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Design of high output DC/DC boost circuit without boost transformer 8

Design of high output DC/DC boost circuit without boost tran...

引用

8th International Conference on Communication and Information Systems, ICCIS 2024

作者： Hong, Qin Zhou, Liang Jia, Zhongning Liu, Chun Liu, Jianbo Software Engineering Institute of Guangzhou Department of Computer Science Guangzhou China

ISBN: (纸本)9798331531171

The development of IOT smart devices has necessitated increasingly stringent requirements for DC power sources in terms of output voltage, size, and cost. For instance, Avalanche Photodiodes (APDs), utilized as photodetectors, require a significantly high reverse bias voltage during the detection of feeble optical signals to achieve optimal gain and high signal-to-noise ratio, thereby ensuring exceptional sensitivity. To address the diverse demands of various smart devices for DC power, a high-output DC/DC boost circuit without a step-up transformer has been innovatively designed. This circuit is comprised of four fundamental components: an oscillator circuit, a comparator circuit, a voltage multiplier circuit, and a constant-current and voltage-stabilizing circuit. The paper conducts a comprehensive theoretical analysis of the circuit's topology, parameter selection, and operational process, which is further validated through Multisim simulation. Additionally, a PCB circuit was designed, fabricated, and underwent rigorous practical testing. The test results conclusively demonstrate that the designed boost circuit proficiently provides APDs with the appropriate bias voltage, eliminating the need for boost transformer. It boasts high output voltage, a simplistic structure, small size, and low cost, enabling it to seamlessly share the same input power source with the APD detection circuit and be integrated into the same PCB layout. © 2024 IEEE.

关键词： avalanche photodiode boost circuit direct current stabilized power supply oscillator voltage multiplier circuit

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Unveiling the Power of Neural Program Synthesis in High Level Code Generation 15

Unveiling the Power of Neural Program Synthesis in High Leve...

引用

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

作者： Sai, Gurram Kumar Harshith, Koganti Sri Sai Varma, Kucharlapati Akash Yashwanth Kiran, G. Swaminathan, J. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

In this research, we delve into a transformative exploration of the realm of code generation, unveiling three fundamental modules that constitute the backbone of our study: the Text-to-Code Generation Model, Code Autocompletion Model, and the formidable Neural Program Synthesis Model. At the forefront of our investigation, we underscore the paramount importance of Neural Program Synthesis - a dynamic force adept at crafting custom programs from input-output examples. Our exploration delves into the intricate landscape of natural language-to-code generation, offering profound insights and invaluable contributions to the field. The ability to automatically generate code from natural language instructions holds immense significance in streamlining software development processes, reducing manual effort, and enhancing productivity. Furthermore, this research addresses a pressing need within the software engineering community for efficient methods of code generation, particularly in domains where human-program interaction is prevalent. Through rigorous experimentation and meticulous analysis, we shed light on methodologies and promising avenues for future research in this burgeoning domain, paving the way for advancements in automated software development and programming assistance tools. © 2024 IEEE.

关键词： Input output programs

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FLARE: Federated Learning And Resilient Encryption for Firewalls 7

FLARE: Federated Learning And Resilient Encryption for Firew...

引用

7th IEEE Pune Section International Conference, PuneCon 2024

作者： Senthilkumar, Lohith Rengarajan, Aaditya Psg College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331527822

Traditional firewalls rely on static rule-based mechanisms, where rules are manually defined and often written in specialized languages. While effective to a degree, these rules are inherently limited and can be easily bypassed by new and evolving types of malware, leading to significant security vulnerabilities. To address these challenges, we propose FLARE: Federated Learning And Resilient Encryption, a novel machine learning-based firewall solution. FLARE dynamically analyzes past network connections to predict and determine the appropriate actions for incoming traffic, thereby adapting to emerging threats in *** the sensitive nature of firewall data, which often contains confidential information, the training of machine learning models poses significant privacy risks. FLARE mitigates these risks by incorporating federated learning, allowing the model to learn from decentralized data sources without requiring raw data to be shared. To further enhance privacy, we introduce an encryption layer that ensures the central model learns from encrypted weights, preventing exposure of sensitive information known to the local model. This combined approach not only improves the resilience of firewalls but also safeguards the confidentiality of the data used in training, offering a robust solution for modern cybersecurity challenges. © 2024 IEEE.

关键词： Federated learning

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Comprehensive Analysis of Attention Deficit/Hyperactivity Disorder using Machine Learning and Deep Learning Algorithms 1

Comprehensive Analysis of Attention Deficit/Hyperactivity Di...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Sindhu, T.N. Sujatha, S.R. Sri Siddhartha Institute of Technology Department of Computer Science and Engineering Tumakuru India

ISBN: (纸本)9798331529246

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects children from childhood to adulthood. The symptoms of ADHD include attention deficit, impulsive behaviour, hyperactivity, and early diagnosis and treatment are imperative, especially for children as it continues into adulthood. ADHD detection is generally carried out using the existing methods such as Machine Learning (ML) and Deep Learning (DL) methods. The ML methods such as Linear Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminative Analysis (LDA), Linear Regression (LR), Random Forest (RF), and Least Square SVM (LS-SVM) are used for the detection and analysis of ADHD. The existing DL methods such as Deep Convolutional Neural Network (DCNN), Graph Recurrent Unit (GRU), Convolutional Long Short-Term Memory (ConvLSTM), and Bidirectional LSTM are also employed for the detection of ADHD. The ML and DL algorithms enhance ADHD detection and diagnosis by automatically determining large patterns in data, thereby offering higher accuracy. The performance of the existing techniques is evaluated in terms of evaluation metrics: accuracy, precision, sensitivity, and F1-score to assess their effectiveness. © 2025 IEEE.

关键词： Adversarial machine learning

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LW-ALDDNet: A Novel Lightweight CNN Model for Automated Detection of Apple Leaf Diseases 15

LW-ALDDNet: A Novel Lightweight CNN Model for Automated Dete...

引用

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

作者： Prito, Rizvee Hassan Hassan, Md. Bayzid Nuzhat, Zarin Tasnim Saha, Ratul Sadia, Umme Fatema Tuz Sounok, Sabbir Ahmed Fahim, Sajid Faysal Mim, Aisha Siddika Siam, Shams E Hussain, Syed Musayedul East West University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350370249

Plant diseases cause substantial losses in crop production, which exerts a major impact on the economic development of agriculture. Early detection of these diseases is inevitable to prevent significant production losses. However, accurate identification is challenging due to similarities in diseases, characteristics. Therefore proper recognition of these diseases has become a challenging task for farmers. But with the help of artificial intelligence and deep learning models in IoT devices, anyone can now take advantage of correctly recognizing diseases of various crops. As the Apple is the most consumed fruit in the world, a novel lightweight CNN model is introduced in this study to accurately identify apple leaf disease from the images of apple tree leaves. Our proposed model is evaluated alongside various popular deep CNN models, including MobileNetV2, InceptionV3, ResNet50v2, AlexNet, SqueezeNet, etc. on two public datasets of five and nine categories of apple leaf diseases with healthy leaves. The evaluation results revealed that our proposed model achieved 91.65%, and 97.53% accuracies on datasets of nine and five classes, respectively, which exceeded the accuracies of six deep CNN models implied on those datasets. Moreover, being a lightweight CNN model, our designed model has the least number of parameters, proving that it can detect apple leaf diseases with better accuracy and with less storage, less computational resources, and shorter detection time requirements. © 2024 IEEE.

关键词： Fruits

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A Study on Machine Learning Techniques for Precision Medicine Recommendation

A Study on Machine Learning Techniques for Precision Medicin...

引用

2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Sugavaneshwari, P. Saranya, K.G. Government College of Engineering Department of Computer Science and Engineering Dharmapuri India PSG College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335835

Precision medicine is emerging now provides decision making for disease treatment and prevention with the help of genome, environment and lifestyle. This study on precision medicine recommendation system mainly focuses to explore the different optimal techniques to improve and meet the objectives of the precision medicine recommendation. It focuses on the different feature selection techniques and the prediction models for effective diagnosis of the disease, improve the survival rate of the patients and also to suggest the best lifestyle practice to carryover. © 2023 IEEE.

关键词： Decision making

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Denoising Historical Text Documents Using Generative Adversarial Networks 8th

Denoising Historical Text Documents Using Generative Adversa...

引用

8th International Symposium on Intelligent Informatics, ISI 2023

作者： Preethi, P. Upadhya, Pradhyumna Likith, M.C. Meghana, N. Karande, Shruti Gunnan Ramkumar, Shreya Department of Computer Science and Engineering PES University Karnataka Bengaluru560085 India

ISBN: (纸本)9789819721467

The field of image-to-image translation, particularly in the context of denoising historical text documents, has witnessed significant advancements driven by the application of Generative Adversarial Networks (GANs). GANs have shown promise in various image restoration tasks, including unpaired image-to-image translation and denoising. This paper explores and contributes to the existing body of knowledge on denoising historical text documents using GANs, with a focus on addressing specific challenges such as stains, faded ink, background noise, and various types of artificial noise. The field of image-to-image translation, particularly in the context of denoising historical text documents, has witnessed significant advancements driven by the application of Generative Adversarial Networks (GANs). GANs have shown promise in various image restoration tasks, including unpaired image-to-image translation and denoising. This paper explores and contributes to the existing body of knowledge on denoising historical text documents using GANs, with a focus on addressing specific challenges such as stains, faded ink, background noise, and various types of artificial noise. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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APFormer: Anti-Phishing Transformer for Website-Phishing Detection Via Joint Feature Learning

APFormer: Anti-Phishing Transformer for Website-Phishing Det...

引用

2024 International Conference on engineering and Computing, ICECT 2024

作者： Memon, Anam Manjotho, Ali Asghar National University of Modern Languages Department of Computer Science Hyderabad Pakistan Mehran University of Engineering and Technology Department of Computer Systems Engineering Jamshoro Pakistan

ISBN: (纸本)9798350349719

Phishing is a cybercrime activity where the perpe-Trator tricks users into believing that a fraudulent website is authentic, revealing sensitive information. Recently, the frequency of phishing attacks has increased, targeting both individuals and corporate sectors, thereby rendering traditional methods of detecting phishing websites less effective. These methods often produce high false positive rates due to the complexities involved in creating a strong correlation between brand identity and website features. Furthermore, sophisticated attacks, such as DOM element manipulation tricks, evade the existing methods and convince the user with visual and behavioural similarity. We propose a transformer-based approach for website phishing detection using joint feature learning. The approach jointly learns textual, visual, and heuristic multi-modal features to establish a stronger connection between brand identity and the webpage. The proposed model comprises a feature extraction module and a transformer network. The feature extraction module extracts textual, visual, and heuristic features from the DOM tree, while the transformer module classifies the input page as legitimate or phishing by learning the feature mapping to brand identity. Our method outperforms current state-of-The-Art methods on benchmark datasets by achieving a 3.5% increase in recall and a 5.9% reduction in false positive rate. © 2024 IEEE.

关键词： Websites

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Evaluating Educational Commentaries: Sentiment Analysis for Student Testimonials Using Machine Learning 4

Evaluating Educational Commentaries: Sentiment Analysis for ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Kasana, Tushar Kaur, Baljinder Rakhra, Manik Lovely Professional University Department of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350387490

This is because in education, evaluation of student data especially in form of feedback cannot be complete without sentiment analysis. The various methods adopted in analysis of sentiments in educational context are discussed in this paper though the use of deep learning and machine learning approaches. Prominent findings show how effectively the mentioned strategies may be implemented to enhance instructional practices and shape institutional decisions. An example of concepts described is such problems as biased datasets or conflict of cultural contexts. The study reveals how self-service feedback tools such as sentiment analysis tools can revolutionize feedback receipt as well as instruction, paving way for individualized learning environment and enhanced performance outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Skin Lesion Segmentation Using Deep Learning 2nd

Skin Lesion Segmentation Using Deep Learning

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Sarkar, Kaustav Haralalka, Varun Shaw, Vishal Singh, Rishav Raj Jaiswal, Rayirth Pramanick, Tridip Department of Computer Application and Science Institute of Engineering and Management Kolkata India

ISBN: (纸本)9789819735938

Dermoscopic image analysis has gained significant importance for dermatological applications due to its potential in eliminating observer bias. Segmenting lesion areas automatically in these images is crucial for expedited disease diagnosis. In this study, an innovative deep learning-based approach for precise lesion delineation has been proposed. Leveraging a deep convolutional neural network, where input images are included after appropriate preprocessing. Pedro Hispano Hospital (PH2) 2013 dataset has been employed, comprising dermoscopic images and lesion binary masks as ground truth for evaluation. The approach uses the SegNet architecture, which achieves remarkable accuracy of 99.14%, surpassing benchmark standards (DC 96.94, JI 99.11).This research offers a promising solution for automated lesion segmentation in dermoscopic images, advancing the field of medical image analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image analysis

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