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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Hema V S. Gowri Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that impacts social communication, behavior, and cognitive functions. Early detection of autism is crucial for timely intervention, which can significantly improve developmental outcomes for individuals. This paper presents a novel methodology for detecting Autism Spectrum Disorder (ASD) using a Cascaded Convolutional Neural Network (CCNN) model. The proposed CCNN model processes neuroimaging data to detect patterns indicative of autism. The dataset used comprises 6,214 samples from the Autism Brain Imaging Data Exchange (ABIDE). Key preprocessing steps include resizing images to 224x224 pixels, noise reduction using a Median Filter, image cropping to focus on the region of interest (ROI), and contrast enhancement using histogram equalization. The CCNN architecture involves multiple convolutional layers, followed by max-pooling and fully connected layers, culminating in a Softmax layer for classification. The model is trained using the Adam optimizer with binary cross-entropy loss, achieving an accuracy of 93.19% on the test set, outperforming traditional models such as SVM, Random Forest, and Logistic Regression.

关键词： Neuroimaging Support vector machines Autism Logistic regression Histograms Noise reduction Brain modeling Data models Convolutional neural networks Random forests

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Large-Scale Network Adaptive Situation Awareness Method in Spatio-Temporal Dimension 1

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Zhang, Hongbin Xu, Ying Liu, Bin Zhao, Dongmei Bai, Yikang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang China

ISBN: (数字)9789819713325

ISBN: (纸本)9789819713318

In large-scale networks, the state space is exploding and changing dynamically. This leads to difficulties in collecting and analyzing situational awareness data, so we construct an adaptive situational awareness model in spatio-temporal dimensions. In the spatial dimension, vulnerabilities’s threats are assessed through attack graphs combined with Shapley values. At the same time, vulnerability threats are dynamically quantified by updating the status node reachability probability in real time. In the temporal dimension, a game model is established by analyzing vulnerability attack graph nodes to dynamically adjust the observation frequency of high-risk vulnerabilities, focusing on the safety status characteristics of high-risk assets. Experimental results show that our proposal integrates the security features of both space and time dimensions. This method can better focus on high-risk vulnerabilities and accurately reflect the dynamic changes in the network security situation, ensuring timeliness and accuracy in network security detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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Supply Chain Management using Soft computing: A Review 2

Supply Chain Management using Soft Computing: A Review

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2nd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2022

作者： Rajbala Nain, Pawan Kumar Singh Kumar, Avadhesh Galgotias University Department of Mechanical Engineering Greater Noida India School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781665437899

Participating members in a manufacturing supply chain usually use an information system like enterprise resources planning (ERP) for planning and scheduling activities independently. Recent research indicates that there is a need to handle such distributed activities in an integrated manner, especially under uncertain and fast changing environments. A multi agent system, a branch of distributed artificial intelligence, is a contemporary modeling technique for a distributed system in the manufacturing domain. This distributed modeling technique is suitable for integrating supply chain networks, which have distributed entities within the system. Each system makes decision locally, i.e., a local information system. By adopting a multi agent modeling technique, supply chain networks can be built efficiently based on an information system. This paper compile empirical findings on using soft computing technology applications insupply chain management to improve the supply chain efficiency. © 2022 IEEE.

关键词： Decision making

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What does functional connectivity tell us about the behaviorally-functional connectivity of a multifunctional neural circuit?

What does functional connectivity tell us about the behavior...

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2022 Conference on Artificial Life, ALIFE 2022

作者： Izquierdo, Eduardo J. Candadai, Madhavun Cognitive Science Program Indiana University Luddy School of Informatics Computing and Engineering Indiana University United States

What insights can statistical analysis of the time series recordings of neurons and brain regions during behavior give about the neural basis of behavior? With the increasing amount of whole-brain imaging data becoming available, the importance of addressing this unanswered theoretical challenge has become increasingly urgent. We propose a computational neuroethology approach to begin to address this challenge. We evolve dynamical recurrent neural networks to be capable of performing multiple tasks. We then analyze the neural activity using popular network neuroscience tools, specifically functional connectivity using Pearson’s correlation, mutual information, and transfer entropy. We compare the results from these tools against a series of informational lesions, as a way to reveal their degree of approximation to the ground-truth. Our initial analysis reveals an overwhelming large gap between the insights gained from statistical inference of the functionality of the circuits based on neural activity and the actual functionality of the circuits as revealed by mechanistic interventions. © Artificial Life Conference Proceedings 2022.

关键词： Recurrent neural networks

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A Novel Method for Rapidly Solving Wideband RCS by Combining UCBFM and Compressive Sensing

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Progress In Electromagnetics Research C 2022年 124卷 33-42页

作者： Wang, Zhonggen Li, Chenwei Sun, Yufa Nie, Wenyan Wang, Pan Lin, Han School of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China Key Lab of Intelligent Computing & Signal Processing Anhui University Hefei230039 China School of Mechanical and Electrical Engineering Huainan Normal University Huainan232001 China

While analyzing wideband electromagnetic scattering problems using ultra-wideband characteristic basis function method (UCBFM), the reconstruction of a reduced matrix and the recalculation of an impedance matrix at each frequency point cost a large amount of time. To overcome this issue, a novel method that combines UCBFM with compressive sensing (CS) is proposed in this paper to rapidly analyse the wideband RCS. The proposed method makes the ultra-wideband characteristic basis functions (UCBFs) generated at the highest frequency as the sparse basis, introduces the CS theory, randomly extracts several rows from the original matrix as the measurement matrix, utilizes the corresponding excitation vector as the measurement value, and then employs the recovery algorithm, through which the solution of target induced current can be obtained. Due to partial filling of impedance matrix and efficient recovery algorithm, the wideband RCS computation time of the object is significantly reduced using the proposed method. Furthermore, the numerical simulation results show that the computation efficiency for the target wideband RCS can be further enhanced compared with that of the stand-alone UCBFM. © 2022, Electromagnetics Academy. All rights reserved.

关键词： Computational efficiency

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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Smart Environmental Monitoring System for Air and Water Quality Index

Smart Environmental Monitoring System for Air and Water Qual...

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IEEE Students' Conference on Electrical, Electronics and Computer science (SCEECS)

作者： Ansh Tripathi Saransh Dubry P. Rama V. Angayarkanni J. Kalaivani Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Department of Computing Technologies Faculty of Engineering and Technology SRMIST Kattankulathur

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The quality of air and water is essential for human health, environmental sustainability, and ecosystem balance. However, pollution from industrial activities, urbanization, and agricultural practices has made effective environmental monitoring a global priority. Traditional monitoring methods, which rely on manual sampling and laboratory analysis, are often time-consuming and lack real-time capabilities. This paper presents a Smart Environmental Monitoring (SEM) system leveraging IoT and sensor technology to monitor air and water quality in real time using two key sensors: the MQ135 for Air Quality Index (AQI) and the pH sensor for Water Quality Index (WQI). By focusing on these two critical parameters, the SEM system offers a streamlined yet robust solution for assessing environmental quality. The system collects data continuously, transmits it to a cloud platform for processing, and employs machine learning algorithms to forecast future pollution trends. A user-friendly dashboard displays real-time AQI and WQI levels, historical trends, and predictive insights, empowering users to respond proactively to environmental risks. Deployment results demonstrate the SEM system’s capability in detecting pollution patterns, identifying high-risk areas, and predicting pollution spikes during peak traffic and in industrial zones. Comparative analysis with traditional monitoring highlights the SEM system’s superior accuracy, scalability, and predictive abilities. Although the SEM system has higher initial setup costs, its operational efficiency and long-term benefits in mitigating environmental risks make it a sustainable choice for public health and environmental management.

关键词： Environmentally friendly manufacturing techniques Scalability Water quality Machine learning Market research Real-time systems Water pollution Indexes Environmental monitoring Public healthcare

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An Efficient Novel Approach for Iris Recognition and Segmentation Based on the Utilization of Deep Learning

An Efficient Novel Approach for Iris Recognition and Segment...

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2023 International Conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Pasha, Syed Nawaz Anitha, D. Anbunathan, R. Gobinath, C. Madhura, G.K. Sr University School of Computer Science & Artificial Intelligence Warangal India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sa Engineering College Department of Mathematics Chennai India Reva University School of Computing and Information Technology Bangalore India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Iris recognition stands out as one of the most stable and accurate biometric identification systems such as facial, voice, fingerprint, handwriting, and iris recognition itself. Deep Learning (DL) plays a pivotal role in enhancing security by leveraging Convolutional Neural Networks (CNNs) for iris recognition. The acquisition of iris images and real-time processing have become focal points demanding heightened attention in the realm of DL. Iris recognition is prized for its high level of protection, making it challenging to reconstruct iris data easily from the database. The CASIA-Iris Dataset serves as a benchmark to verify the uniqueness of iris patterns and evaluate the performance of iris recognition. However, due to limitations in iris image access, the application of CNNs in the segmentation approach remains critical for iris localization and pupil boundary identification, ensuring both efficiency and specificity in iris images. The results obtained from this study reveal that the proposed CNN, utilizing DL models, achieves an impressive accuracy of 99.89% on the CASIA-Iris dataset, better performance of other existing models, such as U-Net and VGG-Net, and ensuring accurate classification. © 2023 IEEE.

关键词： Contrast-Limited Adaptive Histogram Equalization Convolution Neural Network Deep Learning Iris Recognition and Support Vector Machine

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An Analysis of Different YOLO Models for Real-Time Object Detection

An Analysis of Different YOLO Models for Real-Time Object De...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Sawari Jamgaonkar Jay Shyam Gowda Siddharth Singh Chouhan Rajneesh Kumar Patel Ankur Pandey School of Computing Science and Engineering VIT Bhopal University Kothrikalan Sehore Madhya Pradesh India Department of Computing Science and Engineering Manipal University Jaipur Jaipur Rajasthan India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Object detection in real-life applications plays a significant role, with advancements in technology enabling enhanced image classification and processing, reducing manual effort and increasing accuracy and speed. The proposed work involves a comparative analysis and hyper-parameterization of YOLO (You Only Look Once) modules, specifically YOLOv7, YOLOv8, and YOLOv9 models. These models utilise Convolutional Neural Networks (CNNs) to process image layers by dividing images into boxes and then segmenting them into pixels for identification and classification based on various parameters. Initially applied to the COCO dataset, these models were further tested on customised datasets. Among the three, YOLOv9 demonstrates superior performance, achieving a mean Average Precision (mAP) of 52 at 0.5:0.95, compared to YOLOv7 and YOLOv8, which achieved mAPs of 35.2 and 39, respectively. Overall, YOLOv9 significantly reduces parameter usage and improves accuracy.

关键词： YOLO Analytical models Image segmentation Accuracy Manuals Real-time systems Convolutional neural networks Expert systems Tuning Image classification

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Introducing on-chain graph data to consortium blockchain for commercial transactions

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Frontiers of Computer science 2024年第2期18卷 255-257页

作者： Pengyi ZHANG Yuchen YUAN Jie SONG Yu GU Qiang QU Yongjie BAI Software College Northeastern UniversityShenyang 110819China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Blockchain Lab Huawei Cloud Computing Technologies Co.Ltd.Shenzhen 518101China

1 Introduction A blockchain can be referred to as a distributed ledger[1].Nowadays,according to our previous work[2],the graphrelated blockchain grows efficiently in both academic studies and commercial *** data,algor... 详细信息

关键词： transactions ledge commercial

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