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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on Engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

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Thermal-Aware Communication

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IEEE Transactions on Information Theory 2025年第6期71卷 4145-4155页

作者： Chee, Yeow Meng Etzion, Tuvi Immink, Kees A. Schouhamer Nguyen, Tuan Thanh Vu, Van Khu Weber, Jos H. Yaakobi, Eitan Singapore University of Technology and Design Singapore Technion — Israel Institute of Technology Department of Computer Science Haifa3200003 Israel National University of Singapore Department of Industrial Systems Engineering and Management Singapore Turing Machines Inc Rotterdam3016 DK Netherlands Singapore University of Technology and Design Science Mathematics and Technology Cluster 487372 Singapore Delft University of Technology Department of Applied Mathematics Delft2628 CD Netherlands

Temperature control is of utmost importance in transmission systems. In this paper, a binary channel model is considered in which the transmission of a one causes a temperature increase while communicating a zero causes a temperature drop. By putting constraints on the input sequences, it is guaranteed that the channel temperature will not exceed a certain pre-determined maximum. In the asymptotic regime, the capacity of such a channel is studied. For the non-asymptotic regime, fixed-length codes are presented, with the property that codewords can be freely cascaded without violating the temperature constraint. Optimization of the code size is investigated and codewords are enumerated using generating functions. © 1963-2012 IEEE.

关键词： Channel capacity

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Diagnosis of Breast Cancer Using Vision Transformers (ViT)

Diagnosis of Breast Cancer Using Vision Transformers (ViT)

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Ravikumar J Prithvi Prabhu Pani V Tejushree R Sunnampalli Varshitha Department of Computer Science and Engineering Atria Institute of Technology Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Breast cancer is one of the leading causes of death in women. Regular testing is of paramount importance since early detection ensures more treatment options and better prognosis rate. Biopsies, x-rays and ultrasounds are some of the diagnostic tools used to detect breast cancer. Out of these, ultrasounds being portable and ionizing radiation free provide the most feasible and harmless solution in areas which cannot accommodate expensive equipment that require rigorous safety standards to be met. Our detection system utilizes Vision Transformer (ViT) technology, i.e., transfer learning to classify tumors of the breast into malignant, benign and normal categories. Data enhancement has been done using Contrast Limited Adaptive Histogram Equalization (CLAHE). Furthermore, we have categorized them based on BI-RADS criteria for greater accuracy and precision scores. We have chosen ViT over the more commonly used Convolution Neural Networks (CNN) since they offer a more flexible approach, capturing global relationships in images, and you can use it for larger datasets.

关键词： computer vision Ultrasonic imaging Accuracy Transfer learning Imaging X-rays Transformers Breast cancer Convolutional neural networks Tumors

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Emotion Classification in Text Using Machine Learning and LSTM

Emotion Classification in Text Using Machine Learning and LS...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： B P Pradeep Kumar Ravi Kumar J Arpita Biswas Bi Bi Hafeeza D Dhaanya Department of Computer Science Design Atria Institute of Technology Bangalore Visvesvaraya Technological University Karnataka India Dept. of Computer Science Engineeering Atria Institute of Technology Bangalore Visvesvaraya Technological University Karnataka India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Emotion categorization is used in domains like customer service, social media content moderation, and interaction between humans and computers. Classification of emotion from text is a fundamental issue in NLP, or natural language processing. This study examines both advanced deep learning models (LSTM networks with embedding layers) and traditional machine learning techniques (Naive Bayes, Logistic Regression, SVM, Random Forest) for the classification of the six primary emotions: joy, sorrow, anger, fear, love, and surprise. The study uses a preprocessed emotion-tagged text dataset, and metrics including accuracy, precision, recall, and F1-score are utilized to evaluate the models. LSTMs perform better than conventional models, which find it difficult to grasp language subtleties and contextual correlations, by processing sequential data and contextual information with ease.

关键词： Deep learning Support vector machines Sentiment analysis Analytical models Social networking (online) Computational modeling Predictive models Data models Sustainable development Long short term memory

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High-accuracy face recognition with the MRYOLO-SCO algorithm: combining residual networks and YOLOv2 for improved precision

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Signal, Image and Video Processing 2025年第7期19卷 1-12页

作者： Ettiyappan, Anitha palani, Hari kumar Duraipandian, Kavitha Selvaganapathy, Nandhini Department of Information Technology Loyola - ICAM College of Engineering and Technology Nungambakkam Chennai600034 India Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Loni Kalbhor Pune India Department of computer science and engineering Sathyabama Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chengalpattu603203 India

Face recognition in real-world scenarios presents significant challenges due to variations in lighting conditions, occlusions, pose changes, and low-resolution images. To address these challenges, this study proposes a Modified Residual YOLO Network-based Single Candidate Optimization algorithm, which integrates a Modified Residual Network with YOLOv2 to improve recognition accuracy while maintaining computational efficiency. Unlike conventional YOLO models, the proposed framework introduces bipolar feature extraction, enabling the network to capture both high-level global features and low-level local details, thus improving robustness against occlusions and illumination variations. Atailored cost function is introduced to minimize bounding box localization errors and classification inaccuracies, incorporating weighted penalties to enhance model precision. Additionally, a Single Candidate Optimization algorithm is employed to fine-tune hyperparameters, ensuring faster convergence and lower computational complexity. The performance of the proposed technique is evaluated using the benchmark datasets and the simulation results that the proposed framework outperforms existing YOLO-based models, achieving an accuracy of 98.8%, a recall of 98.0%, and an F1-score of 98.1%while maintainingalower computational cost than YOLOv3, YOLOv4, and YOLOv5. The experimental results also indicate that the tailored cost function reduces misclassification errors by 6.1% and accelerates convergence by 20%, making the proposed framework highly suitable for real-time face recognition applications in resource-constrained environmentssuch asIoT-based surveillance and smart security systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Face recognition

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Prediction of Insurance Claims for Health Sector using Machine Learning Techniques 3

Prediction of Insurance Claims for Health Sector using Machi...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pragatheeswari, E. Kavitha, M.N. Saranya, S.S. Dhanushree, D. Jasmitha, S. Abiradhie, P.V. Department of Computer Technology Kongu Engineering College Perundurai Erode India Department of Computer Science and Design Kongu Engineering College Perundurai Erode India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Nowadays, health issues play a tremendous role in day-to-day life and the medical expenditure to get treatment becomes more difficult for the ordinary people. Health insurance has become a vital aspect of people's lives. In this massive community, to access healthcare services such as insurance policies, LIC, ICICI, HDFC ERGO, Star Healthcare are benefits for claiming an amount for their medical expenses. The dataset encompasses three categories, such as generalized data, hospitalized data and claim data. Each record in the dataset represents an individual’s health insurance charges along with corresponding demographic and health-related characteristics. In this paper, Machine Learning techniques are used to group the claim amount that has been adopted by the individuals based on the hospitalized expenditure and insurance charges paid by the individuals. In the health sector, machine learning techniques are used to forecast insurance claims by building predictive models from historical data on claims, patient demographics, treatment types, and results. By predicting future claims based on trends seen in the data, these models help insurance firms better manage risk and allocate resources. In case of a fresher to this concept, it feels challenging to understand whereas generating as a pattern can be more flexible to acquire knowledge about how much amount has been claimed. © 2025 IEEE.

关键词： Electronic health record

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Email Validator Dashboard: A Comprehensive Tool for Accurate Email Validation

Email Validator Dashboard: A Comprehensive Tool for Accurate...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Syed Riyyan Chand Uzair Ahmed T Mohammed Ismail B.P. Pradeep Kumar Syed Bilal Chand Chaitra P Dept. of Computer Science Design Atria Institute of Technology Bangalore India Dept. of Computer Science Engineering Atria Institute of Technology Bangalore India HostUp Cloud Technologies Private Limited Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Email authentication is of the utmost importance in maintaining the reliability and quality of email communication, specifically in database management and bulk email marketing. The centerpiece of the project is the development of an Email Validator Dashboard, which is a very user-friendly functionality filled with other useful parts that guarantee the accurate verification of email addresses. The dashboard really presents valuable inputs quickly in terms of deliverability, risk factors, and domain reliability due to its dynamic visualization along with real-time email validation procedures. The project's structure is such that the users get real-time data in the form of interactive charts and graphs, which make them see the transformations occurring as validation is going on, thus distinguishing it from the traditional tools. It comes with cutting-edge back-end data integrity validation processes, such as mailbox verification, domain DNS checks, and syntax verification, alongside powerful analytics. This project is really useful for organizations and developers who work with large email lists and at the same time can ensure high deliverability and compliance with data standards.

关键词： Accuracy Standards organizations Organizations Syntactics Real-time systems Electronic mail Reliability Servers Usability Sustainable development

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Blockchain-Enabled Secure Offloading for VEC: A Multi-Agent Reinforcement Learning Approach

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2978-2995页

作者： Lu, Xiaozhen Xiao, Liang Xiao, Yilin Xiong, Zehui Liu, Zhe Zhang, Yanyong Zhuang, Weihua Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Xiamen University Department of Information and Communication Engineering Xiamen361005 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Zhejiang Lab Hangzhou310027 China University of Science and Technology of China School of Computer Science and Technology Hefei230026 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Vehicular edge computing (VEC) helps improve the task computational performance of vehicles on roads but has difficulty in defending against eavesdropping and selfish attacks simultaneously. In this paper, we design a reputation-based smart contract with blockchain and propose a multi-agent reinforcement learning (RL) based secure offloading scheme for VEC against both eavesdropping and selfish attacks. This scheme has a three-level hierarchical structure for each vehicle and uses the reputations obtained from the blockchain as the basis to optimize the edge node selection, offloading ratio, and power allocation, which aims to reduce the task computational latency, the vehicle energy consumption and eavesdropping rate. By using a punishment function based on the constraints, this scheme avoids exploring dangerous policies that can cause task failure or severe data leakage. A multi-agent deep RL-based secure offloading scheme is proposed for vehicles with sufficient resources, which evaluates the long-term risk rather than the punishment function to further improve the secure offloading performance. The regret bound is analyzedand the cumulative reward upper bound is provided. Simulation results verify the effectiveness of our schemes as compared with the benchmark. © 2004-2012 IEEE.

关键词： Computation offloading

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