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A deep learning study on understanding banglish and abbreviated words used in social media 5

A deep learning study on understanding banglish and abbrevia...

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5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Das, Suharto Islam, Md Sanzidul Mahmud, Imran Daffodil International University Dept of Software Engineering Dhaka Bangladesh Daffodil International University Dept of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665412728

Day by Day, the trend of using social media(SM) has increased among the people. With the increasing rate, the use of Banglish(Merge of Bangla and English) and shortcut words has also being increased. In this research, Banglish and Shortcut words have been fully converted to English. For this type of conversation, we have used the CNNs method and the method consist of multiple layer and these layers are connected to each other. This method is considered to be the best method because it does not need any feature extraction. The convolutional neuron network is used in many areas such as image and pattern recognition, speech recognition, natural language processing and video analysis. In this research we have used CNN method because we have decided to use computer vision as some of the words are close to each other and at first we need to convert all the shortcut words into images for pre-processing the data. With the help of CNN method searched for the Banglish and shortcut words that people use for their daily conversation. At first we find the different representative of a single word and then we converted those shortcut and Banglish words to main word using CNNs method. © 2021 IEEE.

关键词： Speech recognition

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting 17

An Image Analysis-Based Automated Method using Deep Learning...

引用

17th IEEE International Conference on Industrial and Information Systems, ICIIS 2023

作者： Ajikaran, Ramesh Hewarathna, Ashen Iranga Palanisamy, Vigneshwaran Joseph, Charles Thuseethan, Selvarajah Sabaragamuwa University of Sri Lanka Dept. of Computing and Infromation Systems Sri Lanka Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sri Lanka Fau Dept. of Computer Science Germany School of Information Technology Deakin University Australia

ISBN: (纸本)9798350323634

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst. © 2023 IEEE.

关键词： Grain (agricultural product)

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Recognition of Leukocytes using a Combination of Deep Features and Ensemble Classifier

Recognition of Leukocytes using a Combination of Deep Featur...

引用

International Conference on Emerging Technologies, ICET

作者： Syeda Tehreem Haider Muhammad Faraz Khushbakht Iqtidar Dept. of Electronics Engineering University of Engg. & Tech. Taxila Taxila Pakistan Dept. of Computer & Software Engineering National University of Sciences & Tech. Islamabad Pakistan

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

Detection of the peripheral blood plays a vital role in the field of medical diagnostics and control of major diseases. In this regard, detecting leukocytes or White Blood Cells (WBCs) is important. Leukocytes have five classes: Basophils, Eosinophils, Lymphocytes, Monocytes, and Neutrophils. The dataset used for this study is Raabin-WBC, containing leukocyte images. Deep learning techniques are applied to the dataset for the detection of mentioned leukocyte classes. In this case, to remove class imbalance, oversampling/replication of data is done. Transfer learning is applied in which a pre-trained ResNet18 model gets adapted to new data. Deep features are extracted from the pool5 layer of the network and are fed to the Ensemble Bagged Trees classifier. A remarkable classification accuracy of 99.5% is achieved through 5-fold cross-validation.

关键词： Deep learning Accuracy Transfer learning Feature extraction Mathematical models Robustness Medical diagnosis MATLAB Random forests Diseases

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RFSNU: Reliable Forwarding Strategy in NDN based UWSN

RFSNU: Reliable Forwarding Strategy in NDN based UWSN

引用

Canadian Conference on Electrical and computer engineering (CCECE)

作者： Ahmed Khalid Rana Asif Rehman Byung-Seo Kim Department of Computer Science National University of Computer & Emerging Sciences Pakistan Dept. of Software and Communications Engineering Hongik University Sejong-si South Korea

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Underwater wireless sensor network (UWSN) is a communication paradigm that works in underwater environment such as in oceans, rivers, lakes, and seas. Multiple sensors are deployed in underwater that collect information according to user demand and forwards the data towards the Underwater Autonomous Vehicle (UAV) that send the data towards the ship or user. UWSN is based on Internet Protocol (IP). There are several limitations in existing IP based architecture such as data redundancy, location dependency, mobility. Therefore, there is need for a new network that enhance the network efficiently. Named Data Network (NDN) is a new communication paradigm that provides several built-in benefits such as in-network caching, mobility, avoid data duplication, and fast content retrieval time. In case of NDN based UWSN (NUWSN), network traffic increases due to bandwidth and large propagation delay. Due to dynamic nature of water the ratio of traffic load, packet duplication, packet retransmission increases. When delay is large then more packets can loss and more number of packet retrans-mission, and data duplication occurs. Therefore, there is need for efficient forwarding mechanism in NUWSN that minimize data duplication and packet retransmission. In this paper, we proposed a forwarding mechanism named as Reliable Forwarding Strategy in NDN based UWSN (RFSNU). RFSNU improves the forwarding process in underwater environment using depth based forwarding. Our scheme improves packets retransmission rate, and packet duplication that are main problems in NUWSN.

关键词： Wireless communication Wireless sensor networks Protocols Redundancy Telecommunication traffic Sensors Rivers

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Metaverse and Healthcare: An Empirical Investigation 5

Metaverse and Healthcare: An Empirical Investigation

引用

5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Mahdi, Hussain Falih Sharma, Bhumika Sille, Roohi Choudhury, Tanupriya University of Diyala Department of Computer and Software Engineering Baquba Iraq Uttarakhand Dehradun248007 India SoCS Uttarakhand Dehradun248007 India Graphic Era Hill University CSE Dept. Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350337525

A virtual setting that provides experiences in real-time is introduced through a new field of research called metaverse. Combining the metaverse with healthcare results in a unique real-space experience that enables both patients and physicians to evaluate the health of their respective patients and engage in in-person interactions. The metaverse is a collection of technologies that includes elements of artificial intelligence, virtual reality, augmented reality, the Internet of Things, and more. The integration of healthcare and the metaverse has made it possible to focus on the patient's health in a more accurate, effective, and precise manner. Using the many imaging technologies in the metaverse, researchers are working to speed up the usage of the metaverse for patient diagnosis in addition to clinical diagnostics. It is no longer necessary for a doctor to be there in person while making a diagnosis. In order to diagnose patients in a highly effective and exact manner, there is a lot of research being done to improve the metaverse environment. This study's main focus will be on how the metaverse has streamlined the healthcare industry and the various healthcare amenities it offers. This article offers a thorough examination of clinical and diagnostic developments achieved in the metaverse in an effort to enhance the field of healthcare in the metaverse. Also, the focus is on the cutting-edge developments that will transform the metaverse healthcare industry. © 2023 IEEE.

关键词： Augmented reality

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Enhancing Wireless Data Transmission: A GAN-based Approach for Time Series Data Restoration

Enhancing Wireless Data Transmission: A GAN-based Approach f...

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International Conference on Information Networking

作者： Daejin Han Woongsoo Na Dept. of Computer Science and Engineering Kongju National University Cheonan Korea Dept. of Software Kongju National University Cheonan Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Recent technological advancements have led to a significant increase in the use of wireless communication for data transmission, particularly in sensor networks and device-to-device communications, necessitating not only rapid but also reliable data exchange. In conventional wireless communication, if a packet is lost in transmission due to retransmission, it is retransmitted. In this case, the subsequent packets will wait for them so that the order of the packets is not reversed, which is called Head of Line (HOL) Blocking. This problem can lead to overall transmission delays and losses, and is one of the reasons for inconsistent data transfer times. In this paper, we propose a technique to significantly reduce the retransmission rate of the sender and data loss by retransmission using Generative Adversarial Net (GAN). To evaluate the performance of this study, we applied our technique to a portion of SolarCube solar data assuming that it was corrupted, such as NaN processing, value change, and sign change, and the average recovery rate was 92.36%, demonstrating that it is possible to detect and recover losses in the data transmission process. This research is expected to have applications in data loss detection and recovery during transmission of time series data.

关键词： Wireless communication Wireless sensor networks Time series analysis Propagation losses Generative adversarial networks Data transfer Device-to-device communication

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Federated Anomaly Detection

Federated Anomaly Detection

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IEEE/IFIP International Conference on Dependable Systems and Networks â€“ Supplemental Volume (DSN-S)

作者： Chunjiong Zhang Byeong-Hee Roh Gaoyang Shan Dept. of AI Convergence Network Ajou University Suwon Korea Dept. of Software and Computer Engineering Ajou University Suwon Korea

ISBN: (数字)9798350395709

ISBN: (纸本)9798350395716

More and more portable intelligent devices are connected to the Internet in recent years. A way to effectively use the isolated cyber data without involving privacy and realize the cyber intrusion anomaly detection on the portable intelligent devices with relatively limited hardware storage resources and computing power is worth exploring. In this paper, we propose a framework of federated anomaly detection, which enables the device effectively detect the anomaly by sharing the parameters of the federated model in a fully distributed fashion. We formulate the model training problem as a distributed robust optimization problem and subsequently devise an efficient algorithm for it. Experimental studies have also been carried out to reveal the superior performance of the proposed framework and underscore the significant benefits of federated anomaly detection.

关键词： Training Performance evaluation Data privacy Federated learning Computational modeling Hardware Internet

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Visual object detection using audio data 5

Visual object detection using audio data

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5th International Conference on Computing and Data Science, CONF-CDS 2023

作者： Chaturvedi, Rajnish Kumar Sahu, Dinesh Prasad Tyagi, Manoj Kumar Diwakar, Manoj Singh, Prabhishek Shankar, Achyut Sathishkumar, V.E. Schoolof Computer Science Engineering and Technology Bennett University Greater Noida India Galgotias University India CSE Dept. Graphic Era Deemed to Be University Dehradun India University of Warwick United Kingdom Department of Software Engineering Jeonbuk National University Jeonju-si Jeollabuk-do Korea Republic of

Nowadays Internet of Things (IoT) and Machine Learning (ML) are growing fields. One application of these two fields is object detection, which detects semantic objects using digital images and videos of classes like humans, vehicles, buildings, etc. Visual object detection systems are very effective and accurate due to the appearance information obtained from the cameras. But they face the problem of a limited Field of View. This paper aims to tackle this issue by using audio data to localize the object. A microphone is used to estimate the angular position of the object emitting the sound. Objects currently in the Field of View of a camera are detected and tracked using optical flow, but when they go out of the Field of View, the sound emitted by the object is used by the microphone to calculate the object's angular position. Once the angle is calculated, the camera is rotated in that direction. This thus ensures that the object can be located even if it goes out of frame. Once the object is located through IoT devices, we use ML to identify the person's face. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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IT/OT Convergence Protocols: MQTT, OPC, and REST

IT/OT Convergence Protocols: MQTT, OPC, and REST

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computer Science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Bassam Zahran Fuad Abu Zahra dept of Computer Science and Software Engineering University of Wisconsin Platteville Wisconsin USA dept of Engineering and Applied Sciences University of Wisconsin Milwaukee Wisconsin USA

Information Technology and Operational Technology convergence refers to the integration and merge of IT and OT systems within an organization. Several protocols are commonly used in IT/OT convergence to facilitate communication and data exchange between IT and OT systems. It is imperative to understand how both systems are linking and exchanging data to be able to examine potential security breaches. In this study, we will discuss some of the common protocols used to achieve this convergence. We will include the known strengths and weaknesses of each. This will be considered an initial foundation to study potential security breaches and countermeasures for IT/OT convergence.

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Development of AI-Based Diagnostic Systems for Wound Images

Development of AI-Based Diagnostic Systems for Wound Images

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Seoyeon Jeong Jaeho Kim Kyusung Choi Young-Rae Cho Dept. of Computer and Telecommunications Engineering Yonsei University Wonju Republic of Korea Dept. of Software Yonsei University Wonju Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Recent advancements in AI technology are revolutionizing the medical field by enhancing diagnostic accuracy, optimizing treatment plans, and providing personalized medical services. AI-based algorithms are particularly effective in analyzing medical images to detect diseases early. Telemedicine, which allows remote medical consultations, benefits from these techniques by improving access to healthcare and enabling accurate diagnoses through efficient data exchange. This work developed an AI-based diagnostic system using a Convolutional Neural Network (CNN) model to analyze wound images. The wound dataset from Kaggle, consisting of 377 images across seven categories, was augmented 25 times to improve model performance. The app allows users to take or upload wound images, which are then classified by the model, providing treatment and aftercare information. This app achieved a high accuracy of 95.716% in classifying wounds and provides basic treatment suggestions. Despite challenges like limited dataset size, the project successfully demonstrated the potential for real-time wound analysis in telemedicine and healthcare. Future improvements should include a more diverse dataset and advanced treatment recommendations to enhance the app's utility.

关键词： Analytical models Accuracy Telemedicine Medical services Wounds Real-time systems Information and communication technology Convolutional neural networks Artificial intelligence Medical diagnostic imaging

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