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Enhancing Pneumonia Detection: CNN Interpretability with LIME and SHAP 6

Enhancing Pneumonia Detection: CNN Interpretability with LIM...

引用

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

作者： Hosen, Md. Hamid Saha, Arnob Uddin, Altaf Ashraf, Kahakashan Nawar, Sadia East Delta University Dept. of Computer Science & Engineering Chittagong Bangladesh

ISBN: (纸本)9798350385779

A bacterial or viral infection of the lungs can cause pneumonia, one of the dangerous and potentially fatal illnesses that can have dire repercussions in a short amount of time. Therefore, a key component of a successful treatment plan is an early diagnosis. Therefore, a sophisticated and automated system that can diagnose chest X-rays and make the process of diagnosing pneumonia easier for both specialists and novices is required. This research aims to create a CNN model that will help with the accurate classification of pneumonia. In this work, we have presented our Deep Learning method for the classification challenge, which is taught using modified images through several pre-processing stages. With an overall accuracy of 93.30%, we were able to classify X-ray images of pneumonia using a custom CNN model. Our proposed model is able to precisely detect pneumonia from X-ray images with amazing accuracy and loss. Furthermore, we employed the LIME and SHAP tools of the XAI technique to generate a noteworthy conclusion to persuade medical practitioners. © 2024 IEEE.

关键词： Lime

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Harmonizing the Musical Metaverse: unveiling needs, tools, and challenges from experts’ point of view

Harmonizing the Musical Metaverse: unveiling needs, tools, a...

引用

International Conference on New Interfaces for Musical Expression, NIME 2024

作者： Boem, Alberto Tomasetti, Matteo Turchet, Luca Dept. of Information Engineering and Computer Science University of Trento Trento Italy

The Musical Metaverse (MM) represents an innovative frontier for the field of New Interfaces for Musical Expression (NIME). The MM holds the potential to redefine areas such as musical composition and performance via immersive environments based on technologically mediated social interactions. Despite substantial research on single-user immersive systems, the intersection of NIME and the MM remains largely unexplored. In this paper, we systematically explore this domain by examining previous and current approaches, alongside conducting interviews with eleven experts who have created multi-user immersive musical environments and authored publications on this topic. The goal is to map such an uncharted territory by collecting valuable insights and leveraging the perspective of experts to provide an understanding of the potentials and challenges inherent in creating immersive social environments for musical activities. Our results reveal that existing multi-user immersive environments make use of diverse implementation approaches but face challenges due to the absence of standardized technology stacks, particularly in networking and data synchronization. © 2024, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Music

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Classification of Foods based on Bijective Soft Set 15

Classification of Foods based on Bijective Soft Set

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Das, Priyanka Bhattacharya, Rupam Dept. of Computer Application and Science Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798350370249

Cuisine data is typically classified using images of the cuisine, cooking methods, cooking utensils, and, most importantly, ingredients. The vast amount of recipe data available on the internet necessitates the creation of specialised systems for storing, retrieving, and effectively utilising stored knowledge and data. Neural networks based expert systems are steadily being used to classify the cuisine data. But it lacks in dealing the vagueness present in the categorical cuisine data. Also, most of the popular recipes available online are mostly unhealthy. As a result, the Bijective soft set theory has been introduced into this study since it is an intelligent method for developing classification rules from food data and then recognising ambiguity and data reliance. The classification guidelines also address the necessity to select healthy/nutritious meals. The investigation outcomes from using bijective soft set analysis to a set of data samples are presented and appraised in this work. Furthermore, the classification rules derived from this approach provides valuable help for developing food recommendation systems. The accuracy of this classification scheme has got really influenced by application of the bijective soft set when compared to other state-of-the-art classifiers. ©2024 IEEE.

关键词： bijective soft set classification classification rule generation cuisine ingredients

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Advances in Multi-Style and Real-Time Transfer 1

Advances in Multi-Style and Real-Time Transfer

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1st International Conference on Software, systems and Information Technology, SSITCON 2024

作者： Kini, Nisha Kamath, Vaishnavi Kelkar, Tanay Kanikar, Prashasti Nanade, Archana Kolhe, Abhay SVKM's Nmims Mpstme Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350352931

This paper provides an overview of neural style transfer techniques, focusing on multi-style and real-time applications for both images and videos. Multi-style transfer refers to the technique of combining several art styles based on a given content image to create a unique and intriguing visual result. In contrast, real-time style transfer involves applying artistic styles almost instantaneously using pre-trained feedforward networks, making it particularly well-suited for video editing and live broadcasts. This paper reviews the most significant research and technical advancements in the field, while also addressing existing challenges that hinder the full potential of these transformative technologies. Furthermore, attention is given to emerging applications and the areas still in need of improvement, to enhance understanding and practical implementation. © 2024 IEEE.

关键词： Image Style Tranfer Multi-Style Transfer Real-time Style Transfer Video Style Tranfer

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Enhancing Supply Chain Efficiency through IoT Integration: A Detailed Examination of the Chances and Difficulties 2

Enhancing Supply Chain Efficiency through IoT Integration: A...

引用

2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Mehra, Chaitanya Sharma, Shaifali Arora, Jayant Sharma, Aakanksha Mahajan, Pranaya Manhotra, Aryan Dept. of Computer Science Engineering Chandigarh University Punjab Mohali India

ISBN: (纸本)9798331541217

The adoption of Internet of Things (IoT) technology in supply chain management has brought about noteworthy transformations, boosting productivity, visibility, and evidence- based decision-making. This research investigates the primary reasons for implementing IoT in contemporary supply chains, tackling issues such as limited real-time monitoring, suboptimal inventory control, and the necessity for preemptive risk manage- ment. The investigation delves into the capabilities of IoT-driven solutions, including sensor technology, RFID systems, and data analysis tools, to confront these obstacles by enabling constant surveillance and anticipatory maintenance, taking into account the impact of innovative technologies such as blockchain and artificial intelligence. The results underscore the considerable effect of IoT in establishing robust, flexible, and eco-friendly supply chains, providing valuable insights for businesses aiming to improve their operational effectiveness in an increasingly digital landscape. © 2024 IEEE.

关键词： Inventory control

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Frame-by-Frame Analysis for Assessing Chickens Flock Movement 25

Frame-by-Frame Analysis for Assessing Chickens Flock Movemen...

引用

25th International Electronics Symposium, IES 2023

作者： Sesulihatien, Wahjoe Tjatur Barakbah, Ali Ridho Prasetyo, Piko Permata Ilham Dept. of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

Assessing the behavior and welfare of chickens is crucial for successful chicken rearing and product quality. Recent research has focused on using advanced cameras and computer vision technologies to analyze chicken behavior. This camera-based approach captures high-resolution video footage, which is then processed using computer vision algorithms to extract insights on movement patterns, feeding behavior, and social interactions. While this approach has advanced our understanding, there is a need for studies employing a frame-by-frame analysis to obtain fine-grained details of chicken behavior. This method allows for precise measurements and real-Time analysis, facilitating informed decision-making in chicken farming. To expedite the assessment process, a bandpass filter 470 nm lens is incorporated into the camera setup, optimizing image segmentation and reducing processing time. The proposed system combines this specialized camera with frame-by-frame calculations to track and measure the velocity, acceleration, and position of chickens within a flock. The integration of these technologies holds the potential for improving our understanding of chicken behavior, enhancing welfare, and driving data-driven decision-making in chicken farming practices. © 2023 IEEE.

关键词： computer vision

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Advancing Medical Diagnostics: Performance Analysis of YOLO Algorithms for Automated Platelet Detection

Advancing Medical Diagnostics: Performance Analysis of YOLO ...

引用

2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Dubey, Soumya Jhang, Kyoungson Chungnam National University Dept. of Computer Engineering Daejeon Korea Republic of

ISBN: (纸本)9798331530839

The significance of health automation, particularly post-COVID-19, emphasizes the critical role of advanced diagnostic technologies. Conventional methods for platelet detection in blood-smear images are often slow and inconsistent, impacting medical response efficiency. This study explores the application of the YOLO (You Only Look Once) algorithm, specifically versions 3, 5, 7, and 8, to improve platelet detection accuracy and efficiency. Through meticulous data preparation and fine-tuning on a curated dataset, we compared the performance of these YOLO versions in terms of accuracy, precision, recall, and computational efficiency. YOLOv5 emerged as the most accurate and reliable model, achieving the highest mean Average Precision (mAP) and superior precision and recall metrics, despite slightly slower inference times compared to YOLOv8. YOLOv8, with the fastest inference times and excellent detection capabilities, is also highly effective for real-time applications. The findings underscore the advancements and tradeoffs among different YOLO versions, with YOLOv5 and YOLOv8 showing significant potential for enhancing automated platelet detection in clinical diagnostics. © 2024 IEEE.

关键词： COVID-19

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Intelligent Route Planning for Post-Earthquake Rescues Using IoT 6

Intelligent Route Planning for Post-Earthquake Rescues Using...

引用

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

作者： Tamzid, Rahat Mahee, Ehfaz Faisal Rahman, Ashikur Tamanna, Tahira Akhtar Rahman, Muntasir Hossain, Md. Mamun Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350385779

Starting rescue operations quickly after an earth-quake is the best way to save lives in such disasters. In the case of large-scale earthquakes, current post-earthquake response systems cannot determine which building or rescue route would save the most lives due to the lack of real-time data availability. Research gaps include a lack of automation in earthquake damage assessment of buildings, location identification, and estimation of the number of trapped people. To fill these gaps, this article proposed an innovative solution using advanced sensors and cloud services for post-earthquake rescue route planning systems. This system aims to improve rescue operations to save the maximum number of lives in the shortest time and minimize impacts by optimizing resource allocation and overall response efficiency. By filling the existing research gap, our system seeks to provide more responsive and effective solutions to post-earthquake emergencies. Tested in a simulated environment with 16 samples, our system successfully collects and processes data from IoT devices deployed in buildings, classifies structural damage, and estimates the number of people affected. Our approach significantly reduces the time required to initiate rescue operations by providing rescuers with optimized routes to disaster sites, which has potential implications for disaster management strategies. © 2024 IEEE.

关键词： Earthquakes

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Land Use Regression on Interpolated Urban Graphs to Assess Personal Exposure to Air Pollution

Land Use Regression on Interpolated Urban Graphs to Assess P...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pala, Daniele Zagami, Giacomo Bosoni, Pietro Vazifehdan, Mahin Bellazzi, Riccardo Dagliati, Arianna University of Pavia Dept. of Electric Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350386226

Past research has demonstrated that continuous exposure to pollutants, such as PM2.5 and PM10, is associated with an increased risk of developing and worsening respiratory and neurodegenerative diseases. Calculating and reducing exposure to these pollutants is crucial to assess these risks and perform proper prevention. In this study, we estimate personal exposure to PM2.5 based on the integration of sensors measurements, meteorological data and land use parameters, which could impact on actual pollution levels, especially in areas located far from the sensors. Pollution data have been collected from a dense network of sensors located in Pavia, Italy, meteorological and geographical data have been collected from public sources. We used geographical data to create graphs that model the city road structure, and applied Land Use Regression methods to estimate air pollution on its nodes, adjusting the measurements interpolated from the sensors with the effects of weather data, land use parameters such as the distance from the closest high-traffic road, and additional temporal information such as weekends/holidays and working days. We tested several regression methods: linear regression, both simple and with regularization (Ridge, LASSO and ElasticNet), Random Forest regression, Gradient Boosting and Support Vector Regression (SVR). Results show that meteorological variables, namely temperature and humidity, and temporal factors do contribute significantly in obtaining pollution values in the graph nodes that differ from values obtained exclusively through sensors interpolation. © 2024 IEEE.

关键词： Support vector regression

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Streamlining Livestock Surveillance: YOLOv8 Approach to Cattle Body Segmentation

Streamlining Livestock Surveillance: YOLOv8 Approach to Catt...

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Chugh, Bharti Kumar, Pushepndra Mishra, Upendra Chaudhary, Himanshi Kiet Group of Institutions Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350375237

This research paper introduces an innovative approach to optimize livestock surveillance by employing YOLOv8 for cattle body segmentation. The study attained a mAP(50), mean Average Precision of 0.598 and 0.464 and mAP(50-95) on a dataset comprising 704 images. Preceding model training, extensive preprocessing and image augmentation techniques were implemented, resulting in a dataset of 1476 training, 171 validation, and 71 testing images. The developed model demonstrates considerable promise in enabling real-time mon-itoring of cattle and their health status. Additionally, to enhance its suitability for edge computing environments, quantization-aware training (QAT) abd weight compression techniques were employed, ensuring efficient deployment on resource-constrained devices. The results highlight the effectiveness and practicality of employing YOLOv8-based segmentation for livestock surveillance, with notable implications for precision agriculture and animal welfare. However, further enhancements are warranted, particularly in achieving higher mAP scores across a broader range of Intersection over Union (IoU) thresholds, to fully address the segmentation challenges in this domain. © 2024 IEEE.

关键词： computer vision

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