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Cross-Database Facial Expression Recognition using CNN with Attention Mechanism 14

Cross-Database Facial Expression Recognition using CNN with ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chandra, Jatin Annappa, B. Rashmi Adyapady, R. National Institute of Technology Dept. of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350335095

Facial expression is one of the most effective and universal ways to express emotions and intentions. It reflects what a person is thinking or experiencing. Thus, the expression recognition is one of the key aspects of understanding non-verbal communication and interpreting emotions in social interactions. Some emotions are very confusing, and separating the features between them becomes difficult because they share the same feature space. For example, the distinction between fear, anger, and disgust is confusing. This work tried to improve the model's class-wise performance to detect each class correctly. A distinct combination of deep-learning models is used to calculate the performance of the model, such as ResNet, XceptionNet, DenseNet, etc. The datasets like Real-world Affective Faces Database (RAF-DB), Japanese Female Facial Expression (JAFFE) & Facial Expression Recognition 2013 Plus (FER+) are used to evaluate the model's performance. The proposed model achieved better results and overcame the previous work's limitations. CDE's performance on RAF-DB and FER+ evaluations was significantly better than the current SOTA methods, with an increase in accuracy of 5.18% and 3.98%, respectively. © 2023 IEEE.

关键词： Face recognition

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An Ensemble-based Deep Learning Model for Multi-class Flower Recognition

An Ensemble-based Deep Learning Model for Multi-class Flower...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Rabbi, Md Fozle Dola, Sraboni Das Taha, Noorjahan Binte Kader Al Mahmud, Tamim Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350316001

Many species around the globe heavily depend on flowers, and even the use of flowers for medical purposes is enormous, which has been in practice since ancient times. In modern botany, agronomy, and species research, the identification and classification of flowers mainly depend on human intervention, which requires a lot of human resources, and the recognition accuracy rate is low. On the other hand, due to the vast number of flowers with different types, it is quite a challenge and increases the complexity of the accurate detection and classification process. Moreover, the traditional CNN model and other pre-trained models like- VGG16, VGG19, Xception, and MobileNet-V2 failed to ensure high validation accuracy. In this research, we have designed an ensemble-based deep learning model consisting of two pre-trained models DenseNet-201 and EfficientNet-B7, which we renamed 'FlowerConvNet'. This paper considers classifying five categories of flowers consisting of 4242 images, collected from the Kaggle flower recognition dataset. In this research study, the proposed deep learning model FlowerConvNet using transfer learning can obtain a competitive result with 95% validation accuracy. © 2023 IEEE.

关键词： Learning systems

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Data Visualisation of Time Tradable Assets Using Machine Learning

Data Visualisation of Time Tradable Assets Using Machine Lea...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Mallikarjunaiah, Nirupama Jain, Rishab S. Vinay, K.R. Navada, Sharan S. Pramod, T.C. Siddaganga Institute of Technology Dept. of Computer Science and Engineering Tumkur India

ISBN: (纸本)9798350300826

Forecasting financial instruments accurately is a complex task due to the volatile, non-linear and dynamic nature of financial markets. Numerous factors, including political events, global economic conditions, and company performance, contribute to the difficulty of predicting outcomes. However, this challenge also provides a wealth of data that can be leveraged to uncover patterns. With the advancement of AI and increased computational power, programmable prediction techniques have emerged as more effective tools for forecasting tradable assets. This paper explores the application of various regression techniques, such as Linear, Support Vector, Polynomial, and Tree Regression, in financial prediction. Additionally, the study incorporates Long Short-Term Memory (LSTM), a method of recurrent neural network model, to analyse an asset's historical behaviour and generate reliable conclusions. The proposed approach aims to address the practical implementation of prediction systems in real-world scenarios while addressing challenges related to the accuracy of the provided values. Furthermore, appropriate visualization techniques are employed to enhance the interpretation and understanding of the results. By combining advanced prediction models and visualization methods, this research endeavours to improve the accuracy of financial predictions and provide valuable insights for decision-making infinancial markets. © 2023 IEEE.

关键词： Data visualization

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Optimized Fog Colony Framework for Efficient Service Placement using Hybrid Genetic Algorithm

Optimized Fog Colony Framework for Efficient Service Placeme...

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2023 International Conference on Decision Aid sciences and Applications, DASA 2023

作者： Verma, Nilesh Kumar Naik, K. Jairam National Institute of Technology Dept. of Computer Science & Engineering Raipur India

ISBN: (纸本)9798350342055

The transition from Cloud Computing to Fog Computing is being investigated as the preferred method for rapid data processing in Internet of Things (IoT) applications. Fog computing is a decentralized computing paradigm, harnesses the computational capabilities of IoT devices at the network edge to perform data processing and provide real-time services. However, effective service placement in fog environments remains a challenge. This research paper focuses on the lack of efficient techniques for organizing fog devices into optimal fog colonies that possess self-rule capabilities and require minimal energy for administration. We propose a hierarchical clustering-based fog colony architecture to fill this void, which will allow for in-dept. investigation and analysis of various colony configurations. The resulting dendrogram includes all feasible colony configurations, and a genetic algorithm is used for precise colony selection based on fitness and accuracy. The multi-objective hybrid genetic algorithm NSGA-II is used to assess how efficiently the suggested framework performs under a variety of conditions. The goal is to reduce the delay between an app's initial interaction with a user and the initial practice of the app. The results show improvements in reaching predefined goals when the proposed strategy is applied to different contexts. © 2023 IEEE.

关键词： Genetic algorithms

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Operating System Command Execution Using Voice Command 3

Operating System Command Execution Using Voice Command

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Singh, Paras Nath Navaneetha, M. Tijare, Poonam Vijay Cmr Institute of Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350302288

Recent years have seen a considerable increase in the use of voice commands as a result of the growing popularity of smart gadgets, virtual assistants, and other related technologies. In this study, we investigate the viability of voice commands for operating system commands. In this paper a system is proposed and demonstrated that enables voice commands to be recognized by the operating system and executed according to parameters. This way it is offering a fresh and more usable voice assistant to carry out typical computer chores. Python tools are used to go over how the system is put into practice, including how speech recognition and natural language processing are used, and how well it executes in terms of accuracy, speed, and usefulness. The main impact factor of this paper is to provide voice assistance to disadvantageous people whose hands and fingers are not working well. © 2023 IEEE.

关键词： Speech recognition

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Enhanced Unimodal Continuous Authentication Architecture on Smartphones for User Identification through Behavioral Biometrics 2

Enhanced Unimodal Continuous Authentication Architecture on ...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Rayani, Praveen Kumar Changder, Suvamoy National Institute of Technology Dept. of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350347982

Nowadays intruders are smart and intelligent to initiate various attacks on the smartphone's entry-point au- thentication methods to access the user's stored information on the smartphone. Accessing users' confidential information rather than personal information by attackers can create a monetary or non-monetary gain. Thus, continuous authentication was proposed on smartphones to recognize the user from the entry point to the end of the session. Even though, we noticed that earlier works on unimodal-based continuous authentication were limited to specific applications on smartphones. This can create a vulnerable gap to access the users' information on the smartphone. Thus, we propose an enhanced unimodal-based continuous authentication architecture for user identification on the smartphone. The proposed approach instantaneously chooses the unimodal behavioral biometrics based on the foreground application of the device and performs the user identification through a machine learning model. The experimental results pro- posed approach show better results than the existing literature. © 2023 IEEE.

关键词： Smartphones

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Identification of Malignant Cells Using Convolutional Neural Network 1

Identification of Malignant Cells Using Convolutional Neural...

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1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Sahu, Rekha Dash, Akshaya Kumar Silicon Institute of Technology Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350315769

Breast cancer has a high impact as a leading cause of death. Doctors face problems with the quick diagnosis of malignant tumors, which would help in the prevention of cancer at an early stage. Automatic detection of malignant cells is a life-saving boon. The deep learning approach extensively manipulates the data non-linearly to obtain suitable information. In this study, a one-dimensional convolutional neural network is implemented on a tumor dataset to detect malignant and benign tumors. The deep learning approach has achieved 99.9% classification accuracy with 45 epochs and 98.8% ROC-AUC. More experiments are needed on new tumor datasets to study the performance of the one-dimensional convolutional neural network approach in identifying malignant cells. © 2023 IEEE.

关键词： Tumors

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Hash Tag-Based Sentiment Analysis Using A Deep Learning Approach 10

Hash Tag-Based Sentiment Analysis Using A Deep Learning Appr...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Venkatesan, R. Sangeetha, M. Nivedha, S. Selvakumar, M. Chandru, K.S. Parthasarathi, P. Bannari Amman Institute of Technology Department of Information Technology Erode India K S Rangasamy College of Technology Department of Information Technology Namakkal India Hindusthan College of Engg and Technology Dept. of Computer Science & Engineering Coimbatore India Bannari Amman Institute of Technology Dept. Computer Science & Business Systems Erode India Bannari Amman Institute of Technology Dept. Computer Science and Engineering Erode India

ISBN: (纸本)9798350384369

Social commerce uses a variety of social networking sites like Facebook, Instagram, Twitter, Pinterest, and business Whatsapp as a platform to promote products and services. Since advertising, a product, or service in s-comm will reach as high as a possible number of users on networking sites. Ads will spread virally by sharing among friends and friends of friends. So that wider reachability is possible and shortly will get the customer for our business. The success of an s-comm campaign is measured by the degree to which consumers interact with the sellers through review comments, likes, and shares between friends. Customer behavior analysis is needed for s-comm marketing because social commerce can significantly boost economic growth. The sentiment analysis of customer recommendations was investigated using a dataset of women's s-comm apparel hashtags and reviews. So for such reviews are experimented with different machine learning algorithms (Decision tree, SVM, Random Forest, KNN, Naïve Bayes and LSTM). These models handled the common English reviews and compared the metrics (Accuracy, Sensitivity and Specificity) with one another. Instead of working with individual model, this paper introduces a novel hybrid approach that integrates CNN and Ada Boost LSTM algorithms to provide better results in sentiment analysis when compared to other machine learning algorithms mentioned above. While considering the hash tag with common review comments the hybrid system is out performed. The comparison analysis is done for performance matrices namely accuracy of 98%, specificity of 98.1%, and sensitivity of 98.1%. In the s-commerce transaction industry, this hybrid approach will help to create a deeper understanding of consumer sentiment analysis and psychologically attract clients. © 2024 IEEE.

关键词： Tweets

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Image Recognition-Based Indoor Positioning System Using Federated Learning 100

Image Recognition-Based Indoor Positioning System Using Fede...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Yu, Heng-Shao Jhuang, Yu-Chiao Leu, Jenq-Shiou University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan

ISBN: (纸本)9798331517786

With the advancement of artificial intelligence (AI), indoor positioning systems have become increasingly important for various applications, leading to the development of diverse indoor positioning methods. Among various approaches, image-based indoor positioning methods have demonstrated relatively good positioning accuracy. However, maintaining user privacy continues to be a significant challenge. To tackle this issue, we propose a Federated Learning (FL) approach and develop an FL-based indoor image recognition positioning system. We conducted experiments to validate our method using two real-world datasets and compare the results to a Non-FL approach. Furthermore, we evaluated the accuracy of our method across various communication rounds, the number of client devices, and the amount of data per client device. Our experimental results indicate that the proposed method effectively preserves client privacy while achieving accuracy similar to the Non-FL approach. By employing FedAvg and FedOpt algorithms with the MobileNet model, our system ultimately reaches 94% accuracy. © 2024 IEEE.

关键词： Federated learning

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Blockchain and IoT-Based Medical Care Utilizing Secure and Protection Safeguarding Watermark

Blockchain and IoT-Based Medical Care Utilizing Secure and P...

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2023 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2023

作者： Shruthi, K. Poornima, A.S. Siddaganga Institute of Technology Dept. of Computer Science and Engineering Tumakuru India

ISBN: (纸本)9798350347296

Patient clinical information is critical information for a well-being application. Clinical information regularly assumes a significant part in illness examination and is ensnared in persuading values for instructing symptomatic techniques and altering analytic outcomes. Privacy of patient well-being reports is more significant, furthermore, subsequently, specialists have considered numerous safety efforts, counting access control, classification, and uprightness of these records. To take care of the security issue, this paper presents a high-level arrangement in light of changing over clinical information into minimized size, as a watermark before being sent on Blockchain. Along these lines, difficult tasks will be securely performed off-chain, like gathering and preprocessing patient information, while accommodation of this information is finished on an hourly premise chain. We suggest an information stream design that consolidates the IoT with blockchain using the Ethereum platform, utilizing brilliant agreements to access and store information against many assaults. The proposed model is successful in sharing and overseeing patient electronic well-being information. The tests need to be carried out on Ethereum chain arrangements on laptops, and desktops. © 2023 IEEE.

关键词： Blockchain

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