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6th World Symposium on Communication engineering, WSCE 2023

作者： Lioupa, Anna Memos, Vasileios A. Stergiou, Christos L. Ishibashi, Yutaka Psannis, Konstantinos E. School of Information Sciences University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350339505

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication. © 2023 IEEE.

关键词： Blockchain

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Black Hole Attacks Detection in Mobile Ad Hoc Networks (MANETs) using Bayesian Inference Method 11

Black Hole Attacks Detection in Mobile Ad Hoc Networks (MANE...

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11th International Conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Afifah, Nurul Fadli, Muhammad Septian, Tri Wanda Sari, Winda Kurnia Hermansyah, Adi Marieska, Mastura Diana Sriwijaya University Faculty of Comp. Science Computer Engineering Dept. Palembang Indonesia Sriwijaya University COMNETS Research Group Faculty of Comp. Science Palembang Indonesia Sriwijaya University Faculty of Comp. Science Information System Dept. Palembang Indonesia

ISBN: (纸本)9798350355314

MANET has vulnerability in terms of security systems against black hole attacks. The main target of black hole as a malicious node is to absorbs all data. The aim of this research is to identify black hole attacks against MANE Tusing Bayesian inference. The methodology of this research involves several testing phases. In the first phase, all data packets sent by the source node to destination within the range of the black hole node are lost. The second and third testing phases showed that the Bayesian method was successful in detecting black holes by preventing data transmission. The results indicated that traffic in the MANET runs smoothly when the nodes are operating under normal conditions. However, when a black hole node is introduced, a significant number of data packets are lost. Implementing the Bayesian method offers a solution to this issue. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Machine Learning-based Automated System for Subjective Answer Evaluation 9

Machine Learning-based Automated System for Subjective Answe...

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9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Dodia, Shubham Spoorthy, V. Chandak, Trupti National Institute of Technology Karnataka Dept. of Computer Science and Engineering Surathkal Mangalore India National Institute of Technology Karnataka Dept. of Information Technology Surathkal Mangalore India

ISBN: (纸本)9798350334395

An examination is a useful tool for assessing students' knowledge. Evaluation of exams is a difficult and time-consuming process. The automatic examination of answer scripts makes this task easier for teachers, reducing the amount of effort and time required. The existing literature has a number of methods that have been proposed for evaluating responses to objective questions using machine learning. However, more work needs to be done on evaluating answers to descriptive questions. This study suggests a way to evaluate students' answers to questions of a descriptive kind without using traditional paper or pencil by teachers. Instead, a computer acts as a teacher and grades the students' submissions. The primary objective is to communicate the outcomes of subjective responses using Bidirectional Encoder Representations from Transformers (BERT), cosine, and Jaccard distance. The proposed model achieved an accuracy of 91%, an error of 9.01, a precision of 83%, and a recall of 79%, respectively. The suggested model has provided the best results in comparison with state-of-the-art systems. © 2023 IEEE.

关键词： Machine learning

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ACNNE: An Adaptive Convolution Engine for CNNs Acceleration Exploiting Partial Reconfiguration on FPGAs

ACNNE: An Adaptive Convolution Engine for CNNs Acceleration ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Chun-Hsian Huang Shao-Wei Tang Pao-Ann Hsiung Dept. of Computer Science and Information Engineering National Taitung University Taiwan Dept. of Computer Science and Information Engineering National Chung Cheng University Taiwan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

In this work, we propose a dynamical function exchange Convolutional Neural Networks (CNN) accelerator architecture named Adaptive CNN engine (ACNNE) that can reconfigure specific convolution layer hardware blocks according to model parameters at runtime. We mainly focus on exploiting reconfigurability for inferencing large-scale CNN on resource- constrained FPGAs. The proposed ACNNE can accelerate the process of convolution layers based on a nested-loop algorithm while a data buffering scheme is presented to reduce the iterations of memory accesses. As a study case, the VGG16 model was implemented on Xilinx ZU3EG MPSoC that can achieve 21.09 giga operations per second in the frequency of 100 MHz with a device resource utilization of 25% to 35%. Experiments show a single-image inference can be completed in 2.5 seconds with an average power consumption of 2.23 W, corresponding to a power efficiency of 9.46 GOPS/W.

关键词： Adaptation models Runtime Power demand Convolution Convolutional neural networks Resource management Logic

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Design and Development of Cognitive Training Systems Based on Extended Reality and BCI Technology

Design and Development of Cognitive Training Systems Based o...

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Educational Innovation (ECEI), IEEE Eurasian Conference on

作者： Pei-Jung Lin Hsiu-Chien Ku Li-Lun Lin Dept. of Information Engineering and Computer Science Feng Chia University Taichung Taiwan Dept. of Computer Science and Information Engineering Hungkuang University Taichung Taiwan

Dementia is considered a Mild Cognitive Impairment (MCI), representing a blurred transition from normal cognitive function to dementia. MCI might be a precursor to dementia being caused by various neurodegenerative diseases, including Alzheimer's disease, stroke, metabolic disorders, and mental health conditions. According to the World Health Organization (WHO), over 55 million people worldwide are suffering from dementia, with nearly 10 million new cases each year. This number is expected to rise to 78 million by 2030. Numerous studies have proposed various training methods targeting specific cognitive domains in dementia. For instance, reminiscence therapy encourages individuals to recall buried memories, while reality orientation therapy focuses on strengthening orientation cues to help patients with dementia establish a stronger sense of control. With aging, changes occur in cognitive and physical functions. Research suggests that memory functions can be improved through training. Therefore, the primary goal of this study was to develop an Extended Reality (XR) cognitive system to assist MCI patients in enhancing logical thinking, increasing independence in daily life, and improving response accuracy. The application of extended reality technology in the medical field is expanding, covering areas such as surgical assistance, medical consultation, disease treatment, and medical training. We integrated mixed reality devices with brain-computer interface (BCI) equipment. By detecting brainwave signals in the frontal region of the brain through sensors, neural activity was quantified, and digital brainwave data was transmitted to the system. This data was then visualized on screen in real-time to analyze changes in brainwave data during training. Through multifaceted task training, the system enhanced cognitive response and sensory perception, stimulating brain activity with the goal of strengthening cognitive functions.

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The Development of an Autonomous Robotic Scanning System with the Laser Rangefinder

The Development of an Autonomous Robotic Scanning System wit...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Ko, Hung-Chin Chen, Syuan-Yi He, Jyun-Hong Chiang, Hsin-Han National Taiwan Normal University Dept. Electrical Engineering Taipei Taiwan National Taipei University of Technology Dept. Computer Science and Information Engineering Taipei City Taiwan National Taipei University of Technology Dept. Vehicle Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

Due to the rise of Industry 4.0, many industries have begun to replace traditional human labor with automated equipment to achieve greater productivity and more accurate inspection results. Due to its advantages of high stability, high accuracy, and low repetition error, many manufacturing industries have chosen to use robotic arms to replace human labor in production and inspection tasks to obtain faster and more accurate work results. Based on the above advantages, this experiment combines a seven-axis robot arm with 2D LiDAR to scan the object to be inspected, and the algorithm is used to analyze, calculate and plan the inspection path of the arm so that the robot arm can automatically scan the surface of the object to be inspected in an equal speed, smooth, and reproducible way. © 2023 IEEE.

关键词： Inspection

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Support Chatbot based on ChatGPT for Customer Relationship Management in Educational Institutions 6

Support Chatbot based on ChatGPT for Customer Relationship M...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Mohamed, Islam Samih Abdelsalam, Mohamed Moawad, Ibrahim F. Dept. Business Information Systems Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt

ISBN: (纸本)9798350373875

Chatbot technology is considered one of the latest artificial intelligence tools used to achieve interaction between the organization and the customer. It relies on natural language processing, as it helps the organization know the desires and needs of the customer, in addition to collecting customer data and displaying the services provided by the organization. This research proposes an architectural model for supporting chatbots based on the ChatGPT model. It receives customer interaction inputs, understands questions, and responds quickly and appropriately. In addition, this model was designed and implemented using the Voiceflow platform. This model was named EDUBot to build communication elements and components to customize the structure of the virtual conversation and manage the dialogue. This model can be applied to the Educational City institution as a case study to improve customer service and help the institution manage customer relationships more efficiently. © 2024 IEEE.

关键词： Sales

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Leveraging the Pre-Trained Whisper Model and Levenshtein Distance for Audio Buginesse Transcription

Leveraging the Pre-Trained Whisper Model and Levenshtein Dis...

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

作者： Mukmin, Amirul Adnan Jalil, Abdul Pallawabonang, Mahabintang Prakasa, Esa Yuyun Handayani University of Makassar Dept. of informatics engineering Makassar Indonesia Handayani University of Makassar Dept. of computer system Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331508579

This research aims to transcribe audio into text using the pre-trained Whisper model, focusing on Bugis, a low-resource language. A key challenge in transcribing local languages like Bugis is the limited availability of language model. To address this, we implemented an algorithmic correction approach that does not require extensive datasets or computationally demanding customizations. The Whisper model provides an initial transcription, serving as a foundation;however, the output often lacks accuracy due to the absence of specific adjustments for Bugis. To enhance accuracy, we applied the Levenshtein distance algorithm to correct word errors by comparing transcription results with entries in a Bugis language dataset based on similarity. Although this method is not fully precise and depends on the completeness of the reference dataset, evaluation results indicate that it effectively lowers the word error rate (WER) and improves transcription accuracy. This research produces SRT files containing transcripts that have been corrected using the Levenhstein distance algorithm, which not only improves accessibility but also contributes to the preservation of Bugis language through digital media. This approach is expected to provide a practical solution to improve transcription accuracy and support more accurate documentation in Bugis language preservation efforts. © 2024 IEEE.

关键词： Speech recognition

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Rice Category Identification through Deep Transfer Learning Features and Machine Learning Classifiers: An Intelligent Approach

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第7期51卷 765-784页

作者： Terlapu, Panduranga Vital Prasan, U.D. Kumar, T. Ravi Bendalam, Vijaya Pappu, Sasibhushana Rao Rao, M. Jayanthi Mohitha, Maddula Ratna Jayaram, D. Dept. of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Sasibhushana Rao Pappu is an Assistant Professor of Dept. of Department of Computer Science and Engineering GITAM Andhra Pradesh Visakhapatnam India Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Department of Information Technology Chaitanya Bharathi Institute of Technology Gandipet Telangana Hyderabad500075 India

Rice category identification by image analysis is essential to ensure the quality and safety of rice production. In this study, we propose an intellectual approach to improve rice category identification using deep transfer learning features and machine learning classifiers. Specifically, we extracted features from three pre-trained models (Inception V3, VGG-19 and VGG-16) using transfer learning techniques. These were used as inputs to train MultiLayerPerceptron (MLP) and support vector machine (SVM) classifiers. The results of our experiments show that the proposed strategic results achieve high accuracy in identifying rice categories. The SVM (polynomial kernel) achieves the second-highest accuracy among all models and features, with an accuracy of 0.9948 using the VGG-19 and 0.9912 using Inception V3. The MLP classifier with (30 30) hidden layers achieve the first high accuracy, with an accuracy of 0.9972 (99.72%) using VGG-19 features. The results also show that the choice of deep transfer learning model and machine learning (ML) classifier can significantly affect the accuracy of rice category identification. Among the three pretrained models, VGG-19 features consistently perform the best, followed by Inception V3 and VGG-16. The choice of MLP hidden layer size also affects the accuracy, with 30 HL neurons achieving the best performance. Our proposed approach using deep TL features and ML classifiers shows promising results in improving rice category identification. Our study provides valuable insights into optimizing ML models for agricultural image analysis. © (2024) International Association of Engineers. All rights reserved.

关键词： Support vector machines

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An Enhanced Smart Intelligent Detecting and Alerting System for Industrial Gas Leakage using IoT in Sensor Network 5

An Enhanced Smart Intelligent Detecting and Alerting System ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Senthil, G.A. Suganthi, P. Prabha, R. Madhumathi, M. Prabhu, S. Sridevi, S. Agni College of Technology Dept. of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept. of Electronics and Communication Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Chennai India Sri Ramachandra Engineering and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

Leakage of any type of gas has emerged as a hazard in recent years, whether in a typical workplace, boiler, a commercial setting, a café, or a canteen, industries natural gas, coal, refinery products, crude oil, cement, electricity, steel, and fertilizers etc. In this article, I proposed a new invention of an IoT-based gas waste monitoring, leak detection, and alerting system. The research extends on the development of such an intelligent system that can intelligently preserve gas, ensure the safety precaution and pre vent accidents. The technique must be connected to the stove. The design incorporates ultrasonic sensors which identify bio gas leakage in circumstance with in square metre is usage area of industries or cooking place. The Proposed work smart intelligence system is cuts off the gas supply using an automated shutting off device. Whenever a gas leak is detected, consumers will be notified through SMS via GSM, location using GPRS, and will be able to resolve the situation as quickly as feasible. The flame sensor in the technology will supervise flame and fire. The buzzer starts to sound when a fire is detected. Aside from that, the system may save data on the cloud. With the help of this cloud storage solution, every user's daily gas use may be traced. Finally, this approach will aid in recognizing per user natural gas consumption. The system has been examined and is capable of monitoring gas loss, evacuation, and sending an SMS notification and location aid to the user. The precipitate demonstrated its usefulness in conserving a considerable amount of domestically squandered gas. © 2023 IEEE.

关键词： Liquefied petroleum gas

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