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共 19715 条记录，以下是101-110 订阅

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To Kill a Mockingbird: Cryptanalysis of an Authenticated Key Exchange Scheme for Drones 15

To Kill a Mockingbird: Cryptanalysis of an Authenticated Key...

引用

15th International Conference on Information and Knowledge technology, IKT 2024

作者： Toghraee, Neda Mala, Hamid Dept. of Information Technology Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

ISBN: (纸本)9798331522254

Drones have evolved into sophisticated autonomous systems with a multitude of applications, including military operations, environmental monitoring, traffic oversight, data transmission, package delivery, fire targeting, and film production. As the Internet of Drones (IoD) ecosystem expands, ensuring secure and real-time access for authorized users becomes increasingly vital. User and drone authentication is crucial for this reason. In response to these challenges, Srinivas et al. proposed a lightweight three-factor authentication protocol designed for the IoD. While the authors assert that their protocol is resilient against known cyber threats, our research identifies critical vulnerabilities that challenge this claim. Although this scheme has already been cryptanalyzed in previous studies, in this paper we propose two new attacks on this scheme. Firstly, we present a concrete attack against the perfect forward secrecy of this scheme, and then we show that it is vulnerable to unauthorized access attack by a valid user to an unauthorized area's information. These weaknesses highlight the pressing need for the development of more secure authentication mechanisms in the IoD environment. Moving forward, addressing these vulnerabilities will be essential for fostering trust and ensuring the safe integration of drones into various applications, ultimately contributing to the advancement of IoD technology. © 2024 IEEE.

关键词： Authentication Protocol

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A Survey Paper on Smart-Farm: Machine Learning and IoT-based Agriculture Decision Support Portal 15

A Survey Paper on Smart-Farm: Machine Learning and IoT-based...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Haripriya, K. Chand, Yeluru Vinay Nallamothu, Bhuvan Mahadhyut, S. Sumanth, D. Sree Sangem, Anish Dept of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331300579

Smart Farm, an innovative agricultural platform, is at the forefront of revolutionizing traditional farming practices by integrating machine learning, deep learning algorithms, and IoT technologies seamlessly. This trailblazing technology goes above and beyond traditional approaches by properly estimating crop yields and analyzing seed quality, giving farmers critical insights for optimal agricultural planning. Smart-Farm, by leveraging the power of machine learning algorithms, becomes a trustworthy ally for farmers, providing precise and fast information critical for agricultural decision-making. The addition of IoT technology expands its capabilities even further, allowing for real-time soil quality evaluations via strategically positioned sensors in the soil. These sensors monitor crucial characteristics like as moisture levels, nutrient content, and pH levels, allowing farmers to perform targeted soil management using a data-driven approach. This not only optimizes crop farming practices but also increases overall productivity tremendously. The Agriculture Portal, a key component of Smart-Farm, exemplifies agricultural empowerment by empowering farmers to make well-informed decisions that result in better outcomes, thereby creating a more sustainable and efficient approach to contemporary farming. © Grenze Scientific Society, 2024.

关键词： Smart agriculture

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Advancing Ophthalmology through Transfer Learning and Channel-Wise Attention for Retinal Disease Classification 6

Advancing Ophthalmology through Transfer Learning and Channe...

引用

6th International Conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Islam, Sudipta Progga Alam, Ashraf Ul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

The global incidence of ocular disorders, which impacts more than 2.2 billion people, requires innovative approaches to ensure early and accurate diagnosis. This paper explores the integration of artificial intelligence (AI) in ophthal-mology, focusing on retinal diseases like cataracts, glaucoma, and diabetic retinopathy. The utilization of AI enables remote examinations and consultations, providing a crucial advantage in early disease detection. This study introduces a comparative analysis of state-of-the-art models, combining pre-trained convolutional neural networks (CNNs) and incorporating a channel-wise attention mechanism for enhanced discriminative ability. The proposed hybrid model, combining EfficientNetBO and Inception V3 with channel-wise attention mechanism, exhibits a remarkable accuracy of 98.13%, outperforming standalone models and other combinations. The findings underscore the efficacy of combined architectures and the integration of attention mechanisms in advancing the classification of retinal diseases. © 2024 IEEE.

关键词： Ophthalmology

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Navigating the Landscape of Intelligent Systems, Smart Technologies, and User-Centric Evolution 2

Navigating the Landscape of Intelligent Systems, Smart Techn...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Komatireddy, Sanjay Reddy Krishna Bhargavi, Y. Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350327533

Human-computer Interaction (HCI) is a dynamic field pi votal in shaping human-technology relationships. HCI is a multidisciplinary field, essential in understanding and enhancing the interaction between humans and technology. Challenges within HCI encompass design, usability, accessibility, and emotional resonance. The aim of this research study is to highlight the multifaceted nature of HCI, offering insights into its transformative and complementing potential and its continuous influence on the digital landscape. By delving into the challenges faced and the current technologies (Multimodal Affect Recognition, Artificial Intelligence, etc.), this review serves as a foundation for further research and development in this vital domain, offering valuable insights into the dynamic HCI landscape. © 2024 IEEE.

关键词： Affect Recognition Human-computer Interaction (HCI) Multimodal Interaction Personalization Psychology Usability Challenges

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Built-Up and Land Use Land Cover Mapping for Sustainable Planning in Himalayan Region by Using Machine Learning and Sentinel-2 Satellite Imagery 1

Built-Up and Land Use Land Cover Mapping for Sustainable Pla...

引用

1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Singh, Suraj Saini, Rashmi Govind Ballabh Pant Institute of Engineering and Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350364590

The Himalaya region, known for its ecological sensitivity, also faces recurring natural hazards such as earthquakes, landslides, sinking, glacier bursts, and flash floods, which necessitate the implementation of effective planning and management strategies to ensure sustainable development. This study focuses on Joshimath, a subdivision of Chamoli district in Uttarakhand, India, situated in the Greater Himalaya region, covering an area of 4563.52 km2, Our objective is to map built-up areas using Machine learning techniques and Sentinel-2 satellite multispectral imagery to support sustainable planning initiatives. Two machine learning models, Random Forest (RF) and Support Vector Machine (SVM), were employed in this study. RF achieved an overall accuracy of 92.62% with a kappa value of 0.911, while SVM achieved an accuracy of 90.557 with a kappa value of 0.886. Precision and recall metrics for the built-up class were calculated, with RF achieving 94% precision and 83.22% recall, and SVM achieving 93.48% precision and 82.24% recall. Additionally, the total area classified as built-up by RF was 236.96 km2, whereas SVM classified 198.67 km2. The results demonstrate the effectiveness of machine learning techniques, particularly RF and SVM, in accurately mapping built-up areas in the Himalaya region. This information is crucial for sustainable planning and management practices, enabling stakeholders to make informed decisions regarding land use and development while preserving the ecological integrity of the region. © 2024 IEEE.

关键词： Mapping

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Revolutionizing Mango Crop Health: A Deep Learning Approach for Precise Identification of Leaf Diseases 10

Revolutionizing Mango Crop Health: A Deep Learning Approach ...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Navadharani, N. Mouthami, K. Devipriya, R. Anandamurugan, S. Kpr Institute of Engineering and Technology Dept. of Computer Science Engineering Coimbatore India Kongu Engineering College Dept. of Information Technology Erode India

ISBN: (纸本)9798350384369

Plant diseases pose significant challenges to the agricultural sector, leading to decreased crop production and financial losses. Mango, being a popular fruit worldwide, is susceptible to various leaf diseases. Traditional diagnosis of these disorders requires labor-intensive and time-consuming manual picture interpretation. To address this issue, deep learning models have been utilized to enhance the precision and effectiveness of identifying leaf illness in mango crops. In this study, ResNet50, ResNext50, Convnet, and Conventv2 deep learning models were compared to find the most successful approach. The results demonstrated that the Conventv2 model achieved the highest accuracy of 98.21%. The major aim of the research work is improvising sustainable agricultural practices by leveraging advanced technology for accurate plant disease classification. The findings have implications for improving mango crop management and ensuring a more sustainable and efficient agricultural industry. © 2024 IEEE.

关键词： Fruits

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IoT-Enhanced Smart Bulletin Board: A Multi-Functional Connectivity Hub 2

IoT-Enhanced Smart Bulletin Board: A Multi-Functional Connec...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gundavarapu, Mallikarjuna Rao Swapna, Veerlapalii Reddy, Enugu Sriamsh Kumar, Pindi Praveen Prathapani, Abhinay Shivaji, Kothapally Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350327533

The problem with traditional notice boards is their static nature, leading to outdated information and manual effort to update. An IoT-based smart notice board solves this by providing real-time updates but requires addressing challenges such as connectivity, power management, compatibility, user interface, security, and maintenance. Addressing these challenges ensures an effective and user-friendly smart notice board. The proposed model involves using an LED dot matrix, ESP32, Kodular app, Firebase Cloud and DS3231 module. The app sends messages to the Firebase then it passes the message to ESP32, which displays message on the LED dot matrix, We use Firebase cloud which provides infinite range. Firebase provides a NoSQL database that allows developers to store and synchronize data in real-time. It is suitable for applications that require real-time updates, such as chat apps, collaborative tools, and live dashboards. The DS3231 module shows real-time information. This study implements a feature that allows the user to retrieve the current time and temperature by pressing a button in the Kodular app. When the button is pressed, a command is sent to the ESP32, which retrieves the relevant data from the DS3231 module and sends it to display. This study creates a buffer to store messages, accessible by indexes, allowing easy retrieval. The proposed system provides a comprehensive solution for displaying messages, real-time data, and efficient message storage. QR codes are also provided to scan. © 2024 IEEE.

关键词： Database Dot Matrix DS3231 ESP32 Firebase cloud IoT Kodular app LED Module NoSQL QR code Real-Time Updates

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A Comprehensive Review of Deep Learning Methods for Hyperspectral Image Compression 3

A Comprehensive Review of Deep Learning Methods for Hyperspe...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Afrin, Afsana Mamun, Md. Al Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

Rapid advancement in the development of hyperspectral image (HSI) sensors has significantly enhanced the capabilities of capturing detailed spectral information across a wide range of wavelengths, enabling more precise analysis and interpretation of the observed scenes. Managing the storage of these extensive images poses a crucial challenge, requiring the application of compression techniques. Deep Learning (DL) algorithms have seen a massive rise in popularity over the past few years for HSI compression as well as HSI analysis. This review paper provides a comprehensive summary of the current literature on deep learning methods for HSI compression. Additionally, it explores the challenges, opportunities, and potential directions for future research in HSI compression. © 2024 IEEE.

关键词： Image enhancement

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'Precision Agriculture Farming' Enhancing Farming Efficiency through technology Integration 1

'Precision Agriculture Farming' Enhancing Farming Efficiency...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Prashant Kumar, Pratyaksh Singh, Praveen Kumar Rana, Arun Kumar Galgotias College of Engineering And Technology Dept. Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350356816

Precision agriculture, or precision farming, or smart farming, refers to a contemporary approach that combines technology and data analytics with agricultural practices. This research paper examines the concept of precision agriculture, its principles, advantages, disadvantages, and potential to transform farming practices. Through the use of various technologies including GPS, sensors, drones, and data analytics, precision agriculture seeks to maximize resource efficiency, enhance crop yields, reduce the environmental footprint, and boost overall profitability for the farm. The paper also addresses case studies and real-life implementations of precision agriculture and illustrates its effectiveness in various sectors of agriculture. It further identifies the challenges and constraints of adopting precision agriculture and proposes possible solutions to overcome these challenges. This paper generally offers insight into the revolutionary potential of precision agriculture in reshaping the future of farming. © 2024 IEEE.

关键词： Smart agriculture

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Spam Detection Using Firefly Optimization Technique 2

Spam Detection Using Firefly Optimization Technique

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Srivastav, Shashank Gupta, Alok Singh, Laxman Singh, Rajesh Kumar Tripathi, Kanak Dwivedi, Anshu Kumar Buddha Institute of Technology Department of Computer Science Engineering Gorakhpur India Babu Banarasi das Engineering College Department of Computer Science Engineering Lucknow India Bennett University School of Computer Science Engineering and Technology Greater Noida India KIPM College of Engineering & Technology Dept of CSE Gorakhpur India Galgotia College of Engineering & Technology Dept of MCA Greater Noida India

ISBN: (纸本)9798350368109

In this electronically era, communication using email has become an part of our lives. However, with the convenience of email comes the nuisance of spam messages flooding our inboxes. Detecting and filtering out these spam emails is crucial to maintain productivity and security in our online interactions. In this research project, we explore the performance of using the firefly algorithm, a nature-inspired optimization technique, for spam detection. By leveraging the unique behavior of fireflies in search and optimization, we aim to develop a robust and efficient spam detection system. We start by gathering a dataset containing a mix of spam and non-spam (ham) emails. After preprocessing the text data and extracting relevant features using techniques like Count-Vectorizer, we train a logistic regression model, a commonly used classifier for binary classification tasks. To enhance the performance of our model, we employ the Firefly algorithm to optimize its hyperactive parameters. This algorithm impersonates the flashing behavior of fireflies to iteratively search for the foremost combination of model parameters, maximizing its ability to distinguish between spam and legitimate emails. Our experimental results demonstrate promising outcomes, with the optimized model achieving high accuracy and performance measures such as precision, recall, and F1-score on a held-out test set. These findings highlight the potential of nature-inspired algorithms like the firefly algorithm to improve spam detection techniques and effectively combat email spam in real-world scenarios. © 2024 IEEE.

关键词： Logistic regression

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