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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Chaurasia, Rahul Sengupta, Anirban Indian Institute of Information Technology Department of Computer Science and Engineering Bhopal India Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331533229

This paper introduces a novel methodology for designing secure hardware accelerator tailored for convolutional neural network (CNN) applications, leveraging security-aware high-level synthesis (HLS). The methodology outlined offers comprehensive security measures, including hindrance against reverse engineering (RE) and intellectual property (IP) piracy concerns through architectural transformations and embedded biometric palmprint constraints, respectively. Key contributions of this methodology include: i) robust hindrance against RE attack, ii) robust piracy detection with no additional design overhead, iii) definitive proof of IP ownership by achieving significantly lower probability of coincidence (P), indicating lower probability of false positive, and iv) enhanced tamper tolerance (TT) and resilience against brute force attacks compared to recent methodologies. © 2024 IEEE.

关键词： High level synthesis

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Optimizing abnormality detection in fundus images with Triplet-OS and orchard search optimization model

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Neural Computing and Applications 2024年第36期36卷 23181-23194页

作者： Venkatraman, K. Hemalatha, R. Radhika, S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha Nagar Thandalam Chennai India Department of Computer Science and Engineering St. Joseph’s College of Engineering Chennai India Department of EEE Sathyabama Institute of Science and Technology Chennai India

The accurate and early detection of abnormalities in fundus images is crucial for the timely diagnosis and treatment of various eye diseases, such as glaucoma and diabetic retinopathy. The detection of abnormalities in fundus images using traditional methods is often challenging due to high computational demands, scalability issues, and the requirement of large labeled datasets for effective training. To address these limitations, a new method called triplet-based orchard search (Triplet-OS) has been proposed in this paper. In this study, a GoogleNet (Inception) is utilized for feature extraction of fundus images. Also, the residual network is employed to detect abnormalities in fundus images. The Triplet-OS utilizes the medical imaging technique fundus photography dataset to capture detailed images of the interior surface of the eye, known as the fundus and the fundus includes the retina, optic disk, macula, and blood vessels. To enhance the performance of the Triplet-OS method, the orchard optimization algorithm has been implemented with an initial search strategy for hyperparameter optimization. The performance of the Triplet-OS method has been evaluated based on different metrics such as F1-score, specificity, AUC-ROC, recall, precision, and accuracy. Additionally, the performance of the proposed method has been compared with existing methods. Few-shot learning refers to a process where models can learn from just a small number of examples. This method has been applied to reduce the dependency on deep learning [1]. The goal is for machines to become as intelligent as humans. Today, numerous computing devices, extensive datasets, and advanced methods such as CNN and LSTM have been developed. AI has achieved human-like performance and, in many fields, surpasses human abilities. AI has become part of our daily lives, but it generally relies on large-scale data. In contrast, humans can often apply past knowledge to quickly learn new tasks [2]. For example, if given

关键词： Medical imaging

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Influence analysis of calculated horizontally layered soil parameters on grounding parameters

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High Voltage 2023年第2期8卷 421-430页

作者： Yihua Dan Junjun Yin Jian Yang Huizhang Yang Department of Electronic Engineering Tsinghua UniversityBeijingChina School of Computer&Communication Engineering University of Science and Technology BeijingBeijingChina

Calculated parameters(soil layer resistivity,soil layer thickness,and the number of soil layers)of horizontally layered soil are usually obtained based on the measured apparent resistivity under different measurement distances,which are significant for the design,operation,and maintenance of grounding *** existing calculation methods of soil parameters are just trying to make the calculation results approach the measurement data,ignoring the relationship among measurement data,the calculated soil parameters,and grounding parameters,which would increase the workload of the *** better balance the distance range of the measurement data and the influence of the calculated horizontally layered soil on grounding parameters,this paper systematically studies the relationship among measured apparent soil resistivity,calculated horizontally layered soil parameters,and grounding *** basic theories of apparent resistivity measurement,soil parameter calculation,and grounding parameter calculation are given,the influence of soil layer thickness on the measured apparent resistivity is studied,and the influence of the calculated soil parameters on the grounding resistance of different grounding models is *** on different scales of grounding grids,the results give a corresponding reference distance range of measured apparent soil ***,this paper can help decrease the workload of soil resistivity measurement during grounding parameters analysis,which has far-reaching engineering significance.

关键词： soil parameters apparent

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Extraction of road using semi-supervised deep learning method 4th

Extraction of road using semi-supervised deep learning metho...

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Mishra, Jalaj Khan, Mohd Jawed Negi, Himanshi Alam, Jitendra Alam, Junaid Singh, Pankaj Pratap Department of Computer Science & Engineering Greater Noida Institute of Technology Greater Noida India Department of Computer Science & Engineering Central Institute of Technology Assam Kokrajhar India

ISBN: (纸本)9781032911571

Detection of road networks using high-resolution aerial or remote sensing imagery constitutes a significant focus within modern research efforts. Currently, deep learning models demonstrate efficiency to a certain degree, they are constrained by a notable reliance on annotated datasets, a practice divergent from optimal methodologies due to the scarcity of extensive, high-quality labeled data. The precision of road prediction is additionally influenced by the latent information embedded within pixels exhibiting diminished confidence values. These pixels play a crucial role in discriminating between road and non-road regions within the images, yet their utilization by deep learning models is suboptimal. To handle these two different issues, opted U-Net as our base model with a sole generator paired with twin dis-criminators and presented this combined setup of U-Net on the Generative Adversarial Network (GAN). At the base of the U-Net architecture, a bottleneck module is incorporated to augment the functionalities of the proposed model. This proposed model attains 98% accuracy on DeepGlobe Road Dataset. Comparative and quantitative assessment demonstrates the enhanced efficiency of our model compared to similar methodologies. © 2025 the Author(s).

关键词： Generative adversarial networks

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Blockchain Integration in Emergency Medical Transportation: Advancing Security and Coordination for Critical Care Scenarios 5th

Blockchain Integration in Emergency Medical Transportation: ...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Rawat, Abhishek Suryavanshi, Raghuraj Singh Nagar, Vishal Yadav, Divakar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Institute of Engineering and Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819771776

Blockchains improve safety and faster teamwork for emergency medical transport in critical care. Emergency medical transport is not the only healthcare sector that has become better and different because of current blockchains. This report also illustrates how blockchains can alter the safety, integrity, and effectiveness of emergency medical transport operations, including a wide-ranging examination of these technologies’ consequences on the healthcare sector. Traditional Emergency Medical Transportation Systems face different challenges, and this paper provides a framework for a complete understanding of the possible benefits and creative solutions that Blockchain offers to the healthcare industry. This paper has given a compelling business example for applying Blockchain to enhance Emergency Medical Transportation. It gives valuable perspectives on smart steps that can be used to improve care and well-being and streamline pool offerings and a feasibility rate that could help to create an effective outcome for Emergency Medical Transport Services with the usage of Blockchain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Healthcare industry Medical Data History Transportation

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A Detailed Review of Current AI Solutions for Enhancing Security in Internet of Things Applications

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computers, Materials & Continua 2025年第6期83卷 3713-3752页

作者： Arshiya Sajid Ansari Ghadir Altuwaijri Fahad Alodhyani Moulay Ibrahim El-Khalil Ghembaza Shahabas Manakunnath Devasam Paramb Mohammad Sajid Mohammadi Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Engineering and Information TechnologyOnaizah CollegesQassim56447Saudi Arabia

IoT has emerged as a game-changing technology that connects numerous gadgets to networks for communication,processing,and real-time monitoring across diverse *** to their heterogeneous nature and constrained resources,as well as the growing trend of using smart gadgets,there are privacy and security issues that are not adequately managed by conventional *** review offers a thorough analysis of contemporary AI solutions designed to enhance security within IoT *** intersection of AI technologies,including ML,and blockchain,with IoT privacy and security is systematically examined,focusing on their efficacy in addressing core security *** methodology involves a detailed exploration of existing literature and research on AI-driven privacy-preserving security mechanisms in *** reviewed solutions are categorized based on their ability to tackle specific security *** review highlights key advancements,evaluates their practical applications,and identifies prevailing research gaps and *** findings indicate that AI solutions,particularly those leveraging ML and blockchain,offerpromising enhancements to IoT privacy and security by improving threat detection capabilities and ensuring data *** paper highlights how AI technologies might strengthen IoT privacy and security and offer suggestions for upcoming studies intended to address enduring problems and improve the robustness of IoT networks.

关键词： Security in IoT applications privacy-preserving blockchain AI-driven security mechanisms

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DFNet: Dense fusion convolution neural network for plant leaf disease classification

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Agronomy Journal 2024年第3期116卷 826-838页

作者： Faisal, Muhamad Leu, Jenq-Shiou Avian, Cries Prakosa, Setya Widyawan Köppen, Mario Department of Electronic and Computer Engineering (ECE) National Taiwan University of Science and Technology Taipei City Taiwan Department of Computer Science and Systems Engineering (CSSE) Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka-shi Japan

The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single-CNN model, DFNet relies on a double-pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases. © 2023 The Authors. Agronomy Journal © 2023 American Society of Agronomy.

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Protecting Recommendations: Combating BIAS and Manipulation in Recommendation 8

Protecting Recommendations: Combating BIAS and Manipulation ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Infant, P.Alwin Kumar, P.Mohan Jainish, G.R. Hindustan Institute of Technology & Sciencce Department of Computer Science and Engineering Chennai India Loyola Icam College of Engineering & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Recommender systems assist consumers in navigating the deluge of information by helping them find services and goods. The effectiveness of recommender systems has been thoroughly examined in research currently available from a number of angles. In the research community, robustness against malicious attack is becoming a more important concern. Collaborative Filtering (CF) recommender systems have been established to accomplish this objective. However, due to the fact that they are collaborative recommender systems are open to attack. To weaken the system, attackers can introduce fictitious and biased profiles. An examination of the random attack, Bandwagon attack, Mixed Attack and Noise Injection on recommender systems is presented in this work. Numerous algorithms are susceptible to profile injection attacks. Some of the earlier approaches suffer from only looking at one aspect of user rating values while assessing the rating matrix, ignoring alternative viewpoints. First, a rating matrix in this research that takes into account both genuine and fictitious user ratings is created. Next, using user rating time as a basis, the rating matrix is examined. Next, build a model to distinguish between real and fraudulent users using a variety of classifiers, including Principal Component Analysis (PCA), K-Nearest Neighbour (KNN), and Bayesian Networks (BN). Ultimately, a number of tests are conducted, and a summary of the findings comparing different approaches' abilities to detect small-scale attacks and detect overall attacks is presented. © 2024 IEEE.

关键词： Collaborative filtering

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Machine learning-driven IoT device for women’s safety: a real-time sexual harassment prevention system

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Multimedia Tools and Applications 2024年 1-30页

作者： Islam, Md Reazul Oliullah, Khondokar Kabir, Mohsin Rahman, Ashifur Mridha, M.F. Khan, Muhammed Fayyaz Dey, Nilanjan Department of Computer Science American International University-Bangladesh Dhaka1229 Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Rupnagar Dhaka1216 Bangladesh Department of Computer Science and Engineering Techno International New Town Kolkata India

Sexual harassment is an all-encompassing problem that affects individuals in diverse environments including educational institutions, workplaces, and public areas. Despite increased awareness and advocacy efforts, many women continue to face harassment daily, especially on the Indian sub-continent, with underreporting and impunity exacerbating the problem. As technology advances, there is a growing opportunity to use innovative solutions to address this problem. In recent years, the Internet of Things (IoT) and machine learning have emerged as promising technologies for developing systems that can detect and prevent sexual harassment in real-time. This study presents a novel approach for real-time sexual harassment monitoring using a machine learning-based IoT system. The system incorporates nine force-sensitive resistors strategically embedded in women’s dresses to capture relevant data. It is portable and can be affixed to any type of dressing. If the user wishes to change their attire, the system can be easily removed from the current dress and attached to another dress of choice. This flexibility allows users to adapt the system to suit various clothing preferences and styles. The sensor data are transmitted to the cloud via the NodeMCU, enabling continuous monitoring. In the cloud, a pre-trained machine learning model, specifically the AdaBoost classifier, was employed to classify incoming data in real time. We applied four ML methods: RF with GridSearchCV, Bagging Classifier, XGBoost, and Adaboost Classifier. The AdaBoost classifier performed best with an accuracy of 99.3% using a dataset prepared by our lab, which consists of 1048 instances and was collected from 50 students. If a sexual harassment event is detected, an alert is generated through a mobile application and promptly sent to appropriate authorities for immediate action to save the victim. By integrating wearable sensors, IoT technology, and machine learning, this system offers a proactive and eff

关键词： Wearable sensors

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Unleashing Deep Reinforcement Learning: A Promising Alternative for Imbalanced Dataset Classification 12th

Unleashing Deep Reinforcement Learning: A Promising Alternat...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Din, Nusrat Mohi Ud Assad, Assif Sabha, Saqib Ul Bhat, Muzafar Rasool Department of Computer Science and Engineering Islamic University of Science and Technology Kashmir India Department of Computer Science Islamic University of Science and Technology Kashmir India

ISBN: (纸本)9789819731794

Class-imbalanced datasets pose a significant challenge for classification tasks in supervised learning, as standard classification algorithms are designed under the assumption that the datasets have balanced class distributions. This can lead to biased models that perform poorly on minority classes. To address this challenge, several approaches have been proposed at both the data level and algorithmic level. In this research study, comparative analysis of Deep Reinforcement Learning (DRL) methods and Oversampling techniques (SMOTE, SVM-SMOTE, ADASYN) along with Deep Learning (DL) algorithms have been performed on two benchmark imbalanced datasets—"Breakhis" and stroke prediction datasets. The experimentation has been carried out by taking different imbalanced ratios of 0.05, 0.1, 0.2, and 0.3 on the Breast Cancer training dataset and 0.05 imbalanced ratio on the Brain Stroke dataset. The comparison of DRL and Oversampling methods has been performed, and the results demonstrate that the DRL model outperformed oversampling techniques on all imbalanced ratios of the "Breakhis" dataset. For the Stroke prediction dataset, Oversampling techniques completely failed in classifying the minority class samples. These findings suggest that the Deep Reinforcement Learning algorithm presents itself as a promising alternative for imbalanced dataset classification in the medical domain and could potentially contribute to improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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