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Spatio-temporal K-NN prediction of traffic state based on statistical features in neighbouring roads

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期40卷 9059-9072页

作者： Priambodo, Bagus Ahmad, Azlina Kadir, Rabiah Abdul Univ Kebangsaan Malaysia IIR4 0 Bangi 43600 Selangor Malaysia Univ Mercu Buana Fac Comp Sci Jakarta Indonesia

Traffic congestion on a road results in a ripple effect to other neighbouring roads. Previous research revealed existence of spatial correlation on neighbouring roads. Similar traffic patterns with regards to day and time can be seen amongst roads in a neighbouring area. Presently, nonlinear models of neural network are applied on historical data to predict traffic congestion. Even though neural network has successfully modelled complex relationships, more time is needed to train the network. A non-parametric approach, the k-nearest neighbour (K-NN) is another method for forecasting traffic condition which can capture the nonlinear characteristics of traffic flow. An earlier study has been done to predict traffic flow using K-NN based on connected roads (both downstream and upstream). However, impact of road congestion is not only to connected roads, but also to roads surrounding it. Surrounding roads that are impacted by road congestion are those having 'high relationship' with neighbouring roads. Thus, this study aims to predict traffic state using K-NN by determining high relationship roads within neighbouring roads. We determine the highest relationship neighbouring roads by clustering the surrounding roads by combining grey level co-occurrence matrix (GLCM) with k-means. Our experiments showed that prediction of traffic state using K-NN based on high relationship roads using both GLCM and k-means produced better accuracy than using k-means only.

关键词： classification algorithm clustering algorithm machine learning algorithm nearest neighbour search intelligent transportation system

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Superpixel-based class-semantic texton occurrences for natural roadside vegetation segmentation

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MACHINE VISION AND APPLICATIONS 2017年第3-4期28卷 293-311页

作者： Zhang, Ligang Verma, Brijesh Cent Queensland Univ Sch Engn & Technol Brisbane Qld Australia

Vegetation segmentation from roadside data is a field that has received relatively little attention in present studies, but can be of great potentials in a wide range of real-world applications, such as road safety assessment and vegetation condition monitoring. In this paper, we present a novel approach that generates class-semantic color-texture textons and aggregates superpixel-based texton occurrences for vegetation segmentation in natural roadside images. Pixel-level class-semantic textons are learnt by generating two individual sets of bag-of-word visual dictionaries from color and filter bank texture features separately for each object class using manually cropped training data. For a testing image, it is first oversegmented into a set of homogeneous superpixels. The color and texture features of all pixels in each superpixel are extracted and further mapped to one of the learnt textons using the nearest distance metric, resulting in a color and a texture texton occurrence matrix. The color and texture texton occurrences are aggregated using a linear mixing method over each superpixel and the segmentation is finally achieved using a simple yet effective majority voting strategy. Evaluations on two datasets such as video data collected by the Department of Transport and Main Roads, Queensland, Australia, and a public roadside grass dataset show high accuracy of the proposed approach. We also demonstrate the effectiveness of the approach for vegetation segmentation in real-world scenarios.

关键词： Vegetation segmentation Texton Superpixel classification algorithm Object recognition

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Digital mapping of soil texture classes for efficient land management in the Piedmont plain of Iran

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SOIL USE AND MANAGEMENT 2022年第4期38卷 1705-1735页

作者： Keshavarzi, Ali Sanchez del Arbol, Miguel Angel Kaya, Fuat Gyasi-Agyei, Yeboah Rodrigo-Comino, Jesus Univ Tehran Dept Soil Sci Lab Remote Sensing & GIS POB 4111 Karaj *** Iran Univ Granada Fac Filosofia & Letras Dept Anal Geog Reg & Geog Fis Campus Univ Cartuja Granada Spain Isparta Univ Appl Sci Fac Agr Dept Soil Sci & Plant Nutr TR-32260 Isparta Cunur Turkey Griffith Univ Sch Engn & Built Environm Nathan Qld Australia

Accurate prediction of digital soil maps allows for the evaluation of larger areas with respect to the design of efficient land management plans at the regional scale. Nowadays, there is an increasing demand for high spatial resolution-gridded soil data for crop planning and management because it saves time and costs. One of the most essential soil physical properties affecting water holding capacity, nutrient availability and crop growth is soil texture. It exhibits a high spatial variability, but accurate maps for larger scales are lacking. The aim of this research was to produce gridded maps of soil texture fractions (clay, silt and sand) using regression-based approaches and to establish soil texture classes using classification-based techniques for the semi-arid Piedmont plain of Iran. To this end, a digital elevation model and derived topographic indices, vegetation and soil-based indices generated from 4-year timeseries of remote sensing products of Landsat 8 OLI were used as covariates. The decision tree (linear) and its improved version of random forest (nonlinear) algorithms were used for both the regression and classification analysis. For both algorithms, the topography-based indices and remotely sensed products were the most important predictors for the soil particle fractions. For the estimation of the different textural classes with multiple algorithms, we recorded a moderate overall accuracy rate of 54% and a Kappa coefficient of 17% for the validation datasets. It was observed that the nonlinear classification method of the random forest was more effective, and this was also the case for the regression modelling. In general, the random forest algorithm produced a more useful gridded map to help to design regional management plans based on soil properties.

关键词： classification algorithm digital soil mapping physical soil properties prediction random forest regional issues

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A POLYNOMIAL-TIME algorithm FOR THE CONSTRUCTION AND TRAINING OF A CLASS OF MULTILAYER PERCEPTRONS

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NEURAL NETWORKS 1993年第4期6卷 535-545页

作者： ROY, A KIM, LS MUKHOPADHYAY, S Arizona State University USA

This paper presents a polynomial time algorithm for the construction and training of a class of multilayer perceptrons for classification. It uses linear programming models to incrementally generate the hidden layer in a restricted higher-order perceptron. Polynomial time complexity of the method is proven. Computational results are provided for several well-known applications in the areas of speech recognition, medical diagnosis, and target detection. In all cases, very small nets were created that had error rates similar to those reported so far.

关键词： POLYNOMIAL TIME algorithm MULTILAYER PERCEPTRONS LINEAR PROGRAMMING classification algorithm SUPERVISED LEARNING CLUSTERING NET DESIGN

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On multiple-domain cooperative diversity for communications with distributed content in *** networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2017年第1期30卷 1-16页

作者： Gacanin, Haris Ligata, Amir Chahibi, Youssef Alcatel Lucent Bell Mot Div Antwerp Belgium IPSA Inst Sarajevo Bosnia & Herceg Georgia Inst Technol Atlanta GA 30332 USA

Broadband data applications can be delivered to homes over the available home network infrastructure (i.e., mediums). Further enhancement of the network performance is possible by exploiting spatial, time, or frequency diversity. Recently, MIMO systems have been proposed for power line communication (PLC) networks. Because of the extremely diverse physical characteristics of the mediums, an implementation of MIMO systems in *** across different domains represents a non-trivial task. In this paper, we present and theoretically evaluate the performance of multiple-domain diversity mechanism in *** using a cross-layer classification algorithm. In the proposed network architecture, the signal is transmitted over different mediums selected by the classification algorithm in order to meet the quality-of-service demands. At the receiver, joint signal combining and equalization are done to take advantage of the multiple-domain cooperative diversity and increase the signal-to-noise ratio. The merit of the proposed multiple-domain PLC network has been confirmed by analytical performance analysis and computer simulation. Copyright (C) 2017 John Wiley & Sons, Ltd.

关键词： G.hn multiple-domain OFDM classification algorithm

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A survey on dynamic mobile malware detection

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SOFTWARE QUALITY JOURNAL 2018年第3期26卷 891-919页

作者： Yan, Ping Yan, Zheng Xidian Univ Sch Cyber Engn State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Aalto Univ Dept Commun & Networking Espoo 02150 Finland

The outstanding advances of mobile devices stimulate their wide usage. Since mobile devices are coupled with third-party applications, lots of security and privacy problems are induced. However, current mobile malware detection and analysis technologies are still imperfect, ineffective, and incomprehensive. Due to the specific characteristics of mobile devices such as limited resources, constant network connectivity, user activities and location sensing, and local communication capability, mobile malware detection faces new challenges, especially on dynamic runtime malware detection. Many intrusions or attacks could happen after a mobile app is installed or executed. The literature still expects practical and effective dynamic malware detection approaches. In this paper, we give a thorough survey on dynamic mobile malware detection. We first introduce the definition, evolution, classification, and security threats of mobile malware. Then, we summarize a number of criteria and performance evaluation measures of mobile malware detection. Furthermore, we compare, analyze, and comment on existing mobile malware detection methods proposed in recent years based on evaluation criteria and measures. Finally, we figure out open issues in this research field and motivate future research directions.

关键词： Mobile malware Dynamic malware detection Security threats classification algorithm Evaluation criteria

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Predictive machine learning-based integrated approach for DDoS detection and prevention

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第3期81卷 4185-4211页

作者： Kebede, Solomon Damena Tiwari, Basant Tiwari, Vivek Chandravanshi, Kamlesh Hawassa Univ Inst Technol Dept Informat Technol Hawassa Ethiopia Hawassa Univ Inst Technol Dept Comp Sci Hawassa Ethiopia Dr SP Mukherjee IIIT NR Dept CSE Raipur Madhya Pradesh India LNCT Dept Informat Technol Bhopal India

Distributed Denial of Service attack has been a huge threat to the Internet and may carry extreme losses to systems, companies, and national security. The invader can disseminate Distributed denial of service (DDoS) attacks easily, and it ends up being significantly harder to recognize and forestall DDoS attacks. In recent years, many IT-based companies are attacked by DDoS attacks. In this view, the primary concern of this work is to detect and prevent DDoS attacks. To fulfill the objective, various data mining techniques such that Jrip, J48, and k-NN have been employed for DDoS attacks detection. These algorithms are implemented and thoroughly evaluated individually to validate their performance in this domain. The presented work has been evaluated using the latest dataset CICIDS2017. The dataset characterizes different DDoS attacks viz. brute force SSH, brute force FTP, Heartbleed, infiltration, botnet TCP, UDP, and HTTP with port scan attack. Further, the prevention method takes place in progress to block the malicious nodes participates in any of the said attacks. The proposed DDoS prevention works in a proactive mode to defend all these attack types and gets evaluated concerning various parameters such as Throughput, PDR, End-to-End Delay, and NRL. This study claimed that the proposed technique outperforms with respect to the AODV routing algorithm.

关键词： CICIDS2017 DDoS Attack Machine learning classification algorithm DDoS Detection DDoS Prevention

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A Computer Aided Approach for River Styles-Inspired Characterization of Large Basins: The Magdalena River (Colombia)

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WATER 2020年第4期12卷 1147页

作者： Nardini, Andrea Yepez, Santiago Zuniga, Lislie Gualtieri, Carlo Dolores Bejarano, Maria Fdn CREACUA Riohacha 440001 La Guajira Colombia Univ Politecn Madrid UPM Escuela Montes PhD Program Adv Forestry Engn Madrid 28040 Spain Univ Concepcion UdeC Fac Ciencias Forestales Dept Manejo Bosques & Medio Ambiente Concepcion 407374 Chile Univ Concepcion UdeC Lab Geotecnol & Modelamiento Recursos Nat LGM Concepcion 407374 Chile Univ Napoli Federico II Civil Architectural & Environm Engn Dept DICEA I-80125 Naples Italy Univ Politecn Madrid UPM Escuela Montes Madrid 28040 Spain

This paper addresses the geomorphic characterization and classification of large rivers in a framework of scarce information. This is inspired by the River Styles Framework with some modifications that make the process more straightforward and accessible to practitioners and more applicable to large basins, while reducing the subjective, expert-based inputs, as the process is now more systematic. To this aim, it utilizes innovative criteria and some computer-aided procedures and tools based on GIS, Excel and Python. This approach sheds light on the character and the behavior of rivers, which is key to informing planning, management and restoration. The application to the Magdalena River (Colombia) illustrates the characterization and classification process and the type of results, which ultimately highlight the great geomorphic diversity of that river. The process is applicable to many other rivers worldwide.

关键词： river styles Magdalena River geomorphic response river behavior classification algorithm

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A novel cyber intrusion detection model based on improved hybrid sampling

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第15期45卷 2903-2913页

作者： Qian, Hong Zhang, Xiantao Zhang, Chaofan Jiang, Cheng Shanghai Univ Elect Power Sch Automat Engn Shanghai 200090 Peoples R China Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China Agr Bank China Data Ctr Shanghai Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China

The class imbalance of samples of network traffic will cause the poor classification performance of intrusion detection models based on machine learning. To solve this problem, this paper researches sampling algorithm and deep learning for intrusion detection in imbalanced network traffic. This paper proposes a deep recurrent neural network (delayed long short-term memory (DLSTM)) intrusion detection model based on the balanced samples. First, an improved hybrid sampling (IHS) method based on chaotic particle swarm optimization (CPSO) algorithm is proposed as the sampling algorithm to balance the imbalanced samples. Next, a DLSTM with long short-term memory (LSTM) function is proposed to realize high-precision classification of intrusion behaviours. Finally, the method is validated on the standard network traffic dataset. The experimental results show that the DLSTM intrusion detection model based on the IHS method outperforms other comparative models at accuracy. The model is available to the computer network information security defence system.

关键词： Intrusion detection sample imbalance improved hybrid sampling deep learning classification algorithm

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Balancing noise sensitivity, response latency, and posture accuracy for a computer-assisted canine posture training system

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES 2017年 98卷 179-195页

作者： Majikes, John Brugarolas, Rita Winters, Michael Yuschak, Sherrie Mealin, Sean Walker, Katherine Yang, Pu Sherman, Barbara Bozkurt, Alper Roberts, David L. North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA North Carolina State Univ Dept Clin Sci Raleigh NC 27695 USA

This paper describes a canine posture detection system composed of wearable sensors and instrumented devices that detect the postures sit, stand, and eat. The system consists of a customized harness outfitted with wearable Inertial Measurement Units (IMUs) and a base station for processing IMU data to classify canine postures. Research in operant conditioning, the science of behavior change, indicates that successful animal training requires consistent and accurate feedback on behavior. Properly designed computer systems excel at timeliness and accuracy, which are two characteristics most amateur trainers struggle with and professionals strive for. Therefore, in addition to the system being ergonomically designed to ensure the dog's comfort and well-being, it is engineered to provide posture detection with timing and accuracy on par with a professional trainer. We contend that providing a system with these characteristics will one day aid dogs in learning from humans by overcoming poor or ineffective timing during training. We present the initial steps in the development and validation of a computer-assisted training system designed to work outside of laboratory environments. The main contributions of this work are (a) to explore the trade-off between low-latency responses to changes in time-series IMU data representative of posture changes while maintaining accuracy and timing similar to a professional trainer, and (b) to provide a model for future ACI technologies by documenting the user-centered approach we followed to create a computer-assisted training system that met the criteria identified in (a). Accordingly, in addition to describing our system, we present the results of three experiments to characterize the performance of the system at capturing sit postures of dogs and providing timely reinforcement. These trade-offs are illustrated through the comparison of two algorithms. The first is Random Forest classification and the second is an algorithm which use

关键词： Animal-computer interaction classification algorithm Machine learning Random Forest Wearable technology

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