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Estimation of the fuel consumption in autonomous vehicles using machine learning techniques

Estimation of the fuel consumption in autonomous vehicles us...

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international conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Abdelkader, Ghazli Benaoumeur, Senouci M. Eddine, Abid Dhiya Mourad, Bouache Department of Mathematics and Computer Sciences Tahri Mohamed University Bechar Algeria Sonderborg Denmark Department of Mathematics and Computer Sciences Tahri Mohamed University Bechar Algeria Intel AI Academy Silicon Valley United States

ISBN: (纸本)9781032803722

Energy consumption is the responsibility of each individual and that we must all adopt the right reflexes to preserve our resources. So many reasons that can impact our fuel consumption such as nervous driving, under-inflated tires, poorly maintained vehicle…etc. Adopting economical and responsible driving can allow us to reduce our fuel budget while reducing our carbon footprint. The main objective of this research is to study the influence of some parameters on fuel consumption through the use of artificial intelligence approach. This paper presents the application of five machine learning techniques to estimate fuel consumption of the light cars, in particular, Linear Regression (LR), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) and finally Deep Neural Network. These models have been developed using a dataset contains lot of criteria such us speed, distance and some weather conditions (rain, sun…). Performance of the algorithms is evaluated by reporting the prediction errors using the three metrics tools, which are median absolute error, mean absolute error and mean squared error. The statistical evaluation procedure finds that ANNs have the lowest median absolute error and mean squared error (0.304 and 0.357) compared to LR (0.401 and 0.919), RF(0.330 and 0.366), DT(0.400 and 1.177) and KNN(0.340 and 0.493) and the DNN have lowest mean absolute error (0.408) and mean squared error (0.305). © 2025 The Author(s).

关键词： Mean square error

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Opportunities and Challenges for Developing Machine learning Models with EHR Data

Opportunities and Challenges for Developing Machine Learning...

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2023 international conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Saraswat, Birendra Kumar Saxena, Aditya Vashist, P.C. Gla University Coputer Sciences & Engineering U.P. Mathura281406 India Institute of Engineering & Technology Gla University Computer Engineering & Applications U.P. Mathura281406 India Gl Bajaj Institute of Technology and Management Affiliated to Aktu Information Technology U.P. Greater Noida201306 India

ISBN: (纸本)9798350380880

EHR statistics gives super opportunities for improving machine studying models that guide healthcare groups in enhancing patient care and operational performance. The vast and varied volumes of statistics available make it exceptionally nicely perfect for programs of system mastering techniques. However, there are also giant demanding situations related to using EHR records. Those consist of constrained interoperability of systems and statistics formats, privacy and protection issues, demanding situations in correctly shooting facts, and troubles associated with statistics and accuracy. Moreover, specific clinical and moral problems must be considered while utilizing EHR information to develop devices gaining knowledge of fashions. In the end, while there may be first-rate ability for the utilization of EHR statistics to develop system learning models, significant challenges also need to be addressed for it to succeed. © 2023 IEEE.

关键词： Statistics

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A novel approach based on LSTM and self-attention mechanism for vessel trajectory prediction 4

A novel approach based on LSTM and self-attention mechanism ...

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4th international conference on computer Science and Communication Technology, ICCSCT 2023

作者： Zhang, Yi Zhou, Liang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China

ISBN: (纸本)9781510671232

Trajectory prediction is a crucial tool for analyzing vessel motion behavior, assessing vessel traffic risks, and planning collision avoidance routes for intelligent ships. We propose a novel deep-learning architecture-based model Trajformer to perform the task of vessel trajectory prediction. By preprocessing AIS data, utilizing the LSTM to learn local dependencies, and self-attention mechanism to enhance the capacity of dependencies learning, Trajformer achieves multi-step vessel trajectory prediction. Inspired by time series preprocessing and forecasting tasks, we also propose Trajformer+SD, a simple yet effective approach that applies Series Decomposition on the input sequence. Our modal can effectively model the vessel trajectory and make predictions, as demonstrated by our experiments on three real-world vessel trajectory datasets. Compared to the sequence to sequence (Seq2Seq) and sequence to sequence with attention (Seq2Seq_attn) model, our proposed model exhibits a better performance in prediction tasks. © 2023 SPIE.

关键词： Forecasting

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Research to Practice of Blended learning in computer Programming Course 53

Research to Practice of Blended Learning in Computer Program...

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53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Zhang, Liang Chen, Tianyi Zong, Yue Zhang, Lijun School of Computer Science and Engineering Beihang University Beijing China School of Aeronautic Science and Engineering Beihang University Beijing China School of Software Beihang University Beijing China

ISBN: (纸本)9798350336429

This research to practice WIP paper presented a Blended learning (BL) approach to teaching Data Structures and Programming in Python (DSPP) course. This approach increased students' learning engagement, academic performance, and course satisfaction. There is an intense need to study the BL approach in higher education, especially during the COVID-19 epidemic. Although BL pedagogy has achieved great success in college, there needs to be more quantitative research and practice in the computer programming course. This paper takes the DSPP course as an example to introduce the BL approach in detail. First, we designed the BL process for the computer programming course, which includes specific pre-class, in-class, and after-class activities. Second, we divide a course's knowledge and BL activities into remember, understand, apply, analyze, evaluate, and create levels according to Bloom's Taxonomy. So the knowledge and activities can be corresponding according to the cognitive level. We take a chapter in the DSPP course as an example to introduce it in detail. Third, to support the efficient implementation of BL activities, we have designed and developed several online learning systems, including an Online Judge system (OJ), Question&Anwser website (Q&A), and Peer Review Studio (PRS). To assess the effectiveness of the BL approach, we conducted the study at the DSPP course for undergraduate students in 2021 and 2023. In 2021, we taught the DSPP course in traditional pedagogy. In 2023, we began to implement BL pedagogy. By analyzing students' learning data, we compared the effect of the two pedagogy. We use the nonparametric independent-samples Kruskal-Wallis test to measure the changes in learning engagement, course performance, and student satisfaction. The results show that they were significantly improved in 2022. The course questionnaire of 2022 shows that most students prefer the BL approach. © 2023 IEEE.

关键词： Active learning Blended learning Bloom's Taxonomy computer programming Online learning System

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Sentiment Analysis and Student Emotions: Improving Satisfaction in Online learning Platforms 9

Sentiment Analysis and Student Emotions: Improving Satisfact...

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9th IEEE international Smart Cities conference, ISC2 2023

作者： Anwar, Aamir Ur Rehman, Ikram Nasralla, Moustafa M. Bin Altaf Khattak, Sohaib Khilji, Nasrullah School of Computing & Engineering University of West London London United Kingdom Prince Sultan University Smart Systems Engineering Lab Riyadh Saudi Arabia

ISBN: (纸本)9798350397758

Human emotion recognition using artificial intelligence is among the most prominent research areas. Human-computer Interaction (HCI) and Sentiment Analysis (SA) are extensively used to detect human emotions. The role of students' sentiments and emotions becomes vital while using online learning platforms due to the need for physical interaction between instructor and learner to understand each other compared to face-to-face learning. This study identifies challenges and issues of online learning and student satisfaction while using online learning platforms. In addition, we analysed the importance of sentiment analysis for students' satisfaction while using online learning platforms. This study retrieved 112 research articles/reports using keywords such as 'Online learning', 'Online learning Post-COVID-19', 'Student Satisfaction', 'Student Sentiment Analysis', 'learning Environments' and 'Student Performance in Online learning'. Among 112 research articles, 37 were identified after excluding duplicate and irrelevant research articles based on defined elimination criteria, such as studies on student emotions related to non-educational purposes. For systematic literature, the study has been divided into two research questions, i.e., (i) exploring key challenges of online learning and (ii) the role of sentiment analysis during online learning. These two questions have been answered based on arguments and evidence from the existing literature. © 2023 IEEE.

关键词： Emotion Recognition

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Long-Tailed Hashing with Wasserstein Quantization 27th

Long-Tailed Hashing with Wasserstein Quantization

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27th international conference on Pattern Recognition, ICPR 2024

作者： Fu, Zujun Lai, Hanjiang Pan, Yan School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China

ISBN: (纸本)9783031783043

Long-tailed hashing is to learn hash functions in unbalanced distribution datasets to represent images as binary hash codes for fast and accurate image retrieval. In contrast to balanced distribution datasets, unbalanced distributions are more common in the real world. However, Existing long-tailed hashing methods only focus on how to better learn from unbalanced datasets to improve performance, without giving good consideration to quantization error, which is very crucial in hash learning. In this paper, we propose a simple but efficient quantization method for long-tailed hashing. Specifically, to address the lack of samples in the tail classes, we take a uniform discrete distribution as the optimal target distribution. We use the Sliced Wasserstein distance as a measure of distribution distance. It makes good use of the discrete nature of hash functions and has low computational complexity. Then we formulate the optimization objective of the quantization error as minimizing the distance between the output of the learned hash function and this objective distribution, which can be added as an additional term of the loss function to existing long-tailed hashing methods. We conduct experiments on two long-tailed datasets, and the results show that our proposed method greatly improves the performance of existing long-tailed hashing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hash functions

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Machine learning-Based Risk Prediction for Coronary Heart Disease Using Clinical Data 1

Machine Learning-Based Risk Prediction for Coronary Heart Di...

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1st international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Dewangan, Leelkanth Shitole, Mahesh V. Patra, J.P. Khan, Jalaluddin Dawda, Gopesh Kumar Parida, Prasanta Kumar Swangi Dept of Computer Science & Engineering Wardha India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur522502 India Pune India Department of Civil Raipur Institute of Technology Raipur India Chhattisgarh Swami Vivekanand Technical University Bhilai India Kiit University India

ISBN: (纸本)9798350330915

Through the use of machine learning models, particularly Random Forest and Support Vector Machines, this work increases the prediction of CHD risk as well as achieves a remarkably high predictive accuracy. The use of interpretability methodologies clarifies important aspects of CHD risk estimation, stressing the importance of elements like age, cholesterol levels, as well as smoking behavior. Additionally, the creation of a user-friendly application streamlines the risk assessment procedure and improves communication between healthcare professionals and patients. Future studies need to focus on prospective validation, data source diversity, and ethical issues to better advance the subject. CHD risk management has a bright future thanks to the possible integration of real-time patient data and the advancement of AI-driven decision support systems. © 2023 IEEE.

关键词： Coronary heart disease interpretability machine learning prospective validation risk prediction user-friendly tool

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Automated Service Robot for Catering Businesses Using Arduino Mega 2560 15

Automated Service Robot for Catering Businesses Using Arduin...

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15th international conference on computer and Automation engineering, ICCAE 2023

作者： Cruz, James D. Domingo, Gian Cris B. Garcia, Ramon G. Mapúa University School of Electrical Electronics and Computer Engineering Manila Philippines

ISBN: (纸本)9798350396225

As technology becomes more advanced, the use of robots will further increase in the near future. It is expected that most of these robots are industrial robots and are most likely to be utilized in the upcoming years. However, the use of other types of robots, such as service robots, is also expected to increase gradually in the future. A waiter robot is a type of service robot that works in a catering business by performing the roles of a waiter, such as serving meals or accepting orders depending on its function. Currently, waiter robots are facing limitations. The main factor is how it navigates and interacts within a human environment. This paper aims to create a service robot that serves orders in a catering business. Arduino Mega 2560 was used as a microcontroller, and the line-following algorithm was used to guide the robot to the location of the dining tables. Two ultrasonic sensors were used to detect obstacles from a distance. Results show that the robot moves at a speed of 0.06 m/s in controlled testing and maintains that constant speed while carrying a maximum of 15kg load. Furthermore, it is shown that the robot is capable of serving multiple orders at a single dining table. The robot was able to serve 56 customers in a restaurant. © 2023 IEEE.

关键词： Mobile robots

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Enhancing Education Through Technology: A Critical Review of computer-Assisted learning 6th

Enhancing Education Through Technology: A Critical Review of...

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6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： Karthik, V. Ilavendhan, A. Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819778614

computer-assisted education, often referred to as computer-assisted learning (CAL) or technology-enhanced learning (TEL), is an educational approach that integrates technology and computer-based tools into the teaching and learning process. It encompasses a wide range of applications and tools that aim to enhance and support educational activities. computer-assisted education (CAE) has become a transformative force in the field of education and is helping in reshaping the traditional teaching methodologies and learning experiences. A computer-assisted education-based solution refers to a technology-driven approach designed to enhance educational processes, improve learning outcomes, and provide support to educators, students, or learners. This paper will explore the multifaceted impact of computer-assisted education, encompassing its effects on student achievement, accessibility, teacher roles, curriculum design, and the evolving educational landscape. This research paper underscores the transformative potential of computer-assisted education in fostering more engaging, effective, and accessible learning experiences and also presents a model to make learning more engaging and effective. Various papers are reviewed and their limitations are also presented and AI-based interactive model is also defined in this paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive learning

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Amount and Location Based Credit Card Fraud Detection 5

Amount and Location Based Credit Card Fraud Detection

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5th IEEE international conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Devarakonda, Nagaraju Mulpuri, Sai Rishitha Tripuramallu, Srilakshmi Thanmayi Sathwika, Maddi VIT AP UNIVERSITY Computer Science and Engineering Amaravati India

ISBN: (纸本)9781665493604

During the pandemic, online transactions have become a usual activity for any purchase. Use of credit card has been growing rapidly, as a result, the number of frauds regarding the credits cards has increased enormously. This includes fraudulent transactions or transfer of money from our accounts. In our paper, an evaluation of machine learning, artificial intelligence, and deep learning algorithms like CNN, Local Outlier Factor, Decision Tree, Cat Boost, XG Boost, Isolation Forest, ANN, K-Means, Naive Bayes, Ada Boosting, Bagging, Multi-Layer Perceptron, KNN, Logistic Regression, Random Forest, SVM, Gradient Boosting, Over Sampling, SMOTE, Extra tree, have been contemplated for credit card fraud detection. In the previous papers where time parameter is considered during the credit card fraud detection whereas, in our paper, we will be considering other factors like location as well as the amount for transaction done. As a result, a distinction was made among different algorithms and datasets based on the factor accuracy. © 2023 IEEE.

关键词： Decision trees

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