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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Huang, Shih-Xiu Lung, An Chen, Jia-You Sung, Wu-Min Chu, Yu-Cheng Tu, Jiun-Yu Hu, Chih-Lin National Central University Dept. Physics Taoyuan City320317 Taiwan National Central University Dept. Communication Engineering Taoyuan City320317 Taiwan Southern Taiwan University of Science and Technology Dept. Computer Science and Information Engineering Tainan710301 Taiwan

ISBN: (纸本)9798350340181

Considering the large-scaled field of mountain rescue, a hybrid self-organized network architecture is developed for multi-hop data delivery against weak network connectivity in challenged areas. UAVs and wireless nodes can self-organize unstructured multi-hop networks to distribute urgent and recuse messages in geographic proximity. To seek lost climbers efficiently, this architecture provides two search methods. (1) In a passive method, climbers send messages via MQTT protocols to neighbor nodes that will further forward messages to the cluster head of a self-organized network. (2) In an active method, UAVs hover over disjoint cluster heads to store, carry, and forward messages, and UAVs are equipped with cameras to perform face recognition of detecting target users. All the messages and camera-initiated video streaming can be passed through self-organized networks and then displayed on the rescue website. © 2023 IEEE.

关键词： Network architecture

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TB Chest X-Ray Diagnostic Technique Using Deep Learning 3

TB Chest X-Ray Diagnostic Technique Using Deep Learning

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Goyal, Manish Kumar Yadav, Dhananjay Kumar Brar, Khushmeen Kaur Mittal, Manisha Ojha, Ananta Alzubaidi, Laith H. Pathani, Ashish Vivekananda Global University Dept. of Computer Science & Application Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chandigarh University Dept. of Electronics and Communication Engineering Mohali India Guru Tegh Bahadur Institute of Technology New Delhi India Dept. of Computer Science and Information Technology Bangalore India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun India

ISBN: (纸本)9798331518592

The bacteria Mycobacterium tuberculosis is responsible for the infectious illness tuberculosis (TB). Although the lungs are the primary organs affected, the kidneys, bones, and brain may also be affected. Whenever an infected person coughs, sneezes, or talks, small droplets comprising the bacteria are released into the air and spread through the airborne system. The elimination of tuberculosis (TB) remains a problem due to factors such medication resistance, co-infection with HIV, and scarce resources in high-burden configurations, even with substantial efforts to treat the disease. As a major worldwide health concern, tuberculosis (TB) necessitates quick and precise diagnosis in order to be effectively managed. This study aimed to develop a TB detection model using chest X-ray images obtained from ***, utilizing Google's Collaboration Platform. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. We introduce a technique for transfer learning that proves preferable to the traditional methodology of adopting ImageN et weights. The hybrid methodology utilizes best first search technique in fusion with ResNet 50 model. The SVM classifier is used for categorizing the classes. An accuracy of 93.91 % is attained. © 2024 IEEE.

关键词： Deep learning

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Selinet: A Lightweight Model for Single Channel Speech Separation 48

Selinet: A Lightweight Model for Single Channel Speech Separ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tan, Ha Minh Vu, Duc-Quang Wang, Jia-Ching National Central University Dept. of Computer Science and Information Engineering Taiwan Thai Nguyen University of Education Thai Nguyen Viet Nam

ISBN: (纸本)9781728163277

The time-domain speech separation methods adopting deep learning have obtained impressive performance. However, the computational complexity, model size, and performance are still the challenges for the implementation on real-time low-resource devices. In this paper, we introduce a lightweight yet effective network for speech separation, namely SeliNet. The SeliNet is the one-dimensional convolutional architecture that employs bottleneck modules, and atrous temporal pyramid pooling. In bottleneck modules, the dept.-wise separable convolution significantly decreases the model size and computational cost meanwhile the squeeze excitation uses a context vector to interact with the entire hidden state vector. Specifically, the atrous temporal pyramid pooling recognizes long-time sequences of various lengths and extracts context at different field-of-views. This helps SeliNet to obtain impressive performance while still maintaining the small computational cost and model size. © 2023 IEEE.

关键词： Convolution

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Scalable, Cost Effective IoT Based Medical Oxygen Monitoring System for Resource Constrained Hospital Environment 6

Scalable, Cost Effective IoT Based Medical Oxygen Monitoring...

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6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Rodrigues, Joel Chandra Shekar, R.K. School of Engineering Dept. of Computer Science and Engg. Bengaluru India BITS Pilani Pilani Campus Dept. of Computer Science and Information Systems India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

Oxygen therapy is one of the critical treatments employed in epidemics, pandemics, and natural calamities. Recent covid pandemic worldwide witnessed many deaths due to improper management, delayed delivery, and wastage of medical oxygen. Therefore, efficient utilization of available oxygen is very important. To monitor and manage oxygen, several hospitals employ IoT-based systems. Scalability is an essential feature in such monitoring systems in order to cater to the needs of a sudden surge in the number of patients requiring oxygen. The most commonly employed technique to monitor and manage an oxygen cylinder uses a pressure sensor where scaling up is an issue. Therefore, in this paper, a scalable solution that efficiently measures and monitors the available oxygen in the cylinder is proposed. The approach measures oxygen level using a weight sensor module and raises alerts during critical conditions such as low oxygen level and blockage or leakage of oxygen. The proposed system is a cost-effective, plug-and-play system that aids rapid deployment thereby providing timely care to the patients. Also, it does not require any change in the existing infrastructure making it suitable for a resource-constrained environment. The proposed system supports a web-based dashboard and mobile app that can be remotely accessed. © 2022 IEEE.

关键词： Oxygen

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Detection of Covid-19 using Transfer Learning Technique 3

Detection of Covid-19 using Transfer Learning Technique

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3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Kumar, Nilesh Tripathi, Atul Tripathi, Isha Pathak Mahatma Gandhi Central University Dept. of Computer Science and It Motihari India Indian Institute of Information Technology Dept. of Computer Science and Engineering Kota India

ISBN: (纸本)9781665462006

The whole world is suffering from the wave of the novel coronavirus that causes the large-scale death of a population and is proclaimed a pandemic by WHO. As RT-PCR tests to detect Coronavirus are costly and time taking. So now these days, the purpose of the researcher is to detect these diseases with the help of Artificial Intelligence or Machine learning-based models using CT scan images and X-rays images. So the testing cost, time taken and the number of data required could be minimized. In this paper, transfer learning based on three fine-tuned models has been proposed for Covid detection. The performance of these proposed fine-tuned models has been also compared with other competing models to check the accuracy and other matrices. © 2022 IEEE.

关键词： COVID-19

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Grammar Error Correction in Indonesian Sports News: Comparing the Performance of Pre-Trained T5 and BART Models

Grammar Error Correction in Indonesian Sports News: Comparin...

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Adnan, Muh Prakasa, Esa Yuyun Umar, Najirah Nasrullah Sadli, Adi Anatasya, A. Edeth Fuari Hazriani Razak, Mashur Handayani University of Makassar Dept. of informatics engineering Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesia Handayani University Makassar Dept. of computer system Makassar Indonesia

ISBN: (纸本)9798331508579

This research compares the performance of two pre-trained models based on the transformer, namely text-to-text transfer transformer (T5) and bidirectional and auto-regressive transformer (BART), through a fine-tuned for correcting grammar in Indonesian sports news texts. We used vennify/t5-base-grammar-correction and onionLad/grammar-correction-bart-base, was trained using a dataset of 10,075 sentences from Indonesian language sports news sites. We tested the model performance using bilingual evaluation understudy (BLEU), google's language evaluation understanding (GLEU), training loss, and validation loss. The results show that BART was more efficient in learning data patterns and has better generalization ability, supported by BLEU and GLEU scores of 0.8560 and 0.9655. Meanwhile, T5 maintains simpler sentence structures, supported by BLEU and GLEU scores of 0.8120 and 0.9564. BART excels at handling more complex contexts, making it more effective at accurately correcting errors in dynamic sports news. Both models have their respective advantages, with BART being better at correcting detailed grammatical errors, while T5 offers consistent correction of more simple sentence structures. © 2024 IEEE.

关键词： Error correction

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Penalty-Enabled Serverless Architecture for Cloud-based Startup Solutions 6

Penalty-Enabled Serverless Architecture for Cloud-based Star...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Benedict, Shajulin Subair, Rubiya Gupta, Tanya Vedanta, S.P. Dept. of Computer Science and Engineering Indian Institute of Information Technology Kottayam Valavoor P.O. Kerala Kottayam686635 India

ISBN: (纸本)9798350398496

The step to the success of startups is through overcoming competitors by adopting software innovations that improve businesses. Serverless computing model, recently, has intrigued a sizable number of startup professionals belonging to various sectors, including financial or IoT-enabled application developers. One of the main flaws is its heavy dependency on cloud providers, which can still result in hefty pricing to startups and stalling functions in applications. This article proposes a penalty-enabled serverless architecture for startups. The architecture can boost the economy of startups and can analyze the serverless-oriented cost-saving options in applications. The penalty-oriented approach could enable cloud architects, developers, and startups, to rethink the utilization of serverless functions;to gleam of with future innovations. © 2023 IEEE.

关键词： Economics

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Spreading Points Using Gradient and Tabu

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Operations Research Forum 2023年第2期4卷 30页

作者： Huang, Xiangyang Huang, LiGuo College of Information Engineering Capital Normal University Beijing 100048 China Dept. of Computer Science Southern Methodist University Dallas 75205 TX United States

Large spreading (packing) problems are hard to be solved exactly. Consequently, heuristic approaches are usually used to find approximate solutions. In this paper, a hybrid heuristic method, consisting of a Neighborhood Tabu (NT) and a Perturbed Gradient (PG), is proposed for solving spreading points problems. NT applies an adaptive random technique to selecting promising candidate solutions from the neighborhood of the current local minimum and PG uses a neighborhood-based perturbed gradient algorithm to seek a better minimum, starting from the candidates. Two procedures work alternatively and cooperatively. When applied to some previously studied packings, the proposed method can improve the accuracy of the previous record solutions. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Gradient Minimum enclosing ball Packing Spreading Tabu search

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Arabic Sentiment Analysis of Mobile Banking Services Reviews 10

Arabic Sentiment Analysis of Mobile Banking Services Reviews

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10th International Conference on Social Networks Analysis, Management and Security, SNAMS 2023

作者： Khabour, Safaa M. Mustafa, Dheya Althebyan, Qutaibah Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Al Ain University Dept. of Cybersecurity Al Ain United Arab Emirates Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9798350318906

Online customer reviews have developed into a significant source of information about a business's performance. Due to shifting consumer expectations and growing internet penetration, the Middle East, especially Jordan, is seeing an increase in the usage of online banking. This propensity toward digital transition has been accelerated by the COVID-19 pandemic. Mobile banking service companies aim to effectively use customers' feedback to identify areas for improvement in customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on mobile banking reviews in Arabic, exploiting machine learning, natural language processing, and resampling methods to obtain subjective feedback, determine polarity, and identify customers' feelings in the banking domain. This work walks the reader through detailed steps of cleaning and preprocessing a manually annotated dataset of online banking reviews in Arabic, followed by features extraction and modeling, dataset splitting, and balancing training data. Finally, we examined classification algorithms including Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Random Forest (RF). Ensemble hard and soft voting ML methods were also evaluated. We achieved a maximum accuracy of about 94% using the LR classifier and a maximum precision of 94% using the LR and hard voting methods. Our methodology will enable banks to acquire insights on how to develop their online presence and meet the demands of customers and stakeholders. © 2023 IEEE.

关键词： Sentiment analysis

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Clustering Method of Driving Scenes based on Road Shape by using Road Map Images 4

Clustering Method of Driving Scenes based on Road Shape by u...

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4th International Conference on Electrical, Communication and computer engineering, ICECCE 2023

作者： Hashimoto, Kohjiro Yanagihara, Daichi Kuniyuki, Hiroshi Doki, Kae Funabora, Yuki Doki, Shinji Dept. Applied Information Engineering Suwa University of Science Nagano Chino city Japan Dept. Mechanical and Electrical Engineering Suwa University of Science Nagano Chino city Japan Dept. Electrical Engineering Aichi Institute of Technology Aichi Toyota city Japan Dept. Electrical Engineering and Computer Science Nagoya University Chikusa-ku Aichi Nagoya Japan

ISBN: (纸本)9798350369694

Currently, vehicles are equipped with various sensors that can collect a series of driving behavior data while driving. The driving behavior data obtained in this way can be used to analyze the causes of traffic accidents and evaluate driver skills. However, when performing the above analysis, a specific driving scene is defined and driving behavior data for that driving scene is required. On the other hand, the series of driving behavior data obtained during one driving operation is enormous, and it is not easy to pick up desired driving scene data from among them. This research aims to establish a driving scene clustering method for a series of driving behavior data. In this paper, a driving scene is defined as a scene in which road shape and operation are associated, and propose a driving scene clustering method considered road shape. Here, a road map image is acquired from position information obtained from a GPS sensor, and the relationship between the road map image and operation data is clustered based on Coupled-GP-HSMM. © 2023 IEEE.

关键词： Deep learning

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