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9th International Conference on System Modeling and Advancement in Research Trends, SMART 2020

作者： Srivastava, Rohit Mishra, R.P. Kumar, Vivek Shukla, Himanshu Kumar Goyal, Neha Singh, Chandrab.an Computer Science and Engineering F.O.E.T. University Lucknow India Electronics and Communication Dr R.M.L. Awadh University Ayodhya India Computer Science and Engineering Teerthanker Mahaveer University Moradabad India Computer Science and Engineering K.M. C. Language University Lucknow India Computer Science and Engineering B.N. College of Engg. and Tech. Lucknow India

ISBN: (纸本)9781728189086

Mob.le is a rapidly growing web.environment that attracts malware developers around the world. Smart phones, especially android phones are widely used and are the most popular new target for malware attacks. Most common type of malware found to attack android users was an unauthorized app repackaged as a normal app through a third party, unofficial app store. New apps found in the app store are hard to identify as malicious. Our work develops a malware detector and analyzer. This paper also links insights ab.ut malware attacks in COVID-19 on mob.le devices. To meet the ob.ectives, a model is implemented that extracts the inherent features of android application file and analyzes them for quick and accurate analysis. The model classifies the apps more accurately as b.nign or malicious. © 2020 IEEE.

关键词： COVID-19

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Internet of things (IoT) applications in dentistry

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Current Medicine Research and Practice 2020年第2期10卷 80-81页

作者： Ab.d Haleem Mohd Javaid Ib.ahim Haleem Khan Department of Mechanical Engineering Jamia Millia Islamia New Delhi India B. Tech (Computer Engineering) Jamia Hamdard New Delhi India

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Automatic detection and prevention of b.east cancer b. ML algorithms with dimensionality reduction tech.ique PCA

Automatic detection and prevention of breast cancer by ML al...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Mohd Firoz Warsi Shiv Narain Gupta Usha Chauhan Rahul Dev Rakhi b.ardwaj Ab.ishek Kaushik Department of Computer Science (AIDS) Greater Noida Institute of Technology Greater Noida Uttar Pradesh India Department of Elec. and Comm. Engg. Greater Noida Institute of Technology Greater Noida Uttar Pradesh India Department of Elec. and Comm. Engg. Galgotias University Greater Noida Uttar Pradesh India Department of Elec. and Comm. Engg. GL Bajaj Institute of Tech. & Mgmt. Greater Noida Uttar Pradesh India Department of Computer Sci. and Engg. College of Smart Computing COER University Roorkee Uttar Pradesh India Department of Elec. and Comm. Engg. Vishveshwarya Group of Institutions G. B. Nagar Uttar Pradesh India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Early detection and prevention of b.east cancer are vital for improving patient outcomes and reducing mortality rates. Machine learning algorithms, coupled with principal component analysis (PCA), offer a promising approach for enhancing the accuracy and efficiency of b.east cancer diagnosis. This paper proposes a novel approach to early b.east cancer detection, comb.ning machine learning algorithms, including support vector machines, neural networks, and random forests, with PCA. The study utilizes a comprehensive dataset comprising clinical and demographic features to train and evaluate the performance of the models. Results demonstrate the efficacy of the proposed approach in accurately identifying b.east cancer at its early stages, thus enab.ing timely intervention and improved patient care. Furthermore, the integration of PCA aids in reducing dimensionality and enhancing the interpretab.lity of the models thereb. is facilitating more effective decision-making in clinical settings. Overall, this research underscores the potential of machine learning algorithms with PCA in revolutionizing b.east cancer detection and prevention strategies, ultimately contrib.ting to b.tter healthcare outcomes and patient wellb.ing. In this study we have applied five ML tech.ique: Logistic Regression, Decision tree, Random forest, Naïve b.es and support vector machine on the b.east Cancer Wisconsin Diagnostic dataset. The evaluation of classification outcomes was conducted b.th with and without the implementation of PCA. Notab.y, upon applying PCA, the random forest classifier emerged as the top performer, surpassing all other classifiers. Impressively, it attained an accuracy rate of 99.8%, showcasing its superior performance in the dataset.

关键词： Support vector machines Dimensionality reduction Logistic regression Machine learning algorithms Prevention and mitigation Decision making Medical services b.east cancer Random forests Principal component analysis

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b.yesian Optimization with Stacked Sparse Autoencoder b.sed Cryptocurrency Price Prediction Model

Bayesian Optimization with Stacked Sparse Autoencoder based ...

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International Conference on Smart Systems and Inventive tech.ology (ICSSIT)

作者： Irfan Ab.ul Karim Shaikh P. Vamsi Krishna Swagat Gourav b.swal A. Sasi Kumar S. b.ranidharan Kamlesh Singh Faculty of Business Bahrain Polytechnic Isa Town Bahrain School of Management Malla Reddy University Hyderabad Telangana India B. Tech Department of Electrical Engineering Odisha University of Tecnology and Research Bhubaneswar Orissa India Department of Cloud Technology & Data Science (Mentor-IT-Inurture Education Solutions Pvt Ltd Bangalore) Institute of Engineering & Technology Srinivas University Dakshina Kannada Karnataka India Department of MBA and Research Center Presidency College (Autonomous) Bangalore Karnataka India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Campus Dehradun India

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyb.r cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial tech.ology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging b.cause of the dynamism and price volatility. The electronic economy is severely hazardous and should b. advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new b.yesian optimization with Stacked Sparse Autoencoder b.sed Cryptocurrency Price Prediction (b.SSAE-CPP) model. The major intention of the b.SSALCPP tech.ique lies in the effectual prediction of cryptocurrency prices. To attain this, the b.SSAE-CPP tech.ique exploits SSAE model for price prediction process. Moreover, the b. tech.ique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To delib.rate the enhanced outcomes of the b.SSALCPP tech.ique, extensive experimentation study is made. The comparison study highlighted the improved performance of the b.SSAE-CPP tech.ique.

关键词： Training Government policies Online b.nking Social networking (online) Legislation Predictive models Cryptocurrency

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AI-Driven Autonomous Rob.ts for Search and Rescue Operations in Disaster Zones

AI-Driven Autonomous Robots for Search and Rescue Operations...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Meenakshi S. Pavaimalar Rama Prab.a K. P S. Ravi N. Kumaran S. b. Priya Department of Artificial Intelligence & Data Science Nitte Meenakshi Institute of Technology Nitte University Bangalore India Department of Information Technology V. S. B. Engineering College Karur Tamilnadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science St Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Search and rescue (SAR) operations designed to save lives in disaster zones face difficulties executing effectively b.cause of dangerous surroundings and challenging terrain features. An AI-controlled rob.tic system exists to deal with existing challenges. The system comb.nes a reinforcement learning-b.sed navigation system that integrates multiple sensors including LiDAR with thermal cameras and RGb.sensors for detecting victims to work with decentralized task distrib.tion frameworks. Real-time simulations tested disaster navigation datasets showing an overall detection accuracy rate of 95.6%, navigation accuracy at 92.8%, while ob.tacle avoidance performed at 97.2%. This deployed system achieved 3.2-minute average response times and processed requests with 85.4% resource efficiency. The system proves superior performance over existing SAR methods while demonstrating speed enhancement, accuracy improvement, and autonomous operational value in SAR operations. Future disaster response will change thanks to AI-powered rob.tics b.cause this tech.ology enab.es scalab.e efficient reliab.e solutions for future deployments.

关键词： Accuracy Navigation Disasters Transforms Thermal sensors Sensor systems Time factors Collision avoidance Rob.ts Optimization

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Prevention from Gas Devastation

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Journal of Physics: Conference Series 2021年第1期1797卷

作者： Sanghita Datta Ankita Sah Sidharth Gautam Upamita Das Dr. Saikat Maity B. Tech Computer Science Engineering JIS University

LPG leakages are the most common issues found in household and gas plants. This issue is life threatening not only for the humans in the vicinity b.t also for the surrounding areas. The idea b.hind our project is to save the lives of those innocents who can get affected due to the carelessness of certain people. So, as soon as the gas gets detected b. the system, the concerned people around will receive messages as well as the alarm rings.

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Correction: Materials lab.ratories of the future for alloys, amorphous, and composite materials (MRS b.lletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS b.lletin 2025年 1-2页

作者： b.nerjee, Sarb.jit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb. David W. Agar, Joshua Mukherjee, Partha P. Melot, b.ent Chapman, Karena Guiton, b.th S. Klie, Rob.rt F. McCue, Ian D. Voyles, Paul M. Rob.rtson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Ab.olrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizb.y-Onat, Melike Mehta, Apurva Graham-b.ady, Lori Maruyama, b.nji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, b. Schwarz, Udo D. Simon, Sindee L. b.own, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Lab.ratory, Lemont, USA." Argonne National Lab.ratory was left off the original pub.ication. © The Author(s) 2025.

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Evolution of spatially anisotropic viscous cosmological models filled with b.rotropic fluid

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AIP Conference Proceedings 2024年第1期3139卷

作者： S. Kotamb.ar P. M. Parkhi G. P. Singh R. K. Kelkar Department of Mathematics Laxminarayan Inst. of Tech. RTMN University Nagpur India Department of Mathematics PGTD RTMN University Nagpur India J.D. College of Engineering and Management Nagpur India Departmentof Mathematics VNIT Nagpur India S. B. JITMR Nagpur India

Motivated b. recent developments in the search of a suitab.e cosmological model of the universe this paper attempts to find a model representing the early stages of the universe represented b. spatially anisotropic space-time Geometry. In this regard exact solution of Einstein’s Field equation under different conditions has b.en analyzed to discuss various aspects of the cosmological model under consideration. It has b.en ob.erved that the graphical pattern of cosmological parameters satisfies its ob.ervational b.havior.

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Mental Health Monitoring System using Artificial Intelligence: A Review

Mental Health Monitoring System using Artificial Intelligenc...

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International Conference for Convergence of tech.ology (I2CT)

作者： Vidhi Mody Vrushti Mody B. Tech Department of Computer Engineering NMIMS-MPSTME

ISBN: (数字)9781538680759

ISBN: (纸本)9781538680766

Mental health is an important pub.ic health concern worldwide and should b. a prominent part of the healthcare industry. However, the pace of development in this field appears to b. quite slow. Recently AI tech.iques have attracted a lot of attention in different industries, including mental health. With the help of advanced AI tech.iques and machine learning algorithms, a personalized care that focuses on providing emotional support catered to a specific individual has b.en facilitated. In this paper, we analyse various systems for mental health monitoring namely virtual counselling, precision therapy, and diagnostic systems b. reviewing the algorithms and parameters used in each system. We conclude b. proposing a system which comb.nes the ab.ve-mentioned systems and is planned to provide personalized mental care.

关键词： Medical treatment Feature extraction Support vector machines Monitoring Artificial intelligence Decision trees Employee welfare

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A Review on Detecting Phishing URLs using Clustering Algorithms

A Review on Detecting Phishing URLs using Clustering Algorit...

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International Conference on Advances in Computing, Communication and Control (ICAC3)

作者： Shaheen Mondal Diksha Maheshwari Nilima Pai Ameyaa b.walkar B. Tech. Computer Engineering Mukesh Patel School of Technology Management and Engineering NMIMS India Mukesh Patel School of Technology Management and Engineering NMIMS India

ISBN: (数字)9781728123868

ISBN: (纸本)9781728123875

Phishing is a kind of a social engineering attack. The attacker poses as a legitimate entity and communicates with the victim through some mode of communication. The user is prompted to open a link which has b.en designed to look similar to a legitimate web.ite, or is prompted to relay sensitive credentials over the phone. The attacker steals the users' information to perform identity theft, account hijacking, etc. In this paper, we focus on URL b.sed phishing attacks. Most of the solutions that we investigated focused more on the classification algorithms rather than clustering. Our aim is to experiment and compare the results of b.th of these types of algorithms. The main premise of our approach is a hyb.id machine learning model comprising of two steps- checking with a b.acklist and whitelist, and heuristics b.sed detection, to increase the accuracy of the proposed algorithm.

关键词： Phishing Clustering algorithms Uniform resource locators Classification algorithms Machine learning algorithms b.acklisting Electronic mail

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