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Sensing on The Edge: Smartening up Sensors

Sensing on The Edge: Smartening up Sensors

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international conference on Fog and Mobile Edge computing (FMEC)

作者： Eiman Kanjo Nottingham Trent University UK

Between the sensors gathering data and the cloud computing services processing data, there is an array of new technologies emerging. These technologies allow for complex processing and storage, closer to where data is collected – right at the edge. While Cloud computing has received tremendous attention from both academia and industry for connecting many devices to the internet, the ever-growing number of IoT and mobile devices has created the demand for lower network latency and processing capability closer to the users. Edge computing provides an opportunity for wearable devices to access more resources without violating the constraints on weight, size, and sensing capabilities. It involves placing computing resources closer to where data originates (i.e. heart rate monitor, pumps, step counter, or other sensors) – or at the “edge.” Furthermore, edge computing provides many required on-device processing capabilities, which can then help in protecting users' private data as raw personal data (such as images and videos) don't need to be shared remotely. These computing resources may be located in the devices themselves or in hyper-local, small-scale data centres. This talk will look at the potential of edge computing for multimodal sensing while protecting users' privacy and will showcase several examples from recent work at the Smart Sensing Lab in this area.

关键词： Sensors Sensor arrays Wearable computers Lead Industries Computer science Cloud computing

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A Comparative Study of Artificial Intelligence based Vehicle Classification Algorithms used to Provide Smart Mobility

A Comparative Study of Artificial Intelligence based Vehicle...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Prarthana V Sushma Narayan Hegde Sushmitha T P Savithramma R M R Sumathi Computer Science & Engineering Siddaganga Institute of Technology Tumakuru India

Due to the rising number of vehicles on the road and the limited resources supplied by current infrastructures, traffic problems are becoming more prevalent. Signalized junctions are the prime locations of congestions where commuters need to wait for long time in front of the signals to get their turn to move. This leads to several issues including wastage of time, additional fuel consumption, and green gas emissions. Optimization of traffic signals based on traffic behavior is widely explored topic in which vehicle detection and classification is one of the leading areas of research of Intelligent Transportation System (ITS). Among the technologies Artificial Intelligence (AI) has emerged as a giant in which vehicle classification has developed as a prominent subject of study because of its usefulness in several applications such as traffic control and surveillance, security systems, traffic congestion, avoidance, and accident prevention. Numerous algorithms and techniques for classifying vehicles have been proposed and implemented so far globally which mimics human intelligence. The goal of the paper is to familiarize the reader with the existing AI-based vehicle classification algorithms and to give a comparison of various vehicle detection and classification methods. The existing vehicle classification algorithms are summarized under two categories based on input type i.e., image or video. When the technologies such as AI, image processing, data mining and sensors are combined, the ITS can observe the road, initiate autonomous vehicle detection and thereby control traffic on road efficiently.

关键词： Support vector machines Roads Image processing Surveillance Urban areas Transportation Traffic control

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Fast data Transmission in Hop-Count Aware WBAN

Fast Data Transmission in Hop-Count Aware WBAN

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Rahul Saxena Anu Sharma Department of Engineering & Technology Jaipur National University Jaipur India Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9781665493970

Long-term physiological monitoring through the wireless body area network (WBAN) has the potential to address issues including unequal access to healthcare and an ageing population. Wireless body-area networks may have disruptions in data transfer because to the complexity of human media. The approach uses a pixel by pixel to keep track of the previous states of the connections among nodes. Following this, a prediction function is used to evaluate the robustness of the relationship. Additionally, distinct health data is given varying broadcast priorities in light of the body area network's data heterogeneity. The node with the least projected transmission cost is chosen as the next hop to increase data transfer dependability, and this cost is estimated by combining residual energy, connection reliability, and length. The experimental results shown that this approach incorporates the features of both opportunistic routing and WBAN to efficiently and effectively communicate health information with significantly reduced latency.

关键词： Wireless communication Costs Sociology Medical services Body area networks data transfer Routing

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Web Usage Mining Cluster Simulation Using K-Means and Fuzzy Approach 3rd

Web Usage Mining Cluster Simulation Using K-Means and Fuzzy ...

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3rd international conference on Machine Learning, advances in computing, Renewable Energy and Communication, MARC 2021

作者： Gangadwala, Hardik A. Gulati, Ravi M. Shri. Shambhubhai V. Patel College of Computer Science & Business Management Veer Narmad South Gujarat University Surat India Department of Computer Science Veer Narmad South Gujarat University Surat India

ISBN: (纸本)9789811928277

The Internet continues to develop at an outstanding rate in both the scale and complexity of Web sites and is properly on its way to being the principal source of information and data. Due to this growth in scale and complexity of Internet, Internet site publishers are having so much trouble in appealing and associating Web users. To layout popular and appealing Web sites, publishers need to apprehend their customers’ wishes. Consequently, inspecting users’ behavior is a crucial part of Web site layout. Web usage mining (WUM) is the utility of mining methods to proxy server log repositories so as to determine the utilization patterns which need to investigate the Web user’s surfing behavior. Clustering method is grouping a set of data points into several clusters. Clustering keeps related data points within the same cluster and keeps unrelated data points into distinct clusters. Paper presents comparative study of K-Means and Fuzzy C-Means methods. These methods offer adequate and precise investigation of proxy server log. The least distance between clusters is calculated with the aid of using the Euclidean distance equation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Websites

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An assessment of water pollutions area in Terengganu River, Malaysia using unsupervised machine learning 4th

An assessment of water pollutions area in Terengganu River, ...

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4th international conference on Innovative data Communication technologies and Application, ICIDCA 2022

作者： Zamri, Nurnadiah Pairan, Mohammad Ammar Azman, Wan Nur Amira Wan Abdullah, Lazim Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut22200 Malaysia Management Science Research Group Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia

River, which supply 90% of the readily accessible water, are key elements of universal water source system. Terengganu River situated in Terengganu, Malaysia is a modern busy city known for tourism, fishing, and industry. Due to that, it has increased risk of water pollution exposure. Therefore, this paper proposes unsupervised ML include Autoencoder and Self-Organizing Map (SOM) for clustering water pollution area along the Terengganu River. Then, uses Silhouette analysis to assess the total of optimum clusters in a dataset. Next, applies Adjusted Rank Index (ARI) to discover the finest comparing within original data with Autoencoder and SOM. Lastly, applies Elbow method to double verify the most excellent clusters for each clustering algorithm. Lastly, lists of polluted area in each cluster are retrieved from 14 main sampling stations with 24 water quality parameters, including 405 water samples. Result shows different cluster with different water samples. Thus, offer different strategies to manage polluted area for Terengganu River. © 2023 The Authors. Published by Elsevier B.V.

关键词： Rivers

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Critically Analyzing the Nature of Deep Learning, The Way, It is Adopted in Organizational Developments

Critically Analyzing the Nature of Deep Learning, The Way, I...

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Advance computing and Innovative technologies in Engineering (ICACITE), international conference on

作者： Melanie Lourens Somanchi Hari Krishna R. B. Dhumale Sonakshi Bhatia Kapil Joshi Vikas Tripathi Department of Human Resources Management Durban University of Technology Berea Durban South Africa Department of Business Management Vignana Bharathi Institute of Technology Aushapur Village Ghatkesar Mandal Malkangiri Medchal Dist. Hyderabad Telangana India AISSMS Institute of Information Technology Pune Maharashtra India Department of Management Uttaranchal University Dehradun Uttarakhand Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The ultimate focus of this study is to assess deep teaching approach and how its benefits business outcomes with a focus on the projects' economic value as deep learning-based transformations. The actions required to finish this research were as follows: Following are the aspects of reinforcement learning: Learning based: What exactly is it? What happens when it works properly? How methods have been employed? What are the drawbacks and issues? What impact may machine learning has upon your organization's performance? vii) Deep learning instances; and (vi) paying close attention neural network. In order to understand how DL business value affects organizational performance, our study has made use of the concept of information technology capabilities. It encompasses a broad spectrum of technological advancements, including algorithms that evolve on their own and chat bots and language processing. The idea of the operation of replying to or agitating their world while contemporaneously increasing and enhancing their strategic and commercial advantages has been the driving force behind the deployment of ai and machine learning technological breakthroughs by enterprises. Due to its ability to detect, anticipate, and interact with people, DL surpasses the adversary when it comes towards improving the effectiveness of current processes and boosting the impact of automation, data, and revolutionary advances.

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Analysis and modeling of contextual activity pattern for identifying lifestyle disorders 12

Analysis and modeling of contextual activity pattern for ide...

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12th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2021

作者： Sakhare, Apeksha V. Sharma, Ashish K. Computer Science and Engineering G H Raisoni University MH Amravati India Computer Science and Engineering G H Raisoni College of Engineering MH Nagpur India School of Engineering and Technology G H Raisoni University MH Amravati India

Nowadays with the help of various government health records and surveys, it has been found that the root cause of the various severe diseases (non-communicable) very frequently occur in the early age of humans and adolescents has not even aware of any symptoms or signs. This type of disease has a root cause of human irregular and very unhealthy, unorganized lifestyle and diet pattern. From the data analysis done on health information collected by various government organizations, it has been found that 309889 adolescents between ages 12-18 (53.8 female genders) from approx. 70 countries have been suffering from this non-communicable disease. Medical departments or health care organizations are producing a very wide amount of health-based data every day which be used for identifying or recognizing or predicting this disease which has no early signs or symptoms during some of its growing stages. This hidden data of healthcare organizations will be useful for taking decisions effectively for affected person treatment and recovery procedure. Here the main challenge is how to extract the hidden data from the collected health records, there some sort of data mining and machine learning technique is required. The expected outcome of our research work is that if the disease is detected and identified at its early stage it can be cured very effectively and easily with more accuracy and thus reducing the risk factor of life of a patient and the cost of treatment required to cure the disease. Early recognition of disease will also increase the percentage rate of cure from a disease of a patient and also decreases the risk of loss of life of a patient. This research paper provides a brief introduction of lifestyle Health problems and its cause. Our proposed research work includes a review of various techniques used by different research scholars, a working block diagram with a brief description of the diagram, and at the end, we have concluded with future work and the scope of

关键词： Diseases

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Head Movement Patterns during Face-to-Face Conversations Vary with Age 24

Head Movement Patterns during Face-to-Face Conversations Var...

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24th ACM international conference on Multimodal Interaction, ICMI 2022

作者： McDonald, Denisa Qori Zampella, Casey J. Sariyanidi, Evangelos Manakiwala, Aashvi Dejardin, Ellis Herrington, John D. Schultz, Robert T. Tunc, Birkan Center for Autism Research Children's Hospital of Philadelphia United States Department of Computer and Information Science University of Pennsylvania United States Department of Psychiatry University of Pennsylvania United States Department of Pediatrics University of Pennsylvania United States

ISBN: (纸本)9781450393898

advances in computational behavior analysis have the potential to increase our understanding of behavioral patterns and developmental trajectories in neurotypical individuals, as well as in individuals with mental health conditions marked by motor, social, and emotional difficulties. This study focuses on investigating how head movement patterns during face-to-face conversations vary with age from childhood through adulthood. We rely on computer vision techniques due to their suitability for analysis of social behaviors in naturalistic settings, since video data capture can be unobtrusively embedded within conversations between two social partners. The methods in this work include unsupervised learning for movement pattern clustering, and supervised classification and regression as a function of age. The results demonstrate that 3-minute video recordings of head movements during conversations show patterns that distinguish between participants that are younger vs. older than 12 years with accuracy. Additionally, we extract relevant patterns of head movement upon which the age distinction was determined by our models. © 2022 ACM.

关键词： Video recording

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Multi-Cloud based Task Scheduling using Many Objective Intelligent Technique in IoT

Multi-Cloud based Task Scheduling using Many Objective Intel...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Isha Srivastava Hina Hashmi Department of Engineering & Technology Jaipur National University Jaipur India Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9781665493970

The Internet of Things is a vast network that connects various smart devices and things all over the place. The IoT's expansion causes unprecedented data growth, standard methods of information processing or storage are inefficient, and there is a higher danger of security breaches if the data is used maliciously. A platform for high-performance, secure computing is multi-cloud. For the Internet of Things, this work develops a multi-cloud task scheduling model with six goals: time complexity, price, internet traffic, energy usage, resource utilization, and load balancing. The sine function is used to create the model in this study's multi-objective intelligent algorithm, which considers that the population's tendency for variation in diversity strategy is similar to the sine function. The trial findings show exceptional scheduling effectiveness, increasing security. This paper offers a fresh approach to the challenging issue of data processing in IoT.

关键词： Cloud computing Processor scheduling Computational modeling Load management Security Internet of Things Task analysis

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Research on Cross-Chain data Integration Technology Based on Trust computing 2

Research on Cross-Chain Data Integration Technology Based on...

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2nd international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2022

作者： Liu, Yitong Zhai, Jianhong Yang, Ru Li, Jun Sun, Yan Yang, Zixuan China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China Harbin Institute of Technology Harbin150006 China Heilongjiang Institute of Technology Harbin150006 China

ISBN: (纸本)9781643683522

With the rapid development of blockchain technology, a large number of blockchain projects have been designed to meet the needs of different scenarios. However, they are developed by different teams based on different application scenarios and design concepts and using different technical architectures. Each project forms an isolated blockchain P2P network, just like isolated information islands. Cross chain technology is an important technical means to realize the interconnection between chains. At present, the main cross chain mechanisms include Hash TimeLock Contract(HTLC), sidechains, notary mechanisms and communication protocol suite. However, they all have their own disadvantages, such as vulnerability and centralized risk, and only focus on the cross chain transfer of value. This paper proposes a data integration mechanism based on the trust of blockchain nodes: firstly, the blockchain network initiating the integration randomly selects several nodes as the evaluation committee to join the blockchain network providing data sources, and then the evaluation committee evaluates the credibility of the chain according to the model. Finally, if the credibility is higher than the threshold, the integration is initiated. In this paper, the above scheme is applied to the domain name cross organization access, and the experimental test proves the feasibility, efficiency and accuracy of the scheme. © 2022 The authors and IOS Press.

关键词： Blockchain

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