Neighbor Algorithm Research Articles

Machine learning approaches to predict the need forintensive care unitadmission among Iranian COVID-19 patients based on ICD-10: A cross-sectional study.

Timely identification of the patients requiring intensive care unit admission (ICU) could be life-saving. We aimed to compare different machine learning algorithms to predict the requirements for ICU admission in COVID-19 patients. We screened all patients with COVID-19 at six academic hospitals in Tehran comprising our study population. A total of 44,112 COVID-19 patients (≥18 years old) were included, among which 7722 patients were hospitalized. We used a Random Forest algorithm to select significant variables. Then, prediction models were developed using the Support Vector Machine, Naıve Bayes, logistic regression, lightGBM, decision tree, and K-Nearest Neighbor algorithms. Sensitivity, specificity, accuracy, F1 score, and receiver operating characteristic-Area Under the Curve (AUC) were used to compare the prediction performance of different models. Based on random Forest, the following predictors were selected: age, cardiac disease, cough, hypertension, diabetes, influenza & pneumonia, malignancy, and nervous system disease. Age was found to have the strongest association with ICU admission among COVID-19 patients. All six models achieved an AUC greater than 0.60. Naıve Bayes achieved the best predictive performance (AUC = 0.71). Naïve Bayes and lightGBM demonstrated promising results in predicting ICU admission needs in COVID-19 patients. Machine learning models could help quickly identify high-risk patients upon entry and reduce mortality and morbidity among COVID-19 patients.

The color features and k-nearest neighbor algorithm for classifying betel leaf image

Piper betle L. (betel) is a species that belongs to the genus Piper and is a type of medicinal plant that is quite well known to the general population. The varieties of the leaf color may distinguish are red, green, and black betel. However, consumers still need assistance determining the differences between the many types of betel leaf. Therefore, using image processing techniques, this research contributes to building a classification method for distinguishing betel leaves based on color attributes. This approach anoints for the region of interest detection, feature extraction, and classification. In addition, three different classifiers, naïve Bayes, support vector machine, and k-nearest neighbors (k-NN), were used during the classification process. The evaluation for this study used a percentage split to divide a total of 180 images between the training and testing phases. The method’s performance provided the highest accuracy value possible, 100%, by utilizing the color characteristics with the k-NN classifier.

Implementasi K-Nearest Neighbor Pada Klasifikasi Prospek Waralaba

Franchising is currently a promising business process where many types of franchises are currently mushrooming and growing rapidly. An algorithm is needed that can classify the type of franchise according to existing attributes. The K-Nearest Neighbor algorithm can produce robust or clean data and effects even for large data sets. The K-Nearest Neighbor algorithm is an algorithm that is able to classify franchise types with an accuracy value of 92.23%. With this high accuracy, it can be ensured that the classification can be clearly seen, where according to the research it was found that the very prospective and prospective classes are in Bakmi Gila and Alfamart. That way, you will get maximum profits by choosing the right franchise

K-Nearest Neighbors (K-NN) Algorithm Model in Predicting the Graduation Rate of Teacher Professional Education Students in Indonesia

Predicting the graduation rate of the PPG program has an important significance in analyzing the factors that affect students' success in completing the PPG program. This study uses the K-Nearest Neighbor model in online learning to predict the pass rate of students in the Teacher Professional Education Program (PPG) at UIN Walisongo Semarang. The study analyzed data from 423 students, focusing on input quality variables, such as pedagogical competence and teaching innovation. Results showed the Wave 1 pass rate in 2023 was 86.7%, with 13.3% failure, a 1.7% decrease from Wave 3 in 2022. The confusion matrix showed significant improvement in True Positives (TP) and True Negatives (TN), with an accuracy of 0.916, precision of 0.3, and recall of 0.9725 students' academic achievement.

Valorization of tomato processing by-products: Predictive modeling and optimization for ultrasound-assisted lycopene extraction

Lycopene is a carotenoid highly valuable to the food, pharmaceutical, dye, and cosmetic industries, present in ripe tomatoes and other fruits with a distinctive red color. The main source of lycopene is tomato crops. This bioactive component can be successfully isolated from tomato processing waste, commonly called tomato pomace, mostly made from tomato skins, seeds, and some residual tomato tissue. The main investigative focus in this work was the application of green engineering principles in each stage of the optimized ultrasound-assisted extraction (UAE) of enzymatically treated tomato skins to obtain functional extracts rich in lycopene. The experimental plan was designed to determine the influence of studied operating parameters: enzymatic reaction time (60, 120, and 180 min), extraction time (0, 5, 10, 15, 30, 60, and 120 min), and temperature (25, 35 and 45 ℃) on lycopene yield. Process optimization was performed based on the yield of lycopene [1018, 1067, and 1120 mg/kg] achieved at optimal operating conditions. An artificial neural network (ANN) model was developed and trained for predictive modeling of the closed extraction system, with operating parameters used as input neurons and experimentally obtained values for lycopene content defined as the output neural layer. Applied ANN architecture provided a high correlation of experimental output with ANN-generated data (R=0.99914) with a model deviation error for the entire data set of RMSE=5.3 mg/kg. The k-Nearest Neighbor algorithm was introduced to predict lycopene yield using experimental key features: operating temperature, extraction time, and time of enzymatic treatment, split into training and testing sets with an 85/15 ratio. The model interpretation was conducted through the SHAP (SHapley Additive exPlanations) methodology.

Geometric characterization and segmentation of historic buildings using classification algorithms and convolutional networks in HBIM

Building Information Models (BIM) are essential for managing information and creating 3D digital representations, especially in the study of historic buildings. However, generating BIM models from point clouds in these structures is challenging due to complex algorithms and architectural forms. Artificial Intelligence (AI) technologies are beginning to automate point cloud classification and segmentation, but fully effective methods for historic buildings are still lacking. This study compares Machine Learning (ML) methodologies and a Deep Learning (DL) classifier. It evaluates the effectiveness of a neighbourhood algorithm with commercial software used by geometers and surveyors, and the applicability of convolutional networks. The methods tested include the Random Forest algorithm in MATLAB, commercial geomatics software, and a variant of the PointNet architecture for DL. The results are evaluated by BIM experts, highlighting the high effectiveness of these approaches and their potential contributions to the field.

Evaluating Handwritten Character Recognition with Hu Moments and K-Nearest Neighbors Algorithm

Handwritten character recognition remains a challenging task in various domains, including handwriting analysis and document digitization. This task poses a significant challenge because of the variability and complexity of handwriting. To address this, it is crucial to develop different methodologies that enhance accuracy and efficiency in recognition. This research evaluates the discrimination capacity of a classifier that combines together the Hu Moments features extraction technique and the k-nearest neighbors (KNN) algorithm for the purpose of handwritten character recognition. The authors worked with handwritten characters from the English alphabet where each character is represented in two handwritten forms. The average accuracy of recognition was 82.69%, that conforms the efficiency of the proposed classifier. This research contributes to the field of automated handwritten recognition and highlights the necessity for improved techniques in character analysis, which has implications for handwriting recognition in different contexts, linguistic studies, document digitization, and other applications where character recognition is crucial. This is the first study that aims to apply the combination of Hu Moments features extraction technique in combination with the KNN algorithm for purpose of handwritten character recognition.

Unbalanced Data-Based Fault Diagnosis Method of Bearing Utilizing Time-Frequency DCGAN Processing

Aiming at the unbalanced datasets of bearing fault samples, a fault diagnosis method based on time-frequency conversion and processing of bearing vibration signals based on deep convolutional generative adversarial network (DCGAN) is proposed in this paper. Firstly, through short-time Fourier transform(STFT), the vibration signals are converted into time-frequency images, and then time-frequency images are input into DCGAN to expand the fault samples.Secondly, the expanded fault samples are evaluated for image quality through the comprehensive method of peak signal-to-noise ratio(PSNR) and structural similarity(SSIM). Thirdly, using the Canny edge detection algorithm to extract features of the time-frequency image, and using the obtained binary image as the feature.Finally, k-nearest neighbor algorithm is used for classification to testify the superiority of time-frequency DCGAN processing. The experimental results show that the expanded samples can effectively improve the imbalance of the samples and improve the accuracy of fault diagnosis of bearing.

Intelligent characteristic objects method (INCOME): a data knowledge-based multi-criteria decision analysis

Multi-criteria decision analysis (MCDA) methods are vital in assessing decision variants under multiple conditions. However, involving domain experts in developing decision models can be challenging and costly, necessitating more scalable and independent solutions. This paper introduces the intelligent characteristic objects method (INCOME), which combines the k-Nearest Neighbor (kNN) algorithm and the COMET method to create a theoretical decision-maker for comparing characteristic objects (COs). INCOME overcomes limitations of classical MCDA methods, such as the TOPSIS approach, which struggles with complex functions and non-monotonic modeling. INCOME influences data-based knowledge to provide a robust framework for assessing decision options. The integration of the COMET method and kNN algorithm enables improved modeling of decision functions based on evaluated data, increasing the flexibility and independence of the INCOME approach. A case study assessing gas power plants based on four criteria is presented to validate the performance of the INCOME method. The results demonstrate high correlations with the reference model and slightly higher classical approaches like TOPSIS and TOPSIS-COMET. However, INCOME exhibits greater stability and flexibility by utilizing all available data instead of relying on limited expert knowledge. The proposed INCOME approach offers several advantages, including creating a continuous decision model, resistance to the Rank-Reversal phenomenon, and the potential for replacing domain experts with artificial experts. This study highlights the effectiveness of INCOME in Multi-Criteria Decision Analysis. It suggests future research directions, such as parameter selection and testing in different decision-making problems.

Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks

Wireless sensor networks are used to monitor the environment, to detect anomalies or any other problems and risks in the system. If used in the transportation network, they can monitor traffic and detect traffic risks. In wireless sensor networks, energy constraints must be handled to enable continuous environmental monitoring and surveillance data gathering and communication. Energy-aware path planning of autonomous ground vehicle charging for sensor nodes can solve energy and battery replacement problems. This paper uses the Nearest Neighbour algorithm for the energy-aware path planning problem with an autonomous ground vehicle. Path planning simulations show that the Nearest Neighbour algorithm converges faster and produces a better solution than the genetic algorithm. We offer robust and energy-efficient path planning algorithms to swiftly collect sensor data with less energy, allowing the monitoring system to respond faster to anomalies. Positioning communicating sensors closer minimizes their energy usage and improves the network lifetime. This study also considers the scenario in which it is recommended to avoid taking direct travelling pathways between particular node pairs for a variety of different reasons. To address this more challenging scenario, we provide an Obstacle-Avoided Nearest Neighbour-based approach that has been adapted from the Nearest Neighbour approach. Within the context of this technique, the direct paths that connect the nodes are restricted. Even in this case, the Obstacle-Avoided Nearest Neighbour-based approach achieves almost the same performance as the the Neighbour-based approach.

Rolling bearing fault analysis based on variational mode decomposition and multiscale arrangement entropy

Rolling bearings in operation will appear nonlinear characteristics of the fault vibration signal. In the process of fault feature extraction, a single permutation entropy (PE) produces unsatisfactory results and low accuracy. In this paper, a new diagnostic method was proposed, which was based on variational mode decomposition (VMD) and multiscale permutation entropy (MPE) to diagnose and analyze rolling bearing faults, multi-scale aligned entropy features of intrinsic mode function (IMF) of faulty vibration signals were extracted, and then support vector machine (SVM) and K-nearest neighbor algorithm (KNN) were used to analyze these features, and the maximum attribution metrics were used to determine classification results. The test results show that this method can improve the detection accuracy by comparing with other test analysis methods.

Practical evaluation of intelligent algorithms in ESG management of manufacturing enterprises

ESG (Environmental, Social and Governance) management practice is an important part of promoting sustainable operation and development of manufacturing enterprises. Currently, traditional evaluation methods have limitations such as low efficiency and lack of objectivity. To improve the efficiency and accuracy of ESG evaluation and promote the optimization of ESG performance in manufacturing enterprises, this article combined data mining and analytic hierarchy process (AHP) to conduct effective research on ESG management practice evaluation in manufacturing enterprises. This article adopted the best priority search strategy to collect and process enterprise ESG data. By using AHP to construct hierarchical and segmented objectives for target problems, a performance evaluation index system for management practices was built based on the evaluation objectives and hierarchical priority order. Finally, based on the performance evaluation of ESG management practices, the K-nearest Neighbor algorithm was applied to analyze historical data of key indicators. According to the weights, various key indicators were re-integrated, achieving practical evaluation and decision support for enterprise ESG management. To verify the effectiveness of data mining and AHP, this article took Z enterprise as the research object and conducted empirical analysis on it. The results showed that in terms of evaluation accuracy, the method proposed in this article achieved the highest evaluation accuracy of 92.51%, 91.16%, and 91.75% in environmental, social, and governance dimension data use case evaluation, respectively. The conclusion indicated that data mining and AHP could improve the accuracy of ESG management practice evaluation in enterprises, provide reliable decision support for enterprise development, and help promote sustainable development of enterprises.

Smart grid stability prediction model using two-way attention based hybrid deep learning and MPSO

In smart grid management, precise stability prediction is a complicated task that adds to the effective allocation of resources with grid stability. Specifically, demand-side management is considered an essential element of the overall Smart Grids system. Hence, predicting future energy demands is crucial to regulating consumption by aligning utility offerings with consumer demand. This research presents a hybrid deep learning model (Convolutional Neural Network [CNN] with Bi-LSTM) with a two-way attention method and a multi-objective particle swarm optimization method (MPSO) for short-term load prediction from a smart grid. The proposed hybrid model utilizes a two-way attention method at its encoding and decoding stages, in which an encoding attention layer helps to recognize all the essential features from an input vector, and a decoding attention layer helps to resolve the fixed context vector problem by offering better memory capacity. A CNN and Bi-LSTM are used to capture the essential features from the dataset. We also utilize a t-Nearest Neighbours algorithm to pre-process the initial dataset. An MPSO method combines the features of CNN and Bi-LSTM methods, resulting in better prediction accuracy. As far as we know, it is the first work to suggest a dynamic short-term load prediction model that considers different significant features and enables precise predicting outcomes. The performance of the proposed model and existing well-known deep learning models such as Recurrent Neural Network, Gated Recurrent Unit, Long Short-Term Memory (LSTM), Time Series Transformer, CNN-LSTM and various performance measuring parameters MAE, MSE, MAPE and RMSE are calculated on online UCI dataset (Electrical Grid Stability Simulated Dataset). The proposed hybrid model achieved a better prediction result, which proves the efficiency of the proposed model.

Defect Rate Prediction in Manufacturing Process Using K-Nearest Neighbor Algorithm

The efficiency and effectiveness in the manufacturing industry are significantly impacted by artificial intelligence technology. An important application involves the improvement of product quality, which is measurable through the defects occurring during the production process. This research is aimed at predicting defects in the manufacturing process using the K-Nearest Neighbor (KNN) algorithm with various distance measurement methods, namely Euclidean, Minkowski, and Manhattan distances. The research methodology is composed of four stages: dataset collection, data preprocessing, modeling, and evaluation. The focus of this research is on the optimal K value and the conditions that yield the highest accuracy, considering various scenarios of training and test data splitting ratios and different random state values. The test results indicate that the Minkowski distance method, with a data division ratio of 80% for training data, 20% for test data, and a random state value of 32, provides the best performance, with an optimal K value of 10 and an accuracy of 86.41%.

Structural Failure Prediction of RCC Structure Under Earthquake Using Machine Learning

Earthquake is a most dangerous and catastrophic disaster in the world. It is essential to introduce earthquake resistance features in construction. An earthquake -prone region must deal with the more challenging problem of earthquakes. And there is no more hundred percent earthquake proof building. The best way to avoid the building damages is to learn from the failures that can occur during a seismic event. This paper presents the failure prediction of RCC building using k-Nearest Neighbors (KNN) algorithm which can be tuned to gives up to 90% accuracy of result. A past- earthquake assessment of building data is used to train and create a model for this project to predict the necessary pattern for a future construction project. Thereby, can be predicted whether the proposed building will be collapsed or facing a minimum damage. Key Words: Earthquake Assessment, Machine Learning, Python, K-Neighbour Algorithm, RCC Building, Failure Prediction, Site issues, Soil and Foundation Condition

A Machine Learning Prediction Model for Myelitis and Multiple Sclerosis Based on Fourier Transform Features from MRI Images

Myelitis is a neurodegenerative disease positioned in the spinal cord, with multiple sclerosis (MS) being a common subtype. Radiological indicators enable the diagnosis of these diseases. This study proposes a classification framework to detect myelitis, MS, and healthy control (HC) groups using magnetic resonance imaging (MRI) images. The feature extraction step involves applying the fast Fourier transform (FFT) to MRI images. FFT is important because it converts spatial data into the frequency domain, making it easier to identify patterns and abnormalities that indicate these diseases. Then, statistical features (mean, minimum, maximum, standard deviation, skewness, kurtosis, and total energy) are extracted from this frequency information. These features are then used to train support vector machine (SVM), k-nearest neighbor (KNN), and decision tree algorithms. In multi-class classification (myelitis vs. MS vs. HC), the proposed method achieves a classification accuracy of 99.31% with SVM, with average precision, recall, and F1-score values of 99.27%, 99.21%, and 99.24%, respectively, indicating effective classification across all classes. In the binary class classification (HC vs. MS, MS vs. myelitis, HC vs. myelitis), the SVM achieves an outstanding classification accuracy of 99.36%, 99.71%, and 100% respectively. This study highlights the efficiency of FFT-based feature extraction in forming detection patterns for classifying HC, MS, and myelitis classes.

Advancing breast ultrasound diagnostics through hybrid deep learning models

Today, doctors rely heavily on medical imaging to identify abnormalities. Proper classification of these abnormalities enables them to take informed actions, leading to early diagnosis and treatment. This paper introduces the “EfficientKNN” model, a novel hybrid deep learning approach that combines the advanced feature extraction capabilities of EfficientNetB3 with the simplicity and effectiveness of the k-Nearest Neighbors (k-NN) algorithm. Initially, EfficientNetB3, pre-trained on ImageNet, is repurposed to serve as a feature extractor. Subsequently, a GlobalAveragePooling2D layer is applied, followed by an optional Principal Component Analysis (PCA) to reduce dimensionality while preserving critical information. PCA is used selectively when deemed necessary. The extracted features are then classified using an optimized k-NN algorithm, fine-tuned through meticulous cross-validation.Our model underwent rigorous training using a curated dataset containing benign, malignant, and normal medical images. Data augmentation techniques, including rotations, shifts, flips, and zooms, were employed to help the model generalize and efficiently handle new, unseen data. To enhance the model's ability to identify the important features necessary for accurate predictions, the dataset was refined using segmentation and overlay techniques. The training utilized an ensemble of optimization algorithms—SGD, Adam, and RMSprop—with hyperparameters set at a learning rate of 0.00045, a batch size of 32, and up to 120 epochs, facilitated by early stopping to prevent overfitting.The results demonstrate that the EfficientKNN model outperforms traditional models such as VGG16, AlexNet, and VGG19 in terms of accuracy, precision, and F1-score. Additionally, the model showed better results compared to EfficientNetB3 alone. Achieving a 100 % accuracy rate on multiple tests, the EfficientKNN model has significant potential for real-world diagnostic applications. This study highlights the model's scalability, efficient use of cloud storage, and real-time prediction capabilities, all while minimizing computational demands.By integrating the strengths of EfficientNetB3's deep learning architecture with the interpretability of k-NN, EfficientKNN presents a significant advancement in medical image classification, promising improved diagnostic accuracy and clinical applicability.

Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications

The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performance analysis of modifications made to enhance the exact kNN techniques, particularly focusing on kNN Search and kNN Join for high-dimensional data. We delve deep into 31 kNN search methods and 12 kNN join methods, providing a methodological overview and analytical insight into each, emphasizing their strengths, limitations, and applicability. An important feature of our study is the provision of the source code for each of the kNN methods discussed, fostering ease of experimentation and comparative analysis for readers. Motivated by the rising significance of kNN in high-dimensional spaces and a recognized gap in comprehensive surveys on exact kNN techniques, our work seeks to bridge this gap. Additionally, we outline existing challenges and present potential directions for future research in the domain of kNN techniques, offering a holistic guide that amalgamates, compares, and dissects existing methodologies in a coherent manner.Graphical

Optimized k-Nearest neighbors search implementation on resource-constrained FPGA platforms

The k-Nearest Neighbors (kNN) algorithm is a fundamental machine learning classification technique with wide-ranging applications. Among various kNN implementation choices, FPGA-based heterogeneous systems have gained popularity due to FPGA's inherent parallelism, energy efficiency, and reconfigurability. However, implementing the kNN algorithm on resource-constrained embedded FPGA platforms, typically characterized by constrained programmable resources shared among various application-specific hardware units, necessitates a kNN accelerator architecture that balances high performance, hardware efficiency, and flexibility. To address this challenge, in this paper, we present a kNN hardware accelerator unit designed to optimize resource utilization by utilizing sequential, i.e. accumulation-based, instead of pipelined/parallel distance computations. The proposed architecture incorporates two key algorithmic optimizations to reduce the iteration count of the sequential distance computation loop: a dynamic lower bound enabling early termination of the distance computation and an online element selection that maximizes partial distance growth per iteration. We further enhance the accelerator's performance by incorporating multiple optimized sequential distance computation units, each dedicated to processing a segment of the training dataset. Our experiments demonstrate that the proposed approach is scalable, making it applicable to various hardware platforms and resource constraints. In particular, when implemented on an AMD Zynq device, the proposed single-core kNN accelerator occupies a mere 5 % of the FPGA's resources while delivering a speedup of 3 – 5 times compared to the kNN software implementation running on the accompanying ARM A9 processor. For the 8-core kNN accelerator, the resource utilization stands at 30 %, while the speedup factor ranges between 25 and 35.

Machine Learning-Based Strategies for Detecting Cyberbullying in Online Chats

This study employed the stacking of three machine learning techniques: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression algorithms to develop a model for detecting cyberbullying using a post dataset acquired from the X Platform. The proposed model's task is to extract keywords from the post dataset and then classify them as either 1 ("cyberbullying word") or 0 ("not cyberbullying word"). The model generated an accuracy of 85.52%, and it was deployed using a simple Graphical User Interface (GUI) web application. This study recommends that the model be included on social media platforms to help reduce the growing use of cyberbullying phrases.