Informatics Publishing Limited

T. Dhanabalu ¹, M. Yuvaraja ¹, S. Vishnu Gopeka ¹, B. Sharanya ¹

Affiliations
1 Department of Electronics and Communication Engineering, P. A. College of Engineering and Technology, IN

Abstract

The underwater wireless sensor networks poses many challenges due to critical underwater environment such as large propagation delay and limited bandwidth capacity of acoustic channels. The sensor nodes under mobility causes the problem of delay, more energy consumption and undetermined propagation losses. In this paper we propose a routing protocol based on geographical dynamic routing (DTDRP) to address some of these problems. We focus on minimizing the delay and maximizing the throughput thereby increasing the energy efficiency. The simulations are carried out to analyze the performance of the proposed algorithm by comparing with the existing protocols in underwater environment conditions.

Keywords

Underwater Wireless Sensor Networks, DTDRP, Underwater Topology, Performance Analysis.

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M. Yuvaraja ¹, G. Aravindh ¹, R. Priya ², V. Suresh Babu ³

Affiliations
1 Department of Electronics and Communication Engineering, P.A. College of Engineering and Technology, IN
2 Department of Artificial Intelligence and Data Science, Pollachi Institute of Engineering and Technology, IN
3 Department of Computer Science and Engineering, Sri Shanmugha College of Engineering and Technology, IN

Abstract

In the era of smart agriculture, efficient irrigation management is crucial for optimizing crop yield and resource use. Traditional methods of plant disease detection often rely on manual inspection, which is time-consuming and prone to errors. Smart agriculture leverages technology to improve agricultural practices. Accurate and timely plant disease detection is vital for effective irrigation management and overall crop health. Current methods are limited by their manual nature and inability to process large volumes of data quickly. Manual plant disease detection is labor-intensive and may not provide timely information, leading to inefficient irrigation practices. This inefficiency can result in reduced crop yield and wasted resources. LeNet integrates advanced image processing techniques with a deep learning architecture tailored for plant disease detection. The model utilizes convolutional neural networks (CNNs) to analyze plant leaf images, identifying disease patterns with high precision. LeNet incorporates preprocessing steps such as image normalization and augmentation to enhance model robustness. The network is trained on a comprehensive dataset of plant disease images, employing transfer learning to leverage pre-trained weights for improved accuracy. Evaluation of LeNet on a test dataset comprising 10,000 images demonstrated an impressive accuracy of 92.5%, with a precision of 90.3% and recall of 94.1%. The model significantly outperforms traditional methods, reducing disease detection time by 60% and enhancing irrigation efficiency by 30%. The reduction in water usage and increased crop yield were observed in practical trials.

Keywords

Plant Disease Detection, Smart Agriculture, Deep Learning, Convolutional Neural Networks, Irrigation Efficiency

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M. Yuvaraja , S. Thavamani

Abstract

Sentiment Analysis (SA) performs on specific domain to achieve higher level of accuracy. Extracting the unstructured data sentimental analyses plays a major role. SA is mainly for automatically predict sentiment polarity of positive or negative aspects of data. Sentiment Analysis problem is machine learning problems which provide the outcome based of supervised and unsupervised methods using labeled and unlabeled data. By extracting the data from this cross domain many techniques were used. This paper provides survey on sentiment analysis of various techniques, methods, algorithm and tools of SA to adapt the data in source and target domain to extract the relevant knowledge.

Keywords

Sentiment Analyses (SA), Survey. Cross Domain Analyses, SA Techniques and SA Methods.

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