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A New Embedded Surveillance System for Reducing COVID-19 Outbreak in Elderly Based on Deep Learning and IoT

A New Embedded Surveillance System for Reducing COVID-19 Out...

2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Othman, Nashwan Adnan Al-Dabagh, Mustafa Zuhaer Nayef Aydin, Ilhan Knowledge University College of Science Department of Computer Science Erbil Kurdistan Region446015 Iraq Firat University Computer Engineering Department Elazig Turkey

ISBN: (纸本)9781728196756

As a result of the fast spread of Coronavirus disease (COVID-19) throughout the world, it became urgent to evolve an aided-intelligent system to help healthcare organizations to control and early detect COVID-19 outbreak, especially after massive development in the computing. Artificial Intelligence and Deep Learning methods could introduce real assistance to many healthcare organizations in this global problem by monitoring, detecting, and reporting on infected persons in early-stage. As is well known, the elderly are most vulnerable to the effects of COVID-19, therefore, we aim through this paper is to present the embedded system has the ability to detect and report on the elderly in the endemic areas. An age estimation from a facial image based on deep learning methods and Internet of Things is also applied to send notification to mobile or any other device systems that the application is embedded on it based on IoT. Depending on the experimental results on the proposed system through the Mean Average Error rate, the proposed system gave better or equivalent to the results of the state-of-the-art techniques, and the prediction results of an average accuracy achieved 89.45%. Finally, the proposed system has the capacity to help reduce the intensity of spread COVID-19 by identified older people and prevent them from prohibited in dangerous areas. © 2020 IEEE.

关键词： COVID-19

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Cloud based automated irrigation and plant leaf disease detection system using an android application

Cloud based automated irrigation and plant leaf disease dete...

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2017 International Conference on Electronics, Communication and Aerospace Technology, ICECA 2017

作者： Ranjith Anas, Saheer Badhusha, Ibrahim Ot, Zaheema Faseela, K. Shelly, Minnuja Department of Computer Science Engineering Eranad Knowledge City Technical Campus ManjeriKerala India

ISBN: (纸本)9781509056873;9781509056866

Due to drastic climatic changes and scarcity of water, the need for proper and sustainable irrigation methods is of high demand. The water demand for plants varies from place to place with the changes in soil content, texture, climatic factors and so on. The plants need to be irrigated according to their water requirements at that climatic conditions. As like as the water requirements, plant diseases are also a factor that keeps the plants not growing properly. Also, a variety of plant leaf diseases have also been detected nowadays. In this paper, a novel smart irrigation system has been proposed which can control the irrigation automatically using an android mobile application. Apart from this, the photos of plant leaves are captured and are sent to the cloud server, which is further processed and compared with the diseased plant leaf images in the cloud database. Based on the comparison a list of plant diseases suspected are given to the user via the android mobile application. © 2017 IEEE.

关键词： Embedded systems

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Processing noisy structured textual data using a fuzzy matching approach: Application to postal address errors

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Soft Computing 2000年第4期4卷 195-205页

作者： Buckley, J.P. Buckles, B.P. Petry, F.E. University of Dayton Computer Science Department 300 College Park Drive DaytonOH45469-2160 United States Department of Electrical Engineering and Computer Science Center for Intelligent and Knowledge-based Systems New OrleansLA70118 United States

A multiparadigm approach is developed and demonstrated for exploiting knowledge about structure for the purpose of extracting information from noisy textual data. A motivating example of a potential application would be an address encoding system for a delivery service such as UPS, Federal Express or the United States Post Office. This approach combines aspects of database organization and clustering of records, fuzzy parsing, fuzzy retrieval, an aggregation algebra, and measures of both performance and accuracy. Fuzzy retrieval, in the form of set and fuzzy operators, is accomplished by considering each symbol of the input text to be imperfect and retrieving non-exact matching records from the database that hold for a particular threshold value. The set of low-level database operators constrains the cardinality and accuracy of retrievals. A hierarchical method of clustering the database is defined, whereby the records are partitioned in a manner such that similar records are in the same cluster. This clustering strategy is guaranteed to be mutually exclusive and a complete cover of the data records. Associated with these clusters is an algebra that combines clusters of data into one window of ranked data. A set of fuzzy measures is defined that are used to aggregate and rank sets of records. © Springer-Verlag 2000.

关键词： Algebra

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Exploring Deep Insights from Vast Data: An Overview of Deep Learning Techniques for Big Data 4

Exploring Deep Insights from Vast Data: An Overview of Deep ...

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4th International Conference on Electrical engineering and computer science, ICECOS 2024

作者： Baez Hasan, Sarhad Kareem, Shahab Wahhab Ameen, Didar Dlshad Hamad Soran University Faculty of Science Computer Science Department Soran Iraq Erbil Polytechnic University College of Engineering Knowledge University Department of Information System Engineering Department of Computer Engineering Erbil Iraq

ISBN: (纸本)9798350368253

Big data has emerged very fast, and this has brought both opportunities and problems that are related to the application of deep learning. This paper explores how deep learning can be implemented using big data and in particular the Apache Mahout library. Unlike most other machine learning tools, Mahout can perform distributed computation, especially while using the MapReduce frameworks including Apache Hadoop and Apache Spark. However, this study does not only focus on the explanation of the need for Mahout's integration and employment but also carries out a high-fidelity argument on how deep learning algorithms can be incorporated in the Mahout framework to optimize the manipulation of large and complicated datasets. Mahout-based contributions include the comparative analysis of key deep learning paradigms including distributed processing, parallel processing and transfer learning. This paper also includes examples of the Mahout deep learning integration use and its application in real contexts. This article describes the challenges that currently organizations face in handling big data and indeed, the potential threats these challenges pose to the success of deep learning techniques when used in organizations' big data processing and analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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Size-fixed group discovery via multi-constrained graph pattern matching

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Information sciences 2025年 690卷

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education 230601 Hefei China School of Computer Science and Information Engineering Hefei University of Technology 230601 Hefei China Department of Computing Macquarie University Sydney 2122 Australia

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

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Cryptosystem using Facial Landmark for Authentication Pairing and Key Generation in Bluetooth Security 2

Cryptosystem using Facial Landmark for Authentication Pairin...

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2nd Global Conference for Advancement in Technology, GCAT 2021

作者： Mondal, Asif Ikbal Mandal, Bijoy Kumar Choudhury, Amitava Dumkal Institute of Engineering and Technology Department of Computer Science and Engineering India Nshm Knowledge Campus Department of Computer Science and Engineering West Bengal Durgapur India University of Petroleum and Energy Studies School of Computer Science Dehrdaun India

ISBN: (纸本)9780738132150

In this paper, the whole system starts with a real time picture of the sender and the receiver. The pairing is done utilizing the image of the sender and the receiver at the place of the pin used for pairing. The key generation for the encryption also utilizes the facial characteristics of the parties involved. This method will always generate a key of variable length. As the same person taking the snap of his/her face will change the coordinates every time making it is hard to predict the exact key. The text converted into ciphertext is also padded from both sides with variable bits to make it hard to identify the starting point of the ciphertext to start the actual decryption. The overall aim of this research is to develop a cryptosystem that can be used for achieving good encryption even if the length of the plain text is very small(one or two words). It is an encryption system with associated authentication. An electronic device cannot distinguish between its real users and fake one just by looking at the number which is used for the authentication process but if trained to identify the picture of the used the device up to a certain point is secure. Next thing if someone has requested a pairing merely by looking at the numbers one cannot predict that a legal connection is made but by looking at the real-time image of the person the same can be done with satisfaction. Previously used encryption used key and ciphertext of fixed length but here, a variable ciphertext and key are created in every session of transmission as the image of every sender and the receiver is different. © 2021 IEEE.

关键词： Cryptography

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Hybrid Threshold Speech Enhancement Scheme Using TEO and Wavelet Coefficients 2

Hybrid Threshold Speech Enhancement Scheme Using TEO and Wav...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Latha, R. Suhas, A.R. Pradeep Kumar, B.P. Ibrahim, M.Mohammed Sathiyapriya, V. HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Sona College of Technology Department of Computer Science and Engineering Salem India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350397635

Speech Enhancement (SE) aims to improve the quality of degraded speech while maintaining its intelligibility. The Wavelet Transform (WT) has become a powerful tool of signal analysis thereby widely used in signal detection and signal denoising. In this paper, we propose an effective means of SE by a hybrid threshold scheme using WT. The proposed methodology looks into both falling the noise and preserving edges of the speech signal unlike the conventional Hybrid Threshold (HT) and Soft Threshold (ST) in the wavelet domain. The threshold value in the wavelet domain is maintained constant for all sub-bands of the signal which reduces denoising efficiency. A novel speech augmentation technique built on the wavelet onsets and time adaption of introduced by calculating wavelet coefficients of the Teager Energy. Performance analysis of speech enhancement techniques using Wavelet coefficients and Teager Energy Operator (TEO) with hybrid threshold method is done. The experiment is carried out for speech data with various values of SNR vacillating from -10 to +10 db with Additive White Gaussian Noise (AWGN). © 2023 IEEE.

关键词： Speech enhancement

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Recurring comparison faults: Sorting and finding the minimum 20th

Recurring comparison faults: Sorting and finding the minimum

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20th International Symposium on Fundamentals of Computation Theory, FCT 2015

作者： Geissmann, Barbara Mihalák, Matúš Widmayer, Peter Department of Computer Science ETH Zurich Zurich Switzerland Department of Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9783319221762

In a faulty environment, comparisons between two elements with respect to an underlying linear order can come out right or go wrong. A wrong comparison is a recurring comparison fault if comparing the same two elements yields the very same result each time we compare the elements. We examine the impact of such faults on the elementary problems of sorting a set of distinct elements and finding a minimum element in such a set. The more faults occur, the worse the approaches to solve these problems can become and we parametrize our analysis by an upper bound k on the number of faults. We first explain that reconstructing the sorted order of the elements is impossible in the presence of even one fault. Then, we focus on the maximum information content we get by performing all possible comparisons. We consider two natural approaches for sorting the elements that involve knowledge of the outcomes of all comparisons: the first approach finds a permutation (compatible solution) that contradicts at most k times the outcomes of comparisons, and the second approach sorts the elements by the number of times an element is returned to be larger in the outcomes of its comparisons with all other elements (score solution). In such permutations the elements can be dislocated from their positions in the linear order. We measure the quality of such permutations by three measures: the maximum dislocation of an element, the sum of dislocations of all elements, and the Kemeny distance compared to the linear order. We show for compatible solutions that the Kemeny distance is at most 2k, the sum of dislocations at most 4k, and the maximum dislocation at most 2k. In score solutions the Kemeny distance is smaller than 4k, the sum of dislocations smaller than 8k, and the maximum dislocation at most k +1. Our upper bounds are tight for compatible solutions, but possibly not tight for score solutions. It turns out that none of the two approaches is better than the other in all measures. For the

关键词： Problem solving

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ML Based Intrusion Detection Scheme for various types of attacks in a WSN using C4.5 and CART classifiers

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Materials Today: Proceedings 2023年 80卷 3769-3776页

作者： Gite, Pratik Chouhan, Kuldeep Murali Krishna, K. Kumar Nayak, Chinmaya Soni, Mukesh Shrivastava, Amit Department of Computer Science and Engineering IPSA-IES Indore AB Road MP Indore India Department of Computer Science and Engineering I.T.S Engineering College Greater Noida India Department of Electronics and Communication Engineering Vignan's Institute of Information Technology Duvvada Andhra Pradesh Visakhapatnam India Faculty of Emerging Technology Sri Sri University Odisha751001 India Department of Computer Science & Engineering Jagran Lakecity University Bhopal India Department of Computer Science Engineering Swami Vivekanand College of Engineering Khandwa Road Near Tolnaka Vivekanand Knowledge City Indore India

A key component for preserving a WSN's integrity is designing an intrusion detection system (IDS). This article covers several sorts of security threats that may occur in a WSN. It proposes a detection of malicious nodes in a WSN. This system identifies four common assaults (i.e., black hole, wormhole, gray hole, and DDoS attacks) and is based on a Base Station machine learning (ML) algorithm (BS). The suggested ML algorithm continually analyzes data patterns from each node. Based on this technique, BS recognizes the network's harmful behavior and provides notifications to neighbor nodes to prevent attacker. First, the various assaults are examined and their characteristics are derived in terms of network parameters. Furthermore, the gathered pattern trains an ML algorithm. Then, the attacker node is effectively and properly classified within the BS. The NS2 simulator simulated the needed secure WSN. The experimental findings revealed excellent accuracy of the suggested method for attack detection. This precision can enhance network performance in terms of energy usage and packet delivery (PDR). © 2021

关键词： Base stations

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Common sub-space transfer for reinforcement learning tasks

Common sub-space transfer for reinforcement learning tasks

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23rd Benelux Conference on Artificial Intelligence, BNAIC 2011

作者： Ammar, Haitham Bou Taylor, Mathew E. Tuyls, Karl Weiss, Gerhard Department of Knowledge of Engineering Maastricht University Netherlands Department of Computer Science Lafayette College PA United States

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