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Spintronic Device Potential in Neuromorphic Computing from a Device-Circuit Modeling Perspective

Spintronic Device Potential in Neuromorphic Computing from a...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Suman, Bashetty Deepika, N.M. Rajalakshm, B. Singh, Jay Nijhawan, Ginni Riyadh, Mazin Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Science Bangalore India Gl Bajaj Institute of Technology & Management Dept. of Eee U.P. Greater Noida India Lovely Professional University Phagwara India AL-Hameed College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350357769

Recent years have seen tremendous progress in the area of neuromorphic computing, which seeks to mimic the parallel processing power and energy efficiency of the human brain. From the standpoint of device-circuit modelling, this work explores how spintronic devices might improve the scalability and performance of neuromorphic systems. Utilizing the inherent spin of the electron and the magnetic moment that goes along with it, spintronics has special benefits in terms of non-volatility, low power consumption, and high integration density. We provide a thorough model that accurately depicts the complex behaviour of spintronic components and how they are incorporated into neuromorphic circuits. Our results suggest that spintronic devices may improve the overall efficiency of neuromorphic systems by reducing the power-delay product substantially. Furthermore, it is possible to realize synaptic capabilities and plasticity processes with better accuracy by using the intrinsic features of spintronic devices, such as magnetoresistance and spin-transfer torque. Additionally, the research identifies problems with device variability and suggests possible fixes. In summary, spintronic devices have great potential to transform the field of neuromorphic computing if they are properly modelled and integrated. © 2023 IEEE.

关键词： Device-Circuit Modeling Magnetoresistance. Top of Form Neuromorphic Computing Spintronics Synaptic Plasticity

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Enhancing Explainability and Transparency in Deep Learning Models for Healthcare Applications

Enhancing Explainability and Transparency in Deep Learning M...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： V. Indhumathi Veeramalai Sankaradass V. K Manindra Manish K. Jagadeesh Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India Bachelor of Medicine and Bachelor of Science Dhanalakshmi Srinivasan Institute of Medical Sciences and Hospital Perambalur India Dept. of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Recently, it has been observed that deep learning models can be very useful in handling healthcare facilities mostly medical diagnosis and management. However, they are not interpretable and transparent enough to be accepted and used in the clinical environments where the matching reasoning is important. This paper proposes a new strategy to improve explainability of deep learning approaches when used in healthcare data set. We introduce an IDNN design with explainable layers and postdoc approaches like SHAP and LIME for visualization and explanation of model predictions. Our framework is applied to multiple healthcare datasets such as MIMICIII and ChestXray14 where we will compare the traditional method with the proposed method by training accuracy and explainability evaluation metrics. Results indicate that this causes minimal compromise in accuracy on the other hand, other explainability measures of model fidelity, and comprehensibility reveal significant gains, signifying the potential of this method in advancing the use of AI solutions in healthcare contexts.

关键词： Deep learning Training Hands Accuracy Explainable AI Statistical analysis MIMICs Medical services Predictive models Medical diagnosis

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Importance of Machine Learning and Deep Learning Algorithms in Earthquake Prediction: A Review

Importance of Machine Learning and Deep Learning Algorithms ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Güneş Gürsoy Asaf Varol Ahad Nasab Dept. of Computer Engineering College of Engineering and Natural Sciences Maltepe University İstanbul Turkey Dept. of Engineering Management and Technology College of Engineering and Computer Science The University of Chattanooga Chattanooga TN US

Earthquakes are the leading natural disasters that have caused loss of life and property since the formation of the world. Machine learning and deep learning are frequently used in studies for earthquake prediction. This article consists of a compilation of studies using machine learning and deep learning algorithms. In the article, studies on topics such as earthquake magnitude estimation, signal discrimination, electron density estimations in the ionosphere, examination of radon gas anomalies using machine learning and deep learning algorithms are included. The studies in this paper show that Deep Learning algorithms are used more frequently in earthquake forecasting. It is expected that Deep Learning will provide more successful results in future studies due to its ability to work with larger data sets compared to Machine Learning and its ability to improve itself from errors.

关键词： Deep learning Machine learning algorithms Earthquakes Radon Neural networks Estimation Prediction methods

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An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the Hypso-1 Satellite

An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Tru...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Jon A. Justo Joseph Garrett Dennis D. Langer Marie B. Henriksen Radu T. Ionescu Tor A. Johansen Dept. Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway Dept. Marine Technology Norwegian University of Science and Technology Trondheim Norway Dept. of Computer Science University of Bucharest Romania

Hyperspectral Imaging, employed in satellites for space remote sensing, like HYPSO-1, faces constraints due to few labeled data sets, affecting the training of AI models demanding these ground-truth annotations. In this work, we introduce The HYPSO-1 Sea-Land-Cloud-Labeled Dataset, an open dataset with 200 diverse hyperspectral images from the HYPSO-1 mission, available in both raw and calibrated forms for scientific research in Earth observation. Moreover, 38 of these images from different countries include ground-truth labels at pixel-level totaling about 25 million spectral signatures labeled for sea/land/cloud categories. To demonstrate the potential of the dataset and its labeled subset, we have additionally optimized a deep learning model (1D Fully Convolutional Network), achieving superior performance to the current state of the art. The complete dataset, ground-truth labels, deep learning model, and software code are openly accessible for download at the website https://***/hypso1_sea_land_clouds_dataset/.

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Customer Segmentation Using Hierarchical Clustering

Customer Segmentation Using Hierarchical Clustering

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International Conference for Convergence of technology (I2CT)

作者： Areeba Afzal Laiba Khan Muhammad Zunnurain Hussain Muhammad Zulkifl Hasan Muzzamil Mustafa Aqsa Khalid Rimsha Awan Farhan Ashraf Zohaib Ahmed Khan Arslan Javaid Department of Computer Engineering Information Technology University Lahore Punjab Pakistan Dept. of Computer Science Bahria University Lahore Campus Department of Computer Science Faculty of Information Technology University of Central Punjab Lahore Pakistan Department of Computer Science National College of Business Administration and Economics Lahore Pakistan Information Technology University Lahore Pakistan Department of Computer Science National College of Business Administration & Economics Lahore Pakistan

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In the dynamic landscape of retail, understanding customer behavior is paramount for businesses seeking to optimize marketing strategies and enhance the shopping experience. This study explores the utilization of hierarchical clustering techniques for mall customer segmentation, with a focus on the paper titled ’MALL CUSTOMER SEGMENTATION USING MACHINE LEARNING’ as the benchmark. Our dataset encompasses a diverse range of mall customers, spanning demographics and behavioral attributes. Hierarchical clustering systematically groups customers into clusters, revealing distinct segments within the mall’s customer base. A comprehensive analysis of these clusters unveils profound insights into customer tendencies, preferences, and purchasing habits. These insights form a solid foundation for tailored marketing campaigns, personalized recommendations, and resource allocation within the mall. The study contributes significantly to customer analytics, providing retailers with a powerful tool to gain a competitive edge in the retail sector. By leveraging hierarchical clustering for mall customer segmentation, businesses can enhance customer satisfaction, drive sales, and foster lasting customer relationships. This paper underscores the importance of data-driven methodologies in understanding customer behavior and offers a practical framework for businesses to harness the potential of hierarchical clustering for strategic decision-making in the retail industry.

关键词： Industries Decision making Customer satisfaction Benchmark testing Solids Resource management Data mining

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An Enhancemnt of Low-Light Images Using Hierarchical Pyramid Attentive Network and Hierarchical Feature Fusion 2

An Enhancemnt of Low-Light Images Using Hierarchical Pyramid...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Suma, K.G. Nagendar, Y. Habelalmateen, Mohammed I. Revanasiddappa Halvi, Srinivas Vit University School Of Computer Science And Engineering Dept. Of Ai & Ml Andhra Pradesh India Sr University School Of Computer Science And Engineering Telangana Warangal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Aiml Bengaluru India Dayananda Sagar College Of Engineering Department Of Medical Electronics Engineering Bengaluru India

ISBN: (纸本)9798350381665

The development of the low-light images assists efficiently in high visioned tasks which often fails in the poor illumination conditions. Most of the old methods, applied has enhanced the low-lighted degrade images which resulted in the undesirable enhancement including the intensive noise, blurred details and slanted color. This paper is used for finding alignment to an end-to-end image along with enhancement of the perceptual information by pixel wise. To reduce the differences in style, Hierarchical Pyramid Attentive Network (HPANet) was used, which results in the betterment of the illuminations and the reveal exposed information's. After aligned images, a hierarchy pyramid enhancements subnetwork is used for optimizing the quality of the image, which helps for removing the noises, results in enhancement low light images. A Hierarchical Feature Fusion (HFF) module is proposed which results in the high resolution of the images in perceptual qualities. Extensive experiments have also performed that effectiveness of HPANet-HFF methods on various datasets have outdone the existing methods in reproduction of the color. According to the results, the proposed HPANet-HFF obtained MAE and RMSE of 106.5 and 181.5 which is superior over other existing architectures. © 2024 IEEE.

关键词： Cell proliferation

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Data Security Enhancement in Cloud Computing by Proposing A DKE Encryption Protocol 23

Data Security Enhancement in Cloud Computing by Proposing A ...

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Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications

作者： Sahar Ebadinezhad Halmat Ayub Abdulmajed Department of Computer Information Systems Near East University North Cyprus and Computer Information Systems Research and Technology Center (CISRT) Near East University North Cyprus Dept. Engineering and Computer Systems Lebanese French University (LFU) Iraq

ISBN: (纸本)9798400708268

This study uses cryptography to tackle the important problem of data security in cloud computing. Two keys are used in the Dual Key Encryption (DKE) method for both encryption and decryption. DKE employs a public key for the first encryption round before cloud upload and a user-only private key for the second round of encryption. Decryption operates oppositely. When tested and simulated with different file sizes on a CloudAnalyst simulator, DKE operates faster and more efficiently than cryptographic methods such as Triple DES (3DES), AES, RSA, and DES. Thanks to developments in information technology, cloud computing has made several services available online. Data security assurance is still very difficult to achieve, nevertheless. In this context, cryptography is essential and has led to the creation of the novel DKE technology. This technology is not only far more efficient than well-known cryptographic techniques such as 3DES, but it also improves data security.

关键词： Cloud Services Cryptography Data Integrity Data Security Dual Key Encryption

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Federated Digital Twin

Federated Digital Twin

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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Christian Vergara Rami Bahsoon Georgios Theodoropoulos Wendy Yanez Nikos Tziritas School of Computer Science University of Birmingham Birmingham UK Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece

Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence; and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems.

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Software Code De-Compilation Techniques and Approaches: A Comparative Study

Software Code De-Compilation Techniques and Approaches: A Co...

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Information technology (ACIT)

作者： Hamed Fawareh Yazan Al-Smadi Mohammad Hassan Faid AlNoor Fawareh Ali Elrashidi Dept. of Software Engineering Zarqa University Jordan Dept. of Computer Science Zarqa University Jordan Dept. Of Mathmitical Science Zarqa University Jordan Dept. of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

De-compilation is the process of executable files as input where the de-compilation process tries to get away the source code, or the majority of it, if the file's source code is lost or damaged for whatever cause. De-compilation has many uses such as code recovery, security testing, and errors corrections, however, de-compilation is cautious since it depends on certain approaches and techniques. Nowadays de-compilation done by many automated and semi-automated tools, but the accuracy and the efficiency of it depend on the proposed approaches and techniques to stand in front of reengineering challenges. This paper aims to highlight up-to-date prior contributions in the field where 8 published research papers from May 2020 until May 2023 were collected to investigate the state-of-art. As a result, some of the researchers tend to develop their own tools and others tend to use popular automated with different approaches and techniques where the majority of the researchers focused on C, C++, and JAVA programming languages where 50% were de-compilation on C language.

关键词： Hands Java Source coding Programming Software Libraries Generators Security Information technology Testing

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An Automated Calibration Scheme for PLL in Time-to-Digital Converters 5

An Automated Calibration Scheme for PLL in Time-to-Digital C...

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5th International Conference on Circuits and Systems, ICCS 2023

作者： Xu, Jiayuan Yu, Hang Li, Yan Xi, Daoming Ge, Binjie Liu, Hongzhao College of Computer Science and Software Engineering Shenzhen University Shenzhen Laboratory of Ic Design for Iot Shenzhen China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Shenzhen MSU-BIT University Dept. of Electronics and Computer Engineering Shenzhen China Quality and Standards Academy Shenzhen Technology University Shenzhen China

ISBN: (纸本)9798350308273

In high performance Time-to-Digital Converters (TDCs), PLL can be adopted to generate the required reference clock, which is in ∼ 100 MHz range. In order to improve the phase noise performance, the full VCO frequency range in such a PLL is generally divided into multiple sectors, which can be selected by digital code. This paper proposes an automated calibration scheme to set the digital code for optimized VCO performance. Primarily based on successive approximation algorithm, the proposed scheme completes the automated calibration with only minimal steps. By co-simulating with a typical PLL, the efficacy of the proposed scheme is fully validated. Tentatively synthesized using CMOS 180nm technology, the automated calibration scheme only occupies 100× 100um2 of silicon area, and consists of 144 logical gates. The power consumption is only ∼ 100 uW. © 2023 IEEE.

关键词： Approximation algorithms

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