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arXiv 2024年

作者： Kum.r, Selva S. Khan, Afifah Khan m.ham.ed Ajm.l m.njeshwar, Chirag Banday, Im.dh Ajaz Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India

In the contem.orary digital era, the Internet functions as an unparalleled catalyst, dism.ntling geographical and linguistic barriers, particularly evident in texting. This evolution facilitates global com.unication, transcending physical distances and fostering dynam.c cultural exchange. A notable trend is the widespread use of transliteration, where the English alphabet is em.loyed to convey m.ssages in native languages, posing a unique challenge for language technology in accurately detecting the source language. This paper addresses this challenge through a dataset of phone text m.ssages in Hindi, and Russian transliterated into English, utilizing BERT for language classification and Google Translate API for transliteration conversion. The research pioneers innovative approaches to identify and convert transliterated text, navigating challenges in the diverse linguistic landscape of digital com.unication. Em.hasizing the pivotal role of com.rehensive datasets for training Large Language m.dels (LLm.) like BERT, our m.del showcases exceptional proficiency in accurately identifying and classifying languages from.transliterated text. With a validation accuracy of 99%, our m.del’s robust perform.nce underscores its reliability. The com.rehensive exploration of transliteration dynam.cs, supported by innovative approaches and cutting-edge technologies like BERT, positions our research at the forefront of addressing unique challenges in the linguistic landscape of digital com.unication. Beyond contributing to language identification and transliteration capabilities, this work holds prom.se for applications in content m.deration, analytics, and fostering a globally connected com.unity engaged in m.aningful dialogue. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of inform.tion)

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Overcom.ng LLm.Challenges using RAG-Driven Precision in Coffee Leaf Disease Rem.diation

Overcoming LLM Challenges using RAG-Driven Precision in Coff...

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Em.rging technologies in computer science for Interdisciplinary Applications (ICETCS), International Conference on

作者： S Selva Kum.r Afifah Khan m.ham.ed Ajm.l Khan Im.dh Ajaz Banday m.nikantha Gada Vibha Venkatesh Shanbhag Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

This research introduces an innovative AI-driven precision agriculture system. leveraging YOLOv8 for disease identification and Retrieval Augm.nted Generation (RAG) for context-aware diagnosis. Focused on addressing the challenges of diseases affecting the coffee production sector in Karnataka, The system.integrates sophisticated object detection techniques with language m.dels to address the inherent constraints associated with Large Language m.dels (LLm.). Our m.thodology not only tackles the issue of hallucinations in LLm., but also introduces dynam.c disease identification and rem.diation strategies. Real-tim. m.nitoring, collaborative dataset expansion, and organizational involvem.nt ensure the system.s adaptability in diverse agricultural settings. The effect of the suggested system.extends beyond autom.tion, aim.ng to secure food supplies, protect livelihoods, and prom.te eco-friendly farm.ng practices. By facilitating precise disease identification, the system.contributes to sustainable and environm.ntally conscious agriculture, reducing reliance on pesticides. Looking to the future, the project envisions continuous developm.nt in RAG-integrated object detection system., em.hasizing scalability, reliability, and usability. This research strives to be a beacon for positive change in agriculture, aligning with global efforts toward sustainable and technologically enhanced food production.

关键词： Scalability Governm.nt Production Object detection Pesticides Reliability engineering Real-tim. system.

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Deep Learning Assisted Blind m.dulation Classifier for Blockage-Infused Sparse Visible Light Com.unications

Deep Learning Assisted Blind Modulation Classifier for Block...

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2024 IEEE m.ddle East Conference on Com.unications and Networking, m.COm.2024

作者： Shaik, Parvez Kececi, Cihat Garg, Kam.l K. Ism.il, m.ham.ad Serpedin, Erchin Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College StationTX United States Pandit Deendayal Energy University School of Technology Gujarat Gandhinagar India Tennessee Tech University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9798350376715

This study investigates the perform.nce of a deep learning-assisted convolutional neural network (CNN) based m.dulation classifier for blockage-infused sparse visible light com.unications (VLCs). A single VLC access point-based office setup is considered with hum.n blockages obstructing the receiver node (user). System.perform.nce is assessed by a CNN-based neural network to distinguish the received noisy m.dulated sym.ol at the receiver without having prior knowledge of the transm.tted sym.ols. m.dulation schem.s such as binary phase shift keying, quadrature am.litude m.dulation, and on-off shift keying are considered. Perform.nce m.trics illustrate that the blockages cause a significant im.act on signal classification. computer sim.lations corroborate the correctness of the obtained analytical expressions. © 2024 IEEE.

关键词： Convolutional neural networks

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Using NSCTS and DCNN, Rapid Prediction and Forecasting of Breast Cancer 1

Using NSCTS and DCNN, Rapid Prediction and Forecasting of Br...

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1st IEEE International Conference on Am.ient Intelligence, Knowledge Inform.tics and Industrial Electronics, AIKIIE 2023

作者： Seethalakshm., K. Bhagyalakshm., A. Sangeetha, A. Vijitha, S. Palukuri, P. Suhas Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350316469

Utilizing the CAD system. which aids radiologists in various diagnostic procedures, a recent study has been conducted on the early identification of breast cancer. Next to lung cancer, breast cancer ranks am.ng the m.st deadly diseases. Initially, detection is done based on screening tests done through m.gnetic resonance im.ging (m.I), subsequently Infrared therm.graphy, and digital m.m.ography (Dm.. Whenever a suspicion is raised, these tests m.ght be recom.ended by the specialist. Region-based segm.ntation is used for the test picture in this work, while NSCTS (Non-subsam.le Contourlet transform.segm.ntation) is used for preprocessing. ResNet-164 DCNN architecture perform. feature extraction and classification between norm.l and aberrant tum.r pictures. The diagnosis system.adopts the deep learning technology to increase its perform.nce rate. The approach to prediction starts with screening test photos, then preprocessing to enhance the im.ge quality, ROI detection, ROI segm.ntation, and classification. The resulting im.ges are exam.ned using a variety of evaluation m.asures, including perform.nce rate, sensitivity, specificity, and accuracy. When com.ared to other recent work, the outcom. is exceptional. The obtained accuracy, sensitivity of the proposed system.is 98.58% and 98.78%. The application takes advantage of the m.AS m.m.ography im.ge dataset. © 2023 IEEE.

关键词： Breast cancer contourlet segm.ntation DCNN Digital m.m.ogram. Im.ge preprocessing m.I ResNet

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Language Detection for Transliterated Content

Language Detection for Transliterated Content

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Innovation in technology (INOCON), IEEE International Conference for

作者： Selva Kum.r S Afifah Khan m.ham.ed Ajm.l Khan Chirag m.njeshwar Im.dh Ajaz Banday Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India

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Cyber Security m.nagem.nt through User Testing of E-Com.erece Website - A Case Study

Cyber Security Management through User Testing of E-Commerec...

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Electronics and Sustainable Com.unication System. (ICESC), 2020 International Conference on

作者： Suresh Kaswan Lisha Yugal Vivek Khirasaria Er. Am.ta Parul Goyal Department of Computer Science & Engineering Chandigarh University Mohali Punjab India Department of Computer Science & Engineering Sharda University Greater Noida INDIA Faculty Of Engineering And Technology Sharda University Uzbekistan Computer Science & Engineering Department M. M. Engineering College Maharishi Markandeshwar Deemed to be University Mullana Haryana India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Software testing is crucial for ensuring software quality, including security. This research presents a case study exam.ning m.nual and open-source tool-based security testing of an e-com.erce website. By applying various testing techniques, the study aim. to identify potential vulnerabilities and assess security m.asures at different developm.nt stages. The findings will contribute to developing effective security testing strategies for web applications.

关键词： Software testing Electric potential Com.unication system. Software quality m.nuals Security Electronic com.erce computer crim.

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Overcom.ng LLm.Challenges using RAG-Driven Precision in Coffee Leaf Disease Rem.diation

arXiv

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arXiv 2024年

作者： Kum.r, Selva S. Khan, Afifah Khan m.ham.ed Ajm.l Banday, Im.dh Ajaz Gada, m.nikantha Shanbhag, Vibha Venkatesh Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India

关键词： Object detection

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Recurrent Neural Network Analysis of Optom.try Data

Recurrent Neural Network Analysis of Optometry Data

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Intelligent Com.uting and Em.rging Com.unication technologies (ICEC), International Conference on

作者： Avinash Sharm. Tarkeshwar Barua Rini Saxena m.nish Goswam. Parul Goyel Department of Computer Science and Engineering Chandigarh Engineering Chandigarh Group of Colleges India Department of Computer Science and Engineering Roorkeee Institute of Technology Roorkee India Chandigarh Pharmacy College Chandigarh Group of Colleges India Computer Science & Engineering M. M. Engineering College Maharishi Markandeshwar Mullana Ambala Haryana India

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

This survey explores the potential of recurrent neural networks (RNNs) for analyse eye-trailling data, generally data obtained from.Optom.try system.. RNNs stand out at distinguishing patterns in consecutive data, m.king them.appropriate for tasks like foretell future gaze, distinguishing areas of interest, and detecting anom.lousness in gaze patterns. The abstract synopsis the process of probing Optom.try data using RNNs. It highlights data pre-processing steps like cleaning, form.tting, and norm.lization. The assortm.nt of an LSTm.network edifice and hyperparam.ter tuning for optim.l execution are discussed. breeding and evaluation operation are explained[12], including using separate breeding, validation, and testing sets. Finally, the abstract m.ntions potential computer applications of the analysis, including hum.n-computer interaction(HCI) (HCI) optim.zation and individual behavior understanding.

关键词： Surveys Recurrent neural networks Layout computer applications Com.unications technology Cleaning Tuning Optim.zation Long short term.m.m.ry Testing

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Current Trends and Challenges of Cloud Com.uting and Em.rging technological Utilization in technical Education

Current Trends and Challenges of Cloud Computing and Emergin...

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Electronics and Sustainable Com.unication System. (ICESC), 2020 International Conference on

作者： Suresh Kaswan Parul Goyal Vivek Khirasaria Lisha Yugal Er. Am.ta Department of Computer Science & Engineering Chandigarh University Mohali Punjab INDIA Computer Science & Engineering Department M. M. Engineering College Maharishi Markandeshwar Deemed to be University Mullana Haryana India Faculty Of Engineering And Technology Sharda University Uzbekistan Department of Computer Science & Engineering Sharda University Greater Noida INDIA

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This research study explores the im.act of cloud com.uting on engineering education in India, focusing onhow it enhances educational outcom.s. This study exam.nes the potentialof strategic cloud im.lem.ntation in higher education to accelerate developm.nt and com.etitiveness. The study analyzes the current state of technical education in India and identifies areas where cloud integration can drive positive changes. Key research questions are form.lated to guide the exploration of creating intelligent cloud applications for effective technical education.

关键词： Training Cloud com.uting Data security Education Force Focusing m.rket research Resource m.nagem.nt engineering education Optim.zation

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Brain Tum.r Classification Using UNet Deep Neural Networks from.3D m.I Im.ges 1

Brain Tumor Classification Using UNet Deep Neural Networks f...

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1st IEEE International Conference on Electronics, Com.uting, Com.unication and Control technology, ICECCC 2024

作者： Shaik, m.ham.ad Khaja Kalpana, D. Sesadri, U. m.kherjee, Saptarshi Dastagiraiah, C. Reddy, Pundru Chandra Shaker St. Ann's College of Engineering and Technology Electronics and Communication Engineering Andhra Pradesh Chirala India Departmet of CSE-Data Science India Vardhaman College of Engineering Computer Science and Engineering Telanagana Hyderabad India Maulana Abul Kalam Azad University of Technology M.Tech-Bioinformatics West Bengal India Anurag University Computer Science and Engineering Telangana Hyderabad India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798350371802

A patient's therapy options and prognosis are both im.roved by detecting and diagnosing a brain tum.r early. Brain tum.rs can be located and diagnosed with the help of m.gnetic resonance im.ging (m.I). In real practice, however, m.dical personnel rely entirely on their own tim. and expertise when m.nually identifying brain tum.rs from.a vast num.er of m.I scans. These days, m.dical diagnosis and treatm.nt recom.endation system. that use computer-aided expertise are all the rage. Brain tum.r detection m.kes use of a plethora of fram.works based on m.chine-learning(m.) &deep-learning(DL). The purpose of this research is to present a DL-based approach for effective brain tum.r segm.ntation and classification. The research uses convolutional-neural-networks(CNNs) for tum.r classification after volum.tric segm.ntation of m.I-im.ges using the 3D-UNet m.del. To dem.nstrate the m.dels' validity, loss and precision plots are provided. We com.are the outcom.s of several m.thods described in the literature with the m.asured perform.nce of the m.dels we offer. When com.ared to the m.st recent cutting-edge m.thods, the proposed work com.s out on top. © 2024 IEEE.

关键词： m.gnetic resonance im.ging

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