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001469657 001__ 1469657
001469657 003__ OCoLC
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001469657 020__ $$a9783031346194$$q(electronic bk.)
001469657 020__ $$a303134619X$$q(electronic bk.)
001469657 020__ $$z9783031346187
001469657 0247_ $$a10.1007/978-3-031-34619-4$$2doi
001469657 035__ $$aSP(OCoLC)1382403356
001469657 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dOCLCF
001469657 049__ $$aISEA
001469657 050_4 $$aQ334
001469657 08204 $$a006.3$$223/eng/20230614
001469657 1112_ $$aMachine Intelligence and Emerging Technologies (Conference)$$n(1st :$$d2022 :$$cNoākhāli, Bangladesh).
001469657 24510 $$aMachine intelligence and emerging technologies :$$bFirst International Conference, MIET 2022, Noakhali, Bangladesh, September 23-25, 2022, proceedings.$$nPart I /$$cMd. Shahriare Satu, Mohammad Ali Moni, M. Shamim Kaiser, Mohammad Shamsul Arefin, editors.
001469657 264_1 $$aCham :$$bSpringer,$$c[2023]
001469657 264_4 $$c©2023
001469657 300__ $$a1 online resource (xxxi, 571 pages) :$$billustrations (some color).
001469657 336__ $$atext$$btxt$$2rdacontent
001469657 337__ $$acomputer$$bc$$2rdamedia
001469657 338__ $$aonline resource$$bcr$$2rdacarrier
001469657 4901_ $$aLecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering,$$x1867-822X ;$$v490
001469657 500__ $$aInternational conference proceedings.
001469657 500__ $$aIncludes index.
001469657 5050_ $$aImaging for Disease Detection: Potato-Net: Classifying Potato Leaf Diseases Using Transfer Learning Approach -- False Smut Disease Detection in Paddy using Convolutional Neural Network -- Gabor Wavelet based Fused Texture Features for Identification of Mungbean Leaf Diseases -- Potato Disease Detection Using Convolutional Neural Network: A Web Based Solution -- Device-friendly Guava fruit and leaf disease detection using deep learning -- Cassava Leaf Disease Classification using Supervised Contrastive Learning -- Diabetes Mellitus prediction using Transfer Learning -- An Improved Heart Disease Prediction Using Stacked Ensemble Method -- Improved and Intelligent Heart Disease Prediction System Using Machine Learning Algorithm -- PreCKD_ML: Machine Learning Based Development of Prediction Model for Chronic Kidney Disease and Identify Significant Risk Factors -- A Reliable and Efficient Transfer Learning Approach for Identifying COVID-19 Pneumonia from Chest X-ray -- Infection Segmentation from COVID-19 Chest CT Scans with Dilated CBAM U-Net -- Convolutional Neural Network Model to Detect COVID-19 Patients Utilizing Chest X-ray Images -- Classification of Tumor Cell Using a Naive Convolutional Neural Network Model -- Tumor-TL: A Transfer Learning Approach for Classifying Brain Tumors from MRI Images -- Deep Convolutional Comparison Architecture for Breast Cancer Binary Classification -- Lung Cancer Detection from Histopathological Images using Deep Learning -- Brain Tumor Detection Using Deep Network EfficientNet-B0 -- Cancer Diseases Diagnosis Using Deep Transfer Learning Architectures -- Transfer Learning Based Skin Cancer Classification using GoogLeNet -- Assessing the Risks of COVID-19 on the Health Conditions of Alzheimer’s Patients Using Machine Learning Techniques -- MRI based automated detection of Brain Tumor using DWT, GLCM, PCA, ensemble of SVM and PNN in sequence -- Pattern Recognition and Natural Language Processing: Performance Analysis of ASUS Tinker and MobileNetV2 in Face Mask Detection on Different Datasets -- Fake Profile Detection in Social Media Using Image processing and Machine learning -- A Novel Texture Descriptor Evaluation window based Adjacent Distance Local Binary Pattern (EADLBP) for Image Classification -- Bornomala: A Deep Learning-Based Bangla Image Captioning Technique -- Traffic Sign Detection & Recognition Using Deep Learning Approach -- A Novel Bangla Spoken Numerals Recognition System using Convolutional Neural Network -- Bangla Speech-based Person Identification using LSTM Networks -- VADER vs. BERT: A Comparative Performance Analysis for Sentiment on Coronavirus Outbreak -- Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models -- Covid-19 Vaccine Sentiment Detection and Analysis Using Machine Learning Technique and NLP -- Sentiment Analysis of Tweets on Covid Vaccine (Pfizer): A Boosting-based Machine Learning Solution -- Matching Job Circular With Resume Using Different Natural Language Processing Based Algorithms -- Transformer-based Text Clustering for Newspaper Articles -- Bangla to English Translation using Sequence to Sequence Learning model based Recurrent Neural Networks -- Bangla Spelling Error Detection and Correction Using N-gram Model -- Bidirectional Long-Short Term Memory with Byte Pair Encoding and Back Translation for Bangla-English Machine Translation -- Face Recognition-based Mass Attendance using YOLOv5 and ArcFace -- A Hybrid Watermarking Technique Based on LH-HL Subbands of DWT and SVD -- A Smartphone Based Real-time Object Recognition System for Visually Impaired People -- Bangla Speech Emotion Recognition Using 3D CNN Bi-LSTM Model -- An RNN Based Approach to Predict Next Word in Bangla Language.
001469657 506__ $$aAccess limited to authorized users.
001469657 520__ $$aThe two-volume set LNICST 490 and 491 constitutes the proceedings of the First International Conference on Machine Intelligence and Emerging Technologies, MIET 2022, hosted by Noakhali Science and Technology University, Noakhali, Bangladesh, during September 23–25, 2022. The 104 papers presented in the proceedings were carefully reviewed and selected from 272 submissions. This book focuses on theoretical, practical, state-of-art applications, and research challenges in the field of artificial intelligence and emerging technologies. It will be helpful for active researchers and practitioners in this field. These papers are organized in the following topical sections: imaging for disease detection; pattern recognition and natural language processing; bio signals and recommendation systems for wellbeing; network, security and nanotechnology; and emerging technologies for society and industry.
001469657 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 14, 2023).
001469657 650_0 $$aArtificial intelligence$$vCongresses.
001469657 655_0 $$aElectronic books.
001469657 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001469657 655_7 $$aConference papers and proceedings.$$2lcgft
001469657 7001_ $$aSatu, Md. Shahriare,$$eeditor.
001469657 7001_ $$aMoni, Mohammad Ali,$$eeditor.
001469657 7001_ $$aKaiser, M. Shamim,$$eeditor.
001469657 7001_ $$aArefin, Mohammad Shamsul,$$eeditor.
001469657 830_0 $$aLecture notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering ;$$v490.$$x1867-822X
001469657 852__ $$bebk
001469657 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-34619-4$$zOnline Access$$91397441.1
001469657 909CO $$ooai:library.usi.edu:1469657$$pGLOBAL_SET
001469657 980__ $$aBIB
001469657 980__ $$aEBOOK
001469657 982__ $$aEbook
001469657 983__ $$aOnline
001469657 994__ $$a92$$bISE