Object Vehicle Motion Prediction based on Dynamic Occupancy Grid Map Utilizing Cascaded Support Vector Machine

Occupancy grid maps are popular tools of representing surrounding environments for mobile robots/ intelligent vehicles. When moving in dynamic real world, traditional occupancy grid mapping is required not only to be able to detect occupied areas, but also to be able to understand the dynamic circumstance. The paper addresses this issue by presenting a stereo-vision based framework to create dynamic occupancy grid map, for the purpose of intelligent vehicle. In the proposed framework, a sparse feature points matching and a dense stereo matching are performed in parallel for each stereo image pair. The former process is used to analyze motions of the vehicle itself and also surrounding moving objects. The latter process calculates dense disparity image, as well as U-V disparity maps applied for pixel-wise moving objects segmentation and dynamic occupancy grid mapping. Principal advantage of the proposed framework is the ability of mapping occupied areas and moving objects at the same time. Meanwhile, compared with some existing methods, the stereo-vision based occupancy grid mapping algorithm is improved. The proposed method is verified in real datasets acquired by our platform SeT-Car.

Remote sensing technology has rendered lots of information in agriculture. It has usually been used to monitor paddy growing ecosystems in the past few decades. However, there are uncertainties in data fusion techniques which can be resolved in image classification on paddy rice. In this study, a series of learning concepts integrated by a probability progress Fuzzy Dempster-Shafer (FDS) analysis is presented to upgrade various models and different types of image data which is the goal of this study. More specifically, the study utilized the FDS to generate a series of probability models in the classification of the system. In addition, Logistic Regression (LR), Support Vector Machine (SVM), and Neural Network (NN) approaches are employed into the developed FDS system. Furthermore, two different image types are Satellite Image and Aerial Photo used as the analysis material. The overall classification accuracy has been improved to 97.27%, and the kappa value is 0.93. The overall accuracy of the paddy field image classification for a multi-period of mid-scale satellite images is between 85% and 90%. The overall accuracy of the classification using multi-spectral numerical aerial photos can be between 91% and 95%. The FDS improves the accuracy of the above image classification results.

<div class="section abstract"><div class="htmlview paragraph">In the domain of advanced driver assistance systems and autonomous vehicles, precise perception and interpretation of the vehicle's environment are not merely requirements they are the very foundation upon which every aspect of functionality and safety is constructed. One prevalent method of representing the environment is through the use of an occupancy grid map. This map segments the environment into distinct grid cells, each of which is evaluated to determine if it is occupied or free. This evaluation operates under the assumption that each grid cell is independent of the others. The underlying mathematical structure of this system is the binary Bayes filter (BBF). The BBF integrates sensor data from various sources and can incorporate measurements taken at different times. The occupancy grid map does not rely on the identification of individual objects, which allows it to depict obstacles of any shape. This flexibility is a key advantage of this approach. Traditional occupancy grid maps fall short when it comes to predicting dynamic environments due to their lack of a process model. A notable enhancement to this static model is the Bayesian Occupancy Filter (BOF), which, unlike its predecessor, estimates a velocity distribution for each grid cell's occupancy using a histogram filter. However, the BOF's computational demands are high. To address this, research propose representing the dynamic state of grid cells using particles. This method enables the computation of dynamic grid maps in real-time applications, even with larger grid cell sizes and higher resolution. Despite these advancements, dynamic occupancy grid maps remain a relatively new field of study, especially when compared to more established object-tracking approaches. Until now, the BOF has been treated as a distinct research area with minimal overlap with other tracking methodologies. This methodology aims to bridge that gap and foster a more integrated approach to dynamic environment estimation.</div><div class="htmlview paragraph">This study introduces a novel approach to dynamic grid mapping, conceptualized as an approximation of a Random Finite Set (RFS) filter. An RFS is a probabilistic representation of a finite, random collection of objects and their respective states. Finite Set Statistics (FISST) provide a framework for Bayesian filtering of these random finite sets and form the foundation for several multi-object tracking methodologies, such as the Probability Hypothesis Density (PHD) filter. By characterizing the grid as an RFS, we can apply sophisticated concepts from the well-established domain of RFS filtering to dynamic grid mapping. The research develops a filter known as the Probability Hypothesis Density/Multi-Instance Bernoulli (PHD/MIB) filter. This filter alternately represents and propagates the dynamic grid map as a PHD and as multiple instances of Bernoulli filters, thereby offering a more integrated and efficient approach to dynamic environment estimation.</div><div class="htmlview paragraph">Furthermore, this research introduces a Sequential Monte Carlo (SMC) implementation of the PHD/MIB filter, as well as an approximation within the Dempster-Shafer framework, termed the Dempster-Shafer PHD/MIB (DS-PHD/MIB) filter. This DS-PHD/MIB filter necessitates fewer particles than the original PHD/MIB filter, thereby enhancing computational efficiency. The study provides a comprehensive description of an efficient, massively parallel implementation of the DS-PHD/MIB filter. The algorithm's pseudo code is also outlined, offering a clear and concise understanding of its workings. This approach further strengthens the integrated and efficient methodology for dynamic environment estimation.</div><div class="htmlview paragraph">In conclusion, the research delineates the attributes of the DS-PHD/MIB filter and debates its pros and cons in comparison to object-based tracking methodologies, using practical examples for illustration. A quantitative assessment using real-world data demonstrates that the DS-PHD/MIB filter yields consistent state estimation outcomes. It effectively models both the stochastic multi-object transition process and the stochastic multi-object observation process. Moreover, the evaluation affirms the real-time capability of the parallelized implementation of the DS-PHD/MIB filter. It validates its utility for state estimation in dynamic vehicle environments, thereby underscoring its potential for practical applications in dynamic environment estimation. This comprehensive approach offers a promising avenue for future research and development in this field.</div></div>

According to statistics from the Global Burden of Cancer Study (Globocon) of the World Health Organization (WHO), cancer, particularly breast cancer, is a severe health issue in Indonesia with 68,858 new cases and 22,000 deaths recorded in 2020. Ultrasonography (USG) technology is acknowledged as one of the potentials to support early detection, which is vital in reducing mortality from breast cancer. This study focuses on classifying ultrasound images using the Support Vector Machine (SVM) algorithm, GLCM feature extraction, Min-Max normalization, and Mutual Information with SelectKBest Feature Selection. From several experiments using the SVM algorithm with various combinations of parameter values that have been set and different Tests, namely using a Train/Test Split with a proportion of 80/20 and K-Fold Cross Validation, it shows that the SVM algorithm is capable of classifying ultrasound images of breast cancer. into two categories (Benign Tumor and Malignant Tumor) with the same maximum accuracy of 79% after applying the SMOTE Balancing Data technique or without using the Balancing Data technique. As a result, the Support Vector Machine (SVM) algorithm has the potential to be an effective model for identifying breast cancer ultrasound images, both on data from the original set that has not been balanced and data from the set that has been balanced.

In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed.

Dilihat dari sisi konsumen, ada berbagai aspek yang mempengaruhi konsumen untuk membeli suatu kendaraan bermotor. Hal ini karena masing-masing orang memiliki minat dan kebutuhan yang berbeda. Oleh karena itu, tujuan dari penelitian ini adalah untuk mengetahui kelompok umur dan profesi apa saja yang menjadi pelanggan potensial di SPS Motor Honda Lombok Timur. Metode penelitian yang digunakan adalah metode analitik deskriptif, dimana peneliti mengambil data-data penjualan sepeda motor dari SPS Motor Honda Lombok Timur, yang selanjutnya diolah dan dianalisis menggunakan software Rapidminer 5. Hasil analisis data tersebut selanjutnya dideskripsikan dengan detail mengenai kelompok pelanggan potensial. Berdasarkan analisis yang telah dilakukan, dapat ditarik kesimpulan bahwa kelompok yang termasuk dalam pelanggan potensial dari SPS Motor Honda Lombok Timur adalah kelompok dengan kriteria umur berkisar 33-46 tahun yang berprofesi sebagai wiraswasta dan petani, Hal ini disebabkan karena, pelanggan tersebut memiliki penghasilan dengan usia produktif. Sedangkan kelompok pelanggan yang tidak potensial ada 2 irisan yaitu yang pertama apabila kategori usia produktif tapi tidak memiliki penghasilan dengan rentang umur 17-32 tahun dengan profesi pelajar dan wiraswasta. Kedua yaitu, apabila kategori usia tidak produktif dengan rentang usia 47-73 tahun dan tidak memiliki penghasilan dengan profesi petani dan wiraswata.DOI : 10.29408/jit.v2i2.1447

In the past few decades, the internet has become an inseparable part of human life. It provides ease of access and permeates almost every aspect of human existence. One of the internet platforms that is widely used by people around the world is social media. Apart from being spoiled with the convenience and efficiency offered by social media to support daily life, it has gained popularity among a wide audience. This has positive implications when utilized effectively, but it cannot be denied that there are negative consequences if not utilized properly. One such consequence is the prevalence of cyberbullying activities on social media. Cyberbullying has become a major concern for the public and social media users, prompting researchers to leverage information technology in developing technologies that can identify the elements of cyberbullying, particularly on social media platforms. Sentiment analysis has been employed by researchers to identify the components of cyberbullying in online platforms. Sentiment analysis involves the application of natural language processing techniques and text analysis to identify and extract subjective information from text. This study aims to compare the Naive Bayes algorithm and the Support Vector Machine algorithm, while utilizing feature selection, specifically chi-square, to enhance the accuracy of both algorithms in classifying Instagram comments. The experimental results indicate that the Multinomial Naive Bayes (MNB) algorithm outperforms the Support Vector Machine (SVM) algorithm, achieving an accuracy of 83.85% without feature selection and 90.77% with feature selection. Meanwhile, SVM achieves an accuracy of 82.31% without feature selection and 90% with feature selection. Evaluation through the confusion matrix and classification report reveals that MNB exhibits better precision and recall rates compared to SVM in identifying bullying and non-bullying classes. The use of feature selection enhances the performance of both algorithms in classifying Instagram comments related to cyberbullying.

Land Use/Land Cover (LULC) classification data have proven to be valuable assets for various governmental agencies, park managers, and natural resource managers. Traditional pixel-based classification methods have difficulty with high resolution imagery, resulting in a “salt and pepper” appearance. Newer object-based methods may prove to be more accurate. This study compared an object based classification procedure utilizing Feature Analyst© software with a traditional pixel-based methodology (supervised classification) when applied to medium-spatial resolution satellite imagery merged with high-spatial resolution aerial imagery. This study utilized two multi-spectral SPOT-5 satellite images, leaf-on and leaf-off, merged with a color infrared aerial image. Because of correlation between some of the bands of the merged image, Principal Component Analysis (PCA) was used to reduce redundancy in the data. Field data was collected in the study area to serve as a reference for the accuracy assessment. A training set was produced by selecting and identifying specific LULC class-types using 1-foot high-spatial resolution aerial imagery. This training set was used by both of the classification methods (supervised and object-based) to identify the various cover types within the study area. An accuracy assessment was performed on each image utilizing error matrices, the Kappa coefficient, and a two-tailed Z-test. Results indicate that the overall accuracy of the object-based classification was 82.0%, while the pixel-based classification was 66.9%. A Kappa analysis and a two tailed Z test were calculated. These values indicated a significant difference in the overall accuracies of the classifications.

In order to improve the accuracy of the prediction model of lung tuberculosis, a prediction model of lung tuberculosis based on grayscale mixed nucleus AR-SVM model is proposed in the Thesis. First, early warning model of the incidence trend of lung tuberculosis was constructed by using support vector machine (SVM) algorithm which includes two circumstances. One is that there is no extreme risk preference; the other is that there is extreme risk preference. And the searching was conducted in the optimum sorting process in training set based on SVM algorithm; Secondly, aimed at the problem that early warning of extreme risk preference of SVM model is easily out of order in the prediction of incidence trend of lung tuberculosis, data record of incidence trend of lung tuberculosis was preprocessed by using improved SVM model and SVM algorithm was improved by polynomial kernel SVM, which achieved improvement of prediction performance of autoregression model of sample data; finally, the effectiveness of algorithm is verified by positive analysis.

Classification of team sport activities using a single wearable tracking device

Classification is data mining techniques which used for the purposes of diagnosis in the medical field as measured by the high accuracy produced. The accuracy of classification algorithm is influenced by the use of features and dimensions in dataset. In this study, Chronic Kidney Disease (CKD) dataset was used where the data is one of the high dimension datasets. Support Vector Machine (SVM) algorithm is used because its ability to handle high-dimensional data. In the dataset, it consists of 24 attributes and 1 class which if all are used results accuracy of classification will be diminished. Method for selecting features with Particle Swarm Optimization (PSO) is applied to reduce redundant features and produce optimal features. In addition, ensemble AdaBoost also applied in this research to increase performance of entirety classification algorithm. The results showed that the optimization of SVM algorithm by using PSO as a selection and ensemble feature of AdaBoost with an average of selected features of 18 features could increase the accuracy of 36.20% to 99.50% in the diagnosis of CKD compared to the SVM algorithm without optimization only resulting in accuracy 63.30%. This research can be used as a reference for further research in focusing on the preprocessing stage.

Sentinel-2A remote sensing satellite system was recently launched, providing free global remote sensing data similar to Landsat systems. Although the mission enables the acquisition of 10 m spatial resolution global data, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and assess the capability of Sentinel-2A imagery in mapping urban areas in Malaysia by comparing its performance against the established Landsat-8 data as well as the fusion datasets from combining Landsat-8 and Sentinel-2A datasets and using Wavelet transform (WT), Brovey transform (BT) and principal component analysis. Pixel-based and object-based image analysis (OBIA) classification approaches combined with support vector machine (SVM) and decision tree (DT) algorithms were utilized in this assessment, and the accuracy generated was analysed. The Sentinel-2A data provided superior urban mapping output over the use of Landsat-8 alone, and the fusion datasets do not yield advantages for single-scene urban mapping. The highest overall accuracy (OA) for pixel-based classification of Sentinel-2A images is 84.77 % by SVM, followed by 65.27 % using DT. BT produced the highest OA for the fusion images of 78.40 % with SVM and 52.21 % with DT. For the object-based classification of Sentinel-2A images, the highest OA is 71.33 % by SVM, followed by 76.38 % using DT. Similarly, the highest OA of fusion images is obtained by BT of 50.35 % with SVM, followed by 65.66 % with DT. From the analysis, the use of SVM pixel-based classification for medium spatial resolution Sentinel-2A data is effective for urban mapping in Malaysia and useful for future long-term mapping applications. HIGHLIGHTS An accurate mapping of urban land is still challenging and requires high image quality of spectral and spatial aspects to identify features Single and fusion image analysis conducted in order to investigate and assess the most performing interpretation result by grouping out the features classes Statistical performance and image classification comparison is relevant to prove the most effective result among the images GRAPHICAL ABSTRACT

Aim: prediction of coronary disease using novel support vector machine and comparing its accuracy with logistic regression algorithm. Materials and methods: Two social affairs are proposed for predicting the accuracy (%) of coronary disease. To be explicit, the novel supports vector machine and logistic regression algorithms. Here we take 20 samples each for appraisal and compare. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result: The logistic regression gives better precision (87.82%) than the novel support vector machine (SVM) accuracy (81.30%). Thus the real significance of logistic regression is better than novel support vector machine algorithms. Conclusion: From the result, it might be gathered that logistic regression helps in expecting the coronary sickness with more accuracy to appear differently in relation to novel support vector machine algorithms.

Multi class SVM algorithm with active learning for network traffic classification

Autonomous vehicles have been developed to reduce traffic accidents and use as means of transporting the elderly. Autonomous vehicles require surrounding environment recognition with LIDAR to detect obstacles. In the conventional method, if dynamic obstacles are detected, dynamic state is estimated using Kalman filter, and if static obstacles are detected, map of static environment is made. However, this method sometimes cannot be consistent between dynamic obstacles and static obstacles. Therefore, this paper introduce Dynamic Occupancy Grid Map using LIDAR information. In Dynamic Occupancy Grid Map, the environment is represented by 2D grid, and this method is able to estimate velocity and object existence probability on each grid, so it is possible to recognize dynamic obstacles and static obstacles at the same time. In this paper, after making the map using actual driving data, we consider the estimation results of dynamic objects and static objects.