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Computational Techniques High-Quality Wireless Communication to the Outside World for Further processing in Wireless sensor networks

Computational Techniques High-Quality Wireless Communication...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE international conference on

作者： C. Jisha Chandra R. Nagarajan K.R.N. Kalis S. Kannadhasan Department of Electrical and Electronics Engineering Gnanamani College of Technology Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy Tamilnadu India Department of Electronics and Communication Engineering Study World College of Engineeirng Coimbatore Tamilnadu India

Due to important advancements in hardware manufacturing technology and the creation of effective software algorithms, a wireless sensor network is a system made up of a lot of small, inexpensive sensors that interact wirelessly. this system is now technically and financially practical. the employment of sensor networks in mission-critical environments like combat zones as well as in more commonplace security and business applications like building and traffic surveillance, habitat monitoring, and smart homes, among others, has a lot of promise. Wireless sensor networks, however, provide unique security challenges. Because WSNs are used for so many security-sensitive applications, security is now the top issue for protocol designers who are working on WSNs. We have made an attempt to create a complete list of all known security weaknesses in wireless sensor networks in this study, which also explores a broad range of WSN attacks and their categorization techniques. both the difficulties encountered and the many security methods that may be used to solve them. We tackled the problem in this study and put up a complete, integrated security that would provide security services for all sensor network tasks. It is anticipated to be utilised extensively in the future because of the wireless connectivity, processing power, and sensor technologies it incorporates. A number of security vulnerabilities are present with wireless communication systems.

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MLNLP 2022 - 2022 5th international conference on Machine Learning and Natural Language processing, conference Proceedings

MLNLP 2022 - 2022 5th International Conference on Machine Le...

引用

5th international conference on Machine Learning and Natural Language processing, MLNLP 2022

ISBN: (纸本)9781450399067

the proceedings contain 66 papers. the topics discussed include: answering questions over tables based on TAPAS and graph attention model;prompt-based few-shot learning for table-based fact verification;human finger motion analysis based on machine learning;relevance calculation of users’ document sending behavior under the network social platform;dense attention memory network for multi-modal emotion recognition;a faster method for generating Chinese text summaries-combining extractive summarization and abstractive summarization;pedestrian trajectory prediction based on improved social spatio-temporal graph convolution neural network;long short-term dynamic graph neural networks: for short-term intense rainfall forecasting;an effective time-aware encoder for temporal knowledge graph reasoning;and keyword extractor for contrastive learning of unsupervised sentence embedding.

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Performance Comparison of Ovarian Tumour Classification Using Deep Neural Network 5

Performance Comparison of Ovarian Tumour Classification Usin...

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5th IEEE international conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Srilatha, K. Chitra, P. Sumathi, M. Jayasudha, F.V. Sanju, I. Mary Sajin Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665493604

Ovarian tumour originates in ovaries posing a serious threat to women. As a result, it leads to abnormal cells which have the ability to spread to the other regions of the body. Ovarian tumour is a sort of risky improvement that impacts ovaries in females, and is hard to perceive at early the phase because of which it stays as one of the guideline wellsprings of illness end. the ultrasound imaging is the low cost and best to detect tumour in ovaries. To improve tumour detection accuracy in ultrasound ovarian images, enhanced preprocessing, image segmentation, feature extraction and feature selection, and classification method are proposed. the enhanced preprocessing, tumour segmentation, feature extraction and feature selection, and tumour classification methods have been analyzed to segment and classify the tumour effectively in ultrasound ovarian tumour images. the Anisotropic Diffusion Filter (ADF) has been applied for the preprocessing of the ultrasound ovarian image to improve the image quality for further processing such as segmentation, feature extraction and feature selection, and classification. the Improved Whale Search Optimization (IWSO) segmentation method has been applied to segment tumour in the ultrasound image efficiently. the Deep Neural Network (DNN) method has given more accuracy in classifying the ultrasound ovarian tumour image into benign or malignant. the DNN has high sensitivity, high specificity, high accuracy, high PPV, high NPV, low FPR, low FNR, low FDR, low time complexity, low error rate, and low false classification ratio during the tumour classification compared with the existing methods. © 2023 IEEE.

关键词： Deep neural networks

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Audio Denoising: Speech Enhancement Using Convolutional Neural networks 5th

Audio Denoising: Speech Enhancement Using Convolutional Neur...

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Aruna, T. Vaidya, Shreya Raikar, Rakshita Chikkamath, Satish School of Electronics and Communication Engineering KLE Technological University Hubballi India

ISBN: (纸本)9789819788644

Imagine someone talking on a busy street and their words suppressed by chatter and traffic sound. Our research sets out to tackle such noisy audio recordings and how CNNs can rise to this challenge, restoring clarity to communication. this paper presents an innovative pipeline for the enhancement of speech, that focuses on improving the quality of speech signals which are corrupted by background noise. Our method combines the Denoising Autoencoder (DAE) and Convolutional Neural Network (CNN) which is integrated with classical signal processing techniques. When trained on a sufficient amount of noisy speech frames, the DAE outperforms traditional ideal ratio mask (IRM) approaches when it comes to estimating in clean speech spectrum, resulting denoised speech is analyzed by using the STFTShor (tTime Fourier Transform) and then reconstructed using the overlap-add technique, providing a refined output. this entire pipeline can adapt to diverse acoustic scenarios, thus presenting an all-purpose solution for real-world applications starting from communication systems to voice- activated technologies. Our research demonstrates exceptional performance and a significant improvement in speech enhancement. the solution of integrating DAE-CNN answers existing difficulties in noise reduction and also contributes significantly to Machine learning advancements related to audio processing. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech enhancement

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Visual sensor Aided By Image processing Based on Optimization Algorithm Research of Mobile Robot Localization 5

Visual Sensor Aided By Image Processing Based on Optimizatio...

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5th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

作者： Peng, Hui Chongqing Vocational Institute of Engineering Chongqing402260 China

ISBN: (纸本)9781839539848

In recent years, with the rapid development of artificial intelligence technology, intelligent robots have become a popular research object in academia and industry. An indoor autonomous mobile robot must face three main problems: where am I, where am I going, and how do I get there. Among them, the "where am I" localization problem is particularly important. this paper focuses on a vision-based multi-sensor fusion indoor localization scheme, which is based on binocular vision and fuses wheeled ranging and IMU, and proposes a high accuracy, high robustness and low cost multi-sensor fusion vision SLAM scheme based on ranging and IMU fusion, with pose update assisted vision ranging at the front end and graphics optimization at the back end. Finally, the shortcomings of traditional vision SLAM algorithms that rely too much on image information are compensated by the optimization and improvement of the algorithms. the EKF algorithm is used to combine the robotic wheeled ranger with the I MU's pose information-assisted vision program as a precursor to the SLAM algorithm. Finally, localization accuracy experiments and localization robustness experiments are conducted, which prove that the algorithm proposed in this paper has high localization accuracy and robustness. © the Institution of Engineering & Technology 2023.

关键词： Mobile robots

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Recognition of Aerobics Movement Posture Based on Multisensor Movement Monitoring 5th

Recognition of Aerobics Movement Posture Based on Multisenso...

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5th EAI international conference on Advanced Hybrid Information processing (ADHIP)

作者： Liu, Ying Huang, Zhong-xing Wuhan Inst Design & Sci Dept Publ Basic Courses Wuhan 430205 Peoples R China Guangzhou Metro Design & Res Inst Co Ltd Guangzhou 510000 Peoples R China

ISBN: (纸本)9783030945510;9783030945503

In the traditional attitude recognition methods, the final recognition rate is low because of the inadequate processing of the motion attitude data. therefore, based on the multi-sensor mobile monitoring of aerobics movement posture recognition. In the design process of aerobics movement posture recognition method, first of all, based on multi-sensor movement monitoring, collected aerobics movement posture data. then in order to improve the recognition rate of aerobics posture, the collected data is preprocessed. Will process the good data, according to the time frequency characteristic complete the data characteristic extraction. Comparing the extracted features with the multi-sensor moving monitoring images, through the multi-level attitude recognition algorithm, the movement attitude recognition of aerobics is finally realized. Experimental results show that the proposed method has a higher recognition rate than the traditional method. Even under the influence of changing factors such as penalty factors and kernel parameters, the proposed method is still predominant.

关键词： sensor Mobile monitoring Attitude recognition Feature extraction

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Multilingual Detection of Cyberbullying on Social networks Using a Fine-Tuned GPT-3.5 Model 5th

Multilingual Detection of Cyberbullying on Social Networks U...

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5th international conference on Deep Learning theory and Applications, DeLTA 2024

作者： Nina-Gutiérrez, Elizabeth Adriana Pacheco-Alanya, Jesús Emerson Morales-Arevalo, Juan Carlos Universidad Peruana de Ciencias Aplicadas Lima15023 Peru

ISBN: (纸本)9783031667046

Cyberbullying on social networks has emerged as a global problem with serious consequences on the mental health of victims, mainly children, and adolescents. Although there are AI-based solutions to address this issue, they face limitations such as a lack of multilingual datasets, detecting sarcasm, and detecting idioms. Research presents an innovative approach to effective cyberbullying detection using a fine-tuned GPT-3.5 model. Our main contribution is the creation of an extensive multi-label dataset of approximately 60,000 data in English, and Spanish, spanning diverse dialects. this data set was obtained by combining and processing multiple datasets from reliable sources. In addition, we developed a fine-tuned model based on GPT-3.5, capable of identifying hate speech, and offensive language in textual content on social networks. We conducted a thorough evaluation comparing our model to specialized solutions such as Perspective API, Moderation, Content Safety, Toxic Bert, and Gemini. the results demonstrate that our approach outperforms existing models in metrics such as precision, f1-score, and accuracy, making it the most suitable choice for effective cyberbullying detection. this research lays the groundwork for a future app where users can be alerted to harmful content online. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Artificial intelligence Cyberbullying GPT Hate detection Offensive language Social media

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Detection of Stage of Cancer Using Machine Learning and Deep Learning 5th

Detection of Stage of Cancer Using Machine Learning and Deep...

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5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Ashtagi, Rashmi Akkalkot, Afsha Imran Kotecha, Ketan Maginmani, Umme Habiba Patil, Pushpalata Patil, Abhijeet Computer Engineering Department Vishwakarma Institute of Technology Pune India Computer Engineering Department College of Engineering Pune Technological University Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India Computer Science and Engineering SECAB Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Vijayapur India Computer Science and Engineering Faculty of Engineering and Technology Exclusively for Women Sharnbasva University Kalburagi India RPD PU College of Arts and Commerce Belagavi India

ISBN: (纸本)9789819761029

Melanoma, an aggressive form of skin cancer known for its rapid spread, poses a significant threat and is responsible for a substantial number of fatalities. the accurate classification of cancer stages is crucial for effective diagnosis, particularly during surgical treatment. this paper introduces two methods for categorizing melanoma cancer stages. Initial system distinguishes between stage 1 and stage 2, while the second system extends the classification to include stage 3 melanoma. Leveraging a Convolutional Neural Network (CNN) algorithm, the proposed system incorporates the Similarity Measure for Text processing (SMTP) as a loss function. the experimental results showcase the efficacy of different loss functions, with a particular focus on the proposed SMTP loss function, which outperforms several others designed for classification problems. this algorithm stands out as a more efficient approach for melanoma stage classification. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A Distributed Real-Time Orbit Determination Algorithm Based on Asynchronous Multi-sensor 8th

A Distributed Real-Time Orbit Determination Algorithm Based ...

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8th international conference on Signal and Information processing, Network and Computers, ICSINC 2021

作者： Sun, Shanpeng Huang, Jingqi Wang, Jue Hong, Junying Su, Yangsongyi State Key Laboratory of Astronautic Dynamics Xi’an710043 China

ISBN: (纸本)9789811933868

To solve the problem of asynchronous sampling and communication delay of sensor network in Real-time Orbit Determination, an asynchronous distributed algorithm based on information filtering is proposed. local state information and measurement information with sampling time are transmitted between local sensor and adjacent nodes in a certain topology structure. the local sensor sorts the received asynchronous information by time, and calculates the target state respectively. this method is simple to implement and the frequency of communication between sensors is small. Algorithm has been checked by real measured data of the first BDS-3 GEO satellite, the results show that the orbit determination results of each station converges to a centralized estimate, and the precision of 100 s real-time orbit determination results can be used to quickly evaluate the control effect. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： sensor networks

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Frequency-Split Inception Transformer for Image Super-Resolution 23

Frequency-Split Inception Transformer for Image Super-Resolu...

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5th international conference on Pattern Recognition and Intelligent Systems, PRIS 2023

作者： Xu, Weibo School of Computer Science and Technology ZheJiang Normal University Jinhua China

ISBN: (纸本)9781450399968

Transformer models have shown remarkable effectiveness in capturing long-range dependencies and extracting features for single image super-resolution. However, their deployment on edge devices is hindered by their high computational complexity. To address this challenge, we propose Inception Swin Transformer (IST), a novel model that leverages frequency domain separation to reduce redundant *** IST, we exploit the strengths of both CNN-based networks and Transformer variants to handle high-frequency and low-frequency features, respectively. By dynamically utilizing frequency factors to separate feature maps, IST ensures that different components are processed appropriately. Additionally, IST maintains a balanced trade-off between model speed and performance by gradually reducing the proportion of high-frequency *** experiments demonstrate that IST effectively reduces the FLOPs while preserving high performance. the combination of Transformers' accuracy and CNN variants' efficiency enables IST to significantly reduce computational strain without compromising quality. Comparative analysis reveals that IST outperforms other models, achieving superior results with less FLOPs. © 2023 ACM.

关键词： Image processing

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