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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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Web Application-Based Enhanced Esophageal Disease Diagnosis in Low-Resource Settings

Web Application-Based Enhanced Esophageal Disease Diagnosis ...

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IEEE International Conference on Biomedical engineering, computer and Information Technology for Health

作者： Abdullah Al Masum Zishad Hossain Limon Md Ariful Islam Md Shahidur Rahman Mahbub Khan Sheikh Shemanto Afridi Md. Redwan Ahmed Shafiur Rahman Dept. of Information Technology Westcliff University CA USA Dept. of Computer Science Westcliff University CA USA Dept. of Business Administration International American University CA USA Dept. of Engineering management Westcliff University CA USA Dept. of Management Information System Pacific State University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331534356

ISBN: (纸本)9798331534363

An essential part of the digestive system, the esophagus permits food and liquids to move easily from the throat to the stomach. Traditional diagnostic techniques, such as endoscopy and biopsy, are invasive and resource-intensive, making them less practical in resource-constrained environments. To address these challenges, we proposed utilizing a hybrid Deep Learning (DL) model called GAN-EfficientNet, which combines the power of Generative Adversarial Networks (GAN) and EfficientNet to effectively classify esophageal diseases. Our approach employs advanced image preprocessing techniques and data augmentation methods to enhance the diversity and balance of the dataset. Feature extraction using the Spatial Gray Level Dependence Method (SGLDM) and Principal Component Analysis (PCA) significantly reduces computational complexity without compromising accuracy. We explored both binary and multiclass classification methods, recognizing their crucial roles in various diagnostic scenarios. Binary classification distinguishes between normal and diseased states, essential for initial screening, while multiclass classification identifies specific esophageal diseases, aiding in personalized treatment plans. The GAN-EfficientNet model demonstrated exceptional performance, achieving an impressive F1 score of 99.44% for binary classification and 99.64% for multiclass classification. Additionally, we developed a web application that facilitates quick and precise disease diagnosis, particularly advantageous in areas with limited resources. This tool significantly improves patient outcomes through early detection, leading to reduced healthcare costs and less burden from advanced esophageal diseases.

关键词： Deep learning Accuracy Computational modeling Generative adversarial networks Feature extraction Data augmentation Data models Medical diagnosis Diseases Principal component analysis

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TraceChainBD: Securing Supply Chains from Counterfeiting in Bangladesh with Blockchain and IoT Integration

TraceChainBD: Securing Supply Chains from Counterfeiting in ...

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International Conference on Innovations in science, engineering and Technology (ICISET)

作者： Sagar Karmoker Md. Adib Khan Sanjida Alam Lamia Mobinul Ahasan Tanjid Shahriar Sadman Dihan Nur Adnan Chowdhury Anik Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

The scheme uses blockchain technology in Bangladesh’s supply chain to eliminate counterfeit products, improve tax compliance, and build consumer trust. By ensuring product authenticity, it safeguards brand reputations and boosts government revenue. Manufacturers, distributors, and retailers can enter product data which will then generate QR codes. In case of receiving the product or checking its authenticity, they can scan these QR codes via their mobile or a dedicated IOT device and all the information will be stored in Blockchain. Since blockchain data is secure and immutable, it cannot be altered, consumers can easily check product details by scanning QR codes, and if a product is flagged as suspicious, the Mobile Court Management system can trace its origin. This blockchain-based approach ultimately helps prevent counterfeit goods from entering the market and enhances the government’s ability to collect taxes.

关键词： Technological innovation Supply chain management Manufactured products Supply chains Government Finance QR codes Blockchains Internet of Things Counterfeiting

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Optimized 1D CNNs for Enhanced Early Detection and Accurate Prediction of COPD and Other Pulmonary Diseases

Optimized 1D CNNs for Enhanced Early Detection and Accurate ...

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IEEE Region 10 Symposium

作者： Prakash Sahu Sneha Jha Santosh Kumar Computer Science & Engineering IIIT-Naya Raipur Dept. of Data Science CSVTU Chhattisgarh

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Artificial intelligence (AI) advancements have opened new avenues in medical diagnostics, particularly for the early detection of respiratory conditions such as Chronic Obstructive Pulmonary Disease (COPD). COPD, the most common respiratory disease, is marked by breathing difficulties, mucus (sputum) production, coughing, and wheezing. Early diagnosis is crucial for effective management and treatment. A one-dimensional convolutional neural network (1D CNN) model optimized with Adam and RMSprop algorithms is proposed for COPD detection. Using the ICBHI 2017 dataset, the model's performance with each optimizer was evaluated based on training and validation accuracy. The Adam optimizer achieved a training accuracy of 94% and a validation accuracy of 90%. In comparison, the RMSprop optimizer attained a training accuracy of 92% and a validation accuracy of 88%. These findings indicate that the Adam optimizer outperformed RMSprop in terms of accuracy. It showcased the potential of the proposed 1D CNN model as a reliable diagnostic tool, contributing to the advancement of respiratory health analytics.

关键词： Training Analytical models Accuracy Production Chronic obstructive pulmonary disease Convolutional neural networks Reliability Medical diagnosis Artificial intelligence IEEE Regions

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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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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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Recognition of Human Anomalies Using Convolution Neural Network

Recognition of Human Anomalies Using Convolution Neural Netw...

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Advances in Data engineering and Intelligent Computing systems (ADICS), International Conference on

作者： Teja Parella Balakrishna Ponnam Santhiya P Praveen. A Sujihelen. L dept. of Computer Science and engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

By instantly identifying suspicious occurrences, people, and things, smart CCTV systems greatly improve monitoring capacities. In this research, we investigate how to enhance CCTV video stream anomaly detection using state-of-the-art machine-vision and machine-learning techniques, namely UNET and ALEXNET. In contrast to more conventional methods that depend on supervised learning, this research makes use of uncontrolled and semi-supervised learning techniques to tackle the variety and complexity of anomalies. Using algorithms like UNET and ALEXNET, the system can learn to spot outliers on its own, with little to no help from humans. Because it can tell the difference between normal and abnormal situations, this method improves monitoring efficiency and decreases false alarms. In addition, the system maintains original quality for unusual occurrences while keeping typical video at a lower level, optimizing storage economy. By combining UNET and ALEXNET algorithms, smart CCTV systems may detect suspicious events before they happen, which increases security and allows for faster reactions to any dangers.

关键词： Training system performance Supervised learning Streaming media Semisupervised learning Recording Security

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Automating Requirements Review in the Automotive Sector: A Tailored AI Approach

Automating Requirements Review in the Automotive Sector: A T...

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IEEE International Conference on Requirements engineering

作者： Sivajeet Chand Chang Li Cristina Martinez Montes Beatriz Cabrero-Daniel Jennifer Horkoff Dept. of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg Sweden

Requirements serve as the foundation for defining what a software product should accomplish, highlighting the importance of clear and well-written specifications [1]. Deficient requirements often lead to defects in de... 详细信息

ISBN: (数字)9798350395112

ISBN: (纸本)9798350395129

关键词： Reviews Software Requirements engineering Artificial intelligence Automotive engineering

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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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In-memory Machine Learning using Adaptive Multivariate Decision Trees and Memristors

In-memory Machine Learning using Adaptive Multivariate Decis...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Akash Chavan Pranav Sinha Sunny Raj Dept. of Computer Science and Engineering Oakland University Rochester MI USA

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

We introduce a framework to design in-memory decision tree machine-learning (ML) circuits using memristor crossbars. Decision trees (DTs) offer many advantages over neural networks, such as enhanced energy efficiency, interpretability, safety, privacy, and speed, along with reduced dependence on extensive training data. We propose an adaptive multivariate decision tree (AMDT) training algorithm, which constructs decision trees that incorporate both univariate and multivariate features, facilitating the creation of higher accuracy and energy-efficient crossbar designs compared to the state-of-the-art (SOTA). Our circuits are realized using pure memristor crossbars, requiring just one memristor per cell and no transistors while employing sneak-paths for flow-based in-memory computations. In comparison to the SOTA, our approach produces designs that are, on average, 4% more accurate and require 12.6% lower energy.

关键词： Training Accuracy Circuits Memristors Training data Machine learning Energy efficiency

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