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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Shakil, Rashiduzzaman Shamrat, F. M Javed Mehedi Sharmin, Sharmin Akter, Bonna Rubi, Maksuda Akter Dutta, Anik Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of Malaya Department of Computer System and Technology Kuala Lumpur Malaysia Daffodil International University Department of General Educational Development Dhaka Bangladesh Guru Nanak Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350344882

Otitis media represents inflammation or infection of the middle ear, often stemming from colds, sore throats, or respiratory infections. To address diagnostic challenges, this study introduces the EARnet-AR method, a novel advanced approach for automatically classifying otoscope tympanic membrane images. We employed 1100 otoscopic augmented images were utilized to conduct this research. To enhance the clarity and accuracy of otoscopic images, our research incorporated median filtering, an effective noise reduction technique, and gamma correction to adjust the image's luminance, ensuring a more consistent visual representation. In our initial evaluations, we tested the capabilities of five renowned pre-trained models using a comprehensive set of evaluation metrics, including accuracy, sensitivity, specificity, precision, f1-score, FPR, and FNR. The confusion matrices also detailed the model's capabilities in classifying each specific ear condition. Moreover, our proposed novel model EARnet-AR, enriched with Attention and RNN features, surpasses the other models, achieving training, testing and validation accuracies of 97.5%, 96.0%, and 96.5%, respectively. With such precise diagnostic tools, medical professionals can potentially achieve more timely and accurate treatments, enhancing patient care and outcomes in otitis media cases. © 2023 IEEE.

关键词： Diagnosis

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How Do Nonlinear Transformers Learn and Generalize in In-Context Learning? 41

How Do Nonlinear Transformers Learn and Generalize in In-Con...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Hongkang Wang, Meng Lu, Songtao Cui, Xiaodong Chen, Pin-Yu Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

Transformer-based large language models have displayed impressive in-context learning capabilities, where a pre-trained model can handle new tasks without fine-tuning by simply augmenting the query with some input-output examples from that task. Despite the empirical success, the mechanics of how to train a Transformer to achieve ICL and the corresponding ICL capacity is mostly elusive due to the technical challenges of analyzing the nonconvex training problems resulting from the nonlinear self-attention and nonlinear activation in Transformers. To the best of our knowledge, this paper provides the first theoretical analysis of the training dynamics of Transformers with nonlinear self-attention and nonlinear MLP, together with the ICL generalization capability of the resulting model. Focusing on a group of binary classification tasks, we train Transformers using data from a subset of these tasks and quantify the impact of various factors on the ICL generalization performance on the remaining unseen tasks with and without data distribution shifts. We also analyze how different components in the learned Transformers contribute to the ICL performance. Furthermore, we provide the first theoretical analysis of how model pruning affects ICL performance and prove that proper magnitude-based pruning can have a minimal impact on ICL while reducing inference costs. These theoretical findings are justified through numerical experiments. Copyright 2024 by the author(s)

关键词： Distribution transformers

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New approach to predict warranty costs using a new bivariate reliability prediction model 32nd

New approach to predict warranty costs using a new bivariate...

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32nd European Safety and Reliability Conference, ESREL 2022

作者： Krini, Abderrahim Börcsök, Josef Engineering of Reliability and Quality Robert Bosch GmbH Germany Computer and System Programming University of Kassel Germany

ISBN: (纸本)9789811851834

An important marketing strategy of automotive manufacturers is to promote high-quality products by extending warranty periods, which inevitably generates additional costs. In general, different loads on a control unit cause a failure. Therefore, a failure can be traced back to the loads experienced. In reliability theory, the operating time t is commonly used as a reference value for describing the load acting on a system, since the electronic control units (ECUs) are only under current and voltage for the duration t and are therefore only loaded during this time. However, obtaining a suitable database is difficult. The failure data of ECUs collected during the warranty period from the supplier in the event of complaints can be regarded as complete and therefore represent a suitable database for determining reliability parameters. The warranty data are time-censored and reflect the field behaviour of the ECUs during the warranty period. As a rule, when collecting the data, the duration (production and complaint date) of the ECUs in the field is recorded in addition to the cause of failure. With a new multivariate prediction model that uses this data to forecast the expected ECU failures and knowledge of the costs of a warranty case, the expected additional costs of a warranty extension can be calculated. © 2022 ESREL2022 Organizers. Published by Research Publishing, Singapore.

关键词： Automotive industry

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Disk based pay-as-you-go record linkage

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Frontiers of computer Science 2022年第4期16卷 227-229页

作者： Chenchen Sun Derong Shen Key Laboratory of Computer Vision and System(Ministry of Education) Tianjin University of TechnologyTianjin300384China College of Intelligence and Computing Tianjin UniversityTianjin300350China School of Computer Science and Engineering Northeastern UniversityShenyang110189China

1Introduction Record linkage(RL)groups_records corresponding to the same entities in datasets,and is a long-standing topic in data management and mining communities[1-2].In big data era,real-time data applications become popular,and callfor payas-you-go RL(PRL),which produces as many match pairs as possible in very limited time(much shorter than the overall RLruntime).

关键词： record linkage mining

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Deployment method of wireless sensor networks based on MaOEA/P-GM algorithm

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Soft Computing 2024年 1-10页

作者： Wang, Jun Luo, Dongxu Chen, Weiru Peng, Funan Li, Zhiwu College of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China Key Laboratory of Industrial Intelligence Technology on Chemical Process Shenyang University of Chemical Technology Shenyang110142 China College of System Engineering Institute Macao University of Science and Technology 00853 China

At present, wireless sensor networks are developing rapidly, but they will also face many challenges. For example, in the deployment problem, many problems such as cost, coverage, connectivity, energy, network life cycle and so on need to be considered, while the traditional multi-objective algorithm does not perform well in dealing with many-objective problems. How to quickly find high-quality solutions is the focus of research today. This paper proposes a deployment method for wireless sensor networks based on MaOEA/P-GM algorithm. This method improves the mutation strategy of the algorithm according to the characteristics of wireless sensor network deployment based on the many-objective algorithm MaOEA/P. Experiments show that using this algorithm in a many-objective environment can quickly get a high-quality solution set with fewer nodes, high coverage, good connectivity, and balanced energy consumption. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Life cycle

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Leveraging Data Analytics to Enhance Smart Home Efficiency and Security 2

Leveraging Data Analytics to Enhance Smart Home Efficiency a...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Vivekanandan, Karthi Kaleeswari, B. Natchiammai, P.L. Anthonisamy, Arun Vijayabharathi, R. Alagarsamy, Manjunathan Builders Engineering College Department of Electrical and Electronics Engineering Tirupur India Department of Electronics and Communication Engineering Chennai India Mookambigai College of Engineering Department of Electronics and Communication Engineering Pudukkottai India Panimalar Engineering College Department of Computer Science and Business System Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

Smart home technology has made daily life easier, automated, and managed. IoT devices like smart speakers, cameras, thermostats, and security systems are increasingly integrated, raising security and privacy concerns. This study examines smart home devices' energy consumption, use trends, device failures, and system efficiency. This research uses smart home devices to identify consumption trends and risks. To better understand smart home ecosystems, the study will evaluate energy consumption, device usage hours, and malfunctions that can affect functionality and security. Using predictive analytics and machine learning, the researchers predict excessive energy usage and device problems. These methods identify trends and irregularities in smart home device operation to improve performance, efficiency, and security. This study investigates how user decisions, usage hours, and device age affect energy consumption, as well as how to identify optimization opportunities. The data analysis study contains statistical summaries and visualisations of device type, usage hours, energy consumption, user preferences, malfunction incidents, and device age. Machine learning algorithms such as linear regression and anomaly detection predict energy usage and locate anomalies. The data illustrate energy and device efficiency trends and recommend improvements to smart home security and effectiveness. This study addresses smart home device vulnerabilities and inefficiencies to improve smart home system optimization. The results offer smart home technology manufacturers and customers ways to improve energy efficiency, device performance, and system security. © 2024 IEEE.

关键词： Smart manufacturing

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DRDnet22: Advanced convolutional techniques for early diabetic retinopathy detection in retinal images 15

DRDnet22: Advanced convolutional techniques for early diabet...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mazumder, Md. Sakib Ali Shamrat, F M Javed Mehedi Mahmud, Imran Sharmin, Sharmin Hossain, Tanzim Biplab, Ekramul Kabir Daffodil International University Department of Software Engineering Dhaka Bangladesh Universiti Malaya Department of Computer System and Technology Kuala Lumpur Malaysia Institute of Information Technology University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9798350370249

Diabetic Retinopathy (DR) is a prevalent eye complication among individuals with diabetes. Early detection of DR can prevent vision loss and lower associated problems. Manual diagnosis via retinal fundus images requires the expertise of medical professionals to assess several complex characteristics, including visual acuity tests, pupil dilation, and optical consistency tomography. The process is challenging, time-consuming, and prone to errors. This paper proposes a deep learning (DL) technique, DRDnet22, to detect and categorize DR into five distinct stages. The APTOS 2019 dataset is utilized in this study, which contains 3662 images. The study employs dense block and transition layer with global average pooling (GAP) techniques, and the combination of distinct Convolutional Networks (ConvNets) results in an enhanced representation beyond the utilization of features from a single ConvNet alone. To conduct an ablation analysis, the conventional pre-trained model's performance was compared. The performance evaluation points out that the proposed model achieved 81.6% accuracy, which is an advantage over other DCNN models. Despite facing a few inaccuracies, the model's competence in spotting vital features underscores its diagnostic potential. © 2024 IEEE.

关键词： Convolutional neural networks

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Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

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computers, Materials & Continua 2023年第1期74卷 607-621页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia 53100 Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital *** Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their *** may result in compromised security of the systems,scams,and other such *** attacks hijack huge quantities of the available data,incurring heavy financial *** the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify *** this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)*** presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine *** attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding *** addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious ***,AFSA is applied to the proposed model to enhance the efficiency of GRU *** proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle *** simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

关键词： Malicious URL cybersecurity deep learning machine learning metaheuristics gated recurrent unit

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The Impact of Stuttering Event Representation on Detection Performance 2

The Impact of Stuttering Event Representation on Detection P...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Banna, Abedal-Kareem Fang, Hui Shubita, Ahmad F. Arafah, Mohammad Eqteeshat, Amani Abuaisheh, Saif B. University Of Petra Department Of Ai And Data Science Jordan Loughborough University Department Of Computer Science United Kingdom University Of Petra Department Of Software Engineering Jordan University Of Petra Department Of Computer Science Jordan Malaya University Department Of Information System Malaysia

ISBN: (纸本)9798350394962

Stuttering or stammering is a common neurogenic, psychogenic fluency disorder wherein people have uncontrolled blocks on the natural flow of speech. Automatic Stuttering Event Detection (SED) is a widely recognised challenge due to the heterogenic and overlapped nature of stuttering speech. Therefore, this comparative study investigates the impact of stuttering event representation on detection performance. The paper evaluates the performance of SED and shows the impact of employing ASR pre-trained feature extractions on each stuttering event. Moreover, the paper provides different groups of experiments that evaluate the impact of three acoustic features, the Zero Crossing Rate (ZCR), the Spectral Flux Onset Strength Envelope (SFO), and Fundamental Frequency (FF) features, on SED performance. The paper demonstrates that the fusion of spectral and ASR features improves SED performance across all stuttering events by 8%. The F1 score of the Block class increased by approximately 10%, from 15% to 25%, while the word repetition and interjection increased by 8%. In addition, our experiments proved that the fusion of temporal features increased the average F1 score of SED by 2% across all stuttering events. The F1 score of the prolongation and sound repetition classes increased by 6% and 3%, respectively. © 2024 IEEE.

关键词： Deep learning

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Mitigation and Swift Curative Procedure on Alluring Smart City Using Falcon Technology 1

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International Conference on Big Data and Cloud Computing, ICBDCC 2021

作者： Nivethini, G.S. Yokesh Srenevas, R. Rahfar Nisha, R. Ignisha Rajathi, G. Computer Science and Business System Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India

ISBN: (数字)9789811921773

ISBN: (纸本)9789811921766

With the help of modern Artificial intelligence—AI tools through which the normalcy and anomaly can be determined based on the available data in traffic monitoring and control processes. Then by employing efficient algorithms the available data along with image and video captures can be filtered to detect possible anomalies. The main filtering is done based on object detection and identification-based classification methods. This system can be viewed as a complex one with a control room constituting some 20 screens with many cameras associated forming a big network. This innovation is a new approach to reduce manpower and complexity of the existing system thereby improving the quality of life. The major concern is reducing road accidents as nearly one third of the mortality lies there. So, by proper pre-detection this can be reduced. This innovation consists of a bird shaped IOT device named "Falcon" with certain built-in functions to effectively monitor our smart cities and save lives. The main activities would be detecting road accidents, giving prior information and improving women safety. This falcon can be controlled via mobile application. Any threats and hazards faced by any woman can be easily identified and the falcon will send appropriate information to the police and pre-configured members. This can also serve to monitor transport in traffic signals and will report immediately in case of any violations of the traffic rule. When monitoring any threats or accidents that have taken place then notifications with captures will be sent to the police and hospitals. During any natural calamities or even on a regular basis also delivery of any medicines or essential commodities in a short time can be done by this falcon. This will also improve surveillance and better coordination amongst the police, smart city authorities and other government departments to perform better services for the citizens and to serve the people in a better way especially during emergenci

关键词： Hospitals

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