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2nd International Conference of the IEEE-Nigeria-Computer-Chapter (NigeriaComputConf)

作者： Gana, Noah Ndakotsu Abdulhamid, Shafi'I Muhammad Fed Univ Technol Minna Dept Cyber Secur Sci Minna Nigeria

ISBN: (纸本)9781728107134

Exponential growth experienced in Internet usage have pave way to exploit users of the Internet, phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false positive rate and low accuracy has been a setback in phishing detection. In this research RandomForest, SysFor, SPAARC, RepTree, RandomTree, LMT, ForestPA, JRip, PART, NNge, OneR, AdaBoostMl, RotationForest, LogitBoost, RseslibKnn, LibSVM, and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms were rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quit a number of classifiers also exist which if properly explored will yield more accurate results for phishing detection. RondomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false positive rate for phishing classification which suggest that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.

关键词： Phishing Attack classification algorithm Accuracy Performance metric RandomForest

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Development of a deep learning system for preventive detection of heart failure due to pulmonary disease 11

Development of a deep learning system for preventive detecti...

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11th International Conference on Software Process Improvement (CIMPS) - Applications in Software Engineering

作者： Felix Jimenez, Axel Frederick Ibarra Belmonte, Isaul Sanchez Lee, Vania Stephany Jaramillo Avila, Uziel Parra Gonzalez, Ezra Federico Natl Polytech Inst Comp Syst Engn Zacatecas Zacatecas Mexico Ctr Res Math Zacatecas Zacatecas Mexico Ctr Res Math Dept Comp Sci Zacatecas Zacatecas Mexico

ISBN: (纸本)9798350398960

In the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the development of severe disease efficiently. Our approach employs the use of medical data retrieved from the user to determine and predict whether there is a likelihood of a potential condition. To solve this problem, according to a users medical measurement history, a deep learning model can be implemented to determine a preventive diagnosis or otherwise to follow up on an already detected condition. By posing the problem as a classification task, it can be taken advantage of a deep learning model focused on heart failure or pulmonary conditions to make a preliminary diagnosis and determine if there are signs of any symptomatology.

关键词： heart failure pulmonary conditions condition prediction preventive diagnosis classification algorithm

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

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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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THE APPLICATION OF MACHINE LEARNING TO PAINT CONDITION ASSESSMENT USING HYPERSPECTRAL DATA 12

THE APPLICATION OF MACHINE LEARNING TO PAINT CONDITION ASSES...

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12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

作者： Alayoub, Ayoub El Rahim, Samer Abed Mustapha, Samir Salam, Darine Tehrani, Ali Khoa, Nguyen Lu Dang Amer Univ Beirut Dept Mech Engn Lab Smart Struct & Struct Integr SSSI Beirut Lebanon Alto Univ Dept Civil & Environm Engn Espoo Finland Alto Univ Sch Chem Engn Espoo Finland CSIRO Data61 13 Garden St Eveleigh NSW 2015 Australia

ISBN: (数字)9781665470698

ISBN: (纸本)9781665470698

Paint provides a shield that protects our structures, particularly steel structures, from corrosion and damage due to harsh environmental conditions such as sunlight and humidity. It is essential to ensure that paint coating works effectively and remains in good structural conditions (without peeling or fracture). Assessing the paint condition is complex, especially on large structures such as bridges, and can be time-consuming and costly. The traditional visual inspection methods depend on the experience and evaluation of inspectors in the field and require hard labor and time to perform. This study aims to employ hyperspectral imaging and image classification approaches to develop a structural condition monitoring system that rapidly assesses paint conditions and degradation. Several classification algorithms, such as Decision Tree, Support Vector Machines, Logistic Regression, and Naive Bayes, were trained and tested. The results obtained showed that the Decision Tree classifier outperformed the rest of the classifiers, achieving a highly accurate assessment of the paint condition and degradation levels with a detection accuracy of 0.98.

关键词： Paint condition assessment hyperspectral imaging machine learning classification 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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Learning Brain Computer Interface model for Amyotrophic Lateral Sclerosis (ALS) people using Random Forest - Linear Discriminant Analysis (RF-LDA) algorithm 19

Learning Brain Computer Interface model for Amyotrophic Late...

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19th IEEE-India-Council International Conference (INDICON)

作者： Kumar, Arun S. Kannur, Anil Akash, C. K. Koushik, Venkatanath P. S. Kushal, S. Anwar, C. Shaik Nagarjuna Coll Engn & Technol Informat Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781665473507

As modern technology grows and the study of the human brain makes it easier to understand its complex activity, we are moving toward the acknowledgment of imagination. It is not a matter of comfort for various paralytics. It's the potential to tweak devices with a single thought. The improvement of the Brain Computer Interface may be the genuinely unambiguous movement of the last few years. Brain Computer Interface is a prompt correspondence channel between the mind and an electrical device. It is the new advancement in HCI. There are numerous things to investigate like Asynchronous method, small signal to noise ratio (SNR) and different noise sources are amongst the greatest challenges in EEG-based BCI application. Challenges are inherent in translating any new technology to practical and useful clinical applications and BCIs are no exception. We discuss the potential uses and users of BCI systems and address some of the limitations and challenges facing the field. In this paper we propose a new algorithm which combines LDA with Random forest algorithm which sub-divides the dataset and apply LDA algorithm to each subdivision and vote for the final output. Experimental results shows that our algorithm enhance the accuracy in printing the target letter.

关键词： Brain Computer Interface Signal Processing Feature Extraction classification algorithm

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Semorph: A Morphology Semantic Enhanced Pre-trained Model for Chinese Spam Text Detection 22

Semorph: A Morphology Semantic Enhanced Pre-trained Model fo...

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31st ACM International Conference on Information and Knowledge Management (CIKM)

作者： Lai, Kaiting Long, Yinong Wu, Bowen Li, Ying Wang, Baoxun Peking Univ Beijing Peoples R China Tencent Platform & Content Grp Beijing Peoples R China Peking Univ Natl Res Ctr Software Engn Beijing Peoples R China

ISBN: (纸本)9781450392365

Chinese spam text detection is essential for social media since these texts affect the user experience of Chinese speakers and pollute the community. The underlying text classification method is employed to explore the unique combinations of characters that represent clues of spam information from annotated or further augmented data. However, based on the diversity of Chinese characters in glyphs, the spammers frequently wrap the spam content in another visually close text to fool the model but make sure people understand1. This paper proposes to adopt the essence of human cognition of these adversarial texts into spam text detection models, by designing a pre-trained model to learn the morphology semantics of Chinese characters and represent their contextual meanings from scratch. The model pre-trains on self-supervised Chinese corpus and fine-tunes on spam-annotated community texts. Besides, cooperating with the pre-trained model that can capture the morphological features of Chinese, a new data perturbation method is introduced to guide the optimization towards the direction of recognizing the actual meaning of a text after spammers tamper with partial characters by visually close ones. The experimental results have shown that our proposed methodology can notably improve the performance of spam text detection as well as maintain robustness against adversarial samples.

关键词： Spam Text Detection Chinese Character Representation Pre-trained Language Model classification algorithm

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Frequency detection of experimental errors through Learning Analytics techniques 15

Frequency detection of experimental errors through Learning ...

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15th International Conference of Technology, Learning and Teaching of Electronics (TAEE)

作者： Costa, Heverton Marcos Alves, Gustavo R. da Silva, Juarez Bento da Mota Alves, Joao Bosco Univ Fed Santa Catarina PPGTIC Ararangua Brazil Polytech Porto CIETI Sch Engn Porto Portugal

ISBN: (纸本)9781665421614

The process that systematically collects and analyzes large volumes of data in order to improve the teaching-learning process is called Learning Analytics. Based on data processing, educational data mining and visualization, it is possible to monitor academic progress, enhancing actions on how the teacher should conduct the discipline. The objective of this research is to mine data from experiments carried out in the remote laboratory called "Virtual Instrument Systems in Reality". In order to create classification groups according to theoretical analysis studied in circuit analysis. The k-NN classification model was used for this research. The algorithm presented a very satisfactory result, its accuracy resulted in samples with values greater than 0.9. Considering an excellent form of classification analysis for circuits with 1:1, 0:1, 1:0 model.

关键词： Learning Analytics classification algorithm VISIR Remote Laboratory Simple Electrical Circuits

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Fuzzy vector quantization with a step-optimizer to improve pattern classification

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 188卷

作者： Saha, Abhinaba Dewan, Dipayan Ghosh, Lidia Konar, Amit Jadavpur Univ Elect & Tele Commun Engn Dept 188 Raja SC Mallick Rd Kolkata 700032 W Bengal India

The paper proposes a novel supervised algorithm for data classification based on fuzzy vector quantization. The novelty here lies in including an optimization procedure to determine the step size of the quantization process. Adoption of fuzzy space for data exploration eliminates the problem of class-overlapping, while dealing with high dimensional non-linear data. In the proposed approach, the input data are first been transformed from the Euclidean space to the fuzzy space with three membership grades. Next, we have used uniform manifold approximation algorithm (UMAP), for projecting the data in visual space by selection of the most contrasting features from the fuzzy vectors. Then these features are passed through a quantization step with a novel step -optimization technique. Optimizing the quantization step and making it independent of data sets significantly speeds up the process. As the class information of all the feature vectors obtained by UMAP is known, majority voting principle has been used to locate the class-centroids in the subsequent step which in turn represent the class labels of the test samples. In test phase, after obtaining the test vectors, the Hyperspherical Direction Cosines (HDC) between the test vectors and the previously obtained class-centroids are evaluated. The test sample is finally assigned that class label where the sum of absolute differences (SAD) of these direction cosines is minimum. We have validated our classifier on various benchmark data sets and achieved higher accuracy with significantly low computation time than the existing state-of-the-art algorithms.

关键词： Fuzzy logic Vector quantization Step size optimization Uniform manifold approximation and projection UMAP classification algorithm MNIST BCI Linear and non-linear classification Multi-class high dimensional features

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