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Plant Disease Detection and Diagnosis

Plant Disease Detection and Diagnosis

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Emerging Technology (INCET), international conference for

作者： Pedapudi. Nagababu Shaik. Nageena Veeranki. Dharani Darsi. Naveen Department of Computer Science and Engineering Lakireddy Bali Reddy College of Engineering (Autonomous) Mylavaram India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

One of the most major occupations and a key contributor to the GDP of our country is agriculture. Food crops have a vital role in the ecosystem and for human health, and plant diseases can result in large losses in food supply. Plant leaves can become infected with a variety of diseases, including blights, leaf spots, and other bacterial and fungal infections. It's critical to identify plant diseases in order to minimise yield losses. Disease identification and plant monitoring are essential to sustainable agriculture. Manually observing plant diseases is a challenging task. We therefore present a novel solution to these issues. We are going to talk about a method that uses photos of the plants' leaves to identify plant illnesses. these flaws have a severe detrimental impact on both the quantity and quality of crop yield. In this research, a review is carried out to examine the outcomes of studies based on the machine learning model for plant leaf disease identification. Many plants die as a result of improper diagnosis of these illnesses, ignorance of the symptoms, and lack of knowledge about treatment options. Image processing, which can extract important information and features from images, is the primary component employed in this research. To achieve the maximum level of precision, leaf colour, leaf damage, leaf area, and leaf texture are all classified qualities. CNN algorithm is used to examine various image parameters or features for the purpose of recognising various plant leaf diseases. this plant disease diagnostics can assist farmers in creating defence systems that lead to robust and fruitful food crops.

关键词： Productivity Plant diseases Microorganisms Reviews Plants (biology) machine learning Feature extraction

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Detection and Classification of Bipolar Disorder Syndrome using Deep learning

Detection and Classification of Bipolar Disorder Syndrome us...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： R.M. Dilip Charaan J Vimala Ithayan M Sankar R Chithambaramani P Sivaprakash D Marichamy Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Vel Tech Rangarajan Dr. Sagunthala Institute of Science and Technology Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala Institute of Science and Technology Chennai India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Neural networks that are inspired by the way a human brain is structured, are known as artificial neural networks represent a unique class of machine learning algorithms. these algorithms allow artificial neural networks to acquire from data, and make resolutions based on the outcome, just like humans do. Non-linear data models, which have a whole load of variables and inputs, can predict new patterns. Artificial neural networks are already leveraged in everything from medical diagnoses to speech recognition as well as the ever-growing field of machine translation. there are multiple processes involved in the deep learning methodology that the method suggests for classifying bipolar disease. First, clinical and demographic data such as the sample’s age, gender, symptom severity, and medication history would be included in the dataset of bipolar disorder patient samples and control samples. the dataset would next undergo preprocessing to remove any outliers or missing values. In the meantime, standardization and normalization would be applied to the data to ensure that each variable is on a consistent scale. Data scientists may also decide to use feature selection to determine which variables are most useful for overall optimization for classification goals. In this proposed system accuracy is better as compared to the remaining conventional models. this makes it easier for us to select the elements that are most crucial to making our part a real feature. the two models ANN are the final features. Findings demonstrate that deep learning models, like as artificial neural networks (ANNs), are effective in treating neuroinformatics illnesses since datasets are readily available. When it comes to public relations, moods, and the dataset, the deep learning model predicts the correct classes with more precision. A popular application of ANN involves approximating a random function, thereby providing an inexpensive way to get to various statistics characterizing its distrib

关键词： Deep learning machine learning algorithms Public relations Artificial neural networks Standardization Speech recognition Predictive models Prediction algorithms Medical diagnostic imaging Biological neural networks

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Cardiac Signal Monitoring System using NonContact Measurement for Physiological Features 23

Cardiac Signal Monitoring System using NonContact Measuremen...

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Proceedings of the 5th international conference on Information Management & machine Intelligence

作者： Kalaiselvi Balaraman Angelin Claret S.P Department of Computer Science SRM Institute of Science and Technology Inida Department of Computational Intelligence SRM Institute of Science and Technology India

ISBN: (纸本)9798400709418

Healthcare is a crucial part of human beings; thus, keeping a check by recording and monitoring certain essential functions of the body is important. Using sensors to record multiple physiological processes can be discomforting for the body. this is especially true if the recording is done for a prolonged period, due to this reason that methods for remote physiological monitoring are becoming popular. these methods do not involve the physical interaction of any sensors with the human body or skin. Several studies have proposed the detection of Blood Volume Pulse, or BVP, to calculate Heart Rate and Heart Rate Variability. In this particular study, the main focus is on measuring the Heart Rate (HR). the algorithm utilized in this study is Independent Component Analysis (ICA). the primary function of this algorithm is to separate the main source signal essential for measurement from the noise in the RGB channels of the facial video. In this paper, the generalization of existential methods is addressed. In addition, the process considered in this approach is much more accurate. It is possible due to the implementation of a light equalization scheme. this scheme helps reduce issues of unequal facial light and minimize the effect of shadows—the selection of the best and most accurate signal as outputted by the ICA module.

关键词： Heart-rate detection

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Design and Application of English Oral Online Dialogue System Based on Reinforcement learning Algorithm

Design and Application of English Oral Online Dialogue Syste...

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5th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2024

作者： Chen, Ting Jiangxi Vocational College of Foreign Studies Jiangxi Nanchang330099 China

In view of the lack of personalization and interactivity in current oral English learning, a dialogue system based on reinforcement learning is introduced, aiming to improve users' oral expression ability by dynamically adjusting the learning path and feedback mechanism. the system adopts a deep Q-learning algorithm, optimizes the voice interaction effect through a reward mechanism, and realizes real-time adaptive adjustment of the dialogue. First, a dialogue framework based on reinforcement learning is designed, in which the user's voice input is converted into text and used as the input of the system. then, the system uses a deep Q-learning algorithm to conduct feedback learning based on the user's voice performance and grammatical errors, and adjusts the dialogue strategy and vocabulary recommendation in real time to improve the interactivity and accuracy of learning. Finally, the system trains multiple rounds of dialogues in a simulated environment to continuously optimize the speech recognition and dialogue response strategies. the whole process uses a reward mechanism to adjust the system behavior based on the actual performance of the user to ensure gradual improvement in the learning process. Learners with a medium foundation (intermediate) have a slight improvement in satisfaction scores, reaching 4.3 points, and their oral test improvement is +15 points. Learners with a high foundation (advanced) have a satisfaction score of 4.5 points in the use of the system, and their oral test improvement is +18 points, thanks to the detailed and accurate feedback provided by the system. By introducing a reinforcement learning algorithm, the designed English oral online dialogue system can adjust learning strategies in real time according to user feedback and improve learning effects. Future research will further optimize the reward mechanism and enhance the system's adaptive ability and intelligent recommendation function to better serve the needs of different learner

关键词： Reinforcement learning

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Fault Detection Method Based on Siamese Convolutional Neural Networks

Fault Detection Method Based on Siamese Convolutional Neural...

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computer Engineering and Intelligent Control (ICCEIC) international conference on

作者： Xuncheng Zhu Yining Zhao Ning Li Dianyi Huang Wenan Li Honglei Zhang Hui Zhang Yunnan Institute of Standardization Kunming China School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

Bearing components are important parts of mechanical equipment and have a significant impact on the operating status of the equipment. However, in actual industrial production, the data generated by bearing components is characterized by high dimensionality, nonlinearity, insufficient fault data, noise content, and operating load variation. Traditional machine learning methods for fault detection require human selection of fault features, and their reliability cannot be guaranteed. therefore, to address the above problems, this paper proposes a fault detection model based on Siamese Convolutional Neural Networks (Siamese CNN) and conducts experiments on the benchmark bearing fault dataset collected by the Case Western Reserve University (CWRU) data center. the results show that the average detection accuracy of the model reaches 99.6% under four working conditions, which is 0.4% higher than that of the Convolutional Neural Networks (CNN) model; the detection accuracy of the model is still higher than that of the CNN model in the case of fewer training samples and stronger noise; the average detection accuracy of the Siamese CNN model reaches 98.87%, which is 3.98% higher than that of the CNN model under six variable load cases and the detection accuracy of the model reaches 97% for all of the six variable load the detection accuracy of the Siamese CNN model reaches 98.87% on average, which is 3.98 % higher than that of the CNN model, and the detection accuracy in the six variable load cases reaches over 97%. the experiment fully verifies the effectiveness of the model.

关键词： Training Employee welfare Accuracy Fault detection Computational modeling Noise Production Data models Convolutional neural networks Load modeling

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Layer-wise Relevance Backward Propagation for Fake Face Detection and Reconstruction

Layer-wise Relevance Backward Propagation for Fake Face Dete...

引用

Image Processing and Capsule Networks (ICIPCN), international conference on

作者： Muhumuza David Mawejje Mark William Nabagereka Bridget Nakayiza Hellen Raudha Rose Nakibuule Ggaliwango Marvin Dept of Computer Science Makerere University Arua Uganda Dept of Computer and Information Science Muni University Kampala Uganda

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Deep learning techniques involving Generative Adversarial Networks (GANs) enable artificially generated facial images. this alone presents difficulties in terms of authenticity resulting in ethical concerns among institutional applications for digital forensics, security and social media moderation. A question remains today, why would someone fake identity and how can we improve performance of the deep neural networks? this project initiated a novel approach called Layer-wise relevance backward propagation for fake face detection and reconstruction that determines the relevance of each layer to the final prediction by extracting the most important features in the input images while giving the reason as to how the model has come to the decision. Layer wise backward relevance propagation (LBRP) being the most effective explainable technique to give relevance scores, the Convolutional Neural Network (CNN) was trained using human face dataset from flickr-face-hq along with pretrained models models including VGG-16, Res-net, Mobile-net, Vision *** improve and monitor model performance, accuracy was consider along other evaluation metrics such as precision and finally machine learning operations (MLOPs) was used for managing the pipeline resources of the model thus an accuracy 99.84% with VGG-16 was obtained along with different visualization of Grad-cam and more to give insights into each prediction made by the model. therefore due tho this effectiveness from the results obtained during image processing tasks we propose an algorithm that combines several techniques that ensure improved accuracy, precision through incorporation of interpretability using explainable methods involving LBRP and grad-cam for fake face detection and reconstruction of faces to enable capture informative features at different scales, integrating transfer learning and ensembled method for scalable performance.

关键词： Accuracy Computational modeling Predictive models Feature extraction Transformers Data models Face detection

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human Routine Analyzer Using machine learning

Human Routine Analyzer Using Machine Learning

引用

Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： M. Arvindhan Akshat Sharma Atul Kumar Prasad department of computer science Galgotias University Greater Noida

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

this project aims to develop a ML model which will have the ability to analyze and interpret the human body gestures and movements with the help of several sensors like - Gyro Sensor, Accelerometer, etc. and will be able to present all the data collected by it graphically for an easier understanding of the *** model will take the readings provided by the sensors into account and that data will be provided by through the user’s smart phone. All that data will be passed onto the model which will then process the data using several ML models like - Random Forest Classifier, KNN Classifier, SVM, and Logistic Regression. Afterward the data processing the model will provide the accuracy score. the final output of the model will lay out the outcome of data processing through graphs like - Bar Chart, Pie Chart, *** model’s scope will range from medical to human computer interaction. the model will be making the user aware of their health by keeping track of their running, sleeping, walking, etc. activities and that data can be used by dietitian, or doctors to help them come up with a better remedy if the user is suffering from any kind of disease.

关键词： Support vector machines Legged locomotion Logistic regression Computational modeling Medical services Data processing Data models

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A Transformer-Based Model for English to Yoruba machine Translation

A Transformer-Based Model for English to Yoruba Machine Tran...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Oluwabusayomi Blessing Olagunju Oluwatoyin Bunmi Abiola Olaide Yetunde Saka-Balogun Bukola Badeji-Ajisafe Abiodun Oguntimilehin Lawrence Olubunmi Adewole Kehinde Abimbola Adigun Stephen Eyitayo Obamiyi Tolani Grace Oluwatoki Department of Computing Afe Babalola University Ado Ekiti Nigeria Department of Maths & Physical Sciences Afe Babalola University Ado Ekiti Nigeria Departement of Computer Science Federal University Oye-Ekiti Oye Ekiti Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

In Nigeria, Yoruba is a predominant language among over 500 spoken languages. Despite its importance, Yoruba is classified as a low-resourced language, lacking the extensive datasets and linguistic resources needed for advanced natural language processing (NLP) applications. While machine translation (MT) has advanced significantly over the past decade, these improvements mainly benefit high-resourced languages due to the large corpora required for training neural networks. Consequently, Yoruba remains under-resourced in computational linguistics, and NLP research for Yoruba is still in its early stages. this research focuses on developing a machine translation model for the agricultural domain, translating from English to the resource-scarce language Yoruba using transfer learning. the research involved creating approximately 11,000 agriculture-related texts and generating human translations for these texts. this translation pair was then used to train a model based on the pre-trained Google T5-small model. the transformer's ability to handle complex linguistic patterns and variable-length sequences improves translation quality and reduces training time and development complexity, even with a small training dataset. the model achieved a BLEU score of 0.5354 on the evaluation set against the BLEU score of 0.5369 obtained during the model training. Additionally, the translation quality was evaluated by humans in terms of representation accuracy, contextual correctness, and syntactic correctness. Using the mean opinion score of ten (10) local speakers, the translation quality of the model generated in this research ranks above translations from a state-of-the-art model in terms of syntactic and semantic correctness. the results demonstrated that the model developed in this research used domain-specific terms from the training corpus, providing more informative and beneficial translations for local farmers than Google Translate, which often relies on transliteration.

关键词： Training Translation Transfer learning Semantics Neural networks Syntactics Linguistics Transformers Internet machine translation

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Autonomous Navigation and Control Method of UAV Swarm Based on Deep Reinforcement learning

Autonomous Navigation and Control Method of UAV Swarm Based ...

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5th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2024

作者： Zheng, Yingsong Chengdu Polytechnic Sichuan Chengdu610041 China

the efficient collaboration of drone swarms depends on real-time information sharing and communication between agents. However, in complex environments, communication delays, packet loss, and bandwidth limitations can affect the collaborative operations between agents and reduce the success rate of missions. this paper investigates the autonomous steering and command methods of drone groups based on profound retraining learning, focusing on the applicability of the multi-agent profound deterministic design gradient (MADDPG) engine in multi-agent restoration learn (MARL). First, this paper constructs an environmental model of the drone swarm, including the monetary function, movement space, and state dimension of every drone. the state space contains the drone's position information, speed, acceleration, etc., as well as global information such as obstacles and target positions in the environment;the action space includes speed control and direction adjustment, and the reward function guides the behavior of the drone through factors such as approaching the target, avoiding obstacles and energy consumption. then, based on the MADDPG algorithm, an algorithm framework suitable for this problem is designed, including the cognitive the agent's value and policies. the policy network selects the best action based on the current state, and the value network is used to evaluate the value of the state, thereby adjusting the behavior of the intelligent agent. through the strategy of centralized training and distributed execution, MADDPG can effectively solve the interaction and cooperation problems in multi-UAV collaborative navigation and achieve the optimization of autonomous navigation tasks. Experimental results show that in the case of three agents, the energy consumption of the MADDPG algorithm is 45J and the mission success rate is 94%. © 2025 the Authors. Published by Elsevier B.V.

关键词： Deep reinforcement learning

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RTIFQLD: An Integrated Framework for RealTime IoT Forensic Analysis via Incremental Qlearning Modelled with Resource Aware DQN Operations 23

RTIFQLD: An Integrated Framework for RealTime IoT Forensic A...

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Proceedings of the 5th international conference on Information Management & machine Intelligence

作者： Gangavarapu Rajesh Babu Virendra Kumar Sharma Computer Science and Engineering Bhagwant University India

ISBN: (纸本)9798400709418

As the proliferation of Internet of things (IoT) devices permeates every facet of human life, the need for effective and efficient forensic analysis within this intricate ecosystem has become more critical than ever. the imperative to swiftly and accurately identify anomalous behaviors or security incidents carries substantial ramifications not only for cybersecurity but also for public safety and critical infrastructure protection operations. Current forensic analysis methods for IoT often suffer from issues related to scalability, adaptability, and real-time analysis capabilities. Traditional machine learning techniques frequently require batch processing, making them unsuitable for real-time deployment. Moreover, these techniques often lack the ability to adapt to the dynamically evolving nature of IoT devices and the myriad threats they face, resulting in suboptimal forensic accuracy and responsiveness. this paper presents an integrated framework for real-time IoT forensic analysis that successfully addresses these limitations. We introduce a novel approach combining Incremental Q-learning operations, Resource-Aware Deep Q-Networks (DQN) enhanced with Bacterial Foraging Optimization, and a robust ensemble of classifiers including Naive Bayes, Support Vector machines, Logistic Regression, and Multi-Layer Perceptron for forensic analysis. Our contributions yield several advantages, including superior scalability, real-time analysis, and adaptability to emerging threats. Empirical results demonstrate a significant improvement in the precision of forensic event classification by 8.3%, accuracy by 4.5%, recall by 3.5%, and specificity by 4.9% across different scenarios. By bridging the gap between real-time requirements, resource awareness, and advanced machine learning techniques, this framework sets a new standard for IoT forensic analysis, enhancing both its effectiveness and efficiency levels. this work represents a significant stride in the march toward a safer

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