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International Conference on Recent Trends in Advance Computing (ICRTAC)

作者： Kotari suresh M. Humera Khanam Department of Computer Science and Engineering S. V. University College of Engineering Tirupati India

The integration of IoT and fog computing technologies has the potential to revolutionize a number of sectors, including healthcare, agriculture, urbanization, and transportation. Real-time human health monitoring, sensor-generated bio-signals, and wireless communication protocols enable effective decision-making, medication management, and diagnostics in the healthcare industry while protecting sensitive medical data. Fog computing and Internet of Things solutions for agriculture improve production management and pest control using wireless sensor networks, giving farmers the most recent data on the state of the environment, the health of their crops, and pest activity. Fog computing and the IoT provide sustainable growth and better urban services, resource allocation, and citizen experiences in smart city efforts, solving difficulties brought on by expanding urban colonies. Fog computing and the IoT also benefit transportation systems, particularly through the use of vehicular ad hoc networks (vANETs), which improve driving experiences, traffic efficiency, and traffic forecasting, ultimately resulting in increased safety, fewer accidents, and better overall infrastructure for transportation. by utilizing real-time data analytics, connectivity, and intelligent decision-making, the convergence of fog computing and IoT presents a significant opportunity to address pressing issues in healthcare, agriculture, urbanization, and transportation. This will help to create a future that is more effective, sustainable, and interconnected across many industries.

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Methodology for Classifying Objects in High-Resolution Optical Images Using Deep Learning Techniques 7th

Methodology for Classifying Objects in High-Resolution Optic...

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7th International Conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2022

作者： Lalitha Kumari, P. Das, santanu Kannadasan, b. sampathila, Niranjana saravanakumar, C. Anand, Rohit Gupta, Ankur Department of Computer Science and Engineering Malla Reddy Institute of Technology Andhra Pradesh Secunderabad Maisammaguda India Department of Biotechnology Seshadripuram First Grade College Yelahanka New Town Bangalore India Department of Civil Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Chennai India Department of Biomedical Engineering Manipal Institute of Technology Karnataka Manipal India Department of Electronics and Communication Engineering SRM Valliammai Engineering College Tamilnadu Kattankulathur India DSEU G.B.Pant Okhla-1 Campus New Delhi India Department of Computer Science and Engineering Vaish College of Engineering Haryana Rohtak India

ISBN: (纸本)9789811988646

The classification of objects that are present in the images or in the videos is being developed progressively to obtain good results because of the use of convolutional networks. In this work, we have used the convolutional networks for the detection of objects that are present in high-resolution satellite images. Tests were carried out on ships that are on the high seas and in the ports. This classification is useful for monitoring the coasts, as well as for analyzing the dynamics of the ships which can be applied in the search of ships. To cover this task of classifying ships in the spectral images, the use of high-resolution satellite images of coastal areas and with a large number of ships is used in order to build a set of images, containing images of the ships. In order to be used for training setting and testing of the convolutional network, a very particular configuration of the convolutional network caused by the particularity of high-resolution satellite images is presented. The methodology developed indicating the procedures performed is also presented in which a set of images containing 300 was built images of ships that are in the sea or are anchored in the ports. The results obtained in the classification using the convolutional networks are acceptable to be able to be used in different applications. © 2023, The Author(s), under exclusive license to springer Nature singapore Pte Ltd.

关键词： ships

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Natural Language Processing: bridging the Gap between Human Language and Machine Understanding 2

Natural Language Processing: Bridging the Gap between Human ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging business Technologies, TQCEbT 2024

作者： Kumar, Keerthipati syed, Mohammad Haider bhargava, saumya Mohakud, Lalit Lalitav serajuddin, Mohammad Lourens, Melanie S.V. College of Engineering Computer Science and Engineering Department Andhra Pradesh Tirupati India Saudi Electronic University Department of Computer Science Saudi Arabia Pgdm Indira School of Business Studies Pune India Jadavpur University Department of Education Kolkata700032 India King Saud University College of Dentistry Saudi Arabia Durban University of Technology South Africa Faculty of Management Sciences South Africa

ISBN: (纸本)9798350384277

NLP in relation to deep learning models is focused on the review of NLP evolution from the middle of the 1950s up to present days. Today, NLP remains the pillar on which rests our digital age, connecting speech nuances of a human language and the computational power of machines. It is tracked along with the historical evolution from rule based systems to the paradigm shift to the machine learning decade since the nineties and constellations of the NLP components. The methodology involves using a conventional linguistic model alongside ultra-modern machine learning models to overcome ambiguity as well as contexts associated with other languages. The technical specifics and continuity loops under the Human-in-the-loop (HITL) framework are discussed. For example, they look at words like "uncommon"words, which have cultural implications in showing how involved human speech is. For instance, the strength of NPL can be depicted by being used in diverse sectors like health and social media. Finally, this article illustrates an advanced time when smart NPL units give topnotch solutions proving that interconnection between linguistics, computing and artificial intelligence is ongoing. © 2024 IEEE.

关键词： Adversarial machine learning

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Unveiling blockchain's Potential with Consensus Algorithms and Real-World Applications in supply Chain Management

Unveiling Blockchain's Potential with Consensus Algorithms a...

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Trends in Quantum Computing and Emerging business Technologies (TQCEbT), International Conference on

作者： M. balasubramani K. subathra swathi Agarwal J bamini Anurag Aeron E. Gangadevi Department of Computer Science and Engineering V.S.B. Engineering College Karur India Department of Mathematics Sri Sairam Engineering College Chennai India Department of IT CVR College of Engineering Hyderabad India Sri Ramakrishna College of Arts & Science Coimbatore India Department of CSE MIET Meerut Meerut India Department of Computer Science Loyola College Chennai India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

blockchain technology in the supply chain is set to transform how businesses conduct and transact their businesses. This study explores the transformative potential of blockchain through an analysis of key results in four critical areas: transparency and traceability, efficiency and cost reduction, security and fraud prevention, and interoperability with novel technologies. The prospects of blockchain technology are enhanced transparency and traceability through real-time v.s.bility on the movement of products and exact tracing of product origins and histories. This high level of transparency creates trust among the participants in supply chain and smoothens the dispute resolution. Automation of blockchain has led to substantial efficiency gains as manual tasks and operational delays reduce. such improvements have reduced the operational costs by 15% making the supply chains more agile and efficient. Through an impressive 98% reduction in security incidents and 95% decrease in fraud incidents after blockchain implementation, security improvements are noticeable. The tamper-resistant properties of blockchain play a huge role in achieving these results and ensure the trust of the stakeholders. The integration of emerging technologies, including the IoT and AI, has only made supply chain capabilities broader. Integrating IoT devices effectively enables real-time data capture and surveillance; AI-powered data analytics give actionable intelligence for data-driven decision-support.

关键词： supply chain management Costs supply chains Real-time systems blockchains Fraud security

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CAsCADING DEEP LEARNING APPROACH FOR IDENTIFYING FACIAL EXPREssION YOLO METHOD 1

CASCADING DEEP LEARNING APPROACH FOR IDENTIFYING FACIAL EXPR...

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1st International Conference on Technologies for smart Green Connected society 2021, ICTsGs 2021

作者： Dhanalakshmi, P. Namratha, P. Ramu, Gandikota Prasad, M. Ravi Computer Science and Systems Engineering SVEC Tirupati India Gates Institute of Technology Gooty India Department of CSE Institute of Aeronautical Engineering Hyderabad India S V college of engineering Kadapa India

ISBN: (纸本)9781607685395

Face detection is one of the biggest tasks to find things. Identification is usually the first stage of facial recognition. and identity verification. In recent years in-depth learning algorithms have changed dramatically in object acquisition. These algorithms can usually be divided into two groups, namely two-phase machines like Faster R-CNN or single-phase machines like YOLO. While YOLO and its variants are less accurate than the two-phase detection systems, they outperform other components with wider genes. When faced with standard-sized objects, YOLO works well, but can't get smaller objects. A face recognition system that uses AI (Artificial Intelligence) separates or verifies a person's identity by analyzing their faces. In this project a single neural network predicts binding boxes and class opportunities directly from the full images in a single test. © The Electrochemical society

关键词： Cascade Deep Learning Face Detection Facial Identification Facial Recognition Yolo Method

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Web User Profile Generation and Discovery Analysis using LsTM Architecture 2

Web User Profile Generation and Discovery Analysis using LST...

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2nd International Conference on Technological Advancements in Computational sciences, ICTACs 2022

作者： sudhakar, K. smail, boussaadi Reddy, Tatireddy subba shitharth, s. Tripathi, Diwakar Ramanuj Fahlevi, Mochammad Pscmr College of Engineering and Technology Department of Computer Science Engineering Andhra Pradesh Vijayawada India Research Center on Scientific and Technical Information Cerist Algiers Algeria B v Raju Institute of Technology Department of Computer Science and Engineering Tuljaraopet India Kebri Dehar University Department of Computer Science Ethiopia S.S. Maniar College of Computer and Management Department of Computer Science Maharashtra Nagpur India Binus Online Learning Bina Nusantara University Management Department 11480 Indonesia

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

In today's technology-driven world, a user profile is a virtual representation of each user, containing various user information such as personal, interest and preference data. These profiles are the result of a user profiling process and are essential to personalizing the service. As the amount of information available on the Internet increases and the number of different users, customization becomes a priority. Due to the large amount of information available on the Internet, referral systems that aim to provide relevant information to users are becoming increasingly important and popular. various methods, methodologies and algorithms have been proposed in the literature for the user analysis process. Creating automated user profiles is a big challenge in creating adaptive customized applications. In this work proposed the method, Long short-Term Architecture (LsTM) is User profile is an important issue for both information and service customization. based on the original information, the user's topic preference and text emotional features into attention information and combines various formats and LsTM (Long short Term Memory) models to describe and predict the elements of informal community clients. At last, the trial consequences of different gatherings show that the concern-based LsTM model proposed can accomplish improved results than the right now regularly involved strategies in recognizing client character qualities, and the model has great speculation, which implies that it has this capacity. © 2022 IEEE.

关键词： Long short-term memory

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Text Transmission Through Li-Fi in WsN Using smart based secure Morse Code Algorithm

Text Transmission Through Li-Fi in WSN Using Smart Based Sec...

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Multi-Agent systems for Collaborative Intelligence (ICMsCI), International Conference on

作者： I. vasudevan P. Malathi s. siddik P.R Therasa M.Mohamed Nasurudeen R.M.Dilip Charaan Dept.of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Dept of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Dept.of EEE Pavaai Engineering College Namakkal India Dept.of CSE R.M.K Engineering College Kavaraipettai India Dept.of CSE V.S.B Engineering College Karur India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Cyber security is one the most challenging task in today's world. Most of the existing Li-Fi technology in wireless sensor network is to detect the data within the short range of distance less accuracy. In this research, we proposed a smart based secure Morse Code (ssMC) technique. Here there are two part, first is encoding and next is decoding. This proposed encoding technique takes user input in text based form and convert these text to standard morse code pattern and decoding does in the opposite direction. The transmitter parts encode the user input and send the pattern through laser module. The receiver part receives the pattern sends to microcontroller and then decodes and displays the data. In our experiment results shows that the proposed technique can transmit data over long distance with help of morse code encoding decoding algorithm. This technique takes 5ms for the whole transmission and process the transmission accurately.

关键词： Wireless communication Wireless sensor networks Codes Transmitters Receivers Light fidelity Encoding Decoding computer crime standards

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An efficient ranking based binary salp swarm optimization for feature selection in high dimensional datasets

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Measurement: sensors 2024年 35卷

作者： Jayachitra, s. balasubramani, M. Kaleem, Abdullah Mohammed Karthik, Jayavarapu Keerthiga, G. Mythili, R. Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Engineering V.S.B. Engineering College Karur India Department of Electronics and Telecommunication Engineering MPGI School of Engineering Maharashtra Nanded India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai India

Feature selection is a major challenge in data mining which involves complex searching procedure to acquire relevant feature subset. The effectiveness of classification approaches is greatly susceptible to data dimensionality. The Higher dimensionality intricate numerous problems like higher computational costs and over fitting problem. The essential key factor to mitigate the problem is feature selection. The main motive is to minimize the number of features through eliminating noisy, insignificant, and redundant features from the original data. The Metaheuristic algorithm attains excellent performance for solving this kind of problems. In this paper, the grading based binary salp swarm optimization has been proposed to solve various complex problems with lesser computational time. The grading system has been used to maintain the balance among exploitation and exploration. The proposed method is examined using ten benchmark real datasets. The comparative result exhibits the promising performance of our proposed method and surpasses with other optimization interms of investigating evaluation measures. © 2024

关键词： Feature selection

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Multi-scale Memory Residual Network based Deep Learning Model for Network Traffic Anomaly Detection

Multi-scale Memory Residual Network Based Deep Learning Mode...

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Intelligent Computing and Networking - Proceedings of IC-ICN 2023

作者： Jayakrishna, M. selvakumar, v. Kumar, Atul Dilip, salunke Mangesh Maaliw, Renato R. Mechanical engineering Sri Sivani Engineering College Srikakulam India Department of Maths and Statistics Bhavan’s Vivekananda College of Science Humanities and Commerce Telangana Hyderabad India Dr. D. Y. Patil B-School Pune India Department of Computer Engineering GHRCEM Pune India College of Engineering Southern Luzon State University Lucban Quezon Philippines

ISBN: (纸本)9789819931767

Models for detecting network traffic anomalies based on deep learning usually exhibit weak generalization, confined representative capacity, and low real-world adaption. In light of this, a multi-scale memory residual network-based model for identifying network traffic anomalies is proposed. The distribution analysis of the three-dimensional feature space illustrates the efficiency of the network traffic data preprocessing technique. The deep learning algorithm enhances the model's capacity to represent data by coupling multi-scale 1DCNN-LsTM networks. The realization of the residual network is shown using the residual network notion as a foundation. Deep feature extraction accelerates model convergence to detect network traffic anomalies accurately and effectively while preventing gradient disappearance, explosion, over fitting, and network damage. The experimental findings show how the multi-scale 1DCNN-LsTM network can improve the model's representational competence and generalization ability. Performance indicators for the model in this study are also superior to those for other deep learning models. © 2023, The Author(s), under exclusive license to springer Nature singapore Pte Ltd.

关键词： Feature extraction

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A Novel Approach to social sustainability with Machine Learning and the best Worst Method in Manufacturing 1

A Novel Approach to Social Sustainability with Machine Learn...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Yogi, Kottala sri Franklin Jino, R.E. Dankan Gowda, v. Murthy, C. Ravindra Prasad, veduri veera srinivas, ved Hyderabad Symbiosis International University Symbiosis Institute of Business Management Department of Operations Pune India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Mohan Babu University School of Engineering Department of Ece Andhra Pradesh Tirupati India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Thiagarajar School of Management Department of Human Resources Tamil Nadu Madurai India

ISBN: (纸本)9798350367720

This paper is aimed at presenting a novel approach for enhancing the measurement of social sustainability in manufacturing domain with the help of ML with bWM. social sustainability when dealing with manufacturing is part of the four sustainability pillars that seeks to address aspects such as;welfare of workers, social organization, and practice within the firm. This is why the conventional tools may not sufficiently inform on the right combination of the said aspects. The information from the big data is then processed by the means of ML to disclose the patterns illuminating the stance influences and bWM is integrated to analyze specialists' insight the power of evolving influential aspects of stance. They are useful in minimizing the time as well as the energy required in the formulation of the decisions because the details and correlations needed are already provided. It is illustrated with the help of an example in the manufacturing setting and significant improvements in the values of the social sustainability indices compared to the classical approach. From these conclusions, it is possible to indicate the prospects for the integration of ML and bWM as the authors' contribution to tackling the problem of social sustainability in the manufacturing sector. © 2024 IEEE.

关键词： smart manufacturing

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