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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Ramasami, Meaga Varsha Thangaraj, Rajasekaran Manoj Kumar, S. Eswaran, Sivaraman Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Curtin University Malaysia Department of Electrical and Computer Engineering Miri Malaysia

ISBN: (纸本)9798350310689

Sales fluctuation is the biggest problem faced by every retailer. The key to solving this issue is finding the invisible pattern of the components that have an effect on sales prediction. Nowadays retailers require a 360-degree vision of their customers to compete in the market. To achieve sales and marketing objectives, retailers must build strong promotions and offers. Retail companies employ the data from the previous year to analyse and predict sales for the upcoming year using data analysis applications. Additionally, it provides retailers with analytical insights, particularly in identifying the products liked by the customers in a particular store. In this paper, Walmart store the world's largest retailer dataset is used to figure out the business drivers and forecast which departments will be impacted by multiple parameters like average temperature, fuel price, and unemployment rate along with holidays reference and their effect on sales at stores of various locations. By visualising the analysed data, the Scala and Python API of the Spark framework is utilised to understand Walmart's marketing initiatives and data-driven plans and to acquire fresh insights into consumer behaviour. © 2023 IEEE.

关键词： Sales

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Multi-granularity feature enhancement network for maritime ship detection

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 649-664页

作者： Li Ying Duoqian Miao Zhifei Zhang Hongyun Zhang Witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Project Management Office of China National Scientific Seafloor Observatory Tongji UniversityShanghaiChina Department of Electrical and Computer Engineering AlbertaEdmontonCanada System Research Institute Polish Academy of SciencesWarsawPoland

Due to the characteristics of high resolution and rich texture information,visible light images are widely used for maritime ship ***,these images are suscep-tible to sea fog and ships of different sizes,which can result in missed detections and false alarms,ultimately resulting in lower detection *** address these issues,a novel multi-granularity feature enhancement network,MFENet,which includes a three-way dehazing module(3WDM)and a multi-granularity feature enhancement module(MFEM)is *** 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three-way decisions and FFA-Net to obtain clear image ***,the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super-resolution reconstruction con-volutional neural network to enhance the resolution and semantic representation capa-bility of the feature maps from *** results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Pre-cision metric on two benchmark datasets,achieving 96.28%on the McShips dataset and 97.71%on the SeaShips dataset.

关键词： object classification object recognition rough sets rough set theory

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Tea Leaf Disease Identification using Improved Convolution Neural Network

Tea Leaf Disease Identification using Improved Convolution N...

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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Thangaraj, Rajasekaran Vadivel, Balasubramaniam Aravind, T. Chandhru, L. Harish, K. Eswaran, Sivaraman Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Curtin University Malaysia Department of Electrical and Computer Engineering Miri Malaysia

ISBN: (纸本)9798350310689

Plant diseases are a significant concern for the agricultural industry, as they can reduce crop yields and cause economic losses. Tea is a popular and widely consumed beverage in India, and the tea crop can be affected by different leaf diseases. Early detection of the diseases is essential to prevent the spread of other crops and minimize production losses. Traditional methods of detecting leaf diseases involve manually interpreting the images, which can be time-consuming and laborious. To address this, artificial intelligence techniques, specifically deep learning models are used for more accurate and efficient detection of tea leaf disease. This study compares the performance of several deep learning models including ResNet50, Resnet50-RS, ResNetlOl-V2, and Modified-ResNet50-V2 (M-RN50V2) and finds that the M-RN50V2 model has the highest accuracy at 90.S4%. Overall, this work aims to promote sustainable agriculture by leveraging cutting-edge technology for disease classification. © 2023 IEEE.

关键词： Deep learning

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FedAudio: A Federated Learning Benchmark for Audio Tasks 48

FedAudio: A Federated Learning Benchmark for Audio Tasks

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Tuo Feng, Tiantian Alam, Samiul Lee, Sunwoo Zhang, Mi Narayanan, Shrikanth S. Avestimehr, Salman University of Southern California Department of Electrical and Computer Engineering United States Michigan State University Department of Electrical and Computer Engineering United States The Ohio State University Department of Computer Science and Engineering United States Inha University Department of Computer Engineering Korea Republic of

ISBN: (纸本)9781728163277

Federated learning (FL) has gained substantial attention in recent years due to data privacy concerns related to the pervasiveness of consumer devices that continuously collect data from users. While a number of FL benchmarks have been developed to facilitate FL research, none of them include audio data and audio-related tasks. In this paper, we fill this critical gap by introducing a new FL benchmark for audio tasks which we refer to as FedAudio. FedAudio includes four representative and commonly used audio datasets from three important audio tasks that are well aligned with FL use cases. In particular, a unique contribution of FedAudio is the introduction of data noises and label errors to the datasets to emulate challenges when deploying FL systems in real-world settings. FedAudio also includes the benchmark results of the datasets and a PyTorch library with the objective of facilitating researchers to fairly compare their algorithms. We hope FedAudio could act as a catalyst to inspire new FL research for audio tasks and thus benefit the acoustic and speech research community. The datasets and benchmark results can be accessed at https://***/zhang-tuo-pdf/FedAudio. © 2023 IEEE.

关键词： Data privacy

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Real-Time Power Monitoring and Fault Detection in Power Distribution System Using FPGA and cRIO 9039

Real-Time Power Monitoring and Fault Detection in Power Dist...

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2024 International Conference on Innovations in science, engineering and Technology, ICISET 2024

作者： Mithil, Md. Salman Khan Talha, Md. Abu Ahmad, Shameem Islam, Khandoker Mainul South Dakota School of Mines & Technology Department of Electrical Engineering and Computer Science Rapid CitySD57701 United States Faculty of Engineering Department of Electrical and Electronic Engineering Dhaka1229 Bangladesh

ISBN: (纸本)9798350355499

This paper details developing and implementing a comprehensive power monitoring system, designed for real-time data acquisition and fault detection. Utilizing a cRIO 9039 platform, the system measures voltage and current, calculates RMS values, frequency, and complex power, and identifies fault conditions in voltage levels, frequency, or power factor. A unique aspect of our approach integrates FPGA-based data processing, ensuring high-speed data handling and error detection without interrupting primary data flow. The system also features a user-friendly interface for real-time data display and fault management. If any errors have been detected, the user will be notified using visual and audio warning and the reading and timestamp corresponding to the fault will be recorded. The system accurately monitors power metrics and detects deviations, enhancing industrial power management systems. The architecture, workflow, and user interface design align with industrial standards, offering practical insights into power system monitoring. © 2024 IEEE.

关键词： Power distribution faults

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Hotel Information System Management Audit Using COBIT 2019 - APO12 2

Hotel Information System Management Audit Using COBIT 2019 -...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Utamajaya, Joy Nashar Supangat, Suhono Harso Gaol, Ford Lumban Ranti, Benny BINUS University BINUS Graduate Program School of Information System Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatic Bandung Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia University of Indonesia Computer Science Department Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350320923

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal. © 2023 IEEE.

关键词： Risk management

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Aerosol jet printing of surface acoustic wave microfluidic devices

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Microsystems & Nanoengineering 2024年第1期10卷 231-242页

作者： Joseph Rich Brian Cole Teng Li Brandon Lu Hanyu Fu Brittany N.Smith Jianping Xia Shujie Yang Ruoyu Zhong James L.Doherty Kanji Kaneko Hiroaki Suzuki Zhenhua Tian Aaron D.Franklin Tony Jun Huang Department of Biomedical Engineering Duke UniversityDurhamNC 27708USA Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA Department of Mechanical Engineering Virginia Polytechnic Institute and State UniversityBlacksburgVA 24061USA Thomas Lord Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Deptartment of Precision Mechanics Faculty of Science and EngineeringChuo UniversityTokyo 112-8551Japan Department of Chemistry Duke UniversityDurhamNC 27708USA

The addition of surface acoustic wave(SAW)technologies to microfluidics has greatly advanced lab-on-a-chip applications due to their unique and powerful attributes,including high-precision manipulation,versatility,integrability,biocompatibility,contactless nature,and rapid ***,the development of SAW microfluidic devices is limited by complex and time-consuming micro/nanofabrication techniques and access to cleanroom facilities for multistep photolithography and vacuum-based *** simplify the fabrication of SAW microfluidic devices with customizable dimensions and functions,we utilized the additive manufacturing technique of aerosol jet *** successfully fabricated customized SAW microfluidic devices of varying materials,including silver nanowires,graphene,and poly(3,4-ethylenedioxythiophene)polystyrene sulfonate(PEDOT:PSS).To characterize and compare the acoustic actuation performance of these aerosol jet printed SAW microfluidic devices with their cleanroom-fabricated counterparts,the wave displacements and resonant frequencies of the different fabricated devices were directly measured through scanning laser Doppler ***,to exhibit the capability of the aerosol jet printed devices for lab-on-a-chip applications,we successfully conducted acoustic streaming and particle concentration ***,we demonstrated a novel solution-based,direct-write,single-step,cleanroom-free additive manufacturing technique to rapidly develop SAW microfluidic devices that shows viability for applications in the fields of biology,chemistry,engineering,and medicine.

关键词： printing wave fluid

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Evolutionary Optimization with a Simplified Helper Task for High-Dimensional Expensive Multiobjective Problems

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-32页

作者： Wu, Xunfeng Lin, Qiuzhen Zhou, Junwei Liu, Songbai Coello Coello, Carlos A. Leung, Victor C. M. College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Mexico City07300 Mexico Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In recent years, surrogate-Assisted evolutionary algorithms (SAEAs) have been sufficiently studied for tackling computationally expensive multiobjective optimization problems (EMOPs), as they can quickly estimate the qualities of solutions by using surrogate models to substitute for expensive evaluations. However, most existing SAEAs only show promising performance for solving EMOPs with no more than 10 dimensions, and become less efficient for tackling EMOPs with higher dimensionality. Thus, this article proposes a new SAEA with a simplified helper task for tackling high-dimensional EMOPs. In each generation, one simplified task will be generated artificially by using random dimension reduction on the target task (i.e., the target EMOPs). Then, two surrogate models are trained for the helper task and the target task, respectively. Based on the trained surrogate models, evolutionary multitasking optimization is run to solve these two tasks so that the experiences of solving the helper task can be transferred to speed up the convergence of tackling the target task. Moreover, an effective model management strategy is designed to select new promising samples for training the surrogate models. When compared to five competitive SAEAs on four well-known benchmark suites, the experiments validate the advantages of the proposed algorithm on most test cases. © 2024 held by the owner/author(s).

关键词： Multitasking

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Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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Towards Non-Invasive Swallowing Assessment: An AI-Powered Interface for Swallowing Kinematic Analysis using High-Resolution Cervical Auscultation 46

Towards Non-Invasive Swallowing Assessment: An AI-Powered In...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Luo, Yuewen Anwar, Ayman Ren, Siyi Coyle, James L. Sejdic, Ervin University of Toronto Division of Engineering Science Faculty of Applied Science & Engineering TorontoON Canada University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing is a pivotal physiological function for human sustenance and hydration. Dysfunctions, termed dysphagia, necessitate prompt and precise diagnosis. Videofluoroscopic swallowing studies (VFSS) remain the gold standard for swallowing assessment but pose accessibility and radiation exposure concerns. High-resolution cervical auscultation (HRCA) presents a non-invasive alternative with comparable accuracy. Yet, existing studies have only assessed HRCA in tandem with VFSS, without applying it to clinical settings. We propose an AI-enhanced PC software deploying HRCA for real-time bedside swallowing screening. This software proficiently detects abnormalities and precisely measures key kinematic events, including upper esophageal sphincter opening and laryngeal vestibule closure duration. The system was evaluated through two studies, encompassing a VFSS comparative analysis and a usability study. Our findings underscored the software's superior operational efficiency and diagnostic accuracy, positioning our HRCA-based system as a refined and pragmatic alternative for dysphagia assessment. © 2024 IEEE.

关键词： User centered design

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