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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Bhadra, Sampa Rani Maity, Santi Prasad Sil, Jaya Department of Computer Science Kalyani Mahavidyalaya West Bengal Kalyani741235 India Department of Information Technology Indian Institute of Engineering Science and Technology West Bengal Howrah711103 India Department of Computer Science and Technology Indian Institute of Engineering Science and Technology West Bengal Howrah711103 India

ISBN: (纸本)9798331532420

Automated detection of Alzheimer disease (AD) is evolving rapidly with the advent of artificial intelligence (AI). Machine learning and deep learning models. Detection of AD at an early stage provides advance planning of treatment to the patients that arrest progression of the disease. The aim of this paper is to improve performance of diagnosing AD by fusing features extracted mainly from two sources, such as deep and nondeep frameworks applied on the sMRI images. This paper introduces two customized convolutional neural networks (CNN) (baseline model and hybrid model) and extracted two different sets of deep features. As an alternative source of features, a set of handcrafted features are extracted from the sMRI images by applying the stack assembling of Gray Level Co-occurrence Matrix (GLCM) and Gabor algorithms. In order to select nonredundant important features, we apply cosine similarity metric between the handcrafted and deep features. Next, a Feature Pyramid Network-based feature fusion technique namely- FusionNet is also proposed to fuse the deep features and the handcrafted features in order to further improve the classification accuracy. For comparing the performance of AD diagnosis, fused feature sets without feature selection and with feature selection are considered. It has been observed that the fused features without feature selection achieves the best classification accuracy implying that for AD diagnosis all the features are important and therefore distinct. All the experiments have been applied on a 5-class dataset obtained from ADNI and classified by Support Vector Machine classifier. © 2024 IEEE.

关键词： Convolutional neural networks

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In silico prediction methods of self-interacting proteins:an empirical and academic survey

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Frontiers of computer Science 2023年第3期17卷 199-216页

作者： Zhanheng CHEN Zhuhong YOU Qinhu ZHANG Zhenhao GUO Siguo WANG Yanbin WANG College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China Institute of Machine Learning and Systems Biology School of Electronics and Information EngineeringTongji UniversityShanghai 201804China School of Cyber Science and Technology Zhejiang UniversityHangzhou 310058China

In silico prediction of self-interacting proteins(SIPs)has become an important part of *** is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab *** goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction,to provide important references for actual work in the *** this review,we first describe the data required for the task of DTIs ***,some interesting feature extraction methods and computational models are presented on this topic in a timely ***,an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding ***,we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.

关键词： proteomics self-interacting proteins feature extraction prediction model

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Detecting Sarcasm in Reddit Comments: A Comparative Analysis 4

Detecting Sarcasm in Reddit Comments: A Comparative Analysis

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4th International Conference for Emerging technology, INCET 2023

作者： Sonare, Babita Dewan, Jaya H. Thepade, Sudeep D. Dadape, Vedang Gadge, Tejas Gavali, Aditya Pimpri Chinchwad College of Engineering Department of Information Technology Pune India Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350335750

Sarcasm is the use of sarcastic words to mock or mockingly show disdain for something. Several people frequently use it on social media sites like Reddit and Twitter. This study investigates the effectiveness of deep learning and machine learning algorithms in detecting sarcasm using SARC dataset consisting of 1.3 million Reddit comments with almost equal amounts of sarcastic and neutral comments. We compare several well-known machine learning classification methods, including Logistic Regression, Naïve Bayes, Decision Tree Classifier, and Convolutional Neural Networks (CNN). Our results, with an accuracy of 73.2%, demonstrate that the model designed using a fusion of CNN and Long Short-Term Memory Networks (LSTM) techniques performed better than alternative classification algorithms. Our findings show how machine learning techniques will be used in the future to identify sarcasm on social networking websites. © 2023 IEEE.

关键词： Convolutional neural networks

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Refinery: Graph Solver as a Service: Refinement-based Generation and Analysis of Consistent Models 24

Refinery: Graph Solver as a Service: Refinement-based Genera...

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46th International Conference on Software engineering: Companion, ICSE-Companion 2024

作者： Marussy, Kristóf Ficsor, Attila Semeráth, Oszkár Varró, Dániel Budapest University of Technology and Economics Department of Measurement and Information Systems Budapest Hungary Linköping University Department of Computer and Information Science Linköping Sweden McGill University Department of Electrical and Computer Engineering Montreal Canada

ISBN: (纸本)9798400705021

Various software and systems engineering scenarios rely on the systematic construction of consistent graph models. However, automatically generating a diverse set of consistent graph models for complex domain specifications is challenging. First, the graph generation problem must be specified with mathematical precision. Moreover, graph generation is a computationally complex task, which necessitates specialized logic solvers. Refinery is a novel open-source software framework to automatically synthesize a diverse set of consistent domain-specific graph models. The framework offers an expressive high-level specification language using partial models to succinctly formulate a wide range of graph generation challenges. Moreover, it provides a modern cloud-based architecture for a scalable graph solver as a service, which uses logic reasoning rules to efficiently synthesize a diverse set of solutions to graph generation problems by partial model refinement. Applications include system-level architecture synthesis, test generation for modeling tools or traffic scenario synthesis for autonomous vehicles. Video demonstration: https://***/Qy-3udNsWsM. Continuously deployed at: https://***/. © 2024 IEEE computer Society. All rights reserved.

关键词： Open source software

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Digital Twin Implementation of Autonomous Planning Arc Welding Robot System

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Complex System Modeling and Simulation 2023年第3期3卷 236-251页

作者： Xuewu Wang Yi Hua Jin Gao Zongjie Lin Rui Yu School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China Institute for Sustainable Manufacturing and also with the Department of Electrical and Computer EngineeringUniversity of KentuckyLexingtonKY 40506USA

Industrial robots are currently applied for ship sub-assembly welding to replace welding workers because of the intelligent production and cost *** order to improve the efficiency of the robot system,a digital twin system of welding path planning for the arc welding robot in ship sub-assembly welding is proposed in this manuscript to achieve autonomous planning and generation of the welding ***,a five-dimensional digital twin model of the dual arc welding robot system is ***,the system kinematics analysis and calibration are studied for communication realization between the virtual and the actual ***,a topology consisting of three bounding volume hierarchies(BVH)trees is proposed to construct digital twin virtual entities in this *** on this topology,algorithms for welding seam extraction and collision detection are ***,the genetic algorithm and the RRT-Connect algorithm combined with region partitioning(RRT-Connect-RP)are applied for the welding sequence global planning and local jump path planning,*** digital twin system and its path planning application are tested in the actual application *** results show that the system can not only simulate the actual welding operation of the arc welding robot but also realize path planning and real-time control of the robot.

关键词： arc welding robot digital twin topology path planning

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Effect of Hyper-Parameter Tuning on the Performance of Augmented Random Search 1

Effect of Hyper-Parameter Tuning on the Performance of Augme...

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1st International Conference on Informatics, ICI 2022

作者： Rana, Misha Kumar, Nimesh Sharma, Himanshi Pandey, Kavita Jaypee Institute of Information Technology Dept. of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9781665437219

Recently, researchers working in the field of Artificial Intelligence have been keen on finding new algorithms that provide optimal results. One such reinforcement learning algorithm is the Augmented Random Search. This algorithm is imperative as it provides better results over other reinforcement learning algorithms. Finding the optimal values of hyper-parameters helps the algorithm to work up to its full potential. In this work, we have made an effort to find out the best hyper-parameter settings for the learning rate and direction, for this specific task. Our results show how a change in these values can impact the final maximum rewards obtained during the training process. We report novel findings of the impact of hyper-parameters on the performance of ARS algorithm. Finally, an effort has been made to find out by what percentage the ARS performs better than other Reinforcement Learning algorithm, TRPO. © 2022 IEEE.

关键词： Reinforcement learning

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Using Markov Chain Based Estimation of Distribution Algorithm for Model-Based Safety Analysis of Graph Transformation

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Journal of computer Science & technology 2021年第4期36卷 839-855页

作者： Einollah Pira Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz ***Iran

The ability to assess the reliability of safety-critical systems is one of the most crucial requirements in the design of modern safety-critical systems where even a minor failure can result in loss of life or irreparable damage to the *** checking is an automatic technique that verifies or refutes system properties by exploring all reachable states(state space)of a *** large and complex systems,it is probable that the state space explosion problem *** exploring the state space of systems modeled by graph transformations,the rule applied on the current state specifies the rule that can perform on the next *** other words,the allowed rule on the current state depends only on the applied rule on the previous state,not the ones on earlier *** fact motivates us to use a Markov chain(MC)to capture this type of dependencies and applies the Estimation of Distribution Algorithm(EDA)to improve the quality of the *** is an evolutionary algorithm directing the search for the optimal solution by learning and sampling probabilistic models through the best individuals of a population at each *** show the effectiveness of the proposed approach,we implement it in GROOVE,an open source toolset for designing and model checking graph transformation *** results confirm that the proposed approach has a high speed and accuracy in comparison with the existing meta-heuristic and evolutionary techniques in safety analysis of systems specified formally through graph transformations.

关键词： safety analysis model checking Markov chain estimation of distribution algorithm graph transformation system

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Self-adaptive rolling motion for snake robots in unstructured environments based on torque control

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Biomimetic Intelligence & Robotics 2023年第3期3卷 59-66页

作者： Shihao Ma Fatao Qin Shufan Chen Longchuan Li Jianming Wang Zengzeng Wang Shuai Li Xuan Xiao School of Electronic and Information Engineering Tiangong UniversityTianjin 300382China College of Mechanical and Electrical Engineering Beijing University of Chemical TechnologyBeijing 100029China College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin 300382China School of Computer Science and Technology Tiangong UniversityTianjin 300382China

Snake robots have great potential for exploring and operating in challenging unstructured environments,such as rubble,caves,and narrow ***,due to the complexity and unpredictability of unstructured environments,designing a controller that can achieve adaptive motion is *** paper proposes a self-adaptive torque-based rolling controller for snake robots,enabling compliant motion in unstructured ***,a controller is designed to modify the torque of each motor by focusing on the different motion states of the rolling ***,an experimental platform is established for snake robots to verify the effectiveness of the ***,a series of rolling experiments are conducted using the torque-based rolling *** conclusion,the self-adaptive torque-based rolling controller enhances snake robot adaptability and mobility.

关键词： Snake robot Torque-based control Self-adaptive rolling gait Unstructured environments

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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

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Green Energy and Intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model Intelligent transportation K-means

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The Performance Analysis of Distributed Denial of Service Attacks in Wireless Networks 2

The Performance Analysis of Distributed Denial of Service At...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Agrawal, Trapty Raghavendra, R. Singh, K. Ranjith Rajiv Gandhi, N. Dhabliya, Dharmesh Katariya, Jitendra Kumar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350308846

Complete studies paper that examines the outcomes that malicious 0.33-celebration actors will have on Wi-Fi networks via dispensed denial of carrier (DDoS) attacks. This paper explores the available numerous assault models and the potential countermeasures that may be applied if you want to mitigate the damage due to such assaults. In addition, this paper focuses on the capability blessings related to deploying preventative measures to lessen the impact of such assaults. Moreover, the paper investigates existing attack trends. It affords an outline of the safety techniques to be had for limiting the harm caused by DDoS attacks in wireless networks. By comparing and analyzing the performance of diverse protection mechanisms, this paper offers an evidenceprimarily based evaluation of the effectiveness of DDoS attack safety answers. ultimately, this paper concludes with the aid of supplying recommended measures for strengthening and ensuring the safety of Wi-Fi networks against malicious actors. © 2023 IEEE.

关键词： Evaluation Measures Performance Recommended Strengthening

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