplitudes of 9 Fourier coefficients-which contribute to VGRF features including peak and local minimum amplitudes and limb loading rates-were different between normal and compliant walking. The results suggest that Fourier series-based methods distinguish between gait strategies.

The effects of selectively blocking the electron transport layer of n-i-p perovskite solar cells with polymer particles on device performance

We use insulating polystyrene colloid particles to selectively cover the electron transport layer of n-i-p perovskite solar cells and investigate the effects of the coverage and lateral blocking distance on device performance.

Commercial Aircraft Selection Decision Support Model Using Fuzzy Combinative Multiple Criteria Decision Making Analysis

Multidimensional decision-making issues invariably entail a profusion of factors and dimensions necessitating consideration. In such intricate environments, decision-making can be notably challenging. To navigate this complexity, the realm of multiple criteria decision-making approaches is indispensable, helping in unraveling convoluted predicaments. This paper introduces a novel approach fuzzy combinative multiple criteria decision-making analysis — crafted to address fuzzy decision analysis challenges. At the heart of this method lies the utilization of the Euclidean distance as the primary measure and the rectilinear distance as the secondary measure, both calibrated in accordance with the negative-ideal solution. Determining the desirability of alternatives rests on these distances. Further augmenting the evaluation, the process involves combining the Euclidean and rectilinear distances through fuzzy combinative multiple criteria decision-making analysis. The efficacy and stability of the fuzzy decision analysis are underscored through a numerical example, illustrating the proposed method's procedural nuances. A comparative analysis comparing both Euclidean distance and rectilinear distance with the weighted sum model and the weighted product model is also conducted. This scrutiny underscores the efficiency of the proposed method while affirming the reliability of its outcomes.

基于改进A*算法与粒子群算法的果蔬采摘机器人路径规划

Aiming at the suboptimal local path, slow convergence speed, and many inflection points in the path planning of fruit and vegetable picking robots in complex environments, a global planning method combining particle swarm optimization (PSO) algorithm and A* algorithm was proposed. Firstly, Manhattan distance was taken as a heuristic function of global programming based on the A* algorithm. Secondly, the step size of PSO was adjusted to optimize the path, shorten the path length, and reduce the number of inflection points. Finally, the planned path of the fruit and vegetable picking robot was smoothed so that it could steadily move along a smoother driving path in real scenarios. The experimental results show that compared with the traditional PSO algorithm, the hybrid algorithm based on the improved A* algorithm and PSO algorithm achieves a smoother path and fewer folding points. In comparison with the PSO algorithm, moreover, this algorithm can guarantee the path generation efficiency and the global optimum. In the end, the effectiveness of the proposed method was verified by shortening the path length and reducing the accumulative number of inflection points.

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.

Anomaly Detection Method for Lithium-Ion Battery Cells Based on Time Series Decomposition and Improved Manhattan Distance Algorithm.

Abnormalities in individual lithium-ion batteries can cause the entire battery pack to fail, thereby the operation of electric vehicles is affected and safety accidents even occur in severe cases. Therefore, timely and accurate detection of abnormal monomers can prevent safety accidents and reduce property losses. In this paper, a battery cell anomaly detection method is proposed based on time series decomposition and an improved Manhattan distance algorithm for actual operating data of electric vehicles. First, time series decomposition is performed on the voltage data of all battery cells in the battery pack to obtain the voltage trend component of each cell. Then, the improved Manhattan distance algorithm is utilized to calculate and compare the Manhattan distance values between adjacent cell trend components, to determine the abnormal cells inside the battery pack. Furthermore, the Manhattan distance values at the same sampling moment are calculated within the data sequence to detect the specific time when the abnormal cells malfunction. The data analysis and experimental verification results based on actual vehicle operating conditions indicate that this method can accurately identify an abnormal cell within the battery pack and diagnose the specific moment of abnormality in the battery cell at an early stage of failure, with good robustness.

Implementation of Recurrent Neural Network (RNN) for Question Similarity Identification in Indonesian Language

In a question-and-answer forum, the identification of question similarity is used to determine how similar two questions are. This procedure makes sure that user-submitted questions are compared to the questions in a database for matches to improve system performance on the online Q&amp;A platform. Currently, question similarity is mostly done in foreign languages. The purpose of this research is to identify question similarities and evaluate the effectiveness of the methods used in Indonesian language questions. The data used is a public dataset with labeled pairs of questions as 0 and 1 where label 0 for different pairs of questions and label 1 for the same pairs of questions. The method used is a Recurrent Neural Network (RNN) with the Manhattan Distance approach to calculate the similarity distance between two questions. The question pairs are taken as two inputs with a reference label to identify the similarity distance between the two question inputs. We evaluated the model using three different optimizers namely RMSprop, Adam, and Adagrad. The best results were obtained using the Adam optimizer with 80:20 ratio split-data and overall accuracy is 76%, precision is 74%, recall is 98.8%, and F1-score is 85.1%.

The Logical Mechanism and Practical Progress of Digital Economy Contributing to Rural Revitalization in the Context of Big Data

Abstract This paper proposes spatial data mining and clustering analysis techniques based on big data and uses four distance similarity calculation methods, namely, Euclidean distance, Manhattan distance, Ming’s distance, and Ma’s distance, to constrain spatial clustering similarity metrics. Combine the K-means algorithm and FCM clustering algorithm to calculate the center value of fuzzy clustering, use the additive synthesis method to solve the combination weights, and construct the combination weighted distance, fuzzy C-mean algorithm model. Research on the mechanism and path of digital economy-enabled rural revitalization through the C-FCM model, and propose three correlation hypotheses for this paper. Empirical experiments are conducted to verify the authenticity of the hypotheses proposed in this paper and the use effect of the constructed model. The experimental results show that the Moran index range of digital economy and rural revitalization is [0.06, 0.12], [0.05, 0.16], respectively, and there is a significant aggregation phenomenon of the two under the geographic matrix. In the analysis of the spatial spillover effect, the values of spatial autoregressive coefficients of rural industrial revitalization are all &gt;0, with significance p&gt;0.05. The digital economy has a positive effect on rural revitalization in neighboring areas through the spatial spillover effect. In the clustering sample extraction test, the accuracy is most stable when the number of samples is 2200, and the accuracy rate is as high as 79.66%.

Remaining useful life prediction based on a PCA and similarity methods

Aircraft engine failures or damages not only incur substantial financial losses but also present risks of injuries or even fatalities. Hence, it is of utmost importance to devise an effective method to predict potential failures in advance, thereby mitigating accidents and minimizing losses. This paper proposes a novel approach that combines a principal component analysis (PCA) with similarity methods to establish a degradation trajectory database and predict the remaining useful life (RUL) of new engines by identifying similar trajectories. Firstly, the data dimensionality is reduced using a PCA to create a health indicator. The validity of the reduced data is confirmed by calculating the Spearman correlation coefficient between the health indicator and the system RUL. During the similarity comparison process, the Manhattan distance is employed for the similarity calculation, and parameter optimization is performed on the length of selected time segments and the number of chosen similar trajectories to optimize the similarity of RUL prediction model, resulting in the optimal prediction results among all engine test sets. Notably, this paper introduces the feasibility of employing the Manhattan distance in similarity method-based prediction, which is superior to the commonly used Euclidean distance calculation method found in most literature. This finding offers innovative ideas and perspectives for advancing RUL prediction methodologies. By adopting the proposed approach, the occurrence of accidents and losses associated with aircraft engine failures can be substantially reduced, leading to enhanced safety and economic benefits.