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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： D Sivaganesan M Venkateshwaran S P Dhinesh Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India Department of Computer Science Sri Krishna Arts and Science College Coimbatore India

This study suggests an innovative method for helping people who are blind or visually handicapped by turning visuals into sounds. In the proposed system, audio descriptions are produced in real-time together with significant features that are extracted from photos using deep learning algorithms. The proposed work is developed to be user-friendly, which includes a simple interface that aids blind individuals to easily capture and process images using a mobile device. A user research was undertaken to assess the efficiency of the suggested method, and the results were encouraging in terms of precision and usability. This initiative offers a promising technique to give people who are blind or visually impaired an alternate means of perceiving and interacting with their environment, therefore improving their quality of life. The suggested picture to audio converter system aims to overcome the drawbacks of current assistive devices that rely on braille or textual descriptions. Blind people can more easily interpret visual information that is necessary for daily life, such as recognising items, interpreting signs, or navigate unfamiliar situations, through offering audio descriptions of images. The system makes use of recent deep learning developments that have significantly improved picture identification as natural language processing. As a result, the suggested technique has the ability to offer audio descriptions that are more precise and comprehensive than current methods. This technology has the potential to be implemented into a variety of products, from cellphones to intelligent glasses, and could significantly improve the lives of people who are blind or visually impaired.

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Retraction Note: Anticipating network failures and congestion in optical networks a data analytics approach using genetic algorithm optimization

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Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Kiran, Ajmeera Kalpana, V. Madanan, Mukesh Ramesh, Janjhyam Venkata Naga Alfurhood, Badria Sulaiman Mubeen, Suraya Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science College of Arts and Applied Sciences Dhofar University Salalah Sultanate of Oman Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of ECE CMR Technical Campus Hyderabad India

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MAXIMIZING BIG DATA FOR DATA-DRIVEN DECISION MAKING: AN IN-DEPTH ASSESSMENT

MAXIMIZING BIG DATA FOR DATA-DRIVEN DECISION MAKING: AN IN-D...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kumar, Maneesh Kaur, Dilrajpreet Sanghi, Akash Yuvaraj, K. Mukuntharaj, C. Tamkhade, Jayashree School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand 248007 India Department of Physics School of Basic & Applied Sciences K. R. Mangalam University Haryana Gurugram122103 India Department of Computer Science & Engineering Invertis University Uttar Pradesh Bareilly243123 India Department of Computer Science Karpagam Academy of Higher Education Coimbatore641021 India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore641032 India E&TC Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350355338

In this essay, we examine the history and current condition. We begin by outlining the history of big information reviewing similar technologies including centers, and Hadoop. The four stages of the chain-data production, data capture, - are then the main emphasis. [1] We lay out the overall context, talk about the difficulties, and cover the most recent developments for each step. Finally, we look at a number of exemplary big business. These conversations are meant a thorough introduction and overall picture of this fascinating field. A discussion of unresolved issues and prospective directions follows this survey's conclusion. © 2024 IEEE.

关键词： Data centers

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Physics Informed Neural Networks with strong and weak residuals for advection-dominated diffusion problems

arXiv

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arXiv 2023年

作者： Sikora, Maciej Krukowski, Patryk Paszyńska, Anna Paszyński, Maciej Institute of Computer Science AGH University of Science and Technology Kraków Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków Poland

This paper deals with the following important research questions. Is it possible to solve challenging advection-dominated diffusion problems in one and two dimensions using Physics Informed Neural Networks (PINN) and Variational Physics Informed Neural Networks (VPINN)? How does it compare to the higher-order and continuity Finite Element Method (FEM)? How to define the loss functions for PINN and VPINN so they converge to the correct solutions? How to select points or test functions for training of PINN and VPINN? We focus on the one-dimensional advection-dominated diffusion problem and the two-dimensional Eriksson-Johnson model problem. We show that the standard Galerkin method for FEM cannot solve this problem. We discuss the stabilization of the advection-dominated diffusion problem with the Petrov-Galerkin (PG) formulation and present the FEM solution obtained with the PG method. We employ PINN and VPINN methods, defining several strong and weak loss functions. We compare the training and solutions of PINN and VPINN methods with higher-order FEM methods. © 2023, CC BY.

关键词： Advection

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Employment Prediction System Based on Recommender System Using NLP, Content Filtering & Collaborative Filtering 10

Employment Prediction System Based on Recommender System Usi...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Gupta, Sanjai Kakde, Vinodkumar Dharamshi, Ravindra R. Sakarkar, Gopal Abiyasa, Agus Putu University of Technology and Applied Sciences Nizwa Oman University of Nizwa Nizwa Oman Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Applications Pune India Universitas Pendidikan Nasional Department of Electrical Engineering Denpasar Indonesia

ISBN: (纸本)9798350363104

Employability is an important measure for Higher education Institutions/universities to measure success. Due to globalization, automation, and advances in Data analytics, the job market landscape is more dynamic than ever. All the stakeholders like employers, graduates, and Academic Institutions are extremely beneficial if they are aware of the factors affecting employability and skills requirements. For better career opportunities, students may need to know their strengths and weaknesses. Academic institutions, Universities, and others involved in designing curricula can develop more appropriate skill sets required for the job market. Recommender systems provide suggestions for items that a user may find useful. Using data obtained from job portals and social networking sites such as LinkedIn, in this research project we aim to develop a job recommendation system. Data collection and Analysis were done through web mining to acquire job portal data, social networking data for CVs/resumes, and curriculum data of various academic institutions in Oman. To extract meaningful information from this data, we use Natural Language Processing (NLP) techniques. To match job seekers with suitable job opportunities, our recommendation system utilizes cosine similarity based on content-based filtering. In this study, the effectiveness of the We also create a user-friendly interface that displays essential details such as skills needed, job roles, and company information based on the user's skills and resumes. The proposed model will be evaluated in terms of its ability to provide accurate recommendations. Users will benefit from an efficient and personalized job search process through this system. © 2024 IEEE.

关键词： Collaborative filtering

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Application of artificial intelligence techniques in modeling attenuation behavior of ionization radiation: a review

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Radiation Detection technology and Methods 2023年第1期7卷 56-83页

作者： Joseph Konadu Boahen Samir A.Elsagheer Mohamed Ahmed S.G.Khalil Mohsen A.Hassan Materials Science and Engineering Department School of Innovative Design EngineeringEgypt-Japan University of Science and Technology(E-JUST)P.O.Box 179New Borg El-Arab CityEgypt Computer Science and Information Technology Programs Egypt-Japan University of Science and Technology(E-JUST)AlexandriaEgypt Faculty of Engineering Aswan UniversityAswanEgypt Institute of Basic and Applied Science Egypt-Japan University of Science and Technology(E-JUST)179 New Borg El-Arab CityAlexandriaEgypt Environmental and Smart Technology Group Faculty of ScienceFayoum UniversityFayoum63514Egypt

Introduction Shielding of ionizing radiations,which are gamma rays,neutrons,and X-rays,can be achieved by attenuating its intensity using different *** is therefore crucial in ensuring the safety of lives and essential equipment in areas such as nuclear power plants,radiotherapy facilities,space exploration,and *** Intelligent technologies have become desirable in modeling shielding materials’attenuation behavior due to their unique *** The overview aims to present the recent application of AI technologies in modeling the radiation attenuation behavior of *** A total of 41 relevant articles were obtained using Scopus and web of science *** search was restricted to articles and conference papers published within the last two *** From the overview,it was realized that AI techniques can predict the attenuation properties of shielding materials and optimize the shield *** methods can be grouped into predictive models which are:fuzzy logic,Support Vector Regression,Neural Networks,and optimization models which include Genetic algorithms,Ant Colony,and Particle Swarm *** networks are the most robust and widely used *** predictive models are used in predicting parameters such as attenuation coefficient,buildup factor,shield thickness,and radiation dose rates,whiles the optimization techniques are employed in single and multi-objective attenuator *** In the overview,the accuracies and complexities of the various AI techniques have been discussed giving insight into their *** AI techniques are easy to model compared to conventional methods and can save computational time when coupled with conventional statistical and deterministic models or employed as a standalone technique.

关键词： Ionization radiation Attenuation behavior Artificial intelligence Modeling

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Inf-TESLA++: A Blockchain-assisted Continuous Authentication Protocol for Broadcast Communication in Resource-Constrained Devices

Inf-TESLA++: A Blockchain-assisted Continuous Authentication...

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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Mallikarjun Reddy Dorsala Charugundla Lakshmi Nishitha Pendem Sathwik Gattu Srilekha Mahesh Kumar Morampudi Department of Computer Science and Engineering Indian Institute of Information Technology Sri City Chittoor India Department of Computer Science and Engineering School of Engineering and Applied Sciences (SEAS) SRM University AP India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

Many applications widely use broadcast communications (BC) due to their efficiency in simultaneously distributing data to many receivers. More specifically, BC is essential in resource-constrained devices (RCDs) for conserving energy and bandwidth, which are vital for increasing the longevity and performance of RCDs. The three major challenges in BC are source authentication, data integrity and continuous authentication (CA). The first two challenges are effectively addressed in TESLA and its variants. However, achieving CA without incurring overheads on the sender and receiver sides remains an unresolved challenge. In this paper, we propose Infinite (Inf) TESLA++, assisted by Blockchain and smart contracts, to address the CA challenge with minimal overhead at sender and receiver. Our theoretical and simulation analysis demonstrates that Inf-TESLA++ incurs minimal overhead compared to existing protocols while eliminating the need for direct communication between the sender and receiver for synchronization.

关键词： Performance evaluation Smart contracts Authentication Receivers Bandwidth Blockchains Telecommunications Security Synchronization Testing

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Detection of Leaf Diseases for Multiple Plants using Convolutional Neural Network 3

Detection of Leaf Diseases for Multiple Plants using Convolu...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Pawar, Shital Thorave, Pradnya Panigrahi, Nibedita Jyothi, A.P. Shedge, Sakshi Sayyad, Samina Bharati Vidyapeeth's College of Engineering for Women Computer Engineering Department Pune India Rv Institute of Technology and Management Information Science and Engineering Department Bengaluru India Ramaiah University of Applied Sciences Computer Science and Engineering Department Bengaluru India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Diseases can be harmful to plant health and affecting their growth. Several leaf diseases control the growth of plants. So, early diagnosis of plant diseases is important in agricultural field to produce a good crop yield. However, current research does not provide an accurate and fast diagnostic tool to ensure the healthy growth of agricultural sector. This paper suggests an in-depth study approach which is based upon a Convolutional Neural Network (CNN) algorithm for detecting the leaf infections in the real-time. In this paper, we have focused on detecting the diseases of multiple plants. Although many existing models are limited to large-scale disease detection, the current model deals with accurate diagnostic detection, which is multidisciplinary detection. The proposed model is designed to address both acquisition speed and accuracy and will be used to detect plant diseases of 10 plants in the real world. The system automatically diagnoses the disease and suggests the pesticide name and contact detail of pesticide supplier to the farmer through SMS/website. In addition to this, this proposed system provides the weather forecasting information to the farmers which is important for them for making the appropriate decisions related to the plant health. The CNN is used to find plant diseases. The proposed system can detect the 21 leaf diseases. The name of diseases which the proposed system can detect are Esca, Early Blight, Late Blight, Bacterial Spot, Red Rot, Black Rot, Leaf Mold etc. © 2022 IEEE.

关键词： Feature extraction

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Graph-Grammar Based Longest-Edge Refinement Algorithm for Three-Dimensional Optimally p Refined Meshes with Tetrahedral Elements 21st

Graph-Grammar Based Longest-Edge Refinement Algorithm for Th...

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21st International Conference on Computational science, ICCS 2021

作者： Mosialek, Albert Szaflarski, Andrzej Pych, Rafal Kisiel-Dorohinicki, Marek Paszyński, Maciej Paszyńska, Anna Institute of Computer Science AGH University of Science and Technology Krakow Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland

ISBN: (纸本)9783030779696

Finite element method is a popular way of solving engineering problems in geoengineering. Three-dimensional grids employed for approximation the formation layers are often constructed from tetrahedral finite elements. The refinement algorithms that avoids hanging nodes are desired in order to avoid constrained approximation on broken edges and faces. We present a new mesh refinement algorithm for such the tetrahedral grids, with the following features (1) it is a two-level algorithm, refining the elements’ faces first, followed by the refinement of the elements’ interiors;(2) for the face refinements it employs the graph-grammar based version of the longest-edge refinement algorithm to avoid the hanging nodes;and (3) it allows for nearly perfect parallel execution of the second stage, refining the element interiors. We describe the algorithm using the graph-grammar based formalism. We verify the properties of the algorithm, by breaking 5,000 tetrahedral elements, and checking their angles and proportions. On the generated meshes without hanging nodes we span the polynomial basis functions of the optimal order, selected via metaheuristic optimization algorithm. We use them for the projection based interpolation of formation layers. © 2021, Springer Nature Switzerland AG.

关键词： Optimization

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Leaf Disease Detection of Multiple Plants Using Deep Learning

Leaf Disease Detection of Multiple Plants Using Deep Learnin...

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Pawar, Shital Shedge, Sakshi Panigrahi, Nibedita Jyoti, A.P. Thorave, Pradnya Sayyad, Samina Bharati Vidyapeeth's College of Engineering for Women Computer Engineering Department Pune India RV Institute of Technology and Management Information Science and Engineering Department Bengaluru India Ramaiah University of Applied Sciences Computer Science and Engineering Department Bengaluru India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

The great advancement in deep learning has increased the interest in technologies of image recognition. Different deep learning techniques help big data analysis with large potential and finding the disease of plant in agriculture sector. The deep learning model extract the features from an input and uses its features for classification. In consideration of climate change, plants are affected, which impacts agricultural yield. When the condition worsens, the plants become more susceptible to many diseases caused by fungi, bacteria, viruses, etc. Diseases can affect the plant growth. In the proposed system, CNN algorithm is used to detect the disease from leaf images and pesticides are suggested based on the disease found. The proposed method uses 15-layer CNN. The proposed system will provide pesticide supplier name according to area as well as it provides additional information about the leaf disease of the affected plant in the area, disease name, complete accuracy, time, and weather forecasting information also. The dataset contains 10 plants - apple, potato, corn, tomato, grape, rice, sugarcane, cucumber, pepper, soyabean. © 2022 IEEE.

关键词： Pesticides

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