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共 20255 条记录，以下是441-450 订阅

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Design and Simulation Approaches of Resistive-Random Access Memory 3

Design and Simulation Approaches of Resistive-Random Access ...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Nayak, Abhijith Jayarajendra Bhoomika, V. Chaithra, P. Divyashree, S. Jamuna, S. Dayananda Sagar College of Engineering Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331515911

Memory refers to a physical device storing data in bits. It may be 0 or 1. Storage and retrieval of information is essential for computer operation and memory acts as the primary foundation. Non-volatile memory is a type of storage that does not lose the data it contains after a power loss, just as volatile memory does. Re-RAM is one of the nonvolatile memories. Re-RAM is another outstanding non-volatile category that applies resistance switching from a high resistive state to a low resistive state and vice versa in metal-oxide layers to store data more quickly and at higher density. For instance, it has a low power rating, high endurance, and good data retention characteristics, and is thus suitable for energy-constrained mobile and embedded systems. This Paper demonstrates the simulation of Re-RAM which can be done using Verilog-A in Cadence Virtuoso and through code-level analysis with MATLAB. © 2025 IEEE.

关键词： MATLAB

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A New dataset for Analyzing Battery Failures in Wheelchairs 25th

A New Dataset for Analyzing Battery Failures in Wheelchairs

引用

25th international conference on intelligent data engineering and automated learning

作者： Manzolli, William M. Rickes, Tiago B. Lucca, Giancarlo Oliveira, Lizandro de Souza Yamin, Adenauer Correa Univ Catolica Pelotas Ctr Ci encias Sociais Tecnol Mestrado Engenharia Eletron & Comp Pelotas RS Brazil Univ Fed Pelotas Pelotas RS Brazil

ISBN: (纸本)9783031777370;9783031777387

This study addresses battery failure in motorized wheel chairs, which are essential for the mobility of individuals with disabilities. The main objective was to concept a comprehensive dataset comprising six attributes that directly impact battery life, consisting of 498 instances. Using the Random Forest algorithm, we demonstrate the ability to accurately predict battery failures. The results highlight the necessity for proactive measures to prevent battery degradation and extend its lifespan.

关键词： data Repository Machine learning Deep learning Model sharing data Symbion EI

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Machine learning based Fault Distance Estimation of Underground Transmission using Impedance Relay 3

Machine Learning based Fault Distance Estimation of Undergro...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mathi, Sri Chaithanya Tammisettti, Pooja Naidu Akthar, Shaik Rohid Manitha, P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Fast development of electric transmission infrastructure necessitates efficient methods of transmission and distribution for reliable cost effectiveness. Between the two main types of transmission, Overhead Transmission Lines involve extensive maintenance cost, use of extensive protection schemes, earthing, and lightning protection. In contrast, Underground Transmission Lines with their insulated and securely laid design are highly resistant to external factors such as lightning, wind, and open conductor faults, showing higher reliability and efficiency. However, faults such as symmetrical and unsymmetrical faults can occur due to conductors being bound together. The traditional fault distance estimation techniques are often inefficient for high-power transmission when using impedance-based fault distance relays. The constant variation in fault characteristics leads to impedance fluctuations, and therefore, fault distance calculations become inaccurate. The current research introduces a Machine learning-based Fault Distance Estimation system for Underground Transmission Lines using Impedance Relays to address the above-mentioned challenges. Through data fed into the training machine learning model based on various fault scenarios, data mining is extracted for predicting precise impedance in faulty states and accurately determining fault locations by utilizing trained models with high-performance K-Nearest Neighbour (KNN), Decision Trees (DT), Support Vector Regression (SVR), Random Forest (RF), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANNs). Through these algorithms and approaches, implementations that emphasize optimal preprocessing, effective feature selection and tuning of specific hyperparameters determine their respective modeling efficiencies. The results show that the models generate very accurate distance estimations, allowing for more accurate fault locations and quick rectification. Not only does it bypass the deficiency of the method

关键词： Nearest neighbor search

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Transforming Agriculture with Cloud Computing: data Processing and Analysis Innovations 3

Transforming Agriculture with Cloud Computing: Data Processi...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Kaur, Harpreet Lovely Professional University Department of Computer Application Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798331515911

Cloud computing is a game-changer in modern agriculture, providing new forms to access enormous data analysis. In this paper, we investigate how cloud computing solutions are being integrated with agricultural practices to improve the efficiency, scalability and availability in managing data pertaining to agriculture. Farming operations generate massive amounts of data from a multitude of sources like sensors, drones, and satellite imagery., the performance bottleneck becomes the traditional methods with which the data are being processed. So the common factor in all these problems lies with Cloud computing itself, it has a scalable infrastructure that provides reliable analysis and Real-time processing of large data sets throwing light on an array of questions people have about various aspects. In this paper, we explore the major innovations in cloud computing that power precise agriculture and examine how machine learning algorithms, data fusion methods, and distributed processing platforms are utilized. It also discusses cloud-based solutions, which can reduce costs of the use of resources, crop yield prediction, and risk management to weather/climate and pests. Cloud computing opens up a world of monitoring, analysis, and optimization to help provide higher yields more efficiently while maintaining a commitment to sustainable agriculture. Challenges and considerations in deploying cloud computing in agriculture, e.g., data security risks, privacy concerns, and the need for stable internet connection for rural environments are also articulated in the paper. The results of this project underscore the vital importance of cloud technology in redefining the agri-future and providing a robust platform for smarter and more sustainable agriculture. © 2025 IEEE.

关键词： agricultural data processing Cloud computing data analysis machine learning precision agriculture sustainable farming

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2023 3rd international conference on Electronic Information engineering and Computer, EIECT 2023

2023 3rd International Conference on Electronic Information ...

引用

3rd international conference on Electronic Information engineering and Computer, EIECT 2023

ISBN: (纸本)9798350357707

The proceedings contain 118 papers. The topics discussed include: RCS reduction based on optimization algorithms and novel polarization conversion meta-surface;using UDP to realize flexible and portable human activity recognition;research on temperature compensation algorithm based on optical fiber inertia combination;advancements in video tracking technology: a deep learning approach with SiamMask;positioning method for covered vehicles based on neutrino detection and reinforcement learning;privacy-preserving data splitting based on machine learning;enhanced lightweight hazardous waste detection algorithm of YOLOv5s;research on the application of intelligent 5G network slicing technology;and a boiler heating surface overtemperature warning method based on multi source data fusion and neural network model.

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Urban Mobility Optimization with IoT and Deep Q Networks for Traffic Density Management 3

Urban Mobility Optimization with IoT and Deep Q Networks for...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Ranganathan, Chitra Sabapathy Jayanthi, L.N. Babu, J. Jagan Manikandan, M. Sivakamy, N. Mphasis Corporation Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Management Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

Traffic density and mobility optimization in urban areas are becoming more difficult to manage to improve transportation efficiency. Urban traffic management is complicated, but this research provides a revolutionary technique using the Internet of Things (IoT) and Deep Q Networks (DQN). Traffic lights, sensors, and vehicles may be monitored in real-time using IoT technology to provide a complete picture of urban mobility. As an intelligent decision-making agent, the DQN uses reinforcement learning to adapt to changing traffic circumstances and optimize traffic density management. The model is educated on historical data and refines its tactics via environmental interaction, solving urban transportation problems. A complex data fusion mechanism processes data from different IoT devices. A reinforcement learning framework uses DQN for decision-making, and a feedback loop updates and optimizes traffic management tactics. The research uses simulations and urban case studies to prove that the technique works. The findings show improved traffic flow, congestion, and mobility efficiency. It uses IoT and DQN to develop intelligent urban transport systems that scale and respond to traffic density in complex and dynamic urban environments. © 2024 IEEE.

关键词： Traffic congestion

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CNN-KNN Model for Assessing Hair Health in Telogen Effluvium 3

CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Gotte Ranjith Shubham Banerjee, Deepak School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

The abstract provides a concise overview of a study that focuses on predicting hair breakage levels using a CNN-KNN hybrid model. The study evaluates the model's performance across five Breakage Degrees (BDs) by utilizing precision, recall, and F1-score metrics. Among these degrees, BD2 stands out with precision, recall, and F1-score values of 94.20%, indicating the model's ability to accurately classify hair damage levels between 21% and 40%. The support values, which indicate the number and proportion of instances in each BD, demonstrate a well-balanced distribution across all categories, ensuring the model's robust training. Researchers used a dataset of 10,050 images to reach an impressive 94.02% accuracy in predicting hair breakage levels caused by Telogen Effluvium in the research. Moreover, the model achieves an impressive overall accuracy of 98%, showcasing its effectiveness in distinguishing between various degrees of hair breakage severity. This high accuracy level highlights the model's potential application in clinical settings for dermatological diagnostics and personalized treatment planning in hair care management. The abstract underscores the CNN-KNN hybrid approach as a promising methodology for automated hair health assessment, providing healthcare professionals with a dependable tool to improve diagnostic accuracy and treatment efficacy. By incorporating machine learning techniques, specifically convolutional and k-nearest neighbor algorithms, the study contributes to the advancement of computational methods in dermatology and cosmetology. The results suggest practical implications for enhancing patient care through early detection and intervention of hair-related conditions, ultimately supporting better health outcomes and quality of life for individuals affected by hair breakage concerns. © 2025 IEEE.

关键词： Nearest neighbor search

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Enhancing data Security and Privacy in Cloud Computing: A Survey of Modern Techniques 3

Enhancing Data Security and Privacy in Cloud Computing: A Su...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Suganya, N. Gouthami, P. Sathiya, R. Sivaranjani, S. Murugesan, M. Department of Computer Science and Business Systems Dr. N.G.P. Institute of Technology Coimbatore India Department of Information Technology Thangavelu Engineering College Chennai India Departement of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Departement of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798331527549

A computing system that is based on the internet and offers users all resources as on-demand services. Servers, storage, databases, software, and networking are among these on-demand services. Usually, the user must pay for the use of the resources;they are not their property. The number of people using this technology is increasing dramatically as a result of these features. Although it has benefits, cloud computing poses certain security and privacy risks. The authorized user’s data can be stolen easily. This current research thoroughly investigates modern strategies to these problems, highlighting identity management protocols, effective encryption and blockchain-based solutions. The article identifies research gaps, focuses attention to the inadequacies of current approaches, and recommends the possible ways to improve the efficiency, scalability and trustworthiness. © 2025 IEEE.

关键词： Cloud security

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Development of an Adaptive Smart Toy with Emotional Feedback 3

Development of an Adaptive Smart Toy with Emotional Feedback

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohandas, Gayathri Sanay Santh, V. Padmakumar, Keshav Thangam, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Traditional toys often do not adapt to the emotional and physical needs of children, particularly those who may have limited verbal communication abilities. The project’s goal is to create a smart toy that is adaptive and can communicate with a child by using nonverbal signs to express his or her physical and emotional states. There are sensors like accelerometers to detect movement, a gesture sensor indicating light intensity, a buzzer sensor to produce sound, and an IR sensor to detect the child’s hand when the child is near the toy. All these components will help the user to get in-time feedback with a set of visual indicators (LEDs) and tactile feedback in the form of vibration motors. This smart toy seeks to improve the experience of play and developmental support for children by being more responsive and engaging with individual needs. © 2025 IEEE.

关键词： intelligent robots

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Ensembled Machine learning Models for Antenna Optimization in Wireless Communication/ Biomedical Applications 3

Ensembled Machine Learning Models for Antenna Optimization i...

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3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Upadhyay, Nidhi Kaur, Amanpreet Singh, Ashima Pattanayak, Arnab Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab India Thapar Institute of Engineering and Technology Department of Computer Science Engineering Punjab India

ISBN: (纸本)9798331528201

In this work, the antenna optimisation process is done by using ML algorithms such as KNN, random forest, Gradient Boost, XG Boost and MLP. Then the predicted results are ensembled using Voting Regressor. A Cylindrical Dielectric Resonator Antenna (CDRA) was designed and simulated in CST MWS'23 for this purpose. dataset is created by varying the radius of the DR using parametric sweep function of the EM simulator. A close prediction between actual values and the predicted ones is obtained using these ML models. It is observed that by ensembling these varied approaches, a model that can handle different types of relationships in the data, can be created. This algorithm can be further utilized for optimisation of other antenna parameters and thus helping in reducing time in optimising complex antenna designs. The accuracy achieved by the ensemble model is 98.6% while maintaining a small MSE of 0.001. The optimized design shows stable characteristics within the ISM band and hence can be used for biomedical as well as wireless communication applications. © 2024 IEEE.

关键词： Random forests

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