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2021 International Conference on Computational Intelligence and Computing Applications, ICCICA 2021

作者： Dixit, Umesh D. Shirdhonkar, M.S. B.L.D.E.A's V. P. Dr. P. G. Halakatti College of Engineering and Technology Department of Electronics and Communication Engineering Vijayapura586103 India B.L.D.E.A's V. P. Dr. P. G. Halakatti College of Engineering and Technology Department of Computer Science and Engineering Vijayapura586103 India

ISBN: (纸本)9781665420402

Document image retrieval is an interesting and popular area of research that has b.en evolved into many stages. The current trends show that it is still an emerging area due to the availability of document scanner app with mobiles and handheld devices. The ob.ective of this paper is to provide an insight into document retrieval, classification, a general architecture, related issues and new opportunities for research in the domain. © 2021 Ieee.

关键词： Image retrieval

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Pose, Velocity and Landmark Position estimation Using IMU and bearing Measurements

arXiv

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arXiv 2024年

作者： Wang, Miaomiao Tayebi, Ab.elhamid The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Department of Electrical Engineering Lakehead University Thunder BayONP7B 5E1 Canada The Department of Electrical and Computer Engineering Western University LondonONN6A 3K7 Canada

This paper investigates the estimation prob.em of the pose (orientation and position) and linear velocity of a rigid body, as well as the landmark positions, using an inertial measurement unit (IMU) and a monocular camera. First, we propose a globally exponentially stab.e (GeS) linear time-varying (LTV) ob.erver for the estimation of body-frame landmark positions and velocity, using IMU and monocular bearing measurements. Thereafter, using the gyro measurements, some landmarks known in the inertial frame and the estimates from the LTV ob.erver, we propose a nonlinear pose ob.erver on SO(3) × R3. The overall estimation system is shown to be almost globally asymptotically stab.e (AGAS) using the notion of almost global input-to-state stability (ISS). Interestingly, we show that with the knowledge (in the inertial frame) of a small number of landmarks, we can recover (under some conditions) the unknown positions (in the inertial frame) of a large number of landmarks. Numerical simulation results are presented to illustrate the performance of the proposed estimation scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： bearings (machine parts)

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Sustainab.e electric vehicles fault detection based on monitoring by deep Learning Architectures in feature extraction and classification

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SUSTAINAb.e eNeRGY TeCHNOLOGIeS AND ASSeSSMeNTS 2023年 57卷

作者： Wongchai, Anupong Aoudni, Yassine Yesubabu, Mannava Reegu, Faheem Ahmad Gowri, N. Vasantha Vijayakumar, P. [a]The Independent researcher and Social Worker Thailand [b]Department of Computers and Information Technology Faculty of Sciences and Arts Turaif Northern Border University Arar 91431 Saudi Arabia [c]Computer & Embedded Systems Laboratory National School of Engineers of Sfax (ENIS) University of Sfax BP 1173 3038 Sfax Tunisia [d]Department Of Computer Science & Engineering Vardhaman College Of Engineering Shamshabad Hyderabad 501218 India [e]College of Computer Science And Information Technology Jazan University Jazan 45142 Saudi Arabia [f]Department of EEE Chaitanya Bharathi Institute of Technology (A) Hyderabad India [g]School of Electronics Engineering Vellore Institute of Technology (VIT) Chennai Tamil Nadu India

Numerous industrial sector paradigms have b.en altered by the necessity to produce more competitive ma-chinery and the introduction of digital technologies from so-called Industry 4.0. This research proposes novel technique in electric vehicle fault detection based on monitoring data classification and feature extraction using deep learning architectures. The real Proton exchange membrane fuel cell (PeMFC) experiment dataset has b.en collected as sustainab.e electric vehicles data using multi-cell parallel electric vehicle and it has b.en processed for noise removal, dimensionality reduction and extraction using deep stacking auto gradient descent for-classifyingthrough monte Carlo regressive Gaussian naive bayes *** of experiments demonstrate that suggested model achieves over 99% accuracy in identifying flooding fault of fuel cell under load-varying situations. The experimental analysis has b.en carried out in terms of accuracy, robustness, reliability, preci-sion, recall. The proposed technique attained 99% of accuracy, 89% of robustness, 85% of Reliability, 95% of precision and 81% of recall.

关键词： electric vehicles Fault detection Monitoring Data classification Feature extraction Deep learning

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Umpire gesture detection and recognition using HOG and non-linear support vector machine (NL-SVM) classification of deep features in cricket videos 2

Umpire gesture detection and recognition using HOG and non-l...

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2nd International Conference on Advances in Physical sciences and Materials 2021, ICAPSM 2021

作者： Nandyal, Suvarna Kattimani, Suvarna Laxmikant Department of Computer Science and Engineering P. D. A. College of Engineering Affiliated to Visvesvaraya Technological University 590018 Kalaburgi Karnataka Belagavi585102 India Department of Computer Science and Engineering B.L.D.E.A's V.P. Dr. P. G. Halakatti College of Engineering and Technology Affiliated to Visvesvaraya Technological University Belagavi-590018 Karnataka Vijayapur586103 India

Gesture Recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human-computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation, monitoring patients or elder people, surveillance systems, sports gesture analysis, human behaviour analysis etc., to virtual reality. In recent years, there has b.en increased interest in video summarization and automatic sports highlights generation in the game of Cricket. In Cricket, the Umpire has the authority to make important decisions about events on the field. The Umpire signals important events using unique hand on signals and gestures. The primary intention of our work is to design and develop a new robust method for Umpire Action and Non-Action Gesture Identification and Recognition based on the Umpire Segmentation and the proposed Histogram Oriented Gradient (HOG) feature extraction oriented Non-Linear Support Vector Machine (NL-SVM) classification of Deep Features. Primarily the 80% of Umpire action and non-action images in a cricket match, about 1, 93, 000 frames, the Histogram of Oriented Gradient Deep Features are calculated and trained the system having six gestures of Umpire pose. The proposed HOG Feature extraction oriented Non-Linear Support Vector Machine classification method achieves the maximal accuracy of 97.95%, the maximal sensitivity of 98.87%, the maximal specificity of 98.89% and maximal Precision of 97.02% which indicates its superiority. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.

关键词： Feature extraction

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exploring Issues of Story-based effort estimation in Agile Software Development

SSRN

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SSRN 2023年

作者： Iqbal, Muhammad Ijaz, Muhammad Mazhar, Tehseen Shahzad, Tariq Abbas, Qamar Ghadi, Yazeed Yasin Ahmed, Waseem Hamam, Habib Institute of Computing and Information Technology Gomal University Dera Ismail Khan KP Pakistan Department of Computer Science Virtual University of Pakistan Lahore55150 Pakistan Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science FCIT International Islamic University Islamabad Pakistan Department of computer science and software engineering Al Ain University Abu Dhabi15322 United Arab Emirates Department of Computer Science and Information Technology University of Malakand Pakistan Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada Commune d'Akanda B.P. Libreville1989 Gabon School of Electrical Engineering Department of Electrical and Electronic Engineering. Science University of Johannesburg Johannesburg2006 South Africa Spectrum of Knowledge Production & Skills Development Sfax3027 Tunisia

Objctives This research delves into the domain of user story-based effort estimation in agile software development, aiming to explore the issues arising from inaccurate estimations. The primary goal is to uncover these issues comprehensively and propose potential solutions, thus enhancing the efficacy. Methods To achieve the research ob.ectives, a systematic literature review (SLR) is conducted, surveying a wide range of sources to gather insights into issues surrounding user story-based effort estimation. Results The SLR reveals a spectrum of issues undermining the accuracy of user story-based effort estimation. It identifies internal factors like communication, team expertise, and composition. © 2023, The Authors. All rights reserved.

关键词： Software design

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Segmentation of Arecanut bunches: A Comparative Study of Different Color Models 1

Segmentation of Arecanut Bunches: A Comparative Study of Dif...

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1st Ieee Mysore Sub Section International Conference, MysuruCon 2021

作者： Dhanesha, R. Umesha, D.K. Shrinivasa Naika, C.L. Girish, G.N. Davangere University Dept. of Studies in Computer Science Davanagere Karnataka India Government Women's Polytechnic Shiralakoppa Dept. of CS&E Shivamogga Karnataka India U. B. D. T. College of Engineering Dept. of Studies in CS&E Davanagere Karnataka India Indian Institute of Information Technology Computer Science and Engineering Group Sri City Chittoor India

ISBN: (纸本)9780738146621

Arecanut is one of the important commercial crop of Agriculture sector. Agriculture sector plays important role towards the economic development of India. The market price of arecanut is determined by it's maturity level of the ripeness. Farmers often incur loss in profit due to the lack of expertise in judging the ripeness maturity level of the arecanut bunches before the harvest. In the recent years, image processing and computer vision based precision agriculture techniques has helped the farmers in identifying the ripeness quality of the crops. So, accurate segmentation of the arecanut bunches plays vital role in the automated identification of the arecanut ripeness maturity level. In this proposed work YUV, YCbCr, YCgCr, YPbPr and HSV color models are used to segment arecanut bunches. Dataset with 1017 images of arecanut bunch are used to conduct experiment and segmentation result of the each color model is evaluated using different segmentation performance metrics. Results of experiment clarifies, segmentation of arecanut bunches were efficient using YCgCr and HSV color models. © 2021 Ieee.

关键词： computer vision

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Safe and Quasi-Optimal Autonomous Navigation in Sphere Worlds

arXiv

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arXiv 2023年

作者： Cheniouni, Ishak Tayebi, Ab.elhamid berkane, Soulaimane The Department of Electrical Engineering Lake-head University Thunder BayONP7B 5E1 Canada The Department of Electrical and Computer Engineering Western University LondonONN6A 3K7 Canada The Department Computer Science and Engineering University of Quebec QCJ8X 3X7 Canada

We propose a continuous feedback control strategy that steers a point-mass vehicle safely to a desired destination, in a quasi-optimal manner, from almost all initial conditions in an n-dimensional euclidean space cluttered with spherical obstacles. The main idea consists in avoiding each obstacle via the shortest path within the cone enclosing the obstacle, and moving straight towards the target when the vehicle has a clear line of sight to the target location. The proposed control strategy ensures safe navigation with almost global asymptotic stability of the equilibrium point at the target location. 2D and 3D simulation results, illustrating the effectiveness of the proposed approach, are presented. © 2023, CC bY-NC-ND.

关键词： Asymptotic stability

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Prediction of the Indian summer monsoon using a stacked autoencoder and ensemb.e regression model (vol 37, pg 58, 2021)

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INTeRNATIONAL JOURNAL OF FOReCASTING 2021年第3期37卷 1309-1309页

作者： Saha, Moumita Santara, Anirban Mitra, Pabitra Chakraborty, Arun Nanjundiah, Ravi S. [a]Department of Computer Science University of Colorado Boulder USA [b]Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India [c]Centre for Oceans Rivers Atmosphere and Land Sciences Indian Institute of Technology Kharagpur India [d]Centre for Atmospheric and Oceanic Sciences Indian Institute of Science Bangalore India [e]Divecha Centre for Climate Change Indian Institute of Science Bangalore India [f]Indian Institute of Tropical Meteorology Pune India

The study of climatic variab.es that govern the Indian summer monsoon has b.en widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variab.es from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variab.es can outperform the predictors derived from an individual variab.e. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.

关键词： Stacked autoencoder Automated feature learning Predictor identification Monsoon prediction ensemb.e regression model Indian summer monsoon

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VLSI implementation using fully connected neural networks for energy consumption over neurons

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SUSTAINAb.e eNeRGY TeCHNOLOGIeS AND ASSeSSMeNTS 2022年第PartA期52卷

作者： Mehbodniya, Abolfazl Kumar, Ravi Mohanty, Sachi Nandan Geetha, A. bedi, Pradeep Tripathi, Rohit [a]Department of Electronics and Communications Engineering Kuwait College of Science and Technology (KCST) Doha Area 7th Ring Road Kuwait [b]Department of Electronics and Communication Engineering Jaypee University of Engineering and Technology Guna Madhya Pradesh India [c]Department of ECE Saveetha School of Engineering SIMATS Thadalam Chennai - 602105 Tamil Nadu India [d]Department of Computer Science & Engineering Vardhaman College of Engineering (Autonomous) Hyderabad India [e]B.Tech Department of ECE Indian Institute of Space Science and Technology Thiruvananthapuram 695547 Kerala India [f]Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu 603203 India

Increasingly, the capacity of artificial neural networks to identify and express top-level concepts in sets of data has become quite significant. Deep learning models like fully connected layer machine learning have demonstrated outstanding results in range of identification and verification applications. It's technology deployments suffered as from complexity and huge silicone areas with energy consumption. Memory accesses, the bulk from which happens in fully connected systems, comprise the energy usage of machine learning. It has many of the depths of neural network. Throughout this work, researchers suggest densely connected networks by demonstrating that there is indeed a drop in the number of links inside fully connected systems of up to 90% enhancing the quality of three popular sets of data. To decrease the memory needs have suggested low-connected grid networks, researchers present perhaps an effective hardware implementation based on the linear response registers. In comparison to the traditional design of fully connected layer neural network models, the suggested design could save up to 90 percent of storage. Findings of execution further reveal that artificial neurons of suggested weak network connections have a power consumption decrease up to 84 percent compared to one single atom of fully connected layer machine learning.

关键词： Neural Network Deep learning energy consumption VLSI

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Applications in Home Improvement Retailer, Koctas

Applications in Home Improvement Retailer, Koctas

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business Analytics for Technology and Security (ICbATS), International Conference on

作者： Nahia Mourad Hammad Raza Muhamamd Amjad Muhammed Ubaid Ullah Nouh Sabri elmitwally College of Engineering & IT British University in Dubai Dubai United Arab Emirates Department of Computer Science National College of Business Administration and Economics Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Minhaj University Lahore Paksitan School of Computing and Digital Technology Birmingham City University Birmingham B4 7XG UK Department of Computer Science Faculty of Computers and Artifcial Intelligence Cairo University Giza

It sounds like Koçtaş is a leader in the home improvement sector in Turkey, and they are focused on providing the best service and customer experience possib.e. They are also actively working to accelerate their digital investments and use their vast amount of customer data to innovate in the industry. One way they are using this data is by collecting and analyzing video camera images using AI. This allows them to detect humans and identify which products and shelves are most viewed in their stores. This information can then be used to optimize store layout and product placement for a better customer experience. Another way Koçtaş is innovating is through the implementation of kiosks that use Natural Language Processing (NLP) to interact with customers. These kiosks can understand and respond to questions asked by customers using AI, providing a more personalized and human-like ***, Koçtaş is using Dynamic Creative Optimization to create personalized advertisements for their customers. This method allows them to optimize the content and format of their ads based on the individual preferences and behavior of their customers, leading to more effective ***, Koçtaş is using technology and data to drive innovation and provide a better customer experience in the home improvement industry.

关键词： Industries Technological innovation Layout Cameras Natural language processing Security Artificial intelligence

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