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arXiv 2024年

作者： Liang, Zhen Wu, Taoran Zhao, Ran Xue, Bai Wang, Ji Yang, Wenjing Deng, Shaojun Liu, Wanwei College of Computer Science and Technology National University of Defense Technology China Institute of Software Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China

Recently, formal verification of deep neural networks (DNNs) has garnered considerable attention, and over-approximation based methods have become popular due to their effectiveness and efficiency. However, these strategies face challenges in addressing the "unknown dilemma" concerning whether the exact output region or the introduced approximation error violates the property in question. To address this, this paper introduces the UR4NNV verification framework, which utilizes under-approximation reachability analysis for DNN verification for the first time. UR4NNV focuses on DNNs with Rectified Linear Unit (ReLU) activations and employs a binary tree branch-based under-approximation algorithm. In each epoch, UR4NNV under-approximates a sub-polytope of the reachable set and verifies this polytope against the given property. Through a trial-and-error approach, UR4NNV effectively falsifies DNN properties while providing confidence levels when reaching verification epoch bounds and failing falsifying properties. Experimental comparisons with existing verification methods demonstrate the effectiveness and efficiency of UR4NNV, significantly reducing the impact of the "unknown dilemma".MSC Codes 68Q60, 68T07 Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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An Efficient Blockchain-Based Healthcare System Using Artificial Intelligence

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computers, Materials & Continua 2022年第5期71卷 2721-2738页

作者： Aitizaz Ali Muhammad Fermi Pasha Ong Huey Fang Jehad Ali Mohammed A.Al.Zain Mehedi Masud Department of Software Systems and Cyber-Security School of ITMonash University47500Subang JayaMalaysia Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwon16499Korea Department of Information Technology College of Computers and Information TechnologyTaif University11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif University11099Taif21944Saudi Arabia

Personal health records and electronic health records are considered as the most sensitive information in the healthcare *** solutions have been provided for implementing the digital health system using blockchain,but there are several challenges,such as secure access control and privacy is one of the prominent ***,we propose a novel framework and implemented an attribute-based access control system using ***,we have also integrated artificial intelligence(AI)based approach to identify the behavior and activity for security *** current methods only focus on the related clinical records received from a medical ***,existing methods are too inflexible to resourcefully sustenance metadata changes.A secure patient data access framework is proposed in this research,integrating blockchain,trust chain,and blockchain methods to overcome these problems in the literature for sharing and accessing digital healthcare *** have used a neural network and classifier to categorize the user access to our proposed *** proposed scheme provides an intelligent and secure blockchain-based access control system in the digital healthcare *** results surpass the existing solutions by collecting attributes such as the number of transactions,number of nodes,transaction delay,block creation,and signature verification time.

关键词： Personal health records blockchain technology artificial intelligence access control attributes health care system

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Application of Two Forms of the Erlang Distribution Law in Queueing Theory 8

Application of Two Forms of the Erlang Distribution Law in Q...

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8th IEEE International Conference on Problems of Infocommunications, Science and Technology, PIC S and T 2021

作者： Bakhareva, Nadezhda Tarasov, Veniamin Povolzhskiy State University of Telecommunications And Informatics Department of Informatics And Computer Engineering Software And Management In Technical Systems Department Samara Russia

ISBN: (纸本)9781665406826

The purpose of this work is to compare two forms of the Erlang distribution law: ordinary and normalized. The ordinary Erlang distribution is a special case of a more general gamma distribution and its mathematical expectation depends on the order of the distribution k. For a normalized distribution, the mathematical expectation will not depend on the order of the distribution k;this is the meaning of the normalization operation. Consequently, these two forms of the Erlang distribution law will differ in their numerical characteristics. The paper considers the problem of how these two forms of the distribution law are applied in the theory of queuing and how they affect the main characteristic of queuing systems (QS)-the average delay of claims arriving for service in the system. The rest of the QS characteristics are derivatives of the average delay. For this, three different QSs are considered, including the Erlang distribution law. The method of spectral decomposition of the solution of the Lindley integral equation was used as a mathematical apparatus for studying the QS. For the practical application of the results obtained, the method of moments of the theory of probability was used. Basically, queueing systems G/G/1 with general distribution laws are in demand for modeling data transmission systems for various purposes, including computer and telecommunication networks. This is especially true since for G/G/1 systems there is no solution for the average latency in the final form for the general case. Therefore, such systems are investigated using examples with particular distribution laws. © 2021 IEEE.

关键词： Laplace transforms

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Utilization of Machine Learning In RTL-GL Signals Correlation 8

Utilization of Machine Learning In RTL-GL Signals Correlatio...

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8th International Conference on Signal Processing and Integrated Networks, SPIN 2021

作者： Alhaddad, Medhat Ashraf Hussein, Seif Eldin Mohamed Helmy, Abanoub Ghadban Nagy, Nagy Raouf Ghazy, Muhammad Ziad Muhammad Yousef, Ahmed H. Computer Engineering and Software Systems Department Ain Shams University Cairo Egypt Ain Shams University Nile University Egypt

ISBN: (纸本)9781665435642

Verification is an important part of the Electronic Design Automation (EDA) design flow which currently takes a considerable amount of time. During the synthesis process, Different optimizations are done to the Register-Transfer-Level (RTL) code to optimize the power, area, and speed of the circuit. These optimizations result in changes in the names of signals at the gate level. Automatic signal mapping can improve the verification process and can be used to guide functional verification activities between the two presentations using (Clock domain crossing (CDC) analysis in RTL, Gate Level CDC) as well as Formal Sequential Equivalence Checking. Machine Learning has applications everywhere in our lives, and EDA is not an exception. Machine Learning can help EDA tools optimize their results by learning from their previous choices which would improve their results. Machine Learning can help in decreasing the amount of time used in the verification process and increase productivity. In this paper, we discuss the methods used in the signal correlation, propose a modification to one of them as well as present a new technique using Machine Learning to solve the problem of signal correlation between the RTL and the gate level. © 2021 IEEE

关键词： Machine learning

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Mathematical models of quality estimating of change point detection algorithm for random processes

Mathematical models of quality estimating of change point de...

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2021 IEEE International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2021

作者： Benderskaya, Elena N. Peter the Great St. Petersburg Polytechnic University Computer Systems and Software Engineering Department St. Petersburg Russia

ISBN: (纸本)9781665412629

The paper is devoted to the change point detection method based on decision-making statistics. The decision-making statistics are constructed on the principle of discrepancy. Under consideration is the problem dealing with quality measures and quality estimating while a priori information about the distribution density of the change moments of the random process properties is absent. Mathematical models have been developed to calculate the average number of delay steps of the change point detection algorithm. The proposed models allow choosing the parameters of the algorithm with the worst-case orientation, it is important for ensuring that the change point detection in real-time systems will be done with a delay not exceeding a given value. © 2021 IEEE.

关键词： Decision making

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Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

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computer systems Science & engineering 2023年第10期47卷 375-391页

作者： Sultan Alahmari Saud Yonbawi Suneetha Racharla ELaxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa King Abdulaziz City for Science and Technology P.O Box 6086Riyadh 11442Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of AIML Aditya Engineering CollegeSurempallemAndhra PradeshIndia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed *** spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine *** accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural *** is significant for the prediction and growth monitoring of crop *** the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature *** article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm *** proposed HMAODL-CTC model mainly intends to categorize different types of crops on *** accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image *** addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature *** hyperparameter tuning of the dilated CNN model,the HMAO algorithm is ***,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC *** comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.

关键词： Crop type classification hyperspectral images agricultural monitoring deep learning metaheuristics

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Energy Optimised Security against Wormhole Attack in IoT-Based Wireless Sensor Networks

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computers, Materials & Continua 2021年第8期68卷 1967-1981页

作者： Hafsa Shahid Humaira Ashraf Hafsa Javed Mamoona Humayun Nz Jhanjhi Mohammed A.AlZain Department of Computer Science and Software Engineering International Islamic University IslamabadIslamabadPakistan Department of Information Systems College of Computer and Information ScienceJouf UniversityAl-JoufSaudi Arabia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

An IoT-based wireless sensor network(WSN)comprises many small sensors to collect the data and share it with the central *** sensors are battery-driven and resource-restrained devices that consume most of the energy in sensing or collecting the data and transmitting *** data sharing,security is an important concern in such networks as they are prone to many threats,of which the deadliest is the wormhole *** attacks are launched without acquiring the vital information of the network and they highly compromise the communication,security,and performance of the *** the IoT-based network environment,its mitigation becomes more challenging because of the low resource availability in the sensing *** have performed an extensive literature study of the existing techniques against the wormhole attack and categorised them according to their *** analysis of literature has motivated our *** this paper,we developed the ESWI technique for detecting the wormhole attack while improving the performance and *** algorithm has been designed to be simple and less complicated to avoid the overheads and the drainage of energy in its *** simulation results of our technique show competitive results for the detection rate and packet delivery *** also gives an increased throughput,a decreased end-to-end delay,and a much-reduced consumption of energy.

关键词： IoT Internet of Things energy wormhole WSN wireless sensor networks

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Deep Learning Approaches for Protein Secondary Structure Prediction

Deep Learning Approaches for Protein Secondary Structure Pre...

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International Conference on computer and Information Technology (ICCIT)

作者： Umme Israt Afroz Shreshthajit Das Nazmul Siddique Department of Computer Science and Engineering Khulna University of Engineering & Technology(KUET) Khulna Bangladesh Department of Software Engineering Shahjalal University of Science & Technology(SUST) Sylhet Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Londonderry UK

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This paper presents a novel hybrid model comprising Evolutionary Scale Modeling (ESM), Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) network for prediction of protein secondary structures from coil (C), helix (H), and sheet (E)—from amino acid sequences using deep learning techniques. Each architecture leverages unique strengths, with LSTMs capturing long-range dependencies, CNNs extracting local spatial patterns, and ESM enhancing contextual understanding of sequences. The hybrid model was trained and tested using two key datasets: the UniProt dataset and the pdb-intersect-pisces dataset, which provide a rich source of protein sequences and structural information. The proposed model achieved an accuracy of 89.22%, demonstrating robust performance in protein secondary structure prediction.

关键词： Proteins Deep learning Accuracy Computational modeling Neural networks computer architecture Predictive models Transformers Information technology Long short term memory

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Opinion Formation and Social Impact in Networks with Natural and Artificial Intelligence systems

Opinion Formation and Social Impact in Networks with Natural...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Dmitriy Tsarev Anna Avdyushina Alexander Alodjants Institute of Advanced Data Transfer Systems ITMO University St. Petersburg Russia Faculty of Software Engineering and Computer Systems ITMO University St. Petersburg Russia

We suggest a new quantum-like approach to study distributed intelligence systems (DIS) consisting of natural (owners) and artificial (avatars) intelligence agents organized in a scale-free network. We demonstrate the complex features of social impact in DIS that imply the hierarchical formation of opinion leaders in the network and their topological protection. We elucidated the effect of significant narrowing of the spread of opinions within the network when most owners chose their position as limiting (extreme).

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Sentiment analysis: A review and comparative analysis of existing approaches 23

Sentiment analysis: A review and comparative analysis of exi...

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Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing

作者： Ilya Mukhin Elena Avksentieva Aleksandr Krotov Faculty of Software Engineering and Computer Systems ITMO University Russian Federation Faculty of Electronics and Telecommunications Riga Technical University Latvia and Saint Petersburg Electrotechnical University Russian Federation

ISBN: (纸本)9798400709241

Nowadays the introduction of artificial intelligence technologies into human life is at the peak of its history, including natural language processing (NLP). Consequently, it is becoming more necessary than ever to find new effective solutions for data labeling, on which machine models will be trained and appropriate algorithms will be built. In this paper, we describe the process of sentiment analysis (SA), as well as review approaches at all stages of analysis, publicly available datasets and produced software solutions within the Russian and foreign markets. In addition, we have traced the line of development of approaches for evaluating Russian-language texts in order to take into account the latest and most effective solutions in future work.

关键词： NLP

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