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检索条件"任意字段=10th International Conference on Computer Analysis of Images and Patterns"

共 4187 条记录，以下是31-40 订阅

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Spike Sorting and Event Detection in Neuromorphic Computing 10

Spike Sorting and Event Detection in Neuromorphic Computing

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10th international conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vaishnavi, Vakada G. Sai Sree Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Department of Computer Science and Engineering Mangalore Surathkal India

ISBN: (纸本)9798350384369

Neuromorphic computing is a cutting-edge field of research that focuses on designing and developing computer systems and hardware architectures inspired by the structure and functioning of the human brain. the main objective of neuromorphic computing is to create computational systems that imitate the parallel processing, learning, and energy efficiency observed in biological neural networks. Two essential tasks in the analysis of brain data from extracellular recordings are spike sorting and event detection. Spike sorting involves identifying and categorizing discrete neural spikes, while event detection focuses on recognizing specific patterns or occurrences within the spiking activity of neuromorphic networks. Typically, each neuron generates spikes with a characteristic shape, and these clusters correspond to the activity of different neurons. Here, the outcome of spike sorting is the identification of which spike corresponds to which neuron. In-depth analysis of the spike sorting and event detection which are two critical processes in interpreting the intricate neuronal activity that underpins brain function are provided. © 2024 IEEE.

关键词： Neurons

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Microarray Gene Expression analysis Using QR Decomposition, Machine Learning Algorithm, and Statistical Tool 10

Microarray Gene Expression Analysis Using QR Decomposition, ...

引用

10th international conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Ali, Mohsin Choudhary, Jitendra Medi-Caps University Department Of Computer Science Indore India

ISBN: (纸本)9798350384369

the Allen Brain Atlas offers maps of gene expression and neuronal connectivity across the brain to help in the study of the molecular and functional architecture of the nervous system. Researchers have identified genes, cells, and structures with the help of this platform to employ several techniques to facilitate the analysis of microarray expression patterns and gene visualization. the first phase utilizes principal component analysis (PCA), the QR decomposition algorithm, and a nonlinear dimensionality reduction technique called t-SNE to reduce the number of dimensions. After t-SNE, the silhouette score and ward linkage were used to identify gene labels and optimal cluster values. In the next phase, we utilized a supervised learning approach, which involved using data and labels to assess the quality of the gene (feature). Furthermore, to measure the effectiveness of these features, we used Logistic Regression, which gave us an accuracy of 93.77%. We also used Tukey's HSD test and ANOVA for a more detailed assessment, revealing noteworthy gene expression differences among clusters. this comprehensive approach provides deeper insight into the genetic and connectivity patterns of the brain, which contributes to the broader field of neuroscientific research. © 2024 IEEE.

关键词： Microarrays

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Artificial Intelligence-based Recognition and Classification of PRPD patterns in Power Cable 10

Artificial Intelligence-based Recognition and Classification...

引用

10th international conference on Condition Monitoring and Diagnosis, CMD 2024

作者： Kim, Jaesang Jeong, Minkyung Lee, Hobin Kim, Yechan Kang, Hyoungku Korea National University of Transportation Dept. of Electrical Engineering Chungju Korea Republic of

ISBN: (纸本)9788986510225

this paper explores the application of AI (Artificial Intelligence) to improve diagnostic accuracy in identifying and classifying PRPD (Phase-Resolved Partial Discharge) patterns in power cables, which is crucial for the maintenance and reliability of electrical infrastructure. Partial discharges, indicating defects like corona, surface discharges, and voids, pose significant challenges in power cable management. By simulating these discharges and capturing the resulting signals, this study transforms them into PRPD images, creating a comprehensive dataset for AI analysis. the focus of this research is on utilizing advanced machine learning techniques to process and analyze these images to classify the unique visual characteristics associated with each defect type. A notable achievement of this research is the development and training of a robust AI model that achieved an accuracy rate exceeding 93.8%, demonstrating the potential of AI to revolutionize power cable diagnostics. this approach offers a new path for predictive maintenance, allowing power utilities to prevent potential failures and enhance operational efficiency. Our methodology involves an in-depth analysis of PRPD images using CNN (Convolutional Neural Network) to extract and learn feature representations specific to each type of discharge. the results not only demonstrate the effectiveness of AI in diagnosing electrical equipment but also pave the way for future innovations in the field, potentially setting new industry standards for maintenance strategies and reliability assessments. © 2024 the Korean Institute of Electrical Engineers (KIEE).

关键词： Convolutional neural networks

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A Cryptographically Secure Image Steganography Scheme Based On Arduino Microcontrollers 10

A Cryptographically Secure Image Steganography Scheme Based ...

引用

10th international conference on Communication and Signal Processing, ICCSP 2024

作者： Khan, Aqib Dhavakumar, P. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

In today's digitally pervasive environment, safeguarding sensitive information concealed within images has become increasingly critical. Conventional steganographic techniques may prove inadequate against sophisticated detection algorithms and statistical analysis. To address this challenge, integrating a pseudo-random number generator with hardware based entropy sources into the channel selection process offers a dynamic and adaptive solution to enhance information hiding security in images. As machine learning techniques for steganography become more sophisticated, they often introduce significant computational overhead. these ML-based algorithms require extensive computational resources for training, inference, and analysis, leading to increased complexity in terms of both time and computational power. By capitalizing on the inherent randomness introduced by the watchdog timer's RC oscillator in an arduino embedded microcontroller, we can amplify the unpredictability of channel selection during embedding. this paper proposes a novel hybrid model integrating Hardware Random Number Generators and Pseudo-Random Number Generators to augment entropy in the channel selection process within steganography. An examination of the performance of this hybrid model is presented. © 2024 IEEE.

关键词： Microcontrollers

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Music Emotion Classification using Harris Hawk Optimization based LightGBM Classifier 10

Music Emotion Classification using Harris Hawk Optimization ...

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10th international conference on Biosignals, images and Instrumentation, ICBSII 2024

作者： Patil, Hemprasad Yashwant Bhagwan Patil, Priyanka Balasubramaniam, Anishkaa Shanmugasundaram, Lackesh Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350350951

this research investigates the relationship between music and human emotions, focusing on emotion classification in Turkish music and the role of acoustics in music emotion recognition (MER). Leveraging a unique dataset stored in the UCI-MLR, the study employs machine learning techniques, including Linear Discriminant analysis and Gradient Boost Classifier, to build a robust model. the accuracy enhancement is primarily driven by the feature selection process, which is powered by the Harris Hawks optimizer. this optimizer, when used with the LightGBM model, enables a more precise selection of features, thereby significantly improving the model's accuracy. the research achieves notable accuracies - 81.25% for Linear Discriminant analysis and 83.75% for the Gradient Boosting Classifier - surpassing previous records. the paper discusses findings in the context of affective computing, emphasizing potential applications in music recommendation systems and therapy, concludes with a comprehensive analysis, and outlines future possibilities. this research contributes to advancing MER, laying the groundwork for innovations in diverse domains, including the potential integration with IoT-based intelligent systems for real-time emotion analysis and personalized music experiences based on camera frames. © 2024 IEEE.

关键词： Emotion Recognition

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Dynamic Connectivity patterns in Resting State and Task-Based MEG: An Instantaneous Amplitude Correlation Approach 10

Dynamic Connectivity Patterns in Resting State and Task-Base...

引用

10th international conference on Biosignals, images and Instrumentation, ICBSII 2024

作者： Suhas, M.V. Mariyappa, N. Anitha, H. Sinha, Sanjib Ravindranadh Chowdary, M. Raghavendra, K. Asranna, Ajay Viswanathan, L.G. Manipal Institute of Technology Manipal Academy of Higher Education Department. of Ece Manipal India Nimhans Meg Research Center Department of Neurology Bengaluru India Nimhans Department of Neurology Bengaluru India

ISBN: (纸本)9798350350951

Understanding dynamic functional connectivity is pivotal in unraveling the intricate dynamics of neural activity. this research leverages Magnetoencephalography (MEG) and Instantaneous Amplitude Correlation (IAC) to explore the evolving patterns of dynamic functional connectivity in the human brain. the study meticulously compares IAC outcomes during resting state and task-based MEG, offering insights into the adaptability of brain connectivity. A comprehensive literature review contextualizes the study within existing research, highlighting the relevance of dynamic functional connectivity analysis. the MEG data preprocessing employs advanced techniques, including artifact reduction and source estimation. the IAC analysis, featuring tensor factorization and k-means clustering, reveals distinctive connectivity patterns in various frequency bands. Results demonstrate pronounced transitions between connectivity states, particularly in the beta frequency bands during resting state MEG. this comparative analysis enriches our understanding of neural dynamics and connectivity fluctuations, paving the way for potential clinical applications. the study underscores the need for broader validation through expanded datasets, emphasizing the implications for cognitive neuroscience and clinical practices. © 2024 IEEE.

关键词： Magnetoencephalography

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Recognition Pattern analysis of Astronomical Object Forms Using the Analytical Model 14

Recognition Pattern Analysis of Astronomical Object Forms Us...

引用

14th international conference on Advanced computer Information Technologies, ACIT 2024

作者： Khlamov, Sergii Savanevych, Vadym Troianskyi, Volodymyr Grebennik, Igor Bondar, Yehor Netrebin, Yuriy Kharkiv National University of Radio Electronics Department of Systems Engineering Kharkiv Ukraine Astronomical Institute of the Slovak Academy of Sciences Tatranska Lomnica Slovakia

ISBN: (纸本)9798350350036

Image processing and pattern recognition in astronomy represents a dynamic field of study where the analysis of astronomical object shapes plays a crucial role. this is associated with the necessity of identifying and classifying various patterns present in astronomical images. An important aspect of processing astronomical images is the recognition and analysis of patterns contained within these images. patterns can vary from circular shapes of stars and galaxies to elongated structures of gas clouds. Understanding these patterns not only helps us identify objects in space but also provides unique information about their structure, evolution, and physical characteristics. Another key aspect is the utilization of an analytical image model. this is a mathematical approach that allows modeling images of objects in space and analyzing their characteristics. the analytical model enables the detection of brightness peaks in images, determining typical object shapes, and conducting various analytical investigations. © 2024 IEEE.

关键词： Object detection

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Criminal Activity Forecasting using Machine Learning 10

Criminal Activity Forecasting using Machine Learning

引用

10th international conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Augustin Sam, N. Bamini, A. M. Anusha Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

Crime analysis is an important topic that uses cutting-edge machine learning. this study focuses on using machine learning algorithms to examine crime trends and give law enforcement organizations useful information. the ML models may find patterns and trends by using past crime data, which makes it possible to identify possible crime hotspots and allocate the necessary resources. To find hidden patterns in crime data, Random forest algorithm was applied. the algorithm has the ability to automatically group criminal episodes into clusters, categorize them according to various attributes, and spot anomalies-that is, deviations from the norm. Furthermore, by using previous data, machine learning algorithms can be trained to support proactive crime prevention tactics. the accuracy and efficacy of crime analysis models are further improved by the integration of many data sources, such as information. the proposed algorithm makes it easier to monitor crimes in real time and permits prompt decision-making in emergency scenarios. Law enforcement organizations can use their resources more wisely, plan out patrol routes, and create focused crime prevention programs. By giving law enforcement agencies useful insights, machine learning turns crime analysis into a potent weapon. © 2024 IEEE.

关键词： Adversarial machine learning

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Securing the Internet of things through Device Identification via Network Traffic analysis 10

Securing the Internet of Things Through Device Identificatio...

引用

10th international conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Ahmadian, Rouhollah Ghatee, Mehdi Wahlstrom, Johan Amirkabir University of Technology Department of Computer Science Tehran Iran University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798331532543

the widespread adoption of the Internet of things (IoT) has led to considerable security concerns, particularly in device identification. To tackle this challenge, we have devised a method that scrutinizes the data transmitted by IoT devices over the network, delving into detailed behavioral patterns that differentiate one device from another. Acknowledging the similarities in device behaviors, our approach concentrates on discerning and categorizing them based on their unique characteristics. this paper introduces an approach to identifying IoT devices by integrating probabilistic fusion and convolutional neural networks (CNNs). Our method involves segmenting input data into overlapping subsequences and using a CNN to independently classify each subsequence, enabling the detection of intricate patterns. the predictions are combined using an averaging method, leading to improvements. Specifically, the MFT dataset F1-score increased by 9.3%, the PC dataset by 2.46%, and the BMSAQ dataset by 24.6%. Empirical validation demonstrates that our model surpasses existing methods, offering a practical and efficient solution for real-world IoT applications. this research underscores the potential of advanced machine learning techniques in fortifying and safeguarding IoT ecosystems. © 2024 IEEE.

关键词： Convolutional neural networks

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Rough Set Based Recipe Clustering Approach 10

Rough Set Based Recipe Clustering Approach

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10th international conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Das, Priyanka Mukherjee, Chandrani Gupta, Samiksha Roy, Sayan Shah, Khushi Singh, Shreya Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350384369

Online cooking instructions including ingredient lists and cooking methods are now widely available due to the proliferation of recipe sharing websites. Finding novel and complex patterns in such vast, ambiguous categorical data sets is a crucial task. Conventional clustering analysis algorithms frequently fail to address the uncertainty surrounding the exact value of features found in these categorical data sets. thus to aid in decision-making, rough set based clustering is frequently used. this study aims to identify the optimal clustering feature from the recipe data that will result in the grouping of related food products by applying the rough purity notion. the recipe data has been regarded as a categorical valued information system in this instance, and information theoretic attribute purity is taken into account using the rough purity technique. © 2024 IEEE.

关键词： Food chemistry

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