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6th International Conference on Smart and Sustainable Technologies, SpliTech 2021

作者： Zuazo, Irati Zapirain Boussaada, Zina Aginako, Naiara Curea, Octavian Camblong, Haritza Sierra, Basilio Faculty of Engeneering of Gipuzkoa Department of Systems Engineering and Control Plaza de Europa 1 Donostia-San Sebastián20018 Spain Univ. Bordeaux ESTIA INSTITUTE of TECHNOLOGY BidartF-64210 France Faculty of Engeneering of Gipuzkoa Department of Computer Science and Artificial Intelligence Plaza de Europa 1 Donostia-San Sebastián20018 Spain

ISBN: (纸本)9789532901122

Electric grid, as we nowadays know it, is undergoing a significant transformation. What we are now witnessing is an undoubted change of trend towards a decentralized and decarbonized electric grid, where the electric generation based on local resources will take on special relevance. In this context, the encouragement of collective self-consumption becomes one of the key issues when it comes to taking steps forward to this end. One of the aspects that will contribute to this aim is the development of a consumption-forecasting tool. Hence, a load-forecasting model based on NARX Neural Network is proposed in the following paper. The prediction of the next day (24h) load profile of an individual building is carried out aiming an optimal management of the flexible loads so to achieve the maximum self-consumption. To ensure a consistent behavior of the NARX Neural Network model, identification and removing of outliers, together with data normalization and fixing common time interval has been carried out. The first results of the research are promising, being obtained a 17, 6% MAPE in NARX and 25, 19% with LSTM model, both evaluated during a regular week on winter in adverse conditions. © 2021 University of Split, FESB.

关键词： Distributed power generation

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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Dynamic Patient Triage Optimization in Healthcare Settings Using RNNs for Decision Support

Dynamic Patient Triage Optimization in Healthcare Settings U...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Chitra Sabapathy Ranganathan Chethan Chandra S Basavaraddi V. Saillaja Pramod Pandey B. Sundaramurthy S. Murugan Mphasis Corporation Chennai Tamil Nadu India Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology VTU Belagavi Karnataka India Faculty of Management Studies SRM Institute of Science and Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

The article presents a sophisticated healthcare system that uses recurrent neural networks (RNNs) to optimize real-time patient triage. The developed model integrates patient data from the Internet of Things (IoT), and it performs dynamic assessments of vital indicators like heart rate, blood pressure, and temperature to prioritize pre-operation care based on the urgency and severity of diseases. The RNN architecture considers the temporal connections included in the data, which enables a more sophisticated comprehension of the changing patient states. The training and evaluation of the model make use of a large dataset. In comparison to more conventional triage methods, the model displays considerable gains in both accuracy and efficiency. The system not only reduces the amount of time needed to respond and allocate resources, but it also improves the flexibility to react to shifting patient conditions. This research represents a significant step toward the development of intelligent decision support systems in the healthcare industry. It demonstrates the potential of more sophisticated machine learning approaches to transform patient care processes in environments with high stakes and a dynamic nature.

关键词： Industries Training Temperature distribution Accuracy Recurrent neural networks Medical services Transforms Real-time systems data models Internet of Things

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Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios

arXiv

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arXiv 2024年

作者： Masri, Sari Ashqar, Huthaifa I. Elhenawy, Mohammed Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Civil Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University 500 W 120th St New YorkNY10027 United States CARRS-Q Centre for Data Science Queensland University of Technology 130 Victoria Park Rd Kelvin GroveQLD4059 Australia

Urban traffic management faces significant challenges due to the dynamic environments, and traditional algorithms fail to quickly adapt to this environment in real-time and predict possible conflicts. This study explores the ability of a Large Language Model (LLM), specifically, GPT-4o-mini to improve traffic management at urban intersections. We recruited GPT-4o-mini to analyze, predict position, detect and resolve the conflicts at an intersection in real-time for various basic scenarios. The key findings of this study to investigate whether LLMs can logically reason and understand the scenarios to enhance the traffic efficiency and safety by providing real-time analysis. The study highlights the potential of LLMs in urban traffic management creating more intelligent and more adaptive systems. Results showed the GPT-4o-mini was effectively able to detect and resolve conflicts in heavy traffic, congestion, and mixed-speed conditions. The complex scenario of multiple intersections with obstacles and pedestrians saw successful conflict management as well. Results show that the integration of LLMs promises to improve the effectiveness of traffic control for safer and more efficient urban intersection management. © 2024, CC BY.

关键词： Traffic congestion

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Multimodal brain tumor segmentation and classification from MRI scans based on optimized DeepLabV3+ and interpreted networks information fusion empowered with explainable AI

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Computers in Biology and Medicine 2024年 182卷 109183-109183页

作者： Ullah, Muhammad Sami Khan, Muhammad Attique Albarakati, Hussain Mubarak Damaševičius, Robertas Alsenan, Shrooq Department of Computer Science HITEC University Taxila47080 Pakistan Department of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Computer and Network Engineering Department College of Computing Umm Al-Qura University Makkah24382 Saudi Arabia Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Information Systems Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Explainable artificial intelligence (XAI) aims to offer machine learning (ML) methods that enable people to comprehend, properly trust, and create more explainable models. In medical imaging, XAI has been adopted to interpret deep learning black box models to demonstrate the trustworthiness of machine decisions and predictions. In this work, we proposed a deep learning and explainable AI-based framework for segmenting and classifying brain tumors. The proposed framework consists of two parts. The first part, encoder-decoder-based DeepLabv3+ architecture, is implemented with Bayesian Optimization (BO) based hyperparameter initialization. The different scales are performed, and features are extracted through the Atrous Spatial Pyramid Pooling (ASPP) technique. The extracted features are passed to the output layer for tumor segmentation. In the second part of the proposed framework, two customized models have been proposed named Inverted Residual Bottleneck 96 layers (IRB-96) and Inverted Residual Bottleneck Self-Attention (IRB-Self). Both models are trained on the selected brain tumor datasets and extracted features from the global average pooling and self-attention layers. Features are fused using a serial approach, and classification is performed. The BO-based hyperparameters optimization of the neural network classifiers is performed and the classification results have been optimized. An XAI method named LIME is implemented to check the interpretability of the proposed models. The experimental process of the proposed framework was performed on the Figshare dataset, and an average segmentation accuracy of 92.68 % and classification accuracy of 95.42 % were obtained, respectively. Compared with state-of-the-art techniques, the proposed framework shows improved accuracy. © 2024 Elsevier Ltd

关键词： data fusion

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A Monetary Incentive DTN Routing for Wireless Sensor Networks

Research Square

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Research Square 2021年

作者： Lin, Zhi Wang, Xia Bie, Rongfang Shi, Hongwei School of Artificial Intelligence Beijing Normal University Beijing China School of Statistics and Data Science Faculty of Science Beijing University of Technology Beijing China Center of Information & Network Technology Beijing Normal University Beijing China

The DTN (Delay/Interrupt Tolerant Network) protocol that relies on nodes to handle network interruptions is one of the important components of the wireless sensor network (WSN) routing protocol. However, due to resource consumption, nodes may be unable to unconditionally relay data. To address this issue, several incentive mechanisms have recently been proposed to encourage node participation. However, the existing solutions either do not fully consider the sender’s budget or do not consider the relay cost limitation, which violated the practical incentive mechanism requirements of the DTN protocol. In this paper, we focus on developing a new incentive mechanism for DTN routing that specifically address the challenges brought up by budget and relay cost limitation. Our core idea is to define the payoff functions of the sender and the next hops, then optimize the strategies under the constraints of the sender’s budget and the relay’s cost. Our experimental results demonstrate that the maximized social welfare for all participants can be realized under these constraints. © 2021, CC BY.

关键词： Network routing

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Enhancing Smart Grid Security: Detecting Electricity Theft through Ensemble Deep Learning

Enhancing Smart Grid Security: Detecting Electricity Theft t...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Sowmya C S Vibin R Praveen Mannam Lakkakula Mounika Subash Ranjan Kabat Jyoti Prasad Patra Department of ECE Presidency University Bengaluru Karnataka India Department of Electrical and Electronics Engineering CMS College of Engineering and Technology Coimbatore Tamilnadu India Dept of Electrical and Electronics Engineering KKR&KSR Institute of Technology and Sciences Guntur Andhra-Pradesh Lakkakula Mounika School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Electrical Engineering Radhakrishna Institute of Technology and Engineering Bhubaneswar Odisha India OUTR Government of Odisha Mahalaxmi Vihar Ghatikia Techno Campus Faculty Electrical Odisha University of Technology and Research Bhubaneswar Odisha India

Theft of electricity is a major problem that causes financial losses and inconsistent service for paying consumers for power distribution companies all over the world. The safety of the power grid depends on the ability to identify and stop electricity theft. The use of deep learning techniques has shown great promise in recent years, particularly in the areas of computer vision and natural language processing. This study recommends a random forest-based ensemble deep learning method for identifying cases of electricity theft. The proposed ensemble deep learning model leverages the best features of many kinds of deep learning architectures, including stacked Convolutional Neural Networks (CNN)and Long Short-Term Memory (LSTM). Each architecture has its own strengths when it comes to monitoring normal and abnormal electrical use for signs of theft. The final forecast is derived by adding together the predictions of the different models in the random forest ensemble. The ensemble model is trained using a massive dataset of energy usage records and theft information. Information about consumption patterns is extracted using feature engineering methods once the dataset has been preprocessed to get rid of noise and outliers. This preprocessed dataset is used to train the ensemble model, which then optimizes its parameters to reduce prediction errors. We use many measures, including accuracy, precision, recall, and F1-score, to assess the proposed ensemble deep learning model’s performance. Experiments are run against both conventional machine learning methods and standalone deep learning models to prove that the ensemble method is superior. The findings demonstrate that the ensemble model is more accurate and has a greater detection rate, making it suitable for spotting energy theft.

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Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm

arXiv

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arXiv 2024年

This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real-time. LLMs centralize traditionally disconnected traffic control processes and can integrate traffic data from diverse sources to provide context-aware decisions. LLMs can also delivere tailored outputs using various means such as wireless signals and visuals to drivers, infrastructures, and autonomous vehicles. To evaluate LLMs ability as traffic controllers, this study proposed a four-stage methodology. The methodology includes data creation and environment initialization, prompt engineering, conflict identification, and fine-tuning. We simulated multi-lane four-leg intersection scenarios and generates detailed datasets to enable conflict detection using LLMs and Python simulation as a ground truth. We used chain-of-thought prompts to lead LLMs in understanding the context, detecting conflicts, resolving them using traffic rules, and delivering context-sensitive traffic management solutions. We evaluated the prformance GPT-mini, Gemini, and Llama as traffic controllers. Results showed that the fine-tuned GPT-mini achieved 83% accuracy and an F1-score of 0.84. GPT-mini model exhibited a promising performance in generating actionable traffic management insights, with high ROUGE-L scores across conflict identification of 0.95, decision-making of 0.91, priority assignment of 0.94, and waiting time optimization of 0.92. This methodology confirmed LLMs' benefits as a traffic controller in real-world applications. We demonstrated that LLMs can offer precise recommendations to drivers in real-time including yielding, slowing, or stopping based on vehicle dynamics. This study demonstrates LLMs transformative potential for traffic control, enhancing efficiency and safety a

关键词： Street traffic control

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An effectual IOT coupled EEG analysing model for continuous patient monitoring

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Measurement: Sensors 2022年 24卷

作者： Khiani, Simran Mohamed Iqbal, M. Dhakne, Amol Sai Thrinath, B.V. Gayathri, PG. Thiagarajan, R. G H Raisoni College of Engineering and Management Pune India School of Computer Science and Engineering VIT-AP University Andhra Pradesh India Department of Computer Engineering Dr. D. Y. Patil Institute of Engineering Management and Research SPPU Pune Akurdi Pune India Department of EEE Sree Vidyanikethan Engineering College Tirupati India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology Tamilnadu Trichy India Department of IT Prathyusha Engineering College India

A persistent neurological condition known as epilepsy is characterized by aberrant brain electrical activity. Epilepsy is a disorder characterized by sporadic symptoms and aberrant electrical brain activity that is spasmodic. Such aberrations are supported by non-stationary, non-linear, and multidimensional clinical data. Such data processing and analysis present a variety of computing research difficulties. One of the most often used signals obtained from the human brain in both science and therapeutic settings is the electroencephalogram (EEG). It is an effective resource for offering insightful knowledge of the mechanics of the brain. EEG signal analyses that are precise and thorough are crucial in the diagnosis of brain diseases like epilepsy. The EEG signal is well-liked in the field of biomedical study because of its notable temporal solution, non-invasive recording setup, and convenience with minimal costs. Currently, skilled neurologists make a diagnosis by visually examining EEG information to determine the most likely course of action. It takes time and is error-prone to visually inspect high-dimensional and non-stationary EEG recordings. An automated seizure detector is a crucial tool for better understanding how seizures are generated as well as a relief for medical experts who become exhausted when viewing a continuous, massive, long-term recording. As it records from at least 16 channels, the multichannel automatic seizure detection system is large. It is difficult to use such a method on distantly located subjects. The suggested system gathers data from IoT devices, and patient history-related electronic clinical data that are saved in the cloud were subjected to predictive analytics. The Bi-LSTM-based intelligent healthcare system for tracking and precisely forecasting brain risk. The trade-off between the current system and the current methods is anticipated in the results section. © 2022 The Authors

关键词： Electroencephalography

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LPFFNet: Lightweight Prior Feature Fusion Network for SAR Ship Detection

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Remote Sensing 2025年第10期17卷

作者： Xiaozhen Ren Peiyuan Zhou Xiaqiong Fan Chengguo Feng Peng Li Institute for Complexity Science Henan University of Technology Zhengzhou 450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou 450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China

SAR ship detection is of great significance in marine safety, fisheries management, and maritime traffic. At present, many deep learning-based ship detection methods have improved the detection accuracy but also increased the complexity and computational cost. To address the issue, a lightweight prior feature fusion network (LPFFNet) is proposed to better improve the performance of SAR ship detection. A perception lightweight backbone network (PLBNet) is designed to reduce model complexity, and a multi-channel feature enhancement module (MFEM) is introduced to enhance the SAR ship localization capability. Moreover, a channel prior feature fusion network (CPFFNet) is designed to enhance the perception ability of ships of different sizes. Meanwhile, the residual channel focused attention module (RCFA) and the multi-kernel adaptive pooling local attention network (MKAP-LAN) are integrated to improve feature extraction capability. In addition, the enhanced ghost convolution (EGConv) is used to generate more reliable gradient information. And finally, the detection performance is improved by focusing on difficult samples through a smooth weighted focus loss function (SWF Loss). The experimental results have verified the effectiveness of the proposed model. © 2025 by the authors.

关键词： lightweight model loss function prior feature residual channel ship detection synthetic aperture radar (SAR)

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