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IAENG International Journal of computer science 2024年第8期51卷 1051-1059页

作者： Panchagnula, Venu m.dhav Satya Keerthi, N.V.L.Ch. Surekha, S. Sujatha, R. Veeraiah, Duggineni Ram.sh, Eluri Lakshm., B. Electronics and Communication Engineering Department Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India AIML Department Vignana Bharathi Institute of Technology Ghatkesar Hyderabad India Information Technology Department M. Kumarasamy College of Engineering Karur India Computer Science and Engineering Department Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Computer Science and Engineering Department R.V.R & J. C. College of Engineering Guntur India Electronics and Communication Engineering Department Rajiv Gandhi University of Knowledge Technologies Andhra Pradesh Nuzvid India

Research in the field of m.lware detection is currently leaning toward m.thods based on artificial intelligence algorithm. due to the increasing lim.tations of traditional detection m.thods and the increasing accuracy of these m.thods. This study therefore introduces a novel approach to m.lware detection. This m.thod integrates a deep learning m.del's autoencoder network with a grayscale graphic depiction of m.lware. By looking at the autoencoder's reconstruction error, we m.y assess if the grey-scale picture approach is feasible to use for m.lware detection. Additionally, it differentiates between m.licious and safe software by utilizing the autoencoder's dim.nsionality reduction capabilities. Using the Android dataset, our newly introduced detection m.del attained a 95% accuracy and m.intained a consistently high F-score of approxim.tely 95%. This outperform.d m.re conventional m.thods of m.chine learning detection. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： m.lware

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Explainable Artificial Intelligence(XAI)m.del for Cancer Im.ge Classification

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computer m.deling in engineering & sciences 2024年第10期141卷 401-441页

作者： Am.t Singhal Krishna Kant Agrawal Angeles Quezada Adrian Rodriguez Aguiñaga Sam.ntha Jim.nez Satya Prakash Yadav Department of Computer Science&Engineering Raj Kumar Goel Institute of TechnologyGhaziabad201017India Department of School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Tecnológico Nacional de México Campus Tijuana Baja California22414México Universidad Autónoma de Baja California TijuanaBaja California22414México Department of Computer Science and Engineering G.LBajaj Institute of Technology and Management(GLBITM)Affiliated to Dr.A.P.J.Abdul Kalam Technical UniversityLucknow201306India School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India

The use of Explainable Artificial Intelligence(XAI)m.dels becom.s increasingly im.ortant for m.king decisions in sm.rt healthcare *** is to m.ke sure that decisions are based on trustworthy algorithm. and that healthcare workers understand the decisions m.de by these *** m.dels can potentially enhance interpretability and explainability in decision-m.king processes that rely on artificial ***,the intricate nature of the healthcare field necessitates the utilization of sophisticated m.dels to classify cancer *** research presents an advanced investigation of XAI m.dels to classify cancer *** describes the different levels of explainability and interpretability associated with XAI m.dels and the challenges faced in deploying them.in healthcare *** addition,this study proposes a novel fram.work for cancer im.ge classification that incorporates XAI m.dels with deep learning and advanced m.dical im.ging *** proposed m.del integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive *** proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%*** will discuss the potential applications of the proposed XAI m.dels in the sm.rt healthcare *** will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable sm.rt healthcare environm.nt.

关键词： Explainable artificial intelligence artificial intelligence XAI healthcare cancer im.ge classification

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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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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

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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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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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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...

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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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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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