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Improved Prediction of Electric Vehicle User Behavior with machine learning based Analysis

Improved Prediction of Electric Vehicle User Behavior with M...

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Smart Technologies for Power, Energy and Control (STPEC), IEEE international conference on

作者： Kavita Kiran Prasad Akshay Mali Vivek Agarwal Dept. of Electrical Engineering Indian Institute of Technology Bombay Mumbai India

With the rapid growth of Electric Vehicles (EVs), charging load demand is increasing significantly. Energy providers need to analyze the load requirements and meet them efficiently. Due to the highly nonlinear characteristics of EV user behavior, researchers are turning to machine learning (ML) models suited for nonlinear data. In the proposed study, the Adaptive Charging Network (ACN)’s public charging dataset is utilized to predict EV user behavior. New features like traffic, charging currents, and number of connection-disconnection events are integrated along with the weather data to improve prediction accuracy. Also, the features are included by observing the pattern in one-hour slots, which results in substantial improvements compared to previous work. While existing research mainly focuses on session duration and energy consumption, this work also predicts the charging duration, which is useful to get an idea of when the EV is ready to use. It is shown that ensemble model performs the best among various ML models, achieving a Mean Absolute Error (MAE) of 56 minutes for session duration and 43.2 minutes for charging duration. Also, the energy consumption is best predicted with Random Forest (RF) regression with MAE of 1.36 kWh. All the key results of this work are presented.

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3rd international conference on Innovative Computing and Communication, ICICC 2020

3rd International Conference on Innovative Computing and Com...

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3rd international conference on Innovative Computing and Communication, ICICC 2020

ISBN: (纸本)9789811551123

The proceedings contain 196 papers. The special focus in this conference is on Innovative Computing and Communication. The topics include: A Primer on Opinion mining: The Growing Research Area;a Generic Framework for Evolution of Deep Neural Networks Using Genetic Algorithms;multimodal Medical Image Fusion Based on Discrete Wavelet Transform and Genetic Algorithm;driver Fatigue and Distraction Analysis Using machine learning Algorithms;a Hybridized Auto-encoder and Convolution Neural Network-Based Model for Plant Identification;curaBand: Health Monitoring and Warning System;optimization Algorithm-Based Artificial Neural Network Control of Nonlinear Systems;mHealth for Mental Health;elman and Jordan Recurrence in Convolutional Neural Networks Using Attention Window;state of Charge Estimation Using data-Driven Techniques for Storage Devices in Electric Vehicles;Application of Stochastic Approximation for Self-tuning of PID in Unmanned Surface Vehicles;dark web Activity on Tor—Investigation Challenges and Retrieval of Memory Artifacts;A Heterogeneous Dynamic Scheduling Minimize Energy—HDSME;face recognition-Based Automated Attendance System;Human Emotion recognition from Spontaneous Thermal Image Sequence Using GPU Accelerated Emotion Landmark Localization and Parallel Deep Emotion Net;delay Based Multi-controller Placement in Software Define Tactical Network;security in Cloud Computing for Sensitive data: Challenges and Propositions;Utility of Game Theory in Defensive Wireless Sensor Networks (WSNs);robust Approach for Emotion Classification Using Gait;routing in Internet of Things Using Cellular Automata;depression Detection Among Social Media Users Using machine learning;Security-Based LEACH Protocol for Wireless Sensor Network;Design of High-Speed FPGA Based CASU Using CBNS Arithmetic: Extension to CFFT Processor;blockchain-Based Boothless E-Voting System;preface.

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Recurrent Neural Networks based Bi-LSTM with Sentiment Analysis and Transfer learning approach for Medicine Recommendation System

Recurrent Neural Networks based Bi-LSTM with Sentiment Analy...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Sandeep Kumar Hegde Rajalaxmi Hegde Thangavel Murugan Department of CSE NMAM Institute of Technology NITTE (Deemed to be University) Karnataka India College of Information Technology United Arab Emirates University Al Ain United Arab Emirates

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The implementation of an efficient medication recommendation system is indispensable for personalized medicine to improve patient outcomes by providing personalized treatment recommendations. This paper presents a comprehensive framework for medication recommendation based on transfer learning using deep learning methods like Recurrent Neural Networks (RNNs) and Bidirectional Long Short Term Memory networks (Bi-LSTM) along with sentiment analysis of user reviews for opinion mining. The proposed method implements a complex-sequential modeling architecture using an RNN-BiLSTM-driven deep network to model the complex sequential patterns and timing relationships among drug-related data, thereby preparing the system for learning and eventual adaptation based on past usage and feedback. Bi-LSTM retains context-information in both forward and reversed directions to attain better context understanding. Transfer learning implementation of the framework gains the knowledge from pre trained model pertaining to medical recommendation system towards ensuring high generalizability of the developed model. The proposed method essentially binds insights from historical users' data, user sentiments, and advanced deep learning to satisfy patients and treatment outcomes towards a more precise and personalized system for drug recommendations. The dataset required for conducting the experiment was gathered from the Kaggle repository. The experimental results have been validated by various measures such as ROC rate, precision, F1 Score, confusion matrix, and accuracy. The experimental results showed that the proposed method achieved a 97.6% accuracy using the measures considered, which is significantly better than traditional machine learning and deep learning algorithms.

关键词： Deep learning Sentiment analysis Adaptation models Accuracy Reviews Precision medicine Transfer learning data models Timing Recommender systems

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Food Calorie Estimation using Convolutional Neural Network 3

Food Calorie Estimation using Convolutional Neural Network

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3rd international conference on Signal Processing and Communication (ICPSC)

作者： Kasyap, V. Balaji Jayapandian, N. CHRIST Deemed Univ Sch Engn & Technol Bengaluru India

ISBN: (纸本)9781665428644

The modern world healthy body depends on the number of calories consumed, hence monitoring calorie intake is necessary to maintain good health. At the point when your Body Mass Index is somewhere in between from 25 to 29. It implies that you are conveying overabundance weight. Assuming your BMI is more than 30, it implies you have obesity. To get in shape or keep up the solid weight individuals needs to monitor the calorie they take. The existing system calorie estimation is to be happened manually. The proposed model is to provide unique solution for measuring calorie by using deep learning algorithm. The food calorie calculation is very important in medical field. Because this food calorie is provide good health condition. This measurement is taken from food image in different objects that is fruits and vegetables. This measurement is taken with the help of neural network. The tensor flow is one of the best methods to classify the machine learning method. This method is implementing to calculate the food calorie with the help of Convolutional Neural Network. The input of this calculated model is taken an image of food. The food calorie value is calculated the proposed CNN model with the help of food object detection. The primary parameter of the result is taken by volume error estimation and secondary parameter is calorie error estimation. The volume error estimation is gradually reduced by 20%. That indicates the proposed CNN model is providing higher accuracy level compare to existing model.

关键词： Convolutional Neural Network Deep learning Food Classification Food Detection pattern recognition Tensorflow

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Automatic Feature Extraction for Bearings' Degradation Assessment using Minimally Pre-processed Time series and Multi-modal Feature learning 3

Automatic Feature Extraction for Bearings' Degradation Asses...

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3rd international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2022

作者： Alfeo, Antonio L. Cimino, Mario G.C.A. Gagliardi, Guido Department of Information Engineering University of Pisa Pisa Italy Bioengineering and Robotics Research Center E. Piaggio University of Pisa Pisa Italy University of Florence Florence Italy Department of Electrical Engineering KU Leuven Leuven Belgium

Maintenance activities can be better planned by employing machine learning technologies to monitor an asset's health conditions. However, the variety of observable measures (e.g. temperature, vibration) and behaviours characterizing the health degradation process results in time-consuming manual feature extraction to ensure accurate degradation stage recognitions. Indeed, approaches able to provide automatic feature extraction from multiple and heterogeneous sources are more and more required in the field of predictive maintenance. This issue can be addressed in a data-driven fashion by using feature learning technology, enabling the transformation of minimally processed time series into informative features. Given its capability of discovering meaningful patterns in data while enabling data fusion, many feature learning approaches are based on deep learning technology (e.g. autoencoders). In this work, an architecture based on autoencoders is used to automatically extract degradation-representative features from minimally preprocessed time series of vibration and temperature data. Different autoencoder architectures are implemented to compare different data fusion strategies. The proposed approach is tested considering both the recognition performances and the quality of the learned features with a publicly available real-world dataset about bearings' progressive degradation. The proposed approach is also compared against manual feature extraction and the state-of-the-art technology in feature learning. Copyright © 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Degradation

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Meta-learning for Text-Based Object Localization in Robotic Manipulation with DNN and CLIP

Meta-Learning for Text-Based Object Localization in Robotic ...

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Mechatronics, Control and Robotics (ICMCR), international conference on

作者： Mariam Kashkash Mohsen Guizani Dept. Machine Learning MBZUAI Abu Dhabi UAE

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

As robotics increasingly intersects with human environments, text-based guidance allows robots to perform tasks flexibly and intuitively, bridging the gap between human intentions and robot actions. However, enabling robots to interpret diverse natural language commands, detect and manipulate a variety of objects, and generalize effectively across different datasets poses significant challenges. This paper presents a novel approach based on meta-learning for robotic manipulation tasks, specifically focusing on pick-and-place operations guided by natural language instructions. Model-Agnostic Meta-learning (MAML) framework is proposed to enable a robotic manipulator to generalize across varying tasks based on text-based commands. Utilizing a dataset of images paired with text instructions, a MAML-based model with a Deep Neural Network (DNN) and CLIP is proposed to localize objects mentioned in text commands within the images. The application of N-way K-shot learning allows the model to learn new tasks with minimal data efficiently. Experimental results reveal the impact of tuning the inner and outer learning rates, shedding light on their roles in enhancing the model's adaptability and accuracy. The performance of the approach is further validated using table-top and CLEVR datasets, where it achieves low loss values in object localization and task execution, demonstrating its effectiveness in generalizing across diverse tasks in a few meta-steps. The proposed model significantly reduces support and query losses, particularly in high N-way and K-shot configurations, highlighting its ability to learn effectively with minimal data.

关键词： Metalearning Location awareness Adaptation models Natural languages Artificial neural networks Predictive models data models Object recognition Robots Convergence

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A Study on Attack pattern Generation and Hybrid MR-IDS for In-Vehicle Network 3

A Study on Attack Pattern Generation and Hybrid MR-IDS for I...

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3rd international conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC)

作者： Kang, Dong Mug Yoon, Sang Hun Shin, Dae Kyo Yoon, Young Kim, Hyeon Min Jang, Soo Hyun Korea Elect Technol Inst Gyeonggi Do South Korea Hongik Univ Dept Comp Engn Seoul South Korea Hongik Univ Dept Artificial Intelligence & Big Data Seoul South Korea

ISBN: (纸本)9781728176383

The CAN (Controller Area Network) bus, which transmits and receives ECU contend information in vehicle, has a critical risk of external intrusion because there is no standardized security system. Recently, the need for IDS (Intrusion Detection System) to detect external intrusion of CAN bus is increasing, and high accuracy and real-time processing for intrusion detection are required. In this paper, we propose Hybrid MR (machine learning and Ruleset)-IDS bused on machine learning and ruleset to improve IDS performance. For high accuracy and detection rate, feature engineering was conducted based on the characteristics of the CAN bus, and the generated features were used in detection step. The proposed Hybrid MR-IDS can cope to various attack patterns that have not been learned in previous, as well as the learned attack patterns by using both advantages of rule set and machine learning. In addition, by collecting CAN data from an actual vehicle in driving and stop state, five attack scenarios including physical effects during all driving cycle are generated. Finally, the Hybrid MR-IDS proposed in this paper shows an average of 99% performance based on F1-score.

关键词： CAN Ruleset machine learning Hybrid MR-IDS Network Intrusion

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3rd international conference on Communication, Devices and Computing, ICCDC 2021

3rd International Conference on Communication, Devices and C...

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3rd international conference on Communication, Devices and Computing, ICCDC 2021

ISBN: (纸本)9789811691539

The proceedings contain 70 papers. The special focus in this conference is on Communication, Devices and Computing. The topics include: Accurate Prediction of Petermann I and II Spot Sizes of First Higher Order Mode for Kerr Type Nonlinear Graded Index Fiber by a Simple Method;Analysis of All-Optical XNOR Gate Using Quantum Dot Semiconductor Optical Amplifier (QDSOA);New COVID-19 Normal: An Experimental Prototype of Smart Face Mask Vending machine—An Indispensable Kiosk;Arduino and ESP32-CAM-Based Automatic Touchless Attendance System;speech Emotion recognition of Tamil Language: An Implementation with Linear and Nonlinear Feature;radiation-Resilient GaN/InxGa1-xN Multi-junction Solar Cells with Varying in Contents;A Simple but Accurate Mathematical Formalism for Prediction of the Far Field pattern of First Higher Order (LP11) Mode in Kerr Type Nonlinear Step Index Optical Fiber;A Priority Based Routing Protocol to Improve Quality of Service (QoS) in Wireless Body Area Network for COVID and Diabetic Patients;accurate Estimation of Dimensionless Vector and Scalar Propagation Constants for First Higher Order Mode of Kerr Type Nonlinear Graded Index Fiber by Simple Mathematical Formalism;Performance Analysis of Berlekamp–Massey-Based KES Block for 3-Byte RS Decoder;a Simple Method for Accurate Prediction of Splice Loss for First Higher-Order Mode of Step-Index Fiber in Presence of Kerr Nonlinearity;issues of Knowledge Management in Deep Web and Its Graph-Based Analysis;a Generic Approach for Interpolation and Image Fusion to Obtain Pan-Sharpening;fastResponse—A Smart Ambulatory System for Monitoring Accident/Disaster Victims Along with Live Tracking Facility;COVID-19 data Forecasting Using Soft Computing Technique;Analyze DGS Antenna Structure.

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Advanced Face Detection Using SVM and Haar-like Features: A Comprehensive Approach in AI and Image Processing

Advanced Face Detection Using SVM and Haar-like Features: A ...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Devendra Somwanshi Archika Jain Kapil Joshi Vishnu Kant Salil Bharany Bura Vijay Kumar Department of EC Poornima College of Engineering Jaipur India Department of CSE Management & Gramothan (SKIT) Swami Keshvanand Institute of Technology Jaipur India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science and Artificial Intelligence SR University Warangal

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Face detection is a vibrant research field spanning artificial intelligence, image processing, video surveillance, human-machine interaction, identity authentication, machine learning, pattern recognition, and so on. While humans effortlessly recognize faces, computers face challenges due to facial variability. The complexity lies in factors like expressions, lighting changes, and head positions. Thus, solving the problem of face detection has an important academic value and provides important enlightenment. There are many aspects to face detection such as facial expression, head position, changing lighting conditions and many others. To deal with face detection problems various approaches are used such as template matching methods, knowledge-based methods, feature invariant methods, and appearance-based methods. The proposed approach for face detection utilizes Support Vector machine (SVM) and Haar-like Features. It begins by generating an integral image from a binary image measuring 19 x 19 pixels. Following this, the Haar feature extraction method is employed to obtain Haar values from both face and non-face isommages. A set of 200 face and non-face images from MIT database have been used. Features have been reduced five times to make different features sets of images. These features are passed to Support Vector machine as input parameters and processed to classify face and non-face datasets. Support Vector machine is used to create, train and test the neural network. Features are extracted on MATLAB and further processed in SVM using NeuroSolutions tool. The results are evaluated based on two scenarios as varying number of images and features.

关键词： Support vector machines Training Face recognition Lighting machine learning Feature extraction Magnetic heads Face detection MATLAB Faces

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Unleashing the Potential of data Analytics Through Music 3rd

Unleashing the Potential of Data Analytics Through Music

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3rd international conference on Smart Multimedia, ICSM 2022

作者： Dawar, Jatin Raheja, Prem Vashisth, Utkarsh Hajari, Nasim Cheng, Irene Department of Computing Science University of Alberta EdmontonABT6G 2R3 Canada Department of Computing Science Concordia University of Edmonton EdmontonABT5B 4E4 Canada

ISBN: (纸本)9783031220609

One of the most significant challenges with sequential data is the identification of certain underlying patterns, which can be easily overlooked during human visualization. To overcome such problem, transforming data into sound (Sonification) has shown great potential to notify human about hidden patterns. Sonification is a process of mapping data into a non-speech audio format. In today’s era, with data analysis being the backbone of most infrastructures, Sonification is gaining interests in various fields like data mining, human-computer interaction, exploratory data analysis, and musical interfaces. It presents a novel way of analyzing and interacting with data. In addition, it provides visually impaired people with an attainable alternative. A considerable amount of work has been done in the field of sonification and music generation. However, producing music from data using machine learning and deep learning techniques is inadequately explored. The conventional sonification methods require human involvement and knowledge of music to produce a tune that is appealing to the ears. This method is time-consuming and requires specialized expertise. In this paper, we aim towards developing a system that molds any time-dependent data into music while retaining the original characteristics of the data using deep learning techniques such as LSTM (Long-short Term Memory). The goals of this research are 1) generate music that is melodious and resembles the music composed by humans, and 2) help people not only auralize but also understand the associated data trend through the generated music. Quantitative and qualitative evaluations were used to validate our approach. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Music

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