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An Advanced Surveillance Motorized Car for Real-Time Inspection with Sensing Capabilities

An Advanced Surveillance Motorized Car for Real-Time Inspect...

引用

International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： Ingale, Kiran Tekade, Om Thakare, Prathamesh Wankhade, Abhishek Wankhade, Purva Department of Electronics and Telecommunication Engineering Vishwakarma Institute of Technology Pune India Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819746491

In recent years, the Internet of things (IoT) has emerged as a promising technology for improving quality of life and addressing social challenges. The development of a proposed car is done with integrated sensors and advanced motor control methods. The ESP8266 module in the car is connected to sensors for measuring distance, temperature, humidity, and air quality. Precise movements in various directions are made possible by servomotors and a motor driver. Programming the ESP8266 and ESP32 CAM modules as well as integrating the Blynk interface for remote control are also included in the software. The applications in surveillance and environmental monitoring show successful integration and functionality. The proposed system offers a comprehensive system for real-time remote data collection and control, advancing robotics and the Internet of things. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Air quality

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OCR-Based Extraction of Expiry Dates and Batch Numbers in Medicine Packaging for Error-Free Data Entry 7

OCR-Based Extraction of Expiry Dates and Batch Numbers in Me...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Kavin, S. Shirley, C.P. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

To guarantee patient safety and regulatory compliance, healthcare providers must accurately enter batch numbers and expiration dates from drug packaging. But manual entry is prone to mistakes, which could have unfavourable effects. This project suggests an automated method for extracting batch numbers and expiration dates from the back of pharmaceutical packaging by using optical character recognition (OCR) technology. In order to improve OCR accuracy, the research makes use of preprocessing techniques and a broad dataset of photos from medical packaging. The retrieved text is analysed to determine the batch number and expiration date, and data correctness is ensured by validation procedures. The end product solution reduces errors in healthcare data management by offering an intuitive user interface for effective data extraction. By means of thorough inspection and evaluation, The experiment shows how the OCR-based system can reliably and effectively extract important information from pharmaceutical packaging. Through the reduction of data entry errors and the facilitation of error-free information retrieval from drug packaging, this effort seeks to improve patient safety and streamline healthcare procedures. © 2024 IEEE.

关键词： Data accuracy

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Design and Development of Fire Detection using Deep Learning Algorithm

Design and Development of Fire Detection using Deep Learning...

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2024 International Conference on Science, technology, engineering and Management, ICSTEM 2024

作者： Santhi, S. Kumar, Kishore S. Department of Computer Science and Engineering KIT-Kalaignarkarunanidhi Institute of Technology Coimbatore India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350376913

Fire detection is a critical aspect of ensuring safety in various environments, including residential, industrial, and commercial settings. In this project, we present the development and the implementation of an IoTbased fire detection systems that integrates image capture capabilities with a machine learning algorithm. The system employs an IoT camera to record images of its surroundings, which are subsequently analyzed by a convolutional neural network (CNN) specifically designed for identifying instances of fire. The CNN model analyzes the images in real-time, identifying potential fire hazards based on visual cues. Upon fire detection, the system generates an alert, enabling prompt response and mitigation measures. The integration of IoT technology with machine learning algorithms enables remote monitoring and early detection of fire incidents, enhancing safety and minimizing potential damage. The experimental findings validate the efficacy and dependability of the proposed system in precisely identifying instances of fire. This project contributes to the advancement of fire detection technologies, offering a scalable and efficient solution for enhancing safety in diverse environments. © 2024 IEEE.

关键词： Fires

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VULDetect: A Lightweight Ensemble Based Framework for Automated Software System Vulnerability Detection

VULDetect: A Lightweight Ensemble Based Framework for Automa...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Ali, Irshad Subba, Basant Department of Computer Science & Engineering Indian Institute of Technology Punjab Ropar India

ISBN: (纸本)9798350350821

This paper presents VULDetect: a lightweight and highly accurate ensemble based software system vulnerability detection framework trained on a large C/C++ file corpus comprising thousands of vulnerable and non-vulnerable source code snippets. VULDetect uses a deep learning based ensemble model comprising multiple classifiers to perform a comprehensive vulnerability assessment of the software programs. Experimental results on a proprietary dataset and a benchmark DIVERSEVUL dataset show that VULDetect is capable of detecting wide range of software system vulnerabilities with high accuracy and low false positive rate. In addition, VULDetect is also highly compact and lightweight in comparison to other Large Language Model (LLM) based frameworks proposed in the literature, which makes it highly suitable for deployment on computation constrained edge devices. © 2024 IEEE.

关键词： C++ (programming language)

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Hyperspectral Image Classification Using Quantum Machine Learning 1

Hyperspectral Image Classification Using Quantum Machine Lea...

引用

1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Priyanka, G.S. Venkatesan, M. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367720

Comprehensive spectrum information is provided by hyperspectral imaging and is necessary for data processing and remote sensing. Yet, efficient categorization is hampered by its great dimensionality and intricate spectral fingerprints. We present a novel method in this paper to tackle these issues by utilizing Quantum Machine Learning (QML), more especially Quantum Neural Networks (QNN). In order to maintain crucial spectral information while lowering data complexity, we use dimensionality reduction techniques like Principal Component Analysis (PCA) and Kernel PCA (KPCA). To develop Modified Quantum Neural Network (MQNN) models for multi-class classification, these reduced representations are coupled with conventional Neural Networks (NN). Tested on the Salinas, Pavia, and Indian Pines datasets, our findings show that QML approaches are more accurate when processing high-dimensional hyperspectral data, presenting new opportunities for use in environmental monitoring, agriculture, and urban planning. © 2024 IEEE.

关键词： Dimensionality reduction

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AI-Driven Customer Segmentation for Personalized Marketing 2

AI-Driven Customer Segmentation for Personalized Marketing

引用

2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Hemalatha, Raavi Amulya, Godi Lalitha, C H. S. N. Sai Vishnu Institute of Technology Department of Computer Science & Engineering Bhimavaram534202 India

ISBN: (纸本)9798350364699

The investigation on AI-drive segmentation In real-time of commercial marketing, client segmentation is an essential method that enables firms to tailor their offerings to specific client groups within their target audience. A client segmentation method driven by artificial intelligence is investigated in this exploratory article, along with its impact on the enhancement of marketing personalization. The research reveals how artificial intelligence can analyze massive datasets to identify subtle customer sections based on gestures, preferences, and demographics. This is accomplished by the utilization of complex machine learning methods such as clustering, bracket, and deep literacy by the researchers. This study demonstrates the advantages of AI-driven approaches over traditional styles, including improved delicacy, scalability, and real-time rigidity. These advantages are highlighted in the publication. There are case studies offered to highlight how artificial intelligence has been successfully utilized in the process of segmenting various client bases and establishing targeted marketing strategies. Based on the data, it appears that client segmentation is driven by artificial intelligence, which not only improves the efficiency of marketing but also optimizes resource allocation and increases customer happiness. The conclusion of the article emphasizes the necessity for continuous innovation and ethical considerations when it comes to the utilization of artificial intelligence for proven marketing. It also raises concerns about upcoming trends and issues in AI-driven segmentation. © 2024 IEEE.

关键词： Sales

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CryptoDomains: Building a Decentralized Domain Marketplace with NFTs

CryptoDomains: Building a Decentralized Domain Marketplace w...

引用

2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Phatangare, Sheetal Tekade, Om Thakare, Prathamesh Wankhade, Abhishek Wankhade, Purva Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune411037 India

ISBN: (纸本)9798350382693

The proposed system introduces a decentralized platform built on the Ethereum blockchain, enabling users to acquire, trade, and utilize unique domain names as cryptographic identifiers. Leveraging the ERC-721 standard for non-fungible tokens (NFTs), the platform ensures the secure and transparent ownership of digital assets. The system integrates with the popular MetaMask wallet for user authentication, offering a seamless and widely-adopted solution. Users can mint new domains, buy existing ones, and list owned domains for sale, all facilitated by smart contracts governing domain creation, ownership transfer, and transaction execution. The architecture emphasizes security measures, such as code audits, access controls, and encryption, to safeguard user assets. The platform not only provides a novel approach to digital asset ownership but also simplifies cryptocurrency transactions by allowing users to conduct transactions using easily memorable domain names rather than complex wallet addresses. This research paper explores the technical intricacies, security considerations, and user interactions within the Crypto Domains ecosystem, contributing to the broader understanding of decentralized applications and non-fungible token platforms. © 2024 IEEE.

关键词： Distributed ledger

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Integrity Verifiable Privacy-preserving Federated Learning for Healthcare-IoT

Integrity Verifiable Privacy-preserving Federated Learning f...

引用

2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Li, Jiarui Yu, Shucheng Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken United States

ISBN: (纸本)9798350350548

In Healthcare Internet of Things, federated learning has emerged as a promising distributed machine learning paradigm, enabling multiple clients to collaboratively train models with huge amounts of medical data while preserving the privacy of sensitive information. Despite its advantages, federated learning faces significant challenges in maintaining the integrity of the global model due to the potential for data and model poisoning attacks. These attacks are exacerbated by the lack of direct oversight in the local training processes, allowing malicious participants to manipulate model updates. This paper introduces Integrity Verifiable Federated Learning (IV-FED), a novel framework that leverages trusted execution environments (TEEs) to ensure the integrity of the training process without compromising privacy. IV-FED employs an accumulator-based integrity verification protocol, allowing the central server to verify the correctness of local training without reproducing the entire training process. Additionally, the framework incorporates an adversarial perturbation-based detection mechanism to prevent the injection of poisoned data by malicious participants. © 2024 IEEE.

关键词： Federated learning

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A Hybrid Learning Model for Tomato Plant Disease Detection using Deep Reinforcement Learning with Transfer Learning 4

A Hybrid Learning Model for Tomato Plant Disease Detection u...

引用

4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Raghuram, Kadambari Borah, Malaya Dutta Department of Computer Science and Engineering National Institute of Technology Silchar Assam Silchar India

Plant diseases play a significant role in damaging crop production and food security. Detecting and diagnosing plant diseases in the early stages obtains better management of diseases. Many plants are affected by these diseases, which are very dangerous for crop yield. This paper introduces advanced plant disease detection using an advanced preprocessing technique and a Hybrid Learning Model (HLM). The advanced preprocessing method uses a digital camera to capture high-resolution images of plant leaves from multiple angles. These images are then processed using an enhancement algorithm to improve the visual quality and clarity. The preprocessed images are subjected to a HLM model, which utilizes Deep Reinforcement Learning with Transfer Learning (DRL-TL). The DRL-TL architecture is designed to extract features from the preprocessed images in a three-dimensional manner, considering the spatial information of the leaves. It enables the model to capture precise patterns and variations indicative of disease symptoms. The pre-trained model MobileNetV2 trained on a tomato disease dataset belongs to labeled images;it consists of standard and affected plant leaves to learn the discriminative features associated with different diseases. Results obtained the effectiveness of the hybrid learning model. The preprocessing technique significantly increases the input images' quality, enhancing the subsequent HLM's performance. The model accurately identifies and classifies various plant diseases, outperforming existing methods. Furthermore, the hybrid learning model shows robustness and abstraction, successfully detecting diseases across plant species and environmental conditions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Deep reinforcement learning

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A Client-Server Based Educational Chatbot for Academic Institutions 4

A Client-Server Based Educational Chatbot for Academic Insti...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Richard, Rohan Paul Veemaraj, Ebenezer Thomas, Juanith Mathew Mathew, Joel Stephen, Caleb Koshy, Richie Suresh Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350349900

The use of Generative AI applications in academia, such as ChatGPT, Bard and Perplexity among others is growing at a rapid pace. With its rise, certain ramifications are being felt in regards to the quality and correctness of knowledge and output of these apps. This is detrimental to the process of learning and understanding subject matter in relevant context. In order to stop this issue in its tracks, a solution must be identified, built, tested and deployed so that students will get reliable outputs from trusted sources. This research analyses and describes a sample architecture as well as implementation for such a solution. It incorporates the latest in AI research and development, such as the Large Language Model Mixture of Experts (MoE) architecture as well as a Retrieval Augmented Generation (RAG) with a vector store to use trusted documents such as presentation files, PDF handouts and more from instructors. This is used to add context to the request to the Large Language Model and enrich the understanding as well as response of the model. Apart from this, the application is packaged into a server that can be run on the intranet, as well as deployed for public access. A frontend client page is served to the user, and communicates with the server for all its functioning. © 2024 IEEE.

关键词： Generative AI LangChain Large Language Model (LLM) Mixture of Experts Retrieval Augmented Generation (RAG)

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