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Embedding Clustering using Lightweight Contrastive learning for Cross-Modal Classification 8

Embedding Clustering using Lightweight Contrastive Learning ...

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8th international conference on computer and Communication Systems, ICCCS 2023

作者： Amendola, José Cenkaramaddi, Linga Reddy Jha, Ajit University of Agder Dept. of Engineering Sciences Grimstad Norway University of Agder Dept. of Information and Communication Technology Grimstad Norway

ISBN: (纸本)9781665456128

Deep learning models excel at computer vision tasks, but they require a lot of memory and computation power. Thermal image classification has gained popularity due to the robustness of this modality in the face of adverse weather and lighting conditions. Images from both visible and/or thermal modalities, on the other hand, must frequently be processed in memory constrained environments, such as embedded edge applications. We propose a novel cross-modal constrastive learning model with a novel objective function that learns and averages mappings from both visible and thermal modalities into a shared embedding space for cross-modal data clustering and classification. A lightweight architecture is used to generate embedding from images in both modalities, and the proposed algorithm yields cross-modal centroids that can be efficiently used for clustering and classification in either modality. We demonstrate the model's accuracy by running experiments on a dataset that contains both thermal and visible counterparts of the same objects. © 2023 IEEE.

关键词： Embeddings

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Health Monitoring System using IoT with Machine learning 9

Health Monitoring System using IoT with Machine Learning

引用

9th international conference on Smart Computing and Communications, ICSCC 2023

作者： Anu, C. Muhammad Rasi, K.P. Salih, Nuha Nanditha, P. Rajeevan, Vaishnavi Computer Science and Engineering St. Thomas College of Engineering and Technology Kerala Mattannur India

ISBN: (纸本)9798350314090

The Internet of Things (IoT) is a young technology that continues to grow with numerous new advancements in the medical and health sectors. Health care is an important part of human life. It helps prevent diseases and improve the quality of life. Today, people don't have the time to visit the hospital for routine checkups and maintain a medical record of their health status. To overcome this, we introduced a system called the Health Monitoring System. It is an IoT framework for ML-integrated healthcare monitoring systems that enables routine and periodic monitoring of patients' health conditions. The patient's data are collected using an IR and blood pressure sensor, compared using the machine learning SVM algorithm, and then it is determined if the patient has diabetes or not. © 2023 IEEE.

关键词： Internet of things

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A Novel Circuit-Level Method for Hardware Trojan Detection in Digital Designs 10

A Novel Circuit-Level Method for Hardware Trojan Detection i...

引用

10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Moulee, S. Chandra Ramesh, S.R. Ram, Saswat Kumar Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350382471

The increased dependency on the technology also increases the possible exploitation of a device by malicious agents. The protection against the insertion of Hardware Trojans in the integrated circuits (ICs) is becoming the biggest concern in the current manufacturing processes. The functionality of a Hardware Trojan is to either modify the circuit's standard running functionality or leak any confidential information from the circuit. Hardware Trojans can be detected in both pre-silicon and post-silicon stages, but vulnerability is more in the postsilicon stage. This research aims to detect a hardware Trojan with a zero probability of getting triggered, no increase in area overhead and without activating it with the help of a golden response. A new method for hardware Trojan detection is proposed here using the power feature, RC parasitic extraction, and machine learning (ML)- based clustering techniques. It can not only detect the presence of a Hardware Trojan but also detect the nets in which it is connected. Experiments are carried out on seven ISCAS'85 and seven ISCAS'89 circuits;out of 14 circuits, results were found satisfactory in 13 circuits. © 2023 IEEE.

关键词： Automatic Test Pattern Generation (ATPG) Hardware Security Hardware Trojan (HT) Machine learning (ML) Very Large Scale Integration (VLSI)

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Deep fake Detection using deep learning techniques: A Literature Review 5

Deep fake Detection using deep learning techniques: A Litera...

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5th international conference on Control, Communication and Computing, ICCC 2023

作者： Mary, Amala Edison, Anitha College of Engineering Trivandrum Computer Vision Lab Kerala India APJ Abdul Kalam Technological University Kerala Trivandrum India

ISBN: (纸本)9798350334128

Deep learning is a sophisticated and adaptable technique that has found widespread use in fields such as natural language processing, machine learning, and computer vision. It is one of the most recent deep learning-powered applications to emerge. Deep fakes are altered, high-quality, realistic videos/images that have lately gained popularity. Many incredible uses of this technology are being investigated. Malicious uses of fake videos, such as fake news, celebrity pornographic videos, financial scams, and revenge porn are currently on the rise in the digital world. As a result, celebrities, politicians, and other well-known persons are particularly vulnerable to the Deep fake detection challenge. Numerous research has been undertaken in recent years to understand how deep fakes function and many deep learning-based algorithms to detect deep fake videos or pictures have been *** study comprehensively evaluates deep fake production and detection technologies based on several deep learning algorithms. In addition, the limits of current approaches and the availability of databases in society will be discussed. A deep fake detection system that is both precise and automatic. Given the ease with which deep fake videos/images may be generated and shared, the lack of an effective deep fake detection system creates a serious problem for the world. However, there have been various attempts to address this issue, and deep learning-related solutions outperform traditional approaches. © 2023 IEEE.

关键词： Fake detection

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A Deep Deterministic Policy Gradient Algorithm Based Controller with Adjustable learning Rate for DC-AC Inverters 2

A Deep Deterministic Policy Gradient Algorithm Based Control...

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2nd IEEE international Power Electronics and Application Symposium, PEAS 2023

作者： Ye, Jian Mei, Sen Guo, Huanyu Zhao, Di Zhang, Xinan School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China School of Electrical Electronic and Computer Engineering University of Western Australia Perth Australia

ISBN: (纸本)9798350313765

This paper proposes an inverter control algorithm based on the deep deterministic policy gradient (DDPG) with adjustable learning rates. This algorithm retains the advantages of model-free control of the inverter and solves the inherent problem of learning rate setting in deep neural networks. As a result, the control algorithm can quickly converge to the desired state during training, accelerate the training process, and not be affected by the control effect. Through simulation, the training results of the adjustable learning rate DDPG algorithm are compared with those of the constant small learning rate and constant large learning rate DDPG algorithms. The simulation results show that the training efficiency is improved by 75%, while the control effect on the inverter is almost unaffected. © 2023 IEEE.

关键词： deep deterministic policy gradient algorithm learning rate reinforcement learning Three-Phase DC-AC Inverters

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Breast Cancer Diagnosis Using a Machine learning Model and Swarm Intelligence Approach 1

Breast Cancer Diagnosis Using a Machine Learning Model and S...

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1st international conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Gad, Ibrahim Elmezain, Mahmoud Alwateer, Majed M. Almaliki, Malik Elmarhomy, Ghada Atlam, Elsayed Tanta University Computer Science Division Faculty of Science Tanta31527 Egypt Taibah University College of Computer Science and Engineering Dept. of Computer Science Yanbu966144 Saudi Arabia

ISBN: (纸本)9781665472753

The features selection for machine learning models requires careful consideration. A good selection of features can enable machine learning models to better identify patterns in data and make more accurate predictions. Also, relevant features in the data have an impact on the accuracy of the model and lengthen the training process. In this study, the suggested feature selection strategy is implemented using pigeon inspired optimizer (PIO). The PIO is a continuous swarm intelligent algorithm. The machine learning models were trained and tested using the proposed PIO optimizer in the context of medical data. Both the training and testing steps use the Wisconsin breast cancer dataset. The best results are generated by the Random Forest model, which has accuracy, F-score, recall, and precision values of 97.2%, 97.3%, 97.3%, and 97.3%, respectively. It was concluded that the selected features perform better for classification than the original high-dimensional features, both in terms of accuracy and the F-score. As a consequence of this, the proposed approach can be utilized to better categorize breast cancer data. © 2023 IEEE.

关键词： Feature Selection

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Transfer learning for Hand Arthritis Prediction from X-Ray Images 3

Transfer Learning for Hand Arthritis Prediction from X-Ray I...

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3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Raman, Ramakrishnan Inbamalar, T.M. Pushpalatha, N. Meenakshi, S. Kumar, Ashok Razia, Shaik Gopinath, N. Symbiosis Institute of Business Management Maharashtra Pune India Symbiosis International Maharashtra Pune India R.M.K. College of Engineering Department of Electronics &communication Tamil Nadu Thiruvallur India Marri Laxman Reddy Institute of Technology Department of Computer Science Telangana Hyderabad India R.M.K. Engineering College Department of Science and Humanities Tamil Nadu Kavarapettai India Banasthali Vidyapith Department of Computer Science Rajasthan 304022 India Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vaddeswaram India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350336238

Arthritis is a bone disorder that includes swelling and pain in one or more joints. Everyone can develop osteoarthritis, but it grows more common as individuals get older. When arthritis deteriorates over time, it can lead to persistent pain, making it challenging to do daily tasks, and making activities like walking and climbing stairs painful and difficult. If arthritis is correctly identified and treated in its early stages, these consequences can be avoided. The goal of this project is to create two transfer learning models that, by spotting arthritis in its earliest stages, can lower the likelihood of acquiring chronic arthritis. For this purpose, Google served as the source of the images used in this study. After being purchased from Google, the data collection is preprocessed using three different methods. Image scaling, noise reduction, and image enhancement are a few of the pre-processing approaches. The transfer learning models are trained and assessed using this preprocessed dataset. In this work, two distinct transfer learning models are established. The models include SegNet and ENet. On a graph, the outcomes for the performances of both models are displayed. The training data from the first few epochs of the ENet model and SegNet model are also used in the analysis. The models' final accuracy and loss values are then assessed. In the end, it was discovered that the SegNet model had a lower loss value and more accuracy than the other. The model created in this study can be utilised as a preliminary test for arthritis when a person exhibits moderate arthritis symptoms because the final accuracy of the model is higher than or equal to 95%. © 2023 IEEE.

关键词： Image enhancement

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Fine Grained Sentiment Analysis using Machine learning and Deep learning

Fine Grained Sentiment Analysis using Machine Learning and D...

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2023 international conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Chauhan, Rahul Gusain, Aman Kumar, Prabhat Bhatt, Chandradeep Uniyal, Ishita Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Hill University Ece Department Uttarakhand Dehradun India Graphic Era Hill University Eee Department Uttarakhand Dehradun India

ISBN: (纸本)9798350329186

Due to recent developments in computational algorithms like machine learning (ML) and more advance algorithm like deep learning, the area of fine-grained sentiment analysis has drawn a lot of attention in the context of natural language processing. This paper explores the domain of fine-grained sentiment analysis, with a specific focus on capturing intricate and subtle emotions in text through the utilization of ML and DL approaches. By going beyond conventional sentiment analysis, fine-grained sentiment analysis enables a more comprehensive comprehension of an individuals' sentiments. The paper provides an overview of various ML and DL techniques employed in fine-grained sentiment analysis, including Support Vector Machines, recurrent neural networks, and transformer-based architectures. Additionally, it addresses the challenges and future prospects in this field, wide aspects such as the scarcity of annotated data and the interpretability of DL models. The paper concludes by highlighting real-world applications that underscores the significance of fine-grained sentiment analysis in customer-centric industries, social media platforms, and public opinion analysis. The advancements in fine-grained sentiment analysis present promising opportunities to gain deeper insights into human emotions and opinions, thereby facilitating informed decision-making processes across diverse domains. © 2023 IEEE.

关键词： Sentiment analysis

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Enhanced Automatic modulation classification Using Leveraging Semi-Supervised learning 9

Enhanced Automatic modulation classification Using Leveragin...

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9th international conference on computer and Communications, ICCC 2023

作者： Zhang, Shusen Wang, Sheng Xing, Weining Xu, Luyan Yang, Jian Academy of Military Sciences Research Institute of System Engineering Beijing China

ISBN: (纸本)9798350317251

Automatic modulation classification (AMC) is essential for identifying the modulation scheme of received signals, a key step bridging signal reception and demodulation. This process traditionally relies on prior signal information. Deep learning's role in AMC has been notable, though its efficacy hinges on the volume of labeled data. Addressing the scarcity of labeled samples, our research introduces a semi-supervised AMC approach using consistency regularization and pseudo-labeling. This method harnesses unlabeled data's distributional information to mitigate the shortfall of labeled data. We designed a dual-component objective function: one segment calculates loss from labeled data training, while the other assesses regularized loss from augmented unlabeled data. These combined losses concurrently refine the model parameters. Comparative tests reveal our method's superiority over established algorithms like decision trees, support vector machines, pi-models, and virtual adversarial training, demonstrating enhanced performance in AMC. © 2023 IEEE.

关键词： Deep learning

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CONCRETE DAMAGE IDENTIFICATION FOR STRUCTURAL HEALTH MONITORING USING computer VISION 11

CONCRETE DAMAGE IDENTIFICATION FOR STRUCTURAL HEALTH MONITOR...

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11th international conference on Fiber-Reinforced Polymer ,FRP Composites in Civil engineering, CICE 2023

作者： Kumar, Abhijeet Martin, Hector Leon, Lee Queen's University Belfast United Kingdom University of the West Indies Trinidad and Tobago

Detecting the early degradation of concrete structures can assist agencies in forecasting and planning future resource demands. Structural defects network SDNET2018 classes were used to create a custom binary convolutional neural network CNN classifier with physical crack damage predictive accuracy of 99.2%. Second, concrete's physical, chemical, and reinforcement damage classes, including a negative class, are stratified to generate a separate dataset using concrete defect bridge image CODEBRIM cropped image database to train a multiclass image classifier model with predictive accuracy of 80%. Comparisons were made based on the computational efficiency of a CNN architecture-specific classifier model and a ResNet50-retrained transfer-learning model predicting at 87.45% accuracy. These findings enable the generalisation and improvement of the damage assessment accuracy of concrete structures. © CICE 2023 - 11th international conference on FRP Composites in Civil engineering. All rights reserved.

关键词： computer vision

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