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Classification of Animal Species Using a Deep Neural Network-Based Feature Extraction Method 21

Classification of Animal Species Using a Deep Neural Network...

21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Ibrahim, Mohammed Al-Kubaise, Khamis Alkapti, Ali Almusa, Abdullah Abdelaziz, Osama Al-Maadeed, Somaya Sadasivuni, Kishor Kumar Qatar University Computer Science and Engineering Department Doha Qatar Center for Advanced Materials Qatar University PoBox 2713 Doha Qatar

ISBN: (纸本)9798350378078

This study presents an innovative approach to animal classification and recognition utilizing machine learning and deep learning methodologies. Leveraging advanced algorithms, the proposed system achieves remarkable accuracy in identifying diverse animal species. By integrating sophisticated image processing techniques, the system enhances image quality, improving overall performance. The research demonstrated that the SVM model combined with deep neural network-based feature extraction achieved the highest accuracy of 95.65%. This paper represents a significant stride toward improving the precision and efficiency of animal classification, offering promising applications in biodiversity conservation and ecological monitoring by using advanced feature extraction approach with deep learning. © 2024 IEEE.

关键词： Animal Recognition computer Vision Deep Learning Feature Extraction KNN

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Revolutionizing Smart Agriculture: Burning Number Analysis and Linear Regression for Optimal Sensor Activation in 3-Ary n-Cube Networks

Revolutionizing Smart Agriculture: Burning Number Analysis a...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Rajeshwari, S. Rajesh, M. School of Advanced Sciences Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350364088

This study introduces a novel approach by combining burning number analysis and supervised machine learning, specifically linear regression, to optimize sensor placement in large agricultural fields modeled as a 3-ary n-cube network. In this unique integration, each node represents a location equipped with environmental sensors, and edges signify wireless communication links. The burning number analysis identifies critical sensor nodes, strategically activating them for efficient information propagation throughout the network. Simultaneously, linear regression is employed to predict and optimize the activation of key sensors, maximizing coverage and data dissemination. This approach aims to enhance network efficiency, providing valuable insights for precision agriculture practices such as crop management, irrigation scheduling, and resource allocation. Through this innovative method, the study not only contributes to the development of adaptive solutions for sustainable farming but also showcases the potential of combining graph theory-based burning number analysis with linear regression for optimizing sensor networks in complex agricultural landscapes. © 2024 IEEE.

关键词： Crops

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The Large Language Models on Biomedical Data Analysis: A Survey

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4486-4497页

作者： Lan, Wei Tang, Zhentao Liu, Mingyang Chen, Qingfeng Peng, Wei Chen, Yiping Phoebe Pan, Yi Guangxi University School of Computer Electronic and Information Nanning530004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research. © 2013 IEEE.

关键词： Data consistency

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High-Boost-Gain Split-Source Inverter With Shared-Ground and No Shoot-Through Issue

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IEEE Transactions on Industrial Informatics 2024年第3期20卷 3252-3265页

作者： Ahmed, Hafiz Furqan Moursi, Mohamed Shawky El Hosani, Khalifa Al Alzaabi, Omar Zahawi, Bashar National Sun Yat-sen University Department of Electrical Engineering Kaohsiung804201 Taiwan Khalifa University Advanced Power and Energy Center Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates Mansoura University Faculty of Engineering Mansoura35516 Egypt

In this article, we propose an improved high-boost-gain split-source inverter (SSI) for renewable energy generation. The proposed inverter retains the features of the existing SSIs, such as single-stage boost inversion with a reduced number of passive components and elimination of the inverse modulation-duty relationship constraint of the impedance-source inverters. It also provides additional features that include higher boost inversion ability than existing SSIs to connect low-voltage renewable energy sources to the grid, no voltage-source short-circuit issue, which increases the robustness of the proposed inverter and pulsewidth modulation dead times can be relieved, and the negative input dc-terminal and ac-neutral are electrically connected in the proposed inverter, resulting in a constant common-mode voltage and elimination of leakage ground currents for grid-interfaced PV power systems. A detailed circuit operation and analysis is provided, and experiments are performed on a 400-VA prototype inverter to confirm the working of the proposed topology. © 2005-2012 IEEE.

关键词： Pulse width modulation

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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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Non-Hermitian polarization quantized by time-reversal symmetry

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science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 141-147页

作者： Jing Cheng Jinbing Hu Zhigao Hu Lin Chen Minghui Lu Songlin Zhuang Technical Center for Multifunctional Magneto-Optical Spectroscopy(Shanghai) Engineering Research Center of Nanophotonics&Advanced Instrument(Ministry of Education)Department of Physics and Electronic ScienceEast of China Normal UniversityShanghai200241China College of Optical-Electrical Information and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan430074China National Laboratory of Solid State Microstructures Science and Engineering Nanjing UniversityNanjing210093China

It is well known that in one-dimensional(1D) crystalline insulators,the electric polarization is a manifestation of Berry phase,which can not be quantized by time-reversal symmetry(TRS) as in Hermitian physics TRS does not induce any topological phase in one *** this paper we report that even though associated with complex eigenenergies a 1D non-Hermitian insulator obeying only TRS is capable of presenting quantized bulk *** underlying physical reason is unveiled:TRS guarantees the complex energies to come in pair(E,E*),and the corresponding decaying and amplifying wave functions also come in pair and have the same variation rate,hence,giving rise to a stable wannier *** electron transport is performed by means of charge pumping process,which verifies the physical mechanism *** last,we discuss the possible experimental implementation of the proposed model by means of twisted-π gauge flux.

关键词： non-Hermitian polarization time-reversal symmetry complex hopping

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A self-stabilizing distributed algorithm for the 1-MIS problem under the distance-3 model

A self-stabilizing distributed algorithm for the 1-MIS probl...

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作者： Kakugawa, Hirotsugu Kamei, Sayaka Shibata, Masahiro Ooshita, Fukuhito Faculty of Advanced Science and Technology Ryukoku University Shiga Otsu Japan Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashihiroshima Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka Japan Faculty of Engineering Fukui University of Technology Fukui Fukui Japan

Fault-tolerance and self-organization are critical properties in modern distributed systems. Self-stabilization is a class of fault-tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self-stabilizing distributed algorithm for the 1-MIS problem under the unfair central daemon assuming the distance-3 model. Here, in the distance-3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1-MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is (Formula presented.) steps and the space complexity is (Formula presented.) bits, where (Formula presented.) is the number of processes. Finally, we extend the notion of 1-MIS to (Formula presented.) -MIS for each nonnegative integer (Formula presented.), and compare the set sizes of (Formula presented.) -MIS ((Formula presented.)) and the maximum independent set. © 2024 John Wiley & Sons Ltd.

关键词： Consensus algorithm

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Detection of Brain Tumor using CNN 4

Detection of Brain Tumor using CNN

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Kumar, Tapesh Yadav, Puneet Kumar Yadav, Vrinda Centre for Advanced Studies Computer Science & Engineering Lucknow India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Brain, which is mainly composed of billions of neurons, hosts the most complicated processes that occur in the body. A brain tumor is a tissue growth in the brain that grows in an area of the brain, which should not be significantly increased. To analyze brain tumors, an open source dataset available in Kaggle is used in the paper. There are photos in the dataset and all images are in different colors and dimensions. All noisy images are converted into gray scale using GaussianBlur filter with dimensions 256*256. Then the dataset is divided into 80% for training and 20% for testing. A CNN model is proposed and evaluated against ROC Curve, Confusion Matrix, and Accuracy Curve. It is observed that the proposed model achieved better accuracy when compared with state-of-the-art models. © 2022 IEEE.

关键词： Brain

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Integrative Remote Sensing Approaches Using Generative Adversarial Networks for Urban Heat Island Analysis and Mitigation

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 681-698页

作者： Sundar, Ganapathy Patchaiammal, Pitchaimuthu Venkateshwarulu, Balaji Vijayan Kumar, Thangavel Pradeesh Rajamannar, Kesavamoorthy Tripathi, Rajesh Kumar Department of Computer Applications Sindhi College Chennai India Department of Information Science and Engineering HKBK College of Engineering VTU Belagum Bengaluru Nagawara India VIT School of Agricultural Innovations and Advanced Learning (VAIAL) VIT Vellore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India

The phenomenon of urban heat islands (UHI) presents a critical challenge for urban sustainability, exacerbating local temperatures, increasing energy demands, and impairing public health. Traditional methods for addressing UHI are often resource-intensive and slow. This study introduces a novel approach, utilizing a Hybrid CycleGAN-SVM (HCGS) model that leverages the synergy of Generative Adversarial Networks (GANs) and Support Vector Machines (SVMs) to efficiently analyze and mitigate UHI effects through high-resolution satellite imagery and temperature data. The model incorporates Enhanced Vision Transformers (EViTs) for superior feature extraction, adept at capturing intricate spatial and spectral urban patterns. The CycleGAN component of the model generates high-quality synthetic imagery, enhancing the dataset and addressing class imbalances, thereby bolstering the SVM classifier’s ability to precisely pinpoint heat-prone urban areas. Implemented in Google Colab, the HCGS model demonstrated exceptional performance, achieving a classification accuracy of 0.98. This indicates its potential as an effective tool for urban heat mitigation, offering actionable insights for urban planning and policy-making. By integrating advanced machine learning techniques with remote sensing data, the HCGS model paves the way for innovative climate adaptation strategies, fostering more sustainable and resilient urban environments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Enhanced Vision Transformers EViTs GANs Generative Adversarial Networks HCGS model Hybrid CycleGAN-SVM Satellite imagery Support Vector Machines SVM UHI effect Urban heat island

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Intrusion Detection using Dense Neural Network in Network System 6

Intrusion Detection using Dense Neural Network in Network Sy...

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6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022

作者： Doherey, Aman Singh, Akansha Kumar, Arun Centre for Advanced Studies Computer Science and Engineering Lucknow India

ISBN: (数字)9781665497428

ISBN: (纸本)9781665497428

An Network Intrusion Detection System can be perceived as a device, either software or hardware which is utilized to screen the organization for suspicious action or policy violation. In this era of digitization where everyone is using computers for all types of communications- personal, political, financial, etc., it becomes necessary to ensure that the medium of the communication is secure or not. Because nowadays every small scale enterprise, big companies, even personal households are having their own server. The new technologies are based on the concept of networking. So, an intrusion in such networks can cause bid risks like data breach financial risk or malfunctioning of the devices connected in that network. It might be possible for small networks to be checked manually because the total connection in such networks is less, but when it comes to the big networks where a lot of connections are sending and receiving requests, it is near to impossible for someone to check all the connections manually. In this paper dense neural network are used for detecting the network intrusion and NSL-KDD dataset are used to test the model. The proposed model achieved 98.29% accuracy. © 2022 IEEE.

关键词： Machine learning

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