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New Frontiers in Communication, Automation, Management and security (iCCAMs), international Conference on

作者： R. Usha R.senthamil selvan A Basi Reddy P. Chandrakanth Department of CSE Madanapalle Institute of Technology & Science Madanapalle India Department of Electronics and Communication Engineering nnamacharya Institute of Technology and Sciences Tirupati India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati India Department of CSE N.B.K.R.I.S.T vidyanagar Kota India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Food grains are sensitive to deterioration owing to precipitation temperature, humidity, and several other variables; thus, researchers are working toward novel approaches to preserve the dietary value of food to improve the shelf lifespan of food. therefore, reliable methods of monitoring food deterioration are necessary to maintain current food safety standards. A prototype has been developed to monitor food quality and manage storage systems within domestic settings. the CNN (Convolutional Neural Network) model was initially utilized for fruit and vegetable classification. the proposed system utilizes actuators and sensors to monitor the emission of gases, humidity, and the temperature of vegetables and fruits to assess the degree of food spoilage. Furthermore, this measure effectively regulates the surrounding conditions and minimizes the occurrence of food deterioration whenever feasible. Customers are notified through text message when the food they ordered is about to deteriorate based on the product’s freshness and condition. the used model has a confirmed accuracy of 96%. some types of food could extend their storage life by two days, meaning the experiment succeeded.

关键词： temperature sensors temperature measurement Humidity sensor systems sensors safety Convolutional neural networks

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An Asymmetric Approach to three-Way Approximation of Fuzzy sets

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iEEE transactions on Fuzzy systems 2025年

作者： Zhao, Xuerong Miao, Duoqian Yao, Yiyu Pedrycz, Witold Shanghai Normal University College of Information Mechanical and Electrical Engineering Shanghai201418 China Tongji University Department of Computer Science and Technology Shanghai201804 China University of Regina Department of Computer Science ReginaSKS4S 0A2 Canada University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute of the Polish Academy of Sciences Warsaw Poland

the three-way approximation of fuzzy sets represents membership values using a three-valued set {1,m,0} , where 1 indicates total belongingness, 0 total non-belongingness, and m an intermediate state. this approach elevates values of membership function above a threshold α to 1, reduces those below β to 0, and assigns the remaining ones to an intermediate value m. A key challenge lies in determining the thresholds α and β and selecting the value of m, as existing models often lack analytical solutions and fail to fully explore the relationship between m and membership structures. this study introduces an asymmetric three-way approximation model for fuzzy sets, removing the constraint α + β = 1. An analytical formula is derived for the thresholds (α,β) by minimizing information loss, and the relationship between m and membership structures is thoroughly examined. An adaptive optimizer is proposed to learn the approximate optimal value of m by minimizing the information loss. the experimental results show that information loss decreases initially before increasing as m grows. Besides, our model achieves the best classification across most datasets. © 1993-2012 iEEE.

关键词： Membership functions

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Early Detection of Gynecological Malignancies Using Ensemble Deep Learning Models: Resnet50 and inception V3

SSRN

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ssRN 2024年

作者： Kwatra, Chetna Vaid Kaur, Harpreet Mangla, Monika Kavita shah, Mohd Asif Mallik, saurav Department of Computer Science & Engineering Lovely Professional University India Department of Computer Science & Engineering Lovely Professional University India Department of Information Technology SVKM’s Dwarkadas J Sanghvi College of Engineering Mumbai India Chandigarh University Mohali India Dept. of Economics Kardan University Afghanistan Dept. of Pharmacology & Toxicology University of Arizona TucsonAZ85721 United States Dept. of Environmental Health Harvard T H Chan School of Public Health Boson MA02115 United States

Enhancing patient outcomes and reducing mortality rates rely on timely detection of gynecological malignancies. Medical image analysis has lately demonstrated potential in cancer detection, specifically with the application of deep learning. this study introduces a hybrid deep learning model that combines the ResNet50 and inception v3 architectures. the purpose of this model is to enhance the precision and reliability of detecting gynecological tumors at an early stage. the ensemble model combines the advantages of both designs to identify intricate characteristics in medical images, offering a reliable method for the early detection of cancer. the suggested model underwent training and evaluation utilizing a substantial dataset of gynecological pictures, showcasing its exceptional performance in comparison to individual models. the ResNet50 model obtained a training accuracy of 83.33%, a validation accuracy of 100.00%, and a test accuracy of 86.00%. in comparison, the inception v3 model had a training accuracy of 12.50%, a validation accuracy of 100.00%, and a test accuracy of 85.00%. the ensemble model outperformed both individual models, reaching state-of-the-art (sOtA) performance with a training accuracy of 99.80%, a validation accuracy of 99.80%, and a test accuracy of 99.80%. the efficacy of the ensemble model was assessed by measuring accuracy, sensitivity, specificity, and F1 score. the results showed that the ensemble model greatly improved detection accuracy and achieved greater sensitivity and specificity rates. this led to a reduction in false negatives and false positives. this research additionally incorporates a thorough juxtaposition with existing works in the Journal of King saud University - computer and information sciences, emphasizing progress and difficulties in the field. the findings indicate that the suggested ensemble model is a valuable instrument for medical professionals, potentially assisting in precise and prompt identification of gyn

关键词： Oncology

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Efficient Privacy-Preserving and secure Authentication Framework for the internet of Vehicles

Efficient Privacy-Preserving and Secure Authentication Frame...

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Blockchain technology and information security (iCBCtis), international Conference on

作者： Om Goel Bipin Gajbhiye Viharika Bhimanapati saketh Reddy Cheruku sumit shekhar Laith H. Alzubaidi Department of CSE-DS ABES Engineering College A.K.T.U. U.P. India Department of CSE Johns Hopkins University Baltimore M.D. U.S.A. Department of Computer Science Southern University and A&M College L.A. U.S.A. Department of Computer Science Wichita State University Wichita U.S.A. Department of CSE Columbia University New York U.S.A. The Islamic university Najaf Iraq Najaf Iraq

ISBN: (数字)9798331528324

ISBN: (纸本)9798331528331

Vehicles connected to the internet of things (iot) have significantly improved the safety and quality of its services. in addition, a semantic security analysis has shown that it is resilient to multiple ioV attack vectors, as well as anonymous, untraceable, and key-secretive. As part of its V2V communication security strategy, this paper introduces a robust protocol to authenticate all components of the V2V communication system. Vehicles, Registration Authorities (RAs), trusted Authorities (tAs), and Vehicle servers (Vss) are the four key participants in the model. initiating communication involves sending queries to the Vs, which keeps track of real-time vehicle data and ensures that it is accessible only to authenticated vehicles. RA registration is mandatory, while tA regulation and authentication ensure vehicle- Vs communication, ensuring network security and data traffic control. As a result of the blockchain infrastructure, vehicles can only communicate within the network if they are registered and authenticated. With this model, we aim to ensure that V2V communication networks are safe and reliable by providing essential security features, such as authentication, integrity, and confidentiality.

关键词： Protocols scalability semantics Authentication traffic control Vectors Blockchains safety Reliability servers

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Microdisk Design for Vertical Collection from Quantum Emitters

Microdisk Design for Vertical Collection from Quantum Emitte...

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Frontiers in Optics + Laser science 2021, FiO+Ls 2021 - Part of Frontiers in Optics, FiO 2021

作者： Hammond, ian M. Duan, Yuqin trusheim, Matthew E. Englund, Dirk R. Camacho, Ryan M. Department of Electrical and Computer Engineering Brigham Young University ProvoUT84602 United States Research Laboratory of Electronics M.I.T. CambridgeMA02139 United States Department of Electrical Engineering and Computer Science M.I.T. CambridgeMA02139 United States U.S. Army Research Laboratory Sensors and Electron Devices Directorate AdelphiMD20783 United States

We report on progress to improve the free space coupling and Purcell enhancement of a diamond microdisk resonator using eigenfrequency modal analysis and robust topology optimization. Frontiers in Optics / Laser scien... 详细信息

ISBN: (纸本)9781557528209

关键词： Modal analysis

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A Novel Method for Exploring the store sales Forecasting using Fuzzy Pruning Ls-sVM Approach

A Novel Method for Exploring the Store Sales Forecasting usi...

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international Conference on Edge Computing and Applications (iCECAA)

作者： Mohd. Asif Gandhi Vusal Karimli Maharram G. Raja s.P. sellapaandi Ketan Rathor Kamlesh singh Department of Mechanical Engineering Anjuman-I-Islam's Kalsekar Technical Campus Panvel Maharashtra India Western Caspian University Azerbaijan/ 2Azerbaijan State University of Economics Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of ECE PSNA College of Engineering and Technology Dindigul Tamilnadu India PMO Gyan Sys Inc Maharashtra India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Campus Dehradun India

intelligent robots, intelligent mobiles, intelligent stores, and so on are just a few of the areas where computer-aided ergonomics is being put to use. Convenience stores (CVs) are adapting to a new era of competition by offering a wider variety of products and services than ever before, such as daily fresh meals, a cafe, ticketing, and a grocery. therefore, it is becoming increasingly difficult to estimate daily sales of’ fresh commodities due to the impact of both internal and external factors. in the long run, a trustworthy sales-forecasting system is going to be critical for enhancing corporate plans and gaining an edge over the competition. in today's internet age, data production has reached unprecedented levels, well beyond what any single human being can comprehend. this has led to the development of a plethora of machine learning methods. in this proposed approach various machine learning methods are explored for predicting store's sales and evaluate them to find the one that works best for the specific scenario. training times are reduced and data quality is enhanced with the help of Normalization in the proposed approach. K-Means is a popular feature selection clustering algorithm. Fuzzy Pruning Ls-sVM is used in the suggested method for training the model. the proposed model has superior performance on sVM and CNN.

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A technique Based on Ensemble Machine Learning for the Analysis of Electronic Nose signals

A Technique Based on Ensemble Machine Learning for The Analy...

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Advance Computing and innovative technologies in engineering (iCACitE), international Conference on

作者： Amit taneja santhosh Kumar Rajamani Deepika shekhawat Yerrolla Chanti Kapil Joshi Ashish Garg AIT-CSE Chandigarh University Department of E.N.T MAEER MIT Pune’s MIMER Medical College and Dr BSTR Hospital TalegaonDabhade Pune Maharashtra India Department of Computer Science & Engineering Manipal University Jaipur Rajasthan School of Computer Science and Artificial Intelligent SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Numerous applications have been documented for electronic nose (e-nose) devices, which are valued for their speed, affordability, and lack of invasiveness. the e-nose scheme is a useful instrument for analyzing volatile chemicals as a distinct 'fingerprint,' especially in the meat industry. the pattern identification algorithm's capacity to decode e-nose signals is crucial to the e-nose system's effectiveness in a wide variety of uses. However, positive results using ensemble approaches have been reported in a number of data types. in this study, an ensemble learning strategy is proposed for e-nose signal analysis, particularly for evaluating beef quality. Not only are learning algorithms and sensor array optimization using ensemble approaches. Ensemble FsA is created by combining three filter-based feature selection algorithms (FsAs)—reliefF, chi-square, and Gini index—for sensor array optimization. When using a single FsA on a collection of homogenous e-nose data for beef quality monitoring, it is common for the results to be inconsistent or unexpected. in addition, multi-class regression and categorization problems are handled by using ensemble learning techniques. We use bagging and boosting algorithms, shown here by Random Forest (RF) and Adaboost. Both a support vector machine (sVM) and a decision tree are used as independent learners, and their results are compared to those of the ensemble. Experiments show that our ensemble method is effective and generalizable for e-nose signal processing. Optimized sensor combination using filter-based FsA consistently outperforms previous methods in both classification and regression. Additionally, despite using a reduced sensor complement, Adaboost as a boosting algorithm yields the highest quality prediction.

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Position: Bayesian Deep Learning is Needed in the Age of Large-scale Ai 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

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41st international Conference on Machine Learning, iCML 2024

作者： Papamarkou, theodore skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr immer, Alexander Karaletsos, theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, stephan Nemeth, Christopher Osborne, Michael A. Rudner, tim G.J. Rügamer, David teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

in the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. this paper posits that BDL can elevate the capabilities of deep learning. it revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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the role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, teng-Yi shen, Wu-Chung tsai, Chon-Haw Lo, Yu-Chien Lee, tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: to evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (Ais) lesion. Methods: this study retrospectively enrolled a total of 212 patients having Ais. Four combos, including ADC-ADC-ADC (AAA), DWi-ADC-ADC (DAA), DWi-DWi-ADC (DDA), and DWi-DWi-DWi (DDD), were used as input images, respectively. three ADC thresholds including 0.6, 0.8 and 1.8 × 10–3sup> mm2sup>/s were applied. Dice similarity coefficient (DsC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: the DsC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10–3sup> mm2sup>/s and 0.8 × 10–3sup> mm2sup>/s (p <.001). the U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10–3sup> mm2sup>/s (p =.062 to 1). the U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10–3sup> mm2sup>/s achieved the highest DsC in the segmentation of Ais lesion. Conclusions: the segmentation performance of U-Net for Ais varies among the input imaging combos and ADC thresholds. the U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10–3sup> mm2sup>/s in segmentating Ais lesion with highest DsC. Key Points: • segmentation performance of U-Net for Ais differs among input imaging combos. • segmentation performance of U-Net for Ais differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10–3sup>mm2sup>/s. © 2023, the Author(s), under exclusive licence to European society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance imaging ischemic stroke Neural Networks, computer Retrospective study

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Capsule Networks for Malaria Parasite Classification: An Application Oriented Model

Capsule Networks for Malaria Parasite Classification: An App...

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2020 iEEE international Conference for innovation in technology, iNOCON 2020

作者： Madhu, G. Govardhan, A. srinivas, B.sunil Patel, shilhora Akshay Rohit, B. Bharadwaj, B.Lalith Vnr Vjiet Dept. of Information Technology Hyderabad-90 T.S India Jntuh College of Engineering Dept. of Computer Science and Engg Hyderabad-85 T.S India Tkr College of Engineering Technology Dept. of Computer Science and Engg Hyderabad-97 T.S India

ISBN: (纸本)9781728197449

the epidemic of malaria is a death-dealing infectious disease caused by mosquito spreading across the world. in this technological era, automated diagnosis is worthwhile for accurate and faster solutions. in this work, a novel application-oriented diagnostic model was deployed to detect and classify thin-blood smear images infected with malaria. the Features from thin-blood smears are extracted using a series of Convolution-Neural-Networks and classified with novel Capsule-Networks by understanding the spatial relationship of thin-blood films. the experimentation is compared to VGG-16, ResNet-50, DenseNet-121 architectures with immense depth varying from 16 to 121 layers. the proposed Capsule-Network is 8 layer deep and tends to outperform attaining classification accuracy of 96.9% and specificity, sensitivity scores were 94.95%, 98.18%. this novel model was deployed as a web-application to act as an aid for such a havoc problem and to transfer applicability to every user in need by classifying images in less than 3 seconds. © 2020 iEEE.

关键词： Routing algorithms

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