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Engineering of A-site cations in APbI3 (A = Cs, Rb, K) perovskites for resistive switching control and self-rectifying memristors for next-generation computing applications

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NANO ENERGY 2025年 138卷

作者： Jain, Muskan Fasnick, C. K. Bismiya Khemnani, Manish Kortstee, Lotte Andola, Bhawana Patel, Mayur Jagdishbhai Guerrero, Antonio Srivastava, Yogesh Kumar Castelli, Ivano E. Solanki, Ankur Pandit Deendayal Energy Univ Flextron Lab Gandhinagar 382426 Gujarat India Pandit Deendayal Energy Univ Sch Energy Technol Dept Phys Gandhinagar 382426 India Tech Univ Denmark Dept Energy Convers & Storage Anker Engelundsvej 411 DK-2800 Lyngby Denmark Indian Inst Technol Hyderabad Dept Phys Ultrafast Terahertz Photon Lab Sangareddy 502284 Telangana India Indian Inst Technol Guwahati Dept Chem Gauhati 781039 Assam India Univ Jaume 1 Inst Adv Mat INAM Castellon de La Plana 12006 Spain

The resistive switching in memristor devices is highly influenced by structural and electronic properties, which can be tailored through material composition. Unlike optoelectronic devices, lattice distortion driven by A-site cation variation (A = Cs, Rb, K) is a key mechanism for enhancing the switching properties of APbI(3)-based flexible memristor devices. This distortion, particularly noticeable in KPbI3's orthorhombic phase, alters crystal structure, electronic as well as charge transport properties. Density functional theory (DFT) calculations are performed to calculate ionic migration barriers in the orthorhombic structures, while X-ray photoelectron spectroscopy (XPS) analysis provides insights into the chemical environment and bonding states. Leveraging this effect, KPbI3 exhibits exceptional non-volatile data storage performance, with a high ON/OFF ratio (similar to 10(3)) and self-rectification (similar to 10(3)). Conversely, CsPbI3 with subtle lattice distortion, displays superior synaptic behavior and efficient spike-timing-dependent plasticity (STDP), ideal for neuromorphic computing. CsPbI3 demonstrates remarkable potential for artificial neural networks (ANNs), achieving similar to 97 % accuracy in Modified National Institute of Standards and Technology (MNIST) dataset, image classification with minimal training, and 85 % accuracy in convolutional neural networks (CNNs) on the Canadian Institute for Advanced Research (CIFAR-10) dataset. Our findings highlight the critical role of small cation size in modulating Schottky barriers, morphology, and ionic migration, and their direct impact on resistive switching properties. These results underscore the potential of cation size modulation as an effective strategy for designing memristors for a wide range of applications, including neuromorphic computing, signal processing, logic gates, and quantum computing.

关键词： Memristor KPbI3 Self-rectifying Perovskites artificial Synapse Neuromorphic Computation Resistive Switching

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TRAINING AN artificial INTELLIGENCE MODEL FOR THE DETECTION OF GESTURES RELATED TO TRICHOTILLOMANIA 22

TRAINING AN ARTIFICIAL INTELLIGENCE MODEL FOR THE DETECTION ...

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22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： de Gois Paulino, Daniel Victor Costa De Sousa Alves, Robinson Luis Instituto Federal de Educação Ciência e Tecnologia do Rio Grande do Norte Brazil

This article presents an artificial intelligence model capable of identifying actions strongly related to trichotillomania, a psychiatric disorder that causes people to have a desire to pull their hair. The model was trained with images and videos collected and variations generated through artificial intelligence to improve the image database. The work focused on the user's frontal perspective to optimize the construction of the dataset and neural network training. As a result, we obtained 89% precision in the model, which requires further testing and optimization to be used in real applications – still limited – that can provide users with statistics and results related to the disorder for possible treatments or alert the user to decrease their involuntary actions. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： artificial Intelligence Behavioral Pattern Recognition Computational Psychiatry image processing Machine Learning neural networks Trichotillomania

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A Lightweight Multiscale-Multiobject Deep Segmentation Architecture for UAV-Based Consumer applications

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3740-3753页

作者： Behera, Tanmay Kumar Bakshi, Sambit Khan, Muhammad Attique Albarakati, Hussain Mobarak Natl Inst Technol Rourkela Dept Comp Sci & Engn Rourkela 769008 India HITEC Univ Dept Comp Sci Taxila 47080 Pakistan Lebanese Amer Univ Dept Comp Sci & Math Beirut 11022801 Lebanon Umm Al Qura Univ Coll Comp & Informat Syst Comp & Network Engn Dept Mecca 24382 Saudi Arabia

Smart UAVs have been developed under the consumer Internet of Drone Things (CIoDTs) framework to improve the quality of service (QoS) for several commercial and consumer applications. artificial intelligence (AI)-inspired algorithms are employed here to make these remote sensing devices more intelligent and agile to perform the task most effectively. However, these AI-based techniques may suffer from obtaining the required feature space, leading to poor performance of the AI system. Thus, to address these intrinsic issues, this manuscript presents a lightweight multiscale convolutional neural network (MCNN), which segments the UAV-captured aerial images based on their object classes. The proposed framework is based on an encoder-decoder architecture developed using depthwise separable convolution. The encoder module here involves extracting the crucial scale-invariant features that are upsampled at the decoder stage to generate the predicted segmented images. Additionally, the proposed architecture is validated using two datasets: the prepared NITRDrone dataset and the Urban Drone Dataset (UDD). Thus, the proposed method achieves an IoU score of 0.877 and 0.802 with the NITRDrone and UDD, respectively. Thus, the proposed lightweight architecture helps enhance the efficiency of these intelligent mobile edge aerial devices for several critical consumer applications demanding real-time data processing.

关键词： Task analysis Remote sensing Computer architecture Autonomous aerial vehicles Convolutional neural networks Consumer electronics Computer vision VHR remote sensing aerial image multiscale CNN semantic segmentation mobile edge devices

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Display and View—NSDN—image Caption Generator and Headcount 8th

Display and View—NSDN—Image Caption Generator and Headcoun...

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8th International Conference on Information System Design and Intelligent applications, ISDIA 2024

作者： Nahar, Shreya Jain, Tanishka R. Shah, Mihir Dilip, Golda Department of Computer Science and Engineering SRMIST Vadapalani Chennai India

ISBN: (纸本)9789819748914

The automatic description of the content of an image is a challenge that combines computer vision with natural language processing in the field of artificial intelligence. Most existing models have limitations in accurately identifying subtle elements of an image and counting the number of people present. To address this challenge, we introduce the natural-language scene description network (NSDN), which incorporates recent developments in computer vision and machine translation to generate descriptive phrases that effectively depict images. This technology has the potential to improve the efficiency, capacity, reliability, and safety of crowd management tasks, particularly in diverse and adaptive crowd situations. Despite challenges such as clutter, occlusion, non-uniform object scale, and irregular object distribution, YOLO shows promise for intelligent crowd counting and analysis in images. This article reviews, categorizes, analyzes distinguishing features, and extensively assesses the effectiveness of crowd-counting methods that rely on convolutional neural networks and provides a detailed analysis of their performance. © The Author(s).

关键词： Convolutional neural networks

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Data Augmentation-based Novel Deep Learning Method for Deepfaked images Detection

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND applications 2024年第11期20卷 1-15页

作者： Iqbal, Farkhund Abbasi, Ahmed Javed, Abdul rehman Almadhor, Ahmad Jalil, Zunera Anwar, Sajid Rida, Imad Zayed Univ Coll Technol Innovat Abu Dhabi 144534 U Arab Emirates Air Univ Dept Creat Technol PAF Complex Islamabad 56300 Pakistan Lebanese Amer Univ Dept Elect & Comp Engn Byblos Lebanon Jouf Univ Coll Comp & Informat Sci Dept Comp Engn & Networks Sakaka 42421 Saudi Arabia Air Univ Dept Cyber Secur PAF Complex Islamabad 56300 Pakistan Inst Management Sci Peshawar Peshawar 25130 Pakistan Univ Technol Compiegne BMBI Lab F-60203 Compiegne France

Recent advances in artificial intelligence have led to deepfake images, enabling users to replace a real face with a genuine one. deepfake images have recently been used to malign public figures, politicians, and even average citizens. deepfake but realistic images have been used to stir political dissatisfaction, blackmail, propagate false news, and even carry out bogus terrorist attacks. Thus, identifying real images from fakes has got more challenging. To avoid these issues, this study employs transfer learning and data augmentation technique to classify deepfake images. For experimentation, 190,335 RGB-resolution deepfake and real images and image augmentation methods are used to prepare the dataset. The experiments use the deep learning models: convolutional neural network (CNN), Inception V3, visual geometry group (VGG19), and VGG16 with a transfer learning approach. Essential evaluation metrics (accuracy, precision, recall, F1-score, confusion matrix, and AUC-ROC curve score) are used to test the efficacy of the proposed approach. Results revealed that the proposed approach achieves an accuracy, recall, F1-score and AUC-ROC score of 90% and 91% precision, with our fine-tuned VGG16 model outperforming other DL models in recognizing real and deepfakes.

关键词： Deepfake detection data augmentation image processing deep learning artificial intelligence transfer learning

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Multifunctional In-Memory Analog-to-Digital Converter for Next-Gen Compute-in-Memory Systems

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2025年第5期7卷

作者： Im, Jiseong Ko, Jonghyun Hwang, Joon Kim, Jangsaeng Shin, Wonjun Koo, Ryun-Han Park, Minkyu Park, Sung-Ho Choi, Woo Young Kim, Jae-Joon Lee, Jong-Ho Seoul Natl Univ Coll Engn Interuniv Semicond Res Ctr Dept Elect & Comp Engn Seoul 08826 South Korea Sungkyunkwan Univ Dept Semicond Convergence Engn Suwon 16419 South Korea

Compute-in-memory (CIM) technology based on emerging nonvolatile memories (NVMs) has shown promise in enhancing artificial intelligence applications by integrating computation directly within NVM arrays. However, the efficiency of CIM systems is often curtailed by the substantial overhead that is caused by traditional complementary metal-oxide-semiconductor (CMOS)-based analog-to-digital converters (ADCs). Here, we report an in-memory ADC (IMADC) that leverages NVMs to perform the dual functionalities of reference generation and voltage comparison, effectively minimizing the area occupancy and energy consumption, is reported. The IMADC not only significantly outperforms traditional ADCs but also enables the inherent processing of nonlinear activation functions such as the sigmoid function, which is required for neural networks. The IMADC-based CIM system achieves software-comparable accuracy in CIFAR-10 image classification on the VGG-9 network. The IMADC exhibits significantly reduced area occupancy (45 mu m2) and energy consumption (29.6 fJ) compared to conventional CMOS-based ADCs. The IMADC, compatible with various types of NVMs, demonstrates significant potential for enhancing the efficiency of CIM systems in terms of area occupancy and energy consumption.

关键词： analog-to-digital converter compute-in-memory flash thin-film-transistor hardware-based artificial intelligence neuromorphic computing

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Research on Human Resource Management and Prediction Model Based on neural Network Optimized by Genetic Algorithm 2

Research on Human Resource Management and Prediction Model B...

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2nd IEEE International Conference on image processing and Computer applications, ICIPCA 2024

作者： Qi, Zexuan Yang, Sihan He, Xinjian Shi, Desheng Xinjiang University of Science & Technology Xinjiang Korla China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Engineering Guangzhou Huali College Guangzhou China Modern Agricultural College Honghe Vocational and Technical College Yunnan Mengzi China

ISBN: (纸本)9798350360240

This paper investigates the application of artificial neural networks optimized based on genetic algorithms in human resource management in hospital enterprises and constructs a human resource management and predictive analysis model. The model aims to support enterprises in achieving the optimal allocation of human resources by mining their internal human resource data and revealing the intrinsic correlation between personnel structure, business type and future development strategy. The study adopts multiple regression analysis method and combines the artificial neural network optimized by genetic algorithm to construct a human resource demand prediction model. Taking the operational data of Hospital X as an example, the model predicts the number of human resources in the hospital and analyzes in depth the demand for staff types in each department. The results show that the model has high accuracy and reliability in predicting the variables of hospital revenue, wage level, R&D investment and number of personnel, with a prediction error range between 0.09% and 1.27%. The research in this paper provides strong data support for enterprises, which helps them to realize the rational allocation of human resources and strategic transformation, and then maintain their competitive position in the market. © 2024 IEEE.

关键词： Prediction models

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A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

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Computer Systems Science & Engineering 2024年第2期48卷 273-285页

作者： Amr Ismail WalidHamdy Aya MAl-Zoghby Wael AAwad Ahmed Ismail Ebada Yunyoung Nam Byeong-Gwon Kang Mohamed Abouhawwash Faculty of Science Port Said UniversityPort SaidEgypt Modern Academy for Computer Science and Management Technology Cairo11742Egypt Faculty of Computers and Artificial Intelligence Damietta UniversityNew DamiettaEgypt Department of ICT Convergence Soonchunhyang UniversityAsan31538South Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Deep learning(DL)plays a critical role in processing and converting data into knowledge and *** technologies have been applied in a variety of applications,including image,video,and genome sequence *** deep learning the most widely utilized architecture is Convolutional neural networks(CNN)are taught discriminatory traits in a supervised *** comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene *** is an essential crop of cereals for people around the *** Genotypes identification has an impact on the possible development of many countries in the agricultural *** quantitative genetics prediction of genetic values is a central *** is an allohexaploid(AABBDD)with three distinct *** sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of artificial Intelligence(AI)are *** paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current *** this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional neural networks(CNN).

关键词： Gene expression convolutional neural network optimization algorithm genomic prediction wheat

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Compressing neural networks on Limited Computing Resources

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IEEE ACCESS 2025年 13卷 80063-80075页

作者： Lee, Seunghyun Lee, Dongjun Hyun, Minju Kim, Heeje Song, Byung Cheol Inha Univ Dept Elect & Comp Engn Incheon 22212 South Korea

Network compression is a crucial technique for applying deep learning models to edge or mobile devices. However, the cost of achieving higher benchmark performance through compression is continuously increasing, making network compression a significant burden-especially for small industries focused on developing compact models. Specifically, existing network compression techniques often require extensive computational resources, rendering them impractical for edge devices and small-scale applications. To democratize network compression, we propose a general-purpose framework that combines novel filter pruning and knowledge distillation techniques. First, unlike conventional filter pruning methods based on static heuristics and costly neural architecture search (NAS)-based approaches, our method leverages meta-learning for rapid and fine examination of the importance of each gate. This enables rapid and stable sub-network discovery, significantly improving the pruning process. Second, to minimize the computational cost of knowledge distillation, we introduce a synthetic teacher assistant that leverages precomputed fixed knowledge-referring to the stored feature maps/logits of the teacher network. By leveraging fixed knowledge, we mitigate the cost incurred by the teacher network and facilitate the transmission of fixed knowledge to the student via synthetic teacher assistants, thereby preventing distribution collapse. Our proposed framework dramatically reduces the compression overhead while maintaining high accuracy, achieving a 55.2% reduction in FLOPs of ResNet-50 trained on imageNet while preserving 76.2% top-1 accuracy with only 199 GPU hours-significantly lower than previous state-of-the-art methods. Overall, our framework democratizes deep learning compression by offering a cost-effective and computationally feasible solution, enabling broader adoption in low-resource environments.

关键词： Costs Knowledge engineering Logic gates Filtering algorithms Graphics processing units Computational modeling Accuracy image coding Deep learning Workstations Deep neural network compression filter pruning knowledge transfer efficient neural networks lightweight deep learning models

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Nature and Biologically Inspired image Segmentation Techniques

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2022年第3期29卷 1415-1442页

作者： Singh, Simrandeep Mittal, Nitin Thakur, Diksha Singh, Harbinder Oliva, Diego Demin, Anton Chandigarh Univ Dept Elect & Commun Engn Gharuan Punjab India Chandigarh Engn Coll Dept Elect & Commun Engn Landran Punjab India Univ Guadalajara Dept Ciencias Computac CUCEI Av Revoluc 1500 Guadalajara Jalisco Mexico Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk Russia

image processing is among the significant areas of growth in the current scenario. It consist of a set of techniques typically used to enhance the raw image obtained from different scenes. Segmentation of images is an essential step in image analysis and pre-processing. During the course of the work, standard multilevel thresholding methods are very effective due to their low computational cost, reliability, reduced convergence time, and precision. Nature-inspired methods of optimization play an essential role in the processing of images. Several optimization procedures have been proposed for different image processing applications. These optimization techniques can improve the performance of image segmentation, image restoration, edge detection, image enhancement, pattern recognition, image generation, image thresholding, and image fusion algorithms. This paper includes an overview of several metaheuristic firefly algorithm (FA), differential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), artificial bee colony optimization (ABC), etc. Moreover, artificial neural networks (ANN) and other machine learning techniques (nature or biological inspired) are discussed in context with image segmentation application and their algorithms.

关键词： image segmentation

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