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Empirical Analysis of Deep Learning Models for Tomato Leaf Disease Detection

Empirical Analysis of Deep Learning Models for Tomato Leaf D...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Shreya Kansal Arunima Jaiswal Nitin Sachdeva Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women New Delhi India Department of Computer Science & Engineering (ASET) Amity University Noida Uttar Pradesh India

Currently, the detection of plant diseases relies heavily on manual observation, which is time-consuming and susceptible to inaccuracies. To address the significant threat posed by plant diseases and harmful insects in the agriculture sector, researchers are actively working on developing an automated system for detecting diseases in plant leaves. This paper specifically focuses on the detection of diseases in tomato leaves using various models, both custom-built and pre-trained, with an emphasis on CNNs. Two datasets, PlantVillage and Tomato Leaf Disease dataset, are employed for evaluation. The results of the analysis reveal that the CNN-KNN Hybrid model performs the best for the PlantVillage dataset. For the Tomato Leaf Disease dataset, VGG16 and VGG19 prove to be the most efficient models. It is noteworthy that there are significant variations in model performance depending on the dataset type. Six models exhibit superior performance on the imbalanced dataset, while the other two show improved results on the balanced dataset. The study offers valuable insights for practitioners aiming to optimize model selection and enhance the accuracy and effectiveness of disease detection systems in agriculture. The integration of Deep Learning and computer Vision holds great promise for improving crop management practices and mitigating losses for farmers.

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Generation of Super - Resolved Images Using Deep Neural Networks

Generation of Super - Resolved Images Using Deep Neural Netw...

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Computing, sciences and Communications (ICCSC), International Conference on

作者： Hariram Sampath Kumar Shambhavi Pande Archana Singh Partha Sarathi Mangipudi Dept. of Artificial Intelligence (of ASET) Amity University Noida Uttar Pradesh India Dept. of Computer Science & Engineering (of ASET) Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

High-resolution aerial imaging is crucial in the field of military applications for gathering intelligence and devising strategic plans. This research examines super-resolution approaches, with a specific emphasis on three Deep Learning Neural Models: Super-Resolution Convolutional Neural Networks (SRCNN), Generative Adversarial Networks (GAN), and Super-Resolution Generative Adversarial Networks (SRGAN). The study does a rigorous comparison investigation of these models, with the goal of determining their effectiveness in producing high-resolution aerial photographs. In order to conduct a thorough evaluation of these models, a wide range of datasets is utilised, which includes MNIST, Fashion MNIST, DIV2K, and DOTA. This extensive range of options enables a comprehensive evaluation of the models' performance across a wide range of data complexities, ranging from simple geometric forms to complicated real-world aerial landscapes. This research offers detailed analysis of the datasets, allowing for a thorough examination of the strengths and shortcomings of the models. It provides valuable insights into how these models might be applied to real-world military situations. The results of this study have important consequences for military intelligence. Efficiently generating super-resolved aerial images can improve target recognition, terrain analysis, and threat assessment. Furthermore, comprehending the relative strengths of various models enables military agencies to customise their image processing approaches to meet unique operational needs. This research facilitates the integration of modern deep learning techniques with the complexities of military intelligence, thereby enabling better informed decision-making processes and eventually strengthening national security operations.

关键词： Deep learning Target recognition Superresolution Decision making Imaging Reconnaissance Generative adversarial networks Threat assessment Complexity theory National security

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Hardware-based Dual Booting Switch with Customizable Setup Script and Cross Platform Support

Hardware-based Dual Booting Switch with Customizable Setup S...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Venkatesh Chaturvedi Deepak Arora ASET Department of Computer Science & Engineering Amity School of Engineering & Technolgy Lucknow Amity University Uttar Pradesh India

Many people use numerous operating systems to do a variety of tasks in today's hectic world. While using Linux for programming and coding, someone may utilize Windows for some document editing. There are no major problems faced while dual booting two operating systems aside from the fact that in order to dual boot, one has to open the boot menu every time in order to select which operating system is to be booted. This process of opening the boot menu and selecting the operating system each time is a very redundant task. Hence, the idea of a hardware-based switch that would help in switching the operating system without opening the boot menu every time was born. Initial thoughts for the switch included using a control board such as the Raspberry Pi for programming the switch. Even modification of the bootloader source code in order to create some hardware-based controller was attempted. However, after several days of research and testing, it was concluded that even though previous attempts had been made to create a dual booting switch using control boards, and were successful to an extent, those solutions were too complex for the value they provided. Hence, work was started on creating a dual booting switch without using a control board and with minimum modifications to the bootloader.

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A Comprehensive Analysis of Obstacles to Wearable Technology Adoption in Healthcare

A Comprehensive Analysis of Obstacles to Wearable Technology...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Khushboo Pandey Purushottam Sharma ASET Amity University Uttar Pradesh Noida India School of Computer Science & Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Healthcare systems have gradually incorporated several technologies in recent years to improve older persons' access to health care and facilitate the provision of effective and efficient treatment for this particular group. These technologies include health information technology created to manage long-term diseases and assistive technologies intended to preserve or enhance the independence, social involvement, and functionality of older persons at home. Wearable technology, mobile health, and telehealth are a few examples of these technologies. The potential of health technology to significantly enhance the independence of older adults is immense. However, while this potential is promising, putting it into practice is still difficult. This study aims to perform an integrative systematic assessment of the research findings about the characteristics that support or impede older adults with chronic illnesses from adopting various forms of technology. The ultimate objective of this assessment is to promote good health and self-reliance by highlighting the elements that encourage technology adoption and the obstacles that stand in their way, specifically focusing on the role of Wearable Technology Devices (WTDs) in healthcare monitoring.

关键词： Systematics Telemedicine Focusing Medical services Biomedical monitoring Wearable devices Older adults Information technology Monitoring Diseases

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A review of sentiment analysis techniques using soft computing approaches 11

A review of sentiment analysis techniques using soft computi...

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11th International Conference on Cloud Computing, Data science and engineering, Confluence 2021

作者： Jha, Upasana Tyagi, Lakshya Kansal, Divya Chakraborty, Subhecha Singhal, Abhishek Department of Computer Science and Engineering ASET Amity University Uttar Pradesh Noida India

ISBN: (纸本)9780738131603

Sentiment analysis and opinion mining have taken a leading position ever since the boom of the internet which led to an enormous amount of opinionated data being posted online. Hence, it has become essential to structure an efficient algorithm for analysing sentiments from text. Even though traditional machine learning algorithms were a breakthrough in the domain, there remained a demand for a better solution. This paper presents a systematic & qualitative literature review for various approaches to sentiment analysis using hybridized soft-computing techniques to cater to noise and improve the flexibility of a fuzzy inference-based sentiment analysis system. This qualitative literature review adds to the body of evidence that soft-computing and fuzzy logic techniques can be effectively applied to a range of applications where noise is inherent, especially in the case of sentiment analysis and opinion mining. © 2021 IEEE

关键词： Sentiment analysis

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Data Augmentation: Synthetic Image Generation for Medical Images Using Vector Quantized Variational Autoencoders 3

Data Augmentation: Synthetic Image Generation for Medical Im...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Singla, Chaitanya Bhardwaj, Rajat Shelke, Nilesh Singh, Gurpreet Chandigarh Engineering College Chandigarh Group of Colleges Department of Computer Science and Engineering Jhanjeri Punjab Mohali140307 India Aset Amity University Department of Computer Science and Engineering Karnataka Bengaluru India University Nagpur Campus Pune India

ISBN: (纸本)9798331519582

Artificial intelligence (AI) has revolutionized medical imaging, significantly improving diagnostic accuracy via the evaluation of X-rays, MRIs, and CT scans. However, the effectiveness of AI models is hindered due to high costs, privacy concerns, and challenges in acquiring large annotated datasets. Artificial data generation techniques, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and hybrid models like Disc-VAE, have gained prominence in dealing with these barriers. These techniques aim to augment datasets while preserving the complicated capabilities inherent to medical images. This paper compares the efficacy of Vector Quantized VAE with GAN and Disc-VAE using metrics Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). The Vector Quantized VAE outperforms GAN and Disc-VAE in terms of SSIM and PSNR metrics. The findings spotlight the transformative capability of synthetic information augmentation for the medical system by enabling early and accurate disease diagnosis. These improved SSIM and PSNR metrics suggest that the generated images preserve essential diagnostic details, thereby enhancing the potential for early and accurate clinical diagnosis. It guarantees advanced performance and robustness of AI models, facilitating timely intervention and improved patient outcomes. © 2025 IEEE.

关键词： Variational techniques

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Edge Intelligence in IoT: Architecture and Applications

Edge Intelligence in IoT: Architecture and Applications

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International Conference on Communication and Electronics Systems (ICCES)

作者： Kajal Jewani Satheesh Abimannan Department of Computer Science and Engineering Amity School of Engineering and Technology (ASET) Amity University Mumbai Maharashtra India

This research survey presents a comprehensive analysis of IoT architectures that incorporate edge computing, examining their classifications, applications, and the challenges and limitations associated with their implementation. The survey also explores future directions and trends in IoT architectures that leverage edge computing, including 5G integration, artificial intelligence (AI), and machine learning (ML) advancements, edge virtualization, edge security, and improved interoperability. The research study highlights the potential of IoT architectures that utilize edge computing to address the challenges posed by traditional cloud-centric architectures while also acknowledging the key challenges and limitations that need to be overcome. By examining the current state of the field and emerging trends and technologies, this survey aims to provide a valuable reference for researchers, practitioners, and industry stakeholders working on the development and implementation of IoT solutions incorporating edge computing.

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GreenGuard: Advanced Sensor Solutions for Enhanced Crop Health and Growth

GreenGuard: Advanced Sensor Solutions for Enhanced Crop Heal...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Ishaan Tyagi Rajat Gupta Divya Upadhyay Ashwani Kumar Dubey Puneet Sharma Computer Science and Engineering ABES Engineering College Ghaziabad India Electronics and Communication Engineering ASET Amity University Uttar Pradesh Noida UP India School of Computer Science & Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

This study investigates an innovative soil management approach integrating advanced sensor technology to enhance crop growth conditions. The research focuses on critical soil parameters: pH, moisture, and temperature, essential for plant health and soil quality. By employing state-of-the-art sensors, the study continuously monitors and adjusts these parameters, promoting sustainable agriculture and increased crop yields. features include real-time tracking of soil pH, providing insights into nutrient availability and microbial activity, crucial for soil health. Moisture sensors accurately measure soil water content, aiding in precise irrigation practices and preventing harmful effects of improper watering. Additionally, temperature sensors help maintain ideal conditions for microbial activity and root development. The research includes extensive experimental work to evaluate the effectiveness of this sensor-based system in enhancing plant growth and soil fertility. The results demonstrate the potential of this technology-driven approach to boost agricultural output while minimizing environmental impacts. A notable aspect of the system is an alert mechanism that informs users about significant changes in soil conditions, aiding timely crop management interventions. The paper contributes to the scientific understanding of plant-soil relationships and offers practical strategies for agricultural practitioners. It presents a comprehensive, data-centric method combining pH, moisture, and temperature sensors, paving the way for a sustainable and efficient future in agriculture.

关键词： Temperature sensors Temperature measurement Soil measurements Scalability Green products Moisture Soil Signal processing Smart systems Real-time systems

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Multilevel Cloud resource Scheduling approach based on imagebased signature system 23

Multilevel Cloud resource Scheduling approach based on image...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Koushik Chakraborty Sweety Singhal Nancy Arya Mohit Tiwari Aasiya Khatoon Ashish Tiwari Office of the Registrar Adamas University India Computer Science SOET Sangam University India Computer Science and Engineering Shree Guru Gobind Singh Tricentenary University India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering India Computer Science and engineering Roorkee Institute of Technology India ASET Amity University India

ISBN: (纸本)9798400709418

Abstract: Cloud computing is arising with developing fame in work process booking, particularly for logical work process. During resource allotment, the cloud computing climate might experience impressive issues as far as execution time and execution cost, which might prompt disturbances in assistance quality given to clients. Text Passwords are at this point a completely used affirmation part, but graphical passwords and biometrics are also used. Nevertheless, to make any system astoundingly secure we really want to improve comfort as well as security. Cloud resource scheduling is a critical aspect of cloud computing that involves efficiently allocating and managing cloud resources to meet the demands of applications and services. Various scheduling approaches aim to optimize resource utilization, minimize costs, and improve overall system performance. Automatically scaling resources up or down based on workload demand. Improves cost-efficiency by dynamically adjusting resources to match varying workloads, ensuring optimal performance during peak times and cost savings during low demand. Our proposed FSOS (Fastest scheduling Optimized system) algorithm is most probably used for the framework so that the performance may increase. It uses components of both Graphical and Text Password plans. In this proposed FSOS (Fastest scheduling Optimized system) mannequin we have in contrast with the most famous algorithms like multi-objective scheduling (MOS), fee conscious scheduling (SCAS) are the various scheduling algorithms to make cloud services secure and highly available to the end users. In this exploration, we endeavor to show the most well-known three static errand booking calculations execution of various algorithm system. All the algorithms are calculating the total execution cost and timing of different algorithms.

关键词： Cloud Service

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Quant-Jack: Quantum Machine Learning to Detect Cryptojacking Attacks in IIoT Networks

Quant-Jack: Quantum Machine Learning to Detect Cryptojacking...

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IEEE International Conference on Communications Workshops, ICC

作者： Pronaya Bhattacharya Aparna Kumari Sudeep Tanwar Ishan Budhiraja Sahil Patel Joel J.P.C Rodrigues Department of Computer Science and Engineering ASET Amity University Kolkata India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India COPELABS Luso fona University Lisbon Portugal

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

The paper presents a scheme, Quant-Jack, which employs Quantum Machine Learning (QML) to combat the cryptojacking threat in Industrial Internet-of-Things (IIoT) networks. We propose a dual-layered QML architecture, based on Quantum Neural Networks (QNN) architecture. The first layer is the QNN detection layer that operates via a weighted sum approach on time, frequency, and network traffic. The problem is modeled as multi-objective optimization, which is solved by an iterative Quantum Approximate Optimization Algorithm (QAOA). At the QML filtration layer, a Quantum Metric (QM) is computed that filters anomalies based on specified thresholds. For performance evaluation, the CSE-CIC-IDS 2018 benchmark dataset is augmented with live IIoT data. The performance is simulated for model parameters like convergence rate, attack detection time, network throughput utilization, and other metrics like precision, recall, and F1-score. For 60 nodes, the measured throughput is 11.84 KBps, which is an average improvement of 23.44% compared to baseline quantum models. the QNN model has an accuracy of 0.97 in classifying malign and benign requests, which indicates the efficacy of the proposed scheme against classical security designs.

关键词： Filtration Conferences Machine learning Telecommunication traffic computer architecture Throughput Iterative algorithms

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