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Physiological-Based Smart Stress Detector using Machine learning Algorithms 11

Physiological-Based Smart Stress Detector using Machine Lear...

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11th IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )

作者： Rosales, Marife A. Bandala, Argel A. Vicerra, Ryan Rhay Dadios, Elmer P. De La Salle Univ Elect & Commun Engn Dept Manila Philippines De La Salle Univ Mfg Engn Amd Management Dept Manila Philippines

ISBN: (纸本)9781728130446

this paper is focused on the development of an intelligent system to identify if one person is stress or not stress using physiological parameters through machine learning. In this study, the dataset was acquired from three hundred (300) male and female participants ages 18 to 25. the gathered dataset is composed of five (5) features (i.e. heart rate, systolic blood pressure, diastolic blood pressure, galvanic skin response and gender). An intelligent system was developed using machine learning algorithms for classification such as Linear Regression (LR), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) using Python IDE with sci-kit learn machine learning libraries. Google Colaboratory (Colab) was utilized to perform optimization using Gridsearch to identify the best parameters of each algorithm. Feature selection methods are implemented to identify the most significant features related to stress condition of one person. After optimization, the results showed that SVM has the best performance to classify if one person is stress or not stress with optimized training-testing accuracy score of 95.00% - 96.67%.

关键词： stress machine learning optimization feature selection google colaboratory

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Spam Detection using ANN and ABC Algorithm

Spam Detection using ANN and ABC Algorithm

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Amar Singh Nidhi Chahal Simranjit Singh Suneet Kumar Gupta Bennett University Noida KIET Group of Institutions Ghaziabad India Bennett University Noida India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Social network becomes an effective method to engage with our friends. It enables the users to extract a number of in-formation and its usage are increasing day by day. Amidst all the social networking sites, Twitter is one of the interactive social networking services. To change the authorized user accounts, many spammers are utilized a vast amount of spam. Machine learning (ML) technique is utilized for spam detection system in social sites and for the detection of spammer. data collection is usually done from H-Spam 14 site with the help of pre-processing mechanism, the data is transforming into lowercase. After the first step, pre-processed data comes under feature extraction phase, which utilizes tokenization process to breakdown each sentence into word group in order to extract the best feature from the raw data. the optimization algorithm referred as Artificial Bee Colony (ABC) is used to pick the optimized value from extracted set of features. It is also utilized to get the optimal sets of features from spam and non-spam data. At the end, performance measure criterion and comparing the existing and proposed work in order to look over the progress of the proposed work. In this work, spam detection system is having higher accuracy, precision, recall, and F-measure as compares to classifiers used previously such as, Naïve Bayes and Support Vector Machine (SVM).

关键词： Support vector machines Social networking (online) Electric breakdown Machine learning Feature extraction Tokenization Optimization

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Multi-Label Fake News Detection using Multi-layered Supervised learning 2019

Multi-Label Fake News Detection using Multi-layered Supervis...

引用

11th international conference on Computer and Automation engineering (ICCAE)

作者： Rasool, Tayyaba Butt, Wasi Haider Shaukat, Arslan Akram, M. Usman Natl Univ Sci & Technol Islamabad 44000 Pakistan

ISBN: (纸本)9781450362870

Rapid spreading of misinformation is a growing worldwide concern as it has the capacity to greatly influence individual reputation and societal behavior. the consequences of unchecked spreading of misinformation can not only vary from political to financial but also effect global opinion for a long time. thus, detecting fake news is important but challenging as the ability to accurately categorize certain information as true or fake is limited even in human. Moreover, fake news are a blend of correct news and false information making accurate classification even more confusing. In this paper, we propose a novel method of multilevel multiclass fake news detection based on relabeling of the dataset and learning iteratively. the proposed method outperforms the benchmark and our experiments indicate that profile of the source of information contributes the most in fake news detection.

关键词： Fake News data mining SVM Decision tree Misinformation

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Towards using graph analytics for tracking covid-19 11

Towards using graph analytics for tracking covid-19

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11th international conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020

作者： El Mouden, Zakariyaa Ait Taj, Rachida Moulay Jakimi, Abdeslam Hajar, Moha Software Engineering and Information Systems Engineering FST Errachidia Moulay Ismail University Meknes Morocco

Graph analytics are now considered the state-of-the-art in many applications of communities detection. the combination between the graph's definition in mathematics and the graphs in computer science as an abstract data structure is the key behind the success of graph-based approaches in machine learning. Based on graphs, several approaches have been developed such as shortest path first (SPF) algorithms, subgraphs extraction, social media analytics, transportation networks, bioinformatic algorithms, etc. While SPF algorithms are widely used in optimization problems, Spectral clustering (SC) algorithms have overcome the limits of the most state-of-art approaches in communities detection. the purpose of this paper is to introduce a graph-based approach of communities detection in the novel coronavirus Covid-19 countries' datasets. the motivation behind this work is to overcome the limitations of multiclass classification, as SC is an unsupervised clustering algorithm, there is no need to predefine the output clusters as a preprocessing step. Our proposed approach is based on a previous contribution on an automatic estimation of the k number of the output clusters. Based on dynamic statistical data for more than 200 countries, each cluster is supposed to group countries having similar behaviors of Covid-19 propagation. © 2020 the Authors. Published by Elsevier B.V.

关键词： Coronavirus

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Toward a Smart Platform for data Science Career 11

Toward a Smart Platform for Data Science Career

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11th international conference on Knowledge and Systems engineering (KSE)

作者： Le, Phuong N. Y. Nguyen, Linh V. Nguyen, Tinh H. Vo, Khoi M. Hoang, Suong N. Data Advising Ho Chi Minh City Vietnam Ind Univ Ho Chi Minh City Fac Informat Technol DI Lab Ho Chi Minh City Vietnam Ind Univ Ho Chi Minh City Fac Informat Technol Ho Chi Minh City Vietnam Kyanon Digital R&D Dept Ho Chi Minh City Vietnam

ISBN: (纸本)9781728130033

In this article, we aim to describe our data Advising system. It is a smart platform to provide users with insight into the data science labor market. It connects learners/applicants with employers/businesses and educational providers via an intelligent recommender system. the system will constantly learn from data and domain expertise in order to get smarter and catch up with the trending in data Science.

关键词： recommender system data science labor market job posting educational content

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A Transfer learning Based Approach for Skin Lesion Classification from Imbalanced data

A Transfer Learning Based Approach for Skin Lesion Classific...

引用

international conference on Electrical and Computer engineering (ICECE)

作者： Zillur Rahman Amit Mazumder Ami Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (数字)9781665422543

ISBN: (纸本)9781665446617

Skin cancer is the most common kind of cancer across the world and some types of skin cancer are deadly if not identified at the early stage. therefore, it is crucial to detect the lesion class as early as possible. But skin lesion classification is a very challenging task and many automated systems have been developed so far based on different deep learning algorithms. In this study, we have used ResNet, Xception, and DenseNet three state-of-the-art deep learning pre-trained models to classify the skin lesions. For the training and evaluation of our models, we used the HAM10000 dataset and obtained balanced accuracy of 78%, 82%, and 82% for the three models respectively. We then combined the three models using the weighted ensemble technique without any further training and got 85.8% balanced accuracy and this improved other evaluation parameters as well by a significant amount.

关键词： Deep learning Training Computational modeling Skin data models Lesions Skin cancer

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An In-Memory Cognitive-Based Hyperdimensional Approach to Accurately Classify DNA-Methylation data of Cancer 11th

An In-Memory Cognitive-Based Hyperdimensional Approach to Ac...

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11th international Workshop on Biological Knowledge Discovery from data, BIOKDD 2020, the 4th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2020, the 2nd international Workshop on Machine learning and Knowledge Graphs, MLKgraphs2019, held at the 31st international conference on database and Expert Systems Applications, DEXA 2020

作者： Cumbo, Fabio Weitschek, Emanuel University of Trento Via Sommarive 9 PovoTrento38123 Italy Department of Engineering Uninettuno University Corso Vittorio Emanuele II 39 Rome00186 Italy

ISBN: (纸本)9783030590277

With Next Generation DNA Sequencing techniques (NGS) we are witnessing a high growth of genomic data. In this work, we focus on the NGS DNA methylation experiment, whose aim is to shed light on the biological process that controls the functioning of the genome and whose modifications are deeply investigated in cancer studies for biomarker discovery. Because of the abundance of DNA methylation public data and of its high dimension in terms of features, new and efficient classification techniques are highly demanded. therefore, we propose an energy efficient in-memory cognitive-based hyperdimensional approach for classification of DNA methylation data of cancer. this approach is based on the brain-inspired Hyperdimensional (HD) computing by adopting hypervectors and not single numerical values. this makes it capable of recognizing complex patterns with a great robustness against mistakes even with noisy data, as well as the human brain can do. We perform our experimentation on three cancer datasets (breast, kidney, and thyroid carcinomas) extracted from the Genomic data Commons portal, the main repository of tumoral genomic and clinical data, obtaining very promising results in terms of accuracy (i.e., breast 97.7%, kidney 98.43%, thyroid 100%, respectively) and low computational time. For proving the validity of our approach, we compare it to another state-of-the-art classification algorithm for DNA methylation data. Finally, processed data and software are freely released at https://***/fabio-cumbo/HD-Classifier for aiding field experts in the detection and diagnosis of cancer. © 2020, Springer Nature Switzerland AG.

关键词： Alkylation

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Automatic acquisition of immune cells location using deep learning for automated analysis

Automatic acquisition of immune cells location using deep le...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Shoya Kusunose Yuki Shinomiya Takashi Ushiwaka Nagamasa Maeda Yukinobu Hoshino System of Engineering Kochi University of Technology Kochi Japan Information Kochi University of Technology Kochi Japan Kagoshima Uniciersity Kagoshima Japan Kochi Medical School Kochi Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

Immune cells are analyzed for the elucidation of the human immune system by researchers. they take a cells' movie and track manually the cells to analyze. But the manual process is hard for them. the aim of this study was reducing the load of theirs with automated cells' picking up. To pick up cells, “Recognition Frequency Space” was generated using immune cells' classifier that was made by CNN that a method of deep learning. the classifier was trained while comparing several conditions to decide the best performance classifier. Recognition Frequency Space was indicated the number of times cells are recognized from the frame image of the movie. then, it was checked if cells were picked up from high recognized points of the space. As the result, 9 immune cells were picked up from the first frame image. the result indicates that multiple immune cells can be picked up automatically if it is used the space, and the method can reduce the load of the cell's analyst.

关键词： Immune system Deep learning Motion pictures Image recognition Training Feature extraction Cells (biology)

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Cognitive Fatigue Detection in Vehicular Drivers Using K-Means Algorithm 2nd

Cognitive Fatigue Detection in Vehicular Drivers Using K-Mea...

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2nd international conference on Innovative Computing and Communication, ICICC 2019

作者： Sharma, Manish Kumar Bundele, Mahesh Computer Engineering Poornima College of Engineering JaipurRajasthan India

ISBN: (纸本)9789811503238

In vehicular drivers, the cognitive fatigue has been one of the major factors that leads to loss of lives or disabilities due to vehicular accidents (Bundele and Banerjee in Proceedings of the 11th international conference on information integration and web-based applications and services, ACM, pp 739–744, 2009 [1]). the factors governing driver fatigue such as monotonous driving, traffic conditions on road, road conditions, insufficient sleep, anxiety, health conditions, work environment, type of vehicle, and driving comfort do affect largely the driving behavior. Many researchers across the world are working finding best suitable methods to minimize vehicular accidents. In this paper, our proposed approach is an alternative solution to detect cognitive fatigue in vehicular drivers to decrease the number of incidence of vehicular accidents specially those occurred due to cognitive fatigue (Hu and Zheng in Expert Syst Appl 36:7651–7658, 2009 [2]). the objective of this work has been to provide simple classification technique using K-means algorithms. the basic K-means and two modified versions have been proposed and validated to reliably detect cognitive fatigue while driving. It is of paramount importance that the sensory parameters are chosen such that they could be used without causing discomfort to the driver and without creating obstruction while driving. the data for simple physiological signals such as skin conductance (SC), oximetry pulse (OP), and respiration (RSP) for pre- and post-driving state of drivers has been used for sensing change in fatigue level of the drivers (Bundele and Banerjee in 2009 second international conference on emerging trends in engineering & technology. IEEE, pp 934–939, 2009 [3]). All features of statistical and wavelet were extracted and analyzed (Brown in D1 Methodological issues in driver fatigue research. Fatigue and driving: driver impairment, driver fatigue, and driving simulation, p 155, 1995 [4]). Selected features were us

关键词： Roads and streets

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Joint Prediction of Multiple Vulnerability Characteristics through Multi-Task learning 24

Joint Prediction of Multiple Vulnerability Characteristics T...

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24th international conference on engineering of Complex Computer Systems (ICECCS)

作者： Gong, Xi Xing, Zhenchang Li, Xiaohong Feng, Zhiyong Han, Zhuobing Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Adv Networking TANK Tianjin Peoples R China Australian Natl Univ Res Sch Comp Sci Canberra ACT Australia Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing 100029 Peoples R China

ISBN: (纸本)9781728146461

Software vulnerabilities seriously affect the security of computing systems and they are continuously disclosed and reported. When documenting software vulnerabilities, characterizing the severity, exploitability and impact of a vulnerability is critical for effective triaging and management of software vulnerabilities. Faced with ever-growing number of new vulnerabilities, we observe a significant lag between the disclosure of a vulnerability and the specification of its characteristics. this lag calls for automated, reliable assessment of vulnerability characteristics to assist security analysts in allocating their limited efforts to potentially most serious vulnerabilities. Existing automated techniques for vulnerability assessment require hand-crafted features and balanced data, and consider each specific characteristic independently at a time. In this paper, we propose a multi-task machine learning approach for the joint prediction of multiple vulnerability characteristics based on the vulnerability descriptions. Our approach gets rid of the requirement of balanced data, and it relies on neural networks that learn to extract features from training data. Using the large-scale vulnerability data in the Common Vulnerabilities and Exposure (CVE) database, we conduct extensive experiments to compare different configurations of neural network feature extractors, study the impact of multi-task learning versus independent-task learning, and investigate the performance of our approach for predicting the characteristics of newly disclosed vulnerabilities and the minimum requirement of historical vulnerability data for training reliable prediction model.

关键词： Vulnerability analysis Multi-task learning Feature representation learning

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