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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ouf, Mohamed Ashraf Amin, Rowan Mohamed Sabry, Moustafa Mamdouh Ali, Abdulrahman Hussien Hamdan, Yehia Ahmed Saleh, Sherine Nagy Arab Academy for Science Technology and Maritime Transport Computer Engineering Department Alexandria Egypt

ISBN: (纸本)9798350351408

Ensuring accurate and effective exercise performance monitoring is essential for sports-focused injury prevention. Maintaining perfect form is crucial since incorrect training increases the chance of injuries dramatically. This article focuses on the popular push-up exercise and introduces a new approach that uses a carefully curated dataset for analysis. The proposed model can accurately discriminate between correct and wrong push-up forms by employing LSTM networks and sophisticated computer vision algorithms. This improved method enables focused interventions, tailored feedback, and the detection of form deviations, providing insights into typical errors. This research aims to improve athlete safety, maximize athletic performance, and establish a framework for using the concept in other significant sports-related injury prevention initiatives. The proposed model significantly improved performance, achieving F1 scores of 0.9 and 0.85 on the training and testing sets, respectively. © 2024 IEEE.

关键词： Machine vision

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Integrity Shield: Defending Genuine Goods

Integrity Shield: Defending Genuine Goods

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Kshitija Nagareddy, G.B. Param Preetham, R. Bavagi, Raghavendra R. Sushmitha, S. Dayananda Sagar Academy of Technology and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372502

The rise in counterfeit products poses a significant challenge for consumers, businesses, and governments worldwide. Counterfeit goods not only impact the economy but also pose health and safety risks. Traditional methods of combating counterfeiting have proven inadequate, necessitating the exploration of innovative solutions. This report focuses on the utilization of blockchain technology for fake product identification. © 2024 IEEE.

关键词： Blockchain

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EmoCNN: Unleashing Human Emotions with Customized CNN Using Different Optimizers 2

EmoCNN: Unleashing Human Emotions with Customized CNN Using ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sahana, M. Umesh, Praneetha Kodipalli, Ashwini Rao, Trupthi Department of Computer Science & Engineering Global Academy of Technology Bangalore India Department of Artificial Intelligence & Data Science Global Academy of Technology Bangalore India

A key development towards enhancing computer-human interaction is emotion recognition. This publication describes a technique called EmoCNN, which uses deep learning techniques to precisely identify and classify human emotions, emphasizing improving model performance using different optimizers. Our research intends to contribute to the creation of more effective systems that improve computer-human interaction by solving the problems associated with emotion recognition. By bridging the gap between humans and robots, accurate emotion detection enables systems to perceive emotions for customized and responsive interactions. AI-powered assistants, chatbots, and social robots all benefit from emotion recognition by providing more responsive, empathic and interesting user experiences. Emotion-aware technologies can also enhance user feedback analysis, human-centered design, and monitoring of mental health. Using a human emotion detection dataset, we carried out comprehensive experiments focusing on the happy, sad, and neutral emotion classes. Constructing a customized EmoCNN model with convolutional layers, a hidden layer, ReLU activation, and max-pooling was the focus of our computational work. We investigated various optimizers and evaluated how they affected accuracy, convergence speed and loss minimization. The results demonstrated that the EmoCNN model, which had been trained using the Adam optimizer, gave the best accuracy in distinguishing between emotions. Our paper provides a comparative analysis, highlighting the superiority of EmoCNN over existing models, showcasing its ability to achieve higher validation accuracy (89%) and more efficient emotion recognition when compared to previous approaches with minimal loss. Our research advances the field of emotional computing by demonstrating how well EmoCNN can identify and categorizes various human emotions. This discovery has significant ramifications for the creation of emotion-aware computers, which can better und

关键词： Emotion Recognition

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The Eye: An AI-Powered Video Streaming Platform to Protect Children from Inappropriate Content 6

The Eye: An AI-Powered Video Streaming Platform to Protect C...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： El Bakri, Ahmad Yehia, Abdullah Ali, Mariam Osama, Reem Adel, Zeyad Gamal, Othman Saleh, Sherine Nagy Arab Academy for Science Technology and Maritime Transport Computer Engineering Department Alexandria Egypt

ISBN: (纸本)9798350378511

The Internet is now a vital part of our daily life including young children who watch their favorite cartoons using different streaming platforms. Unfortunately, these platforms often host content deemed inappropriate for children due to limited regulations. Manually reviewing the vast content is impossible, leaving parents with limited options to protect their children. Since parents in the Arabic culture have different beliefs on the content their children should be exposed to, and cannot rely on generic Kids' filters, accordingly 'The Eye' is proposed. The Eye is a streaming platform that allows children to watch videos while providing parents full flexibility to choose the exact content flags they deem inappropriate, ensuring their children enjoy a safe and age-suitable entertainment environment. The proposed model presents a hybrid CNN-RNN model to classify video content based on the predefined flags. A dataset was meticulously collected serving the different flags needed to train the proposed model and accordingly, the proposed trained model achieved an accuracy of approximately 67% on the test data. The model is deployed as a cloud-based service utilizing AWS infrastructure for efficient video processing and classification. © 2024 IEEE.

关键词： Video streaming

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BIOWATCH: Detecting the Lake Pollution to Safeguard Biotic Life

BIOWATCH: Detecting the Lake Pollution to Safeguard Biotic L...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Shalini, S. Mounika Sree, K. Prajwal, M.H. Nitin Reddy, N.V. Govardhan Reddy, P. Dayananda Sagar Academy of Technology and Management Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350372502

This study presents an integrated system devised to combat plastic pollution in lakes autonomously, eliminating the need for human intervention. Utilizing sensor data and camera imagery processed through the YOLO algorithm. The detected plastic is then used to compute its density, which is subsequently compared against a predetermined threshold. Upon surpassing the threshold, an automated email notification containing the density values is dispatched to relevant authorities. Furthermore, water quality sensors are integrated to monitor environmental conditions continuously. Regular updates are provided to facilitate proactive measures in pollution prevention. This initiative underscores the application of cutting-edge technology to address environmental issues and uphold the well-being of aquatic ecosystems. Through automated detection and monitoring processes, the system offers a sustainable approach to combat plastic pollution in lakes, promoting environmental conservation efforts. © 2024 IEEE.

关键词： Lakes

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A Message Digest Based Secured APUF Model to Prevent Machine Learning Attacks

A Message Digest Based Secured APUF Model to Prevent Machine...

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International Conference on computer, Communication, Control and Information technology (C3IT)

作者： Prasenjit Das Chandan Giri Computer Science and Engineering Academy of Technology Adisaptagram Hooghly India Department of Information Technology Indian Institute of Engineering Science and Technology Shibpur Howrah India

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

In the era of the Internet of Things (IoT), cloud computing huge amounts of data are generated by machines, humans and it is communicated over the internet. We need a stringent security to protect information as well as the devices also from the intruder. The electronic devices such as RFID, smart card and different sensors are used in different applications like smart automation and health monitoring but they are small and resource limited. The devices are placed in public places and exposed to the adversary for physical and cloning attacks. Hardware-based security using Physical unclonable function (PUF) overcomes such a problem but it is vulnerable to machine learning attacks with high precision. A variety of PUF designs have been proposed by the different researchers to provide energy-efficient, cost-effective and machine learning attack re-sistant such as Arbiter PUF(APUF), double APUF, XOR-PUF and multi-PUF(M-PUF) but either they have low reliability or they are not fully machine learning attack resistant. In this paper, we propose a message digest based APUF (MD-PUF) model which is energy-efficient, cost-effective and better machine learning attack resistant compare to other APUF models. We have added a message digest module which changes the input challenges to an arbitrary random challenges its make the intruder confuse to predict the response for unknown challenges and then it is fed into the APUF. We design the message digest module using columnar transposition which requires a very less amount hardware cost of n-bit registers for n-stages PUF. We mathematically prove that the prediction probability of MD-PUF is less than the classical APUF. We evaluate and compare the quality metrics of MD-PUF and APUF. The prediction accuracy of the proposed model is less compare to APUF against the machine learning algorithm LR, SVM and using evolutionary strategy CMA-ES.

关键词： Resistance Support vector machines Smart cards Machine learning algorithms Machine learning Physical unclonable function Energy efficiency Registers Security Internet of Things

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MLP-FOX: A Metaheuristic Approach-Based Training Multilayer Perceptron for Data Classification 1

MLP-FOX: A Metaheuristic Approach-Based Training Multilayer ...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Mohapatra, Swagatika Panda, Madhumita Sarangi, Partha Pratim Mishra, Bhabani Shankar Prasad Parihar, Nikhil Sahoo, Biswajit School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Trident Academy of Technology Computer Science & Engineering Bhubaneswar India

ISBN: (纸本)9798350361780

Training a Multilayer Perceptron (MLP) using metaheuristic optimization algorithms serves as a benchmark problem for demonstrating the effectiveness of emerging metaheuristic optimizers. In this study, we explore the efficacy of a novel metaheuristic optimizer namely, Fox algorithm, to optimize the weights and biases of an MLP. However, backpropagation algorithm is a traditional and well-known gradient-based optimizer is commonly used in training MLP, may become trap in local minima in many cases due to complex multimodal training error curves. This issue can diminish the classification accuracy. To overcome this problem, stochastic optimization algorithms play a vital role in improving error convergence and generalization capability of the MLP. In our work, exhaustive experimentation and analysis has been performed using five standard classification medical datasets to show the effectiveness of the Fox algorithm. The experimental results compare with backpropagation (generalized delta rule) and three recently proposed metaheuristic optimizers to evaluate its capability to converge to optimal solutions and enhance the overall performance of the MLP. Based on error convergence rate, accuracy, and F1-score, the proposed approach reveals promising classification performance compared to competitors. © 2024 IEEE.

关键词： Multilayer neural networks

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Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

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Big Data Mining and Analytics 2024年第1期7卷 55-74页

作者： Guosheng Cui Ye Li Jianzhong Li Jianping Fan Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern *** has been widely used and studied in the multi-view clustering tasks because of its *** study proposes a general semi-supervised multi-view nonnegative matrix factorization *** algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different *** specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is *** on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

关键词： multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy

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A Deep Learning Model to Detect Fake News about COVID-19

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Recent Advances in computer science and Communications 2023年第9期16卷 58-66页

作者： Shanmugavel, Selva Birunda Rangaswamy, Kanniga Devi Muthukannan, Muthiah Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Chennai India Department of Civil Engineering KCG College of Technology Chennai India

Aims/Background: Twitter has rapidly become a go-to source for current events coverage. The more people rely on it, the more important it is to provide accurate data. Twitter makes it easy to spread misinformation, which can have a significant impact on how people feel, especially if false information spreads around COVID-19. Methodology: Unfortunately, twitter was also used to spread myths and misinformation about the illness and its preventative immunization. So, it is crucial to identify false information before its spread gets out of hand. In this research, we look into the efficacy of several different types of deep neural networks in automatically classifying and identifying fake news content posted on social media platforms in relation to the COVID-19 pandemic. These networks include long short-term memory (LSTM), bi-directional LSTM, convolutional-neural-networks (CNN), and a hybrid of CNN-LSTM networks. Results: The "COVID-19 Fake News" dataset includes 42,280, actual and fake news cases for the COVID-19 pandemic and associated vaccines and has been used to train and test these deep neural networks. Conclusion: The proposed models are executed and compared to other deep neural networks, the CNN model was found to have the highest accuracy at 95.6%. © 2023 Bentham science Publishers.

关键词： COVID-19

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Cryptocurrency Trading Bot with Sentimental Analysis and Backtracking Using Predictive ML 10th

Cryptocurrency Trading Bot with Sentimental Analysis and Bac...

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Srinivas Murthy, Abhishek Akshay, A. Hanji, Bhagyashri R. Computer Science and Engineering Global Academy of Technology Bangalore India

ISBN: (纸本)9789811974540

Algorithmic trading is a process of converting a trading strategy into computer code which buys and sells the shares or performs trades in an automated, fast, and accurate way. Sentiment analysis is a powerful social media tool that enables us to understand its users. It is an important factor because emotions and attitudes toward a topic can become actionable pieces of information useful in understanding market trends, saving time and effort by the means of automation. Bringing together the art of sentimental analysis of social media and backtracking of historical price data, the paper, coupled with state-of-the-art APIs from leading crypto exchanges, is set to predict the best options and place trade orders, taking into account variables set by the user such as STOP LOSS and risk profiles. Combining the historical data from Binance APIs, coupled with sentimental analysis of Twitter tweets, our work aims at delivering highly accurate trade orders and executing them in real time without any human intervention. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Commerce

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