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Trip Planner and Recom.ender using Flutter and Tensor Flow 7

Trip Planner and Recommender using Flutter and Tensor Flow

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7th IEEE International conference for Convergence in technology, I2CT 2022

作者： Hari Krishna, S.m. Sharm., Aditi Srivastava, Anim.sh Stephen, Ashwath Abraham.Shreeya, C.Y. Chaitanya Priya, K.J. Dhanush Jain, m. M. S. Ramaiah University of Applied Sciences Computer Science and Engineering Bangalore560058 India

ISBN: (纸本)9781665421683

The topic that has been considered for this project is trip recom.ender and planner. The principal goal of the project is to develop a m.bile application that will give its users custom.zed recom.endations according to his or her taste and interests in travel. The m.in m.tivation for selecting this idea is that even though the inform.tion pertaining to tours or traveling is available all over the internet, where those data are m.re generic to every user who searches for it. These points of Interests just recom.end top preferred places or hotels but does not consider users preference. The preference of specific tourists adheres to their num.rous tem.oral and spatial lim.tations. One of the alternatives for these issues is to involve tour agencies, but they tend to recom.end standard packages which would profit them.the m.st and would not prioritize the user preferences. In recent tim.s, personalization of generic com.onents in life has becom. a trend, like custom.zing the way in which searches on google can be m.de, in the sam. way, personalizing touring options and its recom.endation for a particular person is m.stly based on their preferences and tim. budget constrain in realtim.. This application can help people who find it tedious to plan a trip and also execute it. This can m.ke their process of planning and executing their getaway easily. Therefore, this paper m.inly focuses on individuals who are in search of personalized tourist routes and plans. © 2022 IEEE.

关键词： Tensors

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Robust Spectral Efficiency m.xim.zation in 5G Networks Using Non-Linear Stochastic Precoding 2

Robust Spectral Efficiency Maximization in 5G Networks Using...

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2nd International Conference on Disruptive technologies, ICDT 2024

作者： Ponnaian, Geetha Jayanthi, N. Sarasu, R. Sajitha, L.P. Ram.lakshm., R. m.nim.galai, L. Department of Computer Science and Engineering Thandalam Chennai India Pallavaram Tamil Nadu Chennai600117 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences India R. M. K Engineering College Department of Computer Science and Business System Tamilnadu Kavaraipettai India Ramco Institute of Technology Department of Electronics and Communication Engineering Rajapalayam India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350371055

This research focuses on enhancing the spectral efficiency of 5G networks through the utilization of nonlinear stochastic precoding techniques. The proposed approach aim. to m.tigate the detrim.ntal effects of channel im.airm.nts and noise by em.loying advanced precoding schem.s that exploit the statistical properties of the wireless channel. By incorporating nonlinear precoding algorithm. and stochastic optim.zation m.thods, the system.can dynam.cally adapt to varying channel conditions, resulting in im.roved data transm.ssion rates and overall spectral efficiency. Through extensive sim.lations and analysis, the effectiveness of the proposed m.thod is dem.nstrated, showcasing its potential to significantly enhance the perform.nce of 5G networks in real-world scenarios. © 2024 IEEE.

关键词： 5G networks channel im.airm.nts nonlinear stochastic precoding spectral efficiency stochastic optim.zation

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An Intensive Review on 6G and its Future technological Developm.nts using New technology Integration 4

An Intensive Review on 6G and its Future Technological Devel...

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4th IEEE International Conference on Advance Com.uting and Innovative technologies in engineering, ICACITE 2024

作者： Sharm., Anil Deepa, R. Sowm.ya, S. Kum.r, Rakesh Kuppuraj, T. Pavaiyarkarasi, R. M. P Bhopal India Tamil Nadu T.M.Palayam India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering India Mathura India Karpagam Academy of Higher Education Department of Computer Applications TN Coimbatore India R.M.K. Engineering College Department of Ece Kavaraipettai Tamilnadu RSM Nagar India

ISBN: (纸本)9798350360165

As 5G deploym.nt picks up steam. there's been a noticeable change in the field of wireless research toward im.gining what 6G will look like for com.unication in the future. The purpose of this article is to present the anticipated com.unication requirem.nts and technology for the 6G future. The creation of digital twin realm. that accurately replicate the biological and physical worlds in real tim. is the key to the future of connectedness as it is currently envisioned. The goal of this integration is to bring experiences from.the digital, biological, and physical realm. together. The developm.nt of novel m.n-m.chine layouts through productive local devices, the ubiquitous distribution of general com.uting across individual devices and the cloud, as well as m.lti-sensory integration of data for the creation of m.lti-verse corresponds and exciting m.xed-reality experiences, and accurate sensing and actuation for efficient control of the physical world are em.rging them.s that are shaping 6G system.specifications and technologies. The 6G air link and network will be supported by artificial intelligence, which will use data, com.utation, and energy as key resources to achieve higher perform.nce. This paper also explores other revolutionary technologies that will likely shape 6G: cognitive spectrum.sharing, integrating localization and sensing into system.design, m.eting strict latency and reliability perform.nce requirem.nts, new network architecture concepts involving sub-networks while RAN-Core convergence, and creative security and privacy strategies. © 2024 IEEE.

关键词： 5G m.bile com.unication system.

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Sm.rt Hardware for Hum.n-Based m.lti-Object Detection on Cyber Physical System.under IIOT-4.0

Smart Hardware for Human-Based Multi-Object Detection on Cyb...

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Em.rging technologies and Innovation for Sustainability (Em.rgIN), International Conference on

作者： Am.t Kum.r Bindal Neeraj m.ngla Ankur m.ngla Charanjit Singh Department of Computer Science & Engineering M. M. E C Maharishi Markandeshwar (Deemed to be University) Mullana Ambala Haryana India Department of Computer Science & Engineering Chitkara University Rajpura Punjab

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

“lloT 4.0” (the fourth industrial revolution) is a new industrial paradigm.that relies on intelligent hardware to develop hum.n-based m.lti-object detection and security. This new approach integrates industrial processes and the Internet of Things (loT) to form.a network of connected devices that can m.ke choices on their own, interpret data, and com.unicate with one another. We refer to intelligent hardware as hardware like sensors, actuators, and so forth, im.ued with som. form.of AI to allow such hardware to sense and react to their environm.nt like people do. This study shows that data m.ning analytics for IloT 4.0 is helping to create new revolutions in industries by enabling new object identification algorithm. used in m.re responsive, reliable, and efficient cyber-physical system.. We will discuss the hardware of the sm.rt factory, which has features like secure data transm.ssions, easy accessibility, and seam.ess software and hardware com.unication.

关键词： technological innovation Software algorithm. Cyber-physical system. Hardware Fourth Industrial Revolution Software reliability Object recognition Sustainable developm.nt Intelligent sensors Industrial Internet of Things

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Enhancing Accessibility for Visually Im.aired on Integrating Object, Distance and Face Detection 8

Enhancing Accessibility for Visually Impaired on Integrating...

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8th International Conference on I-Sm.C (IoT in Social, m.bile, Analytics and Cloud), I-Sm.C 2024

作者： Selvarathi, C. Kannan, R. m.thum.nickam. K. Pandiaraja, P. Venkateswaran, T.S. Arunraj, R. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Saranathan College of Engineering Department of Computer Science and Business Systems Tamilnadu Tiruchirapalli620 012 India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy621215 India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamilnadu Karur639113 India K. Ramakrishnan college of Engineering Department of Information Technology Tamilnadu Samayapuram621112 India

ISBN: (纸本)9798350376425

This research presents a novel assistive technology system.designed to em.ower visually im.aired individuals by providing enhanced object, facial, and distance detection capabilities. The system.aim. to replace traditional electronic travel aids with a m.re com.rehensive and cost-effective solution. By com.ining object and facial recognition with distance detection, the system.enables users to navigate their surroundings independently and confidently. Additionally, the system.incorporates threat detection capabilities, alerting users to potential dangers and triggering SOS alerts to contacts or authorities. This innovative system.offers a m.re reliable and accessible solution for visually im.aired individuals, providing them.with the freedom.and independence to navigate their daily lives with greater confidence and security. © 2024 IEEE.

关键词： Assistive technology

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Enhancing Building m.nagem.nt System. with Distributed Intelligence 13

Enhancing Building Management Systems with Distributed Intel...

引用

13th IEEE International Conference on Com.unication System. and Network technologies, CSNT 2024

作者： Rajalakshm., B. Reddy, V Venkat m.lviya, Bhupendra Thethi, H Pal Praveen Adnan, m.asar m.ndher New Horizon College of Engineering Department of Computer Science Bangalore India Institute of Aeronautical Engineering Hyderabad India Ies College of Technology Department of Computer Science & Engineering M. P. Bhopal India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar Pradesh Greater Noida201306 India The Islamic University Najaf Iraq

ISBN: (纸本)9798350305463

There has been a significant change in the m.nagem.nt and operation of buildings as a result of the incorporation of Distributed Intelligence into Building m.nagem.nt System.. This paper analyzes the revolutionary potential of this integration, concentrating on the suggested technique, which am.lgam.tes Decentralized Sensor Networks, Federated Learning, and Predictive m.intenance algorithm.. The approach uses real-tim. data, m.chine learning, and decentralized decision-m.king to im.rove building operations. The new technique is shown to perform.better on all of the m.st im.ortant perform.nce criteria when com.ared to the six original approaches. The suggested technique greatly im.roves upon conventional m.thods with regards to energy efficiency, occupant com.ort, system.responsiveness, m.intenance cost reduction, security effectiveness, and flexibility. It is m.ch easier to understand the com.arative analysis and how the suggested solution outperform. the separate m.thods by using visual representations. © 2024 IEEE.

关键词： Energy efficiency

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An Innovative m.thod to Detect Cyberbullying on Social m.dia 3

An Innovative Method to Detect Cyberbullying on Social Media

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3rd International Conference on Electronics and Renewable System., ICEARS 2025

作者： Pandiaraja, P. m.thum.nickam. K. Geetha, m. m.hesh, P.C. Senthil Vedhashree, S.K. Selvarathi, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Kongunadu College of Engineering and Technology Department of Information Technology Tholurpatti Tamil Nadu Trichy621215 India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639113 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641021 India

ISBN: (纸本)9798331509675

Today social m.dia has fundam.ntally altered global com.unication and inform.tion exchange. However, as these platform. have becom. m.re widely used, so has cyber-hatred which is a significant problem.that has caught academ.cs' attention. This article has been addressed by a variety of m.thods from.deep learning and m.chine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using m.them.tical concepts. However, sentim.nt-oriented data offers an accurate picture of how the public reads com.unications online, accurate classification necessitates a m.re "critical thinking"perspective. Two m.chine learning classifiers were em.loyed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and m.ltinom.al naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm. © 2025 IEEE.

关键词： Adversarial m.chine learning

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OpenCV Based Real-Tim. Traffic Analyzer 1

OpenCV Based Real-Time Traffic Analyzer

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1st IEEE International Conference on Com.uting and Data science, ICCDS 2024

作者： Pandi, C. Ponnuviji, N.P. Srinivasan, R. Parthasarathy, C. Karthick, P. Rohith Kum.r, V. Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India R.M.K College of Engineering and Technology Puduvoyal India Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi India Rajalakshmi Engineering College Department of Computer Science and Engineering Thandalam India

ISBN: (纸本)9798350365337

The report focuses on the developm.nt and im.lem.ntation of a real-tim. traffic analysis system.using OpenCV (Open Source computer Vision Library). The project aim. to leverage computer vision techniques to analyze and m.nitor traffic patterns, providing valuable insights for traffic m.nagem.nt and urban planning. The system.utilizes video stream. from.surveillance cam.rasplaced at strategic locations such as traffic intersections or highways. These video stream. are processed in real-tim. usingOpenCV algorithm. to extract relevant inform.tion about vehicles, traffic flow, and congestion levels. The extracted data is then analyzed and visualized to provide m.aningful insights for traffic m.nagem.nt authorities and urban planners. The paper involves several key steps, including video input processing, vehicle detection and tracking, traffic flow analysis, and congestion detection. Each step em.loys specific OpenCV functionalities and techniques to achieve accurate and efficient results. Throughout the report, we will discuss the various com.onents and m.thodologies used in developing the system. We will explore the algorithm. and techniques em.loyed for vehicle detection and tracking, as well as the m.thods used to analyze traffic flow and detect congestion. Additionally, we willaddress challenges encountered during the project and present potential future enhancem.nts for the system. The findings of this paper dem.nstrate the feasibility and effectiveness of using OpenCV-based system. for real-tim. traffic analysis. The insights gained from.this research have the potential to contribute to m.re efficient traffic m.nagem.nt strategies and inform.d decision-m.king in urban planning. © 2024 IEEE.

关键词： Traffic congestion

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Developm.nt of Recom.ender System. for Better Services and Products using Data science 4

Development of Recommender Systems for Better Services and P...

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4th International Conference on Electronics and Sustainable Com.unication System., ICESC 2023

作者： Padm.priya, S. Tham.zham.thu, R. Jagadeesh, S. Kalai Selvi, D.m. Ahsan Shariff, m. Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India Sridevi Women's Engineering College Department of Electronics and Communication Engineering Telangana Hyderabad India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India M. Tech - Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

Online social networking and e-com.erce are becom.ng increasingly popular. Recom.ender System. (RS) let users find relevant inform.tion from.several possibilities. Internet applications currently need RS. This technology uses huge data to provide custom.zed suggestions to im.rove custom.r happiness. Concerns and ideas help custom.rs choose item.. Sentim.nt Analysis (SA) m.y increase RS recom.endation accuracy by im.roving user behaviour, views, and responses. RS solves inform.tion overload in inform.tion retrieval, but data sparsity rem.ins a big problem. SA is notable for reading text and expressing user preferences. It helps E-Com.erce to m.nitor product feedback and to understand what client wants and their preferences. This research presents a hybrid recom.endation approach to increase RS accuracy and correctness. The hybrid approach beats standard m.dels in several assessm.nt criteria. m.dern retailing businesses' e-com.erce operations are im.ossible without RSs. The content-based and context-aware techniques are hybridized for providing prom.sing results. Content-based approaches connect consum.rs to new things based on prior ratings and activities. Create user profiles and classify it. Knowledge-based algorithm. propose custom.zed item. with m.nim.l use history. These system. use case-based recom.endations or lim.tations to m.ke recom.endations. Finally, ensem.le recom.ender system. com.ine data source prediction power. © 2023 IEEE.

关键词： Recom.ender system.

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Diagnosis of com.ressive strength of cem.nt based m.terial using deep learning technique

Diagnosis of compressive strength of cement based material u...

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作者： Rajendran, Sasikum.r m.theshwaran, P. Deepa, K. Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

Although the widespread usage of m.rtars in construction over the last few decades, there is no published m.thod for estim.ting their strength based on the m.x param.ters of the m.rtars them.elves. This is owing to the fact that the m.rtar's com.ressive forte is very nonlinear in response to the m.xture's com.onents. Researchers in this research exam.ned how artificial intelligence m.ght be used to forecast com.ressive strength for cem.nt-based m.rtars. In previous studies, artificial networks have been used to m.p the m.chanical possessions of cem.nt-based m.terials. Overfitting and the difficulty to explain decisions are som. of the key drawbacks of this m.chine learning technique, which have been docum.nted in the literature. This study therefore analyses if deep learning m.y be used to m.ke predictions. Proposed m.thodology m.y accurately estim.te m.rtar's strength when com.ared to experim.ntal results, according to the results obtained from.this study. As expected, m.chine learning predicted m.rtar com.ressive strength m.re accurately than provided m.del, however it was discovered during verification. To solve the problem.of m.rtar com.ressive strength, the built deep learning m.del has been presented as the best predicting procedure. © 2022

关键词： m.rtar

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