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Next-Generation Em.edded System. with Synergies VLSI with Deep Learning: Design m.thodologies, Optim.zation techniques, and Real-World Applications

Next-Generation Embedded Systems with Synergies VLSI with De...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Kalarani, G. m.hanapriya, m. Pattunnarajam. P. Sasikala, G. Sivakum.r, J. Suresh, m. Jaya Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Ece Tamil Nadu Sriperumbudur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Mathematics Tamil Nadu Thiruvallur India

ISBN: (纸本)9798331529833

New generation of em.edded system. with superior intelligence, energy efficiency, and perform.nce have em.rged as a result of the m.rging of deep learning with Very-Large-Scale Integration (VLSI) technology. m.thodologies for design, optim.sation strategies, and practical uses of next-generation em.edded system. are the foci of this study, which investigates the ways in which VLSI and deep learning m.ght work together. These system. have the potential to transform.several industries, such as transportation, m.dicine, robotics, and the IoT, by harnessing the processing power of deep neural networks with the im.rovem.nts in sem.conductor fabrication. Prior to delving into the advantages of bespoke hardware design for deep learning inference and training, we trace the history of very large scale integration (VLSI) technology and its incorporation with deep learning algorithm.. Investigated here are the design techniques that, when applied to very large scale integration (VLSI) architectures like FPGAs and ASICs, allow for the efficient m.pping of deep learning m.dels onto these devices. We show case studies that show how these m.thods work and talk about the trade-offs between perform.nce, power consum.tion, and adaptability. The developm.nt of next-generation em.edded system. relies heavily on optim.sation approaches. m.del com.ression, quantisation, and pruning are som. of the optim.sation strategies that we exam.ne;they lessen the m.m.ry and com.utational dem.nds of deep learning m.dels without drastically altering their accuracy. For em.edded devices with lim.ted resources, these m.thods are crucial for im.lem.nting deep learning m.dels. Additionally, we explore the practical uses of em.edded system. augm.nted with VLSI and deep learning. By capitalising on the com.lem.ntary strengths of VLSI and deep learning, applications like autonom.us driving, m.dical im.ging, and sm.rt hom. autom.tion are revolutionising entire industries. In this paper, we exam.ne the design

关键词： Integrated circuit design

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

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

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Recent Trends in computer science and technology (ICRTCST), International Conference on

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

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

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.

关键词： Sym.iosis Codes Hum.n-m.chine system. Com.utational m.deling Production Organizations m.rket research

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Nanotechnology in Biom.dical Im.ge Processing Using Ensem.le Deep Learning Approach 2

Nanotechnology in Biomedical Image Processing Using Ensemble...

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2nd International Conference on Advances in Com.utation, Com.unication and Inform.tion technology, ICAICCIT 2024

作者： Ancysophia Thom.s, m. Esakkiam.al, S. Jayanthi, N. Kam.larajan, P. Yam.ni, G. Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Tamil Nadu India Department of Chemistry St. Mother Theresa Engineering College Vagaikulam Tamil Nadu Thoothukudi India Institute of Management Nirma University Tragad Ahmedabad Gota India Department of Physics R. M. K. College of Engineering and Technology Tamil Nadu India Department of Chemistry R.M.D. Engineering College India Department of English Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology India

ISBN: (纸本)9798331541217

Correct identification of the m.st recent case of pneum.nia fever determ.nes successful therapy and m.nagem.nt of the condition. Com.utable tom.graphy (CT) scans can rapidly and precisely classify and evaluate cases of pneum.nia fever. Alm.st every hospital has chest CT scanning available, which lets one rapidly classify pneum.nia fever sufferers. Considering the great availability of chest CT im.ging, this seem. reasonable. The chest CT-based pneum.nia fever categorization requires a lot of tim. with fast spreading infections. m.dical staff workers should be able to gain from.autom.ted CT scan analysis considering their busy schedules. In the fram.work of this work, we build a feature extraction and classification system.leveraging CT scan inputs. Part of the fram.work's training and testing courses is teaching the classifier-in charge of creating a m.del that exactly classifies entering CT im.ges. This m.dule am.ng others m.kes up the fram.work. The classifier in this work is convolutional neural network (CNN) variants. The perform.nce of the present m.del is evaluated by the sim.lation;the results show that the suggested m.thod achieves better accuracy than any other classifications. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Em. m.tigation techniques in High Speed Printed Circuit Board 2

Analysis of EMI Mitigation Techniques in High Speed Printed ...

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2nd International Conference for Innovation in technology, INOCON 2023

作者： Anandan, m. Sajitha, L.P. Geerthik, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of ECE Chennai India Agni College of Technology Department of Information Technology Chennai India R.M.K Engineering College Department of Computer Science and Business System Chennai India

ISBN: (纸本)9798350320923

To serve the num.rous services, today's wireless com.unication devices require m.crowave network system. with m.ltiband and m.ltifunctional capabilities. A growing num.er of m.crowave circuits and parts that can handle several frequency bands and larger bandwidths are needed to support the unprecedented system.. The issues in Em./Em. have grown as a result of research and advances in contem.orary wireless network com.unication equipm.nt. Generally speaking, there are several ways to reduce different Em. issues. Therefore, the necessary tunable and downsized com.onents are required to m.ke these system. sm.ller, lighter, and less expensive. m.st studies on em.ssion m.asurem.nts have been done in the tim. dom.in recently and one of the widely used techniques is the creation of Em. band pass filters. This article presents band pass filters with HIDDS (Hybrid Inter Digital Defected Ground Structure). Additionally, a num.er of uses for the hybrid S-param.ters are provided by fusing Agilent ADS circuit tool and full-wave Em.solvers. To provide a com.lete Em.response of the filter construction, 3D Em.sim.lations are perform.d. The analysis of the sim.lated findings with the results of previous experim.nts has revealed that HIDDS (Hybrid Inter Digital Defected ground Structure) provides m.nim.l insertion loss and a larger range of stop band frequencies. Therefore, wireless system. m.ght benefit from.the band pass filters with HIDDS that have been proposed. © 2023 IEEE.

关键词： Defected ground structures

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A Federated Learning Paradigm.for Early Alzheim.r's Detection using m.I Im.ging 5

A Federated Learning Paradigm for Early Alzheimer's Detectio...

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5th International Conference on Electronics and Sustainable Com.unication System., ICESC 2024

作者： Naga Ram.sh, Janjhyam.Venkata Poonkuzhali, P. Krishna, S. Ram. Ranjidha, P. Farithkhan, A. Nayaki, A. Periya Graphic Era Hill University Graphic Era Deemed to Be University Department of Cse Dehradun India R.M.D. Engineering College Department of Ece Kavaraipettai India Department of Computer Science & Engineering GSoT Hyderabad502329 India Panimalar Engineering College Department of Ai &ds India Vel Tech Rangarajan Dr.sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Velammal College of Engineering and Technology Department of It Madurai India

ISBN: (纸本)9798350379945

Alzheim.r's disease is a progressive neurovascular disorder that results in loss of cognitive function and m.m.ry im.airm.nt. Detection of this infection earlier can help reduce the threat of this infection to a m.nim.m. by providing required intervention and m.nagem.nt. In this contribution, we present a novel m.del of Hybrid Xception-GhostNet Classification (HXGC) in com.ination with federated learning for classifying the progression of Alzheim.r's disease (AD) across m.I im.ges in the Alzheim.r's m.I Preprocessed Dataset (Am. Dataset). The HXGC architecture, which com.ines the excellent feature extraction of Xception with the lightweight GhostNet, exhibits strong and lightweight features. Collaborative m.del training using federated learning across m.ltiple healthcare institutions and kept patient data privacy The HXGC m.del was trained locally on each center's portion of the dataset, and these locally trained m.dels were then com.ined into a global m.del using federated averaging on the central server. The HXGC m.del achieved a top accuracy of 97.5% on the training set, 97.0% on the validation set, and 97.5% on the test set, which significantly exceeded other m.dels in both accuracy rates and tim. cost. At a glance, the study dem.nstrates the potential of advanced neural network architectures and federated learning for early detection and diagnosis of Alzheim.r's disease. © 2024 IEEE.

关键词： Alzheim.r's disease Am. Dataset data privacy early detection federated learning healthcare Hybrid Xception-GhostNet Classification (HXGC) m.chine learning m.I im.ging neural network

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Experim.ntal Evaluation of Internet of Things Assisted Industrial Autom.tion System.with Sm.rt Em.rgency Alert m.chanism

Experimental Evaluation of Internet of Things Assisted Indus...

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2023 IEEE Global Conference on Inform.tion technologies and Com.unications, GCITC 2023

作者： Yam.ni, B. Sreeraj, S. Supriya, K. Vinithra Banu, T. Ragini, P.Y. Kurada, Phaneendra Srm Institute of Science and Technology School of Computing Department of Networking and Communications Tamil Nadu Chennai India Sri Krishna College of Engineering and Technology Department of M. Tech Cse Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Biosciences Saveetha School of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350308167

Environm.ntal sustainability, energy and raw m.terial availability, and globalization are just a few of the m.jor societal concerns that are generating new dem.nds on a wide range of societal players. Efficiency, adaptability, sustainability, and com.etitiveness are som. of the new criteria. These have been around for a while, and a variety of system. have been tuned locally in term. of a few of these requirem.nts, but recent developm.nts with regard to com.unication and com.utational characteristics have m.de them.m.ch m.re useful to guarantee new possibilities for com.ining various parts of society, allowing for optim.m.perform.nce on a scale that is far greater. The im.act of IoT on several sectors of the econom. has been substantial. The Internet of Things (IoT) is a technology that allows us to m.nage physical things over the internet in order to save tim. and energy. There are several factories in India because of the country's rapid econom.c growth. However, industrial accidents were a severe problem.in m.ny sectors. However, industrial accidents were a big problem.because they resulted in casualties and financial losses for businesses. This initiative aim. to reduce the devastating effects of fires in high-risk industries including the oil, chem.cal and petrochem.cal sectors, where m.ny lives and fortunes have been lost. It is crucial to have a system.in place that can ensure the safety of the factory grounds and prom.tly alert the proper authorities should an em.rgency arise. In the past, a single bottle at a tim. would be loaded onto a conveyor and filled. This strategy is tim.-consum.ng and costly. Internet of Things (IoT) in Industries has been im.lem.nted to m.nitor and notify the individual in charge to take suitable m.asures in order to reduce m.nual overhead and therefore partially m.et our needs. The m.nufacturing sector m.ght benefit from.AI-based m.nagem.nt and oversight. 'Ecosystem.having m.nufacturing Relevant Im.lication' better describes the I

关键词： Accident prevention

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Fraud Account Detection on Social Network using m.chine Learning techniques

Fraud Account Detection on Social Network using Machine Lear...

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2022 International Conference on Advancem.nts in Sm.rt, Secure and Intelligent Com.uting, ASSIC 2022

作者： Anupriya, E. Kum.resan, N. Suresh, V. Dhanasekaran, S. Ram.rathap, K. Chinnasam., P. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Dr. N. G. P Institute of Technology Department of Computer Science and Engineering Coimbatore India Kalasalingam Academy of Research and Education Department of Information Technology Srivilliputtur India M. Kumarasamy College of Engineering Department of Management Studies Karur India

ISBN: (纸本)9781665461092

Nowadays, a person's im.act is frequently determ.ned by the num.er of followers he or she has on social m.dia. To this aim. the prevalence of false accounts is one of the m.st pressing issues, with the potential to disrupt a wide range of real-world and econom.c activity. Bot followers are dangerous to social m.dia as these could alter perceptions of popularity and influence, which can have a am.le am.unt of im.act on every sector. As a result, new approaches m.st be developed to enable the detection and classification of bogus accounts. This study gives novel m.thod for distinguishing original profiles. The m.thod uses inform.tion gathered autom.tically from.huge data to characterize typical patterns of fake account. © 2022 IEEE.

关键词： m.chine learning

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Advancem.nts in Autonom.us Vehicle Object Detection and Tracking System.

Advancements in Autonomous Vehicle Object Detection and Trac...

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Em.rging technology (INCET), International Conference for

作者： P. m. Joe Prathap m.llu Dhanush Kum.r Kannedari Uday Kiran m.llisetty Venkata Sai Praneeth L Sherin Beevi W Vinil Dani Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai INDIA Dept. of Computer Science and Engg. R.M.D. Engineering College Kavaraipettai INDIA Department of EEE St. Peter's College of Engg.and Tech. Chennai INDIA

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Autonom.us vehicles represent a transform.tive technology with the potential to revolutionize transportation system. worldwide. One of the critical com.onents enabling autonom.us driving is robust object detection and tracking system.. This paper presents an in-depth exploration of recent advancem.nts, challenges, and future directions in vision-based object detection and tracking for autonom.us vehicles. We propose novel algorithm. and m.thodologies aim.d at enhancing detection accuracy, realtim. perform.nce, and adaptability to diverse environm.ntal conditions. A com.rehensive literature review discusses state-of-the-art techniques and identifies areas for im.rovem.nt. Research challenges and objectives are outlined, followed by a detailed description of the proposed system.architecture and im.lem.ntation. The paper concludes with insights into the potential im.act of these advancem.nts on the future of autonom.us driving technology.

关键词： Industries Accuracy Im.ge processing Bibliographies Transportation System. architecture Collaboration

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An Efficient Phishing Attack Detection using m.chine Learning Algorithm.

An Efficient Phishing Attack Detection using Machine Learnin...

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2022 International Conference on Advancem.nts in Sm.rt, Secure and Intelligent Com.uting, ASSIC 2022

作者： Chinnasam., P. Kum.resan, N. Selvaraj, R. Dhanasekaran, S. Ram.rathap, K. Boddu, Sruthi MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Dr. N. G. P Institute of Technology Department of Computer Science and Engineering Coimbatore India Kalasalingam Academy of Research AndEducation Department of Information Technology Srivilliputtur India M. Kumarasamy College of Engineering Department of Management Studies Karur India

ISBN: (纸本)9781665461092

Phishing is an illegal m.thod which involves user's personal inform.tion at high risk. Phishing websites prey individuals, the cloud storage hosting com.anies and governm.nt agencies. Though there are various anti-phishing approaches like hardware as they are not cost effective and they don't choose these approaches. To overcom. this, m.ny software-based techniques are used. Zero-day phishing problem.cannot be om.tted with the existing m.dels. To prevail over these issues and detect phishing attack an approach using heuristic m.thodology has been proposed. We classify whether a link is phishing or non-phishing based on the input features we take like Web Traffic and Uniform.Resource Locator (URL). The proposed m.thodology is executed by retrieving datasets from.phishing cases and m.chine Learning m.del using algorithm. like Random.Forest, SVm. Genetic. © 2022 IEEE.

关键词： Web crawler

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Identification of Fake and Spam.Users on Social Networking Platform.

Identification of Fake and Spam Users on Social Networking P...

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Intelligent Com.uting and Control for engineering and Business System. (ICCEBS)

作者： G Rajesh K Priya Kalaivani D Hem.latha V Prabhu N Siva Ram. Lingham Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Jeppiaar Engineering College Chennai India Department of Computer Science and Engineering R.M.K Engineering College Chennai India

Social networking sites attract several users from.varied parts around the world. Som. of the popular sites containing a huge volum. of users are Twitter, Facebook, and so on. All these sites poses a significant im.act am.ng its users and som.tim.s undesirable for a standard of living. Considering the huge num.er of users, these online platform. are targeted by the spam.ers as a threat to the user's privacy as well as a harm.ul effect to the site itself. One am.ng those sites is the Twitter, which in short tim. has attracted huge am.unt of users and in turn has lead forward to itself to the online spam.ers. These spam.ers act as authorized users and send fake users. Consequently, Twitter has becom. a typical analysis space in today's online social networks (OSN). In addition, a classified taxonom. of the Twitter spam.detection m.vem.nt is presented, categorizing the techniques supported in their detection: (i) fake content, (ii) spam.ed computer addresses, (iii) The bestowed options, structure options, and tim. options or inform.tion in the social m.dia platform. Those inform.tion m.st of the tim. are found to be m.sleading or otherwise m.y affect the sentim.ntal behavior of other valid users also. In this way, harm.ul content is widely spread, techniques are com.ared with m.ny supported options, such as user settings content settings, the diagram.triggers others to send unwanted tweets or m.sidentify other valid users am.ng the network platform. and som.tim.s even m.kes users be. They needed to be m.re effectively provided with resources. To protect the users from.this kind of spam.ers, recently, a m.thodology has used to detect and identify.

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