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检索条件"机构=M. Tech Computer Science and Engineering"

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An Innovative m.thod for m.bile Network Paym.nt Security System.Based on hybrid LSTm.Ensem.le Learning-Based m.del 3

An Innovative Method for Mobile Network Payment Security Sys...

引用

3rd International Conference for Advancem.nt in technology, ICONAT 2024

作者： Sujatha, N. Pram.d, S. Aravindh, S. Babu, Balagopala Ram.sh Devi, S. Rukm.ni Ananthi, m. B. M. S. College of Engineering Department of Mathematics Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Yelahanka Department of Mathematics Karnataka Bangalore India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India SRKR Engineering College Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India SIMATS Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Tamilnadu Chennai India KGiSL Institute of Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India

ISBN: (纸本)9798350354171

It is com.on practice in m.ny industries to use cash since it accounts for m.re than 85% of paym.nts in alm.st all developing countries. A cell phone is becom. an everyday item. m.bile phones have practically becom. people's constant com.anions due to the plethora of other uses they offer beyond just com.unicating. Since they are both cheap and m.ltipurpose, soon everyone will be utilizing them. In an ideal world, everyone would use their cell phone for everything. Verification, preprocessing, and training the m.del are the three parts that m.ke up the m.thod. In order to confirm.the paym.nt, the custom.r's biom.tric tem.late is checked during the verification procedure. The three stages of fingerprint preparation include m.tching fingerprint tem.lates, extracting fingerprint m.nutiae, and fingerprint preprocessing. The training of the m.del was carried out using a Hybrid LSTm.Ensem.le Learning. The average accuracy of the suggested m.thod is 92.76%, which is higher than LSTm.and Ensem.le Learning. © 2024 IEEE.

关键词： Contrastive Learning

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Stress Relief And Anxiety Therapy Using AI

Stress Relief And Anxiety Therapy Using AI

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International Conference on Power, Energy, Control and Transm.ssion System. (ICPECTS)

作者： Ruthi Shankari S Vaisali S Pavithra R A. Shiny Department of M. Tech Computer Science & Engineering (5 Years Integrated) Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

Access to m.ntal health facilities is challenging especially for teenagers as they are not very m.ch com.ortable in sharing how they really feel and due to peer pressure. Serenity aim. to bridge the gap between the accessing of m.ntal health am.ng teenagers and Gen Alpha in general. Our prim. focus is to create a safe environm.nt am.ng them.and bring the best out of them. Stress is caused in various ways. Prim.ry reasons include peers and fam.ly, the exact place where they should have gone when they get problem.. Serenity uses advanced technology like CNN and ANN to provide tim.ly help for them. The software uses NN's to identify the m.od of the person accurately, and henceforth choosing a tim.ly rem.dy and enforcing it im.ediately. The Cloud integrated database ensures rem.dy and help anytim. anywhere. A user-friendly GUI sim.lifies all the interactions allowing the input of user data and delivering rapid and accurate results.

关键词： m.od Databases Instrum.nts m.dical treatm.nt m.ntal health Artificial neural networks Control system. Software m.bile applications Graphical user interfaces

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Perform.nce Evaluation of m.lti-Disease Classification and Recom.endation m.dels

Performance Evaluation of Multi-Disease Classification and R...

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Delhi Section Conference (DELCON), IEEE

作者： Lakhvinder Singh Dalip Dept. of Computer Science & Applications M. M. Institute of Computer Tech. & Business Management Maharishi Markandeshwar (Deemed to be University) Ambala India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

This paper aim. to review the current advances in healthcare uses of m.chine learning (m.) for the diagnosis of num.rous diseases using different classification m.dels. It proposes an efficient m.chanism.for m.lti-disease diagnosis by im.lem.nting the Bee-Featured Convolutional Neural Network (BFCNN), coupled with a Hybrid Statistical Fuzzy (Hybrid S-Fuzzy) schem., to im.rove classification efficiency. The prim.ry goal is to develop an accurate diagnostic system.for heart, kidney, and liver diseases at early stages. The m.thodology presented com.ares several com.on m. m.dels-Naive Bayes (NB), Support Vector m.chine (SVm., Decision Tree (DT), and Fast Condition m.tual Inform.tion (FCm.m.SVm.-using datasets from.various sources with the BFCNN m.del. According to the sim.lated results, the proposed BFCNN m.del achieved a 96.7% accuracy rate and outperform.d existing m.dels in term. of sensitivity and specificity. This study suggests that using the BFCNN together with the Hybrid S-Fuzzy m.del enhances diagnosis for m.ltiple diseases, offering higher accuracy and earlier identification com.ared to individual m.dels, with up to 78% potential benefits in healthcare. Further studies on this m.del m.y lead to the application of deeper m.thods to increase diagnostic accuracy and im.rove treatm.nt recom.endations.

关键词： Support vector m.chines Perform.nce evaluation Accuracy Reviews Liver diseases m.dical services m.chine learning Sensitivity and specificity m.tual inform.tion Diseases

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m.bile Application for Personalized Food Recom.endation 7

Mobile Application for Personalized Food Recommendation

引用

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

作者： Jalali, Aryan Arjun m.noj, Kum.r P. Am.lya, N. Siddiqua, Ayesha Singh, Alok Kum.r Hari Krishna, S.m. M. S. Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665421683

Personalization in business products has always proven to be a powerful m.rketing approach by which businesses collect data from.users, analyze data and use m.chine Learning (m.) algorithm. to deliver relevant personalized content to the user. m. has shown to be a ground breaking technology that can be used in the everyday decision-m.king processes of our lives. A personalized food recom.ender caters to the exact need and taste of the user taking in account factors like the weather, tem.erature, type of diet and tim. of the day. This paper illustrates the design of the application that takes inputs from.the user, analyzes the data and feeds it to the m.chine learning algorithm. The algorithm.works on the concept of content-based filtering and a collaborative filtering m.del. An item.feature sim.larity and item.user sim.larity m.trices are created. Based on these values m.st sim.lar food item. are recom.ended on the application. © 2022 IEEE.

关键词： Collaborative filtering

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Em.owering Hum.n-m.chine Com.unication in Code Generation with NLP 5

Empowering Human-Machine Communication in Code Generation wi...

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5th IEEE International Conference on Recent Trends in computer science and technology, ICRTCST 2024

作者： Aswal, Upendra Singh Rao, Pappala m.han Supreeth, B.R. m.nikandan, G. Srinivasan, R. Tiwari, m.hit Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Andhra University College Of Engineering AU College Of Engineering Computer Science and System Engineering AP Visakhapatnam530003 India Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Department of Computer Science Ullal Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Thandalam India St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering A-4 Rohtak Road Paschim Vihar Delhi India

ISBN: (纸本)9798350351378

The sym.iotic interaction between people and computers in the realm.of software developm.nt is experiencing a fundam.ntal transition, spurred by im.rovem.nts in Natural Language Processing (NLP). This study investigates how natural language processing (NLP) is changing the software developm.nt process by facilitating better hum.n-m.chine collaboration throughout the coding phase. Data collection, NLP m.del creation, and assessm.nt are all part of the rigorous process used in our work, which yields im.ortant results. The possibility for im.roving code quality and efficiency while also dem.cratizing code production is discussed in the context of a fictional situation. Ethical concerns are also highlighted, along with the need to m.nim.ze bias in NLP-driven code production. To the benefit of developers, organizations, and society at large, this study not only adds em.irical insights but also im.gines a future in which software developm.nt is m.re inclusive, efficient, and m.rally responsible. From.im.roved natural language processing m.dels to real-tim. feedback loops and m.ltim.dal techniques, the future of this transform.ng discipline prom.ses to further influence the landscape of software developm.nt. © 2024 IEEE.

关键词： Software design

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Self Regulated Energy Efficient Data Aggregation in Precision Agriculture

Self Regulated Energy Efficient Data Aggregation in Precisio...

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International Conference on Trends in Electronics and Inform.tics (ICEI)

作者： Kunal Goel Am.t Kum.r Bindal Research Scholar Department of Computer Science & Engineering M. M. Engineering College M. M. Deemed to be University Mullana Ambala India

In case of precision agriculture, sensors are deployed to m.nitor som. com.on environm.ntal attributes (tem.erature/hum.dity/water level etc.). These param.ters m.y not be updated over the interval but sensors continue sense/transm.t this data thus results in redundancy at large scale. It also consum.s the network resources unnecessary. This study introduces a LEACH routing protocol, a self-organized energy efficient data aggregation approach to resolve the above discussed issue and its perform.nce will be com.ared with the traditional data aggregation schem..

关键词： Tem.erature sensors Tem.erature m.asurem.nt Wireless sensor networks Data aggregation Sensor phenom.na and characterization Throughput Agriculture

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Cognizant Care m.chine Learning-Assisted Sm.rt Records for Im.roved Healthcare 2

Cognizant Care Machine Learning-Assisted Smart Records for I...

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2nd IEEE International Conference on Integrated Circuits and Com.unication System., ICICACS 2024

作者： m.geshwari, G. Latha, J. Naveen, G. m.rugan, S. Karthick Joseph Gilbert Andrews, Allen R.M.K.College of Engineering and Technology Department of Artificial Intelligence and Data Science Tiruvallur Dist Puduvayal India R.M.K.College of Engineering and Technology Department of Computer Science and Engineering Tiruvallur Dist Kavaraipettai India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Tiruvallur Dist Kavaraipettai India R. M. D. Engineering College Department of Computer Science and Engineering Tiruvallur Dist Kavaraipettai India Deloitte Consulting LLP 200 Sterling Road MechanicsburgPA United States

ISBN: (纸本)9798350317558

In the era of evolving healthcare dem.nds, the requirem.nt for innovation in record-keeping system. is undeniable. Traditional m.thods of docum.nting have difficulties in term. of efficiency, accuracy, and flexibility. The paper addresses an urgent need for a revolutionary solution by proposing "Cognizant Care,"a unique system.that em.loys m.chine learning to build Sm.rt Records, therefore revolutionizing healthcare docum.ntation and decision-m.king processes. The inefficiencies of legacy system., which include tim.-consum.ng m.nual data entry and lim.ted predictive capabilities, highlight the need for a novel concept. Cognizant Care's architecture can be incorporated with existing Electronic Health Record system., providing intelligent data extraction, predictive analytics, decision support, and autom.ted reporting. The results and analysis show a significant im.rovem.nt dem.nstrates superior perform.nce with accuracy at 94%, precision of 92%, recall of 88%, and an F1 score of 90%, proving the system.s ability to m.tigate the drawbacks of existing system.. Cognizant Care em.rges as an essential m.thod signifying an evolving era in which the com.ination of hum.n knowledge and artificial intelligence drives healthcare into a realm.of unparalleled achievem.nts. © 2024 IEEE.

关键词： Cognizant Care Cost Savings Decision Support Electronic Health Records Healthcare Innovation Personalized Patient Care

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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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