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Automated Fruit Classification Based on Deep Learning Utilizing Yolov8 10

Automated Fruit Classification Based on Deep Learning Utiliz...

10th IEEE Uttar Pradesh Section International Conference on Electrical, electronics and computer engineering, UPCON 2023

作者： Dewi, Christine Kamlasi, Oksam M Chhabra, Gunjan Dai, Guowei Kaushik, Keshav Khan, Inam Ullah Satya Wacana Christian University Department of Information Technology Salatiga Indonesia Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Agricultural Information Institute of Caas National Agriculture Science Data Center Beijing China School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Seas Isra University Department of Electronic Engineering Campus Islamabad Pakistan

ISBN: (纸本)9798350382471

The act of fruit identification entails the discernment and classification of various fruit varieties, predicated upon their visual attributes. This task can be accomplished through various methods, including manual inspection, traditional computer vision techniques, and more advanced approaches using machine learning and deep learning. Our work recognized 15 types of fruit. Our experiment used the fruit imagery dataset, consisting of class Avocado, Banana, Cherry, Apple Braeburn, Apple golden 1, Apricot, Grape, Kiwi, Mango, Orange, Papaya, Peach, Pineapple, Pomegranate and Strawberry. Yolov8 is one of the most popular learning models in the field of object detection. Convolutional neural networks (CNNs), thus simultaneously predicting multiple bounding boxes and probability classes from these bounding boxes. Yolov8 trains the entire image and directly optimizes detection performance, modeled as a Convolutional neural network (CNN). The percentage of detection of test results using *** for each of the 15 categories of fruit images with the average accuracy above is 0.97%, so it can be said to be quite good. After collecting data, Train training and system design implementation of Val Test Convolutional neural networks (CNN) algorithms enabled machine learning and were successfully detected using yolov8n. During the experimental process, we employ 100 epochs with the use of Google Collaboratory. Google Collaboratory has provided GPUs up to 12GB, making it easier and speeding up the fruit detection process. © 2023 IEEE.

关键词： Classification Convolutional Neural Network(CNN) Google Collaboratory YOLOv8

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Investigating Internal Dynamics in Monkey's Primary Motor Cortex during Reaching 46

Investigating Internal Dynamics in Monkey's Primary Motor Co...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Qiu, Shicheng Mao, Hongwei Wu, Shenghui Wang, Yiwen The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong University of Pittsburgh School of Medicine Systems Neuroscience Institute Department of Neurobiology PittsburgPA15213 United States

ISBN: (纸本)9798350371499

Neural dynamics are processes that describe how neurons in the brain change their activities through time in a coordinated manner. In motor control, neural dynamics, governed by both local dynamics of the motor cortex as well as inputs from other brain regions, drive the population neural state to evolve from an initial value. A notable feature is the emergence of rotation-like dynamics in neural state space. However, the causes of rotational dynamics in motor neural systems remain elusive. In this study, our objective is to investigate the impact of kinematics, specifically, the velocity and acceleration of the monkey's hand reaching movement, on rotational dynamics. We propose to employ a linear model to decompose the overall neural dynamics into one driven by the common input and the internal dynamics using single-trial data. Then, we assess the rotational features by comparing the power of internal dynamics with that of the overall dynamics, and quantifying the rotational strength of internal dynamics vs. the overall dynamics. We implement the proposed method on real M1 neural activities from the monkey's center-out reaching task. Our preliminary results demonstrate that the internal dynamics have much weaker rotational features than the overall dynamics. Given recent evidence from animal experiments showing the necessity of continuous common inputs to motor cortex during arm reaching, it indicates that the rotational dynamics in motor cortex may be mainly input driven when the subject is engaged in the movement task. © 2024 IEEE.

关键词： Mammals

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Smart Personal Protective Equipment in Ambulance Services with IoT Integration for Safety 2

Smart Personal Protective Equipment in Ambulance Services wi...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Senkamalavalli, R. Merlin Livingston, L.M. Guruva Reddy, Elangovan Basavaraddi, Chethan Chandra S. Latha, N. Department of Computer Science and Engineering Kammavari Sangham Institute of Technology Karnataka Bangalore India Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur Karnataka Tumkur India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Internet of Things (IoT) technology has improved ambulance safety and efficiency. In ambulance services, Smart PPE (Personal Protective Equipment) improves patient and healthcare worker safety via IoT connectivity. In crises, ambulances are vital. However, healthcare professionals risk contagious infections and physical damage. Though vital, traditional PPE lacks real-time monitoring and communication, leaving healthcare workers exposed. These issues are addressed with Smart PPE using IoT. This novel invention adds sensors and communication devices to paramedic and EMT PPE. These sensors measure air quality, temperature, and the wearer's heart and body temperature. A centralized system can remotely monitor healthcare personnel's safety and well-being using real-time data. Smart PPE has two-way communicators and emergency alert systems. Emergency coordination is improved by ambulance crews and dispatch centers communicating seamlessly. The Smart PPE can automatically inform the team and dispatch in urgent situations, increasing response times and patient outcomes. IoT enables predictive maintenance and data analytics. Trends and dangers may be identified from gathered data, allowing proactive risk mitigation. Smart PPE can measure use and remind users to maintain and replace equipment, assuring dependability when required most. By continually monitoring conditions, allowing real-time communication, and enabling data-driven decision-making, this unique technology improves ambulance services and reduces dangers to devoted personnel. © 2023 IEEE.

关键词： Ambulance Services Efficiency Improvement Emergency Healthcare Real-time Alerts Safety Enhancement

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Cloud Computing Based POC Diagnostic Device: Rapid Infectious Disease Testing by data analytics 1

Cloud Computing Based POC Diagnostic Device: Rapid Infectiou...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Basha, H. Anwer Ramalingam, L. Hasan, Md Ashfaqul Ananda, M.H. Velmurugan, S. Maniraj, S.P. Saveetha College of Liberal Arts & Sciences Simats University Department of Computer Science Tamil Nadu Chennai India Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Reva University School of Electrical and Electronics Engineering Karnataka Bangalore India Tjs Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350330915

To meet the growing need for affordable, efficient, and accurate diagnostic tools, the system has developed a Raspberry Pi-based Point-of-Care (POC) gadget for speedy and reliable detection of infectious illnesses. This groundbreaking gadget, built on the flexible and powerful Raspberry Pi computer, ushers in a new era of state-of-the-art POC diagnostics by combining molecular and image analysis methods. Complex testing for pathogens like COVID-19 and malaria may be carried out using this state-of-the-art system, which integrates a camera module and custom biosensors. The device's software parses sensor data, improves diagnostic precision using machine learning techniques, and presents a straightforward interface for clinicians and patients alike. This study takes a holistic approach by considering all relevant factors, including adherence to regulations, user education, upkeep, and data management. Our POC diagnostic gadget driven by a Raspberry Pi provides a low-cost, high-throughput method for diagnosing illness, which may have far-reaching implications for the future of healthcare delivery in both under-resourced and well-endowed regions. © 2023 IEEE.

关键词： Healthcare Infectious Disease Internet of Things, Cloud Computing Point-of-Care Diagnostics

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Channel Estimation for Optical IRS-Assisted VLC System via Spatial Coherence

Channel Estimation for Optical IRS-Assisted VLC System via S...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Sun, Shiyuan Yang, Fang Mei, Weidong Song, Jian Han, Zhu Zhang, Rui University of Electronic Science and Technology of China Chengdu611731 China Department of Electronic Engineering Beijing100084 China Research Institute Tsinghua University Shenzhen518057 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Shenzhen Research Institute of Big Data Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore

ISBN: (纸本)9798350351255

Optical intelligent reflecting surface (OIRS) has been considered a promising technology for visible light communication (VLC) by constructing visual line-of-sight propagation paths to address the signal blockage issue. However, the existing works on OIRSs are mostly based on perfect channel state information (CSI), whose acquisition appears to be challenging due to the passive nature of the OIRS. To tackle this challenge, this paper proposes a customized channel estimation algorithm for OIRSs. Specifically, we first unveil the OIRS spatial coherence characteristics and derive the coherence distance in closed form. Based on this property, a spatial sampling-based algorithm is proposed to estimate the OIRS-reflected channel, by dividing the OIRS into multiple subarrays based on the coherence distance and sequentially estimating their associated CSI, followed by an interpolation to retrieve the full CSI. Simulation results validate the derived OIRS spatial coherence and demonstrate the efficacy of the proposed OIRS channel estimation algorithm. © 2024 IEEE.

关键词： Channel estimation

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A power optimization approach for mixed polarity Reed–Muller logic circuits based on multi-strategy fusion memetic algorithm

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Frontiers of Information Technology & electronic engineering 2025年第3期26卷 415-426页

作者： Mengyu ZHANG Zhenxue HE Yijin WANG Xiaojun ZHAO Xiaodan ZHANG Limin XIAO Xiang WANG Intelligent Agricultural Equipment Research Institute Hebei Agricultural UniversityBaoding 071001China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural UniversityBaoding 071001China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Electronic Information Engineering Beihang UniversityBeijing 100191China

The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization *** optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power ***,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation ***,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit *** results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization *** achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.

关键词： Power optimization Multi-strategy fusion memetic algorithm(MFMA) Mixed polarity Reed-Muller(MPRM) Combinatorial optimization problem

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Vulnerability Analysis of Passwords Based on Real data 6

Vulnerability Analysis of Passwords Based on Real Data

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Yu, Wei Ni, Lulin Shi, Yu Liu, Cheng Ji, Qingbing No.30 Institute of Cetc National key laboratory of security communication Sichuan Chengdu610041 China School of Computer Science and Engineering University of Electronic Science and Technology of China No.30 Institute of Cetc National Key Laboratory of Security Communication Sichuan Chengdu610041 China

ISBN: (纸本)9798331541750

A password represents the initial line of security for the majority of information systems, and plays a pivotal role in the protection of personal information and the security of digital assets. Based on a substantial corpus of password data, this paper employs a data-driven research methodology to analyse the length distribution, structure, and personal information embedded in user passwords, with the objective of elucidating users' susceptible password behaviours. Subsequently, it proffers a series of recommendations to enhance the robustness of passwords. © 2024 IEEE.

关键词： Authentication Protocol

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AggFollower: Aggressiveness Informed Car-Following Modeling

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Chen, Xianda Yuan, Xinyi Zhu, Meixin Zheng, Xinhu Shen, Shaojie Wang, Xuesong Wang, Yinhai Wang, Fei-Yue Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China School of Transportation Engineering Tongji University Shanghai China Department of Civil and Environmental Engineering University of Washington Seattle WA USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection, acceleration pattern, and fuel consumption. However, existing car-following research used limited categories of driving style through pre-defined patterns and failed to encode driving style into data-driven car-following models. To address these limitations, we propose the Aggressiveness Informed Car-Following (AICF) modeling approach, which embeds driving style as a dynamic input feature in data-driven car-following models. In detail, We design driving aggressiveness tokens using four physical quantities (jerk, acceleration, relative speed, and relative spacing) to capture the heterogeneity of driving aggressiveness. These tokens were then embedded into a physics-informed Long Short-Term Memory (LSTM) based car-following model for trajectory prediction. To evaluate the effectiveness of our approach, we conducted extensive experiments based on 12,540 car-following events extracted from the HighD dataset and 24,093 events from the Lyft dataset. Compared to models devoid of considerations for driving aggressiveness levels, AICF exhibits superior efficacy in mitigating the Mean Square Error (MSE) of spacing and collision rate. To the best of our knowledge, this is the first work to directly incorporate real-time driving aggressiveness tokens as input features into data-driven car-following models, enabling a more comprehensive understanding of aggressiveness in car-following behavior. IEEE

关键词： Long short-term memory

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Graph-Based Network for Depth Completion with Ground Completion

Graph-Based Network for Depth Completion with Ground Complet...

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Chinese Intelligent Automation Conference, CIAC 2021

作者： Chen, Siyuan Duan, Zhuohua University of Electronic Science and Technology of China Chengdu611731 China School of Computer Engineering University of Electronic Science and Technology of China Zhongshan Institute Guangdong528402 China

ISBN: (纸本)9789811663710

The purpose of depth completion is to recover a dense depth map from the sparse depth data and the corresponding single RGB image. This paper proposes a depth completion method that separates ground and non-ground data, and use the equation of the ground directly predict the depth of ground. Specifically, we first separate the ground point cloud and calculate the equation of the ground. Since the point cloud is sparse on the depth map, we use the rasterization method to identify the point cloud area. For the ground area in the image, we combine the ground equation to make depth prediction and use the ground-completed data as training data to train the network. In addition, we also designed a smoothing loss based on depth changes to ensure the stability of the output. Our method has proved its effectiveness on the KITTI dataset. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphic methods

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An Inconsistency-Based Hybrid Feature Selection Approach for Enhancing Medical Classification Modeling 2nd

An Inconsistency-Based Hybrid Feature Selection Approach for...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Zhao, Rong ALDharhani, Ghassan Saleh Ratnavelu, Kuru Thambiayya, Sathiyaraj Jasser, Muhammed Basheer Majeed, Anwar P. P. Abdul Luo, Yang Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Institute of Information Science and Technology Dalian University of Science and Technology Dalian China Faculty of Information Science and Technology UKM University Selangor Bangi43000 Malaysia Institute of Mathematical Sciences University of Malaya Kuala Lumpur56000 Malaysia Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Artificial Intelligence is an essential tool for early disease recognition and supporting patient condition monitoring in the future. Timely and exact conclusions about the type of disease are significant for treatment and life extension. A combination of classification and feature selection algorithms can effectively handle complex datasets in the medical field and improve the accuracy of disease diagnosis and treatment. In this paper, we propose a new hybrid feature selection algorithm that uses inconsistency metrics as a filter method for the first step, and then feeds the resulting dataset into a wrapper method, which ultimately results in a reduced dataset. The proposed HFSIM algorithm is tested on five datasets in the medical domain from Kaggle. The obtained feature subsets are verified on three classification algorithms, KNN, LR and RF of machine learning to validate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm obtains feature subsets with low dimensionality on most of the datasets and also has high classification accuracy after testing on the classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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