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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 895-903页

作者： Wang, Leyao Tiwari, Prayag Ding, Yijie Guo, Fei College of Intelligence and Computing Tianjin University Tianjin300350 China School of Information Technology Halmstad University Halmstad30118 Sweden University of Electronic Science and Technology of China Quzhou324000 China School of Computer Science and Engineering Central South University Changsha410083 China

N4-methylcytosine (4mC) is a common DNA methylation that has been implicated in epigenetic regulation and host defense. Accurate prediction of 4mC sites in DNA sequences will help to better explore the biological processes and mechanisms. For this problem, computational methods based on machine learning and deep learning are faster, less complex, and less expensive than experimental detection methods. However, the existing computational methods are still unsatisfactory in terms of prediction accuracy, so we propose a new method with better performance. In this work, we propose a weighted fuzzy system for identifying DNA 4mC sites by kernel entropy component analysis (KECA). We named it as W-TSK-FS-KECA. This model is improved based on the Takagi-Sugeuo-Kang fuzzy system (TSK-FS). We use position-specific trinucleotide propensity to construct feature vectors on representative benchmark datasets. Then we use KECA to get the reconstruct error. Finally, we put the calculated reconstruction error add to the regular term of TSK-FS as the weights to enhance the model performance. Comparative experiments with other methods show that it has good classification perfor- mance. © 2023 IEEE.

关键词： Entropy

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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge computing Environments

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Tsinghua Science and technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Wang, Ze Yang, Kailun Shi, Hao Zhang, Yufan Xu, Zhijie Gao, Fei Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China School of Computing and Engineering University of Huddersfield Huddersfield U.K State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

In this paper, we propose LF-PGVIO, a Visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. The purpose of our research is to unleash the potential of point-line odometry with large-FoV omnidirectional cameras, even for cameras with negative-plane FoV. To achieve this, we propose an Omnidirectional Curve Segment Detection (OCSD) method combined with a camera model which is applicable to images with large distortions, such as panoramic annular images, fisheye images, and various panoramic images. The geodesic segment is sliced into multiple straight-line segments based on the radian and descriptors are extracted and recombined. Descriptor matching establishes the constraint relationship between 3D line segments in multiple frames. In our VIO system, line feature residual is also extended to support large-FoV cameras. Extensive evaluations on public datasets demonstrate the superior accuracy and robustness of LF-PGVIO compared to state-of-the-art methods. The source code will be made publicly available at https://***/flysoaryun/LF-PGVIO. IEEE

关键词： Feature extraction

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Siamese capsule gorilla troops network-based multimodal sentiment analysis for car reviews

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Soft computing 2024年第13-14期28卷 7627-7647页

作者： Kothuri, Sri Raman RajaLakshmi, N.R. Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India

Sentiment analysis of online car reviews is a significant process in natural language processing. Numerous car enthusiasts and influencers create video reviews of automobiles on platforms for example YouTube. In realistic settings for sentiment analysis within this context, the task includes evaluating spoken reviews, the video content, and the emotions expressed by both the content creators and the viewers as reflected in the comments section. The vulnerability to facing circumstances in real-world scenarios where critical components are necessary for performing sentiment analysis on multimedia car reviews may unexpectedly be absent. To overcome this issue, siamese capsule gorilla troops network-based multimodal sentiment analysis for car reviews is proposed in this research work. Initially, pre-processing and feature extraction are performed on the audio, video and text information. The feature fusion is performed by enhancing spiking neural networks with geometric attention fusion network. The fused feature is fed as an input to siamese capsule gorilla troops network which classifies the emotion states as valence, arousal, and trustworthiness. Also, the algorithm called gorilla troop optimization (GTO) optimizes the classification error of the proposed siamese capsule network. Finally, this research work assesses high- and low-level features across modalities, introduces a novel enhancing spiking neural networks with geometric attention fusion network, enhances classification accuracy through new audio, video, and text embeddings, and boosts system robustness by integrating incomplete multimodal data. The achieved accuracy, precision, recall, and F-score values are 99%, 98.55%, 98.75%, and 98.02%, respectively, for the active multimodal sentimental analysis in car review (MuSe-CaR) dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Sentiment analysis

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Efficient Intelligent Based Compliance, Detection, Tracking, and Proximity Model for Traffic System

Efficient Intelligent Based Compliance, Detection, Tracking,...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Revadala, Abhinay Reddy, Sri Ram Charan Ram Kumar, J. Kumar, Manoj School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India University of Wollongong Faculty of Engineering and Information Sciences Dubai United Arab Emirates

ISBN: (纸本)9798350375237

The Intelligent Traffic Management System (ITMS) is a cutting-edge solution designed to address multiple aspects of traffic management and safety. This advanced system incorporates various technologies to tackle issues such as helmet compliance, traffic signal violations, number plate identification, cell phone usage tracking, and proximity-based police station alerts. By leveraging the power of artificial intelligence and machine learning algorithms, the ITMS can accurately detect and enforce helmet compliance among motorcyclists, ultimately reducing the risk of head injuries in road accidents. Furthermore, the system utilizes advanced video analytics to detect violations of traffic signals, ensuring that motorists adhere to traffic rules and regulations. Additionally, the ITMS employs automatic number plate recognition (ANPR) technology to quickly and efficiently identify vehicles with illegal or expired license plates, enabling law enforcement agencies to take appropriate action. Moreover, the system includes a cell phone usage tracking feature which uses computer vision techniques to identify drivers who are using their phones while driving, contributing to a decrease in distracted driving incidents. Lastly, by introducing proximity-based police station alerts, the ITMS enables prompt response from law enforcement agencies by automatically notifying the nearest police station in the event of a traffic violation or accident. In summary, the Intelligent Traffic Management System provides a comprehensive and holistic approach to traffic management and safety through its features of helmet compliance, traffic signal violation detection, number plate identification, cell phone usage tracking, and proximity-based police station alerts. © 2024 IEEE.

关键词： Traffic signals

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Design and Development of User-friendly Bi-lingual Translation System Employing Machine Translation 5 Base Deep Learning Neural Network Framework Based NLP 2

Design and Development of User-friendly Bi-lingual Translati...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Raman Deep, S. Nagar, Sanjay Shrinivas Upendra, Raje Siddiraju Karthik, R. REVA University School of Computing and Information Technology Karnataka Bengaluru India REVA University School of Electronics and Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350349221

Natural language Processing (NLP) is the fledgling field of computer comprehension and analysis of human language. One of the world's most multilingual and multicultural countries is India. Given that Indians live in different regions and speak different regional languages, the country has the second-highest number of languages in the world. Whereas in India English is offered as the second language. But India uses little English, which creates a communication barrier. It is nearly difficult for the people to fill this language gap by converting one language into another, but it can be accomplished through Machine Translation (MT). The primary goal of this paper is to translate Indian languages using neural networks translating Hindi into English and vice versa, with the help of a Neural Machine Translation (NMT) and sequence to sequence model with an encoder-decoder attention mechanism is put forth. © 2024 IEEE.

关键词： Neural machine translation

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Blockchain-Based Trust Model for Inter-Domain Routing

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Computers, Materials & Continua 2025年第3期82卷 4821-4839页

作者： Qiong Yang Li Ma Sami Ullah Shanshan Tu Hisham Alasmary Muhammad Waqas Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia School of Computing andMathematical Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerthWA 6027Australia

Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain *** research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP *** this paper,we propose a blockchain-based trust model for inter-domain *** model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the *** BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective *** incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing *** forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing *** use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust *** with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.

关键词： Inter-domain routing BGP security blockchain trust model trust mechanisms trust evaluation

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Analyzing the Role of Time Series Analysis in Early Detection of Pancreatic Cancer 5th

Analyzing the Role of Time Series Analysis in Early Detectio...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Kavitha, R. Singh, Yudhveer Madhusudhan, M.V. Mishra, Awakash Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Pancreatic cancer is a vital disorder that is still the number one motive of lack of lifestyle, with a five-12 months survival fee of only 9. Early detection of pancreatic cancers is fundamental to beautifying outcomes. However, it could be tough to stumble on based totally on scientific and imaging outcomes myself. Time collection evaluation offers a progressive technique to sources in the early detection of pancreatic cancer. This paper proposes to research time series analysis for analysis and early detection of pancreatic cancers. Time series evaluation is a powerful series of techniques for analyzing statistics and temporal records, biological alerts, or healthcare facts. It uses a sequence of observations taken through the years to assess patterns and traits in facts, which could tell osteoporotic fracture hazards or provide early sickness signs and symptoms. Previous research has assessed time series evaluation's efficacy in detecting cardiovascular ailments and influenza. However, there may be a shortage of research examining the application of time series evaluation for pancreatic cancer detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

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Computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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Approximating(mB,mP)-Monotone BP Maximization and Extensions

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Tsinghua Science and technology 2023年第5期28卷 906-915页

作者： Ruiqi Yang Suixiang Gao Lu Han Gaidi Li Zhongrui Zhao Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China School of Mathematical Sciences University of Chinese Academy SciencesBeijing 100049China School of Science Beijing University of Posts and TelecommunicationsBeijing 100876China

The paper proposes the optimization problem of maximizing the sum of suBmodular and suPermodular(BP)functions with partial monotonicity under a streaming *** this model,elements are randomly released from the stream and the utility is encoded by the sum of partial monotone suBmodular and suPermodular *** goal is to determine whether a subset from the stream of size bounded by parameter k subject to the summarized utility is as large as *** this work,a threshold-based streaming algorithm is presented for the BP maximization that attains a(1-k)/(2-k)-O(e)-approximation with O(1/e^(4)1og^(3)(1/s)log(2-k)k/(1-k)^(2))memory complexity,where k denotes the parameter of supermodularity *** further consider a more general model with fair constraints and present a greedy-based algorithm that obtains the same *** finally investigate this fair model under the streaming fashion and provide a(1-k)^(4)/(2-2k+k^(2))^(2)-O(e)-approximation algorithm.

关键词： submodular maximization streaming model threshold technique approximation algorithm

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