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2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Mohandas, R. Prasanna, D.S.John Deva Baiju, B.V. Nathiya, S. Rajkumar, T. Dhiliphan Sivashankar, C. Srm Institiute Of Science And Technology Department Of Computational Intelligence Chennai Kattankulathur India Srm Institiute Of Science And Technology Department Of Datascience And Business Systems Chennai Kattankulathur India Vit University TamilNadu Vellore India Prince Shri Venkateshwara Padmavathi Engineering College Department Of Electronics And Communication Engineering Ponmar Chennai India Kalasalingam Academy Of Research And Education Dept. Of Computer Science And Engineering Tamilnadu Krishnankovil India Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks;therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities. © 2024 IEEE.

关键词： Internet of things

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Optimizing Production and Inventory Management for Defective Items in Manufacturing Systems 3

Optimizing Production and Inventory Management for Defective...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ahmed, Khandakar Rabbi Hossain, Arafat Asif, MD Amaddus Bepary Mohammad, Mahathir Rahaman, Mustafizur Dewan, MD Arafat Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Central Michigan University Dept. of Business Data Analytics Mt Pleasant United States Southern California State University Dept. of Business Administration and Management Los AngelesCA United States Southern California State University Dept. of Business Administration and Management United States Westcliff University Dept. of Cloud Engineering IrvineCA United States Los Angeles City College Dept. of Computer Science Los Angeles United States

ISBN: (纸本)9798331523893

Manufacturing defective items is one of the challenges for production efficiency and inventory control. Conventional approaches like Economic Order Quantity (EOQ) and Just-in-Time (JIT) often fall short of the flexibility needed to respond to quality variations in real time. It promotes defect detection and control by employing machine learning (ML) for inventory management. Four classifiers were tested using the UCI SECOM dataset (Bagging, AdaBoost, Gradient Boosting, and Random Forest) to identify defective items. We used effective preprocessing techniques to boost model performance, such as SMOTE for class balancing and PCA for reducing dimensionality and standardization. The bagging and random forest classifiers produced 96.6% and 96.9% test accuracies, with good precision and recall scores. Within this context, we propose an Adaptive Inventory Management Framework comprising machine learning algorithms incorporating predictive analytics to reduce waste, optimize production scheduling(s), and improve resource allocation. These results are consistent with defining Industry 4.0 strategies and show that ML has the potential to transform defect management and inventory optimization in manufacturing systems. © 2025 IEEE.

关键词： Industry 4.0

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SYSTEMS WITH SWITCHING CAUSAL RELATIONS: A META-CAUSAL PERSPECTIVE

arXiv

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arXiv 2024年

作者： Willig, Moritz Tobiasch, Tim Nelson Busch, Florian Peter Seng, Jonas Dhami, Devendra Singh Kersting, Kristian Department of Computer Science Technical University of Darmstadt Germany Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands Centre for Cognitive Science Technical University of Darmstadt Germany Germany

Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents’ actions or tipping points in the environmental process can change the qualitative dynamics of the system. As a result, new causal relationships may emerge, while existing ones change or disappear, resulting in an altered causal graph. To analyze these qualitative changes on the causal graph, we propose the concept of meta-causal states, which groups classical causal models into clusters based on equivalent qualitative behavior and consolidates specific mechanism parameterizations. We demonstrate how meta-causal states can be inferred from observed agent behavior, and discuss potential methods for disentangling these states from unlabeled data. Finally, we direct our analysis towards the application of a dynamical system, showing that meta-causal states can also emerge from inherent system dynamics, and thus constitute more than a context-dependent framework in which mechanisms emerge only as a result of external factors. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Improving Relocalization in Visual SLAM by using Object Detection 19

Improving Relocalization in Visual SLAM by using Object Dete...

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19th International Conference on Electrical Engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2022

作者： Mahattansin, Nithid Sukvichai, Kanjanapan Bunnun, Pished Isshiki, Tsuyoshi Kasetsart University Faculty of Engineering Dept. of Electrical Engineering Bangkok Thailand National Science and Technology Development Agency National Electronics and Computer Technology Center Phathum Thani Thailand Tokyo Institute of Technology School of Engineering Dept. of Information and Communications Engineering Tokyo Japan

ISBN: (纸本)9781665485845

Visual simultaneous localization and mapping (VS-LAM) is the useful algorithm for localizing and mapping especially for the indoor mobile robot application. The VSLAM works based on features extracted from surrounding images. The limitation of VSLAM is that a speed of its relocalization algorithm is slow due to a large number of candidates. This research aims to improve the relocalization of VSLAM by using semantic information as a new constraint to determine candidates. The basic idea is to use a deep neural network which is YOLO, to classify objects from an image frame and create a high-level feature array to represent objects in the frame. By using this array, the algorithm can discard many poor candidates and decrease the computation time of the relocalization process. The proposed approach was implemented into the 3 popular VSLAM frameworks which are ORB-SLAM2, OpenVSLAM and RTAB-MAP. Experiments have been conducted on a pre-record video with known ground truth. The results showed that by using the proposed approach, the execution time of the relocalization process was decreased for all selected VSLAM frameworks. © 2022 IEEE.

关键词： Object recognition

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Blockchain technology: A Comprehensive Review and Future Directions 2

Blockchain Technology: A Comprehensive Review and Future Dir...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Kumar, K Parish Venkata Lakshmi, B. Sai Kumar, S. Muralidhar, V. Sai, N. Raghavendra Nagamalleswara, Veerla Dept. of Computer Applications Kanuru India Pvp Siddhartha Institute of Technology Department of It India VVIT- Autonomous Nambur Csm Dept India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Dhanekla Institute of Engineering & Technology Department of Cse AP Ganguru India

ISBN: (纸本)9798350325799

Blockchain technology is a revolutionary innovation that has the power to transform numerous industries and sectors. This research study comprehends the dept.s of blockchain technology, exploring its underlying principles, unique features, and practical applications across various industries. This study thoroughly examines the foundational concepts of distributed ledger technology, cryptographic algorithms, consensus mechanisms, and smart contracts - the essential components of blockchain systems. Furthermore, this article meticulously evaluates the advantages and disadvantages of adopting blockchain technology, addressing key factors such as scalability, privacy, and regulatory concerns. By critically analyzing these aspects, a comprehensive understanding on the potential challenges and benefits associated with implementing blockchain solutions can be gained. Lastly, a comprehensive assessment of the current state of blockchain research is presented by highlighting the ongoing advancements and breakthroughs. Additionally, this study outlines potential future research directions and development, envisioning how blockchain technology can continue to evolve and shape the future. © 2023 IEEE.

关键词： Blockchain

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Cost-Effective and Dynamic Migration for Microservices in Hybrid Cloud-Edge Systems

Cost-Effective and Dynamic Migration for Microservices in Hy...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Mobile edge computing (MEC), as a promising paradigm, delivers computation and storage capacities at the edge of the network. It supports delay-sensitive services for mobile users (MUs). However, dynamic and stochastic characteristics of MEC networks necessitate constant migration of installed services across edge servers to keep up with the mobility of MUs. As a result, the cost of maintaining the network increases significantly. Existing studies of MEC rarely consider the cost of service migration due to MU mobility. To minimize the long-term cost for microservices in a hybrid cloudedge system comprising of MUs, small base stations (SBSs), and a cloud data center (CDC), the total cost minimization is formulated as a constrained mixed-integer nonlinear program. To solve it, this work designs a novel meta-heuristic optimization algorithm called Multi-swarm Grey-wolf-optimizer based on Genetic-learning (MGG), which effectively combines strong local search capabilities of grey wolf optimizer with superior global search capabilities of genetic algorithm. MGG simultaneously optimizes service request routing among MUs, SBSs, and CDC, CPU speeds of SBSs, service deployment of SBSs, service migration cost of SBSs, as well as MUs' transmission power and channel bandwidth allocation. Simulation results with Google cluster trace demonstrate that MGG outperforms several state-of-the-art peers with respect to the overall cost of the hybrid system. © 2023 IEEE.

关键词： genetic algorithm grey wolf optimizer Mobile edge computing resource allocation service migration

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Financial Viability of PV Grid for an Intelligent System of Residential Buildings in sub-urban Areas of Punjab, Pakistan

Financial Viability of PV Grid for an Intelligent System of ...

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Power Electronics and Control (SEPOC), Seminar on

作者： Moazzam Shehzad Nasir Jamal Hafiz Abdul Muqeet Noman Sohaib Qureshi Dept. of Electrical Engineering Lincoln University College Petaling Jaya Malaysia Dept. of Electrical Engineering Punjab Tianjin University of Technology Lahore Pakistan Dept. of Computer Science University of Engineering and Technology (RCET Campus) Lahore Pakistan

ISBN: (数字)9798350379419

ISBN: (纸本)9798350379426

With fuel prices rising and environmental concerns intensifying, the financial burden on consumers and businesses has shown an upward trajectory, prompting a search for sustainable alternatives sources. This study investigates the financial feasibility of installing a grid-tied solar photovoltaic (PV) system in residential buildings in suburban areas of Punjab, Pakistan. The research uses a complete intelligent financial model to assess the feasibility of a grid-connected solar PV system, incorporating parameters such as solar irradiance levels, system size, capital costs, operating expenses, energy rates, its incentives, and payback periods in a systematic computational optimization methodology. The shift to solar photovoltaic systems provides an affordable way to lower energy bills that can also lessen the dependency on fossil fuels. Additionally, solar PV systems help significantly to reduce greenhouse gas emissions and environmental damages by utilizing the clean energy of the sun. This is in line with worldwide efforts to battle climate change and protect the planet from environmental concerns. This research uses RET Screen software to evaluate the financial feasibility with GHG reduction analysis as well. The study’s findings are useful for homeowners, policymakers, and stakeholders interested in encouraging renewable energy adoption and sustainable development in Punjab, Pakistan’s suburban region.

关键词： Climate change Meters Computational modeling Optimization methods Fuels Pricing Power grids Photovoltaic systems Financial management Energy management Buildings Solar power generation Economics

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Blockchain-enabled Transparent and Trustworthy Regulation System for Smart Cities 1

Blockchain-enabled Transparent and Trustworthy Regulation Sy...

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1st International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2023

作者： Puri, Vikram Solanki, Vijender Kumar Kataria, Aman Long, Cu Kim Duy Tan University Center of Visualization and Simulation Da Nang Viet Nam Cmr Institute of Technology Dept. of Computer Science and Engineering TS Hyderabad India Amity University Amity Institute of Defence Technology Uttar Pradesh India Information Technology Center Hanoi Viet Nam

ISBN: (纸本)9798350323566

The development and growth of the shared economy in the smart cities are significantly influenced by technology. An online community-based ecosystem that enables peer-to-peer trade between buyers and sellers as well as share- and rental-based access to goods and services. Lack of trust and transparency is the main problem in the shared economy system. A blockchain-enabled architecture is created for the shared economy ecosystem to address this problem. In this study, product registration and product services policies are implemented using smart contracts. This technology aids in distinguishing between genuine and counterfeit goods while also giving consumers access to the product's metadata via QR codes. An experimental testbed that makes use of the polygon network is taken into consideration in order to validate the suggested framework. © 2023 IEEE.

关键词： Smart city

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AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentation

AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentat...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Samruddhi Maheshkumar Kolekar Agnesh Chandra Yadav Suchita Yadav Daba Prasad Dash Dept. Electrical Engineering Thaper Institue of Engineering and Technology Patiala India Dept. Electrical Engineering India Instite of Technology Patna Patna India Dept. of Computer Science and Engineering Bennett University Gr. Noida India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Accurate segmentation of brain tumors in MRI images is crucial for diagnosis, treatment planning, and disease monitoring. Traditional manual segmentation methods are time- consuming and prone to variability, while standard deep learning architectures such as U-Net face challenges in capturing the complex, heterogeneous structures of brain tumors. In this study, we proposed AttRes-UNet, a novel dual-model architecture combining Attention U-Net and Residual U-Net to enhance segmentation accuracy. Attention Gates allow the model to focus on critical tumor regions, while Residual Blocks improve deep feature learning, enabling the model to handle diverse tumor morphologies with greater precision. We evaluated the model using the BraTS 2021 dataset, achieving high performance with an accuracy of 0.996, a dice coefficient of 0.8340, and an Intersection over Union of 0.8210. These results demonstrate the effectiveness of AttRes-UNet in improving segmentation accuracy, making it a valuable tool for automated brain tumor delineation, ultimately reducing manual workload and improving clinical outcomes.

关键词： Representation learning Image segmentation Accuracy Three-dimensional displays Magnetic resonance imaging Brain tumors Morphology Manuals Logic gates Brain modeling

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Energy Conservation Strategies in Wireless Sensor Networks: A Review

Energy Conservation Strategies in Wireless Sensor Networks: ...

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2021 International Conference of Modern Trends in Information and Communication technology Industry, MTICTI 2021

作者： Inad, Sarah Amjad Ali Alheeti, Khattab M. Al-Rawi, Salah Sleibi University of Anbar College of Computer and Information Technology Computer Science Department Ramadi Iraq University of Anbar College of Computer Sciences and Information Technology Computer Networking Systems Department Ramadi Iraq University of Anbar College of Computer and Information Technology Information Systems Dept. Ramadi Iraq

ISBN: (纸本)9781665414449

Wireless Sensor Networks (WSNs) consist of small devices that sense environmental and physical phenomena. WSNs have proven their high capabilities in various fields, but there are some challenges facing sensor nodes, such as limited battery power and their communication and storage capabilities. This review of recent literature on energy improvement strategies is compressed in this review paper. The concepts of WSNs, power sources, WSNs node component, Energy Saving Techniques, power consumption were extracted and clarified. Finally, the results of previous studies regarding energy improvement were extracted, discussed, and compared at the end of the paper. © 2021 IEEE.

关键词： Electric power utilization

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