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作者： Hayashi, Shinpei Yokoyama, Tetsuo Nagura, Masataka Igaki, Hiroshi School of Computing Tokyo Institute of Technology Japan Faculty of Science and Technology Nanzan University Japan Faculty of Information Science and Technology Osaka Institute of Technology Japan

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Flexible TiO_(2)-WO_(3−x) hybrid memristorwith enhanced linearity and synaptic plasticity for precise weight tuning in neuromorphic computing

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npj Flexible Electronics 2024年第1期8卷 180-191页

作者： Jianyong Pan Hao Kan Zhaorui Liu Song Gao Enxiu Wu Yang Li Chunwei Zhang Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinanChina State Key Lab of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghaiChina School of Integrated Circuits Shandong UniversityJinanChina

Tungsten oxide(WO_(3))-based memristors show promising applications in neuromorphic ***,single-layer WO_(3) memristors suffer from issues such as weak memory performance and nonlinear conductance *** this work,a functional layer based on the hybrids of WO_(3−x) and TiO_(2) is proposed for constructing flexible memristors featuring outstanding synaptic *** diverse electrical stimulations to the memristor enables a range of synaptic functions,elucidating its conduction mechanism through the conductive filament *** incorporation of TiO_(2) not only enhances the memristor’s memory characteristics but makes its conductance more linear,symmetrical and uniform during the long-term ***,in view of the enhanced device performance by TiO_(2) doping,the potential of this device for simple behavioral simulation and processing of complex computing problems is ***“learning-forgetting-relearning”characteristics and device integrability are visually *** the device to a convolutional neural network,the recognition accuracy of MNIST handwritten digits reaches 98.7%.

关键词： computing tuning precise

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A Design Thinking Approach to the Development of Digitalized Hotel Valet Services

A Design Thinking Approach to the Development of Digitalized...

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2025 International Conference on Advancement in Data science, E-learning and information System, ICADEIS 2025

作者： Makky, Muhammad Al Raharjo, Tito Pandu Rahman, Sheina Fathur Yasirandi, Rahmat School of Computing Telkom University Bandung Indonesia School of Applied Science Telkom University Bandung Indonesia School of Information Technology King's Mongkut Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9798331513320

Technological advancements in Indonesia have brought numerous benefits, including to hospitality industries like valet parking. However, traditional valet services still rely on manual processes, leading to inefficiencies such as long wait times, miscommunication, and security concerns. This study focuses on developing a digital valet system using the Design Thinking Approach to address these challenges. The proposed prototype demonstrates strong acceptance, addressing user expectations and improving overall service efficiency. During the testing phase, the System Usability Scale (SUS) was employed with 50 participants who had experience in valet services, resulting in a score of 77.7. This score places the prototype in the 'Good' grade and 'Acceptable' category, indicating that the proposed system successfully addresses the functionality and requirements of its intended users. By modernizing valet operations, this study highlights the potential for broader adoption of digital solutions in the hospitality sector, ultimately improving guest experiences and operational effectiveness. © 2025 IEEE.

关键词： Efficiency

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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

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Computer Systems science & Engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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Artificial Intelligence based System to Prevent Animal Accidents in the Railway Tracks 8

Artificial Intelligence based System to Prevent Animal Accid...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Kannan, Balaji Lakshmi, R.Bagavathi Sakthivanitha, M. Maruthi, R. School of Computing Science Department of Information Technology Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350386578

The wildlife population has been in danger due to frequent animal accidents on railway tracks. In India, animal train collisions cause an average of 20 elephant deaths every year all over the nation. At the same time railway departments are taking necessary steps to identify the cause and implement prevention methods. Despite this, lots of accidents are happening in the railway tracks near the forest areas. This problem has been addressed by proposing a well-defined technological mechanism to alert the animals to be away from the railway tracks to reduce the wildlife deaths. In this study, Machine Learning(ML) frameworks are employed to detect the animals that are coming near the railway tracks and the developed module will provide appropriate warning or signals to the animals as well as to the loco pilot. © 2024 IEEE.

关键词： Invertebrates

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Multimodal attention-driven visual question answering for Malayalam

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Neural computing and Applications 2024年第24期36卷 14691-14708页

作者： Kovath, Abhishek Gopinath Nayyar, Anand Sikha, O.K. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

Visual question answering is a challenging task that necessitates for sophisticated reasoning over the visual elements to provide an accurate answer to a question. Majority of the state-of-the-art VQA models are only applicable to English questions. However, applications such as visual assistance and tourism necessitate the incorporation of multilingual VQA systems. This paper presents an effective deep learning framework for Malayalam visual question answering (MVQA), which can answer a specific natural language question about an image in Malayalam. As there is no available dataset in English–Malayalam VQA, a MVQA dataset was created by translating English question–answer pairs from the visual genome dataset. The paper proposes an attention-driven MVQA model on the developed dataset. The proposed MVQA model uses a deep learning-based co-attention mechanism to jointly learn the attention for images and Malayalam questions. A second-order multimodal factorized high-order pooling is used for multi modal feature fusion. Different VQA models using combinations of classical CNNs and RNNs were experimented on the developed MVQA dataset, and the performance was compared against the proposed attention-driven model. Experimental results show that the proposed attention-driven MVQA model achieves state-of-the-art results as compared to other models for MVQA on the custom Malayalam VQA dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep learning

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Prediction of Cyber Attacks Utilizing Deep Learning Model using Network/Web Traffic Data 3

Prediction of Cyber Attacks Utilizing Deep Learning Model us...

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3rd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Kannan, Balaji Sakthivanitha, M. Jayashree, S. Maruthi, R. School of Computing Science Department of Information Technology Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350375190

Nowadays, cyber-attacks are growing predominantly due to the development of technologies. It will lead to financial losses to a company and the other problems related to attacks. It is very important to predict such attacks from outsiders to safeguard our networking systems to provide effective security. The Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) techniques leverage enormous amounts of data to identify the cyber attacks. These learning approaches are used to identify a broad range of cyber-attacks by analyzing the web traffic or network traffic to identify potential threats such as malware, network intrusions and other types of attacks. The study demonstrates the various deep learning methods to predict the anomalies and other potential threats with more accuracy in real time. © 2024 IEEE.

关键词： Forecasting

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Multi-Stage Confidence-Guided Diffusion and Emotional Bidirectional Mamba for Robust Speech Emotion Recognition

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IEEE Signal Processing Letters 2025年 32卷 2184-2188页

作者： Liu, Yang Chen, Xin Li, Yongwei Wang, Longbiao Zhao, Zhen Qingdao University of Science and Technology School of Information Science and Technology Qingdao266061 China Chinese Academy of Sciences CAS Key Laboratory of Behavioral Science Institute of Psychology Beijing100089 China Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin300072 China

Speech Emotion Recognition (SER) in noisy environments is challenging due to the overlap between emotional and noise-related signals. We propose a novel emotion-diffusion approach to enhance SER performance in noisy conditions that transfers emotional information from clean to noisy speech through a multi-stage diffusion process. First, Mel Frequency Cepstral Coefficients and their delta features are extracted to capture emotional dynamics. Additionally, an Emotional Bidirectional Mamba Encoder with a Multi-time View Bidirectional State Space Model is designed to capture temporal emotional patterns. Next, the Emotion-Transferring Diffusion Network (ETDN) applies confidence filtering to retain key emotional features, ensuring effective emotion transfer despite noise. Finally, the Confidence-Guided Mutual Learning strategy refines noisy features for the Classification Network (CN), while the CN supervises the ETDN to maintain label consistency. Experiments on the IEMOCAP dataset show significant improvements in weighted accuracy, unweighted accuracy, and weighted F1 score across various signal-to-noise ratios. © 1994-2012 IEEE.

关键词： Speech analysis

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Invasive ductal carcinoma (IDC) detection in breast histopathology images using enhanced transfer learning of convolutional neural networks

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International Journal of information technology (Singapore) 2025年 1-13页

作者： Mahbub, Hafsa Binte Olowolayemo, Akeem Omari, Swaleh Maulid Adeleke, Imran Ademola Department of Computer Science KICT International Islamic University Malaysia Kuala Lumpur Malaysia School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Department of Computer Science Education College of Information and Technology Education Lagos State University of Education Lagos Nigeria

This study introduces a deep learning methodology for the automated identification of tissue regions indicative of Invasive Ductal Carcinoma (IDC) within Whole Slide Images (WSI) associated with breast cancer. Deep learning demonstrates notable efficacy in such applications, particularly when an ample number of samples are available for training purposes. The proposed framework extends across various convolutional neural networks (CNNs). The method underwent evaluation using a WSI dataset encompassing specimens from 162 patients diagnosed with IDC. The experimental outcomes indicate commendable accuracies of 89%, 88%, 84%, and 82%, for CNN, EfficientNet, EfficientNetV2, and VGG16, respectively. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Computer aided diagnosis Convolutional neural networks (CNNs) Deep learning(DL) Efficientnet Efficientnetv2 Invasive ductal carcinoma (IDC) Transfer learning VGG16

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