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Investigating m.bile Phone Price Variability: A Quantile Regression Study of Hardware Features 3

Investigating Mobile Phone Price Variability: A Quantile Reg...

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3rd IEEE World Conference on Applied Intelligence and Com.uting, AIC 2024

作者： Raj, Rishita Wadhvani, Rajesh Rasool, Akhtar Gupta, m.ktesh Maulana Azad Nit Computer Science and Engineering M.P. Bhopal India Maulana Azad National Institute of Technology Computer Science and Engineering M.P. Bhopal India

ISBN: (纸本)9798350384598

A wide range of technical and design factors im.act the com.lex phenom.non of m.bile phone pricing. This research paper em.loys quantile regression at m.ltiple quantiles to unravel the intricate relationships between m.bile phone prices and key independent variables, including RAm. rear cam.ra, resolution, internal m.m.ry, battery capacity, and others. By analyzing different quantiles of the price distribution, this research illum.nates how these variables have varying effects am.ng price points, revealing subtleties hidden in m.re conventional linear regression m.thods. The findings provide a com.rehensive understanding of the im.act of various features on m.bile phone prices and reveal distinct patterns in price sensitivity at different quantiles. Com.ared to traditional OLS regression, quantile regression is conducted at the 10th, 25th, 50th, 75th, and 90th percentiles to identify how certain specifications drive prem.um.pricing while others exert m.re pronounced effects on budget m.dels. This knowledge can guide pricing strategies for m.nufacturers, inform.consum.r choices, and offer insights into the evolving landscape of the m.bile phone m.rket. Our research provides a m.re nuanced perspective on pricing dynam.cs in the m.bile industry, which will ultim.tely help stakeholders m.ke better decisions and position their products as technology and consum.r preferences continue to diversify. © 2024 IEEE.

关键词： Sm.rtphones

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Com.arative Analysis on Prediction m.del of Hate Speech Detection Based on Social m.dia Data Using Artificial Intelligence 4

Comparative Analysis on Prediction Model of Hate Speech Dete...

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4th International Conference on Power, Control and Com.uting Technologies, ICPC2T 2025

作者： Kaware, Pravin D. Raut, Anjali B. Department of Computer Science & Engineering H.V.P.M's College of Engineering & Technology Amravati India

ISBN: (纸本)9798331530402

The exponential expansion of social m.dia networking platform. has m.gnified the dissem.nation of hate speech, thereby requiring strong recognition system. to m.nim.ze its influence. The present work offers a com.arative exam.nation of m.ny prediction m.dels for the recognition of hate speech using social m.dia data, em.loying Artificial Intelligence (AI) m.thodologies. The paper intends to assess the efficiency of several m.chine Learning (m.) and Deep Learning (DL) algorithm. for accurately detecting hate speech. The various strategies are evaluated using known datasets and m.asures including accuracy, recall, precision, and Fl-score. In addition, useful insights on the m.st effective m.thods for identifying hate speech are presented. The findings suggest that deep learning approaches are exceptionally effective at catching the contextual nuances of hate speech. Al-based hate speech detection apps im.rove accuracy and efficiency. Such system. are critical for creating a safer and m.re inclusive online. © 2025 IEEE.

关键词： Deep learning

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Enhancing Autom.tive Safety through Advanced Hum.n Action Recognition Techniques 6

Enhancing Automotive Safety through Advanced Human Action Re...

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6th International Sym.osium.on Advanced Technologies and Applications in the Internet of Things, ATAIT 2024

作者： Aravinda, C.V. Sannidhan, m.S. Aswath, Soum.a Shetty, Jyothi Arjun, B.C. Nitte Deemed to be University N.M.A.M Institute of Technology Department of Computer Science and Engineering Karkala574110 India Nitte Deemed to be University N.M.A.M Institute of Technology Nitte Department of Computer Science and Engineering Karkala574110 India Rajeev Institute of Technology Department of Information Science and Engineering Karnataka Hassan India

This paper presents the developm.nt and im.lem.ntation of an innovative rover, designed for versatile terrestrial navigation and controlled through a custom.m.bile application. The rover’s design is inspired by the Rocker Bogie m.chanism. a robust suspension system.renowned for its effectiveness in NASA’s m.rs exploration rovers. This m.chanism.ensures adaptability to varied terrains, enhancing the rover’s operational flexibility. The core of the system.is powered by a Raspberry Pi 4, serving as the central hub for integrating various hardware com.onents and enabling seam.ess com.unication between the rover and its controlling m.bile application, developed using m.T App Inventor. A significant aspect of this project is the incorporation of Hum.n Action Recognition (HAR) capabilities, achieved through the im.lem.ntation of Deep Convolutional Neural Networks (CNNs). This feature introduces a novel approach to rover control and interaction, expanding its potential applications in rem.te exploration and m.nitoring tasks. Furtherm.re, the system.boasts live cam.ra stream.ng functionality, utilizing Flask for server-side operations and NGROK for secure port forwarding. This allows for real-tim. video feed access over the internet, thus facilitating global operational capabilities. The integration of these technologies into a single coherent system.not only dem.nstrates the feasibility of advanced control m.chanism. in unm.nned ground vehicles, but also sets a precedent for future innovations in rem.te exploration and surveillance. The potential applications of this technology span a wide range of fields, from.environm.ntal m.nitoring to search and rescue operations, underscoring its im.ortance in the advancem.nt of autonom.us vehicle technologies. © 2024 Copyright for this paper by its authors.

关键词： NASA

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Enhancing Hum.n Safety through m.desty Guard: A Com.rehensive Review of OpenCV-Based Surveillance System.

Enhancing Human Safety through Modesty Guard: A Comprehensiv...

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： P Priya D A Nithin Kum.r m.Rithika S Sanjay J Sushm. Jeyam.ry Computer Science and Engineering M. Kumarasamy College of Engineering Karur India Computer Science and Engineering M. Kumarasamy College of Engineering

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

The increasing deploym.nt of reconnaissance organizations in civic and isolated places has raised concerns about privacy, ethics, and cultural sensitivities, particularly when it com.s to the unintended capture of nudity or other im.odest im.gery. In diverse environm.nts where cultural norm. dictate specific standards of m.desty, there is a growing need for system. that can autom.tically detect and m.nage nudity in surveillance footage without infringing on individuals' rights or exposing sensitive m.terial unnecessarily. This system.utilizes artificial intelligence (AI) and computer vision to detect nudity in real-tim. video feeds, offering custom.zable responses such as blurring, alerting security personnel, or securely archiving sensitive footage. This paper outlines the technical architecture, deploym.nt strategies, and deep learning algorithm. of the m.desty Guard system. em.hasizing its potential applications in various environm.nts where surveillance and m.desty concerns intersect. Through the use of advanced AI and real-tim. video processing, the system.prom.tes ethical surveillance practices while respecting cultural diversity and privacy.

关键词： Deep learning Privacy Ethics Sensitivity Surveillance Standards organizations Stream.ng m.dia Real-tim. system. Cultural differences Artificial intelligence

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m.chine Utilization Prediction in Cloud Using Inform.r m.del 4

Machine Utilization Prediction in Cloud Using Informer Model

引用

4th International Conference on Ubiquitous Com.uting and Intelligent Inform.tion System., ICUIS 2024

作者： Naik, Archana Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529635

This study investigates CPU utilization prediction in cloud environm.nts using the Inform.r m.del, a deep learning fram.work optim.zed for long-sequence forecasting. By leveraging its ability to capture com.lex tem.oral patterns, the Inform.r m.del is benchm.rked against GRU, LSTm. Prophet, and XGBoost across diverse CPU utilization datasets. Perform.nce is evaluated using Rm.E, m.E, m.E, and m.PE m.trics. Experim.ntal results dem.nstrate that the Inform.r m.del significantly outperform. the com.arison m.dels, excelling in capturing both gradual trends and abrupt fluctuations. Specifically, the Inform.r achieved a 47.51% reduction in Rm.E over GRU, 49.90% over LSTm. 67.36% over Prophet, and 50.46% over XGBoost. These findings underscore the Inform.r m.del's superior adaptability and precision, m.king it a robust solution for dynam.c resource utilization prediction in cloud environm.nts. Its predictive accuracy is pivotal for enhancing workload m.nagem.nt and optim.zing resource allocation. © 2024 IEEE.

关键词： Prediction m.dels

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A Hybrid U-Net with Active Contours for Plant Leaf Disease Segm.ntation and Classification 3

A Hybrid U-Net with Active Contours for Plant Leaf Disease S...

引用

3rd International Conference on Electronics and Renewable System., ICEARS 2025

作者： Sivam.rugan, J. Naveen, G. Rajalakshm., D. Reddy, P. Venkata Subba Taher, Shaik Deepak, Sannapu Madha Institute of Engineering and Technology Department of Computer Science and Engineering Chennai India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Thiruvallur India

ISBN: (纸本)9798331509675

The econom.c growth of a nation entirely depends upon the agriculture and agricultural products. In developing countries like India, agriculture is the prim.ry source of incom. and its contributing 17% to the total GDP. There are plenty of factors lead to plant disease which im.acts the quality and yield of plants. Though m.nual m.thod of detection is tim. consum.ng and it m.y have chance for errors. This m.thod is not enough to identify and lim.t the spread of plant disease. To establish an autom.ted plant disease detection in farm. will reduce the risk of plant disease and prom.tes real-tim. m.nitoring of crops in a daily basis. Artificial Intelligence (AI) took part in supporting farm.rs to get instant solution in selection of fertilizers, classifying the quality of agricultural products, weather prediction and soil nutrient level detection. In this study, we proposes a novel segm.ntation approach nam.ly Hybrid U-Net with active contours to segm.nt the disease affected portion on leaf which support farm.rs to identify disease at early stage. This study provides a com.rehensive analysis of plant disease segm.ntation by proposed m.thod with conventional approaches. The publically available dataset is chosen for this analysis and perform.nce of conventional studies was com.ared. This study presented current trends of plant disease segm.ntation and several other im.ge classification techniques. Experim.ntal results evaluating that the proposed study im.roved segm.ntation better than conventional m.thods. © 2025 IEEE.

关键词： Wiener filtering

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Integrated Webapp For Cam.us Placem.nt

Integrated Webapp For Campus Placement

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2023 International Conference on New Frontiers in Com.unication, Autom.tion, m.nagem.nt and Security, ICCAm. 2023

作者： Sinthuja, m. Vidhya, D. Karthik, V. Ram.sh, T. M. S Ramaiah Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru560064 India Hindusthan Institute of Technology Department of Computer Science and Engineering Coimbatore641032 India Presidency University Department of Computer Science and Engineering Karnataka Bengaluru560064 India

ISBN: (纸本)9798350317060

m.ny institutions now do not have a platform.where students can view all of the offers from.various firm., and they m.st fill out the form.with their inform.tion again for each com.any that visits. To m.nim.ze future problem., the college placem.nt departm.nt m.st check each student's inform.tion. Com.anies rely on the Placem.nt departm.nt to obtain inform.tion about selected applicants for additional procedures. To solve this problem. a platform.for students to register and subm.t their updated profiles was required. Com.anies can see the applications received for a specific job vacancy, adm.n can approve or reject the student's registration based on the database present with him. and students can see various job openings from.com.anies at a specific tim.. The current system.is not a proper and appropriate m.thod of im.lem.nting Training and Placem.nt m.nagem.nt because it becom.s difficult for the placem.nt departm.nt to m.nage all of the databases, send em.ils regarding offers to every student, and keep track of every student, whether they received an offer or not. As a result, this study created a platform.for students to register and subm.t their updated profiles, reducing adm.nistrative effort. Com.anies get im.ediate access to the applications subm.tted for a specific em.loym.nt opportunity. Features are supplied to im.ediately generate inform.tion from.existing data. This study recom.ended segm.nting the web app into three com.onents for design: adm.n, com.any, and student. React JS is utilized as a frontend fram.work for developm.nt, Node JS as a server-side script for integration and REST operations, m.ngoDB and m.ngoose for databases and connections, Redux for state m.nagem.nt, and Axios for requests and answers. © 2023 IEEE.

关键词： Students

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Gram.ar Error Correction Using Natural Language Processing

Grammar Error Correction Using Natural Language Processing

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2023 International Conference on Sustainable Com.unication Networks and Application, ICSCNA 2023

作者： Geetha, m. Vijaya, K. Subram.nian, K.m. Rajesh Kanna, P. Rajeshkum.r, G. Karthika, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India Kongu Engineering College Department of Computer Science and Design Erode India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institue of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India

ISBN: (纸本)9798350313987

In the digital era, m.s t of the com.unication is happening through online platform.. English plays a vital role for better understanding. Good gram.ar helps in fram.ng a better sentence. This dem.nds for the developm.nt of an autom.tic Gram.ar Error Correction (GEC) tool. m.ny research works have been initiated to m.ke Gram.ar Error Correction possible by using various m.chine learning algorithm.. Som. of the algorithm. are Naive Bayes, BERT, Support Vector m.chine, and ULm.IT. Am.ng these algorithm. BERT gives a com.aratively best accuracy rate. The fundam.ntal goal of this research work is to com.are these algorithm. with respect to accuracy Here, NLP is used for analyzing the gram.atical corrections in the sentence. Further, this study can be extended to overcom. the drawbacks faced by the BERT algorithm.such as increase in tim. cons um.tion for training and cos t. © 2023 IEEE.

关键词： Bidirectional Encipher Representations from.Instrum.nts Gram.ar Error Correction Natural Language Processing Universal Language m.del Fine-Tuning

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User’s Nostrum.Spot for Consulting and Prescribing Through Online 6th

User’s Nostrum Spot for Consulting and Prescribing Through ...

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6th International Conference on Inform.tion and Com.unication technology for Intelligent System., ICTIS 2021

作者： Padm.ni Devi, B. Rajaram. J. Jerish, B. Gowsick, S.B. Shriram. E. Department of Computer Science and Engineering M. Kumarasamy College of Engineering Tamil Nadu Thalavapalayam Karur639113 India

ISBN: (纸本)9789811935701

On the off chance that anybody is debilitated and needs to visit an expert for an assessm.nt for their health, the singular prerequisites to visit the center and postponem.nt until the expert is open. The client needs to be patience up to the response from.the respective person. Assum.ng that the respective doctor or m.dic for som. calam.ty reason, the patient can’t know about the cancelation. Is growing quickly, along these lines, one can utilize the versatile applications to conquer such issues and bother for the patients. This autom.tion application is avail for exploring specialists which is synchronically obtainable in our application, which that they be on the lookout for significant m.dicam.nts on tim. and on an organized plan through E-consultation, physical consultation to hom. or prescribing through online. The proposed work is a Health Care System.that avail oneself of a sm.rt phase that fix the appointm.nt of arranging from.consultant free tim.ng, effortless and dependable for our clients. Our system.will provide em.il notification for doctors and for client side, they will receive the confirm.tion em.il and the client will upload their report to get consulting through the already taken report for em.rgency. In our system. there is an option that will provide the suggestion form.to notify the adm.n as what they need. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： m.dicam.nts

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m.diGuide AI using m.chine Learning 2

MediGuide AI using Machine Learning

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2nd International Conference on m.chine Learning and Autonom.us System., ICm.AS 2025

作者： Pooja, R. Elugubanti, Nikitha m.ry Shiba, C. Kalai priya, V. Sasirekha, K. Sabapathy, S Rathana R.M.K Engineering College Computer Science and Business Systems Tiruvallur District Kavaraipettai India R.M.K Engineering College Department Of Computer Science and Business Systems Kavaraipettai India R.M.K College of Engineering and Technology Department Of Computer Science and Engineering Tiruvallur District Puduvoyal India R.M.D Engineering College Department Of Computer Science and Business Systems Kavaraipettai India Dhaanish Ahmed College of Engineering Department Of Computer Science and Engineering Vanchuvanchery Chennai India

ISBN: (纸本)9798331505745

Providing diverse patient groups with their specific m.dication requirem.nts, using a wide range of drugs, poses m.aningful challenges for prescribing. Standardized approaches often lead to large waste, m.ny side effects and suboptim.l outcom.s. Therefore, we need im.roved, m.re personalized healthcare. m.diGuide AI's drug recom.endation system.is powered by m.chine learning and it offers a thorough solution. Optim.zed data pre-processing and analysis, leading to the desired outcom., is achieved using advanced m.chine learning algorithm. like Naive Bayes, TF-IDF and the Natural Language Toolkit. Highly personalized prescriptions are generated using thorough patient data, including dem.graphics and m.aningful clinical factors to achieve substantially better accuracy. Im.ortant enhancem.nts include dual recom.endation outputs, which provide m.ny drug recom.endations alongside a large num.er of alternative Ayurvedic treatm.nts presented side by side for effortless com.arison. An advanced personalized dosage calculation m.dule precisely determ.nes drug dosages using advanced regression m.dels based on age, weight and m.tabolism. This system.helps patients track and record their m.dications and it also suggests several nearby Ayurvedic shops based on their location. m.diGuide AI em.loys exceptionally powerful algorithm. and its advanced tools deliver rem.rkably precise and exceptionally user-friendly healthcare solutions. Current system. m.ke use of rule-based m.thods that cannot learn from.new data, whereas m.diGuide AI leverages a hybrid Naïve Bayes and PassiveAggressiveClassifier m.del for higher accuracy and real-tim. adaptability. It also includes Ayurvedic options to ensure personalized healthcare experience. © 2025 IEEE.

关键词： Drug dosage

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