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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Abin George Sham H. Mankar N V S Suryanarayana Manish Gupta R Sathish A Priya Knowledge and technology management Division Council of Scientific & Industrial Research National Aerospace Laboratories Bangaluru Karnataka India Department of Mechanical Engineering Pimpri Chinchwad College of Engineering and Research Pune Maharashtra India Administrative Officer Central Tribal University of Andhra Pradesh Vizianagaram Andhra Pradesh India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Computer Science Shri Nehru Maha Vidayala College of Arts and Science Coimbatore Tamil Nadu India School of Architecture Koneru Lakshmaiah Educational Foundation Gundur Andhra Pradesh India

The solid waste management is the process of proper decomposition of waste materials within a period of time. This includes the collection of garbage's and then proceeded through certain measures for decomposition. There are various methods adopted in the garbage separation process. This includes the artificial intelligence techniques for the estimation and determination of the solid waste through automatic detection and separation of the garbage waste using control and sensing units. They are integrated with the internet of things to enable the two way communication system. This helps to visualize the functioning of the system adopting the digital platform. The proposed system is implemented through the smart dust bin held in every household that automatically senses the non-biodegradable and biodegradable waste materials. The classification of the waste materials are identified through the image processing techniques.

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Uncertainty-based Model Acceleration for Cancer Classification in Whole-Slide Images

Uncertainty-based Model Acceleration for Cancer Classificati...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zeyu Gao Anyu Mao Jialun Wu Yang Li Chunbao Wang Caixia Ding Tieliang Gong Chen Li School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China Department of Pathology the First Affiliated Hospital of Xi’an Jiaotong University Xi’an China Department of Pathology Shaanxi Provincial Cancer Hospital Xi’an China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Computational Pathology (CPATH) offers the possibility for highly accurate and low-cost automated pathological diagnosis. However, the high time cost of model inference is one of the main issues limiting the application of CPATH methods. Due to the large size of Whole-Slide Image (WSI), commonly used CPATH methods divided a WSI into a large number of image patches at relatively high magnification, then predicted each image patch individually, which is time-consuming. In this paper, we propose a novel Uncertainty-based Model Acceleration (UMA) method for reducing the time cost of model inference, thereby relieving the deployment burden of CPATH applications. Enlightened by the slide-viewing process of pathologists, only a few high-uncertain regions are regarded as “suspicious” regions that need to be predicted at high magnification, and most of the regions in WSI are predicted at low magnification, thereby reducing the times of image patch extraction and prediction. Meanwhile, uncertainty estimation ensures prediction accuracy at low magnification. We take two fundamental CPATH classification tasks (i.e., cancer region detection and subtyping) as examples. Extensive experiments on two large-scale renal cell carcinoma classification datasets demonstrate that our UMA can significantly reduce the time cost of model inference while maintaining competitive classification performance.

关键词： Pathology Costs Uncertainty Limiting Hospitals Biological system modeling Computational modeling

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Various Deep Learning Methods for Hyperspectral Images

Various Deep Learning Methods for Hyperspectral Images

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2020 International Conference on Computing and Information Technology, ICCIT 2020

作者： Sivaram, M. Shobana, S. Jeya Khan, Mujeebudheen Ramakrishnan, Jayabrabu Goel, Pallavi Murghai Maseleno, Andino Lebanese French University Department of Computer Networking Erbil Kurdistan Region Iraq College of Science Knowledge University Department of Computer Science Erbil Kurdistan Region Iraq Ibra College of Technology Department of Electrical and Electronics Oman Jazan University College of Computer Science and Information Technology Department of Information Technology and Security Saudi Arabia Galgotias University School of Computing Science and Engineering Greater Noida India Universiti Tenaga Nasional Institute of Informatics and Computing Energy Department of Business Administration and Information System Malaysia

ISBN: (纸本)9781728126807

Hyperspectral imagery is widely used in remote sensing applications that take into account thousands of spectral channel compositions over a single scene. Hyperspectral imagery requires accurate models of learning to extract the hyperspectral features in an image. Due to the presence of its spatial and spectral resolution, the image learning model presents a core challenge due to its complicated nature of image frames. In order to assist it during the learning process, several attempts have been made to address its complicated nature. However, these methods failed to provide the hyperspectral imagery with a deeper understanding. Because of the presence of mixed pixels, limited training samples and redundant data, the utilization of deep learning techniques addresses the problems. The deep learning process addresses the complex image data relationship. In this paper, various deep learning methods are studied which are used for the learning of hyperspectral imagery. Initially, we present an overview on various deep learning methods for various image processing techniques. A system review is then carried out on various hyperspectral image learning models based on deep learning. © 2020 IEEE.

关键词： Remote sensing

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Time-resolved Hanbury Brown-Twiss interferometry of on-chip biphoton frequency combs using Vernier phase modulation

arXiv

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arXiv 2022年

作者： Myilswamy, Karthik V. Seshadri, Suparna Lu, Hsuan-Hao Alshaykh, Mohammed S. Liu, Junqiu Kippenberg, Tobias J. Weiner, Andrew M. Lukens, Joseph M. School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Electrical Engineering Department King Saud University Riyadh11421 Saudi Arabia Lausanne1015 Switzerland Knowledge Enterprise Arizona State University TempeAZ85287 United States

Biphoton frequency combs (BFCs) are promising quantum sources for large-scale and high-dimensional quantum information and networking systems. In this context, the spectral purity of individual frequency bins will be critical for realizing quantum networking protocols like teleportation and entanglement swapping. Measurement of the temporal auto-correlation function of the unheralded signal or idler photons comprising the BFC is a key tool for characterizing their spectral purity and in turn verifying the utility of the biphoton state for networking protocols. Yet the experimentally obtainable precision for measuring BFC correlation functions is often severely limited by detector jitter. The fine temporal features in the correlation function—not only of practical value in quantum information, but also of fundamental interest in the study of quantum optics—are lost as a result and have remained unexplored. We propose a scheme to circumvent this challenge through electro-optic phase modulation, experimentally demonstrating time-resolved Hanbury Brown–Twiss characterization of BFCs generated from an integrated 40.5 GHz Si3N4 microring, up to a 3 × 3-dimensional two-qutrit Hilbert space. Through slight detuning of the electro-optic drive frequency from the comb’s free spectral range, our approach leverages Vernier principles to magnify temporal features which would otherwise be averaged out by detector jitter. We demonstrate our approach under both continuous-wave and pulsed pumping regimes, finding excellent agreement with theory. Importantly, our method reveals not only the collective statistics of the contributing frequency bins but also their temporal shapes—features lost in standard fully integrated auto-correlation measurements. Copyright © 2022, The Authors. All rights reserved.

关键词： Phase modulation

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An Efficient Embedded Security System for Reduce Car Accident to Build Safer World Based on IoT

An Efficient Embedded Security System for Reduce Car Acciden...

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2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019

作者： Othman, Nashwan Adnan Aydin, Ilhan Karakose, Mehmet Computer Science Department College of Science Knowledge University Erbil Kurdistan Region Iraq Computer Engineering Department Firat University Elazig Turkey

ISBN: (纸本)9781728129327

Nowadays, people need to work hard to afford their life and this results in people to live under the stress of the modern life style. This pressure of the modern life style might cause problems and accidents, for example, when a driver feels drowsy while driving his car, there is a great possibility of making an accident. For the sake of solving or reducing the amount of such accidents, we designed an embedded system. This system presents an enhanced technique for detecting drowsiness in real-Time and reducing car accident. In this paper, we proposed an eye blinking technique for real-Time video by using machine learning and computer vision techniques. When a driver often feels drowsy, instead of stopping the car for a while to rest and gain back his/her conscious, he/she continues driving, so we utilize Internet of Things (IoT), which is a system connected to internet. When a driver feels sleepy, directly an alert message will be sent to one of his nearest kin or friends. Then the alerted person can call the driver and oblige him/her to stop the car. Meanwhile, there is a speaker inside the car to warn the driver. To implement this system, a jetson TX2 embedded platform is used and it uses its own camera board for taking frames. © 2019 IEEE.

关键词： Internet of things

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Improved Conservation of Energy in Fog IOT Services Using Machine Learning Model

Improved Conservation of Energy in Fog IOT Services Using Ma...

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2020 International Conference on Computing and Information Technology, ICCIT 2020

作者： Prakash, M. Viju Porkodi, V. Rajanarayanan, S. Khan, Mujeebudheen Ibrahim, Banar Fareed Sivaram, M. College of Science Knowledge University Department of Computer Science Erbil Kurdistan Region Iraq Lebanese French University Department of Information Technology Erbil Kurdistan Region Iraq Arba Minch University Department of Business Administration and Information System Ethiopia Ibra College of Technology Department of Electrical and Electronics Oman Cyprus International University Computer Engineering Department Cyprus Lebanese French University Department of Computer Networking Erbil Kurdistan Region Iraq

ISBN: (纸本)9781728126807

This Fog Computing is an extension of Cloud Computing technology to the network edge, which enables a newer breed of services and applications. Resource sharing and resource discovery is considered to be critical for the performance of fog computing applications. In general, the methods to reduce are consumption of energy in heterogonous network is very minimal. Considering the higher consumption of energy in heterogeneous networks as a problem statement, this work proposes a machine learning model to reduce the consumption of energy demand in fog computing Internet of Things (IoT) services. Considering the problem, the machine learning model adopts network density, latency and mobility as its energy constraints and designs an objective function to support the lower energy consumption in the network. The simulation of the proposed method is carried out between the proposed and existing methods in terms of various performance metrics. The result shows that the proposed machine learning method in Fog IoT environment is efficient in conserving the energy than the other methods. © 2020 IEEE.

关键词： Fog computing

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Computationally Efficient Approximations for Matrix-based Rényi's Entropy

arXiv

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arXiv 2021年

作者： Gong, Tieliang Dong, Yuxin Yu, Shujian Dong, Bo The School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an710049 China The Machine Learning Group UiT - The Arctic University of Norway Department of Computer Science Vrije University Amsterdam Amsterdam Netherlands

The recently developed matrix-based Rényi's αorder entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi-definite (PSD) matrices in reproducing kernel Hilbert space, without estimation of the underlying data distribution. This intriguing property makes this new information measurement widely adopted in multiple statistical inference and learning tasks. However, the computation of such quantity involves the trace operator on a PSD matrix G to power α (i.e., tr(Gα)), with a normal complexity of nearly O(n3), which severely hampers its practical usage when the number of samples (i.e., n) is large. In this work, we present computationally efficient approximations to this new entropy functional that can reduce its complexity to even significantly less than O(n2). To this end, we leverage the recent progress on Randomized Numerical Linear Algebra, developing Taylor, Chebyshev and Lanczos approximations to tr(Gα) for arbitrary values of α by converting it into a matrix-vector multiplication problem. We also establish the connection between the matrix-based Rényi's entropy and PSD matrix approximation, which enables exploiting both clustering and block low-rank structure of G to further reduce the computational cost. We theoretically provide approximation accuracy guarantees and illustrate the properties for different approximations. Large-scale experimental evaluations on both synthetic and real-world data corroborate our theoretical findings, showing promising speedup with negligible loss in accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Matrix algebra

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ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction 28

ToHRE: A Top-Down Classification Strategy with Hierarchical ...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Yu, Erxin Han, Wenjuan Tian, Yuan Chang, Yi School of Artificial Intelligence Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China International Center of Future Science Jilin University China College of Biological and Agricultural Engineering Jilin University China Department of Computer Science National University of Singapore Singapore

ISBN: (纸本)9781952148279

Distantly Supervised Relation Extraction (DSRE) has proven to be effective to find relational facts from texts, but it still suffers from two main problems: the wrong labeling problem and the long-tail problem. Most of the existing approaches address these two problems through flat classification, which lacks hierarchical information of relations. To leverage the informative relation hierarchies, we formulate DSRE as a hierarchical classification task and propose a novel hierarchical classification framework, which extracts the relation in a top-down manner. Specifically, in our proposed framework, 1) we use a hierarchically-refined representation method to achieve hierarchy-specific representation;2) a top-down classification strategy is introduced instead of training a set of local classifiers. The experiments on NYT dataset demonstrate that our approach significantly outperforms other state-of-the-art approaches, especially for the long-tail problem. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Classification (of information)

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Correction: Toward rumor detection in social networks using multi‑layer autoencoder neural network

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Social Network Analysis and Mining 2024年第1期14卷 1-1页

作者： Qourbani, Amin Khodaparast, Mahdi Yahya, Rebaz Othman Habibi, Mohammadhossein Nouralishahi, Atieh Rezaeipanah, Amin Department of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran Department of Human Resources Studies and Evaluation Research Center of Knowledge-Based Businesses and Resource Management Studies Tehran Iran Department of Computer Science College of Science Cihan University-Erbil Erbil Iraq Faculty of Technical Engineering Malayer University Malayer Iran Sub‑Department of Operations and Analytics Department of Management Faculty of Environment Science and Economy University of Exeter Exeter UK Department of Computer Engineering Persian Gulf University Bushehr Iran

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Periodic boundary cellular automata based wear leveling for resistive memory

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2020年第2期47卷 310-321页

作者： Sarkar, Sutapa Ghosh, Manisha Sikdar, Biplab Kumar Saha, Mousumi Department of Electronics and Communication Engineering Seacom Engineering college Kolkata India Department of Electronics and Communication Engineering NSHM Knowledge Campus Durgapur India Department of Computer science and Technology Indian Institute of Engineering Science and Technology Shibpur India Department of Computer Science and Engineering National Institute of Technology Durgapur India

Locality of reference is preferable every time while writing to cache blocks. The write process may repeat only on a few adjacent cache blocks. As a result, it creates a stress on those blocks. If resistive memory is chosen to be the fundamental technology for the write purpose, the write sensitivity on those blocks increases more. It causes a loss to the durability of the memory. It can be damaged earlier in comparison to NAND/NOR Flash memories (105 to 106 program/erase cycles). This non-uniformity in writes as well as malicious attacks in CMPs cache can cause sudden breakdown of those systems. The wear out of memory blocks at their primary stages can be avoided by wear leveling through distribution of writes to different blocks. This work represents an efficient scheme of wear leveling applied specifically for resistive memories. The major part of this work is developed around one dimensional two state CA. It has the aim to achieve uniform writes throughout all memory blocks with spatial access pattern predictions. The adjacent write-overloaded blocks are considered as an area subjected to remapping. The periodic boundary cellular automata (PBCA) employed for the scheme performs density classification task (DCT) to choose the remapping zone of memory. Remapping is executed through changing the current address location of the identified memory blocks to the new location. Further, the proposed memory write management policy can be implemented together with the fault tolerant memory architecture to develop a memory subsystem with more durability and robustness. © 2020, International Association of Engineers.

关键词： Cellular automata

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