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Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

Utilizing Web Scraping and Machine Learning for Improving Ac...

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Advances in Data Engineering and Intelligent Computing systems (ADICS), international conference on

作者： V. Vasanth Kumar C. TamizhSelvan L. Manoharan R. Vishnu Sekhar V. Vinoth Kumar Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Biotechnology Integrated Bioprocessing Laboratory School of Bioengineering SRM Institute of Science and Technology India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric.

关键词： measurement Computational modeling Linear regression Predictive models Feature extraction Boosting Data models

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Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments

Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Qua...

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IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Duisterhof, Bardienus P. Li, Shushuai Burgues, Javier Reddi, Vijay Janapa de Croon, Guido C. H. E. Delft Univ Technol Delft Netherlands Univ Barcelona Barcelona Spain Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781665417143

Nano quadcopters are ideal for gas source localization (GSL) as they are safe, agile and inexpensive. However, their extremely restricted sensors and computational resources make GSL a daunting challenge. We propose a novel bug algorithm named 'Sniffy Bug', which allows a fully autonomous swarm of gas-seeking nano quadcopters to localize a gas source in unknown, cluttered, and GPS-denied environments. The computationally efficient, mapless algorithm foresees in the avoidance of obstacles and other swarm members, while pursuing desired waypoints. The waypoints are first set for exploration, and, when a single swarm member has sensed the gas, by a particle swarm optimization-based (PSO) procedure. We evolve all the parameters of the bug (and PSO) algorithm using our novel simulation pipeline, 'AutoGDM'. It builds on and expands open source tools in order to enable fully automated end-to-end environment generation and gas dispersion modeling, allowing for learning in simulation. Flight tests show that Sniffy Bug with evolved parameters outperforms manually selected parameters in cluttered, real-world environments. Videos: https://***/37MmtdL

关键词： measurement Location awareness Three-dimensional displays computer bugs Pipelines Sensors Task analysis

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Vision-language navigation with energy-based policy 24

Vision-language navigation with energy-based policy

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Proceedings of the 38th international conference on Neural Information Processing systems

作者： Rui Liu Wenguan Wang Yi Yang The State Key Lab of Brain-Machine Intelligence and College of Computer Science and Technology Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798331314385

Vision-language navigation (VLN) requires an agent to execute actions following human instructions. Existing VLN models are optimized through expert demonstrations by supervised behavioural cloning or incorporating manual reward engineering. While straightforward, these efforts overlook the accumulation of errors in the Markov decision process, and struggle to match the distribution of the expert policy. Going beyond this, we propose an Energy-based Navigation Policy (ENP) to model the joint state-action distribution using an energy-based model. At each step, low energy values correspond to the state-action pairs that the expert is most likely to perform, and vice versa. Theoretically, the optimization objective is equivalent to minimizing the forward divergence between the occupancy measure of the expert and ours. Consequently, ENP learns to globally align with the expert policy by maximizing the likelihood of the actions and modeling the dynamics of the navigation states in a collaborative manner. With a variety of VLN architectures, ENP achieves promising performances on R2R, REVERIE, RxR, and R2R-CE, unleashing the power of existing VLN models.

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Design of Enterprise Financial and Economic Risk Early Warning System Based on Machine Learning Algorithms

Design of Enterprise Financial and Economic Risk Early Warni...

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computer Simulation and modeling, Information Security (CSMIS), international conference on

作者： Hanjun Li Donghong Zhang Department of Financial Management Department of Accounting Wenhua College Wuhan Hubei China

ISBN: (数字)9798350343724

ISBN: (纸本)9798350343731

The design of enterprise financial and economic risk (ER) warning systems based on machine learning algorithms has important background significance. In the current fierce market competition and complex financial environment, enterprises are facing unprecedented financial and ER. Traditional early warning systems often have problems such as low timeliness, missed reports, and false positives. Therefore, introducing machine learning algorithms can more accurately predict and identify potential financial risks, and help enterprises take timely measures to reduce risks. Taking the collected financial data of a manufacturing enterprise as an example, a financial and ER warning model was constructed through the training and validation of machine learning algorithms. In the past five years, the company's operating revenue fluctuated between 9 million to 13 million yuan, and profits fluctuated between 400000 to 700000 yuan; the total assets increased from 18 million yuan to 24 million yuan, and the total liabilities increased from 10 million yuan to 14 million yuan. The designed warning system is based on real-time monitoring and historical data analysis, which can detect potential financial and ER as early as possible in the event of abnormal changes in financial indicators. By establishing a feedback mechanism, the system continuously optimizes accuracy and reliability. This system design provides enterprises with a more accurate and timely financial and ER warning mechanism, which helps to maintain the sustainable development and stable operation of the enterprise.

关键词： Economics Machine learning algorithms Data analysis Current measurement Alarm systems Data models Real-time systems

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Detecting Hallucinations in Large Language Models Using Machine Learning Classifiers: A Comparative Study

Detecting Hallucinations in Large Language Models Using Mach...

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Computational Intelligence Approaches and Applications (ICCIAA), international conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Douaa Al-Rousan Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of computer engineering College of Engineering AIBalqa Applied University Salt Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University Amman Jordan School of Civil Engineering Universiti Sains Malaysia Nibong Tebal Pulau Pinang Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This study is concerned with the comprehensive evaluation of machine learning models intended for the detection of hallucination in Large Language Model output. The preprocessing of the prompt and answers using TF-IDF vectorization has been done as a preliminary step before developing a hallucination prediction model. Afterwards, the generated dataset was then divided into training and testing, with percentages equal to 70% and 30%, respectively. The selected machine learning models were trained with the dataset created. In this research, eight various classifiers are implemented and compared. To evaluate the results of our approach, multiple metrics were taken into consideration: accuracy, precision, recall, and Fl-score. While the training results showed exceptional performance with accuracies exceeding 99% in several models, the test results indicated huge challenges in generalization. Random Forest had the highest testing accuracy of 94.77% at 65% precision but with a very low recall of 4.85%. Besides, cross validation is used with random forest and decision tree, the results showed that the cross-validation is better than the test and train splitting model. The study shows the significant trade-offs between precision and recall in hallucination detection, which is especially problematic while working with imbalanced datasets.

关键词： Training measurement Accuracy Large language models Computational modeling Predictive models Decision trees Random forests Computational intelligence Testing

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Research on Enhanced Perception and Attention-Driven Person Re-Identification

Research on Enhanced Perception and Attention-Driven Person ...

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international conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Cancan Wu Dong Li Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Hubei China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

When matching similarity among pedestrians in images, pedestrian re-identification algorithms are often disturbed by occlusions. A typical tactic is to improve the robustness of occlusion features in the model. However, this approach neglects the noise introduced by other pedestrians in the image, leading to limited learning of relevant information between images. To address these issues, this paper introduces a pedestrian re-identification model that leverages perception-enhanced and attention mechanisms. By enhancing the perception of non-pedestrian noise and erasing non-pedestrian features, we then employ a cross-attention mechanism to subtly embed global and local features for finer feature recognition. Finally, through context attention, we learn the interaction between pedestrian images to improve re-identification accuracy. Results from studies performed on the Market-1501, CUHK03-D, and Duke-ReID datasets indicate that the suggested model surpasses leading contemporary models regarding Rank-1 scores and mean Average Precision (mAP), while maintaining good robustness.

关键词： Pedestrians Accuracy Attention mechanisms Computational modeling Noise Knowledge discovery Robustness Noise measurement Identification of persons Fuzzy systems

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Trustworthy Decision-Making Model for Autonomous Vehicles using Graph Neural Networks and Proximal Policy Optimization

Trustworthy Decision-Making Model for Autonomous Vehicles us...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： K M Monica Mohammed Ihsan Sowmya M R. Manikandan Mamatha Talakoti School of Computer Science and Engineering VIT Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Nandha Engineering College (Autonomous) Erode India Department of Computer Science and Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

In recent decades, autonomous vehicles mostly use advanced systems and Machine Learning (ML) algorithms to make accurate decisions, without involvement of humans. Even though many researchers have developed robust model to enhance the robustness of the system with safety and reliability of autonomous vehicles still there are challenges in handling the events which are unpredictable. Therefore, this research proposes a model namely Graph Neural Networks and Proximal Policy Optimization (GNN-PPO) for decision making of autonomous vehicles. Firstly, the data is collected from Performance measurement System - Bay Area (PEMS-BAY) dataset. Then, the data collected is preprocessed using Kalman Filter (KF) and min-max scaling for handling noise and normalization. Further, the preprocessed data is embedded into Mutual Information (MI) for optimal selection of features. Finally, the proposed GNN-PPO is incorporated to train with spatial dependencies between traffic sensors and adaptive decision making respectively. The proposed GNN-PPO acquired greater results with low level density return of (198.76 ± 1.06) and high-level density return of (165.30 ± 19.35) respectively when compared to the existing Observation Adversarial Reinforcement Learning (OARL) model.

关键词： Solid modeling Decision making Noise Graph neural networks Data models Sensors Kalman filters Autonomous vehicles Optimization Mutual information

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Smart Flood Detection and Communication in Hazardous Zones via MANET-Based IoT and Parallely Distributed Slimmable Neural Networks

Smart Flood Detection and Communication in Hazardous Zones v...

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Sustainable Expert systems (ICSES), international conference on

作者： V. Sathya Shuchi Bhadula R. Srinivasan Bhargav H K Bhavani Sankar Panda Amit Barve Department of Computer Science Periyar Maniammai Institute of Science and Technology (Deemed to be University) Vallam Thanjavur Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Tiruvallur Tamil Nadu India Department of Computer Science and Engineering Industry Institute Interaction Cell (IIIC) and Associate Professor Shridevi Institute of Engineering and Technology Tumakuru Karnataka India Department of Computer Science and Engineering Raghu Engineering College Visakhapatnam Andhra Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Limda Gujarat India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Disasters can be mitigated by an early warning signal and proper communication within the hazardous environment using the MANET technology. However, the exact prediction of disaster situation is needed for the timely disaster management. Hence in addition to MANET technology, deep learning algorithms and Inter of Things (IoT) can also be integrated into disaster detection and communication systems. The presented research developed a novel parallely distributed slimmable neural network with orchard algorithm for the accurate flood disaster prediction. The model processed the dataset containing various measurements such as temperature, humidity, precipitation, air, soil moisture and rainfall level that are collected by the IoT sensor deployed in the various location of the hazardous environment. The gathered data are initially pre-processed using the correlation coefficient min-max normalization approach. Further, the relevant characteristics of the flood disaster from the data are extracted through spike driven transformer process. These refined and dimensionality reduced data are then entered into the proposed framework for the disaster prediction. Here the integrated orchard algorithm increased the disaster monitoring accuracy on the basis of better optimized parameters. The model resulted 95% accuracy, 93% precision, 94% recall and 96% f-score. Therefore, the presented method is an effective prediction mechanism for the disaster management in IoT MANET.

关键词： Temperature sensors Temperature measurement Accuracy Disasters Neural networks Disaster management Prediction algorithms Transformers Mobile ad hoc networks Load modeling

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Boosting Software Quality: A Machine Learning Technique for Predicting Defects

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

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

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

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

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.

关键词： Computational modeling Software algorithms Software quality Predictive models Prediction algorithms Data models Software reliability Decision trees Software measurement Embedded software

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Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reduction

Blockwise Principal Component Analysis for monotone missing ...

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international joint conference on Neural Networks (IJCNN)

作者： Tu T. Do Anh M. Vu Tuan L. Vo Hoang Thien Ly Thu Nguyen Steven Hicks Pål Halvorsen Michael Riegler Binh T. Nguyen Faculty of Mathematics and Computer Science University Of Science Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Ho Chi Minh City Vietnam Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Department of Holistic Systems Simula Metropolitan Oslo Norway University of Science Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Monotone missing data is a common problem in data analysis. However, imputation combined with dimensionality reduction can be computationally expensive, especially with the increasing size of datasets. We propose a Blockwise Principal Component Analysis Imputation (BPI) framework for dimensionality reduction and imputation of monotone missing data to address this issue. The framework conducts Principal Component Analysis on the observed part of each monotone block of the data and then imputes on merging the obtained principal components using a chosen imputation technique. BPI can work with various imputation techniques and can significantly reduce imputation time compared to conducting dimensionality reduction after imputation. This makes it a practical and efficient approach for large datasets with monotone missing data. Our experiments validate the improvement in speed while achieving an accuracy that is comparable to the common strategy of imputation prior to dimensional reduction.

关键词： Dimensionality reduction Support vector machines Accuracy Neural networks Noise reduction Imputation Noise measurement

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