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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Smart Crop Protection Against Animal Encroachment using Deep learning 4

Smart Crop Protection Against Animal Encroachment using Deep...

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4th international conference on Intelligent Engineering and Management, ICIEM 2023

作者： Patel, Rishi Sanjaykumar Amin, Dhrumi Devendra Patel, Sachin Rahevar, Mrugendrasinh Patel, Chirag Nayak, Amit Department of Information Technology India U and PU Patel Department of Computer Engineering India

ISBN: (纸本)9798350341126

In today's world, agriculture faces numerous challenges, one of which is animal intrusion that poses a threat to crop yields and food security. However, with the help of IoT and DL techniques, farmers can now tackle this challenge effectively. By using devices such as LED, Buzzer, Pi Camera, and ESP8266 Wireless Fidelity module, farmers can execute a machine algorithm that is capable of identifying animals and alerting them when they are approaching their fields. the success of this technique lies in the use of Convolutional neural networks that are based on a region and one-shot detection algorithms, both of which use machine learning methods to categorize animals and identify objects in photos. While both techniques are critical, the location-based convolutional neural network is outperformed by the Single Shot Detection approach. this is because it is faster and more accurate. To ensure that farmers receive timely information, the Twilio API-interfaced program provides them with the relevant information they need to take action in their fields. With this system in place, farmers can now safeguard their crops and improve their profits, ultimately contributing to food security. © 2023 IEEE.

关键词： Crops

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4th EAI international conference on Artificial Intelligence for Communications and Networks, AICON 2022

4th EAI International Conference on Artificial Intelligence ...

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4th EAI international conference on Artificial Intelligence for Communications and Networks, AICON 2022

ISBN: (纸本)9783031291258

the proceedings contain 13 papers. the special focus in this conference is on Artificial Intelligence for Communications and Networks. the topics include: A Research of Infectivity Rate of Seasonal Influenza from Pre-infectious Person for Data Driven Simulation;creating Trust Within Population of Evolutionary Computation in an Uncertain Environment Using Blockchain;efficient Inductive Logic Programming Based on Particle Swarm Optimization;fault Diameter of Strong Product Graph of Two Paths;Design and Implementation of SF Selection Based on Distance and SNR Using Autonomous Distributed Reinforcement learning in LoRa Networks;QBRT: Bias and Rising threshold Algorithm with Q-learning: Implementation of the Tower of Hanoi;A Study on Effectiveness of BERT Models and Task-Conditioned Reasoning Strategy for Medical Visual Question Answering;deep Robust Neural Networks Inspired by Human Cognitive Bias Against Transfer-based Attacks;efficient Estimation of Cow’s Location Using machine learning Based on Sensor Data;A Time Series Forecasting Method Using DBN and Adam Optimization;unity-Bounded Function and Benchmark Design Specifications Targeted for Designing Typical Variable Digital Filters.

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Revolutionizing Maize Disease Management with Federated learning CNNs: A Decentralized and Privacy-Sensitive Approach 4

Revolutionizing Maize Disease Management with Federated Lear...

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4th international conference for Emerging Technology, INCET 2023

作者： Mehta, Shiva Kukreja, Vinay Gupta, Amit Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350335750

For sustainable maize production, reliable and prompt disease identification is crucial. Maize diseases represent a danger to world food security. In this work, utilizing decentralized and privacy-sensitive data, to constructed and evaluated a Federated learning CNN model to identify and diagnose maize illnesses. the 9878 maize photos in to dataset, divided into training, validation, and test sets, depict a variety of diseases and conditions. To prepare the dataset for training the model, to did data pre-processing, such as picture scaling, normalization, augmentation, and label encoding. the Federated learning CNN model outperformed conventional CNNs and other frequently used machine learning algorithms in agriculture, achieving an overall accuracy of 89.4% on the test set. the Federated learning CNN model can precisely identify and categorize maize illnesses, as shown by the model's superior precision, recall, and F1-score values compared to other algorithms for each disease class. the CNN model from Federated learning also showed resilience and consistency across the various disease classes, demonstrating the model's ability to adjust to local circumstances and variances in maize illnesses across multiple locations and farms. Additionally, to visualized the feature maps and activation patterns to understand the model, revealing how it generates predictions and which aspects of the maize photos are most crucial for disease diagnosis. To work underscores the significance of creating decentralized, privacy-preserving machine learning models in agriculture and shows the promise of federated learning CNNs for crop disease diagnosis and management. © 2023 IEEE.

关键词： Diseases

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Draw4CM: Detecting Cervical Myelopathy via Hand Drawings Captured by Mobile Devices 26

Draw4CM: Detecting Cervical Myelopathy via Hand Drawings Cap...

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26th international conference on Mobile Human-computer Interaction (MobileHCI)

作者： Watanabe, Takuro Yamada, Eriku Fujita, Koji Sugiura, Yuta Keio Univ Yokohama Kanagawa Japan Inst Sci Tokyo Tokyo Japan

ISBN: (纸本)9798400705069

Cervical myelopathy (CM), which causes numbness and pain in the hands, can affect manual dexterity and render daily life difficult. the characteristics of this disease are expected to be apparent owing to the necessity of fine motor control for drawing in daily life. therefore, this study proposed a method to screen for CM by asking patients to draw a figure on a tablet screen using a stylus. the drawing was captured as an image and fed into our machine-learning model to predict CM. the proposed models exhibited sensitivity and specificity comparable to or better than conventional physical methods while avoiding examiner subjectivity and bias. Further, we developed a smartphone application that uses the camera to capture images of figures drawn on paper for CM screening. Moreover, the proposed methods can enable smartphones and tablets to capture disease characteristics and are ubiquitous, thus rendering it easier to reach potential patients.

关键词： Screening Cervical Myelopathy Tablet Device Smartphone Hand Drawing Manual Dexterity Mobile Health

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Adversarial Example Attacks and Defenses in DNS Data Exfiltration 1

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4th international conference on Emerging Information Security and applications (EISA)

作者： Savic, Izabela Yan, Haonan Lin, Xiaodong Gillis, Daniel Univ Guelph Sch Comp Sci Guelph ON Canada Xidian Univ Sch Cyber Engn Xian Peoples R China

ISBN: (数字)9789819996148

ISBN: (纸本)9789819996131;9789819996148

the Domain Network System (DNS) protocol is used on a daily basis to access the internet. It acts as a phone book that allows users to access websites using words rather than remembering address numbers. In recent years it has become clear that there are serious vulnerabilities in the DNS protocol, and the lack of attention to these vulnerabilities (e.g. data exfiltration) is concerning. the widespread use of the DNS protocol opens a door that could possibly allow for companies and users to have their data stolen through data exfiltration. machine learning is a popular tool for malicious traffic detection, however they are vulnerable to adversarial examples. this leads to the security arms race, where researchers aim to accurately detect and counter new malicious threats exploited by malicious actors. In this work, we demonstrate the success of adversarial examples of DNS exfiltration packets in bypassing machine learning detection techniques. We then propose a voting ensemble method to improve adversarial attack detection. the voting ensemble proposed increases the accuracy of adversarial detection, providing a new level of protection against adversarial example attacks.

关键词： DNS protocol data exfiltration machine learning adversarial attack network traffic analysis

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15th international conference on Intelligent Human computer Interaction, IHCI 2023

15th International Conference on Intelligent Human Computer ...

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15th international conference on Intelligent Human computer Interaction, IHCI 2023

ISBN: (纸本)9783031538261

the proceedings contain 71 papers. the special focus in this conference is on Intelligent Human computer Interaction. the topics include: Cross Cultural Comparison of Emotional Functional Networks;emotion Recognition Using Phase-Locking-Value Based Functional Brain Connections Within-Hemisphere and Cross-Hemisphere;context-Aware Facial Expression Recognition Using Deep Convolutional Neural Network Architecture;exploring Multimodal Features to Understand Cultural Context for Spontaneous Humor Prediction;development of Pneumonia Patient Classification Model Using Fair Federated learning;artificial Intelligence in Medicine: Enhancing Pneumonia Detection Using Wavelet Transform;Maximizing Accuracy in AI-Driven Pattern Detection in Cardiac Care;Development of IMU Sensor-Based XGBoost Model for Patients with Elbow Joint Damage;application of Daubechies Wavelets in Digital Processing of Biomedical Signals and Images;adopting Pre-trained Large Language Models for Regional Language Tasks: A Case Study;An Integrated System for Stroke Rehabilitation Exercise Assessment Using KINECT v2 and machine learning;gastric Ulcer Detection in Endoscopic Images Using MobileNetV3-Small;exploring Quantum machine learning for Early Disease Detection: Perspectives, Challenges, and Opportunities;subject-Independent Brain-computer Interfaces: A Comparative Study of Attention Mechanism-Driven Deep learning Models;PPHR: A Personalized AI System for Proactive Robots;classifying Service Robots in Commercial Places Based on Communication: Design Elements by Level of Communication;Cascade Mentoring Experience to Engage High School Learners in AI and Robotics through Project-Based learning;leveraging Human-machine Interactions for computer Vision Dataset Quality Enhancement;A Comprehensive Survey on AgriTech to Pioneer the HCI-Based Future of Farming;roboRecycle Buddy: Enhancing Early Childhood Green Education and Recycling Habits through Playful Interaction with a Social Robot;Text Mining Based GPT M

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A Broad learning System Based on the Idea of Vertical Federated learning 4th

A Broad Learning System Based on the Idea of Vertical Fed...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Ge, Junrong Wang, Xiaojiao Li, Fengyin Gaurav, Akshat Qufu Normal University Jining China Ronin Institute Montclair United States

ISBN: (纸本)9783031200984

As a new type of deep neural network, the broad learning system has been attracting attention since it was proposed. It shows good performance in image processing, face recognition, etc. To train a broad learning model requires comprehensive data. But the data on which the models are trained is often held by different companies and these companies are reluctant to share the data. To address this problem, we propose a broad learning system based on Vertical Federated learning. Compared with the existing broad learning systems, it can integrate multi-party data to obtain comprehensive information, and realize the training of broad learning systems in the case of isolated data islands. this facilitates the application and advancement of broad learning systems. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Improvement of Particle Swarm Optimization Algorithm and Its application in Power Data Mining 4

Improvement of Particle Swarm Optimization Algorithm and Its...

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4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Hu, Hongbin Zhang, Yifan Han, Junfei Wang, Yuqiang Zhong, Ming He, Qianjin Zhang, Longlong Inner Mongolia Power Research Institute Inner Mongolia Enterprise Key Laboratory of Smart Grid Simulation of Electrical Power System Hohhot China Inner Mongolia Tianda Tiancai Information Technology Co. Ltd. Hohhot China

ISBN: (纸本)9798350308082

In power data mining, in order to cope with the rapidly growing data volume, particle swarm optimization algorithms should be improved and applied to power systems to improve performance and efficiency. In the process of particle search, the size of the particle is changed to affect the size of the ELM network structure. Such operation can not only expand the solution space of the problem, but also solve the shortcoming that traditional Particle Swarm Optimization-Extreme learning machine (PSO-ELM) can only find the optimal network structure in the network structure of the same scale. Experiments show that the PSO algorithm with mutation operator can converge to the optimal value more quickly. In addition, compared with the traditional ELM algorithm, Improved Particle Swarm Optimization-Extreme learning machine (IPSO-ELM) can obtain higher generalization performance and prediction progress in short-term power load prediction. the 4 curves respectively represent the MAPE value obtained by IPSO-ELM on the training set 1-4, and the fluctuation range is all within 4%, which proves that IPSOELM has good stability. By improving the particle swarm optimization algorithm and applying it to power data mining, the efficiency of power system can be enhanced, energy waste can be reduced, and power supply quality and reliability can be improved. © 2023 IEEE.

关键词： Data mining

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2023 4th international conference on Clean and Green Energy Engineering, CGEE 2023

2023 4th International Conference on Clean and Green Energy ...

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4th international conference on Clean and Green Energy Engineering, CGEE 2023

ISBN: (纸本)9798350339796

the proceedings contain 25 papers. the topics discussed include: research on drilling and backfilling treatment of ground source heat pump in buried pipe;modeling and analysis of a thermoelectric power generator;mitigation of cyber-threat on dynamic state estimation;an efficient approach for short-term load forecasting using the regression learner application;renewable energy experiment and teaching innovation: experimental teaching mode of solar cell manufacturing process based on PV factory;evaluation of power sharing mechanism of a PEM fuel cell-battery powered electric vehicle;numerical study on the catalytic combustion characteristics of ultra-low calorific value coalbed methane;a hybrid feature selection and construction method for detection of wind turbine generator heating faults;and a short-term prediction model for multi-energy load of IES based on comprehensive meteorological index and deep learning multi-network fusion.

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Implementation of Optimal Crop Recommendation System using machine learning Algorithms 4

Implementation of Optimal Crop Recommendation System using M...

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4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Sanjana, Siram Kumar, K Nithin Satyanarayana, P. Suresh Varma, K.N.V. Anitha, Inumula Krishnan, V. Gokula V. R. Siddhartha Engineering College Department of ECE AP Vijayawada India S.R.K.R. Engineering College Department of ECE A.P Bhimavaram India Department of CSE Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

Agriculture consumes a significant 85% of the world's freshwater resources, a trend expected to persist due to growing population and food demand. To overcome this challenge, developing strategic solutions blending scientific principles and advanced technology are essential. Leveraging machine learning technologies, an innovative model is proposed to minimize crop loss due to adverse weather conditions. the proposed integrated system addresses three critical aspects of agriculture: crop selection, fertilizer recommendations, and automated irrigation. By analyzing various parameters such as pH levels, current weather, and soil nutrients (potassium, phosphorous, nitrogen), the proposed model adeptly selects optimal crops based on various environmental factors. A mobile application translates soil conditions into graphical data for easy interpretation. Evaluation of machine learning algorithms reveals Support Vector machine (SVM) as the highest accuracy at 95.89%, followed closely by Naïve Bayes (NB) at 95.24%. Decision Tree also performs competitively at 95.05%, while K-nearest Neighbors (K-NN) trails slightly with 93.81% accuracy. these findings depict the efficacy of SVM in accurately classifying datasets, presenting a promising path for addressing water demand in agriculture. © 2024 IEEE.

关键词： Adversarial machine learning

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