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International Conference of Experimental and Numerical Investigations and New Technologies, CNNTech 2024

作者： Ratkovic Kovacevic, Nada Molnar, Daniel Z. Dihovicni, Djordje Mitrovic, Aleksandra Computer-Mechanical Engineering Section The Academy of Applied Technical Studies Belgrade Belgrade Serbia Technical High-School Stara Pazova AP Vojvodina Stara Pazova Serbia Faculty of Information Technology and Engineering University "Union – Nikola Tesla" Belgrade Serbia

ISBN: (纸本)9783031786341

The aim of this work was to make a protective system of the beehive, with automatic measurement of the mass of the beehive, construction of the alarm system and detection of the movement in the vicinity of the apiary. The steps of making and protecting a smart beehive are shown. The paper describes the components of the system and lists their technical characteristics. The Arduino Uno board as hardware and Arduino IDE (Integrated Development Environment) as software are exploited in order to develop the system described here and to monitor selected parameters of the beehive. The sensors are chosen to measure the mass of the beehive, to detect the opening or tilting of the hive lid, to make sound alarm in case the hive is being tampered with, to make sound alarm in case intruder(s) are detected in the hive vicinity. Implementation and mounting of electrical components in the housing of the hive and the protective system are shown. It is explained how the alarm system works for protection of the beehive. The scale that reads the mass of the hive is used to assess the yield of honey as well as to detect if the beehive is lifted to be taken. A practical experiment was performed in order to test subsystems and integration into a system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： engineering education

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Biological Signals for Diagnosing Sleep Stages Using Machine Learning Models 28

Biological Signals for Diagnosing Sleep Stages Using Machine...

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28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Mostafaei, Sahar Hassanzadeh Tanha, Jafar Sharafkhaneh, Amir Agrawal, Ritwick Mostafaei, Zohair Hassanzadeh University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran Baylor College of Medicine Section of Pulmonary Critical Care and Sleep Medicine Department of Medicine HoustonTX United States Islamic Azad University of Tabriz Branch Department of Computer Engineering Tabriz Iran

ISBN: (纸本)9798350338195

Sleep quality is important for health and can prevent diseases. Polysomnography is a standard scientific and clinical tool to evaluate sleep. Sleep staging is one of the main tasks in the sleep study that characterizes sleep cycles. In recent years, many studies are conducted using machine learning approaches to classify sleep stages. These studies can improve the accuracy and speed of the classification task, but most of them have not considered the performance of minority sleep classes. Since the number of samples in the sleep classes varies greatly, it can be considered as imbalanced data. In this study, we propose an ensemble method to handle class imbalance problem in the sleep staging task. For this purpose, we select nine biomedical signals including two EEG channels, two EOG channels, EMG, ECG, Abdominal, Thorax, and Airflow from the Sleep Heart Health Study (SHHS1) dataset. Then we use an ensemble of data-level resampling methods to rebalance the data space of sleep classes. Finally, we employ different machine learning algorithms to classify sleep stages. To evaluate the performance of the proposed method, in addition to general metrics, we use various measures such as Geometric mean (G-mean) and Matthew's Correlation Coefficient (MCC), which are proper metrics for the imbalanced data classification. The results of the developed method show that it achieves high accuracies of 0.9727, 0.9410, 0.8816, and 0.8725 for two, three, five, and six sleep classes, respectively. © 2023 IEEE.

关键词： Machine learning

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Classification of Breast Cancer using a Novel Neural Network-based Architecture 14

Classification of Breast Cancer using a Novel Neural Network...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mishra, Priti Khare, Sangita Singh, Tripty Nair, Rekha R Sharma, Mansi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India Cloud Computing Sw Research Section Future Computing Research Division Korea Republic of

ISBN: (纸本)9798350335095

Breast cancer is currently one of the deadliest types of cancer, and the death rate has considerably grown as a result of a lack of knowledge about the disease, its symptoms, and prevention techniques. Therefore, early detection at a nearly stage is crucial and vital in order to limit the progression of cancer. Malignant and benign breast cancer are the two further subtypes. In order to categorise the different forms of breast cancer, an automated system with logistic regression and neural network is proposed. The classification of the breast cancer data utilises the DNN with various levels of processing. The Digital Database for Screening Mammography (DDMS) dataset was used in the proposed study on the Kaggle platform. The dataset was divided into various train-test splits. On the basis of accuracy, ROC AUC cuve the system's performance is evaluated. The outcome demonstrates that proposed-2 outperforms in a comparable sense with a training accuracy of 98.99% and testing accuracy of 98.83%. © 2023 IEEE.

关键词： Diseases

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Parameter estimation of nonlinear systems: dwarf mongoose optimization algorithm with key term separation principle

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16921-16931页

作者： Mehmood, Khizer Chaudhary, Naveed Ishtiaq Khan, Zeshan Aslam Cheema, Khalid Mehmood Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering International Islamic University Islamabad44000 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Electronic Engineering Fatima Jinnah Women University Rawalpindi46000 Pakistan

In recent years, swarm intelligence-based metaheuristics are extensively explored for optimizing challenging and stiff nonlinear problems. This research explores the identification of the input nonlinear output error (NOE) model through a swarm intelligence-based metaheuristic called dwarf mongoose optimization algorithm (DMOA). Further, the key term separation method (KTSM) based identification model is presented to address the issue of over-parameterization. The DMOA performance is deeply examined for various generations, population sizes, and signal-to-noise ratios. The comparative analyses of the DMOA with the recent counterpart through observations of non-parametric statistical tests confirmed the effectiveness of the DMOA for NOE identification. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Nonlinear systems

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Green Leaf Disease Detection Using Raspberry Pi and Random Forest Machine Learning Algorithm: An Automated Approach for Precision Agriculture 10

Green Leaf Disease Detection Using Raspberry Pi and Random F...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Ranganathan, Chitra Sabapathy Basha, Amjath Ali Jakul Vijayalakshmi, A. Mohankumar, N. Sujatha, S. Sasikala, C. Mphasis Corporation Tamil Nadu Chennai India University of Technology and Applied Sciences Computer Engineering Electrical and Electronic Section Department of Engineering Ibra Oman Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Maharashtra Nagpur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350363104

Farm production and food security are affected by green leaf diseases. Modern disease detection technologies may discover plant health issues early. This work uses Raspberry Pi, a cheap and flexible single-board computer, along the advanced Random Forest machine learning technique to identify a green leaf disease. To identify crop diseases, the study uses automated, non-invasive technologies. From labeled images of good and sickly leaves, the algorithm extracts color histograms and texture, and form. It feeds a Random Forest classifier these properties for classifying data with high dimensions. Several green leaf diseased plant species were examined. This method detected plant health concerns with high precision. Movement and low cost make the Raspberry Pi ideal for remote and resource-limited agriculture, increasing farming accuracy. Helping farmers and users identify and develop green leaf diseases improves efficient farming. This methodology has the potential to improve crop management, optimize pesticide usage, and increase agricultural productivity. These results might contribute to the development of advanced automatic disease detection techniques to address green leaf diseases in modern agriculture. © 2024 IEEE.

关键词： computer Vision Green Leaf Plant Disease Detection Precision Agriculture raspberry pi

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A sketch recognition method based on bi-modal model using cooperative learning paradigm

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Neural Computing and Applications 2024年第23期36卷 14275-14290页

作者： Zhang, Shihui Wang, Lei Cui, Zhiguo Wang, Shi School of Information Science and Engineering Yanshan University Hebei Street West Section Hebei Province Qinhuangdao066000 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Hebei Street West Section Hebei Province Qinhuangdao066000 China School of Information Technology Hebei University of Business and Economics Xuefu Road Hebei Province Shijiazhuang050061 China School of Mathematics and Information Science and Technology Hebei Normal University of Science and Technology Hebei Street West Section Hebei Province Qinhuangdao066000 China

Static image is an important form of displaying a sketch, representing the appearance information of the sketch. And a stroke sequence composed of several points can also express the shape and contour information of the sketch. Therefore, it is very reasonable to treat a sketch as point-modal data and image-modal data simultaneously. In this paper, a method based on bi-modal model using cooperative learning paradigm is proposed for the sketch recognition task. Specifically, in the point-modal branch, a structural point convolution block is developed by properly dividing local regions to preserve the structural information. In the image-modal branch, the hierarchical residual structure is used to fully extract image-modal features. To reduce the negative impact of noisy samples on the recognition performance, a cooperative learning paradigm is designed based on different perceptual abilities of two modal branches on noisy samples, that is, when training the two branches, the noisy samples can be filtered out through information exchanges and mutual learning. Extensive experiments on the sketch datasets TU-Berlin and QuickDraw show that the proposed method outperforms most baseline methods and has many advantages such as no dependence on additional data and stroke information. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks

arXiv

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arXiv 2025年

作者： Orfanoudakis, Stavros Palensky, Peter Vergara, Pedro P. Section Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

Maintaining grid stability amid widespread electric vehicle (EV) adoption is vital for sustainable transportation. Traditional optimization methods and Reinforcement Learning (RL) approaches often struggle with the high dimensionality and dynamic nature of real-time EV charging, leading to sub-optimal solutions. To address these challenges, this study demonstrates that combining Large Language Models (LLMs), for sequence modeling, with Graph Neural Networks (GNNs), for relational information extraction, not only outperforms conventional EV smart charging methods, but also paves the way for entirely new research directions and innovative solutions. © 2025, CC BY.

关键词： Charging stations

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EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation 14

EMPATHIC: Emulating Human-Like Multimodal Personality Archit...

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Varshini Devi, I. Oviya, I.R. Raja, Kalpana Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India School of Medicine Yale University Section for Biomedical Informatics and Data Science New HavenCT United States

ISBN: (纸本)9798350344837

In the evolving landscape of human-computer interaction, this paper introduces an innovative framework poised to revolutionize chatbot systems. Our framework, meticulously designed for emotionally aware multimodal chatbots, seamlessly integrates real-time face and emotion recognition with natural language processing. Currently in the developmental phase, the framework exhibits a distinctive proficiency in executing a diverse array of human instructions. These instructions span tasks such as generating detailed captions, object counting, and responding to general queries. Leveraging parameter-efficient fine-tuning from OpenFlamingo, augmented by the inclusion of the Low-rank Adapter (LoRA), our framework strategically prioritizes versatility and operational efficiency. The core of our approach lies in the establishment of instruction templates that intricately merge both vision and language data. This strategic integration forms the backbone of our multi-modality instruction tuning methodology, enhancing the adaptability of the chatbot. Recognizing the pivotal role of high-quality training data, we emphasize its significance in shaping the dialogue performance of the chatbot. As the framework progresses through its developmental stages, this research lays a robust foundation for the creation of a versatile conversational agent. The envisioned applications of this agent range from enhancing customer service experiences to providing valuable support in mental health scenarios. © 2024 IEEE.

关键词： Emotion Recognition

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A setup for live AI support in interventional radiology

A setup for live AI support in interventional radiology

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作者： Komposch, Jan Malzacher, Till Baumgärtner, Timo Braun, Michael Roßkopf, Johannes Franz, Alfred M. Schmitz, Bernd Institute for Computer Science Ulm University of Applied Sciences Ulm Germany Neuroradiology Section District Hospital Guenzburg Guenzburg Germany Institute for Medical Engineering and Mechatronics Ulm University of Applied Sciences Ulm Germany

Artificial intelligence (AI) has the potential to support time-critical stroke treatment. In a previous study we demonstrated the feasibility of deep learning based automatic classification for thrombus detection during thrombectomies, a catheter-guided procedure to remove occlusions of cerebral vessels. However, this method has yet to be tested during a live intervention. In this work, we present a setup to integrate AI based support in an angiography suite. A classification PC was connected to the angiography by means of a real-time video connection as well as a research interface for control signals. We found that video conversion in real-time does not affect the classification result in comparison to offline classification of DICOM data. Analyzing 50 video streams of previous cases, the system could classify digital-subtraction angiography (DSA) sequences within 13.3 seconds on average. This processing time can further be reduced to an average of 7.9 seconds with GPU acceleration. Additionally, the system successfully classified two DSA sequences acquired during live thrombectomy, identifying the presence of thrombi in less than 5 seconds. So far, the classification result has only been displayed in the control room of the angiography suite to demonstrate feasibility. In the outlook, however, we also discuss how the result can be displayed directly on the angiography screen. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Radiology

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Intelligent Caching Strategies for 5G Edge Networks using Machine Learning 4

Intelligent Caching Strategies for 5G Edge Networks using Ma...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Guduri, Naga Venkata Ramakrishna Sethu, S. Shalinirajan, R. Reena, R. Karthikeyan, M. Uthayakumar, G.S. University of Technology and Applied Sciences-Al Musannah Electrical and Electronics Section Engineering Department Oman Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Computer Science and Business System Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Tamilnadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%. © 2023 IEEE.

关键词： 5G mobile communication systems

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