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检索条件"机构=Computer Science And Engineering V.S.B Engineering College"

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Deep Learning Approaches for Real-Time Data Analytics in IoT sensor Networks 3rd

Deep Learning Approaches for Real-Time Data Analytics in IoT...

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3rd International Conference on Universal Threats in Expert Applications and solutions, UNI-TEAs 2024

作者： Mary, P. Arockia sharma, Avinash Dekka, satish Gowda, v. Dankan singh, Mandeep Department of Information Technology V.S.B. Engineering College Tamilnadu Karur India Director of Computer Application Chandigarh Group of Colleges Sahibzada Ajit Singh Nagar India Department of Computer Science and Engineering Lendi Institute of Engineering and Technology Andhra Pradesh Vizianagaram India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Department of University Institute of Computing Chandigarh University Mohalli Punjab Gharuan India

ISBN: (纸本)9789819751457

The rise of the Internet of Things (IoT) has made sensor networks ubiquitous, generating vast amounts of real-time data. However, the challenge lies not only in collecting this data but also in swiftly extracting meaningful insights. Traditional data processing methods often fall short in handling real-time IoT data streams. This research delves into deep learning algorithms as a solution for real-time analytics in IoT sensor networks. We examine suitable architectures for time-series data, such as 1D CNNs, GRUs, and LsTMs. The study also emphasizes the importance of efficient model training, rapid inference techniques, and data preprocessing. Our tests demonstrate that deep learning outshines traditional methods in accuracy and speed. Hence, when applied effectively, deep learning can offer dependable real-time analytics for IoT sensor networks, paving the way for smarter decisions in various IoT applications. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2024.

关键词： Data Analytics

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Recommendation of Personalized Learning Path in smart E-learning Platform Using Reinforcement Learning Algorithms 8th

Recommendation of Personalized Learning Path in Smart E-lear...

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Deepa, s. Arunkumar, M.s. Kanimozhi, T. Eswaran, balamurugan Duraivelu, vivek sweatha, M. Department of Computer Technology-PG Kongu Engineering College Tamil Nadu Erode Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India School of Information Technology Texila American University Lilayi Zambia Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Narsapur502313 India Kongu Engineering College Tamil Nadu Erode Perundurai638060 India

ISBN: (纸本)9789819777099

The exponential expansion of e-learning has significantly broadened educational opportunities globally, empowering students to engage in learning experiences from anywhere. However, the abundance of online courses poses a challenge in customization, often resulting in diminished learning outcomes as learners struggle to select suitable courses tailored to their unique requirements. Personalized learning addresses this by adapting activities to individual needs, fostering the development of capabilities and personality. The proposed system integrates Markov Decision Processes (MDPs) and Reinforcement Learning (RL), leveraging deep Q-learning for sequential recommendations and MDP adaptability to tailor learning paths. This combined approach holds promise for revolutionizing e-learning by delivering personalized, adaptive experiences. Learners benefit from tailored courses, activities, and learning paths, optimizing their educational journey. Continuous adaptation and improvement, facilitated by the core MDP framework and user feedback, further enhance effectiveness in e-learning environments. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2024.

关键词： Federated learning

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Revamping Urban Parking with IoT and CNN

Revamping Urban Parking with IoT and CNN

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2023 International Conference on sustainable Computing and smart systems, ICsCss 2023

作者： santhana Krishnan, R. Jegadeesan, s. Deepa, N. Manivannan, K. Antony vasantha Kumar, C. Lakshmi Narayanan, K. Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Artificial Intelligence and Data Science Dindigul India V.S.B. Engineering College Department of Information Technology Karur India Scad College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India Francis Xavier Engineering College Department Electronics and Communication Engineering Tirunelveli India

ISBN: (纸本)9798350333602

Automobiles have become an integral part of our daily routines as the society evolves towards a more sophisticated way of existence. Car usage has also increased dramatically in the last decade. As a result, traffic surges during peak hours, and parking lots near congested locations are in short supply. Inadequate parking lots cause a tense situation in everyday life. Drivers of cars wander throughout the area looking for an appropriate place to park, resulting in wasteful fuel consumption and massive pollution on the environment. In order to tackle such a problem, a smartphone app is being created that allows users to reserve parking spaces ahead of time for their pre-planned outing. A two-way checking mechanism is used for admitting automobiles into parking areas in order to prevent stolen automobiles from getting parked there. This strategy also allows customers to choose the ideal parking place for their requirements. The technology serves in tracking the prevalence of face masks among people, so as to reduce the transmission of COvID-19 disease. © 2023 IEEE.

关键词： Internet of things

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Temperature variation Modelling in Mushroom Growing Hall with IAN-bidirectional GRU Model 4

Temperature Variation Modelling in Mushroom Growing Hall wit...

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4th International Conference on sustainable Expert systems, ICsEs 2024

作者： Kalpanadevi, D. Devi, s. Nandhini silas stephen, D. Jadhav, sunita Ali Khan, P.M.D. Muralidharan, J. School of Science Gitam University Department of Computer Science Karnataka India V. S. B. Engineering College Department of Cse Karudayanpalayam Tamilnadu Karur India Panimalar Engineering College Department of Electrical and Electronics Engineering Tamilnadu Chennai India Krishna Mahavidyalaya Rethare Bk Department of Botany Maharashtra Satara India School of Computing Mohan Babu University Andhra Pradesh Tirupati India Kpr Institute of Engineering and Technology Department of Ece Tamilnadu Coimbatore India

ISBN: (纸本)9798331540364

Modern developments in computing and electronics have made intelligent systems suitable for use in the mechanization of agricultural processes. This proposed used multilayered perceptron and radial basis function networks to forecast the temperature shifts in the mushroom cultivation chamber based on independent variables including water temperature, ambient temperature, water tap, fresh air and circulation air dampers, and water tap. Preprocessing, feature extraction, and training the model are the three phases that comprise this proposed method. Reflectance calibration, normalization, and spectral smoothing are the steps involved in data preprocessing. Feature extraction made quick work of extracting information from mushroom photos by employing the sIFT algorithm. The proposed model was trained using an IAN-bidirectional GRU. When compared to more conventional approaches, the proposed methodology is superior. The method's application resulted in a 93.75% improvement in accuracy. © 2024 IEEE.

关键词： Radial basis function networks

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Prediction and Electricity Forecasting on the Individual Household Level based on PsO-LssvM Approach 7

Prediction and Electricity Forecasting on the Individual Hou...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Padmaja, voleti Tamizhselvi, A. Kathir, I. Mohan, Arvind Pal, subharun Rastogi, Ravi Geethanjali College of Engineering and Technology Electrical and Electronics Engineering Hyderabad India St. Joseph's College of Engineering Department of Information Technology Chennai India V.S.B. Engineering College Department of Electrical & Electronics Engineering Karur India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India Indian Institute of Technology Jammu Department of Computer Science and Engineering Jammu & Kashmir Jagti India NIELIT Electronics Division Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350340600

When smart metering systems are used to encourage energy conservation at the residential level, new research challenges arise in the areas of monitoring usage and providing accurate load forecasts. Intelligent smart meters rely on accurate predictions of future electricity use. Many energy and operational improvements, such as more efficient appliances, renewable energy sources, and advanced metering systems, are included into micro-grids. Here is an alternative approach to estimating annual energy use in single-family homes. Incorporating the effects of residents' daily activities and appliance usages on overall home power consumption improves the accuracy of the forecasting model. Data preparation, feature selection, and model training are the three phases of the suggested approach. Data preprocessing usually entails cleaning the data and preparing the features to be used. When it comes to feature selection, the K-means clustering approach is frequently utilized. After using information gain for feature selection, the models are trained using PsO-LssvM. The proposed method outperforms both GRU and LssvM, two well-established competitors. © 2023 IEEE.

关键词： K-means clustering

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Enhanced security Model For Heterogeneous Wireless sensor Networks

Enhanced Security Model For Heterogeneous Wireless Sensor Ne...

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International Conference on Computing and Networking Technology (ICCNT)

作者： M. santhanalakshmi A.sampath Dakshina Murthy Mohit Ranjan Panda s Prabakaran G.sambasiva Rao M.s. Nidhya Dept of Computer Science Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women Chennai Dept of ECE Vignan’s Institute of Information Technology(A) Vishakhapatnam School of Computer Engineering KIIT Deemed to be University V.S.B Engineering College HOD AIML Nawab Shah Alam Khan College of Engineering and technology Osmania University Hyderabad Dept of CS & IT Jain Deemed-to-be University Bangalore

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Wireless sensor Networks (WsNs) have emerged as a transformative technology with diverse applications across environmental monitoring, healthcare, smart cities, and industrial automation. However, the security of WsNs remains a critical challenge, particularly in heterogeneous environments where nodes possess varied capabilities and communication protocols. This paper explores the unique security challenges specific to heterogeneous WsNs and proposes an enhanced security model tailored to address these challenges effectively. The introduction highlights the vulnerabilities of WsNs, including broadcast communication exposure, making sensor nodes susceptible to attacks like jamming and data interception. The absence of centralized infrastructure necessitates decentralized and efficient security mechanisms to protect sensitive data and ensure network integrity. The literature survey reviews existing security models and protocols for WsNs, emphasizing the need for lightweight authentication, secure key management, intrusion detection, and secure routing. The proposed security model incorporates dynamic key management, lightweight authentication, secure data aggregation, and intrusion detection to fortify heterogeneous WsNs against emerging threats and vulnerabilities. Future research directions aim to develop scalable and adaptive security frameworks to withstand evolving threats in dynamic network environments. by exploring these challenges and proposing innovative security solutions, this paper contributes to enhancing the security posture of heterogeneous WsNs across various industries and applications.

关键词： Industries surveys Wireless sensor networks Adaptation models Technological innovation Computational modeling Intrusion detection Authentication security stakeholders

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An Indispensable Contemplation on Natural Language Processing Using Ensemble Techniques for Text Classification 8

An Indispensable Contemplation on Natural Language Processin...

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8th International Conference on Advanced Computing and Communication systems, ICACCs 2022

作者： Chahar, Ankit Patil, Ninad Walunj, Darshan sai Rohith, T. shah, Rajat saratkar, Himanshu R.V. College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India College of Engineering Pune Department of Computer Science and Engineering Maharashtra Pune India P.E.S Modern College of Engineering Department of Information Technology Maharashtra Pune India Department of Computer Science and Engineering Karnataka Manipal India Kj Somaiya College of Engineering Department of Mechanical Engineering Maharashtra Mumbai India Yeshwantrao Chavan College of Engineering Department of Computer Technology Maharashtra Nagpur India

ISBN: (纸本)9781665408165

NLP is a computer-based software technology that used primarily to translate the natural human language into machine understandable format efficiently. The inputs for the machine could be text, audio, video, image, and speech. NLP is emerging globally and is being used in many applications. One popular application of NLP is siri introduced in Apple Inc. This created a greater impact on iPhone community. Machines being trained with respect to voice search will facilitate the future generation in performing day-to-day activities. Machine learning (ML) algorithms have contributed extensively for building this popular framework. The paper aims to provide an NLP solution to a text classification problem and solve it using NLP and machine learning to get the best results and the best accuracy. © 2022 IEEE.

关键词： Regression analysis

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Water body Area Identification and Classification Using Hyperspectral Images 7

Water Body Area Identification and Classification Using Hype...

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7th International Conference on Computing in engineering and Technology, ICCET 2022

作者： Kalaiyarasi, M. saravanan, s. Karthi, s. Karunanithi, K. Priya, P. V.S.B Engineering College Karur India B V Raju Institute of Technology Telangana India Vel Tech Rangarajan Dr.Sagunthala R&D Insitute of Science and Technology Tamil Nadu India Kalasaingam Academy of Research and Education Tamil Nadu India

ISBN: (纸本)9781839537042

The water resource monitoring system based on satellite images has a significant drawback, that it does not deliver reliable data. Main drawbacks in the existing methodologies are, the water resources and their existence could not be correctly recovered, and the results obtained were not conspicuous. The proposed system properly retrieves the existence of water resources and allows monitoring using satellite images. In the proposed system, deep learning based segmentation and svM classifier have been adopted, which properly retrieves the existence of water resources. The proposed method has been applied on the pond in Karur district and has been found that, 8% of water resource has been reduced during 2019-2020. The proposed method produces the outcomes with the Accuracy of 98% when compared with other existing methods. This method may be used to track water resources with ranges in the future, as well as monitor occupied spots in water resources throughout all districts. © 2022 The Institution of engineering and Technology.

关键词： Water resources

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Cloud-based Road safety for Real-Time vehicle Rash Driving Alerts with Random Forest Algorithm 3

Cloud-Based Road Safety for Real-Time Vehicle Rash Driving A...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Presitha Aarthi, M. Reddy, Chilukala Mahender Anbarasi, A. Mohankumar, N. Ishwarya, M.v. Murugan, s. Shree Sathyam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Salem India Gitam University Department of Computer Science and Engineering Telangana Hyderabad India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

The cloud-based road safety technology introduced in this research improves real-time vehicle behavior monitoring and warns for rash driving. It uses cloud computing to gather and interpret data from onboard car sensors and other traffic monitoring equipment. Powerful machine learning technology the Random Forest (RF) algorithm analyses this data intelligently. The RF algorithm is trained on a varied array of driving metrics to detect rash driving tendencies. Real-time data streams may be processed efficiently and scalable on the cloud, enabling speedy decision-making for rash driving detection. A network sends alerts to authorities and nearby vehicles. The system is adaptable to different road conditions, resistant to sensor data noise, and able to enhance rash driving detection accuracy via model updates. Cloud architecture makes the system available worldwide. With its focus on real-time notifications utilizing the RF algorithm, this cloud-based road safety system may reduce the hazards of reckless driving and avoid accidents. © 2024 IEEE.

关键词： Cloud computing architecture

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Parallel and batch Multiple Replica Auditing Protocol for Edge Computing 21

Parallel and Batch Multiple Replica Auditing Protocol for Ed...

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21st IEEE International symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big Data and Cloud Computing, 16th IEEE International Conference on social Computing and Networking and 13th International Conference on sustainable Computing and Communications, IsPA/bDCloud/socialCom/sustainCom 2023

作者： Li, Yi Zheng, Wenying Pandi, vijayakumar bhuiyan, Md Zakirul Alam Thamilarasi, C. Nanjing University of Information Science and Technology School of Computer Science Nanjing China Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Melpakkam India Fordham University Department of Computer and Information Sciences BronxNY United States P.S.V College of Enfgineering and Technoogy Department of Electronics and Communication Engineering Krishnagiri India

ISBN: (纸本)9798350329223

The edge computing architecture exposes data transmission and storage to unauthorized access, compromising data integrity. Users typically employ multiple data backups to ensure data reliability and availability, enhancing data redundancy and recovery capabilities. Nevertheless, verifying the integrity of multiple backup data under edge computing entails computational and communication overhead. In response, we present a parallel and batch multiple replica auditing protocol tailored for edge computing. Contributions include an efficient multiple replica construction method based on RsA, minimizing computational overhead, and a parallel data integrity verification approach that reduces communication overhead. A prototype implementation is evaluated in a real edge computing environment and compared to existing multiple replica auditing approaches. © 2023 IEEE.

关键词： Edge computing

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