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Enhancing Agricultural Quality Control: Freshness Detection in Root Vegetables Through Deep Neural Networks 15

Enhancing Agricultural Quality Control: Freshness Detection ...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shorna, Aklema Akther Ave, Abida Sultana Mahi, Hasibul Hasan Chowdhury Shital, Sabrina Bari Sowrna, Nourin Jahan Nahid, Aynul Hasan Daffodil International University Dept. computer science and engineering Dhaka Bangladesh

ISBN: (纸本)9798350370249

In monarchy agricultural technology, this research pioneers the classification of freshness in root vegetables, a field that has been abruptly neglected in earlier research on a variety of vegetables. This study addresses this research gap and employs a transfer learning-based system to swiftly determine the freshness of root vegetables through images. To conduct this research, we create our own dataset which contains six different vegetables, 'Potato', 'Radish', 'Carrot', 'Onion', 'Taro', and 'Turnip'. Vegetables were collected from the local market and clicked manually by personal mobile phone, giving the dataset authenticity and real-world application. In total we collect 6384 images, divided into 3 classes: 'Fresh', 'Aged' and 'Rotten' based on their physical attributes and texture. Different types of transfer learning model VGG19, ResNet50, InceptionResNetV2 and DenseNet201 used to classify freshness of root vegetables. Although every model achieved decent accuracy, DenseNet201 achieved a remarkable result. DenseNet201achived highest accuracy 98.75% where data loss was 0.02%. Conversely, though ResNet50 achieved lowest accuracy 60.97% among all models. The variety of algorithms used seeks to determine the best course of action for this particular agricultural setting in addition to precisely classifying freshness. This research not only defines a novel benchmark but also sets the stage for future explorations in agricultural technology, ushering in a new era of precision farming for root vegetables © 2024 IEEE.

关键词： classification deep neural network freshness Root vegetable transfer learning

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Physical Layer Authentication via Underwater Acoustic Multipath Channel Crafting

Physical Layer Authentication via Underwater Acoustic Multip...

引用

OCEANS 2024 - Halifax, OCEANS 2024

作者： Eccher, Davide Casari, Paolo University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798331540081

With the increasing number of actors in the under-water environment and the development of new applications, such as large-scale monitoring and autonomous underwater vehicle control, securing underwater communications is becoming a primary necessity. Security was not prioritized in the past due to the constraints of underwater acoustic communications, which cannot sustain the overhead of typical cryptographic techniques. In this paper, we propose a method to authenticate a network device by exploiting the physical properties of the acoustic channel. In particular, our method hinges on the uniqueness and quasi-reciprocity of the channel, from which the authenticator (Alice) node can extract several parameters such as the number of multi path channel components, their delay and amplitude. These values are similar on both ends of a link between Alice and a legitimate transmitter (Bob), and can be used as a seed to craft a new artificial channel, that is then applied to transmissions from Bob to Alice. With this procedure, Alice can distinguish Bob from an impersonating attacker (Eve), given a previous message exchange history. Eve can try to bypass the protocol by estimating the channel parameters and by trying to replicate Bob's signal by crafting a similar channel. In our tests, we observe that the estimation error for Eve, caused by her wrong channel estimates, becomes significant even for short distances betwen Eve and Bob. This error results in a discrepancy between the signal generated by Eve and the one expected by Alice, and reveals Eve as an attacker. © 2024 IEEE.

关键词： Underwater acoustics

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Image Captioning: Analyzing CNN-LSTM and Vision-GPT Models 9

Image Captioning: Analyzing CNN-LSTM and Vision-GPT Models

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Karthik, Abburi Sai Karthik, Maddala H. S. M. Krishna Yashwanth, Samudrala Anjali, T. Amrita Vishwa Vidyapeetham Dept. of Computer Science And Engineering Amritapuri India

ISBN: (纸本)9798350394474

Image captioning, which exists at the point of intersection of computer vision and natural language processing, is essential for enhancing image comprehension, allowing applications like content discovery, visual aid for the blind, and more. The hunt for more precise and reliable picture captioning models continues to be an important research goal as technology develops quickly. The two prominent image captioning techniques used in this study Image Captioning Using LSTM+CNN and Image Captioning Using VisionGPT2 are thoroughly compared. We examine these models' internal workings, assess their effectiveness, and offer insights into their advantages and disadvantages for diverse application *** neural networks (CNNs) for extracting visual features and long short-term memory (LSTM) networks for producing sequential language are combined in the LSTM+CNN model, a tried-and-true methodology. It has shown adept.in creating insightful descriptions for a variety of photographs. On the other hand, VisionGPT2, a GPT-2 architectural extension, makes use of transformers and pretrained language models to provide cutting-edge outcomes in a range of natural language processing applications. We analyze the viability of each technique by taking into account elements like model complexity, training data needs, and deployment simplicity. This comprehensive comparison enlightens academics, programmers, and businesses on the ideal picture captioning solution for their particular requirements, fostering development in this area and its numerous uses. © 2024 IEEE.

关键词： Image enhancement

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DubasAlert: AI-Powered Detection System for Dubas Infestation on Palm Leaves

DubasAlert: AI-Powered Detection System for Dubas Infestatio...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Nagaraju, Y. Venkatesh Uvce Bangalore University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

In Middle Eastern countries, Dubas insects pose a significant threat, causing plant death by draining sap from date palm leaflets and fronds. This study presents a novel method for detecting Dubas insects at different stages of their growth. Existing models perform ineffectively and have large trained weights, making integration with edge devices difficult. To address these issues, we propose a pruned YOLOv7 model which is lighter and has fewer parameters, resulting in 75% lower FLOPs. Pruned Yolov7 outperforms baseline models with a mAP@0.5 of 80.1%. Our approach enables real-time detection and seamless integration into edge devices, resulting in practical solutions to combat Dubas insect infestations. © 2024 IEEE.

关键词： Plant diseases

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Smart Agriculture Inventory Management System Using Machine Learning Algorithms 1

Smart Agriculture Inventory Management System Using Machine ...

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Kanyal, Hoshiyar Singh Jain, Harshit Tyagi, Harsh Yadav, Satyarth Prakash ABES Institute of Technology Ghaziabad Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350353648

In the agricultural sector, smallholder farmers in India face significant challenges with post-harvest losses, fluctuating market prices, and unsustainable practices. This research proposes a mobile-based application designed specifically for farmers in region of India, focusing on specific crops. To reduce agricultural waste, the app features an optimal storage location based on crop type. To improve farmer income, the app includes a crop recommendation module that integrates historical yield data, weather forecasts, soil characteristics, and real-time market prices. Machine learning models, validated with field data, suggest high-yield, profitable crops and optimal sales timing. A market information hub provides real-time mandi prices and demand forecasts. With a user-friendly interface and visual explanations, the app addresses the technological constraints of smallholder farmers. This research aims to reduce agricultural waste, improve farmer income, and promote sustainable agriculture practices. © 2024 IEEE.

关键词： Agricultural economics

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Decentralized e-KYC System for Secure Identity Verification in Bangladesh 3

Decentralized e-KYC System for Secure Identity Verification ...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Karmoker, Sagar Khan, Md. Adib Anik, Nur Adnan Chowdhury Hossain, Md. Sabbir Rahman, Habibur Rumpa, Ummay Khadiza Hasan, Mahamudul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350354348

In this modern age where everything is developing and moving forward to achieve new heights, Fraud and scam-related cases are also increasing. That's why we need to be more careful than ever before. nowadays whether we want to open a bank account or use a financial app we first need to verify ourselves by doing e-KYC. But the problem is this e-KYC verification is needed in so many sectors and we have to do it multiple times. This paper shows how this situation can be solved by using a blockchain-based e-KYC (Electronic Know Your Customer) system. This will be a one-stop solution for e-KYC, where people need to do KYC only one time and then they can use it in multiple organizations. Because of blockchain technology, our system will be transparent, decentralized, efficient, and trustworthy. Where users can choose whom to give the e-KYC information and can transfer all the information or the state of the information within a second. The proposed e-KYC systems' motive is to create a more secure and widespread digitalized ecosystem that will bring benefits to any country. © 2024 IEEE.

关键词： Decentralized finance

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Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches 9

Text-Based Sentimental Analysis to Understand User Experienc...

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Gokulapriya, R. Sambandam, Rakoth Kandan Balamurugan, M. Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350397376

Data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general *** primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. Data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches. © 2023 IEEE.

关键词： Python

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A CNN Based Ensemble Deep Learning Approach for Melanoma Detection

A CNN Based Ensemble Deep Learning Approach for Melanoma Det...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Mullick, Mohammad Abu Obaida Khan, Nafi Sadman Fahid, Ahnaf Tahmid Mahmud, Mahin Rahman, Raiyan United International University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350364866

Melanoma, the most aggressive form of skin cancer, necessitates early detection for successful treatment. This paper proposes an ensemble Convolutional Neural Networks (CNNs) approach to classify dermoscopic images of Melanoma (MEL) and Melanocytic Nevi (NV), by systematically selecting and comparing various pre-trained models with different optimizers and preprocessors through trial and error, as well as optimizing loss functions. The ensemble method takes predictions from multiple pre-trained models and selects the most reliable outcome using a custom metric. The result presents an optimal CNN ensemble model built of DenseNet121 and VGG16 with cropping and scaling, contrast enhancing, and exposure adjusting as image preprocessors that achieved a notable accuracy of 97.5% on a customized and balanced version of the Kaggle HAM10000 dataset. This approach aims to improve patient outcomes by providing a valuable tool for early melanoma detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Blockchain Based Credit System to Increase the Creditability through Carbon Offset Level 5

Blockchain Based Credit System to Increase the Creditability...

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

作者： Padmavathi, A. Reddy, G. Charan Santhosh, Gauri Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Our country has relied significantly on agriculture as its main source of income for several decades. The demand for food and crops is growing due to the growing population, which offers the agriculture sector tremendous prospects for growth. However, despite the soaring demand and bright future, India's agriculture industry has failed to demonstrate appreciable profitability. In the paper that follows, we provide a novel strategy meant to revolutionize agricultural marketing networks. Blockchain technology is included in our approach to enable the virtualization of products and carbon offset certificates. This system encourages agricultural producers to reduce carbon emissions and address environmental concerns. By utilizing blockchain in a particular way, carbon offset credits can be used to securely track, trade, and for other fostering sustainability practices. This integration of technologies assures to change how agricultural products are marketed currently. It also helps in promoting sustainability and provides consumers with all the valuable information about the environmental impact of their purchases. © 2024 IEEE.

关键词： Blockchain carbon offset credits Product visualization virtualization

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Analysis of Ensemble Machine Learning Models for Fraud Detection

Analysis of Ensemble Machine Learning Models for Fraud Detec...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Chaurasia, Siddharth Kesharwani, Satyam Sharma, Shanu Sharma, Swati Chugh, Bharti Kiet Group of Institutions Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350375237

Recent developments in electronic payment and e-commerce have led to a rise in digital fraud cases, including credit card fraud. Within the financial services industry, identifying credit card fraud is still a difficult task that can have significant effects on consumers' and financial institutions' reputations. Therefore, it is crucial to implement systems capable of detecting credit card fraud. This research utilizes the European Cardholder Data Collection to assess the efficacy of ensemble machine learning techniques in identifying credit card fraud. The European cardholder data set is subjected to a rigorous assessment and evaluation of series of machine learning models, such as Gradient Boosting, Random Forest and CAT Boost, to detect fraudulent transactions. It has been noted that the dataset is unbalanced, which may indicate that the models' performance is not very ideal. The study suggests using data sampling strategies to achieve a balanced distribution of data across various algorithms that yield optimal outcomes. The main goal is to identify the model that performs best for machine learning classification problems by comparing the performance of different models. The results of this study will support further initiatives to improve credit card transaction security and lower financial losses brought on by fraud. © 2024 IEEE.

关键词： Losses

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