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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Yupu Xi, Runping Chen, Qian School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Province Key Laboratory of Speech & Image Processing Xi'an China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9781839539848

The motion mode of near-space targets is complex due to their high threat level. The target imaging faces low SNR and susceptibility to background noise. The existing detection and classification algorithms struggle to meet the demands of performance and speed on such targets. To address these issues, a YOLOv5-based target detection algorithm utilizes CARAFE upsampling and the small detection head to extract internal features and edge information of dim target. The algorithm adeptly captures dim target features, enhancing real-time performance and detection accuracy while overcoming subtle feature granularity and extraction difficulties. Aiming at the problem that there is no existing trajectory classification algorithm in the near-space field, we propose an infrared dim target trajectory classification algorithm. The improved attention module is used to enhance the extraction of motion features, and the target category is accurately identified by the prediction module, improving the capacity to extract dependencies between trajectory fragments. We execute comparative experiments on common infrared dim target dataset, near-space infrared dim target dataset and real dataset to confirm the viability of our methods. The outcomes indicate that our proposed method can be applied to the above three types of dim targets and significantly outperforms state-of-the-art methods. © The Institution of engineering & Technology 2023.

关键词： Extraction

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A Unified Framework for Image Caption Generation Using Deep Reinforcement Learning

A Unified Framework for Image Caption Generation Using Deep ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Ritu Shree T Narmadha Vishakha Joseph Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Management ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper introduces a unified framework for picture caption generation with the use of deep reinforcement mastering (RL) that combines convolution neural networks (CNNs) with RL. The paper first introduces a country representation as a remark for RL by means of combining CNN-primarily based photo representation and natural language processing (NLP) natural language representation. Then, the paper describes an RL-based reinforcement mastering (RL-RL) set of rules which uses the statement to generate captions. Eventually, the paper shows the experimental consequences of the proposed RL-RL set of rules on real-world datasets and shows that the RL-RL set of rules outperforms the present RL-NLP and CNN-RL algorithms. The paper additionally demonstrates the very best overall performance on the benchmark dataset COCO with a BLEU score of 0.645 and a METEOR score of 0.348. The proposed framework affords a sensible technique to generate interesting and affordable captions for photographs.

关键词： Knowledge engineering Convolution Neural networks Switches Semisupervised learning Benchmark testing Deep reinforcement learning Natural language processing Meteors Convolutional neural networks

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Using Neural Network-Based Time Series Analysis in Wireless Sensor Networks

Using Neural Network-Based Time Series Analysis in Wireless ...

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Artificial Intelligence and Machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Manish Kumar Goyal Nagaraj Patil Akash Kumar Bhagat Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Neural community-primarily based time series analysis (NNTSA) is an effective technique for research records collected from wireless sensor networks (WSNs). NNTSA uses models stimulated by means of biological neural networks to capture complicated time-various sensor statistics. It requires minimum training of the enter facts and offers a high degree of modelling accuracy. This method has turned out to be increasingly famous for WSN facts evaluation when you consider that it is miles appropriate for streaming statistics and seizing complicated patterns that might not be effortlessly detected with the aid of other techniques. NNTSA fashions are commonly carried out the use of supervised gaining knowledge of processes, together with lower back-propagation, wherein education styles are fed to a neural network at the same time as its parameters are adjusted the use of a fixed of desired output values, together with an average temperature over some time. After the NNTSA c section is complete, new patterns can be evaluated with the trained model to analyze their temporal conduct. NNTSA techniques have been applied in diverse areas, inclusive of fitness monitoring, site visitors management, and weather tracking, with suitable result.

关键词： Training Wireless sensor networks Analytical models Biological system modeling Scalability Time series analysis Artificial neural networks

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Adaptive Index Modulated OFDM Spread Spectrum for Underwater Acoustic Integrated Sensing and Communication Networks

Adaptive Index Modulated OFDM Spread Spectrum for Underwater...

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2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

作者： Wang, Junfeng Cui, Yue Zhou, Mingzhang Sun, Haixin Liu, Lanjun Zhang, Ailing School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application MNR China College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China School of Information Science and Engineering Xiamen University Xiamen361005 China College of Engineering Ocean University of China Qingdao266100 China

ISBN: (纸本)9798350316728

It is predicted that underwater acoustic integrated sensing and communication networks (UAISACNs) would be one of the main enablers for supporting a variety of emerging applications due to benefits of communication-Assisted sensing, sensing-Assisted communication and their integration. Mean-while, link adaptation and index modulated orthogonal frequency division multiplexing spread spectrum (IM-OFDM-SS) are a kind of powerful transmission technique for underwater acoustic communications networks, which can exploit the time-varying behavior of underwater communication channels to transmit much higher speeds. Thus, this paper aims to propose the adaptive IM-OFDM-SS for the UAISACNs, where the adaptive technique is integrated with the IM-OFDM-SS. The presented scheme is capable of adaptive transmitting the downlink information to the communication node in the UAISACNs by employing the estimating sensing parameters of its echo, which can reduce large amount of the signaling overhead and the long latency resulted from the conventional feedback-based protocol. Our suggested framework can choose the best possible transmission mode to maximize the achievable throughput, which can be verified through computer simulations, where we show the benefits of our proposed adaptive transmission scheme. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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Music Generation using Time Distributed Dense Stateful Char-RNNs 7

Music Generation using Time Distributed Dense Stateful Char-...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Banerjee, Shobhan Rath, Manas Swain, Tanmaya Samant, Tapaswini Birla Institute of Technology and Science - Pilani Rajasthan India Kalinga Institute of Industrial Technology School of Computer Application Bhubaneswar India Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India

ISBN: (纸本)9781665421683

Sequence generation is one of the state-of-the-art topics in recent days, where given a sequence of inputs, the aim is to generate a similar sequence of outputs in a given context. The applications range from sentence autocompletion in mail bodies to text suggestions for automatic replies, etc. A similar idea can therefore be utilized to generate a sequence of musical notes using some LSTM/GRU based architecture, where we train our model based on given sequences of musical notes. If we look at music of a certain genre, it's simply a time series data where the data is in frequency domain. Fortunately for musicians these frequencies have a notation, which can serve as texts to train any given time series model. Hence, in this paper we propose a Char-RNN based model which can understand the patterns in each composition or a raga and generate new piece of music based on that. The model must not simply copy paste the sequence or generate any random note at a given instant of time but be capable enough to grasp the patterns in which the given piece of music is based upon and create a similar, new piece of music out of that. © 2022 IEEE.

关键词： Music

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Deep Distributed Learning and Spatial-Temporal Features for Facial Depression Detection 7

Deep Distributed Learning and Spatial-Temporal Features for ...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Arunachalam, Sripriya Ghate, Shubhangi N. Kopperundevi, N. Balamurugan, M.S. Kanagajothi, D. Srimathi, S. SRM Institute of Science and Technology Computer Science and Application Vadapalani Campus College of Science and Humanities India Ramrao Adik Institute of Technology Electronics and Telecommunications Engg. D. YiPatil University Maharashtra Navi-Mumbai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Panimalar Engineering College Department of ECE India Vel Tech Rangarajan Dr Shangunthala R&D Institute of Science and Technology Department of Mathematics India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350340600

One of the most frequent and debilitating mental illnesses is major depression. Video-based identification of depression using facial expressions has been proposed by a number of deep learning architectures. These structures are frequently modeled for regression with Euclidean loss to foretell the degree of depression. The ordinal correlation between depressive symptoms and exposure to facial depictions is not explored, and the models' resilience to noisy and ambiguous labeling is constrained as a result. Famous deep architectures have shown guarantee in facial depression acknowledgment, but they still lack proper discriminative power because of problems with a lack of labeled depression information for deep transfer learning, large variations in facial expression between people who have identical symptom severity, and subtle variations in facial expression between varying depression severities. This study models the challenge of facial depression identification as one of the learnings to distribute labels, and this study offers a deep combined label distribution and metric learning approach to this problem. Using distribution learning, this research study provides a deep learning architecture for predicting depressive symptoms. The proposed deep learning on spatiotemporal feature improves the process of depression prediction because the two learning modules work together to improve the feature's representation ability and discriminatory strength. The results of empirical evaluation of the proposed method on publicly available datasets prove the system efficacy. © 2023 IEEE.

关键词： Deep learning

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Exploring the Benefits of Interactive Communication Media Algorithms for Networking applications

Exploring the Benefits of Interactive Communication Media Al...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Zahid Ahmed N. Beemkumar Debanjan Ghosh Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India

Interactive communication media algorithms are useful tools for creating efficient and effective networking applications. Such algorithms enable the application to accurately predict user behaviour and preferences, use natural language processing to automate conversations, and integrate with other systems. This paper investigates the benefits of using interactive communication media algorithms for developing networking applications, including improved user experience, scalability, and quicker response times. Additionally, this paper will address potential limitations, such as the need for significant data collection and availability of relevant computing resources. Finally, this paper will conclude with a discussion of the potential implications of further investigations into interactive communication media algorithms for networking applications.

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ReIoT-Powered Smart Agriculture in Controlled Environments

ReIoT-Powered Smart Agriculture in Controlled Environments

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Phuke Rakesh Rao Ramandeep Sandhu Sardar M N Islam School of Computer Application Lovely Professional University Phagwara Jalandhar Punjab India School of Computer Science and Engineering Lovely Professional University Phagwara Jalandhar Punjab India Institute for Sustainable Industries & Liveable Cities Victoria University Australia

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The Internet of Things (IoT) is crucial in improving the agricultural sector. To achieve sustainability and sustained production an innovative approach should be planned, tested, implemented, and proposed to achieve the targeted goals parameters such as pollution, lack of water, and drastic climate changes should be analyzed. This study provides information on how to alleviate these problems, and as a result attain the proposed controlled environment with good-quality air, water, and climate is needed. If these goals cannot be achieved globally, alternate solutions must be set to match current climatic conditions and which crops can sustain unaltered weather patterns and disasters. An in-depth analysis is needed to fine-tune these areas. One application of IoT is the development of a system called "Plants for Survival" (P4S) which aims to solve outdoor problems through detailed studies in a controlled environment. This case study helps predict climatic changes and develops appropriate solutions in a prototype setup. The Data-Driven Development Methodology for IoT-based Systems guides P4S, with quality checks and surveys to ensure efficiency and effectiveness Technology and Parameters that influence weather conditions should be considered while testing in a simulated environment.

关键词： Smart agriculture Surveys Prototypes Water pollution Internet of Things Sustainable development Robots Meteorology Farming Testing

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Optimizing Cloud Security with CNN and XGBoost Models for Intrusion Detection Systems

Optimizing Cloud Security with CNN and XGBoost Models for In...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Amit Karbhari Mogal V. Anitha Preeti Nitin Bhatt Kriti Srivasatava S. Rukmani Devi Anvesh Perada Department of Computer Science and Application MVP Samaj's CMCS College Nashik India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy India Department of Electronics & Communication Engineering IIMT College of Engineering Greater Noida India Department of Computer Science and Engineering (Data Science) Dwarkadas J Sanghvi College of Engineering Vile Parle India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai India Department of Electrical and Computer Engineering Drexel University Philadelphia United States of America

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Cloud computing and Internet growth have simplified certain formerly difficult activities. This advancement has also revealed many security weaknesses. Due to cyberattacks, organizations need Intrusion Detection Systems (IDS) to protect their data and networks. Preprocessing, feature selection, and model training comprise the suggested strategy. Preprocessing includes data cleansing, standardization, and labeling. Information gain, chi-square, and PSO are used to choose features. CNN-XGBoost trains the model. The CNN-XGBoost model outperforms solo CNN and XGBoost with an average accuracy of 92.08%. This accuracy proves the hybrid method's cloud computing breach detection usefulness. The study underlines the importance of enhanced IDS models in cyber risk reduction. CNN's feature extraction and XGBoost's classification power provide a powerful, efficient, and reliable cloud intrusion detection model. Modern organizations benefit from this network security method.

关键词： Training Cloud computing Accuracy Computational modeling Standards organizations Intrusion detection Standardization Feature extraction Reliability Integrated circuit modeling

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RailPC: A large-scale railway point cloud semantic segmentation dataset

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1548-1560页

作者： Tengping Jiang Shiwei Li Qinyu Zhang Guangshuai Wang Zequn Zhang Fankun Zeng Peng An Xin Jin Shan Liu Yongjun Wang Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Normal UniversityNanjingChina Technology Innovation Center for Integrated Applications in Remote Sensing and Navigation Ministry of Natural ResourcesNanjingChina Eastern Institute of Technology(EIT) NingboChina Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology TianjinChina College of Computer Science and Engineering Northwest Normal UniversityLanzhouChina Mckelvey School of Engineering Washington University in St.LouisSt.LouisMissouriUSA School of Electronic and Information Engineering Ningbo University of TechnologyNingboChina

Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value,but its development is severely hindered by the lack of suitable and specific ***,the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non-overlapping special/rare categories,for example,rail track,track bed *** harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation,we introduce RailPC,a new point cloud *** provides a large-scale dataset with rich annotations for semantic segmentation in the railway ***,RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning(MLS)point cloud dataset and is the first railway-specific 3D dataset for semantic *** covers a total of nearly 25 km railway in two different scenes(urban and mountain),with 3 billion points that are finely labelled as 16 most typical classes with respect to railway,and the data acquisition process is completed in China by MLS *** extensive experimentation,we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation *** on our findings,we establish some critical challenges towards railway-scale point cloud semantic *** dataset is available at https://***/NNU-GISA/GISA-RailPC,and we will continuously update it based on community feedback.

关键词： data benchmark MLS point clouds railway scene semantic segmentation

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