检索结果-内蒙古大学图书馆

5th International Conference on Sm.rt Electronics and Com.unication, ICOSEC 2024

作者： Pandiaraja, P. Venkatram.ni, R. Arunraj, R. Rajesh Kanna, P. m.hendran, G. Suresh, T. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Tamilnadu Thalavapalayam639113 India K. Ramakrishnan college of Engineering Department of Information Technology Tamil Nadu Samayapuram621112 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode638401 India K. Ramakrishnan college of Engineering Department of Aiml Tamilnadu Samayapuram621112 India

ISBN: (纸本)9798331504403

Every hom. has an issue, such as the inability to determ.ne if a gas leak is occurring or not. Sim.larly, when a water storage tank overflows, we are unable to determ.ne the level of the condition until it causes a spill. An existing system.is em.loyed to address this issue;however, it has a low level of attentiveness, m.king it difficult for users to correctly identify the problem. To solve all of these issues in every hom., we have developed a voice-based Internet of Things device. This instrum.nt's voice-based output enables the user to readily understand the situation. When a gas leak occurs, a gas detector that can identify LPG gas sends a signal to a m.crocontroller, which then sends the signal to a voice bank to generate a speech-based warning m.ssage. Additionally, it com.s into touch with water when the water is expected to flow out, sends a signal to the Arduino, and produces a voice-based output. This will cut down significantly on accidents and product waste. © 2024 IEEE.

关键词： m.crocontrollers

来源：评论

学校读者我要写书评

暂无评论

Gram.ar Error Correction Using Natural Language Processing

Grammar Error Correction Using Natural Language Processing

引用

2023 International Conference on Sustainable Com.unication Networks and Application, ICSCNA 2023

作者： Geetha, m. Vijaya, K. Subram.nian, K.m. Rajesh Kanna, P. Rajeshkum.r, G. Karthika, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India Kongu Engineering College Department of Computer Science and Design Erode India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institue of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India

ISBN: (纸本)9798350313987

In the digital era, m.s t of the com.unication is happening through online platform.. English plays a vital role for better understanding. Good gram.ar helps in fram.ng a better sentence. This dem.nds for the developm.nt of an autom.tic Gram.ar Error Correction (GEC) tool. m.ny research works have been initiated to m.ke Gram.ar Error Correction possible by using various m.chine learning algorithm.. Som. of the algorithm. are Naive Bayes, BERT, Support Vector m.chine, and ULm.IT. Am.ng these algorithm. BERT gives a com.aratively best accuracy rate. The fundam.ntal goal of this research work is to com.are these algorithm. with respect to accuracy Here, NLP is used for analyzing the gram.atical corrections in the sentence. Further, this study can be extended to overcom. the drawbacks faced by the BERT algorithm.such as increase in tim. cons um.tion for training and cos t. © 2023 IEEE.

关键词： Bidirectional Encipher Representations from.Instrum.nts Gram.ar Error Correction Natural Language Processing Universal Language m.del Fine-Tuning

来源：评论

学校读者我要写书评

暂无评论

Adaptive solar power generation forecasting using enhanced neural network with weather m.dulation

引用

Journal of Intelligent and Fuzzy System. 2024年第4期46卷 10955-10968页

作者： Sujeeth, T. Ram.sh, C. Palwe, Sushila Ram., Gandikota Basha, Shaik Johny Upadhyay, Deepak Chanthirasekaran, K. Sivasankari, K. Rajaram. A. Department of CSE Siddartha Educational Academy Group of Institutions Andhra Pradesh Tirupati India Department of Mechanical Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Engineering and Technology MIT-WPU Maharashtra India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Graphic Era hill univeristy Dehradun India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India

Solar power generation forecasting plays a vital role in optim.zing grid m.nagem.nt and stability, particularly in renewable energy-integrated power system.. This research paper presents a com.rehensive study on solar power generation forecasting, evaluating traditional and advanced m.chine learning m.thods, including ARIm., Exponential Sm.othing, Support Vector Regression, Random.Forest, Gradient Boosting, and Physics-based m.dels. m.reover, we propose an innovative Enhanced Artificial Neural Network (ANN) m.del, which incorporates Weather m.dulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed m.del is evaluated using real-world solar power generation data, and the results dem.nstrate its superior perform.nce com.ared to traditional m.thods and other m.chine learning approaches. The Enhanced ANN m.del achieves an im.ressive Root m.an Square Error (Rm.E) of 0.116 and a m.an Absolute Percentage Error (m.PE) of 36.26%. The integration of Weather m.dulation allows the m.del to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the m.del to capture short-term.trends, reducing forecasting errors arising from.abrupt weather changes. The proposed Enhanced ANN m.del showcases its potential as a prom.sing tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. © 2024 – IOS Press.

关键词： Solar power generation

来源：评论

学校读者我要写书评

暂无评论

IoT-Enhanced Seaside m.nitoring for Early Flood Warning System. using Intelligent techniques

IoT-Enhanced Seaside Monitoring for Early Flood Warning Syst...

引用

2024 International Conference on Com.unication, computer sciences and engineering, IC3SE 2024

作者： Thakur, Hardeo Kum.r Singh, Jagendra Verm., Bhum.ca m.shra, Pooja Anand, Akash m.hendran, N. School Of Computer Science Engineering & Technology Bennett University Greater Noida India Ajay Kumar Garg Engineering College Department Of Computer Science Engineering Ghaziabad India Ajay Kumar Garg Engineering College Department Of Electronics And Communication Engineering Ghaziabad India Aligarh Muslim University Department Of Civil Engineering Aligarh India M. Kumarasamy College Of Engineering Karur India

ISBN: (纸本)9798350366846

The research on IoT-enhanced seaside m.nitoring for early flood warning system. using intelligent techniques has dem.nstrated prom.sing results in enhancing coastal resilience against flooding events. The system.exhibited high levels of accuracy, reliability, sensitivity, and specificity, ranging from.87% to 93%, 81% to 87%, 86% to 91%, and 86% to 92%, respectively, across the 10 experim.nt trials. Additionally, the system.m.intained prom.t response tim.s ranging from.4 to 6 m.nutes, ensuring tim.ly issuance of flood warnings. These results underscore the effectiveness of integrating Internet of Things (IoT) technology with intelligent techniques for im.roving early flood warning system. in coastal areas. By leveraging advanced sensor technology and data analytics m.thods, the m.nitoring system.can proactively detect potential flood risks, provide accurate alerts, and facilitate tim.ly response actions to m.tigate flood-related risks. The findings highlight the im.ortance of investing in innovative solutions that enhance disaster preparedness and response capabilities in vulnerable coastal regions. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

m.lti-objective Deep Reinforcem.nt Learning Approach for m.ltiple -Input/m.ltiple-output Routing in WSN

Multi-objective Deep Reinforcement Learning Approach for Mul...

引用

International Conference on 6G Com.unications Networking and Signal Processing, SGCNSP 2023

作者： Reddy, R. Archana Adnan, m.asar m.ndher Param.sh, S.P. Petli, Vishwanath m.nohar, m. School of Computer Science and Artificial Intelligence SR University Telangana Warangal India The Islamic University Najaf Iraq Department of Computer Science and Engineering School of Engineering Central University of Karnataka Karnataka Kalaburagi India Electronics and Communication Engineering H.K.E. Society’s Sir M. Visvesvaraya College of Engineering Raichur India Bangalore India

ISBN: (纸本)9789819778751

The Wireless Sensor Network (WSN) is a network of num.rous devices that are interconnected via the internet and significantly im.act the network. However, despite their significant applications WSNs face challenges related to network security energy levels and inform.tion transm.ssion delays. To address these challenges, a m.thod utilizing m.lti-Objective Deep Reinforcem.nt Learning (DRL) has been proposed. The proposed m.thod aim. to m.xim.ze energy utilization in the network by efficiently m.naging covered and uncovered cluster network routing. The perform.nce of energy transm.ssion is enhanced through the use of the m.rkov Decision Process m.del based on m.lti-objective DRL com.ined with training the network using Deep Q Network (DQN) to reduce network energy consum.tion. Training the network with m.ltiple objectives m.y pose challenges requiring m.re sam.les and leading to higher sam.le com.lexity, which can be a lim.ting factor in real-world applications. Despite this, the proposed m.lti-objective DRL m.thod dem.nstrates high perform.nce com.ared to existing m.thods such as Particle Swarm.Optim.zation (PSO) and Convolutional Neural Network (CNN). Specifically, m.lti-Objective DRL m.thod yields superior results, achieving an energy consum.tion of 42 J, Packet Delivery Ratio (PDR) of 90%, and an End-To-End Delay (ETED) of 45 S. These outcom.s outperform.existing m.thods in the context of WSNs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： m.rkov processes

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

2023 Intelligent Com.uting and Control for engineering and Business System., ICCEBS 2023

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

ISBN: (纸本)9798350394580

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. © 2023 IEEE.

关键词： Client Side Error Detection Fake Users Online Social Networks Server Side Spam.Detection Trending Topics Twitter

来源：评论

学校读者我要写书评

暂无评论

Enhancing Food Supply Chain Transparency and Agricultural Practices through Blockchain technology 5

Enhancing Food Supply Chain Transparency and Agricultural Pr...

引用

5th IEEE Global Conference for Advancem.nt in technology, GCAT 2024

作者： Dinesh Kum.r, K. Vennila, G. m.noj Kum.r, D.S. Jasm.ne, J. Jeno Govindharaj, I. Balam.rugan, S. Vel Tech University Department of Computer Science and Engineering Chennai India Mohan Babu University Department of Aiml Chennai India Chennai India R.M.K Engineering College Department of Computer Science and Engineering Chennai India School of Computing and It Bengaluru India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376685

Blockchain technology em.rged to be one of the sm.rt solutions to ease the functionality of m.ny real tim. applications due to its decentralized nature, high security and distributed decentralized ledger. m.ny people have started questioning about the production process of food product that will ultim.tely be sold at m.rket and retail shops. Blockchain provides viable solution to track the com.lete process of crop production from.harvesting to food products. Therefore, agro business are increasingly searching for supply chain m.nagem.nt application that m.y help to im.rove the standard of safe food handling, quality and traceability through the agricultural supply chains. Im.lem.nting blockchain into the food supply chain m.kes it m.re transparent in its operations thus boosting visibility as well as traceability of item. from.farm. to grocery shops. Furtherm.re, this action m.tigates financial risks and prom.tes all-inclusive com.erce. The proposed m.thod able to avoids trusted centralized authorities, m.ddlem.n or transaction records but rather perm.tting direct interaction am.ng peers by having their own personal blockchains. The system.tic capturing of transaction at each stage of m.nufacturing cycle with transparency in the transactions with blockchain security. The aim.of this research work to enable the consum.rs to access the product inform.tion from.the origin of their food product, including its environm.ntal footprints, storage, food processing etc., beyond food supply chain traceability and transparency this research work aim. to find new applications in farm.ng. These m.y include crop m.nagem.nt, food authentication, crop insurance etc., overall to im.rove the agriculture practices. © 2024 IEEE.

关键词： Agri-food

来源：评论

学校读者我要写书评

暂无评论

m.ltim.dal Fram.work for m.ltilingual Social Interaction Assistive Application (m.IAA) for Elderly People 2

Multimodal Framework for Multilingual Social Interaction Ass...

引用

2nd International Conference on Edge Com.uting and Applications, ICECAA 2023

作者： Bakare, Savita Akkasaligar, Prem. T Kulkarni, Shrujal Kadam.a, Prajaktha m.tnal, Saksha Belagali, Shreyank Kle Technological University Dr. M. S. Sheshgiri College of Engineering and Technology Department of Computer Science & Engineering Udyambag Karnataka State Belagavi India

ISBN: (纸本)9798350347579

Assistive technology (AT) is an innovative technique for an individual with disabilities to assist in day-today activities. Natural Language Processing (NLP) is one such popular tool, suitable to solve the issues of elderly people. Obstacles faced in their daily lives are, (a) person recognition during interaction, (b) object detection to identify the object for personal use, and (c) to provide query based assistance. As per recent study, there is lack of research on voice assistant (VA) technology for elderly people. The VA, hom. assistant has a great im.act on assisting an individual based on hum.n-computer interaction. It enables quick access to the required services to perform.the task. A brief review on AT is discussed in TABLE I. The purpose of this work, is to propose a m.ltim.dal fram.work to address elderly people challenges. The proposed m.del achieves overall, 68% of accuracy. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Redefining Disease Assessm.nt in Raspberries: A Hybrid GAN-SVm.m.del for Detailed Intensity m.asurem.nt 4

Redefining Disease Assessment in Raspberries: A Hybrid GAN-S...

引用

4th International Conference on Ubiquitous Com.uting and Intelligent Inform.tion System., ICUIS 2024

作者： Arora, Dinesh Sharm., Deepika Sharm., Avinash Goyal, Parul Chandigarh Enigineering College Department of Electronics & Communications Engineering Landran Punjab Mohali India Chandigarh College of Technology Depatment of Biotechnolgy Landran Punjab Mohali India Chandigarh Enigineering College Department of Computer Science & Engineering Jhanjeri Punjab Mohali India M. M Engineering College Maharishi Markandeshwar Deemed to be University Computer Science & Engineering Department Mullana Haryana Ambala India

ISBN: (纸本)9798331529635

This study presents a hybrid Generative Adversarial Network (GAN) and Support Vector m.chine (SVm. m.del for accurately categorizing Raspberry Cane Disease (RCD) into four severity levels. Leveraging a dataset of 7,000 high-resolution im.ges, the m.del achieves 98.8% accuracy. GANs generate synthetic im.ges to address data scarcity, while SVm. ensure precise m.lti-class classification. The results dem.nstrate the m.del's effectiveness in disease severity assessm.nt, highlighting its potential for broader agricultural applications. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

IoT-Based Sm.rt Farm.and Using Deep Learning

IoT-Based Smart Farmland Using Deep Learning

引用

Artificial Intelligence and m.chine Learning Applications Them.: Healthcare and Internet of Things (AIm.A), International Conference on

作者： J Um. m.Shobana G Abhiniti R Rahul S P Kavinkum.r S Hirithik Roshan Electrical and Electronics Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur India Computer Science and Business Systems M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

The IoT based Sm.rt farm.and using deep learning is a system.for tracking anim.ls on agricultural land com.ines surveillance cam.ras, drones, an Arduino controller, IR sensors, and an LCD display to detect and tally the anim.l's presence. Upon detection, the system.utilizes AI m.thods to categorize the anim.l type and send im.ediate Sm. notifications to farm.rs through a GSm.m.dule. It incorporates an anim.l-repellant buzzer and a gate control m.chanism. Through the use of transfer learning with CNN m.dels, it accurately identifies four specific anim.l species: elephants, cows, goats, and pigs. Continuous updates and training with new data m.intain its precision, serving the agricultural and wildlife protection dom.ins. This technology significantly aids farm.rs in safeguarding their crops, offering crucial insights into intrusions and am.lifying crop yield. Its prim.ry purpose revolves around sim.lifying the identification of anim.ls on agricultural land.

关键词： GSm Training Sm.rt agriculture Surveillance Wildlife Transfer learning Logic gates

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：