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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Goojar, Arunya Verma, Parth Singh, Nitin Debnath, Shubhradeep Tyagi, Neha Computer Science and Engineering Amity School of Engineering and Technology UP India

ISBN: (纸本)9798350383522

In order to encourage carpooling between workplace personnel and college students, this research article gives a unique method that makes use of machine getting to know and synthetic intelligence (AI) algorithms. The file contends that although carpooling remains an underused mode of transportation, it has the ability to significantly lessen visitors' congestion and air pollution in metropolitan areas. The look at shows a brand-new web tool that compares commuter routes for drivers and passengers while taking vicinity, vacation spot, and preference into attention. Users might also chat with each other and prepare excursions at the utility's user-pleasant platform. The effectiveness of the advised software in encouraging carpooling and decreasing traffic congestion and air pollution is classified within the article. The studies' conclusions offer insightful information on how carpooling can be used to alleviate urban transportation issues. © 2024 IEEE.

关键词： AI Algorithms Air Pollution Carpooling Sustainable Transportation Traffic Congestion

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Fault diagnosis of wind turbine gearbox based on wavelet packet denoising and CNN-Swin Transformer-LSTM 3

Fault diagnosis of wind turbine gearbox based on wavelet pac...

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2024 3rd International Conference on Image Processing, Object Detection, and Tracking, IPODT 2024

作者： Zhang, Tao Wang, Yi School of Computer Science and Engineering Sichuan University of Science & Engineering Yibin644000 China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

The working environment of wind turbine gearboxes is complex and variable, with strong noise, which makes traditional fault diagnosis methods inadequate for accurate fault identification. To address this issue, this paper proposes a fault diagnosis method based on Wavelet Packet Denoising combined with CNN-Swin Transformer-LSTM. Firstly, the original signal is decomposed, denoised, and reconstructed using wavelet packets to highlight the effective periodic impact components within the signal, and the reconstructed signal is converted into two-dimensional wavelet time-frequency images. Then, Convolutional Neural Networks (CNN) are used to extract basic feature information from the images. The feature maps are then input into a Swin Transformer model to automatically extract multi-scale feature information based on the self-attention mechanism. Following this, Long Short-Term Memory (LSTM) networks are employed to capture temporal features of the data. Additionally, the Convolutional Block Attention Module (CBAM) is introduced to enhance feature representation capability. Finally, the method classifies different fault types. Experimental verification shows that the proposed method achieves an accuracy of 99.62% and 99.46% on two working condition datasets, respectively. Under conditions of strong noise and variable working conditions, the fault diagnosis accuracy reaches 92.24% and 96.16%. The experimental results demonstrate that this model possesses strong feature learning capabilities, robust anti-interference ability, and good generalization performance. Compared to other existing diagnosis techniques, it exhibits superior diagnostic performance and reliability. © 2024 SPIE.

关键词： Wind turbines

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Emotion-oriented Cross-modal Prompting and Alignment for Human-centric Emotional Video Captioning

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IEEE Transactions on Multimedia 2025年 27卷 3766-3780页

作者： Wang, Yu Liu, Yuanyuan Zhou, Shunping Huang, Yuxuan Tang, Chang Zhou, Wujie Chen, Zhe China University of Geosciences School of Computer Science School of Geography and Information Engineering Wuhan430074 China China University of Geosciences School of Computer Science Wuhan430074 China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou310018 China La Trobe University Cisco-La Trobe Centre for AI and IoT School of Computing Engineering and Mathematical Sciences BundooraVIC3086 Australia

Human-centric Emotional Video Captioning (H-EVC) aims to generate fine-grained, emotion-related sentences for human-based videos, enhancing the understanding of human emotions and facilitating human-computer emotional interaction. However, existing video captioning methods primarily focus on overall event content, often overlooking sufficient subtle emotional clues and interactions in videos. As a result, the generated captions frequently lack emotional information. To address this, we propose a novel Emotion-oriented Cross-modal Prompting and Alignment (ECPA) approach for large foundation models to enhance H-EVC accuracy by effectively modeling fine-grained visual-textual emotion clues and interactions. Using large foundation models, our ECPA introduces two learnable prompting strategies: visual emotion prompting (VEP) and textual emotion prompting (TEP), as well as an emotion-oriented cross-modal alignment (ECA) module. In VEP, we develop two-level learnable visual prompts, i.e., emotion recognition (ER)-level and action unit (AU)-level prompting, to assist pre-trained vision-language foundation models to attend to both coarse and fine emotion-related visual information in videos. In TEP, we correspondingly devise two-level learnable textual prompts, i.e., sentence-level emotional tokens, and word-level masked tokens, for obtaining both whole and local textual prompt representations related to emotions. To further facilitate the interaction and alignment of visual-textual emotion prompt representations, our ECA introduces another two levels of emotion-oriented prompt alignment learning mechanisms: the ER-sentence level and the AU-word level alignment losses. Both enhance the model's ability to capture and integrate both global and local cross-modal emotion semantics, thereby enabling the generation of fine-grained emotional linguistic descriptions in video captioning. Extensive experiments not only demonstrate that our ECPA outperforms existing state-of-the-art ap

关键词： Semantics

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Stacked autoencoder with weighted loss function for intrusion detection in IoT application

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Multimedia Tools and Applications 2024年 1-29页

作者： Gangula, Rekha Vutukuru, Murali Mohan Kumar, M. Ranjeeth Department of Computer Science and Engineering Vaagdevi Engineering College Telangana BollikuntaWarangal506005 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur522502 India School of Computer Science & amp Artificial Intelligence SR University Anantasagar Telangana Warangal506371 India

The fast increase of network traffic in recent times causes significant detection of intrusions in Internet of Things (IoT) environments. Currently, Deep Learning (DL) models play a crucial role in cyber security for malicious identification and intrusion detection in IoT networks. The existing methods have drawbacks like overfitting, data imbalance, and not completely capturing complex dependencies and relationships among input and features that are significant for intrusion detection. To overcome these limitations, Stacked Autoencoder (SAE) with weighted loss function is proposed for effective Intrusion Detection System (IDS). The SAE includes weighted loss function to minimize the overfitting issue and finally, the One Class-Support Vector Machine (OCSVM) is used in the classification layer to classify the intrusions. The database used for intrusion detection in IoT environment are Bot-IoT and ToN-IoT databases, which undergo pre-processing by using standard scalar and min–max normalization to remove duplicates and inconsistent data in the databases. Then, the optimal features are selected by using firefly optimizer which selects active features values for classification. In the resulting phase, the stacked autoencoder obtains 99.99%, and 99.70% of classification accuracy on the Bot-IoT and ToN-IoT databases, respectively, which are superior while compared to the traditional autoencoder model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Support vector machines

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Development of a diacritic-aware large vocabulary automatic speech recognition for Hausa language

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International Journal of Speech Technology 2024年第3期27卷 687-700页

作者： Abubakar, Abdulqahar Mukhtar Gupta, Deepa Vekkot, Susmitha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics & amp Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

Research on voice recognition for African languages is limited due to the scarcity of digital resources for training and adaptation, despite its broad usefulness. The Hausa language, spoken by almost fifty million inhabitants in West and Central Africa, is an example of a linguistic domain that has not been thoroughly studied. The Hausa language employs diacritics, which are symbols located above alphabetical characters to convey further information. By removing diacritics, the number of homographs increases, making it difficult to distinguish between similar words. This paper presents a study on speech recognition in the Hausa Language, specifically focusing on diacritized words. The study utilises the state-of-the-art wave2vec2.0 and Whisper deep learning architecture models, for transcribing audio signals into corresponding Hausa text. According to the results obtained in the study, the Whisper-large deep model emerged as the best, achieving a word error rate of 4.23% representing a considerable improvement of 43.9% when compared to the existing state-of-the-art model for Hausa language speech recognition. Additionally, the Whsiper-large model demonstrated a diacritic coverage of 92%, precision of 98.87%, with a diacritic error rate of 2.1%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Certrust:An SDN-Based Framework for the Trust of Certificates against Crossfire Attacks in IoT Scenarios

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computer Modeling in engineering & sciences 2023年第3期134卷 2137-2162页

作者： Lei Yan Maode Ma Dandan Li Xiaohong Huang Yan Ma Kun Xie School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijingChina College of Engineering QatarUniversityDohaQatar

The low-intensity attack flows used by Crossfire attacks are hard to distinguish from legitimate *** methods to identify the malicious flows in Crossfire attacks are rerouting,which is based on *** these existing mechanisms,the identification of malicious flows depends on the IP ***,the IP address is easy to be changed by *** the IP address,the certificate ismore challenging to be tampered with or ***,the traffic trend in the network is towards *** certificates are popularly utilized by IoT devices for authentication in encryption *** proposed a new way to verify certificates for resource-constrained IoT devices by using the SDN *** on DTLShps,the SDN controller can collect statistics on *** this paper,we proposeCertrust,a framework based on the trust of certificates,tomitigate the Crossfire attack by using SDN for *** goal is ***,the trust model is built based on the Bayesian trust system with the statistics on the participation of certificates in each Crossfire ***,the forgetting curve is utilized instead of the traditional decay method in the Bayesian trust system for achieving a moderate decay ***,for detecting the Crossfire attack accurately,a method based on graph connectivity is ***,several trust-based routing principles are proposed tomitigate the Crossfire *** principles can also encourage users to use certificates in *** performance evaluation shows that Certrust is more effective in mitigating the Crossfire attack than the traditional rerouting ***,our trust model has a more appropriate decay rate than the traditional methods.

关键词： Trust model certificate SDN Crossfire attack bayesian trust system forgetting curve IoT

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Progressing Breast Cancer Assessment: Precise Tumor Categorization through DenseNet201-based Deep Learning 2

Progressing Breast Cancer Assessment: Precise Tumor Categori...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Logu, K. Thangaraj, S. John Justin Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

In this groundbreaking research endeavor, we present a novel approach to breast cancer assessment, leveraging the power of deep learning and transfer learning techniques. Our methodology involves the fine-tuning of a pre-trained DenseNet201 model using the extensive BreakHis dataset, aiming to achieve precise categorization of breast cancer tumors. The primary objective of our study is to enhance the accuracy and reliability of breast cancer diagnosis through the utilization of state-of-the-art deep learning architectures. Employing transfer learning, we fine-tuned the pre-trained DenseNet201 model on the BreakHis dataset, a comprehensive and diverse collection of breast histopathological images. This dataset encompasses various benign and malignant breast tumor cases, providing a robust foundation for our model to learn intricate patterns and features. During the training phase, our model exhibited remarkable performance, achieving an impressive accuracy of 97.00%. The validation phase further reinforced the model's capabilities, yielding a validation accuracy of 92.00%. These compelling results underscore the efficacy of our approach in accurately categorizing breast tumors, thereby contributing to the advancement of breast cancer diagnostics. This research not only showcases the potential of deep learning in the field of medical image analysis but also emphasizes the importance of leveraging transfer learning to optimize model performance. The ability to discern subtle patterns in histopathological images enables our model to provide clinicians with reliable information for more accurate and timely breast cancer diagnosis. Our study signifies a significant step forward in the ongoing efforts to improve breast cancer assessment methodologies, with potential implications for enhancing patient outcomes through early and precise detection. The integration of advanced technologies, such as deep learning, into medical diagnostics holds promise for revolutionizing the w

关键词： Medical imaging

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A Graph-Assisted Digital-Twin-Driven Multiagent Shared Offloading for Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17349-17363页

作者： Zahangir Alam, Md Rahman, Suryaia Asif Bin Khaled, Md Islam, Ashraful Jamalipour, Abbas Independent University Bangladesh Department of Computer Science and Engineering Center for Computational and Data Sciences Dhaka1229 Bangladesh The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Vehicular edge computing (VEC) allows vehicles to process part of the tasks locally at the network edge while offloading the rest of the tasks to a centralized cloud server for processing. A massive volume of tasks generated by the Internet of Vehicles (IoV) leads to buffer overflow that causes higher latency. Elevating latency, in turn, can increase network energy consumption. Both higher latency and energy consumption lead to a degradation of network performance. Therefore, VEC design requires a balance between latency and energy consumption tradeoff. To reduce overwhelming amount of offloading to edge servers, a cooperative cluster-based shared offloading strategy has been proposed in this work. We use digital twin technology in VEC for managing and adapting to environmental dynamic changes. Then, we leverage Lyapunov (Ly) optimization to transform the stochastic offloading problem into a more manageable deterministic form. Finally, we present a decentralized coordination graph (CG)-driven Ly-based multiagent deep deterministic policy gradient (CG-LyMADDPG) algorithm that trains agents toward energy efficient optimal offloading policy while maintaining queue stability at a maximum delay constraint. The experimental result shows that the proposed learning significantly outperforms the baseline algorithms for energy savings while maintain queue stability. © 2014 IEEE.

关键词： Stochastic systems

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An Efficient Multilingual Text Classification using IndicCorp dataset 5

An Efficient Multilingual Text Classification using IndicCor...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Madanbhavi, Lalitha Desai, Padmashree Sirur, Neha Dhirendra Deshpande, Ananya Hiremath, Risheek V. Patil, Chetan M. Kle Technological University School of Computer Science and Engineering Hubballi India Rv College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Language detection is a crucial preprocessing step in natural language processing (NLP) tasks, especially in a multilingual environment. This paper presents a language detection system utilizing the Naive Bayes classifier, applied to the IndicCorp dataset, which includes a diverse range of languages prevalent in the Indian subcontinent. The IndicCorp dataset provides a rich source of textual data across multiple Indian languages, enabling the development of robust and accurate language models. Our system leverages the simplicity and effectiveness of the Naive Bayes algorithm, known for its high efficiency in text classification tasks. The proposed approach involves pre processing the dataset, extracting features using the CountVectorizer, and training the Naive Bayes classifier to identify the language of a given text. Experimental results demonstrate the system's capability to achieve an accuracy of 73.37% in language detection across various Indian languages within the IndicCorp dataset. This performance underscores the system's effectiveness in classifying languages like Hindi, Bengali, Tamil, and others, highlighting its potential for broader NLP applications and multilingual content processing. The necessity for multilingual classification arises from the need to support diverse linguistic communities and facilitate seamless communication. Accurate language detection enables various downstream applications such as machine translation, sentiment analysis, and information retrieval, enhancing user experience and accessibility. This study emphasizes the practical application of Naive Bayes for language detection and underscores the value of the IndicCorp dataset in developing language processing tools tailored to India's linguistic landscape. The achieved accuracy demonstrates our approach's robustness in handling diverse linguistic characteristics and showcases its potential impact on communication and information retrieval in multilingual contexts. © 2024 IEEE.

关键词： Data assimilation

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Self-Attention Mechanism-Based Activity and Motion Recognition Using Wi-Fi Signals

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China Communications 2024年第12期21卷 92-107页

作者： Kabo Poloko Nkabiti Chen Yueyun Tang Chao School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Shunde Innovation School University of Science and Technology BeijingGuangdong 528399China

Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent *** research studies have achieved splendid results with the help of machine learning models from different applications such as healthcare services,sign language translation,security,context awareness,and the internet of ***,most of these adopted studies have some shortcomings in the machine learning algorithms as they rely on recurrence and convolutions and,thus,precluding smooth sequential ***,in this paper,we propose a deep-learning approach based solely on attention,i.e.,the sole Self-Attention Mechanism model(Sole-SAM),for activity and motion recognition using Wi-Fi *** Sole-SAM was deployed to learn the features representing different activities and motions from the raw CSI *** were carried out to evaluate the performance of the proposed Sole-SAM *** experimental results indicated that our proposed system took significantly less time to train than models that rely on recurrence and convolutions like Long Short-Term Memory(LSTM)and Recurrent Neural Network(RNN).Sole-SAM archived a 0.94%accuracy level,which is 0.04%better than RNN and 0.02%better than LSTM.

关键词： CSI human activity and motion recognition Sole-SAM Wi-Fi

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