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Design and development of political rider competitive swarm optimiser enabled deep learning model for air quality detection

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International Journal of Intelligent systems Technologies and Applications 2024年第1期22卷 77-104页

作者： Patil, Deepika Dadasaheb Thanuja, T.C. Melinamath, Bhuvaneshwari C. Department of Computer Science and Engineering Vishveshvaraya Technological University Belgavi India Sanjay Ghodawat University Kolhapur India Department of VLSI and Embedded System Visvesvaraya Technological University Belagavi India Department of Computer Science and Engineering BMSIT&M College Bangalore India

The air quality prediction process is a more significant one for air pollution prevention and management because air pollution becomes crueller. The precise identification of air quality has become a more significant concern for controlling air pollution. Recently, the weight of particulate matter (PM) on the human physical condition has become an important research area. In this paper, the political rider competitive swarm optimiser (PRCSO)-based deep recurrent neural network (DRNN) algorithm is devised for air quality and carbon monoxide prediction. The missing value imputation scheme is employed to perform pre-processing. Moreover, technical indicators and location information are extracted for the prediction process. The DRNN is employed for prediction, which is trained by the PRCSO and the training process is performed based on every location independently. The PRCSO-based DRNN outperforms existing techniques in terms of mean square error (MSE) of 0.0313, and mean absolute percentage error (MAPE) of 3.08%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Carbon monoxide

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Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity

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Alexandria engineering Journal 2025年 127卷 522-540页

作者： Alzahrani, Abdulrahman Kostkova, Patty Alshammari, Hamoud Habibullah, Safa Alzahrani, Ahmed Department of Information System and Technology College of Computer Science and Engineering University of Jeddah Saudi Arabia University College London United Kingdom Department of Information systems College of Computer and Information Sciences Jouf University Saudi Arabia

Artificial Intelligence (AI) and the Internet of Things (IoT) are developing so fast that they can bring revolutionary changes in ecological sustainability, public health, and community welfare. In contrast, the present waste management system has a set of inefficiencies due to some challenges, such as poor waste stream segregation, limited real-time data analysis, and negligible integration of recent technology. These challenges lead to environmental degradation, public health hazards, and inefficient usage of resources. This research targets these challenges by designing an IWM framework like AI-IoT for smart waste management. The system employs AI models powered by IoT sensors for efficient waste collection, classification, and optimization of recycling schedules. CNN (convolutional neural networks) with transfer learning enabled by Res-Net provides high-accuracy image recognition, which can be used for waste classification. Bidirectional Encoder Representations from Transformers (BERT) allow multilingual users to interact and communicate properly in any linguistic environment. Data collected from IoT-enabled smart bins is transmitted in real-time to a central control system for dynamic decision-making and follow-up analysis. A pilot exercise to verify the system's effectiveness was implemented in metropolitan settings to show the transformation: landfill dependency was decreased by 30 %, recycling efficiency was greatly increased to 90 %, and thus the cost of waste management was optimized. At the same time, environmental health inequity, causing pathogen-related threats, was reduced by 35 %. The model has an accuracy of 96.8 %. The features of the proposed framework not only provide solutions to the existing inefficiencies but also enhance scalability, cost-effectiveness, and global environmental standardization. This dawns the futuristic growth of AI- and IoT-enabled waste management systems, which hinge on sustainability, public health, and resource efficienc

关键词： Artificial Intelligence Internet of Things BERT Medical Services Res-Net Waste Management

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Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

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computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer Nouf Abdullah Almujally Saud S.Alotaibi Abdulwahab Alazeb Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information System Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

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A Fully LTPS-TFT-Based Bi-Directional Biomedical Interface Circuit for Large-Area Healthcare Applications

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第12期3卷 526-532页

作者： Zhang, Hanbo Lou, Yuqing Zhang, Zhihang Li, Yongfu Rokhan, Fakhrul Wang, Guoxing Zhao, Jian Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication System Engineering Selangor Serdang43400 Malaysia

This paper proposed a fully LTPS-TFT-based bi-directional biomedical pixel interface with integrated a high linearity stimulator and a high-performance bio-potential sensing front-end circuit to amplify and digitize biological signals for high robustness data transmission. All circuits are designed and manufactured under a 3-μm LTPS process. We demonstrated the experiment in saline using the stimulator circuit to simulate stimulation inside the organism, achieving stimulation with different average currents under different duty cycles. The proposed front-end circuit has a gain of 40 dB. The referred input voltage noise of the entire signal chain is 86.2 μV/√Hz, and the ENOB is 9.13 bits at an input of 0.5 mVPP. Simultaneously conducting statistical testing on 20 chips has achieved a 1.05 dB SNDR standard deviation. Finally, we use a function signal generator to simulate the input signals of ECoG, ECG, and EOG, then use front-end circuits to read and reconstruct them to demonstrate that the proposed pixel interface can correctly read common biomedical signals for healthcare applications. © 2022 IEEE.

关键词： Image segmentation

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Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset

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IEEE Access 2024年 12卷 9369-9381页

作者： Ainan, Ummey Hany Por, Lip Yee Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The challenge of bankruptcy prediction, critical for averting financial sector losses, is amplified by the prevalence of imbalanced datasets, which often skew prediction models. Addressing this, our study introduces the innovative hybrid model XGBoost+ANN, designed to leverage the strengths of both ensemble learning and artificial neural networks. This model integrates a comprehensive set of features with parameters optimized through genetic algorithms, eschewing traditional feature selection approaches. Our research focuses on an unbalanced dataset of Polish companies and reveals that the XGBoost+ANN model, in particular, exhibits outstanding performance. Optimized using genetic algorithms and without feature selection, this model achieved the highest AUC (0.958), sensitivity (0.752), and accuracy (0.983) scores, surpassing other models in our study. This remarkable outperformance, along with the robust results, marks a substantial advancement in the field of bankruptcy prediction. It underscores the efficacy of our approach in addressing the persistent challenge of data imbalance, offering a more reliable and accurate solution for financial risk assessment. © 2013 IEEE.

关键词： Feature extraction

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SSVEP Based BCI Control of an Exoskeleton 18th

SSVEP Based BCI Control of an Exoskeleton

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18th International Conference on Interdisciplinarity in engineering, INTER-ENG 2024

作者： Sibișanu, Remus Constantin Olar, Marius Leonard Roșca, Sebastian Daniel Leba, Monica Computer and System Control Engineering Department University of Petrosani 20 Universitatii Petrosani332006 Romania

ISBN: (纸本)9783031816840

This paper explores recent innovation in the field of robotic teleoperation, presenting a state-of-the-art system for a robotic arm, configurable as an exoskeleton or prosthetic limb. Based on noninvasive neural headset technology, the system captures movement intentions directly from the user’s neural activity. By precisely interpreting neural signals and translating them into robotic arm movements, the system bridges the gap between human intention and robotic action. Its uniqueness lies in its ability to interpret the subtleties of human intent through non-invasive means, providing intuitive and fluid control over the robotic arm. The system is distinguished by the efficient use of rich neural activity, thus improving usability and opening up new possibilities for people with mobility impairments. The paper analyzes technical complexities, detailing the functionality of neural headsets, signal decoding algorithms, and the command formation process for the robotic arm. It also presents empirical evidence of the effectiveness of the system and discusses possible applications and future improvements. This research not only contributes to robotic teleoperation, but also paves the way for more human and empathetic interactions with machines in the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Exoskeleton (Robotics)

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A systematic Literature Review on the Security Attacks and Countermeasures Used in Graphical Passwords

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IEEE Access 2024年 12卷 53408-53423页

作者： Por, Lip Yee Ng, Ian Ouii Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

This systematic literature review delves into the dynamic realm of graphical passwords, focusing on the myriad security attacks they face and the diverse countermeasures devised to mitigate these threats. The core objective of this paper is to identify existing security threats to graphical password schemes and the corresponding countermeasures developed to mitigate these attacks. The study process begins by identifying the usable databases and search engines to identify all the relevant resources. The inclusion and exclusion criteria were carefully selected to prioritize the study, focusing mostly on attacks and countermeasures related to graphical password schemes between 2009 and 2023. After thorough identification and selection progress, 59 studies met all the criteria. Among these studies, 47 mentioned shoulder surfing as a threat to graphical password schemes, while 20 discussed brute force attacks. Additionally, there were 21 papers on dictionary attacks, 13 on smudge attacks, spyware attacks, and social engineering, and 19 that discussed guessing attacks as threats to graphical password schemes. Furthermore, the papers identified several other attacks, including frequency of occurrence analysis attacks, video recording, eavesdropping, computer vision, sonar, and image gallery attacks, with the corresponding numbers of papers being 9, 17, 5, 2, 2, and 1, respectively. The results also highlight the countermeasures proposed in the study papers to mitigate the aforementioned attacks. Among the various countermeasures identified, most revolve around randomization, obfuscation, and password space complexity as the most commonly used techniques for enhancing the security of graphical password schemes. © 2013 IEEE.

关键词： Authentication

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Dynamic generative R-CNN

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Neural Computing and Applications 2025年第10期37卷 7107-7120页

作者： Saffarini, Rasha Khamayseh, Faisal Awwad, Yousef Sabha, Muath Eleyan, Derar IT Engineering Arab American University Jenin Palestine Department of Computer Science Palestine Polytechnic University Hebron Palestine Department of Computer System Engineering Palestine Technical University Tulkarm Palestine Department of Multimedia Technology Arab American University Jenin Palestine Department of Applied Computing Palestine Technical University Tulkarm Palestine

Different techniques have been developed for object detection and recognition. These techniques can be divided into single-shot and two-shot methods. Single-shot methods focus on real-time applications, while two-shot methods are used in applications requiring higher accuracy. However, different versions of the two-shot techniques produce limited results in terms of accuracy and speed, or both. Therefore, this study proposes a novel model called dynamic generative R-CNN (DGR-CNN) that reduces the number of proposed regions using a dynamic programming model that applies the graph similarity method over graph-based image segmentation. Additionally, the proposed model employs DCGAN technique to improve detection performance. DGR-CNN reduces the overall detection and classification time and enhances the detection accuracy. The PASCAL VOC2007 and MS COCO datasets were utilized to evaluate the model. The results showed that DGR-CNN significantly reduces the number of candidate regions compared to the selective search algorithm employed in R-CNN and fast R-CNN. Although fast R-CNN utilizes 2000 regions and faster R-CNN utilizes 300 regions, DGR-CNN reduces the number of regions to approximately 130. The mean average precision of the proposed method was 75.1% on the PASCAL VOC2007, while fast and faster R-CNN scored 66.9% and 69.9%, respectively. Moreover, the DGR-CNN model significantly improved the classification accuracy when tested on the MS COCO dataset, achieving an MAP of 68.76%, compared with 32.64% and 42.3% for fast and faster R-CNN. This increase in accuracy was achieved without significantly compromising the speed compared with faster R-CNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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Architecture and Operational Control for Resilient Microgrids 60

Architecture and Operational Control for Resilient Microgrid...

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60th IEEE/IAS Industrial and Commercial Power systems Technical Conference, I and CPS 2024

作者： Correia, Alexandre F. M. Cavaleiro, Miguel Neves, Miguel Coimbra, A. Paulo Almeida, Tony R. O. Moura, Pedro De Almeida, Aníbal T. University of Coimbra Institute of System and Robotics Department of Electrical and Computer Engineering Coimbra Portugal

ISBN: (纸本)9798350345308

The increasing frequency of natural disasters has led to situations in which small urban centers and critical infrastructures become isolated from the main utility grid. The microgrids' ability to work autonomously from the utility grid presents a viable solution to this problem. Microgrid resiliency is the characteristic related to the capacity of a microgrid to minimize the impact of disruptive events and ensure that the power supply is maintained under a variety of adverse conditions. This is especially important for critical infrastructures such as hospitals, communications, computer networks and military bases. The objective of this paper is to simulate strategies and propose an algorithm for the design and management of electric microgrids with a focus on resiliency towards critical and disaster situations. The proposed solutions were validated, using an experimental microgrid implemented at the University of Coimbra as a pilot. As a result of the work, the study presents an algorithm that efficiently manages loads of a microgrid, including automatic islanding operation, in order to increase its resilience when the microgrid is isolated from the utility grid. © 2024 IEEE.

关键词： Smart power grids

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Architecture of AC-DC Energy Nodes in Supergrids: A Technical Foundation for Future Renewable Integration 5

Architecture of AC-DC Energy Nodes in Supergrids: A Technica...

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5th National and the 1st International Conference on Applied Research in Electrical engineering, AREE 2025

作者： Mousavi, Mohammad Hossein Moradi, Hassan Rouzbehi, Kumars Razi University Department of Computer and Electrical Engineering Kermanshah Iran University of Seville Department of System Engineering and Automatic Control Seville Spain

ISBN: (纸本)9798350377170

The AC-DC Energy Nodes (ADENs) concept offers a transformative approach to modernizing power grids, particularly in the context of supergrids. By centralizing power flows from diverse renewable energy sources, such as offshore wind farms, ADENs enable efficient long-distance power transmission through HVDC technology. This design enhances the balance of supply and demand across regions and countries, significantly improving grid stability and resilience. However, the implementation of ADENs presents substantial technical, regulatory, and cybersecurity challenges. This study delves into these challenges while also addressing the geopolitical complexities of transnational connections, underscoring the critical importance of policy alignment for successful deployment. © 2025 IEEE.

关键词： Wind power integration

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