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Flexible Hybrid Electronics for Printing of Nano and Chip Scale Structures 1

Flexible Hybrid Electronics for Printing of Nano and Chip Sc...

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Singh, Neelima Gupta, Radha Singh, Rakesh Kumar Shruthi, T.S. Gupta, Parbhat Singhal, Abhinav Uttaranchal University Department of Computer Science and Engineering Dehradun India Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India IIMT College of Engineering U.P. Greater Noida India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad Modinagar India Tula's Institute Department of Computer Science and Engineering Dehradun248197 India

ISBN: (纸本)9798331518578

Adaptable sensors, circuits, and substrates are joined with unbending electronic parts to make adaptable flexible hybrid electronics (FHE). The printing of nanomaterials has benefits over the costly, multi-step, and blunder inclined cleanroom-based nano-microfabrication process, like expense adequacy, high-throughput, steadfastness, and adaptability. FHE usually consists of devices that range in size from nano to chip, and it might be difficult to integrate them using a standard procedure. The mix of ultrathin chips and nanowires (NWs) based electronic layers on a solitary substrate is shown here utilizing printed gadgets. The performance of the transistors on UTCs (ultrathin chips) is thoroughly studied in order to optimise the printing process. The adequacy of the printing system is affirmed by electrical information accumulated from 14 semiconductors on 3 unmistakable chips, which uncover next to no adjustment of execution following exchange printing. Building UV photodetectors utilizing DIW printing further exhibits the unrivaled grade nature of ZnO-NWs-put together electronic layers delivered with respect to a similar substrate. © 2025 IEEE.

关键词： Substrates

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Directed Acyclic Graph-Based Optimization of Apache Flink to Process Streaming Big Data of Industry 4.0

Directed Acyclic Graph-Based Optimization of Apache Flink to...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Samadder, Milton Barman, Anup Kumar Munshi, Shiladitya Madhu, Utpal Central Institute of Technology Department of Computer Science & Engineering Assam Kokrajhar783370 India Techno India University Department of Computer Science & Engineering Tripura 799022 India Swami Vivekananda Institute of Science & Technology Department of Mechanical Engineering Kolkata700145 India

ISBN: (纸本)9798350378047

This paper presents a new approach to improve the management of workflow for DAG processing in Apache Flink for further optimization of stream big data. The proposed optimization techniques focus on enhancing the performance metrics of Average Task Execution Time, CPU & Memory Utilization, and System Throughput. Reported experiments demonstrate that the optimized Apache Flink significantly outperforms the baseline version, making it a promising solution for real-time big data analytics. © 2024 IEEE.

关键词： Data streams

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Comparative analysis of twelve transfer learning models for the prediction and crack detection in concrete dams,based on borehole images

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Frontiers of Structural and Civil engineering 2024年第10期18卷 1507-1523页

作者： Umer Sadiq KHAN Muhammad ISHFAQUE Saif Ur Rehman KHAN Fang Xu Lerui CHEN Yi LEI School of Computer and Information Science Hubei Engineering UniversityXiaogan 432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan 432000China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China School of Computer Science and Engineering Central South UniversityChangsha 410083China College of Aviation Zhongyuan University of TechnologyZhengzhou 451191China School of Civil Engineering Central South UniversityChangsha 410083China

Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and *** research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack *** deep-learning models are trained on 192 crack *** research aims to provide up-to-date detecting techniques to solve dam crack *** finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%*** study’s pre-trained designs help to identify and to determine the specific locations of cracks.

关键词： concrete dam borehole closed-circuit television deep learning models crack detection water resources management management

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Indian fake currency detection using image processing and machine learning

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International Journal of Information technology (Singapore) 2024年第8期16卷 4953-4966页

作者： Bandu, Sai Charan Deep Kakileti, Murari Jannu Soloman, Shyam Sunder Baydeti, Nagaraju Department of Computer Science and Engineering National Institute of Technology Nagaland Chumukedima Nagaland Dimapur 797103 India

The escalating production of counterfeit notes, facilitated by advancements in color printing and scanning, poses a significant global challenge impacting economies and security. This issue, prevalent in countries like India, has negative ramifications, including the funding of illegal activities and terrorism. Despite efforts, such as demonetization in 2016, counterfeits persist, necessitating innovative solutions. The proposed model introduces a fake note detection system utilizing computer vision and machine learning, specifically a Convolutional Neural Network (CNN). CNN effectively extracts intricate features from input data, showcasing its proficiency in pattern recognition. Notably, the system focuses on individual security features within banknotes, distinguishing it from other approaches that analyze entire note images. The primary goal is swift and accurate detection and reduction of counterfeit circulation, contributing to the overall security of the economy. The proposed model resulted in an impressive accuracy of 91.66% for all the six security features in the Indian denomination of Rs. 500, 95.25% for all the six security features in the Indian denomination of Rs. 200, 92.66% for all the six security features in the Indian denomination of Rs.100. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Convolutional neural network Counterfeit notes Image processing Machine learning Security features extraction

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Artificial gannet optimization enabled deep convolutional neural network for autism spectrum disorders classification using MRI image

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Multimedia Tools and Applications 2024年第30期83卷 74757-74783页

作者： Prasad, Vadamodula Ganeshan, R. Rajeswari, R. Department of Computer Science & amp Engineering Lendi Institute of Engineering & ampTechnology Jonnada Village Andhra Pradesh Vizianagaram District 535005 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore466114 India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

Autism Spectrum Disorder (ASD) is a neurodevelopment-based disability caused by variations in the brain. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. This paper aims at ASD classification using MRI Images by Artificial Gannet Optimization enabled Deep Convolutional Neural Network (AGO_DCNN). Here, an MRI image is assumed that be subjected to pre-processing of the image and feature extraction phases as an input. The bilateral filter is employed to remove the noises from the input MRI image and in the pre-processing of an image stage Region of Interest (ROI) extraction is conducted. The pivotal region extraction phase receives a filtered image after which AGO is used to extract the pivotal region. AGO is a newly designed approach by merging two optimizations namely Artificial Ecosystem-based Optimization (AEO) and Gannet Optimization Algorithm (GOA). At the same time, features are extracted from the input image during the feature extraction stage. In a feature extraction stage, some features like texture and statistical features are extracted. At last, the classification of ASD is conducted using DCNN, wherein by AGO the classifier is tuned. Furthermore, AGO_DCNN attained better outcomes in terms of maximal True Positive Rate (TPR), True Negative Rate (TNR), and accuracy values of about 93.7%, 90.8%, 96.8% and False Positive Rate (FPR) and minimal False Negative Rate (FNR) and values of 78.7% and 74.7%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Comparative Evaluation of Metaheuristic Algorithms for Hyperparameter Selection in Short-Term Weather Forecasting 15

Comparative Evaluation of Metaheuristic Algorithms for Hyper...

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15th International Joint Conference on Computational Intelligence, IJCCI 2023

作者： Sen, Anuvab Mazumder, Arul Rhik Dutta, Dibyarup Sen, Udayon Syam, Pathikrit Dhar, Sandipan Electronics and Telecommunication Indian Institute of Engineering Science and Technology Shibpur Howrah India School of Computer Science Carnegie Mellon University PittsburghPA United States Information Technology Indian Institute of Engineering Science and Technology Shibpur Howrah India Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur Howrah India Computer Science and Engineering National Institute of Technology West Bengal Durgapur India

ISBN: (纸本)9789897586743

Weather forecasting plays a vital role in numerous sectors, but accurately capturing the complex dynamics of weather systems remains a challenge for traditional statistical models. Apart from Auto Regressive time forecasting models like ARIMA, deep learning techniques (Vanilla ANNs, LSTM and GRU networks) have shown promise in improving forecasting accuracy by capturing temporal dependencies. This paper explores the application of metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO) to automate the search for optimal hyperparameters in these model architectures. Metaheuristic algorithms excel in global optimization, offering robustness, versatility, and scalability in handling non-linear problems. We present a comparative analysis of different model architectures integrated with metaheuristic optimization, evaluating their performance in weather forecasting based on metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The results demonstrate the potential of metaheuristic algorithms in enhancing weather forecasting accuracy & helps in determining the optimal set of hyper-parameters for each model. The paper underscores the importance of harnessing advanced optimization techniques to select the most suitable metaheuristic algorithm for the given weather forecasting task. © 2023 by SCITEPRESS – science and technology Publications, Lda.

关键词： Weather forecasting

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Experimental investigation on banana stem fibre in the production of M40 grade concrete: A review

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Materials Today: Proceedings 2022年 68卷 2520-2524页

作者： Prakash Chandar, S. Murugan, D. Department of Civil Engineering SRM Institute of Science and Technology KTR Campus 603203 TN India

This Review paper investigate the natural fiber in M40 grade concrete. Introducing the banana stem fiber in M40 grade concrete to enhancing the mechanical properties and durability behaviour of concrete. The different aspect ratio had been studied by many researchers with using concrete specimens with range from 30 mm to 150 mm based on the nature of the material. For this banana fiber comparing the various aspect ratio about 50 mm, 70 mm and 90 mm. The optimum results in compressive strength, flexural strength and tensile strength achieving greater than the other conventional concrete. Mainly, the banana stem fiber concrete attains the maximum value of flexural strength due to elastic nature of the fiber in it. The experimental results shows that the banana fiber concrete is suitable material for construction and sustainable material in construction field. © 2022

关键词： Aspect ratio

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A Comprehensive Review of Recent Artificial Intelligence Techniques and IOT Applications in Dairy Farms 10

A Comprehensive Review of Recent Artificial Intelligence Tec...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Anusree, K. Rajeswari, M. Priyadharshini, C. Karunya Institute of Technology and Science Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sahrdaya College of Engineering and Technology Kerala Kodakara India

ISBN: (纸本)9798350384369

Each and every aspect of daily life has undergone a change thanks to the Internet of Things technology that renders everything intelligent. The wide range of IoT applications has captivated numerous researchers, and IoT enabled dairy farming is being leveraged to undertake innovative studies integrating IoT and Artificial Intelligence (AI) technology. More prospects for intelligent dairy farming are being created by the IoT and data-driven methods. Since the global population is increasing, there is a constant appetite for milk. Developed countries use a greater amount of dairy products than developing nations do. Enhanced technological approaches are required to elevate milk production and meet the escalating demand for dairy products. It is anticipated that a farmer would be able to significantly increase milk output and conquer several traditional farming obstacles with the help of IoT and various AI solutions. This study discusses various difficulties that dairy farmers deal with on every day of their lives. The primary objective of this paper is to undertake a comprehensive review on various Io'T, machine learning(ML) and deep learning(DL) technologies used in the field of smart dairy farming. It also looks into the difficulties and issues that traditional methods encounter as well as their results. Furthermore, the development of an IoT -enabled automated system for dairy farming may benefit further from this evaluation of research. © 2024 IEEE.

关键词： Smart agriculture

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Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

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computers, Materials & Continua 2023年第5期75卷 2957-2979页

作者： Zibo Wang Chaobin Huo Yaofang Zhang Shengtao Cheng Yilu Chen Xiaojie Wei Chao Li Bailing Wang School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China National Computer System Engineering Research Institute of China Beijing100083China School of Cyber Science and Technology Harbin Institute of TechnologyHarbin150001China Weihai Cyberguard Technologies Co.Ltd. Weihai264209China Faculty of Science Vrije Universiteit AmsterdamAmsterdam1081HVNetherlands

With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecast potential ***,it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis *** address these challenges,we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph(KG)in which relation paths contain abundant potential evidence to support the *** reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical *** proposed method consists of three primary building blocks:KG construction,relation path representation,and query relation reasoning.A security-oriented ontology combines exploit modeling,which provides a guideline for the integration of the scattered knowledge while constructing the *** emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate *** order to acquire a high-quality representation,the entity and relation embeddings take advantage of their local structure and related *** critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation *** particular,similarity calculation is introduced into a critical path selection algorithm,which improves search and reasoning ***,the proposed algorithm avoids redundant paths between the given pairs of *** results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.

关键词： Path-based reasoning representation learning attention mechanism vulnerability knowledge graph industrial control component

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AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation 39

AIF-SFDA: Autonomous Information Filter-driven Source-Free D...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Haojin Li, Heng Chen, Jianyu Zhong, Rihan Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Adaptive Systems Southern University of Science and Technology China Department of Computer Science and Engineering Southern University of Science and Technology China Beijing Information Science & Technology University China Institute of High Performance Computing Agency for Science Technology and Research China

ISBN: (纸本)157735897X

Decoupling domain-variant information (DVI) from domain-invariant information (DII) serves as a prominent strategy for mitigating domain shifts in the practical implementation of deep learning algorithms. However, in medical settings, concerns surrounding data collection and privacy often restrict access to both training and test data, hindering the empirical decoupling of information by existing methods. To tackle this issue, we propose an Autonomous Information Filter-driven Source-free Domain Adaptation (AIF-SFDA) algorithm, which leverages a frequency-based learnable information filter to autonomously decouple DVI and DII. Information Bottleneck (IB) and Self-supervision (SS) are incorporated to optimize the learnable frequency filter. The IB governs the information flow within the filter to diminish redundant DVI, while SS preserves DII in alignment with the specific task and image modality. Thus, the autonomous information filter can overcome domain shifts relying solely on target data. A series of experiments covering various medical image modalities and segmentation tasks were conducted to demonstrate the benefits of AIF-SFDA through comparisons with leading algorithms and ablation studies. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image segmentation

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