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Plant Leaf Disease Detection Incorporating IoT and XAI-Enhanced Deep Learning

Plant Leaf Disease Detection Incorporating IoT and XAI-Enhan...

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International Conference on computer and Information Technology (ICCIT)

作者： Pijush Kanti Roy Partho Md. Abid Hasan Rafi Pankaj Bhowmik Dept. of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Dept. of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Plant diseases represent a significant global threat, affecting both food supply and economic growth. Despite technological advances, early plant disease recognition remains difficult for small-scale farmers because of the lackings of infrastructure and resources. Thus, early disease detection using machine learning, deep learning, and computer vision techniques can improve the prediction process and make it easier. Previous studies that were implemented for plant-specific purposes, require high-resolution images and lack real-time detection methods. Therefore, to address these issues, we proposed a deep learning and IoT-based system that uses convolutional neural networks (CNN) and other supporting models, such as InceptionNetV3, VGG16, and EfficientNetB5, with a fine-tuning process to achieve higher accuracy in predicting 38 diseases over 14 different plant species in lower resolution, noisy data. Besides, to facilitate the system, we incorporated an ESP32CAM microcontroller and a server host that can be used to detect diseases in a real-time environment, which makes our research study robust and meaningful. We also implemented Explainable Artificial Intelligence (XAI), particularly Grad-CAM, along with our CNN model for visualizing the infected region in a plant leaf. The individual-trained models achieved an average accuracy of around 99%. We believe, our study can contribute to the agricultural sector by providing real-time disease detection, smart decision support systems, and low-cost solutions for farmers, mainly in resource-limited territories.

关键词： Deep learning Plant diseases Accuracy Microcontrollers Explainable AI Computational modeling Biological system modeling Real-time systems Convolutional neural networks Internet of Things

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Deep Learning-Based Flood Detection system Using Semantic Segmentation

Deep Learning-Based Flood Detection System Using Semantic Se...

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

作者： Divya V Chandran J Anitha Anusree A R Gowri Nair Dept.of Computer Science Engineering Karunya Institute of Technology and Sciences Coimbatore India Dept. Electronics and Communication Engineering Adi Shankara Institute of Engineering and Technology Ernakulam India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Floods cause immense damage worldwide, resulting in significant financial losses and loss of life. Existing flood detection methods often face limitations, including delays and errors due to manual data interpretation and limited coverage. In this study, we propose a novel flood detection system that leverages deep learning techniques for semantic segmentation. Our system employs a U-Net architecture to analyze aerial and satellite images, identifying flood-affected areas at a pixel level. Trained on a diverse dataset of labeled flood images from various regions, our model is both robust and adaptable. Notably, our system achieves an Intersection over Union (IoU) score of 0.85, surpassing traditional methods in accuracy. This advanced system offers the potential to provide real-time flood information, enhancing disaster response and management. With real-time flood mapping, emergency services can better understand the disaster’s scope and plan rescue operations more effectively. Improved emergency response and resource management become possible with timely, precise data on flooded areas, allowing for informed decisions regarding evacuation, relief distribution, and infrastructure maintenance. Future work will focus on integrating additional data sources, such as weather conditions and water flow rates, to further enhance system performance.

关键词： Accuracy Soft sensors Semantic segmentation Refining computer architecture Real-time systems Data models

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Ultra-Wideband Pattern Reconfigurable Antenna for Base station Applications

Ultra-Wideband Pattern Reconfigurable Antenna for Base stati...

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India Council International Subsections Conference (INDISCON), IEEE

作者： Shalini Bhickta Naveen Jaglan Amit Kumar Dept. of Electronics and Communication Engineering Jaypee University of Information Technology Waknaghat Solan India Dept. of Computer Science & Engineering and Information Technology Jaypee University of Information Technology Waknaghat Solan India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

The proposed work aims at 5G base station antenna communication applications. The Reconfigurable Antenna is built with an idea of parasitic loaded elements in antenna. The proposed design has a zig-zag S-shape structure with a driven Monopole antenna located along the central axis and a parasitic structure enfolding around the monopole. Reconfiguration is accomplished by adjusting the PIN diodes bias voltages. The 2.44-9.46 GHz frequency range i.e., the Ultra-Wide-Band frequency spectrum is used at which this reconfigurable antenna is designed to operate. This paper has three radiation pattern which are obtained by two PIN diodes. Beam-steering capability in the elevation plane is achieved i.e., θ (0°-180°). The outcome shows the design’s potential and its wide range concurrent applications for base station antennas.

关键词： Base stations PIN photodiodes 5G mobile communication Ultra wideband antennas Loaded antennas Antenna radiation patterns

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Advancements in Flood Mapping Methodologies: A Comprehensive Review

Advancements in Flood Mapping Methodologies: A Comprehensive...

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IEEE Recent Advances in Intelligent Computational systems (RAICS)

作者： Divya V Chandran J Anitha Amrutha Vijayakumar Anjana Raveendran Dept. of Electronics and Communication Engineering Adi Shankara Institute of Engineering and Technology Kalady India Dept.of Computer Science Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

This review aims to comprehensively assess the recent advancements in flood mapping methodologies and technologies. By analyzing diverse techniques and approaches employed in flood mapping, this review seeks to present an in-dept. overview of the current landscape of this field. The primary focus is on evaluating the strengths and limitations inherent in various flood mapping methods. Special attention will be given to exploring the integration of remote sensing technologies and geographic information systems (GIS) in flood mapping. Additionally, the review will delve into the transformative impact of machine learning and artificial intelligence on enhancing the precision and efficiency of flood mapping processes. Recent developments in flood mapping have significantly elevated the accuracy and reliability of the techniques employed. The integration of AI technologies with remote sensing methods has notably contributed to this advancement. Conventional approaches to flood mapping have traditionally relied on multiple methodologies to comprehend potential flood impacts. However, these methods often prove laborious, resource-intensive, and may lack access to real-time data, posing limitations for precise flood mapping and prediction. These identified limitations underscore the critical need for the development of more robust flood mapping techniques. Addressing these challenges is essential for enhancing flood risk assessment, improving management strategies, and ensuring better preparedness for future flood events.

关键词： Accuracy Reviews Heuristic algorithms Real-time systems Satellite images Floods Reliability

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A Decision Support Framework for Sustainable Waste Disposal Technology Selection

A Decision Support Framework for Sustainable Waste Disposal ...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Lakkireddy, Venkateswara Reddy Mohana, R. Madana Ganesh, B. Rama Reddy, Lakkireddy Udanth Gochhait, Saikat Chogle, Shrish Hyderabad India Chaitanya Bharathi Institute of Technology Department of Artificial Intelligence and Data Science Hyderabad India Sri Venkatesa Perumal College of Engineering and Technology Department of Computer Science and Engineering Tirupathi India Ap University Dept. of Electronics and Communication Engineering Vit Amaravathi India Symbiosis Institute of Digital and Telecom Management Symbiosis International Deemed University India

ISBN: (纸本)9798350372229

Waste Disposal Technology (WDT) selection is a primary issue in Municipal Solid Waste (MSW) that affects the development of the environmental and economic perspectives/aspects, particularly in developing countries. The selection of appropriate WDT is a complex Multi-Attribute Decision-Making (MADM) problem with both qualitative and quantitative elements. The existent MADM approaches with fuzzy sets (removal of uncertainty), different subjective weight methods (significance of attributes), and rank reversal phenomenon leads to improper selection of WDT due to the involvement of different opinions of decision-makers. To avoid this, a Decision Support Framework (DSF) was proposed for optimal WDT selection for the growth of economic and environmental development. The proposed DSF integrates Preference Selection Index (PSI) and a Modified-Comprehensive distance Based Ranking (M-COBRA) approaches to determine the significance of attributes and ranking the alternatives, respectively. The DSF is illustrated using a case study collected from Iran and compared with state-of-the-art MADM approaches. Further, the DSF is validated in terms of sensitivity analysis, rank reversal phenomenon, and Pearson's rank correlation coefficient to ensure the stability of ranking. © 2024 IEEE.

关键词： multi-attribute decision-making Municipal solid waste preference selection index rank reversal phenomenon sensitivity analysis waste disposal technology

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Development of Framework to Find Lung Canker Using GAN Technique 5

Development of Framework to Find Lung Canker Using GAN Techn...

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5th IEEE International Conference on Computing, Power, and communication Technologies, IC2PCT 2024

作者： Ramakrishnan, Rakesh Muniandi, Balakumar Yennapusa, Haritha Jiwani, Nasmin Kiruthiga, T. University of the Cumberlands Department of Information Technology Union CityCA United States Lawrence Technological University Department of Electrical and Computer Engineering SouthfieldMI United States University of Central Missouri Department of Computer Science AustinTX United States University of the Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798350383522

Lung cancer is one of the main reasons of lack of existence globally, accounting for approximately 1. Eight million deaths in past one year. Early prognosis of lung most cancers is crucial for a hit remedy, however it stays a task due to its subtle early signs and symptoms and the limitations of present-day diagnostic techniques. In recent years, the improvement of deep information of era has proven promising consequences in medical photograph analysis and ailment evaluation. On this context, the improved Generative adverse network (GAN) framework has emerged as a powerful device for improving the accuracy and overall performance of lung cancer evaluation. The stepped forward GAN framework is an extension of the conventional GAN, which incorporates a generator and a discriminator working collectively in a competitive gaining knowledge of the approach. The generator generates pics which might be then evaluated with the aid of the discriminator, which identifies if they may be actual or fake. This technique is repeated till the generator efficiently generates sensible photos, fooling the discriminator. Within the context of lung most cancers prognosis, the progressed GAN framework makes use of radiological snap shots, along with CT scans, to generate practical snap shots of lung nodules. Those generated photos are used to enhance the training dataset, developing the range and complexity of the facts, main to a miles higher deep getting-to-know version. This lets in higher differentiation between cancerous and non-cancerous nodules. © 2024 IEEE.

关键词： Better Differentiation Discriminator Extension Prognosis

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Evaluating 10Gbps Wireless Optical communication Using NRZ Modulation: Analyzing BER, Signal Quality, and system Performance

Evaluating 10Gbps Wireless Optical Communication Using NRZ M...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Shailesh Khant Mayur Makwana Atul Patel Faculty of Computer Science and Applications Charusat Changa Anand India Dept. Electronics and Communication Engineering Charusat Changa Anand India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Free space optical (FSO) communication is a fast and wireless data transfer technique that serves as a viable alternative to traditional fiber-optic systems. This study illustrates how FSO can effectively address the growing demand for rapid data transmission and suggests its potential applicability in various scenarios. FSO can be employed to establish connections between buildings within a corporate campus without the need for physical wiring, or it can provide high-speed internet access in locations where installing cables is challenging. We used Optsim software to assess critical parameters such as Bit Error Rate (BER), eye diagrams, power, and signal quality. The results offer valuable insights into enhancing the efficiency and reliability of FSO systems. FSO can find applications in cellular networks to interconnect network equipment, as well as in high-definition video surveillance. These findings highlight the areas in which FSO can advance communication technology and provide valuable insights for future developments.

关键词： Wireless communication Wiring system performance Bit error rate Optical fiber networks Video surveillance Software Software reliability Optical fiber devices Optical signal processing

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HSV Model based Skin Color Segmentation using Uncomplicated Threshold and Logical AND Operation

HSV Model based Skin Color Segmentation using Uncomplicated ...

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International Conference on Advanced Computing and communication systems (ICACCS)

作者： P Ganesan B. S. Sathish L. M. I. Leo Joseph G. Sajiv R. Murugesan A. Akilandeswari S. Gomathi Dept. of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India Dept. of Computer Science Engineering AI Sri Venkateshwara College of Engineering Bengaluru India Dept. of Electronics and Communication Engineering SR University Warangal Telangana India Saveetha School of Engineering SIMATS Chennai India Dept. of Electronics and Communication Engineering Narshima Reddy Engineering College Secunderabad India

In human, skin tone is significantly varying from one extend (darkest) to other (lightest) due to the difference in the amount of pigmentation (melanin). Even though skin color detection and segmentation is the challenging and complex task which finds applications in face recognition, human tracking, video conferencing, content based image retrieval. The variation in the pigmentation is the consequence of the exposure to the amount of solar radiation and heredity. The segmentation is the clustering process wherein the whole image is clustered into small groups according to the color or texture characteristics. The attainment of image analysis is exclusively based on the upshot of the segmentation. The proposed segmentation procedure is provocative due to the attributes like illumination, the characteristics of the image capturing device, ethnicity and individual characteristics. In skin color segmentation, pixels clustered as the skinny or non-skin pixels. The proposed method utilized HSV color model as a tool for the skin tone detection and segmentation. The outcome of the proposed system is weighed against with RGB color model. Experimental results clearly illustrated that the proposed method has a very good efficiency to segment skin color pixels.

关键词： Image segmentation Image color analysis Computational modeling Image retrieval Lighting Skin Solar radiation

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GRAMM: Fast CGRA Application Mapping Based on A Heuristic for Finding Graph Minors

GRAMM: Fast CGRA Application Mapping Based on A Heuristic fo...

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International Conference on Field Programmable Logic and Applications

作者： Guanglei Zhou Mirjana Stojilović Jason H. Anderson Dept. of Electrical and Computer Engineering University of Toronto Toronto Canada School of Computer and Communication Sciences EPFL Lausanne Switzerland

A graph $H$ is a minor of a second graph $G$ if $G$ can be transformed into $H$ by two operations: 1) deleting nodes and/or edges, or 2) contracting edges. Coarse-grained reconfigurable array (CGRA) application mapping is closely related to the graph minor problem, where $H$ is the application's dataflow graph and $G$ is the CGRA's device-model graph. A heuristic algorithm to find graph minors has proven to be practical for sparse graphs with hundreds of vertices in a quantum computing application. In this work, we adapt the heuristic to CGRA application mapping, where the graphs have directed edges, and the vertices have unique types (e.g., representing ALUs or interconnect). Additionally, we alter the original cost function, taking inspiration from PathFinder, an iterative negotiated-congestion routing algorithm. In an experimental study comparing with a CGRA mapper based on integer linear programming, we demonstrate a higher rate of successful mappings and from 80× to up to orders of magnitude lower runtime.

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Design and Comparative analysis of an Optimized 5x32 SRAM Address Decoder for High Speed and High Density Appliactions at 22nm Technology

Design and Comparative analysis of an Optimized 5x32 SRAM Ad...

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International Conference on computer and Information Technology (ICCIT)

作者： Anik Paul Md Nazmul Hasan Umme Hani Irin Md. Minhajul Azmir Dept. of Circuit and System Design Ulkasemi Pvt. Limited Dhaka Bangladesh Dept. of Electrical and Computer Engineering University of South Alabama Mobile Alabama United States

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Address decoders are key components of random-access memory (RAM), determining the performance of Static Random-Access Memory (SRAM). Their speed is particularly important in high-performance computing applications like port register files and multi-level data caches, where speed is critical. Because these decoders can account for up to 50% of total access time and consume a large amount of power during read/write cycles, improving their design is critical. Thus, effective design solutions that focus on circuit techniques and transistor sizing are critical for reducing power consumption while increasing speed and efficiency in memory systems. This study presents the design of a 5:32 SRAM address decoder in a pre-decoded and final wordline decoded architecture that uses custom NAND, NOR, and AND gates. The proposed design is implemented at 22 nm CMOS technology and compared with 5:32 conventional, universal block, and pre-decoded decoders in terms of transistor count, speed, and power consumption. The proposed design reduces transistor count by 56.3% compared to conventional designs and achieves reductions of 30% and 12.5% over universal and pre-decoded decoder architectures, respectively. In terms of performance, it demonstrates a speed increase of 58.4%, 42.1%, and 30.6% over conventional, universal, and pre-decoded decoders, respectively. Additionally, the design consumes 46.7% less power than the conventional decoder, but results in 9.56% and 10.43% higher power consumption than the universal and pre-decoded decoders, respectively.

关键词： Integrated circuit synthesis MOSFET Power demand Memory management Random access memory Logic gates CMOS technology Decoding Registers Transistors

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