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FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-Rays

FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneu...

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Annual IEEE Symposium on computer-Based Medical systems

作者： Ayush Roy Anurag Bhattacharjee Diego Oliva Oscar Ramos-Soto Francisco J. Alvarez-Padilla Ram Sarkar Dept. of Electrical Engineering Jadavpur University Kolkata India Dept. of Computer Science and Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Universidad de Guadalajara CUCEI Guadalajara Mexico Dept. of Computer Science and Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Pneumonia is a respiratory infection caused by bacteria, fungi, or viruses. It affects many people, particularly those in developing or underdeveloped nations with high pollution levels, unhygienic living conditions, overcrowding, and insufficient medical infrastructure. Pneumonia can cause pleural effusion, where fluids fill the lungs, leading to respiratory difficulty. Early diagnosis is crucial to ensure effective treatment and increase survival rates. Chest X-ray imaging is the most commonly used method for diagnosing pneumonia. However, visual examination of chest X-rays can be difficult and subjective. In this study, we have developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We have used DenseNet-121 and ResNet50 as the backbone for the binary class (pneumonia and normal) and multi-class (bacterial pneumonia, viral pneumonia, and normal) classification tasks, respectively. We have also implemented a channel-specific spatial attention mechanism, called Fuzzy Channel Selective Spatial Attention Module (FCSSAM), to highlight the specific spatial regions of relevant channels while removing the irrelevant channels of the extracted features by the backbone. We evaluated the proposed approach on a publicly available chest X-ray dataset, using binary and multi-class classification setups. Our proposed method achieves accuracy rates of 97.15% and 79.79% for the binary and multi-class classification setups, respectively. The results of our proposed method are superior to state-of-the-art (SOTA) methods. The code of the proposed model will be available at: https://***/AyushRoy2001/FA-Net

关键词： Solid modeling Pneumonia Fluids Computational modeling Lung Feature extraction Task analysis

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Balancing Real-world Interaction and VR Immersion with AI Vision Robotic Arm 20

Balancing Real-world Interaction and VR Immersion with AI Vi...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Yang, Xiaoran Zhan, Yang Iwasaki, Yukiko Shi, Miaohui Tang, Shijie Iwata, Hiroyasu Waseda University Dept. Modern Mechanical Engineering 3-4-1 Ookubo Shinjuku-ku Tokyo169-8555 Japan Waseda University Dept. Computer Science and Engineering 3-4-1 Ookubo Shinjuku-ku Tokyo169-8555 Japan

ISBN: (纸本)9798350320831

With the widespread adoption of Virtual Reality (VR) technology, VR devices can be more realistic and make the tasks they perform feel real to the user. It is this sense of realism that makes more and more people are using VR headsets. While using VR devices, the need to interact with the real world will also exist. For example, when people are using a VR headset to study and work, they have a physiological need (thirst, hunger, etc.), and need to interact with reality. But the closed nature of the VR headset causes this interaction to be very disruptive to the immersion of the user's current task. To address this issue, we propose an interaction method that combines an AI vision algorithm and a robotic arm to preserve immersion during task performance in VR. This method addresses the challenge of interacting with the real world while using closed VR devices. Our experiments show that our method outperforms see-through and mapping methods, allowing users to quickly re-immerse themselves after interruptions and reducing interruptions during real-world task performance. Future research will focus on improving this method without sensitization and expanding the range of real-world operations that can be performed. © 2023 IEEE.

关键词： Virtual reality

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Movie Genre Prediction Using AI-Generated Data and Natural Language Processing

Movie Genre Prediction Using AI-Generated Data and Natural L...

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International Conference on computer and Information Technology (ICCIT)

作者： Sifat Tanvir Ayesha Parpia Abontee Md. Abu Rayhan Md. Fuadur Rahman Tasnim Sultana Ajwa Ahmed Dept. of Computer Science and Engineering Brac University Dhaka Bangladesh Dept. of Computer Science Brac University Dhaka Bangladesh

Every movie belongs to one or more specific genre. In this work, a dataset was prepared consisting of AI-generated movie titles and plots of 10 different genres. Text vectorization was used to convert every single word of the titles and plots into vectors. These vectors were used to predict which genre each movie belongs to. For training, a sequential neural network model was built and used, and an accuracy of almost 80% was achieved.

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Weakly Supervised Road Mark Detection for Advanced Driver Assistance systems in Consumer Automotive Technology

Weakly Supervised Road Mark Detection for Advanced Driver As...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Xiu-Zhi Chen Xiao-Wei Huang Yen-Lin Chen Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Road mark detection is a critical component of Advanced Driver Assistance systems (ADAS), ensuring accurate lane-keeping, navigation, and driver safety. However, the high cost of acquiring fully annotated datasets for supervised learning poses a significant challenge for real-world applications. This paper proposes a novel weakly supervised learning method for road mark detection tailored to consumer automotive technology. Leveraging weak annotations, the method efficiently learns to detect and classify road marks with minimal human intervention. The approach was evaluated on the publicly available CeyMo dataset, achieving a classification accuracy of 99.3% and a localization accuracy of 71.5%, demonstrating its effectiveness and practicality. The proposed method reduces the dependency on fully annotated data while maintaining competitive performance, making it a scalable solution for consumer-level ADAS applications. This work contributes to advancing cost-effective, reliable, and scalable ADAS technologies, paving the way for broader adoption in intelligent vehicles.

关键词： Location awareness Accuracy Weak supervision Intelligent vehicles Roads Supervised learning Safety Reliability Advanced driver assistance systems Automotive engineering

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Predictive Insights Beyond Boundaries: Integrating Clinical Data for Hepatitis C Forecasting Using Machine Learning and Deep Learning

Predictive Insights Beyond Boundaries: Integrating Clinical ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Fatema Tuj Jenin Al-Amain Khandaker Mohammad Mohi Uddin Faizul Ahmed Robin Md. Ferdous Bin Hafiz Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Information and Electrical Engineering Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A person's well-being and wellness are seriously hampered by the chronic diseases' lasting implications and recurrent signs. A startling 58 million people worldwide suffer from an incurable chronic form of hepatitis C, disease is brought about by a virus called hepatitis C (HCV). Additionally, it results in 290,000 deaths every year from cirrhosis and liver cancer, both of which linked to HCV. The goal of this venture is to reliably identify and anticipate hepatitis C utilizing Machine Learning (ML) and deep Learning (DL) techniques. It does this by utilizing ML and DL algorithms' capacity to scan big datasets and spot patterns and associations that help with diagnosis. Predictive models were created utilizing blood values from 615 patients, including both hepatitis patients and healthy blood donors, using a variety of approaches comprising SVM, Logistic regression (LR), KNN, LSTM and others. The dataset underwent extensive preprocessing, which included managing missing values, scaling, and diminished dimensionality using Principal Component Analysis (PCA). After a variety of ML along with the SVM classifier showed the best result, giving an accuracy concerning 98.33%, recall concerning 94.17%, precision concerning 93.99%, and F1-score concerning 94.16%.

关键词： Support vector machines Deep learning Accuracy Liver diseases Predictive models Reliability Long short term memory Viruses (medical) Medical diagnostic imaging Principal component analysis

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Facial Biometrics-Driven Feature Encoding and Deep Learning Integration for Secure Password Retrieval

Facial Biometrics-Driven Feature Encoding and Deep Learning ...

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Disruptive Technologies (ICDT), International Conference on

作者： Akash Immanuel Solomon A Ashwin M Swaminathan K Dept. of Computer Science St. Joseph's College of Engineering Chennai India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The proposed system includes face recognition-based systems designed for secure identity verification and decentralized data storage. The working is supposed to be done in three phases: data capture, training of the machine learning model, and decentralized data storage. Collection in the first phase will include face images of individuals and government-issued identification details, such as Aadhar and PAN. A machine learning model will be trained in the second phase for improving the accuracy in the face recognition process. The third phase ensures secure storage of the recognized faces and associated personal data through a decentralized approach similar to blockchain technology. Security, privacy, and resilience against tampering are further developed in this design. The proposed system will reliably meet the application requirements that involve strong authentication processes and protection of data, therefore making the sectors using these systems include government services and secured personal data management.

关键词： Training Privacy Face recognition Government Memory Passwords Reliability Protection Secure storage Resilience

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Sensing Together: Cooperative Task Adaptation and Scheduling for IoT-Nets Using Renewable Energy

Sensing Together: Cooperative Task Adaptation and Scheduling...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor systems (MASS)

作者： Elizabeth Liri K. K. Ramakrishnan Koushik Kar Flavio Esposito Dept. of Computer Science and Engineering University of California Riverside Riverside CA USA Computer Science Dept. Saint Louis University St. Louis MO USA Electrical Computer and Systems Engineering Dept. Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

IoT devices used in various applications, such as monitoring agricultural soil moisture, or urban air quality assessment, are typically battery-operated and energy-constrained. We develop a lightweight and distributed cooperative sensing scheme that provides energy-efficient sensing of an area by reducing spatio-temporal overlaps in the coverage using a multi-sensor IoT network. Our “Sensing Together” solution includes two algorithms: Distributed Task Adaptation (DTA) and Distributed Block Scheduler (DBS), which coordinate the sensing operations of the IoT network through information shared using a distributed “token passing” protocol. DTA adapts the sensing rates from their “raw” values (optimized for each IoT device independently) to minimize spatial redundancy in coverage, while ensuring that a desired coverage threshold is met at all points in the covered area. DBS then schedules task execution times across all IoT devices in a distributed manner to minimize temporal overlap. On-device evaluation shows a small token size and execution times of less than 0.6s on average while simulations show average energy savings of 5% per IoT device under various weather conditions. Moreover, when devices had more significant coverage overlaps, energy savings exceeded 30% thanks to cooperative sensing. In simulations of larger networks, energy savings range on average between 3.34% and 38.53%, depending on weather conditions. Our solutions consistently demonstrate near-optimal performance under various scenarios, showcasing their capability to efficiently reduce temporal overlap during sensing task scheduling.

关键词： Performance evaluation Schedules Renewable energy sources Simulation Redundancy Soil moisture Smart systems Sensors Internet of Things Meteorology

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Enhancing Women's Safety Through Customized Smartphone Sensor Data: A Human Activity Recognition system 9

Enhancing Women's Safety Through Customized Smartphone Senso...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Islam, Yamina Nowrin, Sanjida Faqih, Majed Ibrahim Sultana, Fozia Bahadur, Erfanul Hoque Masum, Abdul Kadar International Islamic University Chittagong Dept. of Computer Science and Engineering Chattogram Bangladesh Signal Corps Institute Director of Research and Development Saudi Arabia Hiperdyne Artificial Intelligence Engineer Tokyo Japan Daffodil International University Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350319651

Human activity recognition (HAR) research using smartphone sensor technology has changed people's daily lives and opened the door for numerous exciting applications due to its extensive applications in addressing real-world humancentric issues. These studies, however, mostly concentrated on a woman's daily activities and behaviours when she is attacked, such as running, walking, sleeping, eating, drinking, lying, straight punching, front kicking, and so forth. The article offers a method to foresee human behaviour using a deep learning model and evaluates the efficiency of the approach using real-world data in response to this. We offer an LSTM structure for detecting the intensity of HAR in order to track activity patterns and build a neural network with binary classification skills to evaluate the situation of women. The proposed model uses the dataset to identify human activity, which records 30 volunteers' body movements while they engage in 18 physical activities. Feature engineering is used to extract additional variables from the data in addition to that. Our proposed model achieves a 90.89% accuracy. © 2023 IEEE.

关键词： Smartphones

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Sentiment Analysis in Greek Clinical Conversations: A Comparative Study of BERT, VADER, and Lexicon Approaches

Sentiment Analysis in Greek Clinical Conversations: A Compar...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chatzimina, Maria Evangelia Oikonomou, Nikolaos Papadaki, Helen Tsiknakis, Manolis Pontikoglou, Charalampos Institute of Computer Science Crete Heraklion Greece Hellenic Mediterranean University Dept. of Electrical and Computer Engineering Crete Heraklion Greece School of Medicine University of Crete Department of Hematology Crete Heraklion Greece

ISBN: (纸本)9798350337488

Sentiment analysis, a subfield of Natural Language Processing (NLP), focuses on the study of emotions expressed in text data. While extensively used in major languages such as English, its application in Greek remains underexplored. We investigate sentiment analysis in Greek clinical dialogues, focusing on hematologic malignancies. These dialogues offer valuable insights into patient experiences, healthcare provider approaches in palliative care, and underscore the critical role of sentiment analysis in accurately assessing patients' emotional status for effective care. Our study compares three methodologies - Greek Lexicon, VADER, and BERT - while addressing the limited Greek resources in healthcare-related NLP. Key findings indicate that BERT outperformed other methods by achieving well-balanced precision and recall in assessing sentiment. The Lexicon-Based approach encountered challenges in identifying both negative and positive sentiments, while VADER showcased robust results. Our study involves data collection, data annotation, model training, and performance evaluation. We aim to address the research gap and the unique challenges of sentiment analysis in Greek, especially in context of hematologic malignancies, where NLP and sentiment analysis resources are limited. © 2023 IEEE.

关键词： clinical dialogues hematologic malignancies natural language processing palliative care sentiment analysis

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Distributed IRSs Mitigate Spatial Wideband & Beam Split Effects

Distributed IRSs Mitigate Spatial Wideband & Beam Split Effe...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： L. Yashvanth Chandra R. Murthy Bhaskar D. Rao Dept. of Electrical Communication Engineering Indian Institute of Science Bangalore India Dept. of Electrical and Computer Engineering University of California San Diego USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We address the problem of the beam split (B-SP) caused by the spatial wideband (SW) effect in intelligent reflecting surface (IRS) aided wideband systems. The SW effect arises when the signal delay across the IRS aperture is comparable to the system sampling time. This leads to a B-SP, wherein the IRS phase shifters fail to form a beam at the desired user equipment (UE) over the complete bandwidth (BW) allotted to that UE, in turn reducing the system throughput. This paper proposes a distributed IRS design that aids in naturally combating the SW/BSP effects by parallelizing the spatial delays across the multiple IRSs. Specifically, we propose to split a single large IRS into multiple smaller IRSs and distribute them over the geographical area. Then, by specifying the maximum permissible number of elements at each IRS, we ensure that the B-SP remains within a specified tolerance level. Finally, we derive the sum-rate and show that the proposed solution yields a nonzero rate over the whole BW of operation and circumvents the SW/B-SP effects. We verify our findings numerically and illustrate the low complexity of the proposed method compared to state-of-the-art techniques.

关键词： Array signal processing Phase shifters Apertures Throughput Acoustics Delays Complexity theory Acoustic beams Speech processing Wideband

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