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Auto Value Estimation: Car Price Prediction 15

Auto Value Estimation: Car Price Prediction

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kumar, Anant Kumar, Ashish Sharma, Vandana Department of Computer Science ABES Engineering College UP Ghaziabad India

ISBN: (纸本)9798331300579

As a result of incredible technological advancements and research of new technical expertise and huge economic growth of our country, people started to buy cars more than other vehicles. Therefore, there arises an enormous demand for cars, as the demand increases for new cars the used car market also booms alike. But the used car market is highly manipulated by few numbers of people who govern the rates of the used cars and also online selling websites designate the values for the used cars. This paper tries to study and investigate the trends in used car prices and predicts the price of used cars with the help of supervised machine learning algorithms. And to suggest which machine learning algorithm performs well among the selected methods for predicting the cars price. There has been related work done with machine learning algorithms like linear regression, multiple regression, random forest and so on. We wanted to study which algorithm predicts the car price more reliably and accurately. © Grenze Scientific Society, 2024.

关键词： Multiple linear regression

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Design of Community Education E-learning Platform Based on Data Network Technology 2

Design of Community Education E-learning Platform Based on D...

引用

2nd international conference on Data Analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Yang, Zhongying Ren, Yu Shandong Institute of Commerce and Technology Intelligence Finance and Economics Industry School Shandong Jinan250103 China

ISBN: (纸本)9798350339765

With the continuous development and popularization of information technology, online education has gradually become a new way of learning. Especially during the epidemic, E-learning has gradually become the mainstream. As an important part of basic education, community education also needs to adapt to the requirements of the development of the times and introduce E-learning into community education. Therefore, this paper aimed to explore the construction of community education E-learning platform based on data network technology, so as to improve the teaching quality and learning effect of community education and promote the development of community education. The research results indicated that under the same other conditions, the average scores (P value greater than 0.05) of Group X and Group Y before the experiment indicated that there was no significant difference between the two. The average scores (P value less than 0.05) of the two groups after the experiment indicated a significant difference between the two groups. This showed that data network technology could significantly improve the teaching effect of community education E-learning platform. © 2023 IEEE.

关键词： E-learning

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An Efficient Image to Speech Generation System for Visually Impaired Individuals Using Deep learning Techniques 10

An Efficient Image to Speech Generation System for Visually ...

引用

10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Priyanka, G. Nivaashini, M. Shree Akshaya, B. Subashini, M. Ritish, G. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

As the world is becoming more visually oriented, visually impaired people are struggling to access and understand visual content. This paper aims to represent the model created to assist blind and visually impaired people. This method leverages deep learning techniques to convert images into meaningful audio outputs. It involves capturing input images and processing them using advanced deep-learning algorithms. The extracted features are translated into speech. The resulting auditory output provides an alternative sensory experience for individuals with visual impairments, enabling them to perceive and interpret visual information through sound. This study implements four deep learning models namely CNN-LSTM, Resnet34, YOLOv5 and Vision Transformer to process sequential visual data capturing intricate patterns and dependencies within images. By integrating these models in this work, the Image to audio converter not only achieves superior accuracy in image recognition but also enhances the richness and depth of the auditory output, creating a nuanced and immersive sensory experience for users. The performance of the proposed models was evaluated using several metrics and it was noted that vision transformer outperformed the remaining models. This seamless integration of deep learning models amplifies the study's effectiveness in translating diverse visual inputs into detailed and coherent auditory output, thereby enhancing accessibility and inclusivity for individuals with visual impairments. © 2024 IEEE.

关键词： Image enhancement

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Remaining Useful Life Prediction of Turbofan Engines based on CNN-LSTM Hybrid Model 3

Remaining Useful Life Prediction of Turbofan Engines based o...

引用

3rd international conference on Cloud computing, Big Data Application and Software engineering, CBASE 2024

作者： Wei, Jianfang Meng, Linggang Du, Junrong Yin, Zhifeng National Computer System Engineering Research Institute of China Beijing China

ISBN: (纸本)9798331506582

Good working condition of mechanical facility is necessary to ensure orderly industrial production, and remaining useful life (RUL) estimation of mechanical facility via current monitoring parameters is an important issue for condition-based maintenance. Deep learning has good capacity of nonlinear feature extraction, high dimension time series modeling and complex pattern recognition, and has good application prospect in the field of RUL prediction of mechanical facility. A hybrid model with CNN-LSTM structure for RUL prediction of mechanical facility is proposed, and a multivariate time series model with optimal length based sliding window is established to extract inner features. The method is validated on NASA's public turbofan engines dataset, achieving accurate remaining useful life prediction of turbofan engines. © 2024 IEEE.

关键词： Turbofan engines

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Boosting Software Quality: A Machine learning Technique for Predicting Defects 6

Boosting Software Quality: A Machine Learning Technique for ...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Akilan Daniel, E. Alwin Infant, P. Jainish, G.R. Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (纸本)9798331522667

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. This work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. The objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. These indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. These algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. This approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges. © 2025 IEEE.

关键词： Data reliability

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Enhancing learning in Pharmaceutical Sales engineering Through Co-Creation 21

Enhancing Learning in Pharmaceutical Sales Engineering Throu...

引用

21st international conference on Information Technology Based Higher Education and Training, ITHET 2024

作者： Lee-Remond, Sora Alavi, Sarah Montbeliard France Esta Belfort France Université de Haute-Alsace Université de Strasbourg Cnrs Lima Umr 7042 MulhouseF-68100 France

ISBN: (纸本)9798331516635

The acquisition of knowledge related to the development of new drugs in the pharmaceutical industry can be challenging. It has been observed that traditional lectures that rely on reading, listening, and observing may not be as effective, especially for sales-engineer learners in cross-disciplinary contexts. Grasping clinical trials, a major chapter of new drug development, is a crucial skill for their training of sales engineers and is highly valued in the pharmaceutical industry. Therefore, it is important to find alternative methods of teaching. The use of game-based learning (GBL) in the health professional sector has been shown to improve learning outcomes. Gamification is a teaching method that actively engages students. However, it is important to note that while most studies have focused on pre-existing gamification support, this work aims to discuss the co-creation of a non-digital social game. This study focused on the creation of tangible games over the use of digital platforms, which may not always facilitate creativity in the same way. Since learning is no longer about repeating what is known, but about creating something new, students are divided into groups and given the task of designing a game using the contents of clinical trials, after receiving a lesson on the elements and mechanisms of games. Following the creation sessions, participants are asked to respond to a Likert scale questionnaire regarding their perceptions of learning and understanding before and after the activity. It is developed using Bloom's taxonomy and the Attrackdiff questionnaire. A semi-structured thematic interview is also carried out to reflect the learning and co-creation experiences encountered at the learning phases, as well as the emotional involvement and perceptions of the learning outcomes. The findings suggest that collaborative game creation in medical sciences courses can enhance students' empathy and understanding, foster motivation, and facilitate proactive lear

关键词： Students

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An Integrated Machine learning Framework Based Liver Disease Diagnosis System

An Integrated Machine Learning Framework Based Liver Disease...

引用

2024 international conference on engineering and computing, ICECT 2024

作者： Imtiaz, Irsa Qaiser, Ayesha Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Sciences and Technology Department of Computer Science Kuwait

ISBN: (纸本)9798350349719

Viral hepatitis increases the likelihood of developing liver cancer, which is a critical public health concern. It is a severe disorder that can be fatal if not recognized and treated promptly. The most common causes of liver cancer are hepatitis viruses. Early-stage diagnosis of HCC is crucial for improving patient outcomes, particularly when hepatitis virus infection is the underlying cause. This study looks into the use of ML algorithms for early detection of liver cancer in hepatitis virus-infected patients. The study will present a unified approach for the early detection of liver cancer that is highly accurate. Early detection of liver cancer is critical in reducing mortality and assisting practitioners in treating patients on time based on results. Machine learning algorithms have been intensively researched and applied to the diagnosis of liver cancer, especially in recent years. Researchers employed a variety of machine learning techniques to create models for detecting liver cancer at different stages, including early diagnosis. To enhance the model's flexibility and efficiency, We used three datasets that contained all of the information required for diagnosing liver cancer. Several machine-learning approaches were used to improve the model's accuracy. Our proposed model acquired a 98.10% accuracy rate with Bagging. Using several datasets in machine learning for liver disease detection enhances model performance by diversifying the training examples, resulting in a more robust and generalizable model with lower overfitting risk. This study aims to improve the quality of life for people who are at risk of or have been diagnosed with liver cancer. © 2024 IEEE.

关键词： Viruses

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A Transfer learning and Feature Ranking-based Feature Extraction Approach for Enhancing Skin Lesion Classification 4

A Transfer Learning and Feature Ranking-based Feature Extrac...

引用

4th international conference on Emerging Systems and Intelligent computing, ESIC 2024

作者： Mohanty, Niharika Pradhan, Manaswini Mallick, Pradeep Kumar Routray, Ashanta Ranjan Fakir Mohan University Department of Information and Communication Technology Odisha Balasore India Deemed to Be University School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350349856

This study investigates skin lesion classification through feature fusion, focusing on transfer learning-based extraction for improved model discernment. Utilizing VGG16, ResNet, and EfficientNet B0, the research ranks features using methods like model weights, RFE, correlation-based techniques, LASSO regression, gradient boosting, and variance thresholding. The approach aims to enhance diagnostic precision by combining diverse features essential for accurate skin lesion classification. Assessing HAM 10000 and BCN 20000 datasets, the study evaluates the impact of ranking on model performance. Results consistently demonstrate that ranked feature sets outperform initial sets across classifiers (KNN, SVM, CNN) for both datasets. Notably, CNN excels with ranked feature sets from RFE applied to fused features from transfer learning networks. © 2024 IEEE.

关键词： Extraction

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Enhancing AI-Generated Content Efficiency through Adaptive Multi-Edge Collaboration 44

Enhancing AI-Generated Content Efficiency through Adaptive M...

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44th IEEE international conference on Distributed computing Systems (ICDCS)

作者： Xu, Changfu Guo, Jianxiong Zeng, Jiandian Meng, Shengguang Chu, Xiaowen Cao, Jiannong Wang, Tian BNU HKBU United Int Coll Guangdong Prov Key Lab IRADS Zhuhai Peoples R China Hong Kong Baptist Univ Hong Kong Peoples R China Guangdong Donghua Faster Software Co Ltd Zhuhai Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Zhuhai Peoples R China

ISBN: (纸本)9798350386066;9798350386059

The Artificial Intelligence-Generated Content (AIGC) technique has gained significant popularity in creating diverse content. However, the current deployment of AIGC services in a centralized framework leads to high response times. To address this issue, we propose the integration of collaborative Mobile Edge computing (MEC) technology to decrease the processing delay of AIGC services. Nevertheless, existing collaborative MEC methods only facilitate collaborative processing among fixed Edge Servers (ESs), limiting flexibility and resource utilization across heterogeneous ESs for different computing and networking requirements associated with AIGC tasks. This poses challenges for efficient resource allocation. We present an adaptive multi-server collaborative MEC approach tailored for heterogeneous edge environments to achieve efficient AIGC by dynamically allocating task workload across multiple ESs. We formulate our problem as an online linear programming problem aiming to minimize task offloading make-span. This problem is proved to be NP-hard and we propose an online adaptive multi-server selection and allocation algorithm based on deep reinforcement learning that effectively addresses this problem. Additionally, we provide theoretical performance analysis, demonstrating that our algorithm achieves near-optimal solutions within approximate linear time complexity bounds. Finally, experimental results validate the effectiveness of our method by showcasing at least 11.04% reduction in task offloading make-span and a 44.86% decrease in failure rate compared to state-of-the-art methods.

关键词： Collaborative edge computing Adaptive multi-server offloading Workload allocation AIGC Deep Q-Networks

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Explainable Trust-aware Selection of Autonomous Vehicles Using LIME for One-Shot Federated learning 19

Explainable Trust-aware Selection of Autonomous Vehicles Usi...

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19th IEEE international Wireless Communications and Mobile computing (IEEE IWCMC)

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Princess Sumaya Univ Technol PSUT King Hussein Sch Comp Sci Amman Jordan Concordia Univ CIISE Montreal PQ Canada Khalifa Univ EECS Abu Dhabi U Arab Emirates Polytech Montreal Dept Comp & Software Engn Montreal PQ Canada

ISBN: (纸本)9798350333398

Autonomous driving has been gaining a lot of attention in the field of transportation technology in recent years. The use of autonomous vehicles has the potential to reduce the number of road accidents caused by human error, improve traffic flow, increase fuel efficiency and save time for travelers. In federated learning systems, selecting trustworthy autonomous vehicles (AVs) to participate in training is critical for ensuring system performance and reliability. In this work, we propose a trust-aware approach to AV selection that incorporates the performance of each AV using the Local Interpretable Model-Agnostic Explanations (LIME) method and One-Shot Federated learning. We modify the XAI LIME Deep Q-learning-based AV selection model to include the trust metric, resulting in the Trust-Aware XAI LIME Deep Q-learning-based AV selection model. Our experiments show that the trust-aware approach outperforms the standard approach in terms of both accuracy and reliability, demonstrating the effectiveness of incorporating trust metrics in AV selection.

关键词： Autonomous driving Explainable artificial intelligence (XAI) One-Shot Federated learning LIME Deep Q-learning

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