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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Delsey, M.J. Bibal Benifa, J.V. Indian Institute of Information Technology Department of Computer Science and Engineering Kottayam India

ISBN: (数字)9798331528348

ISBN: (纸本)9798350352931

Underwater object detection plays a significant role in marine exploration activities such as ecological monitoring, conservation of undersea ecosystems, and underwater robotics. In contrast to detection in the atmosphere on land, underwater detection is challenging due to color distortion and attenuation, which render the detection process more difficult and imprecise. To address these issues, this paper proposes an improved detection framework, YOLOv7-ConvTrans. The YOLOv7 architecture is optimized by integrating a convolution vision transformer in the neck region to enhance the detection accuracy of small and occluded objects. The proposed method reduces redundancy, computational cost, and recognition time to extract significant feature maps from the imagery. In the experimental assessment on the URPC2019 dataset, a mAP of 80.39 and on the Brackish dataset, 92.61, indicate high detection accuracy. Finally, our proposed approach provides a valuable framework for designing underwater vision systems that can be utilized in various scientific and exploratory domains. © 2024 IEEE.

关键词： Machine vision

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MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugin 62

MARVEL: Unlocking the Multi-Modal Capability of Dense Retrie...

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62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Zhou, Tianshuo Mei, Sen Li, Xinze Liu, Zhenghao Xiong, Chenyan Liu, Zhiyuan Gu, Yu Yu, Ge Department of Computer Science and Technology Northeastern University China Language Technologies Institute Carnegie Mellon University United States Department of Computer Science and Technology Institute for AI Tsinghua University China Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798891760943

This paper proposes Multi-modAl Retrieval model via Visual modulE pLugin (MARVEL), which learns an embedding space for queries and multi-modal documents to conduct retrieval. MARVEL encodes queries and multimodal documents with a unified encoder model, which helps to alleviate the modality gap between images and texts. Specifically, we enable the image understanding ability of the well-trained dense retriever, T5-ANCE, by incorporating the visual module's encoded image features as its inputs. To facilitate the multi-modal retrieval tasks, we build the ClueWeb22-MM dataset based on the ClueWeb22 dataset, which regards anchor texts as queries, and extracts the related text and image documents from anchor-linked web pages. Our experiments show that MARVEL significantly outperforms the state-of-the-art methods on the multi-modal retrieval dataset WebQA and ClueWeb22-MM. MARVEL provides an opportunity to broaden the advantages of text retrieval to the multimodal scenario. Besides, we also illustrate that the language model has the ability to extract image semantics and partly map the image features to the input word embedding space. All codes are available at https://***/OpenMatch/MARVEL. © 2024 Association for Computational Linguistics.

关键词： Semantics

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TMFN: A Target-oriented Multi-grained Fusion Network for End-to-end Aspect-based Multimodal Sentiment Analysis 30

TMFN: A Target-oriented Multi-grained Fusion Network for End...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wang, Di He, Yuzheng Liang, Xiao Tian, Yumin Li, Shaofeng Zhao, Lin School of Computer Science and Technology Xidian University Xi'an710071 China Guangzhou Institute of Technology Xidian University Guangzhou510555 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9782493814104

End-to-end multimodal aspect-based sentiment analysis (MABSA) combines multimodal aspect terms extraction (MATE) with multimodal aspect sentiment classification (MASC), aiming to simultaneously extract aspect words and classify the sentiment polarity of each aspect. However, existing MABSA methods have overlooked two issues: (i) They only focus on fusing image regional information and textual words for two subtasks of MABSA. Whereas, MATE subtask relies more on global image information to assist in obtaining the quantity and attributes of aspects. Ignoring the integration with global information may affect the performance of MABSA methods. (ii) They fail to take advantage of target information. Nevertheless, the fine-grained details of targets are important for classifying sentiments of aspects. To solve these problems, we propose a Target-oriented Multi-grained Fusion Network (TMFN). It fuses text information with global coarse-grained image information for MATE subtask and with fine-grained image information for MASC subtask. In addition, a target-oriented feature alignment (TOFA) module is designed to enhance target-related information in image features with target details. In such a way, image features will contain more target emotional-related information which is beneficial to sentiment classification. Extensive experiments show that our method outperforms state-of-the-art methods on two benchmark datasets. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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Wireless Sensor Networks: Revolutionizing Explosive Detection 5

Wireless Sensor Networks: Revolutionizing Explosive Detectio...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sujith Kumar, P.S. Anand, C. Vasuki, N. Ramesh, V. Santhoshkumar, S.P. Sree Buddha College of Engineering Department of Computer Science and Engineering Kerala Alappuzha India Nandha College of Technology Department of Information Technology Erode India Government College of Engineering Department of Computer Science and Engineering Erode India School of Computing Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India

ISBN: (纸本)9798331518097

One of the important technologies for protecting civilian life from terrorist attacks is explosive detection techniques. The global incidence of terrorist activities has been considerably reduced by the development of explosive detection techniques, which has been made possible by ongoing technology improvements. Wireless sensor networks (WSNs) are one of these developments that has become an innovative tool for explosives detection. This work explores recent empirical research on the application of WSNs to explosives detection and includes statistical analysis of the investigations. Wireless sensor network with compatible specialized sensors is required for effective explosive detection. Three main wireless sensor system aspects that are relevant to explosives detection are covered in this research. It first looks at how scalable wireless sensors are in terms of explosives detection technologies. In the second section, it looks at the mobility and communication capabilities of WSNs and sensors in explosives detecting systems. Finally, it highlights potential hazards and weaknesses and addresses the crucial topic of data security inside wireless sensor networks used for explosive detection. The study also suggests incorporating drone assisted detection as a novel way to improve the coverage and effectiveness of explosives detection systems, adding to the continuous advancement of security technologies meant to shield civilian populations from terrorist threats. © 2024 IEEE.

关键词： Terrorism

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Deep Reinforcement Learning with Graph Neural Networks: An Indepth Analysis of Algorithms and Applications 2

Deep Reinforcement Learning with Graph Neural Networks: An I...

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2nd International Conference on Advanced computing and Communication Technologies, ICACCTech 2024

作者： Srivastava, Sakshi Mittal, Harish Kumar Vasesi, Sonika Paramjeet Jain, Avdhi Chauhan, Divya B M Institute of Engineering and Technology Computer Science and Engineering Sonipat India

ISBN: (纸本)9798331519056

Deep Reinforcement Learning (DRL) enable several areas of artificial intelligence, including perception recognition, expert system, recommender program and game. Also, graph neural networks (GNN) have shown excellent performance in supervised machine learning of graph structure data. Recently, combining GNN and DRL in graphical arranged environments has captivated much attention. This article provides a panoramic overview of all hybrid work. In order to facilitate generalisation, we incorporate Graph Neural Networks (GNN) into DRL agents in this research and create an action space that is problem specific. Deep Learning models called GNNs are innately made to generalise across graphs with varying sizes and architectures. This enables a broad range of network designs to ne learnt by proposed GNN-based DRL operator. © 2024 IEEE.

关键词： Deep Learning Deep Reinforcement Graph Neural System Network Hybrid DRL -GNN Query

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Exploring The Trade-off between Efficient Service Placement Time and Optimized Fog Colonies Utilizing Advanced Genetic Algorithm with Diverse Network Topologies 24

Exploring The Trade-off between Efficient Service Placement ...

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13th International Conference on Software and computer Applications, ICSCA 2024

作者： Verma, Nilesh Kumar Khetavath, Jairam Naik Department Of Computer Science And Engineering National Institute Of Technology Raipur Raipur India

ISBN: (纸本)9798400708329

Optimized Fog colony is one of the aid of fog computing, consisting of fog devices, enable efficient management of large fog domains. Well-designed fog colonies can operate independently, resulting in effectively resource utilization and improved system performance. However, there is a lack of optimization approaches for organizing fog devices into colonies and determining which fog colony should be used to stipulated and execute the services required by an IoT application is its primary challenge known as the "efficient service placement time"(ESPT). Contrary to the prevalent practice of treating ESPT as a single objective optimization problem, which often proves insufficient for accommodating the escalating complexities of engineering practice, our study takes a different approach. We present a modeling framework that considers the ESPT in fog computing as a constrained multi-objective optimization problem. Our secondary objective is to minimize the response time of services within the fog colonies. We employ the advanced elitist non-dominated sorting genetic algorithm (MS-NSGA) to optimize the fog colonies for constrained multi-objective service placement problem. We compare our experiment for three different network topologies with various configuration. The experimental result demonstrate the best trade-off between service placement time and proposed scheme for Barabasi-Albert network topology. Additionally, the results also indicate a clear trend towards reduced response time. © 2024 ACM.

关键词： Fog

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Detecting Alzheimer Disease in ADNI MRI Data Using CNN Algorithm 2

Detecting Alzheimer Disease in ADNI MRI Data Using CNN Algor...

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2nd IEEE International Conference on Measurement, Instrumentation, Control and Automation, ICMICA 2023

作者： Saturi, Rajesh Rao, Panuganti Hanumantha Vishnu, Yenugandhula Raghuveer, Yannam Sravan, Yalla Vignana Bharathi Institute of Technology Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350355048

In response to the World Health Organization's unexpected revelation of over 50 million Alzheimer's disease (AD) cases across the globe, this study highlights the significance for early recognition, which is critical to effective AD medical treatment. The research presents an original method to examining MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), using a customized six-layered Convolutional Neural Network (CNN). This improved model attempts to improve accuracy and efficiency in detecting early AD indications as compared to traditional methods. The CNN's complicated architecture allows for the extraction and learning of complex patterns in MRI data that are frequently undetectable to human interpretation. In contrast to standard approaches such as 3D RESNET, which are known for their effectiveness but potential limits in early-stage sensitivity, the tailored CNN expects to beat these models in identifying subtle neuroimaging markers indicative of early. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection Of Polycystic Ovary Syndrome Using Machine Learning and Deep Learning Techniques 2

Detection Of Polycystic Ovary Syndrome Using Machine Learnin...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Arpitha, C.N. Eshanya, C.Y. Bhoomika, G.V. Sinchana, B.R. Sridevi, K.S. Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Karnataka Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Polycystic Ovary Syndrome (PCOS) is a complex endocrine illness that affects women of reproductive age. It is characterized by hormonal imbalance and ovarian malfunction. Its reasons are numerous, including genetic susceptibility, insulin resistance, and environmental variables. PCOS is common, with estimates indicating that it affects one out of every ten women worldwide. Understanding the intricate interplay of components is critical for appropriate management and treatment techniques, emphasizing the need for additional study into this common and serious disorder. For early-stage detection and prediction, the existing method is insufficient. To address this issue, this research introduces a strategy for predicting PCOS based on a set of parameters. We're testing several machine learning algorithms and comparing their outcomes. Datasets are obtained from Max Hospital clinical report. This paper has two different datasets used for detection. Phase-1 involves finding important features causing PCOS and predicting disorder using SVM and KNN. Phase-2 involves using image dataset that classify as normal or PCOS using CNN. © 2024 IEEE.

关键词： Ultrasonic imaging

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A Lightweight Security Algorithm for Authenticating Mobile Transactions using Wearable Devices and ECC Based Signcryption 10

A Lightweight Security Algorithm for Authenticating Mobile T...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Harrish, A. David, Shibin Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

Internet of Things (IoT) devices are intelligent and act as the core component for capturing transactional data, initiating communication with wearable devices. Wearable devices such as smartwatches or smart wristbands serve as the user's payment interface. It aligns with near field communication (NFC) technology which gets connected with payment terminals at a closer range. As these terminals have secure payment gateways like Unified Payment Interface (UPI) and other intermediaries, transactions between the user's bank account and the merchant will be easier. To prove authorization in the transaction, an additional layer of security called vein pattern authentication is invoked. Alongside, elliptic curve cryptography (ECC) based signcryption ensures secure transactions by encrypting and digitally signing the payment data which guarantees confidentiality and non-repudiation. Therefore, a system that adds additional security to the mobile transactions by appending vein pattern and ECC-based signcryption technique. As a result, confidentiality, authorization, and robustness in the transaction is achieved at a higher rate by inculcating forward secrecy, verifiability and non-repudiation. © 2024 IEEE.

关键词： Gateways (computer networks)

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Giant saturation absorption of tungsten trioxide film prepared based on the seedless layer hydrothermal method

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Chinese Physics B 2023年第3期32卷 286-290页

作者：马晓光胡芳珍陈希王艺盟郝晓剑顾敏张启明 Science and Technology on Electronic Test and Measurement Laboratory North University of ChinaTaiyuan 030051China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Nonlinear materials have gained wide interest as saturable absorbers and pulse compression for pulsed laser applications due to their unique optical *** work investigates the third-order nonlinear phenomenon of tungsten trioxide(WO_(3))thin *** giant nonlinear absorption and nonlinear refractive index of WO_(3)thin films were characterized by Z-scan method at 800 *** experimentally observed the giant saturable absorption(SA)and nonlinear refractive index of WO_(3)thin films prepared by the seedless layer hydrothermal method,with SA coefficient being as high as-2.59×105cm·GW^(-1).The SA coefficient is at least one order of magnitude larger than those of the conventional *** nonlinear refractive index n_(2)of WO_(3)film has been observed for the first time in recent studies and the corresponding coefficient can be up to 1.793 cm^(2)·GW^(-1).The large third-order nonlinear optical(NLO)response enables WO_(3)thin films to be promising candidates for optoelectronic and photonic applications in the near-infrared domain.

关键词： tungsten trioxide Z-scan saturable absorption

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