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Plant Disease Identification Tracking and Forecasting Using machine learning

Plant Disease Identification Tracking and Forecasting Using ...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： M. Sowmya Vani S. Girinath V. Hemasree Lomada Hars Havardhan Poli Sandhya Department of Masters of Computer Applications School of Computing Mohan Babu University (Erstwhile SreeVidyanikethan Engineering College) Tirupati AP India

How safe our food supply is and how productive our agriculture is are both significantly impacted by plant diseases. For prompt cures and efficient management of plant diseases, accurate diagnosis, close observation, and foresight are necessary. Methods like deep learning, feature extraction, and picture recognition are frequently used in disease detection. For farmers, agronomists, and decision-makers, machine learning can be used to distinguish, monitor, and forecast illness affecting plants. The Plant Village data set is meticulously segmented for disease prediction training and testing; as a result, several plant species are acknowledged and given new names to make an accurate database. The classifier is then trained using training data, and the output will then be detected with the highest accuracy possible. Therefore, this study presented a CNN-based system for disease detection in plants and also evaluated the overall performance of various classifiers at the study's records set to decide which had the most accuracy. The performance and usability of machine learning models must be improved through ongoing research and development if we are to eventually see more effective and sustainable farming practices. The created model also maps the soil and crop database and recommends appropriate crops depending on the number of nutrients available in the soil, enabling farmers to choose the right crops to be sown in their fields. Therefore, it is important to identify crop diseases as soon as possible. Farmers will profit from using a quick, creative approach and a crop recommendation system.

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Fake Review Identification using Hybrid Fusion of machine learning and Natural Language Processing Techniques

Fake Review Identification using Hybrid Fusion of Machine Le...

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Electrical Power engineering, Communication and Computing Technology(ODICON), Odisha international conference on

作者： Chitti Reddy Veda Namburu Apoorva N. Vishnu Muni Sekhar Velpuru Hammikolla Akshaya Sai Prakhyath Siripuram Department of Information Technology Vardhaman College of Engineering (Autonomous) Hyderabad India Department of CSE Vardhaman College of Engineering (Autonomous) Hyderabad India Department of ECE Vardhaman College of Engineering (Autonomous) Hyderabad India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

The proliferation of online shopping platforms has brought about a surge in user-generated product reviews, making it susceptible to the infiltration of fake reviews. For these platforms to continue to be dependable and reliable, it is necessary to identify and mitigate the impact of fake reviews. When depending on reviews for the product present on various web pages and applications, the rate of false reviews has been growing in the e-commerce sector. The goal is to anticipate and identify fraudulent reviews on e-commerce sites, namely Amazon, by using a hybrid model that combines classic machine learning (ML) with natural language processing (NLP). The proposed hybrid approach that has been suggested seeks to improve detection accuracy and interpretability by utilizing the combined abilities of ML and NLP technologies. Our method combines the power of Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art language model and Bag of Words (BoW) with traditional ML algorithms like Random Forest and XGBoost. To enhance model performance, we employ stacking ensemble method with logistic regression as the meta learner. The machine learns complex linguistic patterns, contextual information, and cooperative behaviors suggestive of fake reviews through training on many datasets. The outcomes of the experimental evaluations demonstrate the effectiveness of the hybrid model, surpassing existing methods in accuracy and robustness. This research contributes to a reliable solution, poised to enhance the trustworthiness of online product reviews and fortify consumer decision-making processes which guarantees continued safety and assurance in online shopping environments.

关键词： Accuracy Reviews Stacking Transformers Natural language processing Encoding Electronic commerce Ensemble learning Fake news Random forests

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A Multiscale Deep Convolutional Malicious Code Classification Method Based on Structural Reparameterization

A Multiscale Deep Convolutional Malicious Code Classificatio...

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Big data, Information and Computer Network (BDICN), international conference on

作者： Sicong Li Jian Wang Yafei Song Wen Quan Yanan Wang College of Air and Missile Defense Air Force Engineering University Xi'an China College of Air Traffic Control and Navigation Air Force Engineering University Xi'an China

ISBN: (数字)9798350363821

ISBN: (纸本)9798350374612

As malicious code countermeasures evolve, attackers have responded by generating numerous malicious code variants through shelling, code obfuscation, and similar strategies. Addressing the shortcomings of current deep learning-based malicious code classification methods-namely, their limited extraction accuracy and efficiency-this study offers a novel approach that integrates CNNs and Transformer for malicious code detection. This method leverages deep neural networks as its foundation and incorporates a unique fusion module for structural reparameterization. This innovative mechanism reduces memory access costs by eliminating jump connections within the network. Furthermore, the implementation of large kernel convolution techniques enhances network accuracy. Experimental results demonstrate that our proposed method consistently outperforms the latest malicious code detection techniques in terms of both accuracy and operational efficiency.

关键词： Convolutional codes Accuracy Costs Convolution data visualization Big data Transformers

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Deep learning Based Leak Detection of Oil Transmission

Deep Learning Based Leak Detection of Oil Transmission

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Intelligent Cybernetics Technology & Applications (ICICyTA), international conference on

作者： Aditya Firman Ihsan Saladin Uttunggadewa Salamet Nur Himawan Choiryaldi Setya Pratama Darmadi Rinaldy Dasilfa School of Computing Telkom University Bandung Indonesia Mathematics Department Institut Teknologi Bandung Bandung Indonesia Informatics Department Politeknik Negeri Indramayu Indramayu Indonesia Center of Mathematical Modeling and Simulation Institut Teknologi Bandung Bandung Indonesia

Leakage of oil pipeline pose significant challenges in the area of petroleum transport and infrastructure management, with far-reaching implications encompassing economic, environmental, and public safety concerns. While machine learning has developed quite rapidly in recent years and has gained prominence in various sectors, its adoption in petroleum engineering domain remains relatively rare. This paper explores the application of machine learning, specifically Artificial Neural Networks (ANN) method, in predicting the location of oil leaks in a pipeline, given the outlet pressure of the pipe and the expected leak rate. Due to very low number of leak occasion in the real practical case, it is quite challenging to obtain enough balanced leak data to train the learning model properly. To manage that issue, we generate the data manually by simulating the leak process using Beggs-Brill pressure drop correlation in various combination of different experimental cases. The generated data incorporates outlet pressure and estimated leak rates, to serve as input features for the ANN model. We fine-tune the ANN architecture to optimize model performance and analyzes its learning capabilities. This paper underscores the potential of ANN technology in addressing the complex issue of oil pipeline leaks and contributes to the ongoing efforts to enhance pipeline safety.

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The Impact of Feature Quality on SDN Traffic learning and Classification

The Impact of Feature Quality on SDN Traffic Learning and Cl...

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3rd international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2022

作者： Alzu'bi, Ahmad Khrais, Jumana Ennab, Noor Abuarqoub, Abdelrahman Department of Computer Science Jordan University of Science and Technology Irbid Jordan Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom

ISBN: (纸本)9781839538186

Network traffic classification is crucial for facilitating sophisticated network management. Software-defined networks (SDN) provide a featured architecture where the control and data planes are decoupled to enable dynamic configuration. The control plane functionality is implemented in a programmable centralized controller that enables effective use of data-driven based on network traffic classification using efficient machine learning (ML) algorithms. However, there is a lack of standardized benchmarking criteria agreed by the SDN community to evaluate the techniques proposed to collect and classify the network traffic features. This paper presents an empirical analysis to investigate the impact of SDN traffic features on the performance of ML-based classifiers. This study has been conducted using a publicly available SDN traffic dataset. The procedure of feature engineering has also been comprehensively evaluated through three approaches of feature selection, which are low variance filter, high correlation filter, and backward feature elimination. The experimental results show that the performance of ML classifiers is highly driven by the quality traffic features fed to them;the highest quality the traffic features are the best accuracy is achieved. © IET conference Proceedings 2022.

关键词： machine learning

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Industrial Production Process Monitoring Based on Multi-modal

Industrial Production Process Monitoring Based on Multi-moda...

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3rd international conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2024

作者： Sun, Yuming Wang, Zhihui Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9789819627660

With the advent of Industry 4.0, intelligent manufacturing and the industrial Internet are changing traditional production methods. In this paper, a multimodal industrial process monitoring method is proposed. By solving the accuracy and robustness of industrial process monitoring, two models are used to solve the problems of multimodal text alignment and adding noise for training to improve the stability, similarity and consistency of the system. At the same time, different types of data sources are integrated to achieve real-time, comprehensive and accurate monitoring of the production process. This method uses deep learning technology and combines multiple modes such as image recognition and sensor data analysis to build an efficient monitoring system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Advancements, Methods, and Obstacles in Emotion Recognition with Multimodal Approaches

Advancements, Methods, and Obstacles in Emotion Recognition ...

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Arunkumar M Athiban R Selva Jothi M S Pavithra Dept of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Emotion recognition has gained an avid curiosity in the research area due to its broad range of applications in human-computer interaction particularly in real time emotion recognition, sentiment analysis, and affective computing. Deep learning techniques are advancing exponentially, automating feature engineering and transcending traditional machine learning techniques. Appeal of the above models can be highly beneficial but also raise certain technical and ethical concerns that are not often discussed enough in the existing studies. While highlighting the recent advancements, the methods involved and obstacles in emotion recognition, this review also proposes future directions in developing a standard unified framework for multimodal approaches. Categorizing and analysing the recent studies depends upon the model types employed, data modalities used and the type of fusion techniques used, this review was able to gain insights into the effect of recent multimodal approaches addressing technical and ethical concerns. Utilization of fusion techniques in multimodal models is still not reliable but they better convey the connection between different modalities and have a huge potential for development in the future. This review provides insights into the state of art multimodal methods using modalities like facial expressions, body gestures, vocal cues, text, and physiological signals for researchers to understand the trends and future directions in this field.

关键词： Deep learning Emotion recognition Ethics Sentiment analysis Affective computing Reviews Real-time systems Physiology Reliability Standards

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Online Active learning for Evolving data Streams with Limited Labels

Online Active Learning for Evolving Data Streams with Limite...

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international conference on Electronics and Information Technology (EIT)

作者： Zhonglin Wu Department of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

The classification of data streams presents considerable challenges, especially in the context of concept drift and label scarcity. Many existing approaches struggle to efficiently classify data streams in the presence of concept drift, particularly when labeled data is scarce. This study proposes an innovative online active learning algorithm for data streams, referred to as OALEDS. The algorithm combines dynamic micro-clustering for concept representation with local class entropy modeling to effectively identify essential concepts and prototypes within continuously evolving data streams. Specifically, the algorithm places greater emphasis on prototypes in regions susceptible to concept drift, thereby concentrating on areas that reflect current data patterns. Additionally, a micro-clustering-based local class entropy strategy is employed to identify the most informative instances for labeling, optimizing the labeling process. Comprehensive experiments performed on both real-world and synthetic imbalanced datasets reveal that OALEDS efficiently adapts to concept drift, dynamically captures significant prototypes, and consistently surpasses state-of-the-art algorithms, even in scenarios with restricted labeling availability.

关键词： Heuristic algorithms Concept drift Prototypes Entropy data models Classification algorithms Labeling Information technology Streams Synthetic data

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Detection of Faulty Nodes in Distributed Environment using machine learning 3

Detection of Faulty Nodes in Distributed Environment using M...

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3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Gaykar, Reshma S. Khanaa, V. Joshi, S.D. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Bharath Institute of Higher Education and Research Information Center Chennai India Bharati Vidyapeeth University Faculty of Engineering and Technology Pune India

ISBN: (纸本)9781665438117

Faulty nodes are expected to have a significant effect on the performance of Big data systems. The cause of discharges, on the other hand, is a complex issue. The previous work has mostly focused on identifying Stragglers, optimizing the quality of programming, and root cause investigation. These methods can't offer useful information unless they can assist users automate their services. We are developing a framework that determines when there will be stragglers and must identify gaps to prevent those circumstances. This research proposed a dynamic log collection of heterogeneous virtual machine data and detect the faulty node based on execution performance using various parameters. We introduce multi-task learning mechanisms in this paper, which exchange knowledge between the different versions of nodes. The system first collects log data from available data nodes and applies an unsupervised machine learning technique to detect the straggler. Moreover, once a straggler has been detected by the learning algorithm, it tries mitigating such nodes instead of eliminating those nodes. The major benefit of this research is that the system can handle massive data with minimum data nodes or even system efficiently work with already heated nodes in a distributed environment. Our compositions absorb the shared context in our data, enhancing sweeping statement efficiency on minimal data, unlike naive inter learning frameworks. © 2021 IEEE.

关键词： Balancing

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Investigating Salient Object Detection Methods Tailored for Edge Computing Infrastructure 3rd

Investigating Salient Object Detection Methods Tailored for...

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3rd international conference on Computing, Communication and learning, CoCoLe 2024

作者： Sebastian, Elsa Martin, John Paul Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam686635 India

ISBN: (纸本)9783031790409

Salient object detection aims in identifying the most significant parts of a scene as viewed by the human eye and plays a vital role in a variety of applications, including object detection, video compression, and augmented reality. Saliency detection has emerged as a fundamental preprocessing step in many computer vision and image processing tasks. Implementing saliency detection in edge computing platforms is challenging, however it promises enhanced real-time responsiveness, reduced bandwidth usage, and improved privacy. This paper examines the most advanced techniques for adapting saliency detection techniques to edge computing and discusses encountered challenges. The paper also gives insights into the evaluation metrics that can be utilized for performance assessment, investigates primary application domain and proposes potential directions for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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