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Fruit and Vegetable Classification and Freshness Detection using Machine learning

Fruit and Vegetable Classification and Freshness Detection u...

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2024 MIT Art, Design and Technology School of computing international conference, MITADTSoCiCon 2024

作者： Akshi Varshney, Poorvi Avasthi, Sandhya Agarwal, Kadambri ABES Engineering College Department of Computer Science and Engineering Ghaziabad India ABES Engineering College Department of CSE Ghaziabad India

ISBN: (纸本)9798350362879

Freshness is a key factor in determining a fruit or vegetable's quality, and it directly influences the physical health and coping provocation of consumers. It ascertains the nutritional value of the specified fruit or vegetable. This paper proposes a well-organized and precise fruit and vegetable classification and freshness detection method. The proposed method employs state-of-The-Art deep learning models, specifically convolutional neural networks (CNNs), to analyze images of fruits and vegetables captured through high-resolution cameras. The dataset used for training and evaluation is extensive and diverse, encompassing a wide variety of fruits and vegetables in various conditions. The freshness of a fruit or vegetable can be ascertained by looking at a variety of features, including color, texture, shape, and size. Fresh produce, for instance, is colorful and free of mold or brown spots. Traditional methods for assessing the quality of fruits and vegetables are both time-consuming and error-prone. These methods include inspection and sorting. It is possible to reduce these issues by utilizing automatic detection techniques. In light of this, we proposed an automated fruit-vegetable freshness detection approach that first recognizes whether the image is of a fruit or vegetable, after which it classifies it into one of three freshness categories: rotten, fresh, or mixed. To identify and categorize fruits and vegetables, two deep learning models are employed: You Only Look Once (YOLO) and Visual Geometry Group (VGG-16). The suggested method's qualitative analysis indicates superior performance on the fruit-vegetable dataset. © 2024 IEEE.

关键词： Vegetables

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Real-Time Brain Signals Monitoring System Using Single-Channel EEG for BCI Applications 2

Real-Time Brain Signals Monitoring System Using Single-Chann...

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2nd IEEE international conference on Advancements and Key Challenges in Green Energy and computing, AKGEC 2024

作者： Ghosh, Soinik Parikshith, C. Yashmeen, Fauzia Srivastava, Alok Kumar Sharma, Shiru Sharma, Neeraj School of Biomedical Engineering Varanasi India

ISBN: (纸本)9798350390179

This study presents the development of a system for real-time monitoring of brain signals using a wearable device equipped with a single-channel EEG sensor. Brain signals, acquired through a custom-designed data acquisition protocol, are used to monitor cognitive and physiological states during various events. The labeled data are utilized to train a deep learning model. EEG signals are processed using a microcontroller and visualized using a graphical user interface (GUI). Data collection from participants was conducted under three different conditions: eyes open, eyes closed, and motor imagery tasks involving hand opening and closing. These conditions serve as training inputs for a deep learning model, enabling it to learn and classify signal patterns associated with different events. The acquired signals are analyzed by extracting various features and visualizing time-frequency domain properties for class differentiation. This system holds potential applications in cognitive and physiological monitoring and applications in brain-computer interface (BCI), such as prosthetic arm control, with the added benefits of low-power, cost-effective edge computing and green computing for sustainable deployment. © 2024 IEEE.

关键词： Graphical user interfaces

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Cardio Prognosis: Machine learning Approaches to Heart Disease Prediction 3

Cardio Prognosis: Machine Learning Approaches to Heart Disea...

引用

3rd Odisha international conference on Electrical Power engineering, Communication and computing Technology, ODICON 2024

作者： Rout, Laxmipriya Mohapatra, Amritakhi Mohapatra, Arpita Jena, Swarna Prabha Paikaray, Bijay Kumar Chakraborty, Mangaldeep Centurion University of Technology and Management Department of Cse Odisha India Centurion University of Technology and Management Department of Ece Odisha India Center for Data Science Department of Cse Odisha India Bishnupur Public Institute of Engineering Department of E&tc West Bengal India

ISBN: (纸本)9798350354379

Global mortality is high due to cardiac disease, which affects heart anatomy and function. Timely identification improves results and lowers healthcare costs. This study uses machine learning (ML) to analyze patient data risk variables and patterns to construct a heart prediction system. This approach will enhance cardiac disease prediction and reduce misdiagnosis. In general, the datasets are heterogeneous and scattered. Therefore, cardiovascular prognostic data is needed. Numerous studies have examined heart disease types, algorithms, applications, and treatments. This study uses Decision Tree, K-Nearest Neighbor, Gaussian Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression to develop heart disease prediction models. Other algorithms include pipeline, cross-validation, hyperparameter optimization, and EDA. Logistic regression, with 99% accuracy, outperforms. © 2024 IEEE.

关键词： cross-validation Heart Disease hyperparameter optimization Machine learning pipeline

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EfficientNetB0-based Automated Diabetic Retinopathy Classification in Fundus Images 3

EfficientNetB0-based Automated Diabetic Retinopathy Classifi...

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Majaw, Ezekiel Alaric Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Venkatesan Ajibesin, Adeyemi Abel Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa

ISBN: (纸本)9798331532420

The most common cause of blindness among diabetics is diabetic retinopathy. This disease must be detected in its early stages as delaying treatment can result in permanent blindness. In today’s modern world with the development of technologies, automated techniques have improved the accuracy and efficiency of detecting and classifying this disease. This study suggests a deep learning model utilizing EfficientNetB0 that focuses on designing a neural network structure that requires fewer parameters and computations compared to traditional architectures. Further to help in increasing the accuracy, the image is being processed with methods like CLAHE which is an image processing algorithm that helps in enhancing the contrast while keeping the noise of the images as low as possible. Also, augmentation is being done on the images and SMOTE is applied to handle class imbalance. We collected the APTOS Blindness Detection dataset consisting of retinal fundus images. The accuracy that is being obtained from the proposed methodology is 98.78% indicating the model's effectiveness in detection and classifying between the various stages of diabetic retinopathy. © 2024 IEEE.

关键词： Contrastive learning

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A Survey on Stock Market Prediction Techniques

A Survey on Stock Market Prediction Techniques

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2023 international conference on Power, Instrumentation, Control and computing, PICC 2023

作者： Krishnapriya, C.A. James, Ajay Government Engineering College Department of Compute Science and Engineering Thrissur India

ISBN: (纸本)9798350334463

The stock market is a meeting point for buyers and sellers to buy and sell stocks. Stock price depends on local and global events. It has a dynamic, non-stationary nature. Thus, it is exceedingly challenging to make accurate stock price predictions. If we can predict the stock prices accurately, it will be helpful for investors. Many researches are going on in this area. Machine learning and deep learning methodologies are used for stock prediction. In this paper, a survey of different stock prediction techniques is done. Additionally, an approach that can give more accurate predictions than the techniques examined is suggested. © 2023 IEEE.

关键词： Financial markets

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international conference on Data Science and Network engineering, ICDSNE 2023

International Conference on Data Science and Network Enginee...

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international conference on Data Science and Network engineering, ICDSNE 2023

ISBN: (纸本)9789819967544

The proceedings contain 39 papers. The special focus in this conference is on Data Science and Network engineering. The topics include: Smart Surveillance System and Prediction of Abnormal Activity in ATM Using Deep learning;a Framework for Extractive Text Summarization of Single Text Document in Tamil Language Using Frequency Based Feature Extraction Technique;an Approach to Mizo Language News Classification Using Machine learning;BASiP: A Novel Architecture for Abstractive Text Summarization;a Hybrid Approach for Leaf Disease Classification Using Machine learning and Deep learning;enhancing Agricultural Decision-Making Through Machine learning-Based Crop Yield Predictions;Pest Detection Using YOLO V7 Model;random Forest Classifier-Based Acute Lymphoblastic Leukemia Detection from Microscopic Blood Smear Images;FedCNNAvg: Federated learning for Preserving-Privacy of Multi-clients Decentralized Medical Image Classification;training Algorithms for Mixtures of Normalizing Flows;acute Lymphoblastic Leukemia Detection Using DenseNet Model from Microscopic Blood Smear Images;a Disease Prediction Framework Based on Predictive Modelling;a Data-Driven Diabetes Predictive Model Using a Novel Optimized Weighted Ensemble Approach;analysis of Image Caechniques Using Encoder–Decoder;security and Energy Efficiency Enhancement for the Internet of Things: Challenges, Architecture and Future Research;spot Pricing in Cloud computing: A Comprehensive Survey of Mechanisms, Strategies, and Future Directions;A Comparative Analysis of Propagation Models Suitable for Non-Line-of-Sight 5G Communication at 26 GHz;validating δ-Currency Using Model Checking;a Batch-Service Queueing Assisted Blockchain System for Supply Chain Management;biFrost: A Blockchain-Based Decentralized Messaging Application;priority Based Load Balancing for Intercloud computing Environments;machine learning Approach to the Internet of Things Threat Detection.

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Detecting and Mitigating Insider Threat Attacks in Cloud using Machine learning 6

Detecting and Mitigating Insider Threat Attacks in Cloud usi...

引用

6th international conference on Cybernetics, Cognition and Machine learning Applications, ICCCMLA 2024

作者： Sivanagireddy, K. Jagadeesh, S. Suresh Kumar, K. Chandrika, B.A. Kranthi Kumar, M. Rajalingam, P. Sridevi Women's engineering college Department of electronics and communication engineering Telangana Hyderabad India

ISBN: (纸本)9798331505790

The proliferation of smart devices has led to an exponential increase in cyber threats, posing significant challenges to cybersecurity, particularly with the centralized nature of cloud computing. Insider threats, leveraging their privileged access, exacerbate these risks, potentially resulting in valuable data breaches. To address this, a novel approach leveraging machine learning is proposed to detect and classify insider threats, aiming to enhance accuracy through ensemble learning techniques. The study utilizes a customized dataset sourced from multiple files of the CERT dataset, reflecting real-world *** of Machine learning algorithms, including Decision Tree classifier, Naive Bayes, KNN Classifier, Logistic Regression, and SVM are evaluated on this dataset. Among these, Support Vector Machine (SVM) emerges as the top performer, achieving an impressive accuracy rate of 98 %. This outcome underscores the efficacy of SVM in identifying and classifying insider threats, highlighting its potential as a key component in cybersecurity frameworks. Implementing such approaches can enhance organizations' resilience against insider threats, safeguarding sensitive data and mitigating potential risks to business operations. © 2024 IEEE.

关键词： Adversarial machine learning

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Supporting Students in Assembler-Level Programming of Processors for Embedded Applications 20th

Supporting Students in Assembler-Level Programming of Proces...

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20th international conference on Remote engineering and Virtual Instrumentation: Open Science in engineering, REV 2023 co-organized with the international Edunet World conference, IEWC 2023

作者： Grout, Ian da Silva, Alexandre César Rodrigues Department of Electronic and Computer Engineering University of Limerick LimerickV94 T9PX Ireland Department of Electrical Engineering São Paulo State University Ilha Solteira Brazil

ISBN: (纸本)9783031424663

In this paper, the role of different laboratory scenarios (local and remote) are considered for the teaching and learning of processor-based embedded systems. Specifically, the development of teaching and learning approaches to microcontroller programming using assembly code (assembler-level) programming techniques, along with C with embedded assembly code, will be presented and discussed. For the student demographics considered (academic level, motivation, and personal circumstances that may include limited ability to obtain physical hardware), different means to access the core content and laboratory resources for practical work are identified and elaborated with the student requirements and accessibility as a focus. The target hardware is the ubiquitous Arduino UNO platform since due to its available online support, low-cost, robustness, and availability. For at-presence teaching and learning, both the Arduino Integrated Development Environment (IDE) and the Microchip Studio environments are utilized as two means for students to access and program the microcontroller hardware. For remote teaching and learning, a simple web page based on HTML, PHP, JavaScript has been prototyped and will be presented. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： accessibility assembly code Programming

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The Work on the Application of Electrical Machines and the Project Method in a Public University of Peru 4

The Work on the Application of Electrical Machines and the P...

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4th IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Rojas, Julio Ernesto Quispe Tuesta, Julio Enrique Quispe Universidad Nacional Pedro Ruiz Gallo Computing and Electronic Departament Lambayeque Peru Universidad Nacional Tecnológica de Lima Sur System Engineering Program Lima Peru

ISBN: (纸本)9798350395914

The purpose of this research is to present an improved project method and the results obtained in its use for the application work of the electrical machines course of an electronic engineering program of the Pedro Ruiz Gallo National University of Lambayeque in Peru. Previously, from 2013 to 2018, the project method was used, and based on these experiences, a proj ect method was prepared with greater detail of the activities and deliverables in each phase of the method, the results are better information, planning and organization by part of the students and a better evaluation by the facilitator, who acts as a referee and not as a coach as in traditional vocational training. The improved project method that has been applied in the 2019-11 semester and completed the validation in the 2023 semesters, due to the COVID 19 quarantine in the years 2020 to 2022, no real application work was carried out. In order to train professionals with new skills, vocational training must be innovated with new teaching-learning modes, such as the project method. The projects carried out in 2019-11 gave 4 very good projects: Automotive alternator as direct current generator, DC generator based on a DC motor, Construction of a windshield wiper system, Construction of a single-phase induction motor;2 good projects and 5 regular projects. © 2024 IEEE.

关键词： AC generator motors

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Robust Multi-agent Federated Reinforcement learning for Task Offloading 7th

Robust Multi-agent Federated Reinforcement Learning for Task...

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7th international conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Yan, Dibao Wang, Yongfeng Hou, Wenjing Song, Huanhuan Wen, Hong Ma, Wendi Sun, Fan School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

With the proliferation of various mobile smart devices, edge computing and computational offloading technologies have emerged as pivotal support mechanisms, enhancing the service quality of these devices. To facilitate the learning of task offloading strategies across diverse and complex scenarios, this study introduces a federated learning strategy algorithm based on multi-agent deep reinforcement learning. This algorithm aims to aggregate the training strategies of multiple edge computing devices, thereby enabling the synthesis of superior task offloading strategies tailored to a wide array of environments while also providing safeguards against malicious nodes. This paper specifically examines the reward inversion attack, illustrating the algorithm’s capability to identify and counteract such malicious threats effectively. Experimental results validate that the proposed algorithm not only robustly defends against these attacks but also adeptly learns the task offloading strategies pertinent to each node. © The Author(s) 2025.

关键词： Federated learning

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