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7th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2022

作者： Singh, Kabrambam Rupabanta Dash, Sujata Atta-ur-Rahman Department of Computer Application Maharaja Srirama Chandra Bhanja Deo University Sriramachandra Vihar Odisha Baripada India DRIEMS School of Professional Studies Odisha Tangi Cuttack India College of Computer Science and Information Technology Imam Abdul Rahman Bin Faisal University Damian Saudi Arabia

ISBN: (纸本)9789819950140

Heart disease is one of the deathliest diseases in the world. As the ECG data is widely available, many machine learning and deep learning models have been developed for the automatic Arrhythmia diagnosis system. In this study, BiLSTM with the Bat algorithm has been proposed for the classification of the Normal ECG signal and the Arrhythmia ECG signal. It can detect the abnormality of the heart rhythm. The novelty of this study is the feature selection using the Bat algorithm that enhanced the performance of the classification model. The performance of this method is compared with the existing methods and it gives better performance than the other existing methods. With the proposed method, an accuracy of 99.75% has been achieved using tenfold cross-validation. It can help the risk patient in the early detection of the abnormality of the heart rhythm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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Improving Search Engine Results Using AI Methods 3

Improving Search Engine Results Using AI Methods

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Katariya, Jitendra Kumar Geetha, B. Mahajan, Rupali A. Singh, Manpreet Yadav, Rakesh Kumar Vivek, V. Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

This paper examines how artificial Intelligence (AI) algorithms can be used to improve seek engine consequences. It outlines key terms, demanding situations, and opportunities related to AI -based totally search engine optimization. initial studies shows that AI-based totally tactics display promising ability to increase accuracy and relevancy in search engine consequences. AI strategies such as natural language processing (NLP), system getting to know (ML), and sentiment analysis are explored in element. additionally, the paper compares current answers that rent AI algorithms to conventional search engines and evaluates their performance, scalability, and applicability. ultimately, the paper discusses the destiny outlook of AI in seo and its potential implications for web users, corporations, and seeks engine developers. © 2023 IEEE.

关键词： algorithms developers engine potential strategies

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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Expanding chiral metamaterials for retrieving fingerprints via vibrational circular dichroism

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Light(science & applications) 2023年第7期12卷 1401-1414页

作者： Cheng Xu Zhihao Ren Hong Zhou Jingkai Zhou Chong Pei Ho Nan Wang Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Center for Inteligent Sensors and MEMS(CISM) National University of SingaporeSingapore 117608Singapore institute of Microelectronics(IME) Agency for ScienceTechnology and Research(A*STAR)Singapore 138634Singapore NUS Graduate School for Integrative Science and Engineering Program(ISEP) National University of SingaporeSingapore 117456Singapore

Circular dichroism(CD)spectroscopy has been widely demonstrated for detecting chiral ***,the determination of chiral mixtures with various concentrations and enantiomeric ratios can be a challenging *** solve this problem,we report an enhanced vibrational circular dichroism(VCD)sensing platform based on plasmonic chiral metamaterials,which presents a 6-magnitude signal enhancement with a selectivity of chiral *** by coupled-mode theory,we leverage both in-plane and out-of-plane symmetry-breaking structures for chiral metamaterial design enabled by a two-step lithography process,which increases the near-field coupling strengths and varies the ratio between absorption and radiation loss,resulting in improved chiral light-matter interaction and enhanced molecular VCD ***,we demonstrate the thin-film sensing process of BSA andβ-lactoglobulin proteins,which contain secondary structures a-helix andβ-sheet and achieve a limit of detection down to zeptomole ***,we also,for the first time,explore the potential of enhanced VCD spectroscopy by demonstrating a selective sensing process of chiral mixtures,where the mixing ratio can be successfully differentiated with our proposed chiral *** findings improve the sensing signal of molecules and expand the extractable information,paving the way toward label-free,compact,small-volume chiral molecule detection for stereochemical and clinical diagnosisapplications.

关键词： chiral vibrational circular

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Filtering of Spurious Sonar Signals 14

Filtering of Spurious Sonar Signals

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Srutheeshna, U. Ashraf, Sharik Koshy, Litty Department of Master of Computer Application SCMS School of Engineering &Technology VidyaNagar Karukutty Ernakulam India Naval Physical and Oceanographic Laboratory P.B.NO.6 NGO Quarters Thrikkara Vazhakkala Kochi India Department of computer science & Engineering SCMS School of Engineering & Technology VidyaNagar Karukutty Ernakulam India

Navy uses a harmonic filter to extract the propel sounds in ships. Usually, Sonar processing uses the harmonic filter to remove unwanted harmonics. The proposed system is used for filtering unwanted noise from lower frequency noise using the machine learning technique. Some sonar may have higher frequency sounds, which can be difficult to hear. Using this sound frequency humans can identify their target by experience. Automate these sounds and using a trained neural network, Classify the sounds. Retrieve non-frequency voice and avoid harmonics using a neural network. © Grenze Scientific Society, 2023.

关键词： High pass filters

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Analyzing STEM Women’s Employability: A Machine Learning Framework with Clustering and Multi-Target Classification

Analyzing STEM Women’s Employability: A Machine Learning Fr...

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computer sciences, Engineering, and technology Innovation (ICoCSETI), International Conference on

作者： Nadzla Andrita Intan Ghayatrie Wina Permana Sari Devi Fitrianah Esyin Chew Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Cardiff School of Technology Cardiff Metropolitan University Cardiff United Kingdom

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

The gender gap in science, technology, Engineering, and Mathematics (STEM) fields highlights significant research opportunities, particularly in examining the employability of female graduates. This study introduces a novel machine learning framework integrating Clustering and Multi-target Classification to analyze employment waiting time and job linearity among women STEM alumni. Using K-Means Clustering and Multi-Target Logistic Regression, the framework achieved a classification accuracy of 77% and a silhouette score of 0.61, demonstrating its effectiveness in predictive analysis. Beyond these results, the framework offers a robust methodology for integrating Clustering with Classification, enabling a nuanced understanding of the employability challenges faced by women in STEM. This approach identifies key patterns in employment data, paving the way for targeted interventions and actionable insights. Furthermore, the findings aim to inform data-driven policymaking and future research to improve employability outcomes. This work contributes to addressing systemic challenges and fostering gender diversity in STEM careers while enhancing opportunities for women.

关键词： Logistic regression Technological innovation Accuracy Computational modeling Employment Linearity Machine learning Predictive analytics STEM Gender issues

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FViT: Finetuned Vision Transformer for Improved Brain Tumor Classification

FViT: Finetuned Vision Transformer for Improved Brain Tumor ...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Lalini Chandra Tej Bahadur Chandra Anirud Rathy Anushka Rai Department of Computer Application National Institute of Technology Raipur Chhattisgarh India School of Computer Science Engineering and Technology Bennett University Greater Noida UP India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Timely detection and treatment of brain tumors (BT) remain a critical challenge due to their rapid growth and complex morphologies. Recent deep learning (DL) approaches and large vision models (LVMs) have promising potential in early and accurate diagnosis of BTs. However, the optimal tuning of hyperparameters is crucial for increasing the model’s efficacy and robustness. Large vision models have significant potential for improving the accuracy of diagnosing BT using MRI images. Additionally, we evaluated and compared the performance of recent DL models ResNet50, VGG16, and Long Short-Term Memory (LSTM) using BT MRI dataset. Through rigorous experimentation, our results highlight the superior diagnostic capabilities of the FViT model with an accuracy (ACC) of 99.68% and an F1-score (FS) of 99.68% for BT classification (using training-validation set). This result is also verified with high ACC of 99.31% and FS of 99.31%using testing set. The study demonstrates how well FViT model performs in classifying BT accurately, showcasing its potential in clinical settings. These findings emphasize the critical role of fine-tuning LVM models in advancing BT diagnosis through brain MRI images.

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Brain modeling Transformers Robustness Long short term memory Tuning Testing

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Fine-Tuned mobileNetV2 and VGG16 Algorithm for Fish Image Classification 4

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Fitrianah, Devi Suryaningrum, Kristien Margi Sagala, Noviyanti Tri Maretta Ayumi, Vina Lim, Siew Mooi School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Mathematics Statistics Department Jakarta Indonesia Universitas Mercu Buana Informatics Engineering Program Faculty of Computer Science Jakarta Indonesia Tunku Abdul Rahman University College Faculty of Computing and Information Technology Computer Science Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665473279

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of mobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%. © 2022 IEEE.

关键词： Fish

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Revolutionizing Plant Disease Management Through Image Processing technology

Revolutionizing Plant Disease Management Through Image Proce...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Kumar, Gaurav Kumar, Ankit Singhal, Manoj Singh, Kamred Udham Kumar, Lalan Singh, Teekam Gla University Department of Computer Engineering & Applications UP Mathura India Gl Bajaj Institute of Technology and Management Department of Information Technology India School of Computing Graphic Era Hill University Dehradun India Gl Bajaj Institute of Technology and Management Department of Master of Computer Application India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Agricultural productivity plays a vital role in the economy, and disease detection in plants is crucial for the agriculture sector as plant diseases are widespread. Failure to take appropriate care can lead to serious consequences for plant quality, quantity, or productivity. For instance, a disease called little leaf disease has been found in pine trees in the United States, which can severely affect plant health. Automated methods for detecting plant diseases are useful as they reduce the need for manual inspection in large crop fields, and they can identify disease symptoms as soon as they appear on plant leaves. This paper presents an algorithm for image segmentation for the automatic identification and classification of plant leaf diseases. It also discusses various disease classification methods that can be used for plant leaf disease detection. Genetic algorithms are used for image segmentation, which is a crucial aspect of disease detection in plant leaves. The paper focuses on identifying the Powdery Mildew disease in organic grape products, which can cause significant damage if not detected in time. The accurate identification and classification of diseases from leaf images are crucial for preventing significant losses in output and agricultural inputs. The proposed process can help improve the accuracy and efficiency of disease detection in plants and lead to better agricultural productivity. © 2023 IEEE.

关键词： Fuzzy logic

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The Impact of Information System and technology of Courier Service During Pandemic 7th

The Impact of Information System and Technology of Courier S...

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7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Siahaan, Andre Timiko Pinajung, Bagas Rizkyka Kennidy Gaol, Ford Lumban Matsuo, Tokuro School of Information System Bina Nusantara University Alam Sutra Tangerang13960 Indonesia Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819951659

Courier services are a means of transport that can be used to deliver orders to customers or claim orders from them, and customers can use technology from the courier service like track order to track their order. The use of courier service nowadays is common that every day, people use courier service as their daily means of transport for delivering goods. However, in situations like pandemic, the courier services need to put more effort into their services, so that customers can use courier services with trust. The courier service must be more careful to deliver customer orders or send orders to customers. For this research methodology, the data collection technique using Google Forms and collecting data directly from people about their opinion toward the research have been employed. The result of the data collecting technique is depicted as the usage of the courier service during COVID-19 pandemic starting from March 2020. The results vary every month because not everyone uses the courier service every day. Moreover, the conclusion on why the months starting from March to June do not show high usage of courier service is that the pandemic has not been that worse than the later months, and so, people were going out to deliver or to do activities like shopping or buying groceries on their own. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Information systems

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