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44th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, SGAI 2024

作者： Shi, Xinming Minku, Leandro L. Yao, Xin School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom School of Computer Science University of Birmingham Birmingham United Kingdom School of Data Science Lingnan University China

ISBN: (纸本)9783031779145

The automated design of analog circuits presents a significant challenge due to the complexity of circuit topology and parameter selection. Traditional evolutionary algorithms, such as Genetic Programming (GP), have shown potential in this domain but are often hindered by inefficient search processes and the large design space. Furthermore, fitness evaluation in the evolutionary design of circuits is often computationally very expensive. In this paper, we introduce a novel evolutionary framework that leverages approximate Shapley values to guide the optimization process in tree-based genetic programming for analog circuit design. Our approach addresses the computational challenges associated with computing Shapley values by introducing a two-stage evolutionary framework that includes a Shapley Value Library (SVlib) and a KNN-based prediction for efficient estimation of Shapley values. Our proposed work not only enhances the search efficiency by focusing on the most beneficial sub-circuits but also leads to more compact and efficient circuit designs. Furthermore, fitness evaluation in the evolutionary design of circuits is often computationally very expensive experiments, we verify that our framework accelerates evolutionary convergence and outperforms traditional methods in terms of circuit optimization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic programming

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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Development of IoT-based Scale Sensor to Measure AWD System Water Level in Crop Field 3

Development of IoT-based Scale Sensor to Measure AWD System ...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Kabir, Md Shahriar Mahmud Niloy, Khalid Hossen, Md Imon Afrose, Sumaiya Hossain Mofazzol, Md. Ismail American International University-Bangladesh Dept. of Electrical & Electronics Engineering Bangladesh Uttara University Dept. of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The paper introduces an innovative Internet of Things (IoT)-based scale sensor that measures the water level in the alternate wetting and drying (AWD) system and displays data in the mobile app to enable remote monitoring. The AWD system mainly consists of 30 cm of PVC pipe or bamboo with a diameter of 10-15 cm inserted into the paddy field, with a lower 20 cm buried in the soil and tiny holes at the bottom to allow field water to enter. The water level within the pipe indicates the field's water level, especially the underground water level. The AWD method optimizes water usage and reduces methane emissions by watering periodically and drying rice fields instead of maintaining continuous flooding. The current manual measurement process for water level needs to go to crop fields daily with a scale, which is difficult for continuous and remote monitoring. The developed scale sensor has IoT connectivity that removes the difficulties. The scale sensor works based on conductive rain sensors to precisely detect each centimeter rise or fall of water. The sensor consists of two open conductive layers that create a short circuit when the water level touches them by raising, triggering the microcontroller to register a voltage drop. The scale can measure up to 20 cm as it has 20 conductive layers, which fulfill the requirements. It has a 1 cm resolution, and in the test, it shows 95-99% accuracy. So, the sensor can effectively measure the water level in the AWD system, and further research is required to add an automatic irrigation system with the sensor so that the whole process can be controlled remotely. © 2024 IEEE.

关键词： Comparators (optical)

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Deep Learning-Based Classification of Brain Tumours on MRI Images Using EfficientNetB0 4

Deep Learning-Based Classification of Brain Tumours on MRI I...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Kumar, Aditya Nelson, Leema Arumugam, Deepak Chitkara University Department of Computer Science and Engineering Punjab Rajpura India Saveetha University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387490

This study presents an innovative approach to brain tumour classification utilising MRI images and deep learning techniques, specifically the EfficientNetB0 architecture. The methodology involves meticulous dataset curation, comprehensive data preprocessing, and the design of an efficient model architecture. The model achieves a notable accuracy of 98% through rigorous training and evaluation procedures, demonstrating its proficiency in distinguishing between various types of brain tumours, including glioma, meningioma, pituitary tumours, and non-tumour samples. The results reveal commendable precision and recall rates, affirming the model's ability to accurately identify pathological conditions from MRI scans. By employing state-of-the-art deep learning methodologies, this study contributes significantly to the advancement of automated brain tumour diagnostics, offering a reliable tool for clinicians to expedite the diagnosis process and improve patient outcomes. The demonstrated accuracy underscores the potential of deep learning algorithms in enhancing medical imaging analysis, paving the way for more efficient and precise diagnostic systems in clinical practice. Overall, this study underscores the transformative impact of artificial intelligence in medical imaging and highlights the promising prospects for integrating deep learning models into routine healthcare practices for improved patient care and outcomes. © 2024 IEEE.

关键词： Deep learning

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A Hyperbolic Micromagnet for Multiple Spin Qubits with Fast Rabi Oscillations and High Addressability

IEEE Electron Device Letters

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IEEE Electron Device Letters 2024年第4期45卷 526-529页

作者： Chang, Che-Hao Li, Yu-Cheng Wu, Yu-Jui Liao, Chen-Yao Lin, Min-Jui Tsao, Hung-Yu Li, Jiun-Yun Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Graduate School of Advanced Technology Taipei10617 Taiwan Taiwan Semiconductor Research Institute Hsinchu30078 Taiwan

We demonstrate a hyperbolic micromagnet to increase the manipulation speed of electron spins in silicon quantum dots using electric dipole spin resonance (EDSR). For single qubits, the hyperbolic magnet provides a large gradient of transverse magnetic fields of 4.9 mT/nm with the corresponding Rabi frequency ( 69 MHz). We then demonstrate that this hyperbolic micromagnet can drive a linear array of qubits with Rabi frequencies of 19 69 MHz and a high addressability (minimum difference of Larmor frequencies) up to 590 MHz to suppress the crosstalk between different qubits. We show that 15 qubits can be effectively addressed for high fidelity by this hyperbolic micromagnet. © 1980-2012 IEEE.

关键词： Magnetic fields

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Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

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computers, Materials & Continua 2023年第11期77卷 2009-2030页

作者： Kalpesh Jadav Vishal Sorathiya Walid El-Shafai Torki Altameem Moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui Department of Electronics&Communication Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Computer Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonSK S7N5A9Canada

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking *** shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and *** of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is *** real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above *** paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle *** method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground *** to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

关键词： Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National Institute of Health vehicle tracking

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Generic Deep-Learning-Based Time Series Models for Aviation Accident Analysis and Forecasting

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SN computer science 2024年第1期5卷 32页

作者： Monika Verma, Seema Kumar, Pardeep Department of Computer Science Banasthali Vidyapith Rajasthan Jaipur India Department of Electrical and Electronics Engineering Education NITTTR Madhya Pradesh Bhopal India Department of Electronics and Communication Engineering SGT University Haryana Gurugram India

The global civil aviation is growing day by which is resulting into more congestion into the air transportation system. Over the past few years, many efforts have been put in the direction to improve the aviation safety and avoid fatal air accidents. Learning from the previous accidents could be crucial to enhance the safety standards and minimise the possibility of such aviation safety events. Hence, this manuscript provides an analysis of time-series-based machine learning models to investigate the impact of aviation accident injuries and fatalities. Time series models are highly capable of predicting aviation accidents due to their statistical association. This study has examined ASRS database containing accident and incident data from 1988 to 2020 by employing time series models such as autoregressive integrated moving average (ARIMA), seasonal ARIMA with an exogenous factor (SARIMAX), deep-learning-based long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and gated recurrent unit (GRU). Here, MSE, RMSE, and MAE are used as performance indicators. The results proved that the Bi-LSTM prediction was superior compared to the other models. Deep-learning-based analysis helps to bring forward the various hidden patterns along with essential information for accident prevention and reducing the accident probability for improving the aviation safety. Ultimately, overall aviation safety level will enhance by minimising the likelihood of occurrence of unsafe events and aviation management optimise their actions towards safety © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Accidents ARIMA Aviation Bi-directional LSTM GRU LSTM Prediction SARIMAX Time series

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Surface flashover in 50 years:*** modification,structureoptimisation,and characteristics enhancement

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High Voltage 2025年第2期10卷 243-278页

作者： Zhen Li Ji Liu Yoshimichi Ohki George Chen Shengtao Li Key Laboratory of Engineering Dielectrics and Its Application Ministry of EducationHarbin University of Science and TechnologyHarbinHeilongjiangChina State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong UniversityXi'anShaanxiChina Research Institute for Materials Science and Technology Waseda UniversityShinjukuJapan School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Surface flashover is a gas-solid interface insulation failure that significantly jeopardises the secure operation of advanced electronic,electrical,and spacecraft *** the widespread application of numerous material modification and structure optimisation technologies aimed at enhancing surface flashover performance,the influence mechanisms of the present technologies have yet to be systematically discussed and *** review aims to introduce various material modification technologies while demonstrating their influence mechanisms on flashover performances by establishing relationships among‘microscopic structure-mesoscopic charge transport-macroscopic insulation failure’.Moreover,it elucidates the effects of chemical structure on surface trap parameters and surface charge transport concerning flashover *** review categorises and presents structure optimisation technologies that govern electric field *** identified technologies highlight that achieving a uniform tangential electric field and reducing the normal electric field can effectively enhance flashover ***,this review proposes recommendations encompassing mathematical,chemical,evaluation,and manufacturing *** systematic summary of current technologies,their influence mechanisms,and associated advantages and disadvantages in improving surface insulation performance is anticipated to be a pivotal component in flashover and future dielectric theory.

关键词： material modification structure optimization surface flashover structure optimisation technologies chemical structure charge transport insulation failure

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Real-Time Incident Monitoring for Smart Cities using IoT 4

Real-Time Incident Monitoring for Smart Cities using IoT

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Pati, Aloukik Praharaj, Ayushi Swain, Kunjabihari Lenka, Manas Ranjan Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Nist Institute of Science and Technology Dept. of Electrical and Electronics Engineering Berhampur India

ISBN: (纸本)9798350349856

In the ever-evolving urban landscape, the concept of the 'smart city' has emerged as a solution to enhance the quality of life for its residents and visitors. Central to this vision is the utilization of the Internet of Things (IoT) technology, which enables the seamless integration of various urban systems and services. Among the critical aspects of smart city management is real-time incident monitoring, which encompasses the rapid detection and response to diverse urban events, including traffic congestion, environmental issues, and public safety concerns. This paper presents a comprehensive exploration of real-time incident monitoring in smart cities through the deployment of IoT-enabled sensors and devices. Leveraging IoT's data collection capabilities, our research delves into the creation of a sophisticated incident monitoring system capable of aggregating, analyzing, and responding to real-time data. The proposed architectural model highlights the data processing algorithms and communication protocols that facilitate the collection and transmission of pertinent data. In the pursuit of resilient and responsive urban environments, this research underscores the pivotal role of IoT-enabled real-time incident monitoring, offering insights into the potential of this technology to enhance the efficiency, safety, and overall well-being of smart city inhabitants. © 2024 IEEE.

关键词： Smart city

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Dual Attention and Question Categorization-Based Visual Question Answering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 81-91页

作者： Mishra, Aakansha Anand, Ashish Guha, Prithwijit Department of Computer Science & Engineering Guwahati781039 India Department of Electronics and Electrical Engineering Guwahati781039 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati Guwahati781039 India

Visual question answering (VQA) aims at predicting an answer to a natural language question associated with an image. This work focuses on two important issues pertaining to VQA, which is a complex multimodal AI task: First, the task of answer prediction in a large output answer space, and second, to obtain enriched representation through cross-modality interactions. This work aims to address these two issues by proposing a dual attention (DA) and question categorization (QC)-based visual question answering model (DAQC-VQA). DAQC-VQA has three main network modules: First, a novel dual attention mechanism that helps toward the objective of obtaining an enriched cross-domain representation of the two modalities;second, a question classifier subsystem for identifying input (natural language) question category. The second module of question categorizer helps in reducing the answer search space;and third, a subsystem for predicting answer depending on the question category. All component networks of DAQC-VQA are trained in an end-to-end manner with a joint loss function. The performance of DAQC-VQA is evaluated on two widely used VQA datasets, viz., TDIUC and VQA2.0. Experimental results demonstrate competitive performance of DAQC-VQA against the recent state-of-the-art VQA models. An ablation analysis indicates that the enriched representation obtained using the proposed dual-attention mechanism helps improve performance. © 2022 IEEE.

关键词： Forecasting

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