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An Efficient Model for Early Detection of Maize Leaf Disease using Deep Learning Approaches 4

An Efficient Model for Early Detection of Maize Leaf Disease...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. K.L.E.Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India K.L.E.Institute of Technology Dept. of Master of Computer Applications Hubballi India

ISBN: (纸本)9798350354218

Maize plays a fundamental role as a staple food for millions worldwide. However, the significant threat posed by various diseases to maize cultivation has led to substantial yield losses on a global scale. Early and accurate detection of these diseases is essential for prompt intervention and effective treatment strategies. In this regard, the development of advanced detection methods employing deep learning and image processing has gained attention, offering the potential to revolutionize disease identification and management practices. Accordingly, this research introduces a Deep Convolutional Neural Network model combined with Transfer Learning techniques, reducing the training time for the AI model. Furthermore, Data Augmentation practices are employed to improve accuracy by addressing dataset variability. The projected model achieves an impressive accuracy of 98.6%, crucial for mitigating current crop wastage resulting from undetected diseases. Enhancing accuracy further is essential for improving current crop yield conditions impacted by undetected diseases. Moreover, a GUI-based application model is developed for early detection of maize diseases, aiding farmers in their fields. This accuracy can be further increased by incorporating additional parameters. Traditional image recognition and Deep Learning approaches are progressively making their way into the field of plant disease recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Transformative Trends in Reinforcement Learning: From Deep Q-Learning to Real-World applications 2

Transformative Trends in Reinforcement Learning: From Deep Q...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Mani, Chandra Sharma, Tarun Kumar Yadav, Kanchan Gupta, Divya Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Applications UP Ghaziabad India Gla University Department of Electrical Engineering UP Mathura India Miet Department of Bca UP Meerut India

ISBN: (纸本)9798350371055

Reinforcement studying (RL) is a type of machine studying that maximizes an agent's cumulative praise in an environment. As such, it has been more and more used to address a ramification of sequential selection-making issues successfully. Currently, there were most important breakthroughs within the RL space that have led to expanded research activity and the development of several novel techniques and packages. Those transformative traits include deep learning, model-free exploration, off-policy gaining knowledge of hierarchical techniques, meta-gaining knowledge of multi-agent training, deep reinforcement learning (DRL), and transfer learning. These new strategies to RL allow for the development of more efficient and powerful algorithms, which in flip allow more a hit education and alertness of reinforcement mastering marketers in lots of one of a kind domain names. Similarly, those additives of RL permit faster, more flexible, and stronger schooling for RL marketers. As a result, RL is rapidly turning into a part of many one of a kind areas of studies that require powerful choice-making systems, inclusive of robotics, herbal language processing, and healthcare. © 2024 IEEE.

关键词： Environment Healthcare Hierarchical Reinforcement Strategies

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410 © 2023 IEEE.

关键词： Image classification

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Improving Implicit Discourse Relation Recognition with Semantics Confrontation 30

Improving Implicit Discourse Relation Recognition with Seman...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Cai, Mingyang Yang, Zhen Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

Implicit Discourse Relation Recognition (IDRR), which infers discourse logical relations without explicit connectives, is one of the most challenging tasks in natural language processing (NLP). Recently, pre-trained language models (PLMs) have yielded impressive results across numerous NLP tasks, but their performance still remains unsatisfactory in IDRR. We argue that prior studies have not fully harnessed the potential of PLMs, thereby resulting in a mixture of logical semantics, which determine the logical relations between discourse arguments, and general semantics, which encapsulate the non-logical contextual aspects (detailed in Sec.1). Such a mixture would inevitably compromise the logic reasoning ability of PLMs. Therefore, we propose a novel method that trains the PLMs through two semantics enhancers to implicitly differentiate logical and general semantics, ultimately achieving logical semantics enhancement. Due to the characteristic of PLM in word representation learning, these two semantics enhancers will inherently confront with each other, facilitating an augmentation of logical semantics by disentangling them from general semantics. The experimental results on PDTB 2.0 dataset show that the confrontation approach exceeds our baseline by 3.81% F1 score, and the effectiveness of the semantics confrontation method is validated by comprehensive ablation experiments. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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Comparative Analysis of Different Types of WSN 10

Comparative Analysis of Different Types of WSN

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Bajaj, Shilpa Bala, Manju Angurala, Mohit I. K. Gujral Punjab Technical University Department of Computer Applications Kapurthala India Khalsa College of Engineering and Technology Department of Computer Science and Engineering Amritsar India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

ISBN: (纸本)9789380544472

Wireless sensor networks (WSNs) have been widely adopted in various fields such as environmental monitoring, industrial control, and military applications. Different types of WSNs have been developed to cater to the specific needs of these applications. This comparative study aims to examine the different types of WSNs including terrestrial, underwater, and underground, and their respective advantages and limitations. The study will also explore the unique challenges faced by each type of WSN and the solutions that have been proposed to overcome these challenges. The main aim of carrying out the comparative study is to make researchers aware of the advantages and challenges of each type of WSN so that they can overcome the limitations with innovative approaches in the future. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Wireless sensor networks

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Effective Integration of Text Diffusion and Pre-Trained Language Models with Linguistic Easy-First Schedule 30

Effective Integration of Text Diffusion and Pre-Trained Lang...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ou, Yimin Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

Diffusion models have become a powerful generative modeling paradigm, achieving great success in continuous data patterns. However, the discrete nature of text data results in compatibility issues between continuous diffusion models (CDMs) and pre-trained language models (PLMs). That is, the performance of diffusion models even degrades when combined with PLMs. To alleviate this issue, we propose to utilize a pre-trained decoder to convert the denoised embedding vectors into natural language instead of using the widely used rounding operation. In this way, CDMs can be more effectively combined with PLMs. Additionally, considering that existing noise schedules in text diffusion models do not take into account the linguistic differences among tokens, which violates the easy-first policy for text generation, we propose a linguistic easy-first schedule that incorporates the measure of word importance, conforming to easy-first-generation linguistic features and bringing about improved generation quality. Experiment results on the E2E dataset and five controllable tasks show that our approach can combine the merits of CDMs and PLMs, significantly outperforming other diffusion-based models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Diffusion

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A Quantitative Study on the FaceNet System 4th

A Quantitative Study on the FaceNet System

引用

4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Gopakumar, Rajesh Kotegar, Karunakar A. Vishal Anand, M. Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819942831

Face recognition is the science of identifying and recognizing human faces in various situations, keeping the constraints like pose variation and occlusion in mind. Due to its impactful applications in safety and security systems, face recognition is becoming extremely popular and is researched extensively even today. The FaceNet method is among the most tested approaches in Deep learning face identification. This method uses a deep convolution neural network for training the data. The face embedding generated can be used to train a face identification system. This study aims to comprehend the FaceNet system, evaluate its performance, and test its accuracy on seven standard datasets. The study also tries to compare how well the FaceNet method works compared to other popular holistic and hybrid methods. From the outcomes of this study, we can conclude that FaceNet showed outstanding results and was better than the other methods. The FaceNet system reached a minimum of 90% accuracy on all standard datasets used on both the pre-trained models, which is a significant number for any face recognition method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Face recognition

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Edge Computing Enabled Anomaly Detection in IoT Environments Using Federated Learning

Edge Computing Enabled Anomaly Detection in IoT Environments...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kansal, Vipashi Husain, Saif O. Kumar, Rakesh Chinthamu, Narender Kumar, Manish Aswini, S. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Mit Cto DallasTX United States Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This research explores the integration of edge computing and unified learning procedures for peculiarity locations in Internet of Things (IoT) situations. Four inconsistency discovery calculations - Isolation Forest, Local Outlier Factor, One-Class SVM, and Recurrent Neural Organize - are assessed over assorted IoT datasets to survey their adequacy. Results show that the Repetitive Neural Network outperforms other calculations with exactness, review, F1-score, and AUC-ROC values of 0.90, 0.92, 0.91, and 0.95 individually. The study illustrates the possibility of leveraging edge computing and unified learning for productive and privacy-preserving inconsistency locations at the organised edge. By passing on computational assignments to edge contraptions and collaboratively planning models utilizing combined learning, our approach finishes flexible and adaptable irregularity revelation in IoT systems. The investigate contributes encounters into tending to challenges such as inertness, security, and flexibility characteristics in centralized idiosyncrasy disclosure approaches, clearing the way for more capable and privacy-preserving courses of action in IoT applications. © 2024 IEEE.

关键词： Anomaly detection

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A comparative analysis of using Deep Learning and Machine Learning technologies for intrusion detection for effective network traffic analysis

A comparative analysis of using Deep Learning and Machine Le...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Divya S B S. Prince Mary Department of Computer Science and Engineering Sathyabhama Institute of Science & Technology Chennai Department Of Computer Applications Mangalam College Of Engineering

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The broad utilization of interconnectivity and interoperability of processing frameworks have turned into an irreplaceable need to improve our everyday activities. All the while, it opens a way to exploitable weaknesses that work out positively past human control capacity. The weaknesses consider network protection instruments fundamental to accepting correspondence trade. Secure correspondence requires safety efforts to battle the dangers and necessities headways to safety efforts that counter advancing security dangers. So, emerging technologies like Deep Learning/ Machine Learning (DL/ML) come in handy for integration and better prediction. So, in this paper, we depict various deep learning/ML strategies used in detecting the imbalance in network traffic. The document also includes research that has been proposed on the topic of intrusion detection in network traffic over the previous four years (2020–2024) by a variety of research specialists. The main achievements of the security measures are recognized in this review together with their quantitative and qualitative success indicators. Additionally, as a guide for upcoming discoveries, this research study examines the major discoveries and the rationale for the lessons discovered.

关键词： Deep learning Performance evaluation Reviews Computational modeling Intrusion detection Telecommunication traffic Security

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Harris Hawks Optimization Based Feature Selection on High Dimensional data 2

Harris Hawks Optimization Based Feature Selection on High Di...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Jena, Suvendu Kumar Kumar, Gotte Ranjith Al-Fatlawy, Ramy Riad Ali, S.Rasheed Mansoor Aruna, T.M. Indira Gandhi Institute of Technology Department of Computer Science Engineering and Applications Odisha Sarang India School of Computer Science & Artificial Intelligence Sr University Warangal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Jamal Mohamed College Affiliated to Bharathidasan University Department of Computer Science Tiruchirappalli India Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798350373110

In recent times, evolutionary algorithms have been demonstrating significant benefits in feature selection due to their simplicity and global search capability. However, most of the existing evolutionary algorithms are computationally intensive for high-dimensional data. In order to decrease computational costs, effective evaluation methods are crucial. In this paper, Harris Hawks Optimization (HHO) technique is proposed for effective feature selection on high dimensional data. Firstly, a Multi-Layer Perceptron (MLP) is used to form an integer optimization problem for efficient feature selection process. Then the proposed HHO method is applied for the process of feature selection. This method has three steps such as Initialization, solutions update and classification. The performance of proposed HHO algorithm is compared with the existing classifiers such as Binary Waterwheel Plant Algorithm (BWWP A) and Community detection based Genetic Algorithm (CDGAFS) and achieved 98.2% of accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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