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Cyberbullying Detection and Prevention: data Mining in Social Media

Cyberbullying Detection and Prevention: Data Mining in Socia...

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

作者： Daniyar Sultan Azizah Suliman Aigerim Toktarova Batyrkhan Omarov Satmyrza Mamikov Gulbakhram Beissenova al-Farabi Kazakh National University Almaty Kazakhstan College of Computing & Informatics Tenaga National University Kuala Lumpur Malaysia International Kazakh-Turkish University Almaty Kazakhstan al-Farabi Kazakh National University Akhmet Yassawi International Kazakh-Turkish University Kazakhstan University of friendship of people’s academician A. Kuatbekov Shymkent Kazakhstan M. Auezov South Kazakhstan University University of friendship of people’s academician A. Kuatbekov Shymkent Kazakhstan

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

this article is devoted to analysis of the articles on cyberbullying of children and adolescents and creating the methodology of working on the own work, which related especially to Kazakh social media. the article provides examples of similar works by foreign researchers, the United States, England and other European countries. Using the methods of theoretical, namely analysis, synthesis, and empirical: comparison and experiment, the analysis of works on this topic was carried out, and this work was aimed at analyzing the problem in the Kazakh space. In brief, we consider the issue of creating a parser, as well as collecting data for training machine learning and deep learning algorithms that could find and block texts containing a humiliating slope in real time. the article will be of interest to both novice specialists who are engaged in data analysis, and experienced ones to expand their *** question of implementing deep learning algorithms requires further study, which is one of the parts of a large work being done, as well as a future topic for a subsequent article.

关键词： Deep learning Training Machine learning algorithms Social networking (online) Planets Real-time systems Internet

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Computer Vision and Iot Based Smart System for Visually Impaired People

Computer Vision and Iot Based Smart System for Visually Impa...

引用

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

作者： Sneha Rao Vishwa Mohan Singh MIT World Peace University Pune India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

For people who are visually impaired, navigation is a challenge they encounter on a daily basis. For the same, use of walking sticks have become a common practice. Although, there are a lot of limitation of just relying on a blind stick. A more suitable method will alert the user about the nature of the obstacle and should also be of assistance in guiding the user to their location. In this paper, we propose an architecture of an assistance system revolving around a shoe that employees IoT devices and sensors along with computer vision algorithms to provide functionalities like obstacle detection, avoidance and navigation. the method uses a smartphone based voice assistance and guides the user with appropriate haptic feedback calculated using various sensors and actuators.

关键词： Computer vision Actuators Navigation Footwear Sensor systems Haptic interfaces intelligent sensors

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A Waste Management Technique to detect and separate Non-Biodegradable Waste using Machine learning and YOLO algorithm

A Waste Management Technique to detect and separate Non-Biod...

引用

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

作者： Aishwarya Aishwarya Parth Wadhwa Owais Owais Vasudha Vashisht Amity University Noida Uttar Pradesh India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

this research Paper proposes an application of Image Processing that works on the principle of machine learning and YOLO (You Only Look Once) algorithm was used to detect the custom object. the proposed application is a method to detect non-biodegradable waste from the bins so that the non-biodegradable waste can easily be separated from the bins. data was divided into three major categories of non- biodegradable waste that were glass, metal and plastic. 450 -500 images of each category were collected in order to train the model. Each image was labelled using a labelling tool in the format of yolo. A machine learning model was created that was trained on the data set of all three categories of the images of non- biodegradable waste. After completion of training a number of files were obtained that were further used in testing model of the application. After completion of testing and checking the accuracy of the model on the webcam all the files were finally imported on the raspberry pi camera. Final testing was done on raspberry pi camera and results were verified accordingly and accuracy was calculated on the basis of the output received.

关键词： Machine learning algorithms Webcams Biological system modeling Machine learning data models Testing Biomedical imaging

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Stacked Autoencoder based Intrusion Detection System using One-Class Classification

Stacked Autoencoder based Intrusion Detection System using O...

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

作者： Prabhav Gupta Yash Ghatole Nihal Reddy Mu Sigma Inc. Bangalore India BITS Pilani Hyderabad India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

the paper presents a study of deep learning based approach for Intrusion Detection System. Already existing models for classification were based on supervised learning methods which fails to classify instances of unknown attacks. Effective prediction of network packets as normal or attack, known and unknown to the model, is imperative requiring detection with minimal false alarm rate. Even for the attacks not known to the model, Stacked Autoencoder turns out to be one such deep learning architecture which identifies complex pattern leading to generation of the best latent representation of inputs. the proposed model was trained on single labeled instances from KDD Cup 99 dataset along with standardizing the inputs using batch normalization to minimize the problem of internal covariance shift and vanishing gradient to some extent. Experimental results obtained show that the proposed method outperforms all the other algorithms giving accuracy of 98.17% and false alarm rate of 0.38%.

关键词： Deep learning Supervised learning Intrusion detection Predictive models Classification algorithms

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An intelligent Intrusion Detection Scheme Powered by Boosting Algorithm

An Intelligent Intrusion Detection Scheme Powered by Boostin...

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

作者： Subham Divakar Rojalina Priyadarshini Rabindra Kumar Barik Diptendu Sinha Roy Persistent Systems C.V. Raman Global University Bhubaneswar Odisha KIIT Deemed to be University Bhubaneswar Odisha National Institue of Technology Meghalaya India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Security is always a major concern in today's world. Due to the prevalent techniques like Internet of things (IoT), Fog/Edge computing and the vast use of social networking, there is a significant increase in the generation of network traffic data. For this reason, proper and fast mechanisms are needed to monitorvariety of data to fight against the vulnerabilities and threats those may occur in the system. In the present article, a machine learning based Intrusion Detection Scheme (IDS) is being proposed. this system can monitor and analyze the incoming network traffic whether is normal. UNSW-NB 15 dataset is used to validate the machine learning model which is powered by boosting *** of the boosting algorithms such as Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost) and Gradient Boosting Classifier (GBC) are trained over the six baseline models such as Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), two variants of Random Forest Model, Gaussian Naïve Bayes (GNB). the performance of the IDS is measured in terms of correct analysis of the network traffic as normal or abnormal and the time taken to detect it. As per the observed results the proposed IDS system is providing the best results for XGB model which gives 95.57 % of accuracy and the time taken to do it is come out as 3.03 seconds. the entire experiment is executed both in Central Processing Unit (CPU) and Graphical Processing UnitGPU) environment and a comparative analysis is done in terms of execution time.

关键词： Adaptation models Intrusion detection Telecommunication traffic Boosting Classification algorithms Central Processing Unit Testing

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A Neuro-inspired Approach for a Generic Knowledge Management System of the intelligent Cyber-Enterprise 11

A Neuro-inspired Approach for a Generic Knowledge Management...

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11th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management (IC3K) / 11th international conference on Knowledge engineering and Ontology Development (KEOD)

作者： Simona, Caramihai Ioan, Dumitrache Mihnea, Moisescu Daniela, Saru Ioan, Sacala Univ Politehn Bucuresti Automat & Comp Sci Fac 313 Splaiul Independentei Bucharest Romania

ISBN: (纸本)9789897583827

the paradigm of Cyber-Physical Systems may be successfully applied to a large number of case-studies, but the most challenging of them are focusing on large scale systems whose dynamics is adapting to various functional scenarios and environmental conditions as energy networks, traffic systems and especially different kinds of cooperative networks of enterprises. However, the large spectrum of possible configurations of such processes raises many issues with respect to the identification of problems to be solved, and furthermore, of the solving method itself, making the efficient use of available knowledge a real challenge. this paper is presenting a neuro-inspired approach for the design of a knowledge management system dedicated to complex networking enterprises organized as Cyber-Physical Systems, and whose functioning is implying dynamical reconfiguration in response to environmental changes, based on the gathering and use of large flows of information, which are referred to as intelligent Cyber-Enterprises. the proposed ideas are based on a human brain model of reasoning and learning.

关键词： Cyber-Physical Systems intelligent Cyber-Enterprise Perception

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Is it Nemo or Dory? Fast and accurate object detection for IoT and edge devices 21

Is it Nemo or Dory? Fast and accurate object detection for I...

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Proceedings of the 11th international conference on the Internet of things

作者： Sudhanshu Agarwal Richard Vuduc College of Computing Georgia Institute of Technology United States School of Computational Science and Engineering Georgia Institute of Technology United States

ISBN: (纸本)9781450385664

Current state-of-the-art object detection neural networks, such as YOLO and SSD, are trained and developed on server-class GPUs. these neural networks do not scale down well to resource-constrained devices, with both accuracy and precision taking a significant hit at the expense of speed. this is particularly concerning as object detection algorithms are often used for low-powered devices, such as surveillance and smart-home cameras, where accuracy is critical. therefore, these devices generally tend to forward data to servers for processing, which adds network latency. In other cases, algorithms developed for these devices shrink neural networks to reduce computation at the expense of accuracy, which is often not acceptable. We create an alternative object detection scheme for static-camera systems such as those used in surveillance and smart-home settings. Our model does not require positions of objects in training data, enabling us to retain only the relevant parts of a video frame for training data and reduce data storage cost while preserving privacy of other subjects in the video. When evaluated on static-camera video feeds using an NVIDIA Jetson Nano (a hybrid Arm and GPU IoT embedded device platform), our method increases throughput compared to Tiny YOLOv3. Further, it takes half of the time that Tiny YOLOv3 takes when run on OpenCV’s CUDA-optimized DNN framework per frame while having high accuracy of bounding box prediction and classification. the lessons learned from this work also suggest other strategies for tailoring the deployment of deep learning algorithms at the edge.

关键词： Background Modeling Object Detection Edge Computing Parallel Computing

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intelligent tutoring chatbot for solving mathematical problems in High-school 11

Intelligent tutoring chatbot for solving mathematical proble...

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

作者： Nguyen, Hien D. Pham, Vuong T. Tran, Dung A. Le, Trung T. Univ Informat Technol VNU HCM Fac Comp Sci Ho Chi Minh City Vietnam Sai Gon Univ Fac Informat Technol Ho Chi Minh City Vietnam Vinh Long Coll Econ & Finance Fac Informat Technol Vinh Long Vietnam

ISBN: (纸本)9781728130033

Nowadays, information technology is applied for teaching and learning in high-school. It is an essential trend for the modern education, especially building intelligent systems to support the learning of mathematics. In the math subjects of Vietnamese high-school, problems about surveying properties of a function are popular in high-school exams. there are some current tools can solve them automatically. However, they only can display the graph of a function;they cannot solve problems about determining the value of a parameter to satisfy some conditions about this function's properties. In this paper, we will present a method to design an intelligent chatbot for solving problems in this knowledge domain. It plays as an instructor to give some tips and tutor the learner how to solve problems automatically. this program can communicate with the learner through the question-answering process by chatbot. this tutoring of our system simulates the tutoring method of the teacher to the students in practice.

关键词： intelligent problem solver intelligent tutoring system knowledge-based system knowledge engineering STEM

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Innovative Design of Environmental Art Dependent on Big data Pattern learning Optimization Cultural Algorithm 11

Innovative Design of Environmental Art Dependent on Big Data...

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11th international conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Lin, Naijing Shanghai Univ Shanghai Peoples R China

ISBN: (纸本)9781728121659

How to carry out innovative design for the environmental art more effectively is the main work of the research at present. In this paper, a kind of method for the innovative design of environmental art that is dependent on the big data pattern learning optimization cultural algorithm is put forward. As the design for the environmental art emerges, the environmental art can be analyzed through the real-time analysis of the big data pattern learning optimization cultural algorithm. Firstly, in accordance with the frequent pattern mining algorithm, a new big data pattern learning optimization cultural algorithm can be formed. through the use of the big data pattern learning optimization cultural algorithm, the innovative design of the environmental art can be implemented. this algorithm can meet the requirement for the innovative design for the discovery of the environmental art as well as in the aspect of precision.

关键词： Big data Pattern learning Optimization Cultural Algorithm Environmental Art Innovative Design Algorithm

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A Novel Neural Model based Framework for Detection of GAN Generated Fake Images

A Novel Neural Model based Framework for Detection of GAN Ge...

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

作者： Samaksh Agarwal Nancy Girdhar Himanshu Raghav Amity University Noida UP India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

With the advancement in Generative Adversarial Networks (GAN), it has become easier than ever to generate fake images. these images are more realistic and non-discernible by untrained eyes and can be used to propagate fake information on the Internet. In this paper, we propose a novel method to detect GAN generated fake images by using a combination of frequency spectrum of image and deep learning. We apply Discrete Fourier Transform to each of 3 color channels of the image to obtain its frequency spectrum which shows if the image has been upsampled, a common trend in most GANs, and then train a Capsule Network model with it. Conducting experiments on a dataset of almost 1000 images based on Unconditional data modeling (StyleGan2 - ADA) gave results indicating that the model is promising with accuracy over 99% when trained on the state-of-the-art GAN model. In theory, our model should give decent results when trained with one dataset and tested on another.

关键词： Deep learning Image color analysis Discrete Fourier transforms Generative adversarial networks Market research data models Internet

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