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Large Language Models versus Natural Language Understanding and Generation 27

Large Language Models versus Natural Language Understanding ...

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27th Pan-Hellenic Conference on Progress in Computing and informatics with International Participation, PCI 2023

作者： Karanikolas, Nikitas Manga, Eirini Samaridi, Nikoletta Tousidou, Eleni Vassilakopoulos, Michael Dept. of Informatics and Computer Engineering University of West Attica Athens Greece Dept. of Electrical and Computer Eng. University of Thessaly Volos Greece

ISBN: (纸本)9798400716263

In recent years, the process humans adopt to learn a foreign language has moved from the strict "Grammar -Translation"method, which is based mainly on grammar and syntax rules, to more innovative processes, resulting to the more modern "Communicative approach". As its name states, this approach focuses on the coherent communication with native speakers and the cultivation of oral skills, without taking into consideration, at least at the first stages, the rules that govern the language. The same trend seems to have been applied to the way machinery can be "educated"to comprehend and reproduce the unfamiliar, human language. The "rule based"Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms, on one hand, and the "text based"Large Language Models (LLMs), on the other, are two, analogous to the two human foreign language learning processes, subareas of Natural Language Processing (NLP). This paper presents these two alternative approaches, LLMs (a technology having surfaced as an influential catalyst of NLP, during last years) on the one hand and NLG/NLU on the other, highlighting their applications, their technologies, their capabilities, their differences, their strengths and weaknesses and the challenges they present, contributing to a deeper comprehension of the evolving landscape of Artificial Intelligence and human-computer communication. © 2023 Owner/Author.

关键词： Natural language processing systems

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Rice Leaf Diseases Classification using Deep Learning Based on EfficientNetB3 Architecture with Transfer Learning 1

Rice Leaf Diseases Classification using Deep Learning Based ...

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1st International Conference on Technology, engineering, and Computing Applications, ICTECA 2023

作者： Yasid, Achmad Wahyuningrum, Rima Tri Ni'mah, Ana Tsalitsatun Ayani, Irham Hamed Universitas Trunojoyo Madura Dept. of Information Systems Bangkalan Indonesia Universitas Trunojoyo Madura Dept. of Informatics Engineering Bangkalan Indonesia Universitas Trunojoyo Madura Informatics Engineering Education Bangkalan Indonesia

ISBN: (纸本)9798350360554

Rice is the most significant agricultural commodity and a staple among Indonesians. One of the primary issues encountered by rice farmers is the pests and diseases that attack their crops, including those that affect the leaves, ultimately decreasing the quality and quantity of rice productivity. In this research, we introduce deep learning, especially Convolutional Neural Networks, to classify rice leaf disease as a first-step approach to accelerating the determination of the appropriate treatment process. Convolutional Neural Networks (CNN) based on the EfficientNetB3 architecture can be used to directly diagnose and classify rice leaf disease image data. Because the CNN method is highly computational and uses many resources, the research proposed using the EfficientNetB3 architecture with transfer learning (pre-trained model). We use various transfer learning techniques to optimize our model's accuracy and performance, including unfreezing a few last layers and unfreezing all layers, allowing the pre-trained model to learn from new data continually. This dataset contains 3355 healthy and three distinct varieties (brown spot, hispa, and leaf blast) of rice leaf diseases. Finally, model EfficientNetB3 with transfer learning unfreezes all layers and achieves the highest testing accuracy of 79.43% and train accuracy of 98.27% with an area under curve (AUC) of 0.9321. © 2023 IEEE.

关键词： Convolutional neural networks

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Modeling Data Set of Indonesian Heart Sound Signals 7

Modeling Data Set of Indonesian Heart Sound Signals

引用

7th International Conference of computer and informatics engineering, IC2IE 2024

作者： Oktivasari, Prihatin Sinantya, F. Agitha Nasywa, A. Hk, Firmansyah Setiawan, Evan Sutarna, Nana Mohd Safuan, Syadia Nabilah Najah Saud Al Humairi, Safaa Politeknik Negeri Jakarta Dept. of Informatics and Computer Engineering Depok Indonesia Politeknik Negeri Jakarta Dept. of Electrical Engineering Depok Indonesia Msu University Faculty of Information Sciences and Engineering Selangor Malaysia

ISBN: (纸本)9798331505530

Vital signs, such as heart and respiratory rates, are the main parameters when assessing a person's physical condition. Advanced technologies for monitoring vital signs include electrocardiography (ECG), photoplethysmograph (PPG), or phonocardiography (PCG). While ECG utilizes electrical stimulation that triggers each heartbeat, PPG measures changes in absorbance caused by blood flow, and PCG measures acoustic waves resulting from the mechanical activity of the heart. In recent decades, analysis of heart sound signals, especially for automatic heart sound segmentation and classification, has been widely studied and reported to have potential value for accurately and clinically detecting pathology. However, comparative analysis of algorithms in the literature has been hindered by the lack of high-quality, rigorously validated, and standardized heart sound databases of recordings. In this research, a heart sound database will be created by the public, especially Indonesians, to represent the condition of Asian society, which in the future can be used as a research reference for other researchers as an open data set. It includes healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. Records are collected from a variety of clinical or nonclinical environments and equipment. Recording length varies from a few seconds to several seconds-minutes. This study will report detailed information about subjects/patients including demographics (number, age, and gender), recordings (number, location, state, and length of time), associated synchronously recorded signals, sampling frequency, and type of sensor used. This research aims to measure acoustic waves resulting from the mechanical activity of the heart and the results of heart sound recordings in various patient conditions, along with their pathologies, will be grouped in an open database based on normal and abnormal. In this study, a deep-ranging S1 pattern was

关键词： Electrocardiography

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Lung Cancer Screening Using computer-Aided Detection and Image Processing Techniques 3

Lung Cancer Screening Using Computer-Aided Detection and Ima...

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

作者： Boddupalli, Anil Rayala, Laxman Lingareddy, Sai Sandeep Reddy, Yetra Mohan Sai Saran Yamuna Devi, M.M. Veeraswamy, A. Koneru Lakshmaiah Education Foundation Dept. of Computer Science & Engineering Vaddeswaram India

ISBN: (纸本)9798350322842

Lung cancer is one of the leading causes of mortality in India., accounting for 11% of all fatalities. Recently, the image processing techniques are increasingly used to find the lung cancer-infected region. Early detection of lung cancer may result in better treatment outcomes. Applied image processing methods are highly preferred to analyze CT scan data to detect the diseases that occur in human lungs. This can also assist in identifying the issue areas using a variety of ways, enabling people to get appropriate healthcare assistance. Here, this research study has used classification algorithms like SVM to clearly identify the infected lung region. This research study provides more research insights on pre-processing, segmentation and feature extraction. © 2023 IEEE.

关键词： computerized tomography

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Crop Intelligent: Weather based Crop Selection using Machine Learning 2

Crop Intelligent: Weather based Crop Selection using Machine...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Jasti, Sri Sai Ram Revanth, V. Rammohan Chowdary, K.D.N. Charan, K. Ch. S. V. Raju, S. Hrushikesava Kavitha, S. Koneru Lakshmaiah Education Foundation Dept. of Computer Science & Engineering Vaddeswaram India

ISBN: (纸本)9781665491990

Agriculture is essential in all places, accounting for 18% of India's overall Gross Domestic Product. Most crops in India are weather, soil, and planting dependent. For weather prediction, machine learning techniques such as recurrent neural networks are utilized, and to choose appropriate crops, a classification of random forests approach is utilized, which helps to boost yield and also indicates the appropriate planting time for the suitable crops. Machine learning algorithms for weather prediction such as, RNN and ANN are utilized, and the random forest is used to identify the suitable crop, while the ANN displays the highest performance data for each specified weather parameter. Weather-based crop selection has challenges due to a lack of understanding of crop sensitivity to weather, the complexity of climate change, the availability of sufficient seed types, and access to financial resources. Because of these challenges, forecasting which crops would perform best in a given place can be difficult, restricting farmers' ability to adopt weather-based crop selection tactics. © 2023 IEEE.

关键词： Crops

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XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding (XLU) 8

XNLI 2.0: Improving XNLI dataset and performance on Cross Li...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Kumar Upadhyay, Ankit Kumar Upadhya, Harsit JSS Academy of Technical Education Dept. of Computer Science and Engineering Bengaluru India JSS Academy of Technical Education Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350334012

Natural Language Processing systems are heavily dependent on the availability of annotated data to train practical models. Primarily, models are trained on English datasets. In recent times, significant advances have been made in multilingual understanding due to the steeply increasing necessity of working in different languages. One of the points that stands out is that since there are now so many pre-trained multilingual models, we can utilize them for cross-lingual understanding tasks. Using cross-lingual understanding and Natural Language Inference, it is possible to train models whose applications extend beyond the training language. We can leverage the power of machine translation to skip the tiresome part of translating datasets from one language to another. In this work, we focus on improving the original XNLI dataset by re-translating the MNLI dataset in all of the 14 different languages present in XNLI, including the test and dev sets of XNLI using Google Translate. We also perform experiments by training models in all 15 languages and analyzing their performance on the task of natural language inference. We then expand our boundary to investigate if we could improve performance in low-resource languages such as Swahili and Urdu by training models in languages other than English. © 2023 IEEE.

关键词： Natural language processing systems

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Mechanical Design and Forward Kinematics Analysis of T-FLoW 3.0 Prosthetic Robot Hand: Lever-based Finger Movement Mechanism 24

Mechanical Design and Forward Kinematics Analysis of T-FLoW ...

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24th International Electronics Symposium, IES 2022

作者： Apriandy, Kevin Ilham Dewantara, Bima Sena Bayu Dewanto, Raden Sanggar Pramadihanto, Dadet Dept. of Informatics and Computer Engineering Indonesia Dept. of Energy Mechanics Engineering Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

In this research, a prosthetic robot hand that features a lever-based finger movement mechanism is proposed as the new approach to the T-FLoW 3.0 humanoid robot's hand development. The proposed approach performs both grasping and releasing movements by pushing or pulling the finger-attached lever. The lever is pushed or pulled by micro-servo, which uses a stiff bar to transfer the force from the servo-horn to the finger's lever. Our prosthetic robot hand is equipped with six joints, six SG92R micro-servos as actuators, and six force-sensitive resistors (FSR) as grasping feedback. 3D printing manufacturing technology is utilized to give the hand a realistic appearance, and PLA filament material is used in the manufacturing process to provide low-cost, lightweight, and easy maintenance. Static structural analysis simulation result lead to the conclusion that our prosthetic robot hand could sustain a load of around 30N. With the lever-based finger movement mechanism, the proposed approach is expected to overcome the mechanical slip issues from finger movements, which are often experienced in the old approach of the T-FLoW 3.0 humanoid robot's hand development. © 2022 IEEE.

关键词： 3D printers

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Stacked Bidirectional-LSTM Network for FakeNews Detection on Twitter Data

Stacked Bidirectional-LSTM Network for FakeNews Detection on...

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2023 International Conference on computer Communication and informatics, ICCCI 2023

作者： Dineshbabu, Paladugula Sampath Kumar, M.D.V. Rao, Boyina Vishnu Vardhan Shaik, Taneem Kowsar Kumar, P. M. Ashok Shankar, R. Koneru Lakshmaiah Education Foundation Dept. of Computer Science & Engineering Vaddeswaram India

ISBN: (纸本)9798350348217

This work suggests a computational approach incorporating deep-learning methods to combat false information. The goal is to raise the false information detection scores by utilizing vectorizers, namely Word2Vec vectorization and the elimination of English stop words. To produce a more precise forecast of false information, our proposed model also makes use of a variety of classifiers, including Stacked Bidirectional-Long Short-Term Memory (Stacked Bi-LSTM), Support Vector Machine (SVM), Decision Tree, etc. By padding each sequence to its maximum length, we offer the network normalized qualities that can affect how effectively and accurately it operates. The network then uses a Stacked BiLSTM to interpret this data. Using the fake news data set, we made predictions regarding the validity of news. Our testing has determined that the proposed solution is more accurate than other machine-learning models. © 2023 IEEE.

关键词： Decision trees

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Design and construction of a medium chamber for a tissue bioreactor system 22

Design and construction of a medium chamber for a tissue bio...

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2024 IEEE 22nd World Symposium on Applied Machine Intelligence and informatics, SAMI 2024

作者： Kolembusova, Petra Ferencik, Norbert Steingartner, William Hudak, Radovan Sedlakova, Veronika Stefanovic, Branko Technical University of Košice Dept. of Biomedical Engineering Košice Slovakia Technical University of Košice Dept. of Computer and Informatics Košice Slovakia

ISBN: (纸本)9798350317206

In the field of tissue engineering and regenerative medicine, understanding the intricate properties and behaviors of cells and tissues within their natural environment is fundamental for translating theory into practical applications. This scientific article introduces the development of a functional medium chamber. The design of this chamber was driven by the need to align it with the volume requirements and specified conditions of bioreactor systems. The objective was to acquire new practical and theoretical insights in the realms of tissue engineering, 3D printing, and bioreactor technology. The emphasis throughout the project was on simplicity in design, with the aim of enabling easy construction and disassembly of the model. This initiative not only involved the creation of a chamber design but also encompassed the 3D printing process and extended functional testing over the long term. The design parameters, including the closing mechanism and chamber shape, were thoughtfully selected to enhance versatility, making the chamber adaptable for various bioreactor systems in the future. Subsequently, the printed chamber underwent rigorous testing using water as the culture medium, laying the groundwork for potential advancements in bioreactor technology and tissue cultivation. © 2024 IEEE.

关键词： Bioreactors

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Real-Time Object Detection with IOT Using a Smart Cart 18

Real-Time Object Detection with IOT Using a Smart Cart

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Omer, Muhammad Ali, Sardar Jaffar Raza, Syed Muhammad Le, Duc-Tai Choo, Hyunseung College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of AI System Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350331011

With Industry 4.0's technological advancements, the convenience and affordability of Artificial Intelligence (AI) and IoT have increased. However, customers still face long waiting times in payment queues during the smart shopping experience. Current solutions rely on expensive sensor-based RFIDs or simple barcodes, making them impractical and limited in scalability. This paper proposes an economically smart solution with two cameras on NVIDIA JETSON NANO for realtime object monitoring and automated shopping. Communication between modules is facilitated by TCP/IP and HTTP protocols. By utilizing the YOLO v7, real-time monitoring is achieved. This innovative approach revolutionizes the customer experience by providing a seamless interface and time-saving features. Additionally, it offers potential integration with e-commerce businesses and payment gateways, further enhancing the overall customer and vendor experience. © 2024 IEEE.

关键词： Graphical user interfaces

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