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Advancing Ophthalmology through Transfer Learning and Channel-Wise Attention for Retinal Disease Classification 6

Advancing Ophthalmology through Transfer Learning and Channe...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Islam, Sudipta Progga Alam, Ashraf Ul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

The global incidence of ocular disorders, which impacts more than 2.2 billion people, requires innovative approaches to ensure early and accurate diagnosis. This paper explores the integration of artificial intelligence (AI) in ophthal-mology, focusing on retinal diseases like cataracts, glaucoma, and diabetic retinopathy. The utilization of AI enables remote examinations and consultations, providing a crucial advantage in early disease detection. This study introduces a comparative analysis of state-of-the-art models, combining pre-trained convolutional neural networks (CNNs) and incorporating a channel-wise attention mechanism for enhanced discriminative ability. The proposed hybrid model, combining EfficientNetBO and Inception V3 with channel-wise attention mechanism, exhibits a remarkable accuracy of 98.13%, outperforming standalone models and other combinations. The findings underscore the efficacy of combined architectures and the integration of attention mechanisms in advancing the classification of retinal diseases. © 2024 IEEE.

关键词： Ophthalmology

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Stock Market Prediction Using Deep Learning

Stock Market Prediction Using Deep Learning

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Johnshaida, S.K. Kumar, G. Shiva Vignan's Foundation for Science Technology & Research Deemed to be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350379525

This project focuses on leveraging One kind of deep learning model is the Long Short-Term Memory (LS TM) networks., to forecast stock prices by analyzing historical data and technical indicators. The methodology involves comprehensive steps such as data collection, preprocessing, feature selection, model selection (including LSTM networks), training, and evaluation taking advantage of performance indicators such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). It utilizes a dataset comprising historical gold stock prices and trading volume to train and test the LSTM model. Furthermore, Naive Bayes, Support Vector Machine (SVM), Linear Regression, and other machine learning algorithms are examined in the study, along with more complex methods like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). Results reveal diverse accuracy levels across models, with LSTM demonstrating a tendency to overpredict stock prices. Future endeavors aim to refine model performance, investigate ensemble methods, and integrate additional data sources like economic news and sentiment analysis to bolster prediction accuracy, thereby offering investors and analysts enhanced decision-making tools in the dynamic stock market landscape. © 2024 IEEE.

关键词： Convolutional neural networks

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Cricket shot classification using Deep Learning 2

Cricket shot classification using Deep Learning

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kallur, Shanta Thotad, Puneeth N. Yaligar, Suman Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9798350388916

Cricket is a popular sport that involves various batting techniques and shot types. Accurate and real-time detection of cricket shots can provide priceless visual images for coaches, players, and analysts. This study proposes an artificial intelligence-based approach for cricket shot detection aimed at automatically recognizing and classifying different shots played by batsmen. Images are extracted and pre-processed to enhance their quality. The pre-processed images extract key features like bat trajectory, player movement, and batsman position. A training dataset is used to enable shot classification, consisting of labeled samples of different cricket shots. Neural network models, such as a convolutional neural network, VGG16, VGG19, are trained using an image set to learn the patterns and characteristics of each shot type. The model is then employed to classify shots in real time for predicting the corresponding shot type based on the selected features. The proposed approach is evaluated on a diverse dataset of cricket match images, encompassing a broad extent of shots played by professional batsmen. Execution metrics such as accuracy, precision, recall, and F1-score are used to assess the effect of the shot detection system. A VGG16 model in cricket shot prediction of the dataset yielded an impressive accuracy of 95.81%. The solution establishes the efficiency of the planned approach in accurately detecting and classifying cricket shots in real-time. The developed system has the potential to enhance cricket coaching, player performance analysis, and broadcast coverage by providing valuable insights into the shot selection and execution of batters. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Comparison of Overlapping Sound Event Detection Methods 14

Performance Comparison of Overlapping Sound Event Detection ...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Tyagi, Akshay Singh, Kartikeya S Jaswanth Kumar, P.V. Garg, Prof. Rakesh Yousef Abdullah, Mohammed Edres Amity University Dept. of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350344837

Sound event detection is pivotal in various applications, including environmental monitoring and surveillance systems, enhancing situational awareness and response strategies. This paper investigates the intricacies of overlapping sound events, where multiple events occur concurrently, creating complexities in accurate detection. Traditional methods falter when confronted with this challenge, necessitating advanced approaches. This research pursues two primary objectives: comprehensive review of existing overlapping sound event detection methodologies and exploration of innovative solutions to enhance accuracy and robustness. Through meticulous analysis of prior research, this paper offers insights into field progress and identifies unexplored areas. Subsequent sections dissect existing methodologies, evaluating their strengths and limitations, and addressing challenges in common evaluation metrics and datasets. This paper contributes to the advancement of sound event detection technology, particularly in complex audio environments, promoting better solutions for real-world applications. © 2024 IEEE.

关键词： Pattern recognition

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Meta-Adaptation Goals: Leveraging Feedback Loop Requirements for Effective Self-Adaptation 5

Meta-Adaptation Goals: Leveraging Feedback Loop Requirements...

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5th IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2024

作者： Groner, Raffaela Caldas, Ricardo Diniz Wohlrab, Rebekka Chalmers | University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798350389760

Self-∗ systems focus on optimizing systems regarding adaptation goals, e.g. availability, that relate to properties of the managed system. While these adaptation goals are important, the feedback loop must also be considered. It is problematic if systems adapt too often, make too time-consuming changes that involve insecure transition states, or react too slowly when planning adaptations. Current approaches do not allow to enforce requirements on the adaptation loop - including sensitivity, sta-bility, and transition states. When planning adaptations, those meta-adaptation goals need to be considered and potentially traded off against traditional adaptation goals. Such a tradeoff might entail, for instance, that it may be worth it to adapt a system less frequently, although the overall availability of the system could be higher with more adaptations. We propose to explicitly consider feedback loop requirements for self-adaptive systems. Concretely, we propose using machine learning to predict quality measures based on the state of the feedback loop and the managed system. This information is used to plan for better adaptations that consider the adaptation goals and the meta-adaptation goals of the feedback loop. The explicit representation of meta-adaptation goals and adaptation goals enables the optimization of adaptation strategies, tradeoff analysis, and evaluation of adaptations. © 2024 IEEE.

关键词： Self-supervised learning

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Deadline Constrained Least Laxity Time Based Cloudlet Scheduling

Deadline Constrained Least Laxity Time Based Cloudlet Schedu...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Datta, Lipika College of Engineering and Management Dept. of Computer Science and Engineering West Bengal Kolaghat India

ISBN: (纸本)9798331517892

Users send their requests to cloud service providers, who then deliver the services over networked systems. With a high volume of requests, it is crucial to schedule cloudlets effectively to ensure that most meet their specified deadlines for optimal performance. This paper focuses on calculating Both slack time and laxity time for cloudlets across all virtual machines and scheduling them accordingly. It also considers the rearrangement of cloudlets already assigned to virtual machines to accommodate new cloudlets. The CloudSim simulation environment is utilized to schedule cloudlets by evaluating their slack time and laxity time across various virtual machines. This approach helps in selecting the most suitable virtual machine for executing the cloudlets. By incorporating laxity time into the scheduling decisions, the system becomes more balanced, reduces makespan, and thus enhances overall performance. The proposed method has been shown to reduce task rejection by at least 10% and the makespan by an average of 17% when compared to the EFD algorithm. © 2024 IEEE.

关键词： Virtual machine

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Malware Exposed: An In-dept. Analysis of its Behavior and Threats 3

Malware Exposed: An In-Depth Analysis of its Behavior and Th...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Anand, Chinmay Korada, Shreya Raksha, S. Sundaram, B. Meenakshi Rajalakshmi, B. Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350322842

Any software that acts maliciously towards a user, device, or network is referred to as malware. Malware analysis consists of four fundamental processes that make use of a variety of technologies to comprehend operation and pinpoint areas for removal. In order to examine the behavior of the malware on the system, the second phase, known as Basic Dynamic analysis, is running the malware in a secure environment. Running malware in a Sandbox environment is a crucial step in the Basic Dynamic Analysis process. In order to determine which sandbox gives the greatest flexibility for running malware for research, this study will examine a variety of sandboxes. Based on the desired characteristics of a sandbox environment, a rubric was developed. Scalability, the capability to examine different file kinds, and the presence of sandbox detection evasion strategies are a few of the parameters taken into account. To do Basic Dynamic Analysis for malware analysis after examining some of the most well-known sandboxes, Norman Sandbox, GFI Sandbox, and Anubis. © 2023 IEEE.

关键词： Cybersecurity

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A Machine Learning Approach to Identifying Employees at Risk of Workplace Injury 2

A Machine Learning Approach to Identifying Employees at Risk...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Pramanik, Bablu Modak, Santanu Brainware University Dept. of Computer Science & Engineering India Bengal College of Engineering and Technology Dept. of Information Technology India

ISBN: (纸本)9798350360523

Workplace injuries are a critical concern, with millions occurring annually, leading to substantial human and economic costs. This study focuses on a qualitative analysis based on statistics from 2013 to 2017, with a specific emphasis on fatal and non-fatal workplace accidents within the European Union (EU) and Romania. The primary objective is to assess Romania's workplace safety status in the context of the EU and to propose strategic measures for improvement. To achieve this goal, four key indicators and statistical datasets are utilized, sourced from the National Institute of Statistics (NIS) of Romania, the Eurostat database of the European Commission, and the Romanian Labor Inspection. These indicators include the rate of incidence frequency index for occupational accidents, the average duration index, the frequency index for fatal accidents, and the severity index, enabling a comprehensive evaluation of accident frequency and severity. The rate of incidence, measuring injuries per 100,000 workers, is a pivotal indicator. Additionally, the study calculates the frequency index of non-fatal accidents (injuries per 1,000 employees) and the fatal accident frequency index (injuries per 1,000 workers). Statistical findings are rigorously validated through ANOVA analysis and T-tests. Following data evaluation, the study offers strategic recommendations informed by national and European strategies, including the National Occupational Safety and Health (OSH) Strategy for 2017-2020 and the "EU Strategic Framework on Health and Safety at Work"spanning 2014 to 2020. These recommendations aim to guide efforts for workplace accident control and prevention in Romania and the broader European *** summary, this study's core objective is to comprehensively analyze workplace accidents in Romania and the EU, employing various indicators and statistical data. Its primary aim is to assess the current status and propose evidence-based strategies for improvement, aligning with

关键词： Machine learning

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Cao Robot for Taiwanese/English Knowledge Graph Application 27

Cao Robot for Taiwanese/English Knowledge Graph Application

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27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Lee, Chang-Shing Wang, Mei-Hui Tseng, Guan-Ying Yue, Chao-Cyuan Hsieh, Hao-Chun Reformat, Marek National University of Tainan Dept. of Computer Science and Information Engineering Tainan Taiwan University of Alberta Dept. of Electrical and Computer Engineering EdmontonAB Canada

ISBN: (纸本)9798331506032

This paper proposes a Content Attention Ontology (CAO) robot for constructing Taiwanese/English Knowledge Graphs (KGs) by prompting audio or texts to Large Language Models (LLMs), including TAIDE, Zephyr, and Llama 3.1. The collected data includes lecture videos from the IEEE WCCI 2024 in Japan and the 2024 National Language Development Forum in Taiwan, along with students' learning data from the 2024 Summer School on Taiwanese/English Human and Robot Co-Learning at Rende Elementary School (RDES). In addition, the fundamental concepts of Computational Intelligence (CI) and Quantum CI (QCI) learning were incorporated into the study. The generative KGs highlight important concepts, relations, and communities within the collected teaching and learning data. Additionally, we utilized data from subjects wearing braincomputer interface (BCI) devices while speaking Taiwanese/English to generate KGs. We also compared the differences in these KGs and analyzed the similarities between the transcribed texts of lectures and learners. In the future, we plan to expand the CAO robot to more validation fields across Taiwan, aiming to engage young students in speaking Taiwanese while concurrently enhancing their English language skills through interaction with the robot. © 2024 IEEE.

关键词： Knowledge graph

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Deep Learning for Enhanced Consumer Behavior Analysis and Predictive Accuracy

Deep Learning for Enhanced Consumer Behavior Analysis and Pr...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Bharti, Jigyasa Dongre, Snehlata G H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350319019

The accelerating digital transformation in retail necessitates advanced analytical approaches to effectively comprehend and predict consumer behavior. Existing methodologies in retail data mining often fall short in handling the complexity and heterogeneity of data, resulting in suboptimal predictive accuracy and operational efficiency. This work addresses these limitations by introducing a novel deep learning framework that synergizes the capabilities of Graph Neural Networks (GNNs) with Q Learning and VARMAx models. The proposed model adept.y fuses data from diverse sources such as Twitter, Amazon, Facebook, and Flipkart, creating a comprehensive analysis platform. By leveraging the relational data structure processing power of GNNs and the dynamic decision-making proficiency of Q Learning, along with the time series predictive strength of VARMAx models, this approach offers a more holistic understanding of consumer trends. The efficacy of this model is demonstrated through extensive testing on varied data samples encompassing social media, e-commerce platforms, and tweets. The results are compelling: an 8.3% increase in accuracy, 8.5% enhancement in precision, 6.5% improvement in recall, and a significant 10.4% reduction in delay compared to existing classification methods. The impact of this work is far-reaching, offering retailers a robust tool for strategic decision-making, enhanced customer insight, and a competitive edge in the rapidly evolving digital marketplace. This approach not only propels the retail industry forward but also sets a new benchmark in the application of deep learning for consumer behavior analysis. © 2024 IEEE.

关键词： Data mining

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