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

作者： Sondhi, Kartikey Dalapati, Poulami Kumar, Saurabh Department of Computer Science and Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9789897587030

Efficient transportation systems are crucial for the ever-growing smart cities. With the increasing urbanization and growth in vehicular traffic, congestion has become a significant challenge. This research paper addresses the critical issue of identifying the fastest, least congested path in road transport networks, aiming to enhance overall travel efficiency and reduce the negative impact of traffic congestion. The study employs an improved version of the Lifelong Planning A∗ (LPA∗) that helps find the fastest route between two points in dynamic changing environments. The proposed methodology is called the Lifelong Dynamic Timed A∗(LTA∗) algorithm with an optimal bound weight factor integrated with it to make the search more guided and efficiently predict optimized traffic paths to provide real-time recommendations. To validate the effectiveness of the developed algorithm, extensive simulations and case studies are conducted on a small area inWashington as well as on GridWorlds. The experimental results show that LTA∗, within accurate weight bounds, always managed to find the fastest path, and in some cases, the time taken was close to half of that produced by A∗. Copyright © 2024 by SCITEPRESS - science and technology Publications, Lda.

关键词： Traffic congestion

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Ethical Dimensions in Data-Driven Business: Balancing Fairness, Accountability, Transparency, and Explainability 15

Ethical Dimensions in Data-Driven Business: Balancing Fairne...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering India

ISBN: (纸本)9798350370249

In the modern business landscape, the widespread adoption of data-driven decision systems underscores the importance of integrating ethical principles such as fairness, accountability, transparency, and explainability (FATE). These principles are critical for ensuring that decision-making processes are conducted ethically and responsibly. Fairness prevents biases and discrimination, accountability ensures organizations are held responsible for outcomes, transparency fosters trust by making processes accessible, and explainability enables users to comprehend algorithmic decisions. This paper examines the significance of FATE principles in business contexts, emphasizing their role in promoting ethical decision-making and societal welfare. Through case studies and best practices, it highlights the practical implementation of these principles to ensure the responsible use of data-driven technologies in business operations. © 2024 IEEE.

关键词： Data assimilation

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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural Computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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The Large Language Models on Biomedical Data Analysis: A Survey

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4486-4497页

作者： Lan, Wei Tang, Zhentao Liu, Mingyang Chen, Qingfeng Peng, Wei Chen, Yiping Phoebe Pan, Yi Guangxi University School of Computer Electronic and Information Nanning530004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research. © 2013 IEEE.

关键词： Data consistency

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Enhanced word vector space with ensemble deep learning model for COVID-19 Hindi text sentiment analysis

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Multimedia Tools and Applications 2025年第9期84卷 5861-5882页

作者： Jain, Vipin Kashyap, Kanchan Lata Department of SCSE VIT Bhopal University Madhya Pradesh Sehore466114 India Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

The SARS-CoV-2 virus has spread worldwide since March 2020 and became a global pandemic. Millions of people worldwide have infected with this virus. This research work proposed an enhanced word vector space and deep learning-based hybrid model for COVID-19 Hindi text sentiment analysis. Total 36,863,393 Hindi tweets are collected for analysis. First, various natural language processing techniques are applied for text preprocessing. Next, The word vector space is enhanced by combining various word embedding techniques namely, Word2vec, Fasttext, Glove, POS2Vec, WordPosition2Vec, Lexicon2Vec, and Elmo. Finally, developed a hybrid model for sentiment classification. According to the experimental findings, highest 89.60%, 78.70%, 77.23% and 91.64% precision, recall, F-score, and accuracy, respectively, is given by proposed model. The obtained results indicate that the proposed approach is highly efficient in classifying Hindi sentiments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vector spaces

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Automatic Timbre Transformation using Enhanced Diffusion Model

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IEEE Access 2025年 13卷 91642-91654页

作者： Wu, Cheng-Han Cheewaprakobkit, Pimpa Shih, Timothy K. Lin, Yu-Cheng Liu, Bing-Ze National Central University Department of Computer Science and Information Engineering Taoyuan City32001 Taiwan Asia Pacific International University Faculty of Information Technology Saraburi18180 Thailand Yuan-Ze University Department of Computer Science and Engineering Taoyuan City32003 Taiwan

We present a novel timbre transfer model that uses an enhanced diffusion architecture to convert music from various instruments into Erhu timbre. The Erhu, a traditional Chinese instrument, is difficult to simulate due to its rich vibrato and smooth note transitions. Existing Musical Instrument Digital Interface systems struggle to capture its nuanced dynamics. Our model integrates a Pitch Encoder, a Loudness Encoder, and a Diffusion Decoder. The encoders extract pitch features and dynamic loudness variations, guiding the decoder in generating realistic Erhu timbre. By extracting general musical features, the system generalizes to unseen input types without retraining. Evaluations based on pitch accuracy, cosine similarity, and Fréchet Audio Distance show that our model achieves 96% pitch accuracy and high fidelity in Erhu timbre reproduction. This study demonstrates the potential of diffusion-based timbre transfer models in music generation and provides new directions for future work on both music generation and timbre transfer. © 2013 IEEE.

关键词： Deep learning

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Prediction of vertical displacement for a buried pipeline subjected to normal fault using a hybrid FEM-ANN approach

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Frontiers of Structural and Civil engineering 2024年第3期18卷 428-443页

作者： Hedye JALALI Reza YEGANEH KHAKSAR Danial MOHAMMADZADEH S. Nader KARBALLAEEZADEH Amir H.GANDOMI Department of Civil Engineering Architecture and UrbanismSadjad University of TechnologyPO Box ***MashhadIran Department of Civil Engineering Faculty of EngineeringEqbal Lahoori Institute of Higher EducationPO Box ***MashhadIran Faculty of Civil Engineering Architectureand ArtScience and Research Branch of Islamic Azad UniversityPO Box ***TehranIran Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia University Research and Innovation Center(EKIK) Óbuda UniversityBudapest 1034Hungary

Fault movement during earthquakes is a geotechnical phenomenon threatening buried pipelines and with the potential to cause severe damage to critical ***,effective prediction of pipe displacement is crucial for preventive management *** study aims to develop a fast,hybrid model for predicting vertical displacement of pipe networks when they experience *** this study,the complex behavior of soil and a buried pipeline system subjected to a normal fault is analyzed by using an artificial neural network(ANN)to generate predictions the behavior of the soil when different parameters of it are *** this purpose,a finite element model is developed for a pipeline subjected to normal fault *** data bank used for training the ANN includes all the critical soil parameters(cohesion,internal friction angle,Young’s modulus,and faulting).Furthermore,a mathematical formula is presented,based on biases and weights of the ANN *** results show that the maximum error of the presented formula is 2.03%,which makes the proposed technique efficiently predict the vertical displacement of buried pipelines and hence,helps to optimize the upcoming pipeline projects.

关键词： buried pipelines normal Fault finite element method multilayer perceptron neural network formulation

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Task Offloading and Resource Management for IIoT with Satellite-Terrestrial Integrated Computing Power Network Based on D3QN

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IEEE Internet of Things Journal 2025年第12期12卷 21770-21783页

作者： Li, Meng Li, Meihui Wang, Kan Yu, F. Richard Wang, Zhuwei Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT, 2) the complex communication environments, 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated computing power network (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a Markov Decision Process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a Dueling Double Deep Q Network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms the comparison schemes significantly. © 2025 IEEE.

关键词： Energy utilization

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IVIDNet: Intelligent iris vitality detection via weighted prediction score level fusion

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Multimedia Tools and Applications 2023年第29期82卷 45959-45981页

作者： Verma, Palak Selwal, Arvind Sharma, Deepika Department of Computer Science and Engineering Indian Institute of Technology Jammu PO-Nagrota Jammu181221 India Department of Computer Science and Information Technology Central University of Jammu Samba181143 India

Iris recognition is one of the most widely used human authentication mechanism that has gained huge popularity due to its security and efficiency. However, recently attackers have breached the security of these systems via synthetically generated fake iris traits using a variety of artefacts such as paper printouts, video attacks, and cosmetic lenses. To countermeasure these attacks, an anti-deception sub-system is instilled between sensor and feature extractor modules of the system. These sub-systems intelligently measure the vitality of the presented iris traits and are popularly known as iris vitality detection (IVID) or iris anti-spoofing techniques. Recently, the deep learning-based models are widely deployed for iris anti-spoofing approaches, but these methods result in additional training overhead as well as require larger training dataset. In this paper, we expound a novel iris vitality detection network (IVIDNet) that employs the robust features of two pre-trained deep convolutional neural networks (DCNN). The extracted features are then integrated via a new weighted score-level fusion technique. To demonstrates the efficacy of the proposed technique, experimental analysis is carried out on benchmark iris liveness detection Notre Dame 2017 and Notre Dame cosmetic lenses dataset (NDCLD) 2015. The proposed IVIDNet exhibits excellent performance in the known attack scenario with an average classification error rate (ACER) of 0.60%. Moreover, IVIDNet shows superior performance in the unseen attacks scenario (i.e. cross-database) with an ACER of 9.72%. In overall, the proposed model outperforms other similar iris anti-spoofing approaches and efficient in terms of computational overhead. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Biometrics

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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