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9th IEEE International Symposium on Smart Electronic Systems, iSES 2023

作者： Bhandekar, Abhinay Udutalapally, Venkanna Das, Debanjan Iiit Computer Science and Engineering Naya Raipur India Iiitdm Computer Science and Engineering Kurnool India

ISBN: (纸本)9798350383249

Poultry farming's pivotal role in meeting global protein demand emphasizes the need for chicken health assurance. This study introduces real-time acoustic monitoring for poultry farms, bridging the gap left by labor-intensive, delayed health issue detection that undermines welfare and profits. We devised a system to capture and analyze farm audio data by harnessing audio analytics and machine learning. Targeted microphones strategically captured chicken vocalizations, with collected audio undergoing preprocessing for Mel-Frequency Cepstral Coefficients (MFCCs). Utilizing a comprehensively trained Support Vector Machine (SVM) model, our solution achieved a remarkable accuracy of 95.66% in real-time detection of abnormal sounds. Rigorous experimentation showcased SVM's proficiency in distinguishing between normal and abnormal, further classifying the chicken's activities, and updating the user every 30 minutes. Farmers can intervene on time, ensuring the well-being of their flock. This real-time insight empowers farmers to take immediate corrective actions, enhancing animal welfare, minimizing losses, and optimizing overall farm management practices. © 2023 IEEE.

关键词： Support vector machines

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Coffee Leaf Disease and Severity Prediction Using Deep Learning 38

Coffee Leaf Disease and Severity Prediction Using Deep Learn...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Chaubey, Harshit Kumar Arelli, Siri Raj, Aryan Naik, Nenavath Srinivas Iiit Naya Raipur Computer Science and Engineering Raipur India Iiit Naya Raipur Data Science and Artificial Intelligence Raipur India Iiitdm Kurnool Computer Science and Engineering Kurnool India

ISBN: (纸本)9798350302196

Coffee production is a vital industry in many countries, but diseases affecting coffee leaves can lead to significant losses for farmers. To mitigate these losses, timely disease detection and accurate assessment of disease severity are crucial. This work proposes a deep learning approach for classifying coffee leaf diseases based on their severity levels. The proposed methodology involves several steps. Initially, U2Net removes the background from the coffee leaf images. Subsequently, the background-removed images are converted into BGR format to identify the diseased regions. DeepLabV3 is then trained to extract and mark the diseased portions of the leaves in red. Using these annotated images, various convolutional neural network (CNN) models, including VGG-16, Inception V3,and MobileNetV2, were trained to classify the diseases based on their severity levels. These models are carefully modified and fine-tuned with hyperparameters to achieve the best performance metrics. Upon model training, the modified MobileNetV2 model performs better than the other CNN models, achieving an impressive F1-score of 97.99%. This outcome highlights the effectiveness of this paper's approach in accurately classifying coffee leaf diseases according to their severity levels. The proposed methodology has significant implications for coffee farmers, enabling them to swiftly detect diseases and assess their severity, allowing for timely and appropriate actions. Furthermore, the findings indicate the superiority of the modified MobileNetV2 model in achieving high accuracy in disease severity classification. This research contributes to advancing deep learning techniques for agricultural applications, offering practical solutions for disease management in the coffee industry. © 2023 IEEE.

关键词： Deep learning

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Weighted Ensemble Learning for Accident Severity Classification Using Social Media Data

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SN computer science 2024年第5期5卷 528页

作者： Raul, Sanjib Kumar Rout, Rashmi Ranjan Somayajulu, D.V.L.N. Department of Computer Science and Engineering National Institute of Technology Telangana Warangal 506004 India Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing (IIITDM) Andhra Pradesh Kurnool 518002 India

Due to the heavy increase in social media usage, like Twitter, there is a growing interest in the research community in developing automation tools like accident severity-based tweet classification models. These tools aid in automatically extracting severity information from the accident tweet content. Moreover, prediction models are essential for predicting the severity of an accident to increase the safety efficacy of the road traffic system. However, the difficulty lies in the collection of sufficient labeled data. We propose a model called weighted ensemble-based self-training with decision tree (WESTDT), a semi-supervised methodology with a dynamic data labeling strategy. The base classifier in this model is a homogeneous weighted ensemble of decision tree classifiers for better prediction of pseudo labels. We also propose a novel performance measure called risk factor to estimate the amount of risk present in the application using the prediction model. The proposed model outperformed the state-of-the-art model, namely reliable semi-supervised ensemble learning (RESSEL), and the baseline models, namely decision tree (DT) and self-training with decision tree (STDT), in terms of both the traditional and the proposed measures. The results indicate that the proposed framework outperforms all other models on all datasets in terms of precision, recall, and accuracy by a range of 5–18.3%, 3.9–9.3%, and 6.6%, respectively. These findings can be helpful for the development of efficient and sustainable systems for traffic management and safety. It is also crucial for assisting government authorities in devising prompt, proactive strategies to prevent traffic accidents and enhance road safety. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Accident severity classification Decision tree Self-training Semi-supervised learning Weighted ensemble learning

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Enhancing Hadoop Performance with Q-Learning for Optimal Parameter Tuning 38

Enhancing Hadoop Performance with Q-Learning for Optimal Par...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Akshay, Garika Naik, Nenavath Srinivas Vardhan, Jai Iiit Naya Raipur Data Science and Artificial Intelligence Raipur India Iiitdm Kurnool Computer Science and Engineering Kurnool India Iiit Naya Raipur Computer Science and Engineering Raipur India

ISBN: (纸本)9798350302196

This paper presents an approach to enhance Hadoop performance by leveraging deep Q-Learning, a form of Reinforcement Learning, to optimize parameter settings. The performance of Hadoop, a widely adopted distributed computing framework, relies heavily on configuring numerous parameters. However, the complex and extensive search space poses challenges in determining the optimal settings. In response, we propose an innovative deep Q-Learning algorithm that iterative discovers and applies the most effective parameter configurations, thereby improving Hadoop's performance. Our approach involves defining state and action spaces, learning from performance feedback, and identifying the optimal configuration to maximize Hadoop's efficiency. Our proposed model consistently outperforms existing solutions by benchmarking with popular jobs such as TeraSort, WordCount, Hive Aggregate, and WordCooccur, demonstrating significant reductions in execution times and improved computational efficiency. This research accelerates big data processing in Hadoop and provides a scalable and efficient solution applicable to similar distributed computing frameworks. Furthermore, our paper reinforces the effectiveness and applicability of Reinforcement Learning in addressing complex optimization problems. By harnessing the power of deep Q-Learning, we showcase its capability to navigate the intricate parameter space of Hadoop, resulting in enhanced performance and streamlined data processing. This research contributes to advancing distributed computing frameworks, offering a novel and efficient approach to optimize parameter settings and improve overall system performance. © 2023 IEEE.

关键词： Reinforcement learning

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Unveiling Network Anomalies: A Comparative Study of Real-time Log-Based Detection Approach 20

Unveiling Network Anomalies: A Comparative Study of Real-tim...

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20th IEEE India Council International Conference, INDICON 2023

作者： Marella, Deepika Muthi, Roopa Navya Avinash Reddy, Buchammagari Saraswathi, Kaniganti Priyanka Busi Reddy, Vijender Naik, Nenavath Srinivas Iiit Naya Raipur Computer Science and Engineering Raipur India Iiit Naya Raipur Data Science and Artificial Intelligence Raipur India Secunderabad Secunderabad India Computer Science and Engineering Iiitdm Kurnool Kurnool India

ISBN: (纸本)9798350305593

In this paper, we address the issue of detecting anomalies in network log files. In laboratory setup, log files are generated by network devices such as routers, switches, firewalls, and servers. These log files contain information about network activities, such as network traffic, device configurations, user activity, and security events. Analyzing log files on computer networks is essential for identifying potential security threats and network issues. Researchers have sought to develop quick and effective methods to identify anomalies in a computer system by examining its log data as a result of this significant utility. In our work, we have used a machine-learning approach called Isolation Forest (iForest) and also examined clustering algorithms, Ordering Points To Identify Cluster Structure, referred to as OPTICS Clustering, and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, for detecting anomalies in real-time log datasets. The performance of each model is evaluated and compared. Finally, we conclude by presenting the experimental results that demonstrate the effectiveness of the suggested method and future directions in real-time log-based anomaly detection, highlighting the need for more advanced and efficient methods to deal with the increasing complexity and scale of modern networks. © 2023 IEEE.

关键词： Anomaly detection DBSCAN Isolation Forest log files OPTICS clustering

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Drone Vision Based Abiotic Stress Monitoring for Smart Agriculture 9

Drone Vision Based Abiotic Stress Monitoring for Smart Agric...

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9th IEEE International Symposium on Smart Electronic Systems, iSES 2023

作者： Sriram, Gonthina Sai Aditya, S.S.V. Teja, Gannoju Akshar Uduthalapally, Venkanna Das, Debanjan Iiit Naya Department of Computer Science and Engineering Raipur India Iiitdm Department of Computer Science and Engineering Kurnool India Iiit Naya Department of Electronics and Communication Engineering Raipur India

ISBN: (纸本)9798350383249

In recent years, significant technological advancements have driven innovative solutions across various domains, including agriculture. The compelling requirement to enhance agricultural methods employing state-of-the-art technologies such as Unmanned Aerial Vehicles and computer Vision has grown with the increasing challenges traditional farming practices face. These technologies offer real-time insights into crop health and farm conditions enabling more efficient farming methods. In the envisioned system, the UAV acquires images of the farmland, subsequently transmitting them to the Raspberry Pi for preprocessing. Further, the algorithm processes Normalized Difference Vegetation Index values and segments the regions of green crops, yellow patches, and soil also identifying regions impacted by abiotic stress. The system demonstrates an accuracy of about 90%, highlighting the algorithm's robustness. The system can be used by farmers to capture images of their farmland, with the software autonomously generating valuable insights about farmland conditions. The proposed system revolutionizes crop monitoring and management for farmers, improving agricultural productivity and sustainability. © 2023 IEEE.

关键词： Drones

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iCrop: a framework for identification of millet crop diseases via segmentation and feature extraction

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Indian Phytopathology 2023年第3期76卷 803-812页

作者： Singh, Sonal Dashore, Swastik Prasad, Jatin Naik, Nenavath Srinivas Data Science and Artificial Intelligence International Institute of Information Technology Chhattisgarh Naya Raipur 493661 India Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing (IIITDM) Kurnool Andhra Pradesh Kurnool 518008 India

Crop diseases are important in agricultural productivity and quality. It is essential to recognise and diagnose these diseases. Deep learning’s ability to correctly classify small data sets is a major research challenge. This work proposes an approach using image segmentation, feature extraction and deep learning techniques to develop a multi-class classifier for millet crop disease identification. The proposed image processing approach and use of Artificial Neural Networks demonstrate disease classification over 2632 images with a training accuracy of 97.25% and a testing accuracy of 96.88%. Compared with the pre-trained model MobileNet V3, the implemented model achieved better training and validation accuracy over 100 epochs and required less training time. The proposed model’s simplicity makes crop disease detection in agriculture fast and efficient. As a result, the suggested method paves the way for large-scale crop disease diagnosis. © 2023, Indian Phytopathological Society.

关键词： Artificial neural networks Deep learning Feature extraction Image segmentation Millet crop disease

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Acoustic Based Chicken Health Monitoring in Smart Poultry Farms

Acoustic Based Chicken Health Monitoring in Smart Poultry Fa...

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IEEE International Symposium on Smart Electronic Systems (iSES)

作者： Abhinay Bhandekar Venkanna Udutalapally Debanjan Das Computer Science and Engineering IIIT Naya Raipur India Computer Science and Engineering IIITDM Kurnool India

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Optimized Job Scheduling in Cloud Data Centers: A Deep Reinforcement Learning Approach for Efficient Big Data Processing

SSRN

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SSRN 2024年

作者： Verma, Vishnu Prasad Kumar, Santosh Naik, Nenavath Srinivas Department of Computer Science and Engineering IIIT Naya Raipur Chhattisgarh Raipur493661 India Department of Computer Science and Engineering IIITDM Kurnool Andhra Pradesh Kurnool518008 India India

Due to the constraints of traditional infrastructure and data management systems and the exponential data expansion, organizations are moving their big data processing to the cloud. Due to the dynamic nature of system states and the range of user demands, efficient resource allocation in the Cloud is difficult. This research article focuses on job scheduling in cloud data centers, which is essential for resource efficiency and system performance optimization. We propose a novel job scheduling algorithm based on deep reinforcement learning (DRL) to address the shortcomings of traditional approaches. Our algorithm leverages DRL techniques within the bin packing problem framework to automatically learn a fitness calculation method, minimizing makespan and maximizing throughput. We validate our algorithm’s convergence and generalization capabilities through trace-driven simulations, providing insights into the underlying learning mechanisms. The dynamic adaptability of our approach to changing workloads, achieved through DRL, ensures efficient resource utilization and improves overall system efficiency. Our research contributes to the advancement of job scheduling techniques in cloud data centers, paving the way for optimized big data analytics in the cloud. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Coffee Leaf Disease and Severity Prediction Using Deep Learning

Coffee Leaf Disease and Severity Prediction Using Deep Learn...

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IEEE Region 10 International Conference TENCON

作者： Harshit Kumar Chaubey Siri Arelli Aryan Raj Nenavath Srinivas Naik Computer Science and Engineering IIIT Naya Raipur Raipur India Data Science and Artificial Intelligence IIIT Naya Raipur Raipur India Computer Science and Engineering IIITDM Kurnool Kurnool India

Coffee production is a vital industry in many countries, but diseases affecting coffee leaves can lead to significant losses for farmers. To mitigate these losses, timely disease detection and accurate assessment of disease severity are crucial. This work proposes a deep learning approach for classifying coffee leaf diseases based on their severity levels. The proposed methodology involves several steps. Initially, U 2 Net removes the background from the coffee leaf images. Subsequently, the background-removed images are converted into BGR format to identify the diseased regions. DeepLabV3 is then trained to extract and mark the diseased portions of the leaves in red. Using these annotated images, various convolutional neural network (CNN) models, including VGG-16, Inception V3,and MobileNetV2, were trained to classify the diseases based on their severity levels. These models are carefully modified and fine-tuned with hyperparameters to achieve the best performance metrics. Upon model training, the modified MobileNetV2 model performs better than the other CNN models, achieving an impressive F1-score of 97.99%. This outcome highlights the effectiveness of this paper's approach in accurately classifying coffee leaf diseases according to their severity levels. The proposed methodology has significant implications for coffee farmers, enabling them to swiftly detect diseases and assess their severity, allowing for timely and appropriate actions. Furthermore, the findings indicate the superiority of the modified MobileNetV2 model in achieving high accuracy in disease severity classification. This research contributes to advancing deep learning techniques for agricultural applications, offering practical solutions for disease management in the coffee industry.

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