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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Spournias, Alexandros Faliagka, Evanthia Skandamis, Theodoros Antonopoulos, Christos Voros, Nikolaos S. Keramidas, Georgios University of the Peloponnese Electrical & Computer Engineering Department Patra Greece School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9798350322910

This paper presents a system for detecting gestures and controlling devices in Ambient Assisted Living (AAL) environments using machine learning and Bluetooth Low Energy (BLE) technology. The system consists of two main components: a device equipped with a set of sensors to detect hand gestures via IMU sensor and a BLE-enabled hub that receives the gesture data and controls the lighting of the house. The hub uses machine learning algorithms to recognize hand gestures and transmit the corresponding commands to the devices. The hub, in turn, uses wifi to communicate with the devices and execute the appropriate actions based on the received commands. The proposed system's performance evaluation was carried out through a series of experiments in a AAL environment. The results demonstrate that the system is capable of accurately detecting hand gestures and controlling various devices such as lights, where the model's performance yields successful predictions with an accuracy rate of 90%. The proposed system provides a user-friendly and intuitive way for elderly or people with disabilities to control their environment without the need for complex interfaces or physical buttons. Furthermore, the system can be easily extended to support more gestures and devices, making it a flexible and scalable solution for AAL environments. © 2023 IEEE.

关键词： Gesture recognition

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South Asian Sounds: Audio Classification 4

South Asian Sounds: Audio Classification

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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Chatterjee, Rajdeep Bishwas, Pappu Chakrabarty, Sudip Bandyopadhyay, Tathagata School of Computer Engineering Kiit Deemed to be University Bhubaneswar751024 India Amygdala Ai Bhubaneswar751024 India Informatics Technical University of Munich Germany

ISBN: (纸本)9798350389128

Sound classification is a significant task in the field of audio processing, with applications ranging from urban planning to noise pollution monitoring. We use Mel-Frequency Cepstral Coefficients (MFCCs) and a 1D Convolutional Neural Network (1D-CNN) model to try to solve the problem of putting urban sounds from Bangladesh into different groups. The proposed approach involves extracting MFCC features from audio recordings of urban environments in Bangladesh, which capture the spectral characteristics of the sounds. A 1D-CNN model then processes these MFCC features to classify the sounds into predefined categories like road-traffic noise, traditional Bengali songs, trains (railways), classroom noise, tanpura, etc. We also assess the performance of our approach on a dataset that has been collected and recorded from various locations in India and Bangladesh, as well as the well-known UrbanSounds8k dataset. Experimental results demonstrate the effectiveness of the proposed method, achieving high classification accuracy in distinguishing between different urban sound classes across both datasets. Our findings suggest that the combination of MFCCs and 1D-CNNs offers a robust solution for urban sound classification, with potential applications in urban planning, environmental monitoring, and smart city initiatives. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparing the Effectiveness of Machine Learning and Deep Learning Techniques for Feature Envy Detection in Software Systems 1

Comparing the Effectiveness of Machine Learning and Deep Lea...

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Menshawy, Rana S. Yousef, Ahmed H. Salem, Ashraf Faculty of Engineering Computer and System Department Ain Shams University Cairo Egypt Egypt University of Informatics Cairo Egypt

ISBN: (纸本)9798350335569

Code smells are common in poorly designed software that can hinder code maintainability. Automatic detection of design flaws assists developers in identifying code smells in theirsoftware programs to avoid low-quality delivery. However, the interpretation of code smells is subjective, and existing machine learning-based detection tools are limited in their ability to capture complex semantic relationships in textual code. To address this limitation, the paper proposes a detection system that exploits machine learning and deep learning techniques and compares their effectiveness in identifying the feature envy code smell. The study applies six deep learning techniques based on code semantics features extracted from abstract syntax trees, and eleven machine learning techniques based on code structural features. The results show that both approaches are effective in detecting code smells, with Random Forest achieving exceptional performance among the machine learning classifiers. © 2023 IEEE.

关键词： Deep learning

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Data Analytics for Volunteer-Based Educational Application 16

Data Analytics for Volunteer-Based Educational Application

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16th IEEE AFRICON, AFRICON 2023

作者： Sherif, Omar Zaghloul, Yasmine Sharaf, Nada Informatics and Computer Science The German International University Cairo Egypt German International University Faculty of Engineering Cairo Egypt

ISBN: (纸本)9798350336214

In the world of technology, data have been available easily and in huge amounts. Because of the large amounts of data, Educational Data Mining (EDM) is increasingly gaining more importance. Educational data mining is trending as it is the analysis method for analyzing educational data. It involves checking the relationship between the characteristics of students and which features affect their final grades the most. It can also involve predictive modeling by predicting the final grades of students in the future to help educational institutes rescue failing students before they actually fail. Predictive analysis was the main focus in past years where most researchers targeted predicting grades of students. However, not all educational institutes are able to collect the amount of data suitable for machine learning models to achieve good accuracy. That is why the main target of the work presented in this paper is to develop an interactive interface that gives the ability to educational institutes to check by themselves the relation between different factors using correlation mining. The output of the tool is visual with message boxes to make it understandable by users. That is the best way to discover hidden patterns and trends without the need for a large amount of data in addition to removing the cost of deploying machine learning models. The second goal of the work is to give teachers the ability to check the quality of the content of their slides to help them provide a better learning process. © 2023 IEEE.

关键词： Students

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Plagiarism Detection Application with Modified Enhanced Confix Stripping - Levenshtein Distance Algorithm Analysis 12

Plagiarism Detection Application with Modified Enhanced Conf...

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12th International Conference on Cyber and IT Service Management, CITSM 2024

作者： Nurhayati Aditya Suryawan, Ida Bagus Wardhani, Luh Kesuma Lee, Kyungoh Suseno, Hendra Bavu Masruroh, Siti Ummi Syarif Hidayatullah State Islamic University Department of Informatics Jakarta Indonesia Sun Moon University Computer Engineering Department Asan-si Korea Republic of

ISBN: (纸本)9798331528799

This research is made to continue the research on Implementation of Levensthein Distance Algorithm on Plagiarism Detection. In that research, it is found that the application can calculate the percentage of plagiarism against 2 documents, and it is suggested for further research to include the stemming algorithm to increase the percentage of plagiarism. This study uses the stemming algorithm of Modified Enhanced Confix Stripping (MECS) to prove the suggestion written in previous research. The study was conducted by comparing plagiarism percentage, system usage, running time, and complexity of Levensthein and MECS - Levensthein algorithm. The result of this research is the use of MECS algorithm can reduce running time by 7s, but the percentage decreased by 0,8% and system usage increased by 11,72%. © 2024 IEEE.

关键词： Confix Stripping Distance Levensthein Modified Enhanced Plagiarism Stemming

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A Novel Inception Deep Learning model for Mobile AI Stroke Prediction system: AI Research and Industry Perspectives for Connected Health 6

A Novel Inception Deep Learning model for Mobile AI Stroke P...

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6th International Conference on Computing and informatics, ICCI 2024

作者： Elbagoury, Bassant M. Maskeliuans, Rytis Zaghow, Marwa Kamel, Nabil Faculty of Computer and Information Sciences Ain Shams University Computer Science and Artificial Intelligence Programs Egypt Faculty of Computer Science and Engineering King Salman International University KSIU Egypt Kaunas University of Technology Faculty of Informatics Department of Multimedia Engineering Lithuania Dell Technologies EMEA Egypt

ISBN: (纸本)9798350373875

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed. © 2024 IEEE.

关键词： Learning systems

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A Comprehensive Framework for Cold Start Problem in Hybrid Recommendation Systems Extending the Efficient Multi-Mode Approach 27

A Comprehensive Framework for Cold Start Problem in Hybrid R...

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27th International computer Science and engineering Conference, ICSEC 2023

作者： Badriyah, Tessy Ramadhan, Yunaz Gilang Syarif, Iwan Informatics and Computer Engineering Department Kampus PENS JL. Arief Rahman Hakim Sukolilo Surabaya60111 Indonesia

ISBN: (纸本)9798350342109

E-Commerce is a prominent application of information technology in business, offering convenience in transactions. However, as more products and users join the platform, the complexity of E-Commerce systems increases. This necessitates the implementation of a recommendation system to enhance the user experience and address individual preferences. E-Commerce platforms often face the challenge of the cold start problem, where new products are introduced to the platform or when there is limited information available about new users. In such cases, traditional recommendation systems struggle to provide accurate recommendations due to the lack of historical data or user preferences. In this research, a comprehensive framework for an efficient multi-mode Hybrid Recommendation System is proposed, focusing on addressing the cold start problem. The system leverages user behavior tracking, specifically search history and product visit history, to capture user preferences effectively. Through experimental evaluation, the system demonstrates its adaptability to different dataset conditions, achieving a high precision rate of 90%. This research addresses the challenges of data sparsity and the cold start problem in recommendation systems, particularly in the E-Commerce context. The flexibility of the recommendation system ensures optimal recommendations in various scenarios, with potential applications beyond E-Commerce. © 2023 IEEE.

关键词： Recommender systems

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Implementation of Convolutional Neural Network for COVID19 Screening using X-Rays Images

Implementation of Convolutional Neural Network for COVID19 S...

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2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Delimayanti, Mera Kartika Mardiyono, Anggi Warsuta, Bambang Puspitaningrum, Eka Suci Naryanto, Rizqi Fitri Naryaningsih, Agustien Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Indonesia Universitas Negeri Semarang Faculty of Engineering Department of Mechanical Engineering Indonesia Fisheries Research Center Indonesia

ISBN: (纸本)9798350320954

Various unwelcome conditions have existed since the introduction of the novel coronavirus illness (COVID-19). COVID-19 can cause fever, muscle soreness, shortness of breath, cough, headache, and other symptoms. Diagnosis at an early stage is a crucial aspect of successful treatment. Therefore, it is necessary to seek out alternate methods for COVID-19 detection. Among the existing imaging resources, X-ray images are generally accessible and inexpensive. Consequently, an alternate diagnostic tool for detecting COVID-19 instances is provided using available resources. In the first stages of COVID-19, X-rays detected the disease before it spread to the lungs and caused more damage. Machine learning models can help clinicians accomplish jobs more quickly and accurately. In addition to chest X-ray pictures and fundus images, deep learning algorithms have been used to diagnose illnesses. This research was conducted to classify the X-ray chest images in COVID-19 and normal cases based on the public datasets which were used. This analysis uses 5600 images from the accessible resources, and a Convolutional Neural Network (CNN) architecture with the VGG16 algorithm was employed to diagnose COVID19. VGG16 is object identification and classification method that can classify with greater precision than most other deep learning algorithms. Transfer learning and fine-tuning were employed to help for improving the performance. The results showed that the VGG16 network had an accuracy of 98.13%. This research has implications for the early detection of COVID-19 by using X-ray images. The experiment and analysis reveal our suggested method's promising and stable performance compared to the current standard. © 2023 IEEE.

关键词： COVID-19

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Medical image segmentation technology based on the SC-UNet model

Medical image segmentation technology based on the SC-UNet m...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Zhu, Xuhui Han, Bo Li, Dongliang School of Computer and Information Engineering Key Laboratory of Functional Materials and Devices for Informatics of Anhui Higher Education Institutes Fuyang Normal University Fuyang China

ISBN: (纸本)9781510681514

This paper proposes a new neural network model, series connection UNet (SC-UNet). The network model is composed of two series of UNet modules. The residual module and the attention mechanism gate are added to the model to make the back-propagation faster and focus the model's attention on the target region. In the medical image segmentation task of lung X-ray pictures, compared with the single UNet model, the average crossover ratio, mean pixel accuracy, average accuracy rate, and mean recall rate of the model structure increased by 4.51%, 3.98%, 0.45%, and 4.00% respectively. Experiments show that the SC-UNet model has significant advantages over the UNet model in medical image segmentation. © 2024 SPIE.

关键词： Neural network models

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A TOPOLOGICAL CHARACTERIZATION OF THE EXISTENCE OF w-STABLE SETS

arXiv

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arXiv 2024年

作者： Andrikopoulos, Athanasios Sampanis, Nikolaos Dept. of Computer Engineering and Informatics University of Patras Patras26504 Greece Department of Computer Engineering & Informatics University of Patras Greece

The theory of optimal choice sets is a solution theory that has a long and well-established tradition in social choice and game theories. Some of important general solution concepts of choice problems when the set of best alternatives does not exist (this problem occurs when the preferences yielded by an economic process are cyclic) is the Stable Set (Von Neumann-Morgenstern set) and its variants (Generalized Stable set, Extended Stable set, m-Stable set and w-Stable set). The theory of w-stable sets solution is more realistic because: (1) It solves the existence problem of solution;(2) It expands the notions of maximal alternative set and (3) The concept of stability is defined in such a way as to prevent a chosen alternative from being dominated by another alternative and sets this stability within the solution. In this paper, we present a topological characterization of the existence of w-Stable sets solution of arbitrary binary relations over non-finite sets of alternatives. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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