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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Tohye, Tewodros Gizaw Qin, Zhiguang Leka, Habte Lejebo Hundera, Negalign Wake School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua China

ISBN: (纸本)9798350318982

Today, glaucoma detection is a severe problem. Because it was predicted that there would be 111.8 million cases of glaucoma worldwide by 2040, up from the expected 76.0 million cases in 2020. Blindness results if early treatment is not given. The CNN model has been used in a variety of studies to identify glaucoma. The results obtained thus far are insufficient to reliably identify glaucoma. In order to detect glaucoma, which is a binary classification, we are utilizing the CNN model. We divided the REFUGUE data set into tanning and testing, with 80% of the photos being utilized for tanning and 20% for testing. Our model's accuracy score was 99.8% for the training set and 95.4% for the testing set. We get result of precision=98% and recall= 97%. We compare the model with other state of arts (SOTA) our experimental results reveal that, the proposed model for glaucoma detection using convolutional neural network is more proficient in glaucoma classification. © 2023 IEEE.

关键词： Convolution

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Object Recognition and Tracking System to Assist Visually Impaired: A Neural Network-Based Deep RBM Technique 1

Object Recognition and Tracking System to Assist Visually Im...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Patil, Sonal Ramagundam, Shashishekhar Sukumar, P. Khan, Mohiuddin Ali Patil, Nisha Deepak Nishant, Neerav G H Raisoni Institute of Engineering and Business Management HoD of CSE-IT Department Jalgaon India Software and Dev Engg4- SDET/ IT COMCAST MonroviaMD United States Department of Computer Science and Engineering Tamilnadu Erode India Jazan University Department of Computer & Network Engineering Jazan Saudi Arabia GCOERC Computer Engineering Nashik India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350371802

Being able to see is fundamental to almost every aspect of our everyday lives, thus those who are visually impaired confront enormous obstacles. Thanks to recent developments in computer vision and computing, a system that can provide auditory input to visually impaired individuals regarding objects and their environments has been designed. Sequencing is crucial for preprocessing, feature selection, and model training. Although extremely accurate data input is still required for object detection, preparation is getting computationally cheaper. We were partially successful in reducing the image size by employing Gaussian Blur, which imparts a smoother appearance to the photos. We use color descriptors and Fourier transforms for feature extraction. Deep RBM training necessitates precise control over the attributes. In comparison to this method, current RBM and DNN algorithms look antiquated. The results demonstrated a notable enhancement in accuracy, reaching 98.31%. © 2024 IEEE.

关键词： Object detection

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Energy Oriented Three-Tier Computation Offloading Scheme in Maritime Edge Computing Network

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8126-8140页

作者： Zhang, Hongxia Xi, Shiyu Shang, Bodong Zhang, Peiying Wu, Sheng Jiang, Chunxiao Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Eastern Institute of Technology Eastern Institute for Advanced Study Ningbo315200 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

With the development of maritime continuing to grow, large-scale maritime wireless devices (MWDs) are being deployed for various maritime applications. The rapid development of maritime Internet of Things (IoT) and growing demand for these applications have led to an increasing number of computation-intensive and resource-sensitive tasks. Considering the limited computing capacity and energy of MWDs, it is challenging to transmit and process massive maritime computing tasks in an energy efficient manner. In this paper, we propose a three-tier maritime edge computing system in which low earth orbit (LEO) satellite and offshore base station (OBS) support to provide communication and computing services for MWDs. The system optimizes association variable, task partitioning, transmission power, and computing resource allocation to reduce system energy consumption under latency constraints. The optimization problem is formulated as a mixed integer non-linear programming (MINLP) optimization problem. We decompose the original problem into four subproblems and design algorithms to solve them. Firstly, the association variable optimization subproblem is solved by slack variable method. Then, we adopt quadratic transformation and design a difference of convex algorithm to solve transmission power optimization subproblem. Further, task partitioning subproblem is solved by obtaining upper and lower bounds of offloading task size by strict deduction, and then solved by the standard convex method. Finally, Lagrangian dual method is utilized to deal with joint computing resource optimization subproblem of LEO satellite and OBS. In simulation results, the proposed algorithm saves 39.3% of system energy consumption compared to benchmark schemes. © 1967-2012 IEEE.

关键词： Computation offloading

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Secure Energy Efficiency for ARIS Networks With Deep Learning: Active Beamforming and Position Optimization

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5282-5296页

作者： Wang, Dawei Wang, Zijun Zhao, Hongbo Zhou, Fuhui Alfarraj, Osama Yang, Weichao Mumtaz, Shahid Leung, Victor C. M. Northwestern Polytechnical University School of Electronics and Information Shaanxi Xi’an710072 China Beihang University School of Electronic and Information Engineering Beijing100191 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210000 China King Saud University Community College Computer Science Department Riyadh11437 Saudi Arabia Nottingham Trent University Department of Computer Science NottinghamNG1 4FQ United Kingdom Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Incorporating an active reconfigurable intelligent surface on an autonomous aerial vehicles (AAVs), denoted as an aerial reconfigurable intelligent surface (ARIS), introduces a novel dimension for secure transmissions. Given the constraint of limited battery capacity in AAVs, energy management emerges as a key challenge within AAV networks. In response, we propose a secure energy efficiency (SEE) transmission scheme for ARIS networks, where active ARIS is strategically deployed to enhance information security. In addition, a SEE optimal problem is formulated by considering the imperfect wiretap channel state information to optimize the active beamforming vector and the ARIS position. For this non-convex problem, we first reformulate the fractional SEE objective into an equivalent form and subsequently decompose it into two distinct subproblems: optimizing the AAV’s position and designing the active beamforming. For the AAV’s position optimization, we propose a sophisticated deep deterministic policy gradient algorithm that enables the AAV to autonomously determine the optimal ARIS position through a self-learning strategy. Regarding beamforming design, we transform this aspect into a quadratic constrained quadratic programming problem and design an alternating direction multiplier method to optimize the reflection coefficient. Subsequently, an alternating optimization algorithm is proposed to synergistically solve these subproblems. Empirical simulations validate our proposed scheme, indicating an improvement in SEE of up to 47.2%. This significant improvement underscores the efficacy of the proposed ARIS-assisted secure transmission scheme in enhancing both security and energy efficiency in AAV networks. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Cryptography of Blockchain 7th

Cryptography of Blockchain

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Long, Ying Gong, Yinyan Huang, Weihong Cai, Jiahong Xu, Nengxiang Li, Kuan-ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

ISBN: (纸本)9783031281235

With the development of digital currencies and 5G technology, blockchain has gained widespread attention and is being used in areas such as healthcare, industry and smart vehicles. Many security issues have also been exposed in the course of blockchain applications. Cryptography can ensure the security of data on the blockchain, the integrity and validity of data as well as the ability to authenticate users and anonymize them. This article therefore examines the cryptography underlying blockchain security issues, providing an overview of cryptographic homomorphic encryption, zero-knowledge proofs and secure multi-party computation commonly used in blockchains. At the same time, the development of quantum computing is bound to affect existing cryptographic systems, and blockchains applying these cryptographic systems are bound to be hit hard, so this article discusses four of the most promising post-quantum cryptography techniques available: hash-based public key cryptography, code-based public key cryptography, multivariate public key cryptography, and lattice-based public key cryptography. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Public key cryptography

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Securing Multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure Multiparty Computation

Securing Multi-Tenant Cloud Environments with Fully Homomorp...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Jagannathan, J. R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India

ISBN: (纸本)9798350388602

The aim of this work is to examine the application of FHE-SMPC as a viable security model for protecting the tenant isolation in multi-tenant cloud model. In multi-tenant environments, where various customers use the same cloud system, controlling the data leakage and protecting privacy is a major issue. Actual safeguards fail to meet the needs of data protection during processing, therefore, the introduction of highly elaborate encryption processes must happen. This research work focuses on the use of FHE technique and SMPC such that data can undergo processing while the plaintext remains unknown to any party. A kind of approach is based on the acquisition of synthetic and real data sets, system-level architecture, and cryptographic algorithms using open-source libraries with cloud service providers. The results of such implementation indicate that despite the fact that the FHE-SMPC system has a considerable computational overhead in comparison with classical schemes it is very secure and protected against different types of cryptographic attacks. The problem of scalability is still an open issue, but the complex is quite efficient with various data types that is why it can be used in various cloud applications. This paper offers a pertinent solution for security of data that is housed in multitenant environment and will go along way in creating fertile ground for research and innovation in secure clouding. © 2024 IEEE.

关键词： Cloud security

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A software Execution Data Component Identification Algorithm Based on Spectral Clustering 10

A Software Execution Data Component Identification Algorithm...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Tang, Yahui Rao, Mengqi Wei, Qingjie Chen, Feng Liu, Xin Chongqing University of Posts and Telecommunications School of Software Engineering ChongQing China Chongqing University of Posts and Telecommunications College of Computer Science and Technology ChongQing China

ISBN: (纸本)9798350304770

In order to understand the architecture of software system and facilitate the future maintenance of the software, we can reconstruct the software system as a component-based software system. To obtain the component information of the software, it is necessary to identify components based on the information contained in the software execution data. This paper presents a software Execution data component identification algorithm based on Spectral Clustering (SESC). The input of the algorithm is the software execution event log, and the output is an evaluation report on the quality of the identified components, yielding the log of software execution events with component information. By using four software execution event logs, we compare the proposed SESC with five component identification algorithms based on the community detection algorithm. SESC takes the number of calls between classes into consideration, so it can identify components more accurately, and has certain advantages in time performance. © 2023 IEEE.

关键词： Clustering algorithms

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An Experiment: Finding Parents for Parentless Synsets by Means of CILI 12

An Experiment: Finding Parents for Parentless Synsets by Mea...

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12th Global Wordnet Conference, GWC 2023

作者： Lohk, Ahti Rebane, Martin Orav, Heili Department of Software Science Tallinn University of Technology Tallinn Estonia School of Engineering University of Warwick Coventry United Kingdom Department of Computer Science University of Tartu Tartu Estonia

ISBN: (纸本)9781713890881

Identifying and correcting inconsistencies in wordnets is a natural part of their development. Focusing only on the sub-problem of missing links, we aim to find automatically possible parents for parentless synsets in IS-A hierarchies of a target wordnet by means of source wordnets where target and source wordnets are in XML-format and equipped with Collaborative Interlingual Index (CILI). In this paper, we describe the algorithm and provide statistics on the possible parents of parentless synsets of the wordnets included in the study. Additionally, we investigate the suitability of the proposed potential parent synsets for correcting noun and verb synsets within the Estonian wordnet. © 2023 12th Global Wordnet Conference, GWC 2023. All rights reserved.

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GTDDA: Graph Convolutional Network and Graph Transformer Structure for Drug Repositioning

GTDDA: Graph Convolutional Network and Graph Transformer Str...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tang, Jialan Chen, Weilin Zeng, Xiaoting Liu, Chuan-Ming Li, Pingkang Lei, Baiying Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen University Medical School Shenzhen University Institute of Biological Therapy Shenzhen China National Taipei University of Technology Computer Science and Information Engineering Taipei Taiwan Shenzhen University Medical School Shenzhen University School of Biomedical Engineering Shenzhen China

ISBN: (纸本)9798350337488

Finding new uses for current drugs can be accomplished with the help of computational drug repositioning. Many deep learning-based techniques have been developed recently to find possible drug-disease associations (DDAs). However, effectively utilizing the connections between biological elements to capture biological interactions to improve DDAs prediction is still a difficult task. To address the above issues, we propose a hybrid graph representation learning method (i.e., GTDDA). Specifically, we replace the self-attention mechanism in the Transformer with graph attention layer and design a new graph Transformer encoder (i.e., GATT) for generating graph embedding. First, drug-drug and disease-disease similarity networks are used to construct the graphs. Secondly, a hybrid graph embedding model is shown to train features for drugs and diseases sequentially at the same time, by combining graph convolutional network (GCN) with GATT. Lastly, predictive models that identify DDAs are constructed by concatenating the learnt features. A series of experiment results show that GTDDA outperforms several advanced DDAs prediction methods, which can provide effective predictions for the discovery of new indications for drugs and new treatments for diseases. © 2023 IEEE.

关键词： DDA Prediction Graph Convolutional Network Graph Transformer

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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