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6th International Conference on Dynamics of Information Systems, DIS 2023

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University GeelongVIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Department of Signal Theory Networking and Communications Universidad de Granada Granada Spain Swinburne University of Technology HawthornVIC3122 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

ISBN: (纸本)9783031503191

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. These motion vectors are then used to analyze facial expressions using some data mining algorithms. This analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. The facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep Learning (DL), Support Vector Machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Estimation of Deforestation Rate and Forest Land Use Land Cover Change Detection

Estimation of Deforestation Rate and Forest Land Use Land Co...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Kalaiyarasi, M. Saravanan, S. Balaji, R. Karthi, S. Rao, D.S. Naga Malleswara Sireesha, D. Nagineni Venkata Department of Computational Intelligence Saveetha School of Engineering Simats Chennai India B v Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering V.S.B. Engineering College Karur India Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Information Technology Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9798350334357

Amazon area is important for global energy, hydrology, and carbon balance. Amazon covers 5.5 million km2. Between 1976-2003, 7.3% of the Amazon was deforested, and 2.6% amid 2000-2010, PRODES monitors Amazon deforestation using satellite imagery. The yearly deforestation rate is anticipated based on the growth of desertification in Brazilian Amazon satellite images. According to INPE and FAO, 768,935 km2 of Amazon Basin have been deforested. In this paper, the deforestation rate and the Forest Land Use Land Cover change has been detected using SAGA GIS. Business As Usual (BAU) Scenario is used to find the rate. As indicated by BAU scenario in the result, Amazon rainforest could be diminished to 43.26% and the overall timberland misfortune would be 38.45% towards the end of 2050. © 2023 IEEE.

关键词： Deforestation

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Design of an Automated Blockchain-Enabled Vehicle Data Management System 5

Design of an Automated Blockchain-Enabled Vehicle Data Manag...

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5th International Conference on Signal Processing and Information Security, ICSPIS 2022

作者： Das, Debashis Banerjee, Sourav Chatterjee, Pushpita Ghosh, Uttam Mansoor, Wathiq Biswas, Utpal University of Kalyani Computer Science & Engineering Kalyani India Kalyani Government Engineering College Computer Science & Engineering Kalyani India Howard University Computer Science & Engineering WashingtonDC United States Meharry Medical College School of Applied Computational Sciences NashvilleTN United States University of Dubai Electrical Engineering Department Dubai United Arab Emirates

ISBN: (纸本)9781665492652

The Internet of Vehicles (IoV) has a tremendous prospect for numerous vehicular applications. IoV enables vehicles to transmit data to improve roadway safety and efficiency. Data security is essential for increasing the security and privacy of vehicle and roadway infrastructures in IoV systems. Several researchers proposed numerous solutions to address security and privacy issues in IoV systems. However, these issues are not proper solutions that lack data authentication and verification protocols. In this paper, a blockchain-enabled automated data management system for vehicles has been proposed and demonstrated. This work enables automated data verification and authentication using smart contracts. Certified organizations can only access vehicle data uploaded by the vehicle user to the Interplanetary File System (IPFS) server through that vehicle user's consent. The proposed system increases the security of vehicles and data. Vehicle privacy is also maintained here by increasing data privacy. © 2022 IEEE.

关键词： Smart contract

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Optimization Models for Teaching Load Formation of University Teachers

Optimization Models for Teaching Load Formation of Universit...

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AITU: Digital Generation, IEEE

作者： Diana Chigambayeva Makar Goryakin Polina Batova Department of Computational and Data Science Astana IT University Astana Kazakhstan Depatment of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350364378

ISBN: (纸本)9798350364385

Functional and mathematical models for the distribution of academic workload at the stage of preparing the educational process at a university are considered, which make it possible to largely determine the uniformity of teachers' workload with various types of academic work when fulfilling the criteria and restrictions adopted for the distribution.

关键词： Training Optimization models Human-machine systems Education Load modeling Information systems

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Quantum Hardware Roofline: Evaluating the Impact of Gate Expressivity on Quantum Processor Design

Quantum Hardware Roofline: Evaluating the Impact of Gate Exp...

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Justin Kalloor Mathias Weiden Ed Younis John Kubiatowicz Bert De Jong Costin Iancu Department of Electrical Engineering and Computer Science University of California Berkeley Computational Research Division Lawrence Berkeley National Laboratory

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

The design space of current quantum computers is expansive, with no obvious winning solution, leaving practitioners with a crucial question: “What is the optimal system configuration to run an algorithm?” This paper explores hardware design trade-offs across NISQ systems to better guide algorithm and hardware development. Algorithmic workloads and fidelity models drive the evaluation to appropriately capture architectural features such as gate expressivity, fidelity, and crosstalk. As a result of our analysis, we extend the criteria for gate design and selection from only maximizing average fidelity to a more comprehensive approach that additionally considers expressivity with respect to algorithm structures. A custom synthesis-driven compilation workflow that produces minimal circuit representations for a given system configuration drives our methodology and allows us to analyze any gate set effectively. In this work, we focus on native entangling gates (CNOT, ECR, CZ, ZZ, XX, Sycamore, √iSWAP), proposed gates (B Gate, 4 √CNOT, - 8 √CNOT), as well as parameterized gates (FSim, XY). By providing a method to evaluate the suitability of algorithms for hardware platforms, this work emphasizes the importance of hardware-software codesign for quantum computing.

关键词： Quantum system Quantum computing computational modeling Logic gates Parallel processing Benchmark testing Hardware Circuit synthesis Topology Integrated circuit modeling

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First-Order Federated Bilevel Learning 39

First-Order Federated Bilevel Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Yifan Xiao, Peiyao Ma, Shiqian Ji, Kaiyi Department of Computer Science and Engineering University at Buffalo United States Department of Computational Applied Math and Operations Research Rice University United States

ISBN: (纸本)157735897X

Federated bilevel optimization (FBO) has garnered significant attention lately, driven by its promising applications in meta-learning and hyperparameter optimization. Existing algorithms generally aim to approximate the gradient of the upper-level objective function (hypergradient) in the federated setting. However, because of the nonlinearity of the hypergradient and client drift, they often involve complicated computations. These computations, like multiple optimization sub-loops and second-order derivative evaluations, end up with significant memory consumption and high computational costs. In this paper, we propose a computationally and memory-efficient FBO algorithm named MemFBO. MemFBO features a fully single-loop structure with all involved variables updated simultaneously, and uses only first-order gradient information for all local updates. We show that MemFBO exhibits a linear convergence speedup with milder assumptions in both partial and full client participation scenarios. We further implement MemFBO in a novel FBO application for federated data cleaning. Our experiments, conducted on this application and federated hyper-representation, demonstrate the effectiveness of the proposed algorithm. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Intervention of Digital Twin for Smart Monitoring of Battery Management System 14

Intervention of Digital Twin for Smart Monitoring of Battery...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Joshi, Kapil Gupta, Amit Kaushik, Vishal Anandaram, Harishchander Gupta, Himanshu Kumar, Anil Uttaranchal University Uttaranchal Institute of Technology Department of Cse Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Upes School of Computer Science Dehradun India Amrita Vishwa Vidyapeetham Centre for Computational Engineering and Networking Tamil Nadu Coimbatore India Coer University Roorkee India Dit University School of Computing Dehradun India

ISBN: (纸本)9798350335095

The use of digital twins (DT) has made important improvements not just from the beginning of the process of automobile development through the final phases of vehicle building, but additionally during its usage, collecting essential information from its everyday activities to make traveling more joyful, relaxed, and secure. This study organizes the DT development history, fundamental principles, and essential technologies, as well as summarizing current research methodologies and problems in battery simulation, state estimate, remaining useful life (RUL) projection, battery protection, and management. In addition, study provide methods for batteries digital simulation, real-time status calculation, variable charging supervision, dynamic heat leadership, and energetic leveling regulation in the smart battery management system (BMS) using DT. The study also provides prospects for DT growth in the industry of battery. Lastly, the article concludes with an examination of the technological economic effects of DT technology, which will revolutionize conventional automobile manufacturing, as well as the barriers to continued growth. © 2023 IEEE.

关键词： Blockchain

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MRI Image Processing and Segmentation on Brain Tumor: A Survey

MRI Image Processing and Segmentation on Brain Tumor: A Surv...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Jayaraj Ramasamy Sreekumar Narayanan Gopirajan PV Ravikumar RN Department of Computer Science Botho University Botswana Africa Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Engineering Marwadi University Rajkot India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Image segmentation is a critical step in many clinical applications and is crucial in computer-assisted diagnosis. Segmentation of brain is the technique of automatically recognizing malignant brain tissues and labeling them depending on tumor kind. Manual tumor segmentation using brain MRI was time consuming and prone to inaccuracy. In brain MRI research, image segmentation is commonly used to detect and interpret anatomical features, analyze changes to the brain, identify sick areas, organize therapy and image guided surgery. Over the last few decades, different segmentation methods were developed and published in the research, with varying degrees of detail and difficulty. This study will focus on the most common segmentation methods. To begin addressing the complex details and challenges of the MRI Brain issue, we have introduced fundamental image preprocessing stages and then we have performed Image segmentation.

关键词： Surveys Support vector machines Image segmentation Magnetic resonance imaging Brain tumors Surgery Watersheds Artificial intelligence Random forests Cancer

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IoT-Enhanced Path Planning and Obstacle Avoidance for Autonomous Mobile Robots 3

IoT-Enhanced Path Planning and Obstacle Avoidance for Autono...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Selvanathan, N. Ramya, S.R. Sureshkumar, C. Kiruthiga, R. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. Sona College of Technology Department of Information Technology Salem India Dr NGP Institute of Technology Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Department of AI&DS Erode India SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350354171

Path planning and obstacle avoidance play a key role in how well autonomous mobile robots work and stay safe when they move through tricky areas. Traditional path-planning methods often can't keep up when new obstacles pop up or existing ones change, which makes them less useful in the real world. To fix these problems, the proposed research work brings in a new IoT-powered path planning and obstacle avoidance system that uses the D∗ Lite and Deep Reinforcement Learning (DRL) algorithms. Our approach brings together D∗ Lite's quick replanning skills with DRL's ability to learn and adapt made even better by live data from IoT sensors. We've put our model through its paces in different simulated environments set up to be as tricky as real-world navigation. Quantitative analysis shows it's a big step up from older models. Adding IoT data leads to a 30% boost in how paths are planned and cut the time robots need to get through complex routes by 20%. Plus, using DRL helps robots deal with moving obstacles 25% better, which means fewer potential crashes. This is a big step forward in the field of autonomous robotics. It gives a robust answer that would not just be reactive to the given environmental inputs but also, through proactivity, prompt possible obstacles, further contributing to smoother and safer output. As we move along with the development of our methodology, we foresee broad applications in many sectors enhancing the autonomy and efficiency of robotic systems working in dynamic and unpredictable environments. © 2024 IEEE.

关键词： Deep reinforcement learning

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A survey of load balancing and implementation of clustering-based approach for clouds

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Recent Advances in computer science and Communications 2021年第3期14卷 669-677页

作者： Sharma, Anju Pandey, Rohit Singh, Simar P. Kumar, Rajesh Department of Computational Sciences Maharaja Ranjit Singh Punjab Technical University Bathinda Punjab India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala Punjab India

Background: Generally, it is observed that there is not a single algorithm that classifies the task using Quality of Service (QoS) parameters requested by the task but instead focuses on clas-sifying resources and balancing the task's using the availability of resources. In past literature, authors divided the load balancing solutions in three main parts workload estimation, decision making, task transferring. Workload estimation deals with identifying requirements for the incoming tasks on the system. Decision making is done to analyze that whether or not load balancing should be performed for the given node. If the decision for load balancing has been made then third step deals with transferring task to appropriate node to reach a saturation point where the system will be in the stable state. Objective: To address this issue, our approach is more focused upon on workload estimation and its main objective is to cluster the incoming heterogeneous task into generic groups. Another issue for this approach is that the client demand varies for the number of tasks. Thus, some attributes may be much more critical to a user then the others and this demand changes from user to user. Methods: This paper classify the tasks using QoS parameters and focused on work-load estimation. The main objective is to cluster the incoming heterogeneous task into generic groups. For this, K-Medoid based clustering approach for cloud computing is devised and implemented. This approach is then compared with its different iterations to analyses the workload execution more deeply. Results: The analysis of our approach is computed using cloudsim simulator. Results and computa-tions shows that the data is very uneven in initial times, as some clusters have only four elements and others are having much more elements. Whereas after the 20th iteration data observed is more normally balanced, so the clusters formed after 20th iteration were more stable than clusters formed initially i.e. 1st iteratio

关键词： Surveys

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