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5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Yenkikar, Anuradha Babu, C. Narendra Ramaiah University of Applied Sciences Department of Computer Science and Engineering Karnataka Bangalore India Pune Institute of Computer Technology Pune India

ISBN: (纸本)9781665475242

Forecasting companies' stock market prices are one the interesting topics for analysts and researchers. Although a company's stock price can be unpredictable, long-term forecasts can help but it is dependent on many factors such as the company's business model, change in leadership, and investors' mood. It has been found to be insufficient to predict stock values just on the basis of historical data or textual information. Previous research in sentiment analysis have shown a strong correlation between the movement of stock prices and the publication of news stories. At different levels, a number of sentiment analysis research have been attempted utilizing methods. In this paper, we made a comparison of various Machine Learning methods on five datasets of financial news related to the company and domains in which the company. Encouraging results are obtained using 13 models i.e., Linear Regression, Ridge Regression, Lasso Regression, Random Forest, Naive Bayes, Logistic Regression, LSTM, ARIMA, Logistic Regression, Support Vector Machines, Decision Tree, Boosted Tree, and ensemble method which depict polarity of news articles being positive or negative and the accuracies are 93.90%, 92.31 %, 92.27%, 85.44%, 84.65%, 84.65%, 94.73%, 90.13%, 82%, 83%, 72%, 70%, 95.11 % respectively. © 2023 IEEE.

关键词： Financial markets

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Simulation-Based Performance Evaluation of 3D Object Detection Methods with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use Case 10

Simulation-Based Performance Evaluation of 3D Object Detecti...

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10th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2024

作者： Patel, Milin Jung, Rolf Institute for Advanced Driver Assistance Systems and Connected Mobility Kempten University of Applied Sciences Benningen Germany Faculty of Computer Science Kempten University of Applied Sciences Kempten Germany

ISBN: (纸本)9789897587030

Safety of the Intended Functionality (SOTIF) addresses sensor performance limitations and deep learningbased object detection insufficiencies to ensure the intended functionality of Automated Driving Systems (ADS). This paper presents a methodology examining the adaptability and performance evaluation of the 3D object detection methods on a LiDAR point cloud dataset generated by simulating a SOTIF-related Use Case. The major contributions of this paper include defining and modeling a SOTIF-related Use Case with 21 diverse weather conditions and generating a LiDAR point cloud dataset suitable for application of 3D object detection methods. The dataset consists of 547 frames, encompassing clear, cloudy, rainy weather conditions, corresponding to different times of the day, including noon, sunset, and night. Employing MMDetection3D and OpenPCDET toolkits, the performance of State-of-the-Art (SOTA) 3D object detection methods is evaluated and compared by testing the pre-trained Deep Learning (DL) models on the generated dataset using Average Precision (AP) and Recall metrics. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Object detection

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Influence of performance and emission of diesel engine with alumina nano material-based catalyst biodiesel using IoT

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International Journal of Power and Energy Conversion 2024年第4期15卷 338-351页

作者： Venkatesh, B. Khan, Mudassir Babu, J. Chinna Nagaraju, C.H. Mohan, R. Madhan Department of Mechanical Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Computer Science College of Computer Science Applied College Tanumah King Khalid University P.O. Box 960-61421 Abha Saudi Arabia Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Electrical and Electronics Engineering Annamacharya Institute of Technology and Sciences India

In this study, the performance and emissions of Al2O3 alkaline cottonseed biodiesel-powered compression ignition engines were predicted. An experiment was conducted using different biodiesel blends made from cottonseed oil under various loading conditions. Biodiesel was produced using ethanol and Cao-mg/Al2O3 as catalysts through the transesterification process and blended with diesel to produce CBDN10, CBDN20, CBDN30, and CBDN40. The CBDN20 blends showed an evident reduction in fuel consumption by 8% and a 12% improvement in thermal efficiency at high static thrust. The use of these blends resulted in minimal CO, CO2, and NOx emissions from engines. These emissions were monitored by an IoT system and analysed with gas analysers, which revealed CBDN20’s ability to reduce nitrogen oxide and hydrocarbon emissions compared to diesel. Additionally, CBDN20 demonstrated improved thermal efficiency and reduced brake-specific fuel consumption compared to the other blends. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Nitrogen oxides

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A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 491-510页

作者： Ling Xiao Beiji Zou Xiaoyan Kui Chengzhang Zhu Wensheng Zhang Xuebing Yang Bob Zhang School of Computer Science and Engineering Central South UniversityChangshaChina Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Central South UniversityChangshaChina The College of Literature and Journalism Central South UniversityChangshaChina Institute of Automation Chinese Academy of SciencesBeijingChina Department of Computer and Information Science University of MacaoMacaoChina

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health *** eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple *** compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate *** addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system ***,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these *** similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate ***,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ***,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system *** demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

关键词： big data cloud computing compression data compression medical applications performance evaluation

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Anticancer Drug Response Prediction Using Deep Learning

Anticancer Drug Response Prediction Using Deep Learning

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2024 International Conference on Horizons of Information Technology and Engineering, HITE 2024

作者： Zaidi, Awais Raza Majid, Abdul Bilal, Muhammad Pakistan Institute of Engineering & Applied Sciences Islamabad Department of Computer & Information Sciences P.O. Box 45650 Islamabad44000 Pakistan

ISBN: (纸本)9798331516055

Precision medicine demands accurate prediction of individual patient responses to drugs, a challenge addressed by various computational methods leveraging genomic profiles or chemical structures. This study introduces a novel deep-learning model designed to amalgamate genetic mutation, gene expression, and cheminformatics data within a multi-task convolutional architecture. we identified pertinent cancer-related genes from oncology genetics databases and L1000 landmark genes. The model, incorporating gene expression and mutations as genomic features, and Chemoinformatics features from PubChem or ChEMBL, exhibited enhanced predictive performance through the synergistic integration of these diverse data types. Our approach extends beyond conventional Graph Neural Networks (GNNs) by replacing them with Recurrent Neural Networks (RNNs) in the variants autoencoder deep-learning network. Additionally, we augmented the model's performance by increasing the number of layers in the Convolutional Neural Network (CNN). The extended architecture, complemented by multi-tasking and self-attention functions, demonstrated superior predictive capabilities compared to recently published methods on the same datasets. Our deep learning model has demonstrated improved performance as compared to the previous models. The results underscore the potential of combining gene expression, genetic mutation, and Chemoinformatics features to enhance the accuracy and effectiveness of predictive modeling in the context of precision medicine. © 2024 IEEE.

关键词： Oncology

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Towards new realities: Implications of personalized online layers in our daily lives

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i-com 2024年第2期23卷 221-229页

作者： Herder, Eelco Stojko, Laura Strecker, Jannis Neumayr, Thomas Yigitbas, Enes Augstein, Mirjam Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Science University of the Bundeswehr Munich Neubiberg Germany Institute of Computer Science University of St. Gallen St. Gallen Switzerland Research Group Personalized Environments and Collaborative Systems University of Applied Sciences Upper Austria Hagenberg Austria Faculty of Electrical Engineering Computer Science and Mathematics Paderborn University Paderborn Germany

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world. © 2024 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Human computer interaction

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From Social Media to Ballot Box:Leveraging Location-Aware Sentiment Analysis for Election Predictions

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computers, Materials & Continua 2023年第12期77卷 3037-3055页

作者： Asif Khan Nada Boudjellal Huaping Zhang Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media ***,traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels,resulting in a limited depth of analysis and *** light of this challenge,this study focuses on predicting elections at the state/regional level along with the country level,intending to offer a comprehensive analysis and deeper insights into the electoral *** achieve this,the study introduces the Location-Based Election Prediction Model(LEPM),which utilizes social media data,specifically Twitter,and integrates location-aware sentiment analysis techniques at both the state/region and country *** predicts the support and opposing strength of each political party/*** determine the location of users/voters who have not disclosed their location information in tweets,the model utilizes a Voter Location Detection(VotLocaDetect)approach,which leverages recent tweets/*** sentiment analysis techniques employed in this study include rule-based sentiment analysis,Valence Aware Dictionary and Sentiment Reasoner(VADER)as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers(BERT),BERTweet,and Election based BERT(ElecBERT).This study uses the 2020 United States(US)Presidential Election as a case *** applying the LEPM model to the election,the study demonstrates its ability to accurately predict outcomes in forty-one states,achieving an 0.84 accuracy rate at the state ***,at the country level,the LEPM model outperforms traditional polling *** a low Mean Absolute Error(MAE)of 0.87,the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other *** the extensive social media data,the LEPM model provides nuanc

关键词： Sentiment analysis big data machine learning election predictions social media analysis

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Enhancing Depression Detection: Employing Autoencoders and Linguistic Feature Analysis with BERT and LSTM Model

Enhancing Depression Detection: Employing Autoencoders and L...

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2023 International Russian Automation Conference, RusAutoCon 2023

作者： Firoz, Neda Beresteneva, Olga G. Aksyonov, Sergey V. Tomsk State University Institute of Applied Mathematics and Computer Sciences Tomsk Russia Tomsk Russia

ISBN: (纸本)9798350345551

Depression is an abstinent health ailment that affronts several people globally, affecting a consistent decline in mood resulting in a trivial impact on their emotions. The article centers around the utilization of BERT based features and Autoencoders to identify depression from textual input, while also exploring gender identity variations. The study emphasizes the importance of feature engineering for text data obtained from the DAIC_WOZ benchmark dataset. By employing BERT embeddings, which capture the semantic meaning of text, in combination with Autoencoders and additional quantitative characteristics such as PHQ-8 participant feedback, assessment of absolutist word usage, and gender details, the model's performance is significantly improved. We conduct trials with BERT embeddings that encode the textual meaning to extract text characteristics. These features are subsequently combined using Autoencoders. Our method surpasses the performance of the baseline models, achieving an accuracy of 95.5%. Additionally, the mean absolute error (MAE) is measured at 3.75 and the root mean squared error (RMSE) at 5.02, demonstrating the exceptional performance for binary depression classification. These discoveries reveal the capacity of machine learning in mental health investigation, especially when considering gender disparities. © 2023 IEEE.

关键词： Semantics

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Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

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computers, Materials & Continua 2023年第9期76卷 3345-3361页

作者： Asif Khan Huaping Zhang Nada Boudjellal Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various *** recent years,the rise of social media platforms(SMPs)has provided a rich source of data for analyzing public opinions,particularly in the context of election-related ***,sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used,including figurative expressions,sarcasm,and the spread of *** address these challenges,this paper proposes Election-focused Bidirectional Encoder Representations from Transformers(ElecBERT),a new model for sentiment analysis in the context of election-related ***-related tweets pose unique challenges for sentiment analysis due to their complex language,sarcasm,*** is based on the Bidirectional Encoder Representations from Transformers(BERT)language model and is fine-tuned on two datasets:Election-Related Sentiment-Annotated Tweets(ElecSent)-Multi-Languages,containing 5.31 million labeled tweets in multiple languages,and ElecSent-English,containing 4.75million labeled tweets in *** outperforms othermachine learning models such as Support Vector Machines(SVM),Na飗e Bayes(NB),and eXtreme Gradient Boosting(XGBoost),with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages,and an accuracy of 0.9930 and F1-score of 0.9899 on *** performance of differentmodels was compared using the 2020 United States(US)Presidential Election as a case *** ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet,with the ElecBERT-English model achieving aMean Absolute Error(MAE)of *** paper presents a valuable contribution to sentiment analysis in the context of election-related tweets,with potential applications in political analysis,social media management,and policymaking.

关键词： Sentiment analysis social media election prediction machine learning transformers

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Vulnerabilities and Challenges in the Development of PUF-Based Authentication Protocols on FPGAs: A Brief Review 7

Vulnerabilities and Challenges in the Development of PUF-Bas...

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7th IEEE Conference on Dependable and Secure Computing, DSC 2024

作者： Yavuz, Sinan Naroska, Edwin Daniel, Kai University of Siegen Department of Electrical Engineering and Computer Science Siegen Germany Niederrhein University of Applied Sciences Department of Electrical Engineering and Computer Science Krefeld Germany Institute of Measurement and Sensor Technology University of Applied Sciences Ruhr West Mülheim Germany

ISBN: (纸本)9798331540289

The security of IoT (Internet of Things) devices and the protection of sensitive information processed by these devices such as personal data, sensor values, process-related information is an important and difficult challenge. A major task in IoT communication is secure identification of devices. Unfortunately, traditional cryptographic methods are often not suitable for IoT devices due to their limited hardware resources. However, typical methods are computationally intensive, require a large amount of memory, and have a high-power consumption. On the other hand, Physical Unclonable Functions (PUFs) are low-cost and lightweight hardware-based primitives that can be used as a security component to protect data against third parties and thus increase security of a device. In this paper, the challenges and vulnerabilities in the development of PUF-based authentication protocols are presented. To this end, a security analysis of different approaches known from literature are discussed. Furthermore, possible attack vectors and prevention techniques are also considered. © 2024 IEEE.

关键词： Authentication Protocol

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