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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Liao, Wenbin Wen, Du Li, Jianqiang Wang, Fangzhong Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Tianjin University Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology China

ISBN: (纸本)9798350337488

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model's robustness. © 2023 IEEE.

关键词： Class Imbalance Data Generation Gene Annotation Positive-Unlabeled Learning

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Machine Learning-Powered Design and Implementation for Classification of Missing Data in IoT Applications 1

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3rd International Conference on Mechanical and Energy Technologies, ICMET 2023

作者： Deepan, S. Suchitra, B. Arya, Namita Ghori, Md. Atheeq Sultan Bora, Ashim Mahender, Nagurla School of CS & AI SR University Telangana Warangal India IT & Cognitive System Tamilnadu Coimbatore India Galgotias University UP Greater Noida India Department of Computer Science and Engineering Telangana University Nizamabad Telangana State Nizamabad India Kampur College Assam Kampur India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

ISBN: (数字)9789819727162

ISBN: (纸本)9789819727155

Missing values are a common issue in machine learning, which has formed the foundation for data analysis and extraction. Several reasons can lead to missing values, such as missing entirely, missing at random, and not missing. However, before conducting any data analysis, it is imperative to tackle missing values since this might result in biased or inaccurate results. One common technique to increase the precision of IoT data classification is to combine many sensors with different data techniques. But all modalities are seldom available even for the evaluation results, and this lack of data creates serious obstacles to multimodal education. Inspired by recent developments in deep learning, we present a cross-neural network for the segmentation of the IoT Data Classification, which is created on data modalities not all obtainable during trials. We educate the framework in IoT Data Classification using a cost function specifically designed for imbalanced classes. We are giving the device an insufficiently full baseline dataset. Our technique extends beyond CNN training and the gathering of two CNNs trained in the missing modality by using temporal data, considering that they are not involved in the research process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cost functions

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Integrating GPS and GSM Technologies for Enhanced Women's Safety: A Fingerprint-Activated Device Approach

Integrating GPS and GSM Technologies for Enhanced Women's Sa...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Kamred Udham Chaudhary, Varun Sharma, Pradeep Kumar Kumar, Pushpendra Varshney, Neeraj Singh, Teekam Graphic Era Hill University School of Computing Dehradun India Ims Engineering College Ghaziabad India Hcl Technologies UP Noida India Scanno System Greater Noida India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350382723

The creation of a safety system that is based on GPS and is developed exclusively for the health and safety of women via the incorporation of novel elements in order to meet the rising concerns about crimes committed against women. The gadget is capable of contacting authorities, conveying the user's position via SMS, and generating a loud noise to attract attention. It may be triggered in anticipation of potential danger and can be activated in the event that danger is imminent. In order to guarantee that only authorized users are able to activate the device, the system makes use of fingerprint recognition in a very unique way. Using technologies such as global positioning system (GPS), global system for mobile communications (GSM), and microcontrollers, this study makes a contribution to the continuing efforts to improve the safety of women via technological innovation. It provides a proactive approach to prevent violence against women and save lives. The research emphasizes the significance of combining hardware and software in order to provide a complete safety tool. It also illustrates the vital role that quick support plays and the potential of such gadgets to promote confidence among women in public places. © 2024 IEEE.

关键词： Microcontrollers

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Range-angle Imaging Based on the Prior of Target Scattering Characteristics in Distributed Arrays 2

Range-angle Imaging Based on the Prior of Target Scattering ...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Mei, Yongjie Zhang, Guangbin Huang, Tianyao Wang, Yan Beijng Institute of Technology School of Information and Electronics Beijing China Yangtze Delta Region Academy Beijing Institute of Technology Radar System and Intelligent Signal Processing Jiaxing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

We consider the problem of target range-angle imaging using distributed arrays. High resolution imaging requires a large array aperture. However, due to the limited number of array elements, the subarrays are usually sparsely located, which results in severe under-sampling and ambiguity problem, degrading the imaging performance. To this end, we propose a range-angle imaging method based on the prior of target scattering characteristic to resolve the ambiguity. The main idea is to use the information increment of priors to reduce the dependence on the measurements. Particularly, we first statistically extract the prior distribution of target scattering intensity by data analysis on the target images. Then we use these priors to design weighting parameters and achieve the ambiguity-free images by solving a weighted sparse recovery problem. Finally, numerical simulation results verify the feasibility of the proposed method. © 2024 IEEE.

关键词： ambiguity distributed array Range-angle imaging the prior of target scattering characteristics weight

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A Hybrid Model Combined with SVM and CNN for Community Content Classification 23

A Hybrid Model Combined with SVM and CNN for Community Conte...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ye, Yukui Xie, Xia Jin, Hai Wang, Duoqiang National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9781665494571

Community websites bring many conveniences to people, and the classification of community content is playing an important role in website management and information searching. As the carrier of community content, posts are difficult to classify manually. According to the characteristics of community content, a hybrid classification model for machine learning is proposed. This model consists of three steps. Firstly, aiming at the problem of fewer features of posts, a weighted word vector is proposed to enrich the features of posts. Secondly, since the single kernel function of SVM can not completely match all data distributions, a mixed kernel function is employed to improve the model. Finally, in order to fully utilize the powerful feature extraction ability of Convolutional Neural Network as well as the classification ability of SVM, a hybrid model is designed and implemented by replacing softmax layer with SVM classifier. The corresponding experiment results indicate that compared with traditional Convolutional Neural Network, the proposed hybrid model has better performance and stability with classification accuracy improved from 0.9% to 1.4% in general. © 2021 IEEE.

关键词： Support vector machines Smart cities Support vector machine classification Machine learning Feature extraction Stability analysis Data models

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Efficient Model-Based OPC via Graph Neural Network

Efficient Model-Based OPC via Graph Neural Network

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2023 International Symposium of Electronics Design Automation, ISEDA 2023

作者： Sun, Shuyuan Chen, Xuelian Yang, Fan Yu, Bei Li, Shang Zeng, Xuan School of Microelectronics Fudan University State Key Lab of ASIC and System China Cogenda Inc China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350304510

As feature size continues to shrink and light source wavelengths remain unchanged, the optical diffraction effects seriously degrade chip yield. Optical proximity correction (OPC) has become an essential step for chip manufacturability. However, OPC cannot achieve satisfactory mask correction results in an affordable number of iterations on some layouts, delaying the chip design cycle. In this paper, we propose to use the Graph Neural Network (GNN) to predict the initial mask correction and speed up model-based OPC. We design a graph model to represent the chip layout, with segments represented as graph nodes and the diffraction effects between them represented as graph edges. The GNN is used to obtain the embedding of the segment, and we predict the shift value based on its output. The proposed GNN-based predictor enhances the original OPC to find a good correction solution for full-chip-size masks in fewer iterations. compared with the original OPC method, experimental results on 55nm and 32nm full chip designs show that the proposed GNN-based OPC method reduces the number of iterations up to 75% and the acquired mask of comparable quality. © 2023 IEEE.

关键词： Diffraction

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Robust Spectral Efficiency Maximization in 5G Networks Using Non-Linear Stochastic Precoding 2

Robust Spectral Efficiency Maximization in 5G Networks Using...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Ponnaian, Geetha Jayanthi, N. Sarasu, R. Sajitha, L.P. Ramalakshmi, R. Manimegalai, L. Department of Computer Science and Engineering Thandalam Chennai India Pallavaram Tamil Nadu Chennai600117 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences India R. M. K Engineering College Department of Computer Science and Business System Tamilnadu Kavaraipettai India Ramco Institute of Technology Department of Electronics and Communication Engineering Rajapalayam India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350371055

This research focuses on enhancing the spectral efficiency of 5G networks through the utilization of nonlinear stochastic precoding techniques. The proposed approach aims to mitigate the detrimental effects of channel impairments and noise by employing advanced precoding schemes that exploit the statistical properties of the wireless channel. By incorporating nonlinear precoding algorithms and stochastic optimization methods, the system can dynamically adapt to varying channel conditions, resulting in improved data transmission rates and overall spectral efficiency. Through extensive simulations and analysis, the effectiveness of the proposed method is demonstrated, showcasing its potential to significantly enhance the performance of 5G networks in real-world scenarios. © 2024 IEEE.

关键词： 5G networks channel impairments nonlinear stochastic precoding spectral efficiency stochastic optimization

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Leveraging Adaptive Algorithms for Real-Time Data Analysis 15

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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

作者： Sahoo, Girija Shankar Hannah Jessie Rani, R. Goyal, Manish Kumar Mohammed, Vazeer Ali Jana, D Little Femilin Wasatkar, Namrata N. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Lewis University Illinois Department of Computer & Information System United States Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area. © 2024 IEEE.

关键词： Adaptive algorithms

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Distributed safe resource allocation using barrier functions

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Automatica 2023年 153卷

作者： Wu, Xuyang Magnússon, Sindri Johansson, Mikael School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Computer and System Science Stockholm University Sweden

Resource allocation plays a central role in networked systems such as smart grids, communication networks, and urban transportation systems. In these systems, many constraints have physical meaning and infeasible allocations can result in a system breakdown. Hence, algorithms with asymptotic feasibility guarantees can be insufficient since they cannot ensure that an implementable solution is found in finite time. This paper proposes a distributed feasible method (DFM) for resource allocation based on barrier functions. In DFM, every iterate is feasible and safe to implement since it does not violate the physical constraints. We prove that, under mild conditions, DFM converges to an arbitrarily small neighborhood of the optimum. Numerical experiments demonstrate the competitive performance of DFM. © 2023 The Author(s)

关键词： Resource allocation

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Smart Contract Vulnerability Detection Based on Mixed Channel Attention 36

Smart Contract Vulnerability Detection Based on Mixed Channe...

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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Wang, Jiao Ran Zeng, Cheng Ba, Kai Yang Quan, Qing Yu Wang, Yi Feng School of Computer Science and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China School of Cyberspace Security Hubei University Wuhan China Key Laboratory of Intelligence Sensing System and Security Hubei University Ministry of Education Wuhan China

ISBN: (纸本)1891706594

As blockchain technology progresses, the deployment of smart contracts has become increasingly prevalent. Despite their growing popularity, smart contracts introduce substantial security risks and vulnerabilities. Present detection methodologies predominantly analyze the entirety of a contract’s source code, a process that tends to indiscriminately extract uniform features, leading to inadequate detection capabilities. Addressing these concerns, we propose a novel vulnerability detection method for smart contracts, predicated on a mixed channel attention mechanism. This approach focuses on extracting multifaceted features from pivotal code segments of smart contracts, utilizing a combination of spatial and frequency domain channel attention mechanisms. These mechanisms dynamically modulate the weights of feature channels during visualization, allowing the model to allocate greater emphasis to features of higher significance and enhance feature differentiation. This strategy is instrumental in capturing salient information within the data, thereby augmenting the model’s performance and accuracy. Empirical results substantiate that our proposed method substantially surpasses the baseline model, evidencing improvements of 5.55%, 5.42%, and 5.49% in the detection of timestamp vulnerabilities, and 4.12%, 4.29%, and 4.20% in the identification of reentrancy vulnerabilities, respectively. Our method offers a significant advancement in the efficacy of smart contract vulnerability detection and presents valuable implications for future research. © 2024 Knowledge systems Institute Graduate school. All rights reserved.

关键词： channel attention key code slicing multiple features smart contracts vulnerability detection

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