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

作者： Pandey, Abhishek Ramesh, V. Mohan, Ravi Kaliappan, S. Swetha Reddy, A. Gurunathan, K. SCSVMV University Department of Computer Applications Kanchipuram India Shri Ram Institute of Technology Department of ECE Madhya Pradesh Jabalpur India Lovely Professional University Division of Research and Development Punjab India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India Mount Zion College of Engineering and Technology Department of Computer Science and Engineering Pudukkottai India

ISBN: (纸本)9798350371802

One out of every eight women will be affected by breast cancer at some point in their lives, making it one of the most common malignancies in females. The most reliable method for finding breast cancer in its early stages is mammography. Due to the low visibility and weak contrast of mammographic photos, early identification of breast cancer is crucial for effective therapy. As a result of several computer-aided detection approaches, radiologists are now able to make more precise diagnoses. Operating Sequencing is crucial for several tasks, including morphology, preprocessing, segmentation, feature selection, and training models. The suggested method makes use of preprocessing technologies that can enhance mammography, remove noise, and identify pectoral muscles. Using image processing techniques, a digital image can be 'segmented' into many layers. Acquiring a precise image of the mass is the goal of the morphological process. Geometric algorithms are employed by the feature selection technique. Training a GRU-XGBoost model requires careful consideration of which features to use. The proposed method outperforms the current gold standard algorithms, GRU and XGBoost, in every respect. The results showed a remarkable improvement, with an accuracy of 95.63%. © 2024 IEEE.

关键词： Diseases

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Invoking an Efficient Deep Learning Approach for Real-Time Detection of False Data Injection Attacks

Invoking an Efficient Deep Learning Approach for Real-Time D...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Shadmand, Mohammad Abdallah, Mohamed Bcgovic, Miroslav Ghrayeb, Ali Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States Hamad Bin Khalifa University Information and Computing Technology Division Doha Qatar University of Illinois at Chicago Department of Electrical and Computer Engineering Chicago United States

ISBN: (纸本)9798331541125

With the increasing complexity of power systems and their vulnerability to cyber-attacks, robust detection mechanisms are crucial for ensuring the grid's transient and steady-state stability and resiliency. This study introduces an efficient deep learning framework to detect false data injection attacks (FDIAs) in power systems. Leveraging the MATPOWER environment, we simulate a realistic power grid scenario to generate datasets comprising normal operations and various FDIA scenarios. Utilizing advanced deep learning techniques, including convolutional neural networks enhanced with channel attention mechanisms and transformer encoders, our model processes sequential power system data to discern subtle attack signatures from normal fluctuations. The proposed methodology employs a stratified k-fold cross-validation approach on resampled datasets to address class imbalance, ensuring robust model evaluation. The model's performance is meticulously assessed using accuracy metrics and confusion matrices, demonstrating its capability to effectively identify and differentiate between normal and compromised states in power systems. Results on the IEEE 39-bus and 300-bus systems highlight that this approach significantly enhances detection capabilities, achieving high accuracy and practical detection times of less than 2 milliseconds for the IEEE 39-bus system, which addresses the real-time requirements. These promising results suggest that such models can be pivotal for preemptive security measures and real-time monitoring of power grids. © 2025 IEEE.

关键词： Convolutional neural networks

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Imaging and structure analysis of ferroelectric domains,domain walls,and vortices by scanning electron diffraction

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npj Computational Materials 2024年第1期10卷 2154-2161页

作者： Ursula Ludacka Jiali He Shuyu Qin Manuel Zahn Emil Frang Christiansen Kasper A.Hunnestad Xinqiao Zhang Zewu Yan Edith Bourret István Kézsmárki Antonius T.Jvan Helvoort Joshua Agar Dennis Meier Department of Materials Science and Engineering NTNU Norwegian University of Science and TechnologyTrondheimNorway Department of Computer Science and Engineering Lehigh UniversityBethlehemPAUSA Department of Mechanical Engineering and Mechanics Drexel UniversityPhiladelphiaPAUSA Experimental Physics V University of AugsburgAugsburgGermany Department of Physics NTNU Norwegian University of Science and TechnologyTrondheimNorway Department of Physics ETH ZurichZürichSwitzerland Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCAUSA

Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the *** electronic and quantum materials,this new capability gives access to,for example,emergent chiral structures and symmetry-breaking distortions that underpin functional *** nanoscale structural features with statistical significance,however,is complicated by the subtleties of dynamic diffraction and coexisting contrast mechanisms,which often results in a low signal-to-noise ratio and the superposition of multiple signals that are challenging to deconvolute.

关键词： properties structure ferroelectric

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A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

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computers, Materials & Continua 2023年第9期76卷 2959-2978页

作者： Muhammad Firdaus Harashta Tatimma Larasati Kyung-Hyune Rhee Department of Artificial Intelligence Convergence Pukyong National UniversityBusan48513Korea School of Electrical Engineering and Informatics Institut Teknologi BandungBandung40132Indonesia College of Information Technology and Convergence Division of Computer Engineering and AIPukyong National UniversityBusan48513Korea

The enormous volume of heterogeneous data fromvarious smart device-based applications has growingly increased a deeply interlaced cyber-physical *** order to deliver smart cloud services that require low latency with strong computational processing capabilities,the Edge Intelligence System(EIS)idea is now being employed,which takes advantage of Artificial Intelligence(AI)and Edge Computing technology(ECT).Thus,EIS presents a potential approach to enforcing future Intelligent Transportation Systems(ITS),particularly within a context of a Vehicular Network(VNets).However,the current EIS framework meets some issues and is conceivably vulnerable tomultiple adversarial attacks because the central aggregator server handles the entire ***,this paper introduces the concept of distributed edge intelligence,combining the advantages of Federated Learning(FL),Differential Privacy(DP),and blockchain to address the issues raised *** performing decentralized data management and storing transactions in immutable distributed ledger networks,the blockchain-assisted FL method improves user privacy and boosts traffic prediction ***,DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing *** model has been deployed in two strategies:First,DP-based FL to strengthen user privacy by masking the intermediate data during model ***,blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular *** simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget(ε)of 4.03,1.18,and 0.522,achieving model accuracy of 95.8%,93.78%,and 89.31%,respectively.

关键词： Edge intelligence federated learning differential privacy blockchain vehicular networks

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Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

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computer Systems Science & engineering 2023年第3期44卷 1939-1950页

作者： Dhanalakshmi R Sovan Samanta Arun Kumar Sivaraman Jeong Gon Lee Balasundaram A Sanamdikar Sanjay Tanaji Priya Ravindran Department of Computer Science and Engineering KCG College of TechnologyChennai600097Tamil NaduIndia Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127Tamil NaduIndia Division of Applied Mathematics Wonkwang UniversityIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of TechnologyChennai600127Tamil NaduIndia Department of Instrumentation Engineering PDEAs College of EngineeringManjari412307PuneIndia Engineering and Research Services HCL TechnologiesChennai600119India

Fuzzy sets have undergone several expansions and generalisations in the literature,including Atanasov’s intuitionistic fuzzy sets,type 2 fuzzy sets,and fuzzy multisets,to name a *** can be regarded as fuzzy multisets from a formal standpoint;nevertheless,their interpretation differs from the two other approaches to fuzzy multisets that are currently *** fuzzy sets(HFS)are very useful if consultants have hesitation in dealing with group decision-making problems between several possible ***,these possible memberships can be not only crisp values in[0,1],but also interval values during a practical evaluation *** bipolar valued fuzzy set(HBVFS)is a generalization of *** paper aims to introduce a general framework of multi-attribute group decision-making using social *** propose two types of decision-making processes:Type-1 decision-making process and Type-2 decision-making *** the Type-1 decision-making process,the experts’original opinion is proces for thefinal ranking of *** Type-2 decision making processs,there are two major aspects we ***,consistency tests and checking of consensus models are given for detecting that the judgments are logically ***,the framework demands(partial)decision-makers to review their ***,the coherence and consensus of several HBVFSs are established forfinal ranking of *** proposed framework is clarified by an example of software packages selection of a university.

关键词： Group decision-making aggregation operators hesitant bipolar-valued fuzzy set

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Unpredictable Switching for Cyber-Physical Security Against Worst-case Attackers 63

Unpredictable Switching for Cyber-Physical Security Against ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Kanellopoulos, Aris Fotiadis, Filippos Vamvoudakis, Kyriakos G. Sandberg, Henrik Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden Georgia Institute of Technology Daniel Guggenheim School of Aerospace Engineering AtlantaGA30332 United States

ISBN: (纸本)9798350316339

In this paper, we investigate the problem of securing a system against actuator attacks. Specifically, we employ an unpredictability-based defense algorithm according to the principles of Moving Target Defense, while explicitly considering the worst-case attack that can be launched into the system. Consequently, we allow the system to alternate between different actuating modes, but also between optimal and robust control schemes. Via game-theoretic methods, we derive mixed strategies dictating the probabilities of utilizing different controllers for a given mode of operation, whereas an entropy-augmented optimization problem gives the probabilities of using these different modes. Finally, the overall stability of the system is analyzed, and simulation results showcase the efficacy of our proposed method. © 2024 IEEE.

关键词： Cyber attacks

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AI Trauma Chatbot Analyzing Voice, Text and Providing Direct Doctor Link

AI Trauma Chatbot Analyzing Voice, Text and Providing Direct...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Janet Sherin. A T. Kavitha Division of Computer Sciences and Engineering Karunya Institute of Technology and Sciences

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Mental health support that is available and fast becoming a necessity because of the rising prevalence of psychological distress caused by trauma events. The research develops an AI-based trauma chatbot with text and voice sentiment evaluation alongside doctor access to provide improved mental health assistance strategies. The system analyzes both written texts and voice signals and specific emergency terminology to evaluate emotional conditions while providing immediate support and both therapeutic and professional services in critical situations. Through NLP and SER technologies the chatbot functions to evaluate mental distress intensity while providing suitable treatment recommendations. The proposed system demonstrates its capability to detect critical emotional states according to assessments made by comparing existing chatbot models and psychological tools. This investigation strives to enhance mental health service access through building a scalable time-responsive conversational agent which effectively detects and helps patients deal with trauma-related emotional distress.

关键词： Couplings Terminology Computational modeling Buildings Mental health Medical services Chatbots Artificial intelligence

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Fully Inkjet-Printed Soft Wearable Strain Sensors Based on Metal/Polymer Composite Sensing Films 5

Fully Inkjet-Printed Soft Wearable Strain Sensors Based on M...

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5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023

作者： Khan, Arshad Ali, Shawkat Nittala, Aditya Shekhar Riaz, Kashif Bermak, Amine College of Science and Engineering Hamad Bin Khalifa University Division of Information and Computing Technology Doha Qatar Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia University of Calgary Department of Computer Science Calgary Canada

ISBN: (纸本)9781665457330

In this paper, we present a high-performance soft strain sensor that uses composite sensing layers made of metal and polymer. This strain sensor is entirely developed by inkjet-printing the composite films on a highly stretchable thermoplastic polyurethane (TPU) substrate and a textile-compatible polymer film, which makes the process easy, affordable, energy-efficient, and scalable. The sensing patches are made up of two films: a PEDOT:PSS-based conductive polymer top film and a metal bottom film composed of silver nanoparticles. The sensor has a decent sensing range (≈10% strain), high sensitivity (GF ≈ 12), and outstanding mechanical stability (>1000 cycles), making it suitable for both stretching and bending deformation detection. As application examples, the soft sensors are used to track the highly deformed human body motions, displaying excellent performance. The outcomes demonstrate the attractiveness and potential of our low-cost soft strain sensors for real-world uses, such as the wide-range tracking of human movements. © 2023 IEEE.

关键词： Polymer films

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Mapping-aware Biased Training for Accurate Memristor-based Neural Networks 5

Mapping-aware Biased Training for Accurate Memristor-based N...

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5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Diware, Sumit Gebregiorgis, Anteneh Joshi, Rajiv V. Hamdioui, Said Bishnoi, Rajendra Delft University of Technology Computer Engineering Lab Delft Netherlands Ibm Research Division Yorktown HeightsNY United States

ISBN: (纸本)9798350332674

Memristor-based computation-in-memory (CIM) can achieve high energy efficiency by processing the data within the memory, which makes it well-suited for applications like neural networks. However, memristors suffer from conductance variation problem where their programmed conductance values deviate from the desired values. Such variations lead to computational errors that result in degraded inference accuracy in CIM-based neural networks. In this paper, we present a mapping-aware biased training methodology to mitigate the impact of conductance variation on CIM-based neural networks. We first determine which conductance states of the memristor are inherently more immune to variation. The neural network is then trained under the constraint that important weights can only take numeric values which directly get mapped to such favorable states. Simulation results show that our proposed mapping-aware biased training achieves up to 2.4× hardware accuracy compared to the conventional training. © 2023 IEEE.

关键词： Memristors

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Read-disturb Detection Methodology for RRAM-based Computation-in-Memory Architecture 5

Read-disturb Detection Methodology for RRAM-based Computatio...

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5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Yaldagard, Mohammad Amin Diware, Sumit Joshi, Rajiv V. Hamdioui, Said Bishnoi, Rajendra Delft University of Technology Computer Engineering Lab Delft Netherlands Ibm Research Division Yorktown HeightsNY United States

ISBN: (纸本)9798350332674

Resistive random access memory (RRAM) based computation-in-memory (CIM) architectures can meet the unprecedented energy efficiency requirements to execute AI algorithms directly on edge devices. However, the read-disturb problem associated with these architectures can lead to accumulated computational errors. To achieve the necessary level of computational accuracy, after a specific number of read cycles, these devices must undergo a reprogramming process which is a static approach and needs a large counter. This paper proposes a circuit-level RRAM read-disturb detection technique by monitoring real-time conductance drifts of RRAM devices, which initiate the reprogramming when actually it needs. Moreover, an analytic method is presented to determine the minimum conductance detection requirements, and our proposed read-disturb detection technique is tuned for the same to detect it dynamically. SPICE simulation result using TSMC 40 nm shows the correct functionality of our proposed detection technique. © 2023 IEEE.

关键词： Memory architecture

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