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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

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

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

To improve the supply-chain management system of Bangladesh for imported cosmetic products this paper proposes a blockchain-based solution. One of the major goals of this project is to increase the tax compliance of the government by stopping counterfeit and illegally imported cosmetic products. Which will enhance the trust of consumers and the brand’s reputation. Using this system is quite easy and convenient, as All the information will be stored in the blockchain, so it will become a secure and immutable product tracking system for importers, distributors, and retailers to maintain their ledgers just by printing and scanning a QR code through an IoT device, and consumers can check the authenticity of these cosmetic products just by scanning them. If the product is verified to be suspicious, The Mobile Court Management System has the power to check its origin. Finally, we can say that it will avoid the sale of counterfeit items in the market and increase government revenue on tax.

关键词： Printing Supply chain management Government Finance QR codes Documentation Blockchains Stakeholders Security Internet of Things

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Deep Learning for Tea Leaf Disease Detection: Comparing VGG16, Vision Transformer, EfficientNetB3, and Xception

Deep Learning for Tea Leaf Disease Detection: Comparing VGG1...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Rokonozzaman Ayon Arif Shahriar Mubin Al-Amin Gazi Hridoy Mayen Uddin Mojumdar Narayan Ranjan Chakraborty Md. Jahidul Islam Dept. of Computer Science & Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The need for efficient disease management in crop production is highlighted by the economic relevance of agriculture. Various leaf diseases cause major losses for tea, a staple product in many places. In this paper, a deep learning-based method is provided for the automated identification of illnesses affecting tea leaves. Using the unique architectural benefits of four models: VGG16, ViT (Vision Transformer), EfficientNetB3, and Xception, their performance is examined. Each model’s accuracy and effectiveness in classifying diseases are evaluated by conducting a thorough series of experiments on a dataset of photos of tea leaves. The results show that ViT and VGG16 perform better than expected, with accuracies of $\mathbf{9 6. 0 9 \%}$ and $\mathbf{9 8. 2 9 \%}$, respectively. These results highlight the effectiveness of transformer-based designs and conventional CNN in the identification of tea leaf disease. Our research also demonstrates how deep learning approaches can improve agricultural practices by enabling early and accurate disease identification, which would facilitate proactive disease control tactics and increase crop output.

关键词： Deep learning Learning systems computer vision Accuracy Biological system modeling Crops Production Transformers Reliability Diseases

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Densely Decoded Networks with Adaptive Deep Supervision for Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Mishra, Suraj Chen, Danny Z. Dept. of Computer Science and Engineering University of Notre Dame United States

Medical image segmentation using deep neural networks has been highly successful. However, the effectiveness of these networks is often limited by inadequate dense prediction and inability to extract robust features. To achieve refined dense prediction, we propose densely decoded networks (DDN), by selectively introducing ‘crutch’ network connections. Such ‘crutch’ connections in each upsampling stage of the network decoder (1) enhance target localization by incorporating high resolution features from the encoder, and (2) improve segmentation by facilitating multi-stage contextual information flow. Further, we present a training strategy based on adaptive deep supervision (ADS), which exploits and adapts specific attributes of input dataset, for robust feature extraction. In particular, ADS strategically locates and deploys auxiliary supervision, by matching the average input object size with the layer-wise effective receptive fields (LERF) of a network, resulting in a class of DDNs. Such inclusion of ’companion objective’ from a specific hidden layer, helps the model pay close attention to some distinct input-dependent features, which the network might otherwise ‘ignore’ during training. Our new networks and training strategy are validated on 4 diverse datasets of different modalities, demonstrating their effectiveness. © 2024, CC BY.

关键词： Deep neural networks

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

Enhancing Agricultural Quality Control: Freshness Detection ...

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International Conference on Computing and Networking Technology (ICCNT)

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

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

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

关键词： Deep learning Accuracy Image processing Transfer learning Quality control Aging Mobile handsets Classification algorithms Residual neural networks Farming

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Identification of Sugarcane Leaf Diseases and Deficiency Disorders Using Transformers

Identification of Sugarcane Leaf Diseases and Deficiency Dis...

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Subhasish Sarkar Gautam Kumar Dept. of Computer Science and Engineering National Institute of Technology Delhi India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Early identification of plant diseases and nutrient deficiencies is crucial for ensuring healthy food production, especially for crops like sugarcane, which contribute significantly to our food supply. Farmers often struggle to differentiate between diseases and deficiencies due to similar symptoms. To overcome such challenges, this study leverages the application of transformers to classify whether the given input is suffering from disease or deficiency. For this purpose, two transformers, i.e., Vision Transformer (ViT) and Swin Transformer is trained on the dataset having leaves of sugarcane. The performance of the model is calculated in terms of precision, recall, accuracy and F-score. From the results, it is found that the Vision transformer with an accuracy of 99.6% can classify diseases and deficiencies more accurately compared to the 99.2% accuracy for Swin transformer.

关键词： computer vision Plant diseases Accuracy Crops Production Transformers Nutrients

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An algorithm frequently mines a set of items for big data to enhance efficiency

An algorithm frequently mines a set of items for big data to...

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Automation and Computation (AUTOCOM), International Conference on

作者： Sanjay Kumar Sonkar Balkar Singh Dept. of Computer Science and Engineering SunRise University Alwar Rajasthan India

In the landscape of rapidly expanding data streams, deriving meaningful insights, particularly frequent itemsets, within massive datasets at a swift pace poses a significant challenge. Addressing this challenge, various methodologies for Frequent Itemset Mining have been proposed, yet they struggle with low support counts and efficiency concerns in handling large datasets. In response, our research introduces Jagged Itemset Counting (JIC) methodologies, aiming to effectively mine Frequent Itemsets from extensive data. The core objective revolves around devising a robust algorithm capable of identifying all Frequent Itemsets, irrespective of database size or the nature of the itemset. Central to this approach is the introduction of a straightforward label representation, GPLN (Geometric Progression Label Number), assigned to each frequent item. Utilizing CGPLN (Cumulative Geometric Progression Label Number), derived from the arithmetic sum of GPLNs within transaction subsets, forms the CGPLN-Label representation for each transaction subset (itemset). Comparative analysis reveals superior performance of JIC over Apriori and Eclat algorithms for small and medium-sized datasets, exhibiting efficiency even at minimal support thresholds. In the realm of Big data, where FP-Growth and Eclat falter, the proposed technique shines with faster execution times, optimized main memory utilization, and efficient disc memory usage.

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Cost-efficient Blockchain-based e-KYC Platform using Biometric verification

Cost-efficient Blockchain-based e-KYC Platform using Biometr...

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International Conference on Information Networking

作者： Sahil Bhatia Lokendra Vishwakarma Debasis Das Computer Science & Engineering Dept. Indian Institute of Technology Jodhpur Rajasthan

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Know Your Customer (KYC) is the authentication and verification process of the user carried out in the financial sector by institutions such as banks, insurance companies, fintech companies, etc., and other sectors such as smart healthcare, real estate, telecommunication, online gaming and gambling companies before engaging in any financial activities. The KYC process is made mandatory for Financial Institutions (FIs) by governments worldwide to keep a check on terror financing and money laundering activities. However, the traditional physical KYC process has limitations such as high operational costs, large time consumption, privacy and security issues, repetitive processes across multiple institutions and user inconvenience. Hence, the FIs are looking for alternatives to the physical KYC. The digitization of the KYC process, known as e-KYC, was explored but had problems such as a single point of failure, repetition of KYC process across multiple FIs and lack of security. Blockchain-based e-KYC has become popular these days and caught the eye of FIs due to inherent properties of the blockchain such as decentralization, transparency, and immutable ledger. Moreover, blockchain-based solution is highly secure and cost-efficient. This paper proposes a blockchain-based e-KYC platform where users can register themselves and get their video KYC done after ether payment. Once successfully verified, the user will receive a KYC key. The user will submit this key to the banks or other FIs in which he wants to register. The FIs can verify whether the user has undergone the KYC process by looking in the blockchain using the KYC key. Our solution requires the user to submit his documents only at the e-KYC platform and not at different FIs for registration, thus preserving his privacy and, at the same time, ensuring the user’s authenticity.

关键词： Privacy Costs Government Insurance Companies Medical services Registers

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On Vertex-Euclidean Deficiency of Complete Fan Graphs and Complete Wheel Graphs

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Journal of Combinatorial Mathematics and Combinatorial Computing 2024年 121卷 41-52页

作者： Gao, Zhen-Bin Guo, Ricky Kwong, Harris Lee, Sin-Min Qiu, Wei College of General Education Guangdong University of Science and Technology Dongguan523000 China Dept. of Computer Science Univ. of Calif. at Los Angeles Los AngelesCA90095 United States Dept. of Math. Sci. SUNY Fredonia FredoniaNY14063 United States 1786 Plan Tree Drive UplandCA91784 United States College of Math. Sci.Harbin Engineering Univ. Harbin150001 China

A simple graph G with p vertices is said to be vertex-Euclidean if there exists a bijection f : V(G) → {1, 2, . . ., p} such that f(v1) + f(v2) > f(v3) for each C3-subgraph with vertex set {v1, v2, v3}, where f(v1) 2) 3). More generally, the vertex-Euclidean deficiency of a graph G is the smallest integer k such that G ∪ Nk is vertex-Euclidean. To illustrate the idea behind this new graph labeling problem, we study the vertex-Euclidean deficiency of two new families of graphs called the complete fan graphs and the complete wheel graphs. We also explore some related problems, and pose several research topics for further study. © 2024 the Author(s)

关键词： Wheels

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A Photovoltaic Interleaved Boost Integrated CL Resonant Converter for Magnetron Application

A Photovoltaic Interleaved Boost Integrated CL Resonant Conv...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Aref Molaei Kajanan Kanathipan John Lam Dept. of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

A new single-stage high-gain interleaved boost modular CL resonant converter is proposed in this paper for photovoltaic-powered magnetron application. The proposed converter is able to provide high voltage gain for the magnetron which is a device for microwave applications. Perturb and Observe (P&O) method was used to achieve Maximum Power Point Tracking (MPPT) in the integrated interleaved boost stage by controlling the duty-cycle of the two primary leg switches. The operational principles of the converter are briefly discussed in this paper. Results on a 1.7kW, 230V/3.8kV design are presented to verify the proposed converter performance.

关键词： Photovoltaic systems Maximum power point trackers Legged locomotion Magnetic resonance Magnetic devices Resonant converters Microwave devices High-voltage techniques Energy conversion Microwave theory and techniques

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A New Half-Bridge/Dual-Stacked-Switches Structured Electrolytic Capacitor-less AC/DC Bi-Directional On-Boad Charger for High-Voltage EV Battery

A New Half-Bridge/Dual-Stacked-Switches Structured Electroly...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Siamak Derakhshan John Lam Dept. Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper introduces a novel half-bridge (HB)/dual-stacked-switches based electrolytic capacitor-less bidirectional AC/DC converter for high voltage (HV) electric vehicle (EV) systems. The proposed two-stage AC/DC converter incorporates a closed-loop Power Factor Correction (PFC) mechanism based on the dq model of the input LCL filter, ensuring high-quality input current with minimized Total Harmonic Distortion (THD) during both conversion and inversion operations of the converter. Moreover, the front-end HB PFC circuit operates in Continuous Conduction Mode (CCM) that results in the elimination of the high peak current in the switches. Additionally, a dedicated closed-loop control system is deployed in the dual-stacked-switches resonant converter to significantly reduce the output voltage ripple, thus eliminating the use of bulky electrolytic-type storage capacitors. Output voltage regulation is realized by using Variable Frequency Modulation (VFM). Soft-switching operation is guaranteed for all the switches and diodes using CLLC resonant circuit for both conversion and inversion modes of operation. The proposed circuit’s performance is verified through a 1kW, 120Vrms/800Vdc proof-of-concept prototype.

关键词： AC-DC power converters Total harmonic distortion Capacitors RLC circuits Switches Resonant converters High-voltage techniques Voltage control Integrated circuit modeling Power harmonic filters

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