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3rd International Conference on Science and Sustainable Development (ICSSD)

作者： Afolabi, Ibukun T. Worlu, Rowland E. Adebayo, O. P. Jonathan, Oluranti

Presently, there are numerous e-marketing and m-marketing mediums that exist such as YouTube, SMS, What Sapp, Google, twitter, yahoo, Facebook, LinkedIn, email and personal blogs. These mediums are beginning to be used for marketing purposes, particularly by the SMEs in Nigeria. The aim of this research is to address the problem of deciding which of the mediums mentioned above is mostly appropriate to target customer of a particular SME and also to discover the type of data that is most appropriate for analysis in making this decision. In order to achieve this, data was gathered by administering questionnaires and pre-processed based on structured and unstructured data sources. The J48 decision tree classification algorithm was used to mine the data, relevant predictions were made from the structured and unstructured data and the results were evaluated. The results revealed that predicting from unstructured data expresses more of popular opinion, so decision can start from unstructured results and be fined tuned or validated with predicting from structured data. Though structured prediction appears to be better than unstructured, unstructured prediction is still very valuable in situations where there are no structured data such as analysing text messages. Also, Models developed for predicting customer behaviour as regards the marketing channels studied, will form the foundation for marketing decision making, in small and medium businesses in Nigeria.

关键词： Data Mining classification algorithm Marketing e-marketing m-marketing Structured data Unstructured data

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Applied Research on Data Mining algorithm in Network Intrusion Detection

Applied Research on Data Mining Algorithm in Network Intrusi...

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1st IITA International Joint Conference on Artificial Intelligence

作者： Xue, Ming Zhu, Changjun Changchun Inst Technol Changchun 130012 Peoples R China HeBei Univ Engn Handan 056038 Peoples R China

ISBN: (纸本)9780769536156

Intrusion Detection is one of network security area of technology main research directions. Data mining technology will be applied to Network Intrusion Detection System (NIDS), may automatically discover the new pattern from the massive network data, to reduce the workload of the manual compilation intrusion behavior patterns and normal behavior patterns. This article reviewed the current intrusion detection technology and the data mining technology briefly. Focus on data mining algorithm in anomaly detection and misuse detection of specific applications. For misuse detection, the main study the classification algorithm;For anomaly detection, the main study the pattern comparison and the cluster algorithm. In pattern comparison to analysis deeply the association rules and sequence rules. Finally, has analysised the difficulties which the current data mining algorithm in intrusion detection applications faced at present, and has indicated the next research direction.

关键词： intrusion detection data mining classification algorithm association rules sequence rules cluster algorithm

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Wearable Pressure Sensor Suit for Real-Time Detection of Incorrect Exercise Techniques

Wearable Pressure Sensor Suit for Real-Time Detection of Inc...

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IEEE Sensors Conference

作者： Kuriakose, Ivin Chauhan, Shirley Fatema, Anis Hussain, Aftab M. Int Inst Informat Technol Ctr VLSI & Embedded Syst Technol CVEST FleCS Lab Hyderabad India

ISBN: (纸本)9781665484640

In recent years, the popularity of weight training has increased significantly. However, incorrect technique or physical form while performing an exercise can not only slow down progress but also cause serious injuries. Deadlift is a famous weight training exercise, which if done incorrectly, can lead to chronic back pain. In this paper, we present a wearable pressure sensor system that checks the posture of the user while performing deadlift. The suit employs a set of flexible pressure sensors, made using velostat (piezoresistive) material, to get the amount of bending for specific muscles. We have developed an algorithm to process the data in real-time so that feedback about incorrect posture can be provided to the user immediately. The algorithm has been configured using data from a single subject, however, it provides accurate classification for multiple subjects in the same weight class, thus eliminating the need for subject-wise configuration. The algorithm classifies a repetition (rep) of the exercise into a good rep or bad rep with an overall accuracy of 95.5%, across three subjects. With some enhancements, the system can also be configured to classify reps of other exercises.

关键词： classification algorithm Deadlift Flexible system Pressure sensor Wearable suit

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Machine Learning-Assisted Detection for BPSK-modulated Ambient Backscatter Communication Systems

Machine Learning-Assisted Detection for BPSK-modulated Ambie...

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IEEE Global Communications Conference (IEEE GLOBECOM)

作者： Wang, Xiyu Duan, Ruifeng Yigitler, Huseyin Menta, Estifanos Jantti, Riku Aalto Univ Dept Commun & Networking Espoo 02150 Finland

ISBN: (纸本)9781728109626

Ambient backscatter communication (AmBC), a green communication technology, is hampered by the continuously and extremely fast varying, strong and unknown ambient radio frequency (RF) signals. This paper presents a machine learning-assisted method for extracting the information of the AmBC device. The information is modulated on top of the unknown Gaussian-distributed ambient RF signals. The proposed approach can decode the binary phase shift keying backscatter signals encoded using Hadamard codes. This method extracts the learnable features for the tag signal by first eliminating the direct path signal and then correlating the residual signal with the coarse estimate of ambient signal. Thereafter, the tag signals are recovered by using the k-nearest neighbors classification algorithm. The recovered signals are decoded by a Hadamard decoder to retrieve the original information bits. We validate the performance using simulations to corroborate the proposed approach.

关键词： Ambient backscatter classification algorithm green communication machine learning signal detection

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Classifying and Passing 3D Obstacles for Autonomous Driving 18

Classifying and Passing 3D Obstacles for Autonomous Driving

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Delp, Michael Nagasaka, Naoki Kamata, Nobuhide James, Michael R. Toyota Res Inst North Amer 1555 Woodridge Ave Ann Arbor MI 48105 USA

ISBN: (纸本)9781467365963

Highly automated driving in real world situations requires perception and planning to account for other forms of human locomotion, principally bicycles, and pedestrians, in addition to vehicles. Here we present a complete perception-planning-control system with high quality perception including object classification and tracking, and a path planning algorithm that makes use of probabilistic classification results. Our classification algorithm is fast enough to provide results for the vast majority of objects in on-line operation, but to ensure output for real-time decision-making the computation and integration of classification results is done using an anytime algorithm. Our focus is on safe driving behavior in the presence of dynamic obstacles on the side of the road, which are typically bicycles, using LIDAR data. Our resulting system is extensively tested in real-world situations, and shows good performance on a large test set of interactions with bicycles.

关键词： bicycles image classification intelligent transportation systems object tracking path planning pedestrians road safety road vehicles 3D obstacles LIDAR data anytime algorithm automated driving autonomous driving classification algorithm dynamic obstacles high quality perception human locomotion object classification path planning algorithm perception-planning-control system probabilistic classification real-time decision-making safe driving behavior vehicles Bicycles Laser radar Radar tracking Robot sensing systems Three-dimensional displays

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Classifying the Sustainability Profile of Clothing Customers

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Circular Economy and Sustainability 2022年第3期2卷 811-828页

作者： Garcia, Solimar de Alencar Nääs, Irenilza da Silva Lima, Nilsa Duarte Graduate Program in Production Engineering Universidade Paulista Rua Dr. Bacelar 1212 SP São Paulo Brazil

The textile sector faces reverse logistics, with activities that can foster a circular economy, such as recycling and reusing materials, a challenge. Industrial waste represents difficulties in all countries, and searching for a solution to the issues related to this subject is highly needed. In 2010 in Brazil, a National Solid Waste Policy (PNRS) was implemented, and after almost a decade, the textile industry is still far from adequate regulation. The consumer society and its unstoppable garbage production need action to reverse this situation. Education and conscious consumption are urgent, especially in developing and poorer countries. The present study’s objective was to propose a clothing consumer classification model based on recycling initiatives. The research was developed based on a questionnaire distributed by email and social media platforms. The questionnaire used the Google Platform to examine the garments consumers’ characteristics and profiles. The data mining approach was used to process the organized data, and random trees were built to express the models. The results describe the clothing consumers’ sustainability characteristics based on their gender, age range, school level, monthly income, purchasing and recycling habits, and commuting. Results indicate that consumer sustainability awareness is mainly related to the period they wear the clothes and how often they purchase them. Consumer behavioral habits such as recycling solid waste and commuting are also indicators of their sustainability degree related to buying and using clothes. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Circular economy classification algorithm Clothes chain Garment industry

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The Advance of Support Tensor Machine 16

The Advance of Support Tensor Machine

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16th IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Xiang, Yi He, Jing Wu, LiWen Jiang, Qian Jin, Xin Yao, Shaowen Yunnan Univ Sch Software Kunming Yunnan Peoples R China

ISBN: (纸本)9781538658864

In recent years, tensor-based machine learning methods, in which the Support Tensor Machine (STM) is a typical technology, have gradually attracted the attention of researchers. Compared with Support Vector Machine (SVM), STM has superior generalization ability that can make full use of the structural information of data. However, it still faces many challenges due to the imperfection of its theoretical basis and model. In order to study the further development of STM, this paper provides a survey about the potential and existing problems in STM.

关键词： Support Tensor Machine Tensor Decomposition classification algorithm

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A DDoS Attack Mitigation Framework for IoT Networks using Fog Computing 17

A DDoS Attack Mitigation Framework for IoT Networks using Fo...

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17th International Learning and Technology Conference

作者： Lawal, Muhammad Aminu Shaikh, Riaz Ahmed Hassan, Syed Raheel King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Sci Jeddah 21589 Saudi Arabia

The advent of 5G which strives to connect more devices with high speed and low latencies has aided the growth IoT network. Despite the benefits of IoT, its applications in several facets of our lives such as smart health, smart homes, smart cities, etc. have raised several security concerns such as Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS mitigation framework for IoT using fog computing to ensure fast and accurate attack detection. The fog provides resources for effective deployment of the mitigation framework, this solves the deficits in resources of the resource-constrained IoT devices. The mitigation framework uses an anomaly-based intrusion detection method and a database. The database stores signatures of previously detected attacks while the anomaly-based detection scheme utilizes k-NN classification algorithm for detecting the DDoS attacks. By using a database containing the attack signatures, attacks can be detected faster when the same type of attack is executed again. The evaluations using a DDoS based dataset show that the k-NN classification algorithm proposed for our framework achieves a satisfactory accuracy in detecting DDoS attacks. (C) 2021 The Authors. Published by Elsevier B. V.

关键词： Internet of Things (IoT) Anomaly mitigation DDoS Fog computing classification algorithm

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First Place Solution for NLPCC 2017 Shared Task Social Media User Modeling 6th

First Place Solution for NLPCC 2017 Shared Task Social Media...

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6th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC)

作者： Qian, Lingfei Wang, Anran Wang, Yan Huang, Yuhang Wang, Jian Lin, Hongfei Dalian Univ Technol Dalian 116023 Liaoning Peoples R China

ISBN: (纸本)9783319736181;9783319736174

With the popularity of mobile Internet, many social networking applications provide users with the function to share their personal information. It is of high commercial value to leverage the users' personal information such as tweets, preferences and locations for user profiling. There are two subtasks working in user profiling. Subtask one is to predict the Point-of-Interest (POI) a user will check in at. We adopted a combination of multiple approach results, including user-based collaborative filtering (CF) and social-based CF to predict the locations. Subtask two is to predict the users' gender. We divided the users into two groups, depending on whether the user has posted or not. We treat this task subtask as a classification task. Our results achieved first place in both subtasks.

关键词： Location prediction Gender prediction User modeling Collaborative filtering classification algorithm

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A static images based-system for traffic signs detection

A static images based-system for traffic signs detection

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IASTED International Conference on Artificial Intelligence and Applications

作者： Moreno, V Ledezma, A Sanchis, A Univ Carlos III Madrid Avda Univ 30 Leganes 28911 Madrid Spain

ISBN: (纸本)0889865566

The localization and interpretation of traffic signs by means of a wise-artificial system is one of the several applications of the recognizing-image techniques. In this work we present techniques addressed to the correct and efficient localization of traffic signs from real images, obtaining information about their location, colour, shape, size and orientation. Moreover, it is shown that colour models non-dependent of illumination combined with non-dimensional features are able to discriminate shapes and provide good results in the traffic signs detection. Several experimental results concerning the application of previously mentioned techniques are shown later on (1).

关键词： traffic signs colour detection techniques shape detection classification algorithm

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