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A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification 18

A New Rhythm in A1: Convolutional Neural Networks for Music ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Javed, Huzaifa Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis. © 2023 IEEE.

关键词： Convolution

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The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection 18

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ahmed, Tafreed Maaz, Ahmad Mahmood, Danyaal Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications. © 2023 IEEE.

关键词： Object detection

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Network Anomaly Detection with Stacked Sparse Shrink Autoencoders and Improved XGBoost

Network Anomaly Detection with Stacked Sparse Shrink Autoenc...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Guan, Ziyue Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Efficient and accurate identification of network anomalies is of great significance to the construction of network security systems in the information age. It is highly challenging to accurately detect abnormal behaviors in the increasing network data. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have poor detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. For network anomaly detection, this work proposes a new network anomaly detection method named SAXP, which integrates Stacked sparse shrink Autoencoders and a XGBoost model based on genetic simulated annealing Particle swarm optimization (GSPSO). Specifically, features extracted by stacked sparse shrink autoencoders are introduced into the XG Boost model for classification, and GSPSO is used to optimize the hyperparameters of XGBoost. Experimental results based on two real-life data sets demonstrate that the proposed SAXP achieves higher recognition accuracy than several state-of-the-art algorithms. © 2023 IEEE.

关键词： autoencoders feature extraction Network anomaly detection particle swarm optimization XGBoost

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Investigating the Role of Block Chain to Secure Identity in IoT for Industrial Automation 2

Investigating the Role of Block Chain to Secure Identity in ...

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2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Veeraiah, Vivek Preetham, Anusha Karthikeyan, P. Kumari, P. Lalitha Mehta, Kamakshi Kumbhkar, Makhan Adichunchanagiri University Dept. of R & D Computer Science Karnataka Mandya India Dayananda Sagar Academy of Technology and Management Dept. of Ise Karnataka India School of Management Studies Kongu Engineering College Perundurai TN Erode India Visakha Institute of Engineering & Technology Dept. of CSE AP Narava Visakhapatnam India Amity College of Commerce Amity University Haryana India Christian Eminent College Dept. of Computer Science Madhya Pradesh Indore India

ISBN: (纸本)9781665437899

Everyone who has an app that can access the block chain can get their hands on the data. There are blocks for each new piece of data that come in. A chronological chain of data is formed when a block is filled with data and chained to the previous block. Smart things are protected from cyber-attacks thanks to the IoT's technology's transparency and virtual incorruptibility. The block chain may be used to enable secure connectivity between IoT devices by storing data in transactions and confirming those transactions with nodes. The Internet of Things (IOT) has helped industrial automation reach new heights. We can improve the security and privacy by adding block chain to it. © 2022 IEEE.

关键词： Internet of things

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Latency-Minimized Computation Offloading in Vehicle Fog Computing with Improved Whale Optimization Algorithm

Latency-Minimized Computation Offloading in Vehicle Fog Comp...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Xue, Xiangdong Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Fog computing provides lower latency and higher bandwidth compared to cloud computing and is widely used in Internet of Vehicles (IoV). Vehicles cannot compute all tasks locally due to their limited computing power and battery capacity. Thus, it is a useful way to offload some tasks of vehicles to other resource-rich servers. However, due to the high mobility of vehicles, there may be a failure of returning computing results. Thus, it is a challenge to minimize the latency of tasks while meeting the constraint of energy consumption. This work proposes a vehicle-fog offloading system that offloads tasks to fog servers or idle vehicles which is a probabilistic offloading problem. So this work proposes an improved optimization algorithm called an adaptive Lévy flight-based Whale optimization algorithm with Hierarchical learning (LWH) to solve this problem. Simulation experiments show that LWH has strong global search capability and outperforms its five typical and widely used algorithms. © 2023 IEEE.

关键词： computation offloading fog computing Lévy flight Vehicular networks whale optimization algorithm

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An Enhancemnt of Low-Light Images Using Hierarchical Pyramid Attentive Network and Hierarchical Feature Fusion 2

An Enhancemnt of Low-Light Images Using Hierarchical Pyramid...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Suma, K.G. Nagendar, Y. Habelalmateen, Mohammed I. Revanasiddappa Halvi, Srinivas Vit University School Of Computer Science And Engineering Dept. Of Ai & Ml Andhra Pradesh India Sr University School Of Computer Science And Engineering Telangana Warangal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Aiml Bengaluru India Dayananda Sagar College Of Engineering Department Of Medical Electronics Engineering Bengaluru India

ISBN: (纸本)9798350381665

The development of the low-light images assists efficiently in high visioned tasks which often fails in the poor illumination conditions. Most of the old methods, applied has enhanced the low-lighted degrade images which resulted in the undesirable enhancement including the intensive noise, blurred details and slanted color. This paper is used for finding alignment to an end-to-end image along with enhancement of the perceptual information by pixel wise. To reduce the differences in style, Hierarchical Pyramid Attentive Network (HPANet) was used, which results in the betterment of the illuminations and the reveal exposed information's. After aligned images, a hierarchy pyramid enhancements subnetwork is used for optimizing the quality of the image, which helps for removing the noises, results in enhancement low light images. A Hierarchical Feature Fusion (HFF) module is proposed which results in the high resolution of the images in perceptual qualities. Extensive experiments have also performed that effectiveness of HPANet-HFF methods on various datasets have outdone the existing methods in reproduction of the color. According to the results, the proposed HPANet-HFF obtained MAE and RMSE of 106.5 and 181.5 which is superior over other existing architectures. © 2024 IEEE.

关键词： Cell proliferation

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Joint Optimization of Cache-Assisted Offloading and Resource Allocation in Mobile Edge Computing

Joint Optimization of Cache-Assisted Offloading and Resource...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Sun, Zhe Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Edge computing is a new architectural model that aims to offer computing, storage, and networking resources to support Internet of Things. Its primary strategy involves transferring computational tasks to the edge of network, which is closer to end-users. This paradigm facilitates offloading of computation, resulting in reduced latency and improved system performance. However, nodes located at the network edge have restricted energy and resources. As a result, running tasks entirely at the edge leads to higher energy consumption. This work proposes a novel three-tier offloading framework comprising of multiple mobile vehicles (MVs), a base station (BS), and a cloud data center (CDC). It jointly optimizes offloading rates of tasks, CPU computation rates of MVs, BS, and CDC, and the allocation of wireless bandwidth resources at MVs during partial computation offloading of tasks. It also considers limits of maximum computational resources and maximum delay of task execution. To further reduce the total system energy consumption, this work actively caches execution codes of tasks in MEC servers to reduce data transmission energy of MVs, which minimizes the total system energy consumption. This work develops a mixed integer nonlinear program and designs a mixed meta-heuristic algorithm with a multi-strategy adaptive particle swarm optimizer. Simulation results demonstrate that it outperforms various state-of-the-art algorithms by achieving lower energy consumption in fewer iterations. © 2023 IEEE.

关键词： cloud computing Computation offloading particle swarm optimization vehicular edge computing

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Plant Leaf Disease Detection Incorporating IoT and XAI-Enhanced Deep Learning

Plant Leaf Disease Detection Incorporating IoT and XAI-Enhan...

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International Conference on computer and Information technology (ICCIT)

作者： Pijush Kanti Roy Partho Md. Abid Hasan Rafi Pankaj Bhowmik Dept. of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Dept. of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Plant diseases represent a significant global threat, affecting both food supply and economic growth. Despite technological advances, early plant disease recognition remains difficult for small-scale farmers because of the lackings of infrastructure and resources. Thus, early disease detection using machine learning, deep learning, and computer vision techniques can improve the prediction process and make it easier. Previous studies that were implemented for plant-specific purposes, require high-resolution images and lack real-time detection methods. Therefore, to address these issues, we proposed a deep learning and IoT-based system that uses convolutional neural networks (CNN) and other supporting models, such as InceptionNetV3, VGG16, and EfficientNetB5, with a fine-tuning process to achieve higher accuracy in predicting 38 diseases over 14 different plant species in lower resolution, noisy data. Besides, to facilitate the system, we incorporated an ESP32CAM microcontroller and a server host that can be used to detect diseases in a real-time environment, which makes our research study robust and meaningful. We also implemented Explainable Artificial Intelligence (XAI), particularly Grad-CAM, along with our CNN model for visualizing the infected region in a plant leaf. The individual-trained models achieved an average accuracy of around 99%. We believe, our study can contribute to the agricultural sector by providing real-time disease detection, smart decision support systems, and low-cost solutions for farmers, mainly in resource-limited territories.

关键词： Deep learning Plant diseases Accuracy Microcontrollers Explainable AI Computational modeling Biological system modeling Real-time systems Convolutional neural networks Internet of Things

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A Real-Time Performance Model for NFT Chains

A Real-Time Performance Model for NFT Chains

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Blockchain Computing and Applications (BCCA), International Conference on

作者： K. Kentner J. Seol N. Park Computer Science Dept. Oklahoma State University Stillwater OK USA Information Technology Dept. Middle Georgia State University Macon GA USA Dept. of Computer Science Oklahoma State University Stillwater OK USA

This paper sets forth a bivariate, real-time NFT (nonfungible token) model based on a Markovian queueing process. This model is proposed with the goal of simulating and observing the performance of an NFT system as it behaves under real-time constraints, as well as to see how various on-/off-chain transaction rates, service rates, blocksizes, and deadline-miss rates affect the overall performance of the system. The system is assumed to operate at a steady state, thus allowing for each probabilistic state (which at a given time consists of ion-chain transactions and j off-chain transactions that have arrived) to be calculated using balance equations (all incoming transactions to a state equal all outgoing from the state when the relevant probabilities are considered). The Markovian assumption necessitates that the full state space must sum to 1.0, and because all states derive from $\mathrm{p}_{0,0}$ , P0,0 may be factored out and solved using this constraint. This in turn facilitates the computation of the remaining probabilities. The on-/off-chain transaction rates $\lambda_{on}$ and $\lambda_{off}$ , service rate $\mu$ , and deadline-miss rate $\lambda_{F}$ are the primary parameters within the model, and $\lambda_{F}$ is the key parameter for modeling the real-time behavior of the system $(\lambda_{F}$ represents a failure for a transaction to meet the deadline, and it causes the current state to self-loop). An analysis of the system via metrics including average wait time, average number of transactions, and throughput is presented, and the implications of the simulation on the overall performance of the system are discussed.

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Energy-aware intelligent routing framework for 6G-based wireless sensor networks

Energy-aware intelligent routing framework for 6G-based wire...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Mishra, Pranati Kandpal, Meenakshi Panigrahy, Supriya Rautaray, Jyotirmayee Dash, Ranjan Kumar School of Computer Sciences Odisha University of Technology and Research Odisha Bhubaneswar751029 India Dept. of Computer Science and Engineering C.V. Raman Gobal University Odisha Bhubaneswar752054 India

In the context of 6G technology, wireless sensor networks can leverage the advanced features and capabilities of 6G networks, including ultra-fast data speeds, ultra-low latency, massive device connectivity and enhanced energy efficiency. However, developing and implementing 6G networks presents several challenges, particularly in ensuring energy efficiency, which is essential to minimize environmental impact and operational costs. This work addresses the critical issue of energy efficiency by formulating the energy-efficient routing problem and mapping it to a Q-learning technique. An intelligent Q-learning-based framework is proposed to efficiently determine energy-efficient routing paths between sensor nodes and the sink. The different states and transitions are defined using a Markov model. Three separate algorithms are presented as part of the proposed routing framework in 6G-based WSNs. The performance of the proposed framework is compared against clustering-based and particle swarm optimization-based algorithms in terms of energy consumption. Simulation results demonstrate that the proposed routing framework significantly prolongs the overall network lifetime and substantially improves transmission efficiency compared to existing techniques. © 2025 The Author(s).

关键词： Sensor nodes

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