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Easy-to-Deploy Living Activity Sensing System and data Collection in General Homes

Easy-to-Deploy Living Activity Sensing System and Data Colle...

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IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Tomokazu Matsui Kosei Onishi Shinya Misaki Manato Fujimoto Hirohiko Suwa Keiichi Yasumoto Nara Institute of Science and Technology Ikoma Nara Japan RIKEN Center for Advanced Intelligence Project Chuo-ku Tokyo Japan

ISBN: (数字)9781728147161

ISBN: (纸本)9781728147178

Emergence of smart appliances and high performance IoT devices is promoting studies on more functional and intelligent home services using these devices. Especially, in developed countries including Japan with aging population and declining birthrate, it is urgent to develop technologies to monitor living situations of residents including elderly persons and improve their quality of life (QoL) through home services based on the activity recognition technology. However, activity recognition systems in general require many types/number of sensors and hence they are difficult to deploy and operate. In this paper, we propose a system consisting of low-cost and easy-to-deploy sensors based on energy harvesting that can collect data of resident's activities of daily living (ADL) for months without maintenance. The system was deployed in 10 homes of senior citizens where we collected ADL data for two months each. We also estimated the ADLs from the collected data by using long short-term memory (LSTM), a deep learning model. As a result, ADLs could be estimated at high recall rate of 82.4% on average and hence we found that the proposed system has high applicability to actual services.

关键词： data collection Senior citizens Activity recognition Magnetic sensors Servers

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Experimental Evaluation of Deep Learning Based Liver Cancer Identification Scheme Using Hybrid Classification Logic

Experimental Evaluation of Deep Learning Based Liver Cancer ...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Mouleeswaran SK J. Bhuvaneswari Sakthi Saravanan. N S. Hemajothi Sivabalan A K. Vijayakumar Department of CSE Dayananda Sagar University Bangalore Department of Artificial Intelligence and Data Science RMK college of Engineering and Technology Chennai Department of EEE S. A. Engineering College Chennai Department of ECE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of ECE Easwari Engineering College Chennai Department of Information Technology St. Joseph's Institute of Technology OMR Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Companies in the healthcare industry would be foolish to disregard medical analytics and decision-making tools that employ multi-modal intelligence. Liver cancer, among the most prevalent malignancies, can only be effectively treated if caught early. The contrast and brightness of liver tumours are identical to those of the surrounding tissues. Also, depending on the stage of cancer and the kind of tumour, abnormal tumour forms might be a major cause for concern. Segmenting a liver tumour entails first locating the cancer, and then cutting it into smaller pieces. However, while traditional methods of interactive segmentation require a large number of intensity levels, newer methods based on convolutional neural networks (CNN) and the Learning Scheme for Liver Cancer Identification (LSLCI) have limited presentation on images of liver tumours. This study showcases a new method for segmentation utilizing Coot Extreme Learning Model, which is based on deep learning, and it shows great efficiency in identifying tumors using accessible information of liver scans. The research process the first segmentation with minimal further user clicks in order to optimize the segmentation process. Doing so allows the suggested geodesic distance encoding method to incorporate inner boundary points. Liver cancer survival rates can be improved with early detection and treatment. The gold standard for liver tumour differential diagnosis is dynamic contrast-enhanced magnetic resonance imaging (MRI). But since MRI diagnoses are susceptible to human error, deep learning may provide a fresh approach to the diagnostic process. We trained a deep learning system using LSLCI and CNN to identify liver tumours from a variety of sources, including enhanced and unenhanced magnetic resonance images, as well as textual and numerical clinical data, as well as the results of laboratory tests.

关键词： Deep learning Liver cancer Image segmentation Magnetic resonance imaging Liver Magnetic resonance Medical services Convolutional neural networks Medical diagnostic imaging Tumors

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Wi-mix: A pedestrian track tracking method combining PDR and Wi-Fi signals

Wi-mix: A pedestrian track tracking method combining PDR and...

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Zhanjun Hao Lihua Yan Jianwu Dang Lei Bai School of Electronic and Information Engineering Lanzhou Jiaotong University College of Computer Science and Engineering Northwest Normal University Lanzhou China College of Computer Science and Engineering Northwest Normal University Gansu Province Internet of Things Engineering Research Center Lanzhou China School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China Academy of Sciences Northwest Normal University Lanzhou China

ISBN: (数字)9781728176475

ISBN: (纸本)9781728176482

With the rapid development of network communication technology and intelligent mobile terminals in recent years, location-based services have been more and more widely used in daily life. In order to meet the requirements of high precision, low cost and wide coverage in complex indoor scenes. This paper proposes an indoor pedestrian track tracking method based on the fusion of Wi-Fi signal and PDR(Pedestrian Dead Reckoning, PDR) model based on kalman filter framework. The method combines pedestrian dead reckoning with Wi-Fi location technology to realize pedestrian track tracking. Through experiments, the accuracy of the tracking error within 1m reaches 85%, and the accuracy can reach more than 90% when the tracking error is 1.5m. Based on the comprehensive tracking accuracy, construction cost, robustness and universality of the system, the human trajectory tracking method is better than other common trajectory tracking methods.

关键词： Wireless fidelity Target tracking Fingerprint recognition Buildings smart phones Sensors Dead reckoning

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Architecture of an intelligent Intrusion Detection System for smart Home

Architecture of an intelligent Intrusion Detection System fo...

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IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Julian Graf Katrin Neubauer Sebastian Fischer Rudolf Hackenberg Ostbayerische Technische Hochschule Regensburg Germany Secure Systems Engineering Fraunhofer AISEC Berlin Germany

ISBN: (数字)9781728147161

ISBN: (纸本)9781728147178

Increasing cyber-attacks on Internet of Things (IoT) environments are a growing problem of digitized households worldwide. The purpose of this study is to investigate how an intelligent Intrusion Detection System (iIDS) can provide more security in IoT networks with a novel architecture, combining multiple classical and machine learning approaches. By combining classical security analysis methods and modern concepts of artificial intelligence, we increase the quality of attack detection and can therefore conduct dedicated attack suppression. The architectural image of the iIDS consists of different layers, which in parts achieve self-sufficient results. The results of the different modules are calculated by means of statement variables and evaluation techniques adapted for the individual module elements and subsequently combined by limit value considerations. The architecture image combines approaches for the analysis and processing of IoT network traffic and evaluates it to an aggregated score. From this result it can be determined whether the analyzed data indicates device misuse or attempted break-ins into the network. This study answers the questions whether a connection between classical and modern concepts for monitoring and analyzing IoT network traffic can be implemented meaningfully within a reliable architecture of an iIDS.

关键词： Artificial intelligence Intrusion detection Protocols Sensors Wireless fidelity Computer architecture

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Deep Reinforcement Learning for Optimal Resource Allocation in Blockchain-based IoV Secure Systems

Deep Reinforcement Learning for Optimal Resource Allocation ...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Hongzhi Xiao Chen Qiu Qinglin Yang Huakun Huang Junbo Wang Chunhua Su Computer and Information System The University of Aizu Aizuwakamatsu Japan School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou City China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Driven by the advanced technologies of vehicular communications and networking, the Internet of Vehicles (IoV) has become an emerging paradigm in smart world. However, privacy and security are still quite critical issues for the current IoV system because of various sensitive information and the centralized interaction architecture. To address these challenges, a decentralized architecture is proposed to develop a blockchain-supported IoV (BS-IoV) system. In the BS-IoV system, the Roadside Units (RSUs) are redesigned for Mobile Edge computing (MEC). Except for information collection and communication, the RSUs also need to audit the data uploaded by vehicles, packing data as block transactions to guarantee high-quality data sharing. However, since block generating is critical resource-consuming, the distributed database will cost high computing power. Additionally, due to the dynamical variation environment of traffic system, the computing resource is quite difficult to be allocated. In this paper, to solve the above problems, we propose a Deep Reinforcement Learning (DRL) based algorithm for resource optimization in the BS-IoV system. Specifically, to maximize the satisfaction of the system and users, we formulate a resource optimization problem and exploit the DRL-based algorithm to determine the allocation scheme. The evaluation of the proposed learning scheme is performed in the SUMO with Flow, which is a professional simulation tool for traffic simulation with reinforcement learning functions interfaces. Evaluation results have demonstrated good effectiveness of the proposed scheme.

关键词： Reinforcement learning Computer architecture Blockchain Throughput Resource management Vehicle dynamics Optimization

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intelligent Navigation System for the Visually Impaired - A Deep Learning Approach

Intelligent Navigation System for the Visually Impaired - A ...

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international conference on computing Methodologies and Communication (ICCMC)

作者： Deepak Kumar Yadav Somsankar Mookherji Joanne Gomes Siddhant Patil Department of Information Technology St. Francis Institute of Technology Mumbai University

ISBN: (数字)9781728148892

ISBN: (纸本)9781728148908

Visually impaired individuals have been gradually claiming a significant stake in the population demographics. The proposed autonomous device aims to provide a holistic solution by engineering a smart navigation system that relentlessly scans the environment, detects and classifies neighboring objects using a 4 layered Convolutional Neural Network (CNN) that has been trained on a data set containing 2513 permutations of various images of household objects that an individual may encounter in daily life. The CNN follows the 80-20 rule for testing and training the self-learning model enabling it to learn recursively from the error rate. The proposed system then calculates distances of neighboring objects from the user and provides adaptive solutions in real time to manoeuvre the user to safety by providing auditory input in a simplistic manner which considers 10-24 frames per second while drafting the kinematic response for the user. The device has achieved an unprecedented success rate of serving within a response time of less than 50 ms. The accuracy of the CNN algorithm being at 94.6%, also sets a distinguished benchmark as an object detection algorithm thereby contributing to the success in simulations of the proposed device in a constrained environment.

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A traceability set up using Digitalization of data and Accessibility

A traceability set up using Digitalization of Data and Acces...

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international conference on intelligent Sustainable Systems (ICISS)

作者： J. Aruna Jasmine V. Nisha Jenipher J. S. Richard Jimreeves K. Ravindran D. Dhinakaran Sri Sairam Engineering College St. Joseph's Institute of Technology Easwari Engineering College Velammal Institute of technology

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

The main intention of this development is to build a smart outcome for managing and maintaining data digitally using radiofrequency digitalization (RFID) tags and Quick Response code (QR) codes. The component traceability system either requires significant human effort and supervision or outsized amounts of subsidy and capital. This task provides better inventory supervision through enhanced clarity and organization of resources. The RFID-QR employed in this project is to afford admittance to the Records 24×7 that are collected via RFID tags-QR codes. Automated data compilation facilitates to improve the precision of conclusion building processes. RFID tool facilitates to evade vast disasters and upset origin by short of fact concerning the methodological state of rail coaches. The QR code supervision system mitigates some of the human efforts. This supervision system works via one application openly reachable to certified users. The Real-Time Dynamics monitoring System (RTDMS) solution is designed to manipulate manifold facilities or allow numerous users to scrutiny fact at a solitary competence. This method is employed in the cloud server and has password fortification, which averts unauthorized users from screening or revises facts.

关键词： Servers RFID tags Cloud computing Rails Industries Task analysis Organizations

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Desirability/Rarity-based Ranks in the Prediction of Acceptability as a Presurfacing Step for Products

Desirability/Rarity-based Ranks in the Prediction of Accepta...

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international conference on Computer and Information Technology (ICCIT)

作者： Shithi Maitra Atanu Das Bapon Data Science Analytics and Insights — DS(Ai) Sheba Platform Limited (Sheba.xyz) Dhaka Bangladesh Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9781665422444

ISBN: (纸本)9781665446686

Artificially intelligent systems simulate humanoid qualities in terms of problem-solving by learning, precluding the need for explicit programming. This mimicry of human behavior can be greatly useful in Business Intelligence owing to its potential of predicting how a user group may react to a particular product. This constitutes a Machine Learning task (T), which improves its predictive performance (P) with the increase in experience (E). This paper attempts to build a User Experience-centric supply rating system to estimate Customer Satisfaction on a scale of one to five by taking into account three factors: (a) the opinions of the users from (b) variegated perspectives of the listings and (c) tangible features thereof. The research starts out by hard, statistical computing in that it initially constructs a ranking system that adjusts weights of five different criteria based on rarity, only after assuring their distinctiveness by a statistical ANOVA. Inferential statistics is further applied to sort attributes affecting the ranks after which a labeled dataset is prepared and divided by clubbing certain sequential ranks. Finally a learning-based soft computing algorithm is applied to recognize the patterns and assign the ratings, with zero rankspecific priors. A Neural Network was employed which could approximate with above 97% accuracy on each of its k-fold cross-validations. Thus the efficacy of the qualitative desirability (also, scarcity)-based ranker was established by a case study on travelogue data, showing great potential to be extended over other businesses as well.

关键词： Ranking (statistics) Neural networks Machine learning Programming Pattern recognition Problem-solving Task analysis

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Arduino based smart and intelligent helmet system for two-wheelers

Arduino based smart and intelligent helmet system for two-wh...

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IEEE Distributed computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Mahesh S Gour Druva Kumar S Pradeep Kumara Manjunatha S Sunil Kumar K Chetan H CMR Institute of Technology Bengaluru India DSCE Bengaluru India VTU centre for PG studies Kalaburgi

ISBN: (数字)9781728198859

ISBN: (纸本)9781728198866

Road accidents are becoming very common in the country. The impact of road accidents can lead to the loss of many lives and can also damage many body parts. This situation becomes more serious if the riders won't wear the helmet which can be prevented by wearing the helmet and can reduce these impacts. While riding the bike, the government made it a mandatory rule to wear the helmet. Using this rule as a base, a smart helmet system is proposed which helps in providing safety to the riders and prevents accidents. The system mainly consists of Arduino Uno as a processor for processing the data, GSM & GPS modules for tracking the location and sending a message to authorized numbers, a wiper for wiping the raindrops on the helmet screen, a vibration sensor for alerting, in case the rider meets an accident and alcohol sensors as breath analyzer for the rider. The system will ensure a safe journey for riders and gives a helping hand in case of emergency. The cost of installing the whole system onto the helmet is affordable.

关键词： Safety Head Accidents Global Positioning System Vibrations GSM Radio frequency

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Design of intelligent Decision Support System With Ensembled Machine Learning to Predict Students' Performance

Design of Intelligent Decision Support System With Ensembled...

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Communication, Computer Sciences and Engineering (IC3SE), international conference on

作者： S Deepan Dineshkumar Rajendran Navdeep Dhaliwal K Praveena Melanie Lourens Anandhi R J School of CS & AI SR University Warangal Telangana India Computer Games Design and Programming School of Computing and Innovative Technologies British University Vietnam Vietnam Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa Department of Information Science Engineering New Horizon College of Engineering Bangalore

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and Educational data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes—Low Performance Student, Average Student, and smart Student—were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments.

关键词： Deep learning Decision support systems Analytical models data analysis Automation Education Companies

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