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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Cheng, Chih-Hong Stockel, Paul Zhao, Xingyu Chalmers & Univ. of Gothenburg Dept. of Computer Science & Engineering Sweden University of Hildesheim Department of Informatics Germany University of Warwick WMG CV4 7AL United Kingdom

ISBN: (纸本)9798331505929

Modeling and calibrating the fidelity of synthetic data is paramount in shaping the future of safe and reliable self-driving technology by offering a cost-effective and scalable alternative to real-world data collection. We focus on its role in safety-critical applications, introducing four types of instance-level fidelity that go beyond mere visual input characteristics. The aim is to ensure that applying testing on synthetic data can reveal real-world safety issues, and the absence of safety-critical issues when testing under synthetic data can provide a strong safety guarantee in real-world behavior. We suggest an optimization method to refine the synthetic data generator, reducing fidelity gaps identified by deep learning components. Experiments show this tuning enhances the correlation between safety-critical errors in synthetic and real data. © 2024 IEEE.

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An Evolutionary Algorithm for the Travelling Sales Man problem

An Evolutionary Algorithm for the Travelling Sales Man probl...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： Shaik Mohammad Waseem Jeshwanth N L Lingesh A Matta Jayanth Bhargavi C H Batini Dhanwanth Dept. of Computer Science and Engineering Panimalar Institute of Technology (Anna University) Chennai India Dept. of Artificial Intelligence and Data Science Panimalar Institute of Technology (Anna University) Chennai India Dept. of Computer Science and Engineering Sai Spurthi Institute of Technology India Dept. of AI Engineering Jönköping University Jönköping Sweden

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conventional techniques because it uses a new randomized crossover threshold. According to experimental assessments, GA-P outperforms a normal Genetic Algorithm with fixed crossover (GA-N, 628.6) and approaches the best Branch and Bound solutions (B&B, 560.6) with an average cost of 617.4 for 16-node graphs. With average execution times of 0.1347 seconds for 16 nodes, GA-P also demonstrates constant computational efficiency, outperforming B&B's exponentially growing temporal complexity (169.37 seconds for 16 nodes). In order to balance solution quality and computational cost, hyperparameter tweaking revealed the ideal values of population percentage (pp = 1.1), crossover proportion (cp = 0.8), mutation threshold (mt = 0.3), and exploration probability (ep = 0.2). The results show that GA-P is a scalable and efficient substitute for TSP, with room for improvement via parallelization and exploratory techniques.

关键词： Renewable energy sources Costs Evolutionary computation Computational efficiency Complexity theory Optimization Tuning Genetic algorithms

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Detect Bangladeshi Mango Leaf Diseases Using Lightweight Convolutional Neural Network

Detect Bangladeshi Mango Leaf Diseases Using Lightweight Con...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Nosin Ibna Mahbub Feroza Naznin Md Imran Hasan Syed Mahfuzur Rahman Shifat Md. Alamgir Hossain Md Zahidul Islam Dept. of Information and Communication Technology Islamic University Kushtia Bangladesh Dept. of Computer Science and Engineering Green University Dhaka Bangladesh Dept. of Computer Science and Engineering Islamic University Kushtia Bangladesh Dept. of Computer Science and Engineering University of Global Village Barishal Bangladesh

This research concentrates on the diagnosis of common mango leaf diseases in Bangladesh using image processing via deep learning. Mango production could be raised by at least 28% globally if the crop could be safeguarded from a variety of diseases. However, without the assistance of an expert, it is challenging for the farmer to detect the disease at the appropriate time. Few studies have been conducted to identify the mango leaf disease present in Bangladesh. So far, no study has been done to identify the seven distinct mango leaf diseases reported in Bangladesh. We proposed a lightweight convolutional neural network (LCNN) in this paper to accurately classify seven distinct mango leaf diseases as well as normal mango leaf. To assess the proposed LCNN model, performance is compared to several pre-trained models such as VGG16, Resnet50, Resnet101, and Xception, and it is found that LCNN achieves the highest testing accuracy (98%).

关键词： Deep learning Computational modeling Image processing Crops Production Convolutional neural networks Diseases

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Transfer Learning in Deep Neural Network for Land Cover Classification 25

Transfer Learning in Deep Neural Network for Land Cover Clas...

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25th International Conference on computer and Information Technology, ICCIT 2022

作者： Sadia, Amina Uddin, S. M. Shihab Islam, Rafiqul University of Chittagong Institute of Forestry and Environmental Sciences Bangladesh Islamic University of Technology Dept. of Cse Bangladesh Dhaka University of Engineering and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350346022

Land cover classification research contributes significantly to sustainable development by enhancing the baseline for urban planning, natural resource management, and environmental monitoring at the local, regional, national, and global levels. In practice, only the RGB and near-infrared (NIR) image bands are obtainable for identifying land cover since high-resolution satellite remote sensing data may not even be accessible due to cost, storage systems, or bandwidth restrictions. This work presents Transfer Learning in Deep Neural Networks (DNN) for land cover classification, where ResNet101 is considered as a DNN model. Numerical experiments are conducted using the EuroSAT dataset, a multispectral dataset based on Sentinel-2 satellite images of 34 European cities covering 13 spectral bands in visible, near-infrared, and short-wave infrared parts. Several experiments were conducted, and the proposed ResNet101-based technique effectively classifies the land cover classes with more than 99% accuracy. © 2022 IEEE.

关键词： Deep neural networks

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Prediction of Crop Yield Using Machine Learning Approaches for Agricultural Data

TechRxiv

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TechRxiv 2023年

作者： Hazra, Simanta Karforma, Sunil Bandyopadhyay, Abhishek Chakraborty, Sayantani Chakraborty, Debasis Dept. of Computer Science and Engineering Asansol Engineering College West Bengal 713305 India Dept. of Computer Science University of Burdwan West Bengal 713104 India The Dept. of Computer Application Asansol Engineering College West Bengal 713305 India The Dept. of Computer Science and Engineering Asansol Engineering College Asansol713305 India

For many developing nations, agriculture is the fundamental source of economic engine. Without a significant increase in food production, the rise in global population during the 21st century would not have been conceivable. Thus, a key issue in agricultural expansion is the accurate forecast of healthy crop output / crop type utilizing machine learning (ML) algorithms. In this paper, the aim is to identify the most effective model that can help farmers in predicting the crop yield / crop type for the benefit of farmers. Seven popular algorithms such as K-Nearest Neighbor (KNN), Random Forest (RF) Classifier, Extreme Gradient Boosting(XGBoost), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN), Support Vector Machine(SVM) and Multi-layer Perceptron (MLP) are investigated on the numerical crop dataset and performances are studied. Experimental results reveal that XGBoost gives the highest accuracy among the seven. Subsequently, Another crop image dataset is explored to identify crop type using transfer learning in convolutional neural networks (CNN). Different models for crop image classification with weights trained on ImageNet such as VGG-19 (Visual Geometry Group-19), VGG-16, Inception v3, ResNet-50 and EfficientNet-B0 are studied. Results based on the accuracy and kappa coefficient show that VGG-19 outperforms other techniques. © 2023, CC BY.

关键词： Image classification

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SiCBit-PUF: Strong in-Cache Bitflip PUF Computation for Trusted SoCs

SiCBit-PUF: Strong in-Cache Bitflip PUF Computation for Trus...

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IEEE European Test Symposium (ETS)

作者： Athanasios Xynos Vasileios Tenentes Yiorgos Tsiatouhas Dept. of Computer Science & Engineering University of Ioannina Greece

Secure computing necessitates hardware root of trust (RoT) integrated in systems-on-Chips (SoCs) for cryptographic keys generation, authentication and identification. In this paper, we observe that bitflips in SRAM cells that appear while accessing multiple cells from the same bitline, are not stochastic, as previously considered, but systematic. Based on this observation, a novel strong in-memory Physical Unclonable Function (PUF) computation is proposed for harvesting static entropy from SRAM arrays. The proposed design is compatible with existing in-SRAM computing architectures. To verify our PUF operation, we implement a 6T SRAM array model that performs in-memory computing using a 32 nm CMOS Technology, and, through SPICE simulation, we evaluate the proposed PUF performance. The proposed PUF operation achieves uniqueness and uniformity of 49.99%, and 49.74%, respectively, and reliability higher than 97.4% when the temperature is varied from 0°C to 100°C, and higher than 95.2% when the nominal voltage supply is varied by 10%. Furthermore, we explore the scaling of the number of Challenge Response Pairs (CRPs) of the proposed PUF, and we compare it against the state-of-the-art. Our PUF offers orders of magnitude higher number of CRPs, therefore it is suitable for integrated mechanisms that assure secure computing in SoCs.

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Switching Activity Reduction in Graphene PN Junction Circuits using Circuit Re-structuring

Switching Activity Reduction in Graphene PN Junction Circuit...

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2023 International Symposium on Devices, Circuits and systems, ISDCS 2023

作者： Das, Subrata Deb, Arighna Fiser, Petr Sister Nivedita University Dept. of Computer Science & Engineering Kolkata India School of Electronics Engineering Kiit Bhubaneswar India Czech Technical University Faculty of Information Technology Prague Czech Republic

ISBN: (纸本)9798350315042

Graphene PN Junction (GPNJ) logic circuits received significant attention from the researchers thanks to the availability of electrostatically doped graphene PN Junction (GPNJ) device- a promising one for designing low-power, high-speed circuits. Several design approaches for GPNJ logic circuits realizing important arithmetic and arbitrary Boolean functions exist in literature. However, no detailed evaluations of the resulting circuit complexities with respect to relevant cost metrics are reported. It remains open, how do we synthesize GPNJ logic circuits with minimal switching activity, an essential cost metric influencing the dynamic power dissipation in the resulting circuits. In this paper, we introduce a synthesis approach for GPNJ logic circuits that interconnects a set of GPNJ devices in parallel. Such parallel circuit structures ensure the reduction of switching activity in the resulting GPNJ logic circuits. To the best of our knowledge, so far only one research work discussed the computation of switching activity of GPNJ logic circuits. Experimental evaluations confirm that an average 67.35% reduction in switching activity can be achieved using the proposed approach over the existing one. © 2023 IEEE.

关键词： Graphene

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Pseudo slicer on three dimensional brain tumor segmentation

Pseudo slicer on three dimensional brain tumor segmentation

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Iqbal, Mohammad Urbach, Fitria Pao, Hsing-Kuo Sensusiati, Anggraini Dwi Hidayat, Nurul Mukhlash, Imam Institut Teknologi Sepuluh Nopember Dept. of Mathematics Surabaya Indonesia National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan Airlangga University Dept. of Radiology Surabaya Indonesia

ISBN: (纸本)9781665480451

A constructive way to assist surgeons before performing brain tumor surgery is by visualizing a three-dimensional (3D) MRI image to determine the brain tumor volume when a pre-operative examination. However, the availability of 3D MRI brain tumors is more limited than 2D, especially for meningioma type. Therefore, we attempt to propose a model to construct a pseudo-3D volume of a meningioma tumor from a few 2D MRI slices. In the proposed model, we first generate pseudo slices for each given 2D MRI slice until satisfying 3D MRI construction from interpolation techniques, called pseudoslicer. Next, we segment the pseudo-3D MRIs to capture the 3D meningioma tumor using 3D volume-to-volume generative adversarial networks (3D-V2V-GAN). That is, we call the proposed model 3D-slicer-V2V-GAN or 3DS-V2V-GAN. In this work, the proposed model was evaluated from 27 meningioma patients at a private university hospital in Indonesia. The proposed model outperformed the SOTA model in terms of the Jaccard Index and dice coefficient. Additionally, we have radiologists to affirm the segmentation results in good quality. © 2022 IEEE.

关键词： Image segmentation

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Crafting Connections: Empowering Kondapalli Artisans through a Mobile Application

Crafting Connections: Empowering Kondapalli Artisans through...

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Intelligent systems and Machine Learning Conference (ISML)

作者： Chinta Vara Prasad J V D Prasad Vallakati Navyasri Dept. of Artificial Intelligence and Data Science V R Siddhartha Engineering College Vijayawada India Dept. of Computer Science and Engineering VR Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

Handicrafts hold historical significance in representing the essence of our nation's native culture. However, these handcrafted goods still need to be explored globally, and the manufacturers frequently need help connecting directly with larger markets and potential customers. The scope of this project is bound to Kondapalli Toys, also known as Kondapalli Bommalu, renowned wooden handicrafts originating from Andhra Pradesh, India. The current resources for resolving this issue are not easily available on the market, and there is a lack of a user-friendly application. A dedicated Android application is needed to strengthen the handicraft business. This application enables artisans and potential customers to communicate effortlessly, guaranteeing secure transactions, offering valuable data insights, and providing resources for skill and capacity development. The user interface for this application was developed using the Flutter framework, which prioritizes data security and is scalable and region-adaptive. Through this application, manufacturers can conveniently reach markets while customers have the opportunity to explore and connect with their businesses, allowing them to display their craft items to a global audience.

关键词： Technological innovation Sentiment analysis Toy manufacturing industry Globalization User interfaces Real-time systems Mobile applications Registers Monitoring Business

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Review on Brain Tumor Detection using Custom CNN Layers and Transfer Learning on MRI images

Review on Brain Tumor Detection using Custom CNN Layers and ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Akshit Saxena Jaspreet Singh Dept. of Computer Science and Engineering Chandigarh University India

Early diagnosis and treatment planning are greatly aided by the early identification of brain tumors. Because of its high resolution, low radiation, and low risk of patient discomfort, magnetic resonance imaging (MRI) is frequently used to diagnose brain tumors. Brain tumor identification is only one area where recent developments in convolutional neural networks (CNNs) have shown exceptional effectiveness. This article summarizes recent progress in detecting brain tumors by utilizing MRI images and bespoke CNN layers with transfer learning. The review kicks off with a discussion of the difficulties of detecting brain cancers, such as the tumors' complexity and heterogeneity and the scarcity of available annotated data. The article proceeds to go into the foundations of CNNs and their applicability to MRI image processing. To improve detection accuracy, we incorporate custom CNN layers that are tailored to capture salient tumor-specific information. The concept of transfer learning, in which CNN models trained on large-scale datasets are repurposed for brain tumor detection, is also discussed at length in the review. Using transfer learning, we can take advantage of what we've learned about general image identification to better train models to spot brain tumors. Fine-tuning, feature extraction, and other transfer learning methods are addressed at length. Recent research using custom CNN layers and transfer learning approaches to detect brain cancers in MRI images is thoroughly analyzed in this study. Among the benefits and drawbacks discussed are the methods' adaptability to small datasets, enhanced detection accuracy, and decreased training time. Also, the significance of using metrics for measuring performance and benchmark datasets for comparing methods fairly is discussed. The analysis concludes with suggestions for future study, such as the combination of functional and diffusion tensor imaging with conventional MRI scans to better detect brain tumors. Further

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