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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Tiwari, Shivam Sharma, Vikrant Rathore, Yogesh Singh Gandhi, Parul Srivastava, Durgesh Sharda University Department of Computer Science & Engineering Greater Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India Sharda University Computer Science and Application UP Greater Noida India Manav Rachna International Institute of Research and Studies Department of Computer Applications Haryana Faridabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350338423

The vast variety of floral species present on Earth poses a considerable challenge in memorizing their individual names. Acquiring knowledge about specific flower types inhabiting diverse environments like forests, mountains, and dense regions becomes crucial for their recognition and differentiation. Consequently, the development of a technique for accurately determining flower categories becomes imperative. The task of distinguishing and categorizing a particular blossom amidst the extensive array of floral species presents a formidable task. Hence, the automation of flower species identification has been a subject of extensive research interest. This research domain is characterized by the utilization of features extracted from floral images in conjunction with specialized algorithms aimed at classifying flower species. A prominent aspect of these endeavors is the employment of three widely recognized flower image datasets. The present study constitutes a comprehensive survey of various methodologies proposed by diverse researchers with the objective of accomplishing precise identification of flower species. © 2023 IEEE.

关键词： Feature extraction

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Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G Communication Environment

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Tsinghua science and Technology 2024年第6期29卷 1728-1751页

作者： Ashish Bagwari Jaganathan Logeshwaran M.Raja P.Devisivasankari Jyotshana Bagwari Vikas Rathi Asma Mohammed Elbashir Saad Department of Electronics and Communication Engineering Uttarakhand Technical UniversityDehradun 248007India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore 641202India Department of Computer Science and Engineering CMR Institute of TechnologyKarnataka 560037India Department of Information Science and Engineering CMR Institute of TechnologyKarnataka 560037India Advanced and Innovative Research Laboratory Dehradun 248001India Department of Electronics and Communication Engineering Graphic Era(Deemed to be University)Dehradun 248007India Department of Physics College of Science and HumanitiesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 16273Saudi Arabia

Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for and buying only 4G Subscriber Identity Module (SIM)-capable mobiles. In this case, the expectation of 5G has increased in line with 2G, 3G, and 4G, where the G stands for generation, and it does not indicate Internet or Internet speed. 5G includes next-generation features that are more advanced than those available in 4G network services. The main objective of 5G is uninterrupted telecommunication service in hybrid energy storage system. This paper proposes an intelligent networking model to obtain the maximum energy efficiency and Artificial Intelligence (AI) automation of 5G networks. There is currently an issue where the signal cuts out when crossing an area with one tower and traveling to an area with another tower. The problem of “call drop”, where the call is disconnected while talking, is not present in 5G. The proposed Intelligent Computational Model (ICM) model obtained 96.31% network speed management, 90.63% battery capacity management, 92.27% network device management, 93.57% energy efficiency, and 88.41% AI automation.

关键词： hybrid energy storage system 4G digital growth 5G downloading Subscriber Identity Module(SIM) generation network energy efficiency Artificial Intelligence(Al)automation

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Simple Industrial Cutting Machine Safety System Based on computer Vision 3

Simple Industrial Cutting Machine Safety System Based on Com...

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3rd IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Chiang, Chien-Lin Peng, Ming-Yuan Lin, I-Long Chou, Yu-Wei Fong, Ji-Men Chiang, Yi-Yuan Department of Computer Science and Engineering Tatung University Taipei Taiwan Graduate Institute of National Development and Mainland China Studies Chinese Culture University Taipei Taiwan Department of Computer Science and Information Engineering Vanung University Taoyuan Taiwan

ISBN: (纸本)9798350333862

Safety devices are often not fitted to many simple panel-cutting machines due to cost. Designing and installing low-cost safety devices on the simple panel-cutting machine improves the safety of operators. We propose a computer vision-based safety system with a camera to capture images of the operator and machine from above. Each captured image is analyzed by a neural network. If the operator's hands are too close to the cutting tool, the safety system sends a warning signal and stops the machine. The neural network used in the vision system is a ten-layer convolutional neural network. In addition to the input and output layers, there are five convolutional layers in the middle, followed by three fully connected layers. The input image undergoes five convolution operations to obtain feature maps of different granularity. With three fully connected layers, a combination of features is used to determine the positions of the operator's hands as a guideline for stopping the machine. To improve the generalization of the model, we collect thousands of training data. The experimental results show that the model has a low bias, low variance, and high accuracy characteristics. The accuracy of the verification data is over 98%. © 2023 IEEE.

关键词： Convolutional neural networks

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Quantum Algorithms and Lower Bounds for Finite-Sum Optimization 41

Quantum Algorithms and Lower Bounds for Finite-Sum Optimizat...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yexin Zhang, Chenyi Fang, Cong Wang, Liwei Li, Tongyang School of Electronics Engineering and Computer Science Peking University China Computer Science Department Stanford University United States National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Center on Frontiers of Computing Studies Peking University China School of Computer Science Peking University China

Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, *** this paper, we initiate the study of solving finite-sum optimization problems by quantum ***, let f1, ..., fn : d → be -smooth convex functions and ψ: d → be a µ-strongly convex proximal *** goal is to find an ϵ-optimal point for F(x) = n1 Pni=1 fi(x) + ψ(x).We give a quantum algorithm with complexity Õ(Equation presented) 1 improving the classical tight bound (Equation presented).We also prove a quantum lower bound Ω˜(n + n3/4(/µ)1/4) when d is large *** our quantum upper and lower bounds can extend to the cases where ψ is not necessarily strongly convex, or each fi is Lipschitz but not necessarily *** addition, when F is nonconvex, our quantum algorithm can find_an ϵ-critial point using (Equation presented) queries. Copyright 2024 by the author(s)

关键词： Quantum computers

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Deep learning based Crop Monitoring for effective Agricultural-IoT Management

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Procedia computer science 2025年 258卷 332-341页

作者： Mahmood Hussain Mir Judeson Antony Kovilpillai J Sulaiman Syed Mohamed Pragya Tawseef Ahmad Mir Banibrata Paul Alliance School of Advanced Computing Alliance University Bangalore India Department of Computer Science GITAM University Bangalore India Department of Computer Science & Engineering Birla Institute of Technology Ranchi India

Achieving sustainable crop production to meet the increasing demand for food depends on the efficient management of agricultural greenhouses. Conventional methods of greenhouse management typically depend on human observation and intervention, resulting in a lack of scalability, efficiency, and precision. In this research, a deep learning technique for managing agricultural greenhouses is proposed by utilizing IoT sensor data obtained from a smart greenhouse. The proposed method aims to enhance crop monitoring and control, hence improving the overall efficiency of greenhouse operations. The utilization of several deep learning models with various activation functions, such as ELU, Swish, ReLU, SELU, and Mish, enables the ongoing surveillance and independent regulation of greenhouse conditions throughout the day. The proposed methodology can be used to enable real-time tracking and autonomous control, leading to resource optimization, increased productivity, and reduced manual intervention. Empirical validation demonstrates the efficacy of utilizing deep learning and IoT in agriculture to improve crop management and sustainability.

关键词： Deep Learning IoT Crop Monitoring Agriculture

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A Few-Shot Learning-Based Domain-Adaptive Lightweight CNN for Robust Hand Gesture Recognition in Real-Time Applications 2

A Few-Shot Learning-Based Domain-Adaptive Lightweight CNN fo...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Shobana, K.B. Kaladeep Yalagi, Pratibha C. Ammu, V. Aldo Stalin, J.L. Kumar, Prashant Richard, Titus Department of Artificial Intelligence and Data Science Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Walchand Institute of Technology Solapur India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Information Technology Sona College of Technology Salem India Department of Information Technology Project Circle BSNL Lalkuthi Kolkata India Department of English Vels Institute of Science Technology & Advance Studies Pallavaram Chennai India

ISBN: (纸本)9798331504960

Surface electromyogram-based hand gesture recognition (sEMG) has promising prospects in human-machine interaction applications, including the neural rehabilitation and assistive technologies. However, this technique also has several disadvantages, such as poor generalization across subjects and days, demanding a large amount of data for calibration, and computational complexity that does not enable the real-time deployment of the system. The proposed work incorporates a novel few-shot learning-based domain-adaptive lightweight CNN that is specifically designed to be used in robust and efficient hand gesture recognition. This minimizes the dependency on the labeled data of other new subjects or days, hence allowing better adaptability in real-world scenarios. The proposed lightweight CNN architecture adapts well to wearable devices with real-time performance but high accuracy. Another would be adding domain-adaptive feature learning to account for variations between inter-subject and inter-day domains while preserving the accuracy of classification and improving model generalization. The proposed framework is able to achieve competitive accuracy using a small calibration set in gesture-based human-machine interfaces continuously and broadly. Results on high-density sEMG datasets have shown that our model achieves a good balance between the accuracy (95.5%) and efficiency on experimental results and holds promise for providing a reliable solution for real-time cross-domain hand gesture recognition applications. © 2024 IEEE.

关键词： Calibration

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Optimization Model for Machine Learning Based Intrusion Detection System (OML-IDS) in Mobile-Edge Computing 4

Optimization Model for Machine Learning Based Intrusion Dete...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Kiruthiga, R. Jeyalaksshmi, S. Vanitha, V. Khan, Abdul Azeem Pal, Souvik Kumar, Rahul SRM Institute of Science & Technology Department of Computer Applications Kattankulathur Chennai India Depart. of IT Vels Institute of Science Technology & Advanced Studies Chennai India Sri Sankara Arts and Science College Department of Computer Applications Kanchipuram India Faculty of Islamic Technology Brunei Sister Nivedita University Department of CS and Engineering Kolkata India Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

The Internet of Things (IoT) and 5G communications now primarily use mobile edge computing (MEC). The fundamental concept behind MEC is the deployment of a hierarchy of edge servers with more computational power to handle the computing duties that are being offloaded by low-end IoT edge devices. Some of the security risks in MEC pertain to the application layer, the network layer, the data, and the node. Out main aim is to protect MEC from different cyber-attacks and continuous monitoring of digital assets. The network layer security for MEC is protected by intrusion detection systems (IDSs). We need an updated IDS system capable of detecting modern cyber-attacks as well as monitoring the devices. Thus, this paper proposes an optimization model for MEC settings that functions in conjunction with an OML-IDS, a machine learning-based intrusion detection system. To begin identifying attack patterns in a MEC environment, this model first develops an optimization-based feature selection strategy for selecting an ideal subset of characteristics. For the purpose of intrusion categorization and detection, a hybrid ML algorithm is also used. Enhancing the offloading latency, synchronization time, and turnaround time is necessary if you want to be able to recognize assaults in real time. © 2024 IEEE.

关键词： Network intrusion

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Sampling from the Mean-Field Stationary Distribution 37

Sampling from the Mean-Field Stationary Distribution

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37th Annual Conference on Learning Theory, COLT 2024

作者： Kook, Yunbum Zhang, Matthew S. Chewi, Sinho Erdogdu, Murat A. Li, Mufan Georgia Institute of Technology United States Department of Computer Science University of Toronto Vector Institute Canada School of Mathematics Institute for Advanced Study United States Department of Operations Research and Financial Engineering Princeton University United States

We study the complexity of sampling from the stationary distribution of a mean-field SDE, or equivalently, the complexity of minimizing a functional over the space of probability measures which includes an interaction term. Our main insight is to decouple the two key aspects of this problem: (1) approximation of the mean-field SDE via a finite-particle system, via uniform-in-time propagation of chaos, and (2) sampling from the finite-particle stationary distribution, via standard log-concave samplers. Our approach is conceptually simpler and its flexibility allows for incorporating the state-of-the-art for both algorithms and theory. This leads to improved guarantees in numerous settings, including better guarantees for optimizing certain two-layer neural networks in the mean-field regime. A key technical contribution is to establish a new uniform-in-N log-Sobolev inequality for the stationary distribution of the mean-field Langevin dynamics. © 2024 Y. Kook, M. S. Zhang, S. Chewi, M. A. Erdogdu & M.(. Li.

关键词： Probability distributions

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Dynamic Visible Light Positioning Based on Enhanced Visual Target Tracking

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China Communications 2023年第10期20卷 276-291页

作者： Xiangyu Liu Jingyu Hao Lei Guo Song Song School of Information Science and Engineering Shenyang Ligong UniversityShenyang 110158China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Hangzhou Institute of Advanced Technology Hangzhou 310056China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China

In visible light positioning systems,some scholars have proposed target tracking algorithms to balance the relationship among positioning accuracy,real-time performance,and ***,there are still two problems:(1)When the captured LED disappears and the uncertain LED reappears,existing tracking algorithms may recognize the landmark in error;(2)The receiver is not always able to achieve positioning under various moving *** this paper,we propose an enhanced visual target tracking algorithm to solve the above ***,we design the lightweight recognition/demodulation mechanism,which combines Kalman filtering with simple image preprocessing to quickly track and accurately demodulate the ***,we use the Gaussian mixture model and the LED color feature to enable the system to achieve positioning,when the receiver is under various moving *** results show that our system can achieve high-precision dynamic positioning and improve the system’s comprehensive performance.

关键词： visible light positioning visual target tracking gaussian mixture model kalman filtering system performance

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Intelligent Kidney Abnormality Detection in CT Images Using GLCM and Neural Networks 4

Intelligent Kidney Abnormality Detection in CT Images Using ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Bajpai, Chhavi Anitha, S. Gaur, Manish Raghini, M. Kaliappan, S. Khan, Imran Centre for Advanced Studies Aktu Department of Computer Science and Engineering Lucknow India Acs College of Engineering Department of CSE-Cyber Security Bangalore India Institute of Engineering and Technology Aktu Department of Computer Science & Engineering Lucknow India Velammal College of Engineering and Technology Department of Artificial Intell igence and Data Science Madurai India Lovely Professional University Department of Research and Development Jalandhar India Sharda School of Nursing Science and Research Sharda University Department of Medical Surgical Nursing Greater Noida India

ISBN: (纸本)9798350352931

Kidney stone illness, one of the most common medical problems worldwide, relieves pain and drives people to the ER. Diagnoses often use imaging, but educated doctors must interpret them. computer-aided diagnostics (CAD) systems help doctors discover kidney stones more accurately. The suggested technique includes preprocessing, feature extraction, and model training. Ultrasound (US) image preprocessing includes restoration, smoothness, sharpness, and contrast enhancement. The Grey Level Co-occurrence Matrices (GLCM) analyze image texture to extract characteristics. Model training uses KELM. The KELM-based model outperforms Extreme Learning Machine (ELM) and K-Nearest Neighbours (KNN) with an average accuracy of 92.54%. This accuracy demonstrates the method can detect kidney stones in ultrasound images. This proposed shows that computer-aided kidney stone detection can be fast and accurate. The combination of GLCM's texture analysis and KELM's classification skills can help medical professionals diagnose kidney stones using imaging. © 2024 IEEE.

关键词： computerized tomography

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